From 697f73f77007b272758c6c9e0d89bed25c767960 Mon Sep 17 00:00:00 2001
From: b380490 <nicole.albern@kit.edu>
Date: Mon, 21 Jun 2021 11:45:26 +0200
Subject: [PATCH] Initial commit of python scripts and ICON runscripts
---
...ignificance_bootstrapping-checkpoint.ipynb | 1092 +++++++++++++++++
.../figure1_robust_response-checkpoint.ipynb | 793 ++++++++++++
.../figure2_jetresponse-checkpoint.ipynb | 738 +++++++++++
...n_mpi_ipsl_total_vs_cloud-checkpoint.ipynb | 635 ++++++++++
...gure4_cloudheating_change-checkpoint.ipynb | 506 ++++++++
.../figure5_tr_ml_po-checkpoint.ipynb | 440 +++++++
...re6_tropical_cloudimpacts-checkpoint.ipynb | 471 +++++++
...free_vs_locked_watervapor-checkpoint.ipynb | 392 ++++++
.../figure_S2_schematics-checkpoint.ipynb | 355 ++++++
.../figure_S3_cloudcover-checkpoint.ipynb | 387 ++++++
.../figure_S4_streamfunction-checkpoint.ipynb | 327 +++++
...cmip5_data_to_common_grid-checkpoint.ipynb | 444 +++++++
pythonscripts/BlueYellowRed.rgb | 256 ++++
.../helper_functions.cpython-36.pyc | Bin 0 -> 14187 bytes
...calculate_significance_bootstrapping.ipynb | 1092 +++++++++++++++++
pythonscripts/figure1_robust_response.ipynb | 793 ++++++++++++
pythonscripts/figure1a_1f.pdf | Bin 0 -> 842594 bytes
pythonscripts/figure2_jetresponse.ipynb | 738 +++++++++++
pythonscripts/figure2a_2f.pdf | Bin 0 -> 61185 bytes
...3_du850_icon_mpi_ipsl_total_vs_cloud.ipynb | 635 ++++++++++
pythonscripts/figure3a_3f.pdf | Bin 0 -> 163206 bytes
.../figure4_cloudheating_change.ipynb | 506 ++++++++
pythonscripts/figure4a_4f.pdf | Bin 0 -> 408850 bytes
pythonscripts/figure5_tr_ml_po.ipynb | 440 +++++++
pythonscripts/figure5a_5c.pdf | Bin 0 -> 170887 bytes
.../figure6_tropical_cloudimpacts.ipynb | 471 +++++++
pythonscripts/figure6a_6f.pdf | Bin 0 -> 329392 bytes
pythonscripts/figure_S1.pdf | Bin 0 -> 84438 bytes
.../figure_S1_free_vs_locked_watervapor.ipynb | 392 ++++++
pythonscripts/figure_S2.pdf | Bin 0 -> 84431 bytes
pythonscripts/figure_S2_schematics.ipynb | 355 ++++++
pythonscripts/figure_S3.pdf | Bin 0 -> 172435 bytes
pythonscripts/figure_S3_cloudcover.ipynb | 387 ++++++
pythonscripts/figure_S4.pdf | Bin 0 -> 163759 bytes
pythonscripts/figure_S4_streamfunction.ipynb | 327 +++++
pythonscripts/helper_functions.py | 587 +++++++++
...nterpolate_cmip5_data_to_common_grid.ipynb | 444 +++++++
runscripts_ICON/exp.nanw0005.run | 762 ++++++++++++
runscripts_ICON/exp.nanw0006.run | 762 ++++++++++++
runscripts_ICON/exp.nanw0007.run | 778 ++++++++++++
runscripts_ICON/exp.nanw0008.run | 778 ++++++++++++
runscripts_ICON/exp.nanw0009.run | 778 ++++++++++++
runscripts_ICON/exp.nanw0010.run | 778 ++++++++++++
runscripts_ICON/exp.nanw0015.run | 781 ++++++++++++
runscripts_ICON/exp.nanw0016.run | 792 ++++++++++++
runscripts_ICON/exp.nanw0017.run | 791 ++++++++++++
runscripts_ICON/exp.nanw0018.run | 781 ++++++++++++
runscripts_ICON/exp.nanw0019.run | 780 ++++++++++++
runscripts_ICON/exp.nanw0020.run | 791 ++++++++++++
runscripts_ICON/exp.nanw0021.run | 791 ++++++++++++
runscripts_ICON/exp.nanw0022.run | 781 ++++++++++++
runscripts_ICON/exp.nanw0023.run | 844 +++++++++++++
runscripts_ICON/exp.nanw0031.run | 796 ++++++++++++
runscripts_ICON/exp.nanw0032.run | 796 ++++++++++++
runscripts_ICON/exp.nanw0033.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0034.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0044.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0045.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0046.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0047.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0048.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0049.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0050.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0051.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0052.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0053.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0054.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0055.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0056.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0057.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0058.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0059.run | 798 ++++++++++++
runscripts_ICON/exp.nanw0060.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0061.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0062.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0063.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0064.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0065.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0066.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0067.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0068.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0069.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0070.run | 800 ++++++++++++
runscripts_ICON/exp.nanw0071.run | 800 ++++++++++++
runscripts_ICON/simulations_overview.txt | 68 +
85 files changed, 51395 insertions(+)
create mode 100644 pythonscripts/.ipynb_checkpoints/calculate_significance_bootstrapping-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure1_robust_response-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure2_jetresponse-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure3_du850_icon_mpi_ipsl_total_vs_cloud-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure4_cloudheating_change-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure5_tr_ml_po-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure6_tropical_cloudimpacts-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure_S1_free_vs_locked_watervapor-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure_S2_schematics-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure_S3_cloudcover-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/figure_S4_streamfunction-checkpoint.ipynb
create mode 100644 pythonscripts/.ipynb_checkpoints/interpolate_cmip5_data_to_common_grid-checkpoint.ipynb
create mode 100644 pythonscripts/BlueYellowRed.rgb
create mode 100644 pythonscripts/__pycache__/helper_functions.cpython-36.pyc
create mode 100644 pythonscripts/calculate_significance_bootstrapping.ipynb
create mode 100644 pythonscripts/figure1_robust_response.ipynb
create mode 100644 pythonscripts/figure1a_1f.pdf
create mode 100644 pythonscripts/figure2_jetresponse.ipynb
create mode 100644 pythonscripts/figure2a_2f.pdf
create mode 100644 pythonscripts/figure3_du850_icon_mpi_ipsl_total_vs_cloud.ipynb
create mode 100644 pythonscripts/figure3a_3f.pdf
create mode 100644 pythonscripts/figure4_cloudheating_change.ipynb
create mode 100644 pythonscripts/figure4a_4f.pdf
create mode 100644 pythonscripts/figure5_tr_ml_po.ipynb
create mode 100644 pythonscripts/figure5a_5c.pdf
create mode 100644 pythonscripts/figure6_tropical_cloudimpacts.ipynb
create mode 100644 pythonscripts/figure6a_6f.pdf
create mode 100644 pythonscripts/figure_S1.pdf
create mode 100644 pythonscripts/figure_S1_free_vs_locked_watervapor.ipynb
create mode 100644 pythonscripts/figure_S2.pdf
create mode 100644 pythonscripts/figure_S2_schematics.ipynb
create mode 100644 pythonscripts/figure_S3.pdf
create mode 100644 pythonscripts/figure_S3_cloudcover.ipynb
create mode 100644 pythonscripts/figure_S4.pdf
create mode 100644 pythonscripts/figure_S4_streamfunction.ipynb
create mode 100644 pythonscripts/helper_functions.py
create mode 100644 pythonscripts/interpolate_cmip5_data_to_common_grid.ipynb
create mode 100755 runscripts_ICON/exp.nanw0005.run
create mode 100755 runscripts_ICON/exp.nanw0006.run
create mode 100755 runscripts_ICON/exp.nanw0007.run
create mode 100755 runscripts_ICON/exp.nanw0008.run
create mode 100755 runscripts_ICON/exp.nanw0009.run
create mode 100755 runscripts_ICON/exp.nanw0010.run
create mode 100755 runscripts_ICON/exp.nanw0015.run
create mode 100755 runscripts_ICON/exp.nanw0016.run
create mode 100755 runscripts_ICON/exp.nanw0017.run
create mode 100755 runscripts_ICON/exp.nanw0018.run
create mode 100755 runscripts_ICON/exp.nanw0019.run
create mode 100755 runscripts_ICON/exp.nanw0020.run
create mode 100755 runscripts_ICON/exp.nanw0021.run
create mode 100755 runscripts_ICON/exp.nanw0022.run
create mode 100755 runscripts_ICON/exp.nanw0023.run
create mode 100755 runscripts_ICON/exp.nanw0031.run
create mode 100755 runscripts_ICON/exp.nanw0032.run
create mode 100755 runscripts_ICON/exp.nanw0033.run
create mode 100755 runscripts_ICON/exp.nanw0034.run
create mode 100755 runscripts_ICON/exp.nanw0044.run
create mode 100755 runscripts_ICON/exp.nanw0045.run
create mode 100755 runscripts_ICON/exp.nanw0046.run
create mode 100755 runscripts_ICON/exp.nanw0047.run
create mode 100755 runscripts_ICON/exp.nanw0048.run
create mode 100755 runscripts_ICON/exp.nanw0049.run
create mode 100755 runscripts_ICON/exp.nanw0050.run
create mode 100755 runscripts_ICON/exp.nanw0051.run
create mode 100755 runscripts_ICON/exp.nanw0052.run
create mode 100755 runscripts_ICON/exp.nanw0053.run
create mode 100755 runscripts_ICON/exp.nanw0054.run
create mode 100755 runscripts_ICON/exp.nanw0055.run
create mode 100755 runscripts_ICON/exp.nanw0056.run
create mode 100755 runscripts_ICON/exp.nanw0057.run
create mode 100755 runscripts_ICON/exp.nanw0058.run
create mode 100755 runscripts_ICON/exp.nanw0059.run
create mode 100755 runscripts_ICON/exp.nanw0060.run
create mode 100755 runscripts_ICON/exp.nanw0061.run
create mode 100755 runscripts_ICON/exp.nanw0062.run
create mode 100755 runscripts_ICON/exp.nanw0063.run
create mode 100755 runscripts_ICON/exp.nanw0064.run
create mode 100755 runscripts_ICON/exp.nanw0065.run
create mode 100755 runscripts_ICON/exp.nanw0066.run
create mode 100755 runscripts_ICON/exp.nanw0067.run
create mode 100755 runscripts_ICON/exp.nanw0068.run
create mode 100755 runscripts_ICON/exp.nanw0069.run
create mode 100755 runscripts_ICON/exp.nanw0070.run
create mode 100755 runscripts_ICON/exp.nanw0071.run
create mode 100644 runscripts_ICON/simulations_overview.txt
diff --git a/pythonscripts/.ipynb_checkpoints/calculate_significance_bootstrapping-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/calculate_significance_bootstrapping-checkpoint.ipynb
new file mode 100644
index 0000000..afe9638
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/calculate_significance_bootstrapping-checkpoint.ipynb
@@ -0,0 +1,1092 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get masks with significant responses based on bootstrapping.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Functions for calculation of significance based on bootstrapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate bootstrap distribution of annual-mean or seasonal-mean\n",
+ "# time series with 1000 samplings with replacement\n",
+ "def calc_bootstrap_dist(data_ym, lats, lons, nreps):\n",
+ " # decide which years to use\n",
+ " ntime = data_ym.shape[0] # number of years in simulation\n",
+ " #nreps = 1000 # number of resamples \n",
+ " time = np.arange(ntime) # artificial time vector\n",
+ " time_bs = np.random.choice(time, (ntime, nreps))\n",
+ "\n",
+ " # find u850 profiles according to time_bs\n",
+ " data_bs = np.full((ntime, nreps, lats.size, lons.size), np.nan, dtype=float)\n",
+ " for l in range(nreps): # number of resamples\n",
+ " for k in range(ntime): # number of years\n",
+ " data_bs[k, l, :, :] = data_ym[time_bs[k, l], :, :]\n",
+ " del k\n",
+ " del l\n",
+ "\n",
+ " # mean over years for each resampling\n",
+ " if np.isnan(data_bs).any() == False:\n",
+ " print(\"No NaN's. Calculation worked.\")\n",
+ " u850_bs_mean = np.mean(data_bs, axis=0)\n",
+ " else:\n",
+ " print(\"Bootstrap array contains NaN's. Exit function.\")\n",
+ " return\n",
+ "\n",
+ " return u850_bs_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_ML = ['T1C2ML', 'T1C1ML', 'T2C2ML', 'T2C1ML']\n",
+ "runs_reg_PO = ['T1C2PO', 'T1C1PO', 'T2C2PO', 'T2C1PO']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_ML + runs_reg_PO + \\\n",
+ " runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR', 'cloud ML', 'cloud notML',\n",
+ " 'cloud PO', 'cloud notPO',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Number of samplings / reputations in bootstrap function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nreps=1000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind and do bootstrap calculations for ICON (locked clouds, interactive water vapor)\n",
+ "\n",
+ "Get the seasonal-mean zonal-mean zonal wind with Climate Data Operators (cdo): cdo seasmean -selvar,u ifile ofile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "###############\n",
+ "T1C1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850_tmym_bs = {}; u850_smym_bs = {}\n",
+ "for run in runs_all:\n",
+ " # time-mean\n",
+ " print('###############')\n",
+ " print(run)\n",
+ " # read zonal wind for each month\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset('../../ICON-NWP_lockedclouds/' + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['lev'][:].data\n",
+ " uwind = ncfile.variables['u'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850 = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " # calculate yearly-mean u850 for each year\n",
+ " u850_tmym = np.full((int(u850.shape[0]/12), lats.size, lons.size), np.nan,\n",
+ " dtype=float)\n",
+ " for i in range(u850_tmym.shape[0]):\n",
+ " u850_tmym[i,:,:] = np.nanmean(u850[i*12:i*12+12,:,:], axis=0)\n",
+ " del i\n",
+ " del u850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### time-mean #####')\n",
+ " u850_tmym_bs[run] = calc_bootstrap_dist(u850_tmym, lats, lons, nreps)\n",
+ " del u850_tmym\n",
+ " \n",
+ " ##########################################################################\n",
+ " # seasonal-mean\n",
+ " # read seasonal-mean zonal wind for each year\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.uwind.seasmean.nc'\n",
+ " ncfile = nc.Dataset('../../ICON-NWP_lockedclouds/' + ifile, 'r')\n",
+ " levs_sm = ncfile.variables['lev'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " levind850 = (np.abs(levs_sm-85000)).argmin() # index of 850 hPa level\n",
+ " # DJF: do not read last timestep, because it only contains the value from\n",
+ " # Dec of the last year (note: first value only contains values from\n",
+ " # Jan and Feb of first year)\n",
+ " # other seasons: read all available time steps\n",
+ " u850_smym = {'DJF': u_wind_sm[0:-1:4, levind850, :, :],\n",
+ " 'MAM': u_wind_sm[1::4, levind850, :, :],\n",
+ " 'JJA': u_wind_sm[2::4, levind850, :, :],\n",
+ " 'SON': u_wind_sm[3::4, levind850, :, :],\n",
+ " }\n",
+ " del levs_sm, u_wind_sm, levind850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### seasonal-mean #####')\n",
+ " u850_smym_bs1 = {}\n",
+ " for s in seasons:\n",
+ " u850_smym_bs1[s] = calc_bootstrap_dist(u850_smym[s], lats, lons, nreps)\n",
+ " u850_smym_bs[run] = u850_smym_bs1.copy()\n",
+ " del s, u850_smym_bs1\n",
+ " del u850_smym\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses and store significance masks for ICON (locked clouds, interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculate response\n",
+ " T1C2TR T1C1TR T2C2TR T2C1TR\n",
+ "Calculate percentiles\n",
+ " cloud TR cloud notTR\n",
+ "Calculate response\n",
+ " T1C2ML T1C1ML T2C2ML T2C1ML\n",
+ "Calculate percentiles\n",
+ " cloud ML cloud notML\n",
+ "Calculate response\n",
+ " T1C2PO T1C1PO T2C2PO T2C1PO\n",
+ "Calculate percentiles\n",
+ " cloud PO cloud notPO\n",
+ "Calculate response\n",
+ " T1C2TA T1C1TA T2C2TA T2C1TA\n",
+ "Calculate percentiles\n",
+ " cloud TA cloud notTA\n",
+ "Calculate response\n",
+ " T1C2IO T1C1IO T2C2IO T2C1IO\n",
+ "Calculate percentiles\n",
+ " cloud IO cloud notIO\n",
+ "Calculate response\n",
+ " T1C2WP T1C1WP T2C2WP T2C1WP\n",
+ "Calculate percentiles\n",
+ " cloud WP cloud notWP\n",
+ "Calculate response\n",
+ " T1C2EP T1C1EP T2C2EP T2C1EP\n",
+ "Calculate percentiles\n",
+ " cloud EP cloud notEP\n"
+ ]
+ }
+ ],
+ "source": [
+ "# calculate total response, SST impact and cloud impact\n",
+ "# time-mean\n",
+ "du850_tmym_glo = np.full((3, nreps, len(lats), len(lons)), np.nan, dtype=float)\n",
+ "du850_tmym_glo[0, :, :, :], du850_tmym_glo[1, :, :, :], \\\n",
+ "du850_tmym_glo[2, :, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_tmym_bs['T1C1'], u850_tmym_bs['T2C2'],\n",
+ " u850_tmym_bs['T1C2'], u850_tmym_bs['T2C1'])\n",
+ "\n",
+ "# seasonal-mean\n",
+ "du850_smym_glo = np.full((len(seasons), 3, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "for s, seas in enumerate(seasons):\n",
+ " du850_smym_glo[s, 0, :, :, :], du850_smym_glo[s, 1, :, :, :], \\\n",
+ " du850_smym_glo[s, 2, :, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_smym_bs['T1C1'][seas],\n",
+ " u850_smym_bs['T2C2'][seas],\n",
+ " u850_smym_bs['T1C2'][seas],\n",
+ " u850_smym_bs['T2C1'][seas])\n",
+ "del s, seas\n",
+ "\n",
+ "# calculate percentiles of bootstrap distributions\n",
+ "lp = 5; up = 95 # 5th-95th percentile range\n",
+ "# set axis to calculate percentiles over nreps=1000\n",
+ "# time-mean dimensions: 3, nreps, lats, lons\n",
+ "du850_tmym_perc = np.percentile(du850_tmym_glo, [lp, up], axis=1)\n",
+ "# seasonal-mean dimensions: seasons, 3, nreps, lats, lons\n",
+ "du850_smym_perc = np.percentile(du850_smym_glo, [lp, up], axis=2)\n",
+ "# new dimensions:\n",
+ "# time-mean: 2, 3, lats, lons\n",
+ "# seasonal-mean: 2, seasons, 3, lats, lons\n",
+ "del lp, up\n",
+ "\n",
+ "# create mask based on percentiles\n",
+ "# true if significant: both percentile values have the same sig and, thus, 0\n",
+ "# is not included\n",
+ "# true if sign is the same and response is \n",
+ "du850_mask_tm_bs = {'total': np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :]),\n",
+ " 'SST': np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_tmym_perc[0, 2, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 2, :, :])}\n",
+ "du850_mask_sm_bs = {'total': np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :]),\n",
+ " 'SST': np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_smym_perc[0, :, 2, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 2, :, :])}\n",
+ "\n",
+ "del du850_tmym_glo, du850_smym_glo, du850_tmym_perc, du850_smym_perc\n",
+ "\n",
+ "##############################################################################\n",
+ "##############################################################################\n",
+ "# regional cloud impacts\n",
+ "for r in range(int(len(runs_reg)/4)):\n",
+ " # calculate u850 response for bootstrap distribtions\n",
+ " print('Calculate response')\n",
+ " print(' ', runs_reg[r*4], runs_reg[r*4+1], runs_reg[r*4+2], runs_reg[r*4+3])\n",
+ " # time-mean response\n",
+ " du850_tmym_reg = np.full((2, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " _, _, du850_tmym_reg[0, :, :, :], du850_tmym_reg[1, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_tmym_bs['T1C1'],\n",
+ " u850_tmym_bs['T2C2'],\n",
+ " u850_tmym_bs['T1C2'],\n",
+ " u850_tmym_bs['T2C1'],\n",
+ " u850_tmym_bs[runs_reg[r*4]],\n",
+ " u850_tmym_bs[runs_reg[r*4+1]],\n",
+ " u850_tmym_bs[runs_reg[r*4+2]],\n",
+ " u850_tmym_bs[runs_reg[r*4+3]])\n",
+ " \n",
+ " # seasonal-mean response\n",
+ " du850_smym_reg = np.full((len(seasons), 2, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " for s, seas in enumerate(seasons):\n",
+ " _, _, du850_smym_reg[s, 0, :, :, :], du850_smym_reg[s, 1, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_smym_bs['T1C1'][seas],\n",
+ " u850_smym_bs['T2C2'][seas],\n",
+ " u850_smym_bs['T1C2'][seas],\n",
+ " u850_smym_bs['T2C1'][seas],\n",
+ " u850_smym_bs[runs_reg[r*4]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+1]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+2]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+3]][seas])\n",
+ " del s, seas\n",
+ " \n",
+ " ##############################################################################\n",
+ " # calculate percentiles of bootstrap distributions\n",
+ " print('Calculate percentiles')\n",
+ " lp = 5; up = 95 # 5th-95th percentile range\n",
+ " # set axis to calculate percentiles over nreps=1000\n",
+ " # time-mean dimensions: 2, nreps, lats, lons\n",
+ " du850_tmym_perc = np.percentile(du850_tmym_reg, [lp, up], axis=1)\n",
+ " # seasonal-mean dimensions: seasons, 2, nreps, lats, lons\n",
+ " du850_smym_perc = np.percentile(du850_smym_reg, [lp, up], axis=2)\n",
+ " # new dimensions:\n",
+ " # time-mean: 2(perc), 2(response), lats, lons\n",
+ " # seasonal-mean: 2(perc), seasons, 2(response), lats, lons\n",
+ " del lp, up\n",
+ "\n",
+ " ##############################################################################\n",
+ " # create mask based on percentiles\n",
+ " # true if significant: both percentile values have the same sig and, thus, 0\n",
+ " # is not included\n",
+ " # true if sign is the same and response is\n",
+ " print(' ', response_all[r*2+3], response_all[r*2+4])\n",
+ " du850_mask_tm_bs.update({response_all[r*2+3]: \\\n",
+ " np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :])})\n",
+ " du850_mask_tm_bs.update({response_all[r*2+4]: \\\n",
+ " np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :])})\n",
+ " du850_mask_sm_bs.update({response_all[r*2+3]: \\\n",
+ " np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :])})\n",
+ " du850_mask_sm_bs.update({response_all[r*2+4]: \\\n",
+ " np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :])})\n",
+ " \n",
+ " del du850_tmym_reg, du850_smym_reg, du850_tmym_perc, du850_smym_perc\n",
+ "del r\n",
+ "\n",
+ "del u850_tmym_bs, u850_smym_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Save masks as numpy arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.save('../../ICON-NWP_lockedclouds/du850_mask_tm_bs.npy', du850_mask_tm_bs)\n",
+ "np.save('../../ICON-NWP_lockedclouds/du850_mask_sm_bs.npy', du850_mask_sm_bs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Delete masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del du850_mask_tm_bs, du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind, do bootstrap calculations, and store masks for MPI-ESM and IPSL-CM5A (locked clouds and locked water vapor)\n",
+ "\n",
+ "Get the seasonal-mean zonal-mean zonal wind with Climate Data Operators (cdo): cdo seasmean -selvar,u ifile ofile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MPI-ESM\n",
+ "###############\n",
+ "T1C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "##############################\n",
+ "##############################\n",
+ "IPSL-CM5A\n",
+ "###############\n",
+ "T1C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "##############################\n",
+ "##############################\n",
+ "###########\n",
+ "## Done! ##\n"
+ ]
+ }
+ ],
+ "source": [
+ "runs = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "models = ['MPI-ESM', 'IPSL-CM5A']\n",
+ "\n",
+ "for m, model in enumerate(models):\n",
+ " u850_tmym_bs = {}\n",
+ " u850_smym_bs = {}\n",
+ "\n",
+ " print(model)\n",
+ " for run in runs:\n",
+ " print('###############')\n",
+ " print(run)\n",
+ "\n",
+ " # time-mean\n",
+ " # read zonal wind for each month\n",
+ " if model == 'MPI-ESM':\n",
+ " ifile = model + '_' + run + '_3d_mm.uwind.nc'\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " ifile = model + '_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " ncfile = nc.Dataset('../../' + model + '/' + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " # pressure levels and zonal wind are named differently\n",
+ " # in the two models\n",
+ " if model == 'MPI-ESM':\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['u'][:].data\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " levs = ncfile.variables['presnivs'][:].data\n",
+ " uwind = ncfile.variables['vitu'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850 = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " # calculate yearly-mean u850 for each year\n",
+ " u850_tmym = np.full((int(u850.shape[0]/12), lats.size, lons.size), np.nan,\n",
+ " dtype=float)\n",
+ " for i in range(u850_tmym.shape[0]):\n",
+ " u850_tmym[i,:,:] = np.nanmean(u850[i*12:i*12+12,:,:], axis=0)\n",
+ " del i\n",
+ " del u850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### time-mean #####')\n",
+ " u850_tmym_bs[run] = calc_bootstrap_dist(u850_tmym, lats, lons, nreps)\n",
+ " del u850_tmym\n",
+ "\n",
+ " ##########################################################################\n",
+ " # seasonal-mean\n",
+ " # read seasonal-mean zonal wind for each year\n",
+ " if model == 'MPI-ESM':\n",
+ " ifile = model + '_' + run + '_3d_mm.uwind.seasmean.nc'\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " ifile = model + '_' + run + '_3d_mm.remapcon.uwind.seasmean.nc'\n",
+ " ncfile = nc.Dataset('../../' + model + '/' + ifile, 'r')\n",
+ " # pressure levels and zonal wind are named differently\n",
+ " # in the two models\n",
+ " if model == 'MPI-ESM':\n",
+ " levs_sm = ncfile.variables['plev'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['u'][:].data)\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " levs_sm = ncfile.variables['presnivs'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['vitu'][:].data)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " levind850 = (np.abs(levs_sm-85000)).argmin() # index of 850 hPa level\n",
+ " # DJF: do not read last timestep, because it only contains the value from\n",
+ " # Dec of the last year (note: first value only contains values from\n",
+ " # Jan and Feb of first year)\n",
+ " # other seasons: read all available time steps\n",
+ " u850_smym = {'DJF': u_wind_sm[0:-1:4, levind850, :, :],\n",
+ " 'MAM': u_wind_sm[1::4, levind850, :, :],\n",
+ " 'JJA': u_wind_sm[2::4, levind850, :, :],\n",
+ " 'SON': u_wind_sm[3::4, levind850, :, :],\n",
+ " }\n",
+ " del levs_sm, u_wind_sm, levind850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### seasonal-mean #####')\n",
+ " u850_smym_bs1 = {}\n",
+ " for s in seasons:\n",
+ " u850_smym_bs1[s] = calc_bootstrap_dist(u850_smym[s], lats, lons, nreps)\n",
+ " u850_smym_bs[run] = u850_smym_bs1.copy()\n",
+ " del s, u850_smym_bs1\n",
+ " del u850_smym\n",
+ " del run\n",
+ " \n",
+ " ##########################################################################\n",
+ " ##########################################################################\n",
+ " # Calculate responses and store significance masks\n",
+ " \n",
+ " # calculate responses\n",
+ " # time-mean\n",
+ " du850_tmym = np.full((4, nreps, len(lats), len(lons)), np.nan, dtype=float)\n",
+ "\n",
+ " du850_tmym[0, :, :, :], du850_tmym[1, :, :, :], du850_tmym[2, :, :, :], \\\n",
+ " du850_tmym[3, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_tmym_bs['T1C1W1'], u850_tmym_bs['T2C2W2'],\n",
+ " u850_tmym_bs['T1C2W2'], u850_tmym_bs['T2C1W1'],\n",
+ " u850_tmym_bs['T1C2W1'], u850_tmym_bs['T1C1W2'],\n",
+ " u850_tmym_bs['T2C2W1'], u850_tmym_bs['T2C1W2'])\n",
+ "\n",
+ " # seasonal-mean\n",
+ " du850_smym = np.full((len(seasons), 4, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " for s, seas in enumerate(seasons):\n",
+ " du850_smym[s, 0, :, :, :], du850_smym[s, 1, :, :, :], \\\n",
+ " du850_smym[s, 2, :, :, :], du850_smym[s, 3, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_smym_bs['T1C1W1'][seas],\n",
+ " u850_smym_bs['T2C2W2'][seas],\n",
+ " u850_smym_bs['T1C2W2'][seas],\n",
+ " u850_smym_bs['T2C1W1'][seas],\n",
+ " u850_smym_bs['T1C2W1'][seas],\n",
+ " u850_smym_bs['T1C1W2'][seas],\n",
+ " u850_smym_bs['T2C2W1'][seas],\n",
+ " u850_smym_bs['T2C1W2'][seas])\n",
+ " del s, seas\n",
+ "\n",
+ " # calculate percentiles of bootstrap distributions\n",
+ " lp = 5; up = 95 # 5th-95th percentile range\n",
+ " # set axis to calculate percentiles over nreps=1000\n",
+ " # time-mean dimensions: 3, nreps, lats, lons\n",
+ " du850_tmym_perc = np.percentile(du850_tmym, [lp, up], axis=1)\n",
+ " # seasonal-mean dimensions: seasons, 3, nreps, lats, lons\n",
+ " du850_smym_perc = np.percentile(du850_smym, [lp, up], axis=2)\n",
+ " # new dimensions:\n",
+ " # time-mean: 2, 3, lats, lons\n",
+ " # seasonal-mean: 2, seasons, 3, lats, lons\n",
+ " del lp, up\n",
+ "\n",
+ " # create mask based on percentiles\n",
+ " # true if significant: both percentile values have the same sig and, thus, 0\n",
+ " # is not included\n",
+ " # true if sign is the same and response is \n",
+ " du850_mask_tm_bs = {'total': np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :]),\n",
+ " 'SST': np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_tmym_perc[0, 2, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 2, :, :]),\n",
+ " 'water vapor' : np.sign(du850_tmym_perc[0, 3, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 3, :, :])}\n",
+ " du850_mask_sm_bs = {'total': np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :]),\n",
+ " 'SST': np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_smym_perc[0, :, 2, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 2, :, :]),\n",
+ " 'water vapor': np.sign(du850_smym_perc[0, :, 3, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 3, :, :])}\n",
+ "\n",
+ " ##############################################################################\n",
+ " # save masks as numpy arrays\n",
+ " np.save('../../' + model + '/' + model + '_du850_mask_tm_bs.npy', du850_mask_tm_bs)\n",
+ " np.save('../../' + model + '/' + model + '_du850_mask_sm_bs.npy', du850_mask_sm_bs)\n",
+ "\n",
+ " del u850_tmym_bs, u850_smym_bs\n",
+ " del du850_tmym, du850_smym, du850_tmym_perc, du850_smym_perc\n",
+ " del du850_mask_tm_bs, du850_mask_sm_bs\n",
+ " del lats, lons\n",
+ " \n",
+ " print('##############################')\n",
+ " print('##############################')\n",
+ "\n",
+ "del model, models\n",
+ "print('###########')\n",
+ "print('## Done! ##')\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure1_robust_response-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure1_robust_response-checkpoint.ipynb
new file mode 100644
index 0000000..09183b9
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure1_robust_response-checkpoint.ipynb
@@ -0,0 +1,793 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Robust zonal wind response\n",
+ "\n",
+ "This script generates figure 1: maps of zonal wind response during DJF for\n",
+ "- coupled CMIP5 models (RCP8.5)\n",
+ "- atmosphere CMIP5 models (amipFuture and amip4K)\n",
+ "- ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "sims_cmip = ['historical', 'rcp85']\n",
+ "sims_amip = ['amip', 'amip4K', 'amipFuture']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)\n",
+ "\n",
+ "The zonal wind fields were extracted from the data in the archive with Climate Data Operators (cdo; https://www.mpimet.mpg.de/cdo):\n",
+ "\n",
+ "cdo selvar,u file.nc file.uwind.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T1C1W1\n",
+ "reading T2C2W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON simulations with locked clouds and interactive water vapor\n",
+ "runs_cld = ['T1C1', 'T2C2']\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " u850_icon[run], lats, lons = fct.read_var_onelevel(ipath + ifile,\n",
+ " 'u', 'lev', 850)\n",
+ " del ifile\n",
+ "del run, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM and IPSL-CM5A simulations with locked clouds and locked water vapor\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2']\n",
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (CMIP5 models)\n",
+ "\n",
+ "Note: All simulations were interpolated to the same grid and stored in numpy arrays with the jupyter notebook \"interpolate_cmip5_data_to_common_grid.ipynb\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "ntime = 360 # length of cmip/amip simulations in months (30 years)\n",
+ "\n",
+ "# coupled models: historical and RCP8.5 simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_hist = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_rcp85 = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_cmip):\n",
+ " u850_hist[:, m, :, :] = np.load(ipath + model + '_u850_historical.npy')\n",
+ " u850_rcp85[:, m, :, :] = np.load(ipath + model + '_u850_rcp85.npy')\n",
+ "del m, model\n",
+ "\n",
+ "# atmosphere models: amip, amip4K and amipFuture simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_amip = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amip4k = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amipfut = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_amip):\n",
+ " u850_amip[:, m, :, :] = np.load(ipath + model + '_u850_amip.npy')\n",
+ " u850_amip4k[:, m, :, :] = np.load(ipath + model + '_u850_amip4k.npy')\n",
+ " u850_amipfut[:, m, :, :] = np.load(ipath + model + '_u850_amipfut.npy')\n",
+ "del m, model\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean for all simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:64: RuntimeWarning: Mean of empty slice\n",
+ " monthly_mean[month] = np.nanmean(monthly_data[month], axis=0)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:71: RuntimeWarning: Mean of empty slice\n",
+ " seasons_dict[season] ], axis=0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM and IPSL-CM5A\n",
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# coupled CMIP5 models\n",
+ "u850_cmip_djf = np.full((len(sims_cmip), len(models_cmip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_cmip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_hist, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_cmip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_rcp85, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# atmosphere CMIP5 models\n",
+ "u850_amip_djf = np.full((len(sims_amip), len(models_amip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_amip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip4k, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[2, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amipfut, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# model mean for historical and amip simulations\n",
+ "u850_cmip_djf_mm = np.nanmean(u850_cmip_djf, axis=1)\n",
+ "u850_amip_djf_mm = np.nanmean(u850_amip_djf, axis=1)\n",
+ "\n",
+ "# delete variables with time information\n",
+ "del u850_icon, u850_mpi, u850_ipsl\n",
+ "del u850_hist, u850_rcp85, u850_amip, u850_amip4k, u850_amipfut"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ICON\n",
+ "du850_icon = u850_icon_djf['T2C2'] - u850_icon_djf['T1C1']\n",
+ "# MPI-ESM\n",
+ "du850_mpi = u850_mpi_djf['T2C2W2'] - u850_mpi_djf['T1C1W1']\n",
+ "# IPSL-CM5A\n",
+ "du850_ipsl = u850_ipsl_djf['T2C2W2'] - u850_ipsl_djf['T1C1W1']\n",
+ "\n",
+ "# CMIP5\n",
+ "du850_rcp85 = u850_cmip_djf[1, :, :, :] - u850_cmip_djf[0, :, :, :]\n",
+ "du850_amip4k = u850_amip_djf[1, :, :, :] - u850_amip_djf[0, :, :, :]\n",
+ "du850_amipfut = u850_amip_djf[2, :, :, :] - u850_amip_djf[0, :, :, :]\n",
+ "\n",
+ "# model mean\n",
+ "du850_rcp85_mm = np.nanmean(du850_rcp85, axis=0)\n",
+ "du850_amip4k_mm = np.nanmean(du850_amip4k, axis=0)\n",
+ "du850_amipfut_mm = np.nanmean(du850_amipfut, axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Determine where responses are robust (CMIP simulations) and where ICON, MPI-ESM and IPSL-CM5A disagree with the robust amip4K response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:58: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:60: RuntimeWarning: invalid value encountered in greater\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:63: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:65: RuntimeWarning: invalid value encountered in greater\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:68: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:70: RuntimeWarning: invalid value encountered in greater\n"
+ ]
+ }
+ ],
+ "source": [
+ "# find indices, where 9 or more AMIP models agree on response\n",
+ "# 9 or more models should agree on the sign: set threshold to 7 (9-2)\n",
+ "# (because we sum over all models and positive and negative signs might cancel\n",
+ "# each other out)\n",
+ "# 9 models agree on sign: sum = 7 or sum = -7\n",
+ "mask_thd = 7\n",
+ "mask_model_amip4k = np.sign(du850_amip4k)\n",
+ "mask_model_amipfut = np.sign(du850_amipfut)\n",
+ "\n",
+ "# calculate sum over mask_model arrays along axis of models\n",
+ "# -> how many models agree on the sign\n",
+ "mask_sum_amip4k = np.nansum(mask_model_amip4k, axis=0)\n",
+ "mask_sum_amipfut = np.nansum(mask_model_amipfut, axis=0)\n",
+ "\n",
+ "# apply threshold to mask_sum arrays\n",
+ "mask_u850_amip4k = np.logical_or(mask_sum_amip4k >= mask_thd,\n",
+ " mask_sum_amip4k <= -1*mask_thd) * 1\n",
+ "mask_u850_amipfut = np.logical_or(mask_sum_amipfut >= mask_thd,\n",
+ " mask_sum_amipfut <= -1*mask_thd) * 1\n",
+ "\n",
+ "del mask_model_amip4k, mask_model_amipfut\n",
+ "del mask_sum_amip4k, mask_sum_amipfut\n",
+ "del mask_thd\n",
+ "\n",
+ "##############################################################################\n",
+ "# find indices, where 30 or more coupled CMIP5 models agree on response\n",
+ "# 30 or more models should agree on the sign: set threshold to 23 (30-7)\n",
+ "# (37 models in total)\n",
+ "# (because we sum over all models and positive and negative signs might cancel\n",
+ "# each other out)\n",
+ "# 30 models agree on sign: sum = 23 or sum = -23\n",
+ "mask_thd = 23\n",
+ "mask_model_rcp85 = np.sign(du850_rcp85)\n",
+ "\n",
+ "# calculate sum over mask_model arrays along axis of models\n",
+ "# -> how many models agree on the sign\n",
+ "mask_sum_rcp85 = np.nansum(mask_model_rcp85, axis=0)\n",
+ "\n",
+ "# apply threshold to mask_sum arrays\n",
+ "mask_u850_rcp85 = np.logical_or(mask_sum_rcp85 >= mask_thd,\n",
+ " mask_sum_rcp85 <= -1*mask_thd) * 1\n",
+ "\n",
+ "del mask_model_rcp85, mask_sum_rcp85, mask_thd\n",
+ "\n",
+ "##############################################################################\n",
+ "# find indices, where sign of robust response in amip4K simulations does not\n",
+ "# agree with ICON, MPI-ESM and IPSL-CM5A\n",
+ "mask_model_icon = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "mask_model_mpi = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "mask_model_ipsl = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "for m in range(len(models_amip)):\n",
+ " mask_model_icon[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_icon * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_icon * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ " mask_model_mpi[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_mpi * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_mpi * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ " mask_model_ipsl[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_ipsl * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_ipsl * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ "del m\n",
+ "\n",
+ "mask_thd = 9 # icon should agree with 9 or more models\n",
+ "mask_u850_icon_na = (np.nansum(mask_model_icon, axis=0) >= mask_thd) * 1\n",
+ "mask_u850_mpi_na = (np.nansum(mask_model_mpi, axis=0) >= mask_thd) * 1\n",
+ "mask_u850_ipsl_na = (np.nansum(mask_model_ipsl, axis=0) >= mask_thd) * 1\n",
+ "\n",
+ "del mask_model_icon, mask_model_mpi, mask_model_ipsl\n",
+ "del mask_thd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude in model-mean historical, model-mean amip, ICON, MPI-ESM, and IPSL-CM5A control simulations for the Northern Hemisphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# latitudes in Northern Hemisphere\n",
+ "latind0 = (np.abs(lats-0)).argmin() + 1\n",
+ "lats_NH = lats[latind0:]\n",
+ "\n",
+ "# shift longitudes from 0deg...360deg to -270deg...90deg for visualization reasons\n",
+ "# and look at NH\n",
+ "u850_cmip_mm_shift, lons_shift = fct.shiftgrid_copy(90.,\n",
+ " u850_cmip_djf_mm[0, latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_amip_mm_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_amip_djf_mm[0, latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_icon_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_icon_djf['T1C1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_mpi_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_mpi_djf['T1C1W1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_ipsl_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_ipsl_djf['T1C1W1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "\n",
+ "jetlat_hist_mm_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_amip_mm_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_icon_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_mpi_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_ipsl_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "for lo in range(lons_shift.size):\n",
+ " # historical simulation\n",
+ " jetlat_hist_mm_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_mm_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ " # amip simulation\n",
+ " jetlat_amip_mm_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_mm_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ " # ICON\n",
+ " jetlat_icon_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_shift[:, lo], lats_NH)\n",
+ " # MPI-ESM\n",
+ " jetlat_mpi_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_shift[:, lo], lats_NH)\n",
+ " # IPSL-CM5A\n",
+ " jetlat_ipsl_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ "del lo\n",
+ "\n",
+ "del u850_cmip_mm_shift, u850_amip_mm_shift, \\\n",
+ " u850_icon_shift, u850_mpi_shift, u850_ipsl_shift\n",
+ "del lons_shift"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot of u850 response\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant in the CMIP5 models, and the hatching for the regions, in which the response in ICON, MPI-ESM and IPSL-CM5A does not agree with the robust amip4K response)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "lons_plot = fct.shiftgrid_copy(90., du850_icon, lons, start=False)[1]\n",
+ "\n",
+ "# North Atlantic region\n",
+ "lonind_west = (np.abs(lons_plot--90)).argmin() # find index of 90°W\n",
+ "lonind_east = (np.abs(lons_plot-35)).argmin() # find index of 35°E\n",
+ "latind_sout = (np.abs(lats-20)).argmin() # find index of 20°N\n",
+ "latind_nort = (np.abs(lats-80)).argmin() # find index of 80°N\n",
+ "\n",
+ "lons_plot = lons_plot[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "# MPI-ESM uses slightly different latitudes\n",
+ "latind_sout_mpi = (np.abs(lats_mpi-20)).argmin() # find index of 20°N\n",
+ "latind_nort_mpi = (np.abs(lats_mpi-80)).argmin() # find index of 80°N\n",
+ "lats_mpi_plot = lats_mpi[latind_sout_mpi:latind_nort_mpi+1]\n",
+ "\n",
+ "# shift zonal wind fields and masks\n",
+ "du850_rcp85_plot = fct.shiftgrid_copy(90., du850_rcp85_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_rcp85_plot = fct.shiftgrid_copy(90., mask_u850_rcp85, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "du850_amipfut_plot = fct.shiftgrid_copy(90., du850_amipfut_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_amipfut_plot = fct.shiftgrid_copy(90., mask_u850_amipfut, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "du850_amip4k_plot = fct.shiftgrid_copy(90., du850_amip4k_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_amip4k_plot = fct.shiftgrid_copy(90., mask_u850_amip4k, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_icon_plot = fct.shiftgrid_copy(90., du850_icon, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_icon_plot = fct.shiftgrid_copy(90., mask_u850_icon_na, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_mpi_plot = fct.shiftgrid_copy(90., du850_mpi, lons,\n",
+ " start=False)[0][latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_mpi_plot = fct.shiftgrid_copy(90., mask_u850_mpi_na, lons,\n",
+ " start=False)[0][latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_ipsl_plot = fct.shiftgrid_copy(90., du850_ipsl, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_ipsl_plot = fct.shiftgrid_copy(90., mask_u850_ipsl_na, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of u850 response in DJF for RCP8.5, amipFuture, amip4K, ICON, MPI-ESM and IPSL-CM5A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 12 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(2, 3, figsize=(12, 6),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].coastlines(rasterized=True)\n",
+ " ax[i].set_aspect('auto')\n",
+ " ax[i].tick_params(labelsize=14) \n",
+ " # extended North Atlantic region\n",
+ " ax[i].set_extent([-90, 30, 25, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if i > 2: # last row\n",
+ " #ax[i].set_xticks([0, 90, 180, 270], crs=ccrs.PlateCarree())\n",
+ " ax[i].set_xticks([-90, -60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[i].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if i in [0, 3]:\n",
+ " #ax[i].set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n",
+ " ax[i].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del i\n",
+ "\n",
+ "# plot contour for 8, 10 and 12 m/s in CTL\n",
+ "levp = [8, 10, 12]\n",
+ "ax[0].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_cmip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[1].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_amip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[2].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_amip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[3].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_icon_djf['T1C1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[4].contour(lons_plot, lats_mpi_plot,\n",
+ " fct.shiftgrid_copy(90., u850_mpi_djf['T1C1W1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[5].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_ipsl_djf['T1C1W1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "del levp\n",
+ "\n",
+ "# cmip5 coupled models\n",
+ "cf = ax[0].pcolormesh(lons_plot, lats_plot, du850_rcp85_plot,\n",
+ " vmin=-1.5, vmax=1.5, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[0].plot(lons_plot, jetlat_hist_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[0].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_rcp85_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-1.5, 2, 1.5)\n",
+ "cb = fig.colorbar(cf, ax=ax[0], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[0].set_title('CMIP5 RCP8.5\\n(37 models)', fontsize=16)\n",
+ "# amipfuture\n",
+ "cf = ax[1].pcolormesh(lons_plot, lats_plot, du850_amipfut_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[1].plot(lons_plot, jetlat_amip_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[1].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_amipfut_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[1], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[1].set_title('CMIP5 AmipFuture\\n(11 models)', fontsize=16)\n",
+ "# amip4k\n",
+ "cf = ax[2].pcolormesh(lons_plot, lats_plot, du850_amip4k_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[2].plot(lons_plot, jetlat_amip_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[2].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_amip4k_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[2], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[2].set_title('CMIP5 Amip4K\\n(11 models)', fontsize=16)\n",
+ "# ICON (locked clouds)\n",
+ "cf = ax[3].pcolormesh(lons_plot, lats_plot, du850_icon_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[3].plot(lons_plot, jetlat_icon_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[3].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(mask_icon_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[3], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[3].set_title('ICON', fontsize=16)\n",
+ "# MPI-ESM\n",
+ "cf = ax[4].pcolormesh(lons_plot, lats_mpi_plot, du850_mpi_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[4].plot(lons_plot, jetlat_mpi_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[4].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(mask_mpi_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[4], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[4].set_title('MPI-ESM', fontsize=16)\n",
+ "# IPSL-CM5A\n",
+ "cf = ax[5].pcolormesh(lons_plot, lats_plot, du850_ipsl_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[5].plot(lons_plot, jetlat_ipsl_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[5].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(mask_ipsl_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[5], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[5].set_title('IPSL-CM5A', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ " \n",
+ "fig.savefig('figure1a_1f.pdf', dpi=400, bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure2_jetresponse-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure2_jetresponse-checkpoint.ipynb
new file mode 100644
index 0000000..edd4ac9
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure2_jetresponse-checkpoint.ipynb
@@ -0,0 +1,738 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Jet response\n",
+ "\n",
+ "This script generates figure 2: jet shift vs. jet strengthening over the North Atlantic and over Europe for\n",
+ "- coupled CMIP5 models (RCP8.5)\n",
+ "- atmosphere CMIP5 models (amipFuture and amip4K)\n",
+ "- ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "# inset small plot in large plot\n",
+ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
+ "import cartopy.mpl.geoaxes\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "sims_cmip = ['historical', 'rcp85']\n",
+ "sims_amip = ['amip', 'amip4K', 'amipFuture']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T1C1W1\n",
+ "reading T2C2W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON simulations with locked clouds and interactive water vapor\n",
+ "runs_cld = ['T1C1', 'T2C2']\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM and IPSL-CM5A simulations with locked clouds and locked water vapor\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2']\n",
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (CMIP5 models)¶\n",
+ "\n",
+ "Note: All simulations were interpolated to the same grid and stored in numpy arrays with the jupyter notebook \"interpolate_cmip5_data_to_common_grid.ipynb\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "ntime = 360 # length of cmip/amip simulations in months (30 years)\n",
+ "\n",
+ "# coupled models: historical and RCP8.5 simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_hist = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_rcp85 = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_cmip):\n",
+ " u850_hist[:, m, :, :] = np.load(ipath + model + '_u850_historical.npy')\n",
+ " u850_rcp85[:, m, :, :] = np.load(ipath + model + '_u850_rcp85.npy')\n",
+ "del m, model\n",
+ "\n",
+ "# atmosphere models: amip, amip4K and amipFuture simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_amip = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amip4k = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amipfut = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_amip):\n",
+ " u850_amip[:, m, :, :] = np.load(ipath + model + '_u850_amip.npy')\n",
+ " u850_amip4k[:, m, :, :] = np.load(ipath + model + '_u850_amip4k.npy')\n",
+ " u850_amipfut[:, m, :, :] = np.load(ipath + model + '_u850_amipfut.npy')\n",
+ "del m, model\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean for all simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:64: RuntimeWarning: Mean of empty slice\n",
+ " monthly_mean[month] = np.nanmean(monthly_data[month], axis=0)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:71: RuntimeWarning: Mean of empty slice\n",
+ " seasons_dict[season] ], axis=0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM and IPSL-CM5A\n",
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# coupled CMIP5 models\n",
+ "u850_cmip_djf = np.full((len(sims_cmip), len(models_cmip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_cmip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_hist, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_cmip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_rcp85, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# atmosphere CMIP5 models\n",
+ "u850_amip_djf = np.full((len(sims_amip), len(models_amip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_amip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip4k, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[2, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amipfut, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# model mean for historical and amip simulations\n",
+ "u850_cmip_djf_mm = np.nanmean(u850_cmip_djf, axis=1)\n",
+ "u850_amip_djf_mm = np.nanmean(u850_amip_djf, axis=1)\n",
+ "\n",
+ "# delete variables with time information\n",
+ "del u850_icon, u850_mpi, u850_ipsl\n",
+ "del u850_hist, u850_rcp85, u850_amip, u850_amip4k, u850_amipfut"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate zonal-mean u850 over the North Atlantic (60W-0E) and over Europe (0E-25E)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:354: RuntimeWarning: Mean of empty slice\n",
+ " var_zm = np.nanmean(var[:, lonind_west:lonind_east+1], axis=1)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:362: RuntimeWarning: Mean of empty slice\n",
+ " var_zm = np.nanmean(var1[:, lonind_west:lonind_east+1], axis=1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# boundaries of regions\n",
+ "box_west = [0, -60]\n",
+ "box_east = [25, 0]\n",
+ "\n",
+ "# coupled models\n",
+ "u850_cmip_box = np.full((len(box_west), len(sims_cmip), len(models_cmip),\n",
+ " len(lats)), np.nan, dtype=float)\n",
+ "u850_cmip_mm_box = np.full((len(box_west), len(sims_cmip), len(lats)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_cmip)):\n",
+ " for m in range(len(models_cmip)):\n",
+ " u850_cmip_box[b, s, m, :] = fct.calcBoxZonalmean(u850_cmip_djf[s, m, :, :],\n",
+ " lats, lons, box_west[b], box_east[b])\n",
+ " del m\n",
+ " u850_cmip_mm_box[b, s, :] = fct.calcBoxZonalmean(u850_cmip_djf_mm[s, :, :],\n",
+ " lats, lons, box_west[b], box_east[b])\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# amip models\n",
+ "u850_amip_box = np.full((len(box_west), len(sims_amip), len(models_amip),\n",
+ " len(lats)), np.nan, dtype=float)\n",
+ "u850_amip_mm_box = np.full((len(box_west), len(sims_amip), len(lats)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_amip)):\n",
+ " for m in range(len(models_amip)):\n",
+ " u850_amip_box[b, s, m, :] = fct.calcBoxZonalmean(u850_amip_djf[s, m, :, :],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " del m\n",
+ " u850_amip_mm_box[b, s, :] = fct.calcBoxZonalmean(u850_amip_djf_mm[s, :, :],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# ICON\n",
+ "u850_icon_box = {}\n",
+ "for run in runs_cld:\n",
+ " u850icon = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " u850icon[b, :] = fct.calcBoxZonalmean(u850_icon_djf[run],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " u850_icon_box[run] = u850icon.copy()\n",
+ " del u850icon, b\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM, IPSL-CM5A\n",
+ "u850_mpi_box = {}; u850_ipsl_box = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850mpi = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " u850ipsl = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " u850mpi[b, :] = fct.calcBoxZonalmean(u850_mpi_djf[run],\n",
+ " lats_mpi, lons_mpi,\n",
+ " box_west[b], box_east[b])\n",
+ " u850ipsl[b, :] = fct.calcBoxZonalmean(u850_ipsl_djf[run],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " u850_mpi_box[run] = u850mpi.copy()\n",
+ " u850_ipsl_box[run] = u850ipsl.copy()\n",
+ " del u850mpi, u850ipsl, b\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude and jet strength based on the zonal-mean u850 profiles for the two regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Northern Hemisphere\n",
+ "latind0 = (np.abs(lats-0)).argmin() + 1\n",
+ "lats_NH = lats[latind0:]\n",
+ "\n",
+ "# coupled models\n",
+ "jetlat_cmip_box = np.full((len(box_west), len(sims_cmip),\n",
+ " len(models_cmip)), np.nan, dtype=float)\n",
+ "jetint_cmip_box = np.full((len(box_west), len(sims_cmip),\n",
+ " len(models_cmip)), np.nan, dtype=float)\n",
+ "jetlat_cmip_mm_box = np.full((len(box_west), len(sims_cmip)), np.nan,\n",
+ " dtype=float)\n",
+ "jetint_cmip_mm_box = np.full((len(box_west), len(sims_cmip)), np.nan,\n",
+ " dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_cmip)):\n",
+ " for m in range(len(models_cmip)):\n",
+ " jetlat_cmip_box[b, s, m], jetint_cmip_box[b, s, m] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_box[b, s, m, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del m\n",
+ " jetlat_cmip_mm_box[b, s], jetint_cmip_mm_box[b, s] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_mm_box[b, s, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# amip models\n",
+ "jetlat_amip_box = np.full((len(box_west), len(sims_amip),\n",
+ " len(models_amip)), np.nan, dtype=float)\n",
+ "jetint_amip_box = np.full((len(box_west), len(sims_amip),\n",
+ " len(models_amip)), np.nan, dtype=float)\n",
+ "jetlat_amip_mm_box = np.full((len(box_west), len(sims_amip)),\n",
+ " np.nan, dtype=float)\n",
+ "jetint_amip_mm_box = np.full((len(box_west), len(sims_amip)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_amip)):\n",
+ " for m in range(len(models_amip)):\n",
+ " jetlat_amip_box[b, s, m], jetint_amip_box[b, s, m] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_box[b, s, m, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del m\n",
+ " jetlat_amip_mm_box[b, s], jetint_amip_mm_box[b, s] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_mm_box[b, s, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# ICON\n",
+ "jetlat_icon_box = {}; jetint_icon_box = {}\n",
+ "for run in runs_cld:\n",
+ " jlat = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jint = np.full(len(box_west), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " jlat[b], jint[b] = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_box[run][b, latind0:], lats_NH)\n",
+ " jetlat_icon_box[run] = jlat.copy()\n",
+ " jetint_icon_box[run] = jint.copy()\n",
+ " del jlat, jint, b\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM, IPSL-CM5A\n",
+ "jetlat_mpi_box = {}; jetint_mpi_box = {}\n",
+ "jetlat_ipsl_box = {}; jetint_ipsl_box = {}\n",
+ "for run in runs_cldvap:\n",
+ " jlatmpi = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jintmpi = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jlatipsl = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jintipsl = np.full(len(box_west), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " jlatmpi[b], jintmpi[b] = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_box[run][b, latind0:], lats_NH)\n",
+ " jlatipsl[b], jintipsl[b] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_box[run][b, latind0:],\n",
+ " lats_NH, -999)\n",
+ " jetlat_mpi_box[run] = jlatmpi.copy()\n",
+ " jetint_mpi_box[run] = jintmpi.copy()\n",
+ " jetlat_ipsl_box[run] = jlatipsl.copy()\n",
+ " jetint_ipsl_box[run] = jintipsl.copy()\n",
+ " del jlatmpi, jintmpi, jlatipsl, jintipsl, b\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet responses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# coupled models\n",
+ "djetlat_cmip_box = jetlat_cmip_box[:, 1, :] - jetlat_cmip_box[:, 0, :]\n",
+ "djetint_cmip_box = jetint_cmip_box[:, 1, :] - jetint_cmip_box[:, 0, :]\n",
+ "djetlat_cmip_mm_box = jetlat_cmip_mm_box[:, 1] - jetlat_cmip_mm_box[:, 0]\n",
+ "djetint_cmip_mm_box = jetint_cmip_mm_box[:, 1] - jetint_cmip_mm_box[:, 0]\n",
+ "\n",
+ "# amip models\n",
+ "djetlat_amip4k_box = jetlat_amip_box[:, 1, :] - jetlat_amip_box[:, 0, :]\n",
+ "djetint_amip4k_box = jetint_amip_box[:, 1, :] - jetint_amip_box[:, 0, :]\n",
+ "djetlat_amip4k_mm_box = jetlat_amip_mm_box[:, 1] - jetlat_amip_mm_box[:, 0]\n",
+ "djetint_amip4k_mm_box = jetint_amip_mm_box[:, 1] - jetint_amip_mm_box[:, 0]\n",
+ "\n",
+ "djetlat_amipfut_box = jetlat_amip_box[:, 2, :] - jetlat_amip_box[:, 0, :]\n",
+ "djetint_amipfut_box = jetint_amip_box[:, 2, :] - jetint_amip_box[:, 0, :]\n",
+ "djetlat_amipfut_mm_box = jetlat_amip_mm_box[:, 2] - jetlat_amip_mm_box[:, 0]\n",
+ "djetint_amipfut_mm_box = jetint_amip_mm_box[:, 2] - jetint_amip_mm_box[:, 0]\n",
+ "\n",
+ "# ICON\n",
+ "djetlat_icon_box = jetlat_icon_box['T2C2'] - jetlat_icon_box['T1C1']\n",
+ "djetint_icon_box = jetint_icon_box['T2C2'] - jetint_icon_box['T1C1']\n",
+ "\n",
+ "# MPI-ESM\n",
+ "djetlat_mpi_box = jetlat_mpi_box['T2C2W2'] - jetlat_mpi_box['T1C1W1']\n",
+ "djetint_mpi_box = jetint_mpi_box['T2C2W2'] - jetint_mpi_box['T1C1W1']\n",
+ "\n",
+ "# IPSL-CM5A\n",
+ "djetlat_ipsl_box = jetlat_ipsl_box['T2C2W2'] - jetlat_ipsl_box['T1C1W1']\n",
+ "djetint_ipsl_box = jetint_ipsl_box['T2C2W2'] - jetint_ipsl_box['T1C1W1']\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot jet shift vs. jet strengthening over the North Atlantic and over Europe\n",
+ "\n",
+ "Add maps in which the region is highlighted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "color_cmip = 'dimgrey'\n",
+ "color_mean = 'lightseagreen'\n",
+ "color_icon = 'tab:orange'\n",
+ "color_mpi = 'tab:red'\n",
+ "color_ipsl = 'tab:purple'\n",
+ "ms = 7.5 # markersize\n",
+ "ylab = ['Europe', 'North Atlantic']\n",
+ "\n",
+ "fig, ax = plt.subplots(len(box_west), 3, figsize=(10, 6))\n",
+ "for b in range(len(box_west)):\n",
+ " # coupled models\n",
+ " ax[b, 0].plot(djetlat_cmip_box[b, :], djetint_cmip_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 0].plot(djetlat_cmip_mm_box[b], djetint_cmip_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # amip models\n",
+ " # amipFuture\n",
+ " ax[b, 1].plot(djetlat_amipfut_box[b, :], djetint_amipfut_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 1].plot(djetlat_amipfut_mm_box[b], djetint_amipfut_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # amip4K\n",
+ " ax[b, 2].plot(djetlat_amip4k_box[b, :], djetint_amip4k_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_amip4k_mm_box[b], djetint_amip4k_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # ICON, MPI-ESM, IPSL-CM5A\n",
+ " ax[b, 2].plot(djetlat_icon_box[b], djetint_icon_box[b],\n",
+ " linestyle='none', marker='o', color=color_icon,\n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_mpi_box[b], djetint_mpi_box[b],\n",
+ " linestyle='none', marker='o', color=color_mpi,\n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_ipsl_box[b], djetint_ipsl_box[b],\n",
+ " linestyle='none', marker='o', color=color_ipsl,\n",
+ " markersize=ms)\n",
+ " \n",
+ " # ylabel\n",
+ " ax[b, 0].set_ylabel(ylab[b] + '\\n$\\Delta$u$_{jet}$ [m s$^{-1}$]', fontsize=15)\n",
+ " ax[b, 0].yaxis.set_label_coords(-0.05, 0.5)\n",
+ "del b\n",
+ "ax[0, 0].set_title('RCP8.5', fontsize=16)\n",
+ "ax[0, 1].set_title('AmipFuture', fontsize=16)\n",
+ "ax[0, 2].set_title('Amip4K', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# plot maps for Europe and North Atlantic as small inserted plot\n",
+ "# Europe\n",
+ "inset_ax = inset_axes(ax[0, 0], \n",
+ " width=\"50%\",\n",
+ " height=\"50%\",\n",
+ " #loc=\"upper right\",\n",
+ " bbox_to_anchor=(0.15, 0.1, 1, 1),\n",
+ " bbox_transform=ax[0, 0].transAxes,\n",
+ " axes_class=cartopy.mpl.geoaxes.GeoAxes,\n",
+ " axes_kwargs=dict(map_projection=ccrs.PlateCarree(central_longitude=-90.)))\n",
+ "inset_ax.coastlines()\n",
+ "inset_ax.set_aspect(1.5)\n",
+ "inset_ax.tick_params(labelsize=12)\n",
+ "# extended North Atlantic region\n",
+ "inset_ax.set_extent([-80, 30, 30, 70], ccrs.PlateCarree())\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# xaxis: longitudes\n",
+ "inset_ax.set_xticks([0, 25], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "inset_ax.xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "# mark west and east boundaries of Europe\n",
+ "lonwest = 0; loneast = 25; latsout=30.8; latnort=69.2\n",
+ "# left vertical line\n",
+ "inset_ax.plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# right vertical line\n",
+ "inset_ax.plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# upper horizontal line\n",
+ "inset_ax.plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "inset_ax.plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "del inset_ax\n",
+ "\n",
+ "# North Atlantic\n",
+ "inset_ax = inset_axes(ax[1, 0], \n",
+ " width=\"50%\",\n",
+ " height=\"50%\",\n",
+ " #loc=\"upper right\",\n",
+ " bbox_to_anchor=(0.15, 0.1, 1, 1),\n",
+ " bbox_transform=ax[1, 0].transAxes,\n",
+ " axes_class=cartopy.mpl.geoaxes.GeoAxes, \n",
+ " axes_kwargs=dict(map_projection=ccrs.PlateCarree(central_longitude=-90.)))\n",
+ "inset_ax.coastlines()\n",
+ "inset_ax.set_aspect(1.7)\n",
+ "inset_ax.tick_params(labelsize=12)\n",
+ "# extended North Atlantic region\n",
+ "inset_ax.set_extent([-80, 30, 30, 70], ccrs.PlateCarree())\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# xaxis: longitudes\n",
+ "inset_ax.set_xticks([-60, 0], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "inset_ax.xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "# mark west and east boundaries of Europe\n",
+ "lonwest = -60; loneast = 0; latsout=30.8; latnort=69.2\n",
+ "# left vertical line\n",
+ "inset_ax.plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# right vertical line\n",
+ "inset_ax.plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# upper horizontal line\n",
+ "inset_ax.plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "inset_ax.plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "del inset_ax\n",
+ "\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].tick_params(labelsize=14)\n",
+ " ax[i].spines['right'].set_color('none')\n",
+ " ax[i].spines['top'].set_color('none')\n",
+ " ax[i].spines['left'].set_position('zero')\n",
+ " ax[i].spines['bottom'].set_position('zero')\n",
+ " # x-ticks\n",
+ " ax[i].xaxis.set_ticks([-5, -2.5, 2.5, 5, 7.5, 10])#, 15])\n",
+ " ax[i].xaxis.set_ticklabels([-5, -2.5, 2.5, 5, 7.5, 10])#, 15])\n",
+ " ax[i].set_xlim(-2.5, 7.5)#7)\n",
+ " # y-ticks\n",
+ " ax[i].yaxis.set_ticks([-1, 1, 3, 5])\n",
+ " ax[i].yaxis.set_ticklabels([-1, 1, 3, 5])\n",
+ " ax[i].set_ylim(-1.6, 5)\n",
+ " # labels\n",
+ " ax[i].set_xlabel(r'$\\Delta \\varphi_{jet}$ [$^{\\circ}$]', fontsize=16)\n",
+ " ax[i].xaxis.set_label_coords(0.5, -0.05)\n",
+ " # hide x labels and tick labels for top plots and y ticks for right plots.\n",
+ " ax[i].label_outer()\n",
+ "del i\n",
+ "\n",
+ "# text for colors\n",
+ "fig.text(0.88, 0.82, 'CMIP models', color=color_cmip, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.78, 'CMIP model mean', color=color_mean, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.74, 'ICON', color=color_icon, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.70, 'MPI-ESM', color=color_mpi, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.66, 'IPSL-CM5A', color=color_ipsl, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.savefig('figure2a_2f.pdf', bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax\n",
+ "\n",
+ "del color_cmip, color_mean, color_icon\n",
+ "del color_mpi, color_ipsl, ms\n",
+ "del ylab"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure3_du850_icon_mpi_ipsl_total_vs_cloud-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure3_du850_icon_mpi_ipsl_total_vs_cloud-checkpoint.ipynb
new file mode 100644
index 0000000..f9cad0b
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure3_du850_icon_mpi_ipsl_total_vs_cloud-checkpoint.ipynb
@@ -0,0 +1,635 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: total response vs. cloud impact\n",
+ "\n",
+ "This script generates figure 3: maps of total zonal wind response vs. cloud impact in ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated\n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (MPI-ESM, IPSL-CM5A with locked clouds and locked water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON with locked clouds and interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons_icon = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months,\n",
+ " seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del u850_mpi, u850_ipsl, u850_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_mpi = np.full((len(response_cldvap), len(lats_mpi),\n",
+ " len(lons_mpi)), np.nan, dtype=float)\n",
+ "du850_ipsl = np.full((len(response_cldvap), len(lats_ipsl),\n",
+ " len(lons_ipsl)), np.nan, dtype=float)\n",
+ "\n",
+ "du850_mpi[0, :, :], du850_mpi[1, :, :], du850_mpi[2, :, :], \\\n",
+ "du850_mpi[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_mpi_djf['T1C1W1'], u850_mpi_djf['T2C2W2'],\n",
+ " u850_mpi_djf['T1C2W2'], u850_mpi_djf['T2C1W1'],\n",
+ " u850_mpi_djf['T1C2W1'], u850_mpi_djf['T1C1W2'],\n",
+ " u850_mpi_djf['T2C2W1'], u850_mpi_djf['T2C1W2'])\n",
+ "du850_ipsl[0, :, :], du850_ipsl[1, :, :], du850_ipsl[2, :, :], \\\n",
+ "du850_ipsl[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_ipsl_djf['T1C1W1'], u850_ipsl_djf['T2C2W2'],\n",
+ " u850_ipsl_djf['T1C2W2'], u850_ipsl_djf['T2C1W1'],\n",
+ " u850_ipsl_djf['T1C2W1'], u850_ipsl_djf['T1C1W2'],\n",
+ " u850_ipsl_djf['T2C2W1'], u850_ipsl_djf['T2C1W2'])\n",
+ "\n",
+ "du850_icon = np.full((len(response_cld), len(lats_icon),\n",
+ " len(lons_icon)), np.nan, dtype=float)\n",
+ "du850_icon[0, :, :], du850_icon[1, :, :], du850_icon[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_icon_djf['T1C1'], u850_icon_djf['T2C2'],\n",
+ " u850_icon_djf['T1C2'], u850_icon_djf['T2C1'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal-mean zonal wind.\n",
+ "\n",
+ "The seasonal-mean zonal wind is calculated with cdo (Climate Data Operators):\n",
+ "\n",
+ "* select zonal wind: cdo selvar,u ICON-NWP_AMIP_run_3d_mm.nc ICON-NWP_AMIP_run_3d_mm.uwind.nc\n",
+ "* calculate the seasonal mean: cdo seasmean ICON-NWP_AMIP_run_3d_mm.uwind.nc ICON-NWP_AMIP_run_3d_mm.uwind.seasmean.nc\n",
+ "\n",
+ "\"run\" can be any of the simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ICON\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "mask_read = np.load(ipath + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_icon = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cld])\n",
+ "del mask_read, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "mask_read = np.load('../../MPI-ESM/MPI-ESM_du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_mpi = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cldvap])\n",
+ "# shift latitudes to go from South to North and not from North to South\n",
+ "du850_mask_mpi = du850_mask_mpi[:, ::-1, :]\n",
+ "del mask_read\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "mask_read = np.load('../../IPSL-CM5A/IPSL-CM5A_du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_ipsl = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cldvap])\n",
+ "del mask_read"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude in the control simulations for the Northern Hemisphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# latitudes in Northern Hemisphere\n",
+ "latind0 = (np.abs(lats_icon-0)).argmin() + 1\n",
+ "lats_NH = lats_icon[latind0:]\n",
+ "\n",
+ "# shift longitudes from 0deg...360deg to -270deg...90deg for visualization reasons\n",
+ "# and look at NH\n",
+ "u850_icon_shift, lons_shift = fct.shiftgrid_copy(90.,\n",
+ " u850_icon_djf['T1C1'][latind0:, :],\n",
+ " lons_icon, start=False)\n",
+ "u850_mpi_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_mpi_djf['T1C1W1'][latind0:, :],\n",
+ " lons_mpi, start=False)\n",
+ "u850_ipsl_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_ipsl_djf['T1C1W1'][latind0:, :],\n",
+ " lons_ipsl, start=False)\n",
+ "\n",
+ "jetlat_icon_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_mpi_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_ipsl_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "for lo in range(lons_shift.size):\n",
+ " # ICON\n",
+ " jetlat_icon_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_shift[:, lo], lats_NH)\n",
+ " # MPI-ESM\n",
+ " jetlat_mpi_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_shift[:, lo], lats_NH)\n",
+ " # IPSL-CM5A\n",
+ " jetlat_ipsl_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ "del lo\n",
+ "\n",
+ "del u850_icon_shift, u850_mpi_shift, u850_ipsl_shift, lons_shift"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot of total response and cloud impact\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "lons_plot = fct.shiftgrid_copy(90., du850_icon, lons_icon, start=False)[1]\n",
+ "\n",
+ "# North Atlantic region\n",
+ "lonind_west = (np.abs(lons_plot--90)).argmin() # find index of 90°W\n",
+ "lonind_east = (np.abs(lons_plot-35)).argmin() # find index of 35°E\n",
+ "latind_sout = (np.abs(lats_icon-20)).argmin() # find index of 20°N\n",
+ "latind_nort = (np.abs(lats_icon-80)).argmin() # find index of 80°N\n",
+ "\n",
+ "lons_plot = lons_plot[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats_icon[latind_sout:latind_nort+1]\n",
+ "\n",
+ "# MPI-ESM uses slightly different latitudes\n",
+ "latind_sout_mpi = (np.abs(lats_mpi-20)).argmin() # find index of 20°N\n",
+ "latind_nort_mpi = (np.abs(lats_mpi-80)).argmin() # find index of 80°N\n",
+ "lats_mpi_plot = lats_mpi[latind_sout_mpi:latind_nort_mpi+1]\n",
+ "\n",
+ "# shift zonal wind fields and masks\n",
+ "du850_icon_plot = fct.shiftgrid_copy(90., du850_icon, lons_icon,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_icon_plot = fct.shiftgrid_copy(90., du850_mask_icon, lons_icon,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_mpi_plot = fct.shiftgrid_copy(90., du850_mpi, lons_mpi,\n",
+ " start=False)[0][:,\n",
+ " latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_mpi_plot = fct.shiftgrid_copy(90., du850_mask_mpi, lons_mpi,\n",
+ " start=False)[0][:,\n",
+ " latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_ipsl_plot = fct.shiftgrid_copy(90., du850_ipsl, lons_ipsl,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_ipsl_plot = fct.shiftgrid_copy(90., du850_mask_ipsl, lons_ipsl,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the total response and cloud impact on u850"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mods = ['ICON', 'MPI-ESM', 'IPSL-CM5A']\n",
+ "vlim_tot = 4\n",
+ "vlim_cld = 2\n",
+ "# boundaries of region with robust response\n",
+ "lonwest = -4; loneast = 25; latsout = 50; latnort = 59\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10,7),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "for i in range(ax.shape[0]):\n",
+ " for k in range(ax.shape[1]):\n",
+ " ax[i, k].coastlines(rasterized=True)\n",
+ " ax[i, k].set_aspect('auto')\n",
+ " ax[i, k].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[i, k].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if i == 2: # last row\n",
+ " ax[i, k].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[i, k].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if k == 0: # first column\n",
+ " ax[i, k].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i, k].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[i, k].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[i, k].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[i, k].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[i, k].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " del k\n",
+ "del i\n",
+ "del lonwest, loneast, latsout, latnort\n",
+ "# total response\n",
+ "# ICON\n",
+ "k = response_cldvap.index('total') # 0\n",
+ "cf = ax[0, 0].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_icon_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[0, 0].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_icon_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# MPI-ESM\n",
+ "ax[1, 0].pcolormesh(lons_plot, lats_mpi_plot,\n",
+ " du850_mpi_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[1, 0].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(1*mask_mpi_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# IPSL-CM5A\n",
+ "ax[2, 0].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_ipsl_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[2, 0].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_ipsl_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "del k\n",
+ "##################################################\n",
+ "# cloud impact\n",
+ "k = response_cldvap.index('cloud') # 2\n",
+ "# ICON\n",
+ "cf1 = ax[0, 1].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_icon_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[0, 1].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_icon_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# MPI-ESM\n",
+ "ax[1, 1].pcolormesh(lons_plot, lats_mpi_plot,\n",
+ " du850_mpi_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[1, 1].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(1*mask_mpi_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# IPSL-CM5A\n",
+ "ax[2, 1].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_ipsl_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[2, 1].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_ipsl_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "del k\n",
+ "##################################################\n",
+ "# jet latitude in control simulation\n",
+ "for k in range(ax.shape[1]):\n",
+ " ax[0, k].plot(lons_plot, jetlat_icon_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " ax[1, k].plot(lons_plot, jetlat_mpi_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " ax[2, k].plot(lons_plot, jetlat_ipsl_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "\n",
+ "# titles\n",
+ "ax[0, 0].set_title('total', fontsize=16)\n",
+ "ax[0, 1].set_title('cloud', fontsize=16)\n",
+ "\n",
+ "# labels for models\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(-0.15, 0.51, mods[i], va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=16, transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.1)\n",
+ "cbar_ax = fig.add_axes([0.09, 0.0, 0.423, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf\n",
+ "\n",
+ "cbar_ax = fig.add_axes([0.545, 0.0, 0.423, 0.023]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf1, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf1\n",
+ "\n",
+ "fig.savefig('figure3a_3f.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "\n",
+ "del mods, vlim_tot, vlim_cld"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure4_cloudheating_change-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure4_cloudheating_change-checkpoint.ipynb
new file mode 100644
index 0000000..745548b
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure4_cloudheating_change-checkpoint.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Changes in atmospheric cloud radiative heating.\n",
+ "\n",
+ "This script generates figure 4: \n",
+ "zonal-mean changes and maps of upper-tropospheric changes in atmospheric cloud-radiative heating during DJF for ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify the months of the year (needed for DJF mean)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:50: RuntimeWarning: Mean of empty slice\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LMD: change order of levels to go from TOA to surface\n",
+ "False -5.538915423676372 5.984612123575062\n",
+ "True -8.794642752036452 10.795256146229804\n",
+ "False -14.69999528490007 12.625148193910718\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "ifile = 'ICON-NWP_prp_T1C1_vs_T1C2_3d_40PL_mm.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_icon = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_icon = np.array(ncfile.variables['lev'][:].data)\n",
+ "dQsw = np.array(ncfile.variables['dQ_c_srad'][:].data) # shortwave\n",
+ "dQlw = np.array(ncfile.variables['dQ_c_trad'][:].data) # longwave\n",
+ "ncfile.close()\n",
+ "# add shortwave and longwave components\n",
+ "dQ_icon = (dQsw + dQlw) * 86400 # K/s -> K/day\n",
+ "del ipath, ifile, ncfile, dQsw, dQlw\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_icon_djf = np.nanmean(np.array([dQ_icon[months.index('Jan'), :, :, :],\n",
+ " dQ_icon[months.index('Feb'), :, :, :],\n",
+ " dQ_icon[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_icon\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_icon[0] > levs_icon[1]:\n",
+ " print('ICON: change order of levels to go from TOA to surface')\n",
+ " levs_icon = levs_icon[::-1]\n",
+ " dQ_icon_djf = dQ_icon_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "ipath = '../../MPI-ESM/'\n",
+ "ifile = 'MPI-ESM_prp_T1C1W1_vs_T1C2W1_3d_mm.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_mpi = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_mpi = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_mpi = np.array(ncfile.variables['plev_2'][:].data)\n",
+ "dQsw = np.array(ncfile.variables['dQ_cld_srad'][:].data) # shortwave\n",
+ "dQlw = np.array(ncfile.variables['dQ_cld_trad'][:].data) # longwave\n",
+ "ncfile.close()\n",
+ "# add shortwave and longwave components\n",
+ "dQ_mpi = (dQsw + dQlw) * 86400 # K/s -> K/day\n",
+ "del ipath, ifile, ncfile, dQsw, dQlw\n",
+ "\n",
+ "# set missing values (-9e33) to NaN\n",
+ "dQ_mpi[dQ_mpi <= -8e33] = np.nan\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_mpi_djf = np.nanmean(np.array([dQ_mpi[months.index('Jan'), :, :, :],\n",
+ " dQ_mpi[months.index('Feb'), :, :, :],\n",
+ " dQ_mpi[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_mpi\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_mpi[0] > levs_mpi[1]:\n",
+ " print('ECHAM: change order of levels to go from TOA to surface')\n",
+ " levs_mpi = levs_mpi[::-1]\n",
+ " dQ_mpi_djf = dQ_mpi_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "ipath = '../../IPSL-CM5A/'\n",
+ "ifile = 'IPSL-CM5A_prp_T1C1W1_vs_T1C2W1_3d_mm.remapcon.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_ipsl = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_ipsl = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_ipsl = np.array(ncfile.variables['presnivs'][:].data)\n",
+ "dtswr = np.array(ncfile.variables['dtswr'][:].data)\n",
+ "dtlwr = np.array(ncfile.variables['dtlwr'][:].data)\n",
+ "dtswr_prpc = np.array(ncfile.variables['dtswr_prpc'][:].data)\n",
+ "dtlwr_prpc = np.array(ncfile.variables['dtlwr_prpc'][:].data)\n",
+ "ncfile.close()\n",
+ "# get change in cloud-radiative heating in K/day\n",
+ "# multiply with 86400 to get K/s -> K/day\n",
+ "dQ_ipsl = 86400 * ((dtswr_prpc + dtlwr_prpc) - (dtswr + dtlwr))\n",
+ "del ifile, ncfile, dtswr, dtlwr, dtswr_prpc, dtlwr_prpc\n",
+ "\n",
+ "# set missing values (9.9e36) to NaN\n",
+ "dQ_ipsl[dQ_ipsl >= 9e36] = np.nan\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_ipsl_djf = np.nanmean(np.array([dQ_ipsl[months.index('Jan'), :, :, :],\n",
+ " dQ_ipsl[months.index('Feb'), :, :, :],\n",
+ " dQ_ipsl[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_ipsl\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_ipsl[0] > levs_ipsl[1]:\n",
+ " print('LMD: change order of levels to go from TOA to surface')\n",
+ " levs_ipsl = levs_ipsl[::-1]\n",
+ " dQ_ipsl_djf = dQ_ipsl_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# check that all missing values are replaced by NaN's\n",
+ "print(np.isnan(dQ_icon_djf).any(), np.nanmin(dQ_icon_djf), np.nanmax(dQ_icon_djf))\n",
+ "print(np.isnan(dQ_mpi_djf).any(), np.nanmin(dQ_mpi_djf), np.nanmax(dQ_mpi_djf))\n",
+ "print(np.isnan(dQ_ipsl_djf).any(), np.nanmin(dQ_ipsl_djf), np.nanmax(dQ_ipsl_djf))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read tropopause height (with interpolated missing values) and calculate vertical mean dQ for a 200hPa thick layer below tropopause.\n",
+ "\n",
+ "NOTE: pressure value that is closer to the ground must be the second plev value that is given to the function get_verticalmean_overp_tropo (due to order of levels in levs array)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../tropopause_T1C1_T1C1W1/'\n",
+ "\n",
+ "# ICON\n",
+ "ifile = 'ICON-NWP_AMIP_T1C1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_icon.size, lons_icon.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_icon.size, lons_icon.size), np.nan, dtype=int)\n",
+ "for la in range(lats_icon.size):\n",
+ " for lo in range(lons_icon.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_icon-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_icon-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_icon_vmean = fct.get_verticalmean_overp_tropo(dQ_icon_djf, levs_icon,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_icon = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "ifile = 'MPI-ESM_T1C1W1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_mpi.size, lons_mpi.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_mpi.size, lons_mpi.size), np.nan, dtype=int)\n",
+ "for la in range(lats_mpi.size):\n",
+ " for lo in range(lons_mpi.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_mpi-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_mpi-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_mpi_vmean = fct.get_verticalmean_overp_tropo(dQ_mpi_djf, levs_mpi,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_mpi = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "ifile = 'IPSL-CM5A_T1C1W1_tropopause_DJF_remapcon_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_ipsl.size, lons_ipsl.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_ipsl.size, lons_ipsl.size), np.nan, dtype=int)\n",
+ "for la in range(lats_ipsl.size):\n",
+ " for lo in range(lons_ipsl.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_ipsl-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_ipsl-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_ipsl_vmean = fct.get_verticalmean_overp_tropo(dQ_ipsl_djf, levs_ipsl,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_ipsl = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get zonal-mean changes in atmospheric cloud-radiative heating."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "dQ_icon_djf_zm = np.nanmean(dQ_icon_djf, axis=2)\n",
+ "dQ_mpi_djf_zm = np.nanmean(dQ_mpi_djf, axis=2)\n",
+ "dQ_ipsl_djf_zm = np.nanmean(dQ_ipsl_djf, axis=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot zonal-mean and maps of upper-tropospheric changes in atmospheric cloud-radiative heating in ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes from 0deg...360deg to -90deg...270deg for visualization reasons\n",
+ "dQ_icon_vmean_shift, lons_icon_shift = fct.shiftgrid_copy(90., dQ_icon_vmean, lons_icon, start=False)\n",
+ "dQ_mpi_vmean_shift, lons_mpi_shift = fct.shiftgrid_copy(90., dQ_mpi_vmean, lons_mpi, start=False)\n",
+ "dQ_ipsl_vmean_shift, lons_ipsl_shift = fct.shiftgrid_copy(90., dQ_ipsl_vmean, lons_ipsl, start=False)\n",
+ "\n",
+ "# limit for colorbar\n",
+ "vlim = 1\n",
+ "\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 7))#figsize(10))\n",
+ "# zonal-mean change\n",
+ "cf0 = ax[0, 0].pcolormesh(lats_icon, levs_icon/100, dQ_icon_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[1, 0].pcolormesh(lats_mpi, levs_mpi/100, dQ_mpi_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[2, 0].pcolormesh(lats_ipsl, levs_ipsl/100, dQ_ipsl_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "# zonal-mean tropopause\n",
+ "ax[0, 0].plot(lats_icon, tropo_icon/100, color='tab:green')\n",
+ "ax[1, 0].plot(lats_mpi, tropo_mpi/100, color='tab:green')\n",
+ "ax[2, 0].plot(lats_ipsl, tropo_ipsl/100, color='tab:green')\n",
+ "\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].tick_params(labelsize=13)\n",
+ " ax[i, 0].set(xticks=np.arange(-90, 91, 30),\n",
+ " xticklabels=['90S', '60S', '30S', '0', '30N', '60N' ,'90N'],\n",
+ " xlim=(-90, 90))\n",
+ " ax[i, 0].set_yticks(np.arange(0, 1100, 200))\n",
+ " ax[i, 0].set_ylim(1000, 10)\n",
+ " ax[i, 0].set_ylabel('Pressure [hPa]', fontsize=15)\n",
+ "del i\n",
+ "ax[2, 0].set_xlabel('Latitude [$^{\\circ}$]', fontsize=15)\n",
+ "# text for models\n",
+ "mods = ['ICON', 'MPI-ESM', 'IPSL-CM5A']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(-0.25, 0.51, mods[i], va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=17, transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "###################################################################\n",
+ "# maps\n",
+ "ax1 = plt.subplot(3, 2, 2, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax1.pcolormesh(lons_icon_shift, lats_icon, dQ_icon_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "ax2 = plt.subplot(3, 2, 4, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax2.pcolormesh(lons_icon_shift, lats_icon, dQ_mpi_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "ax3 = plt.subplot(3, 2, 6, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax3.pcolormesh(lons_icon_shift, lats_icon, dQ_ipsl_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "\n",
+ "for axis in [ax1, ax2, ax3]:\n",
+ " axis.coastlines()\n",
+ " axis.set_aspect('auto')\n",
+ " axis.tick_params(labelsize=13)\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " axis.set_xticks([-150, -70, 40, 120], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " axis.xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " axis.set_yticks([-60, -30, 0, 30, 60],\n",
+ " crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " axis.yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del axis\n",
+ "\n",
+ "# add lines for tropical regions in ICON\n",
+ "latnort = 30 # northern boundary: 30°N\n",
+ "latsout = -30 # southern boundary: 30°S\n",
+ "lon1 = -150 # 150°W\n",
+ "lon2 = -70 # 70°W\n",
+ "lon3 = 40 # 40°E\n",
+ "lon4 = 120 # 120°E\n",
+ "# upper horizontal line\n",
+ "ax1.plot([90, -269.999], [latnort, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "ax1.plot([90, -269.999], [latsout, latsout],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "# vertical lines\n",
+ "ax1.plot([lon1, lon1], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon2, lon2], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon3, lon3], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon4, lon4], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "del latnort, latsout, lon1, lon2, lon3, lon4\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs1 = ['(a)', '(b)', '(c)']\n",
+ "labs2 = ['(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(0.01, 1.02, labs1[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "for i, axis in enumerate([ax1, ax2, ax3]):\n",
+ " axis.text(0.01, 1.02, labs2[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=axis.transAxes)\n",
+ "del i, axis\n",
+ "del labs1, labs2\n",
+ "\n",
+ "del ax1, ax2, ax3\n",
+ "\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar\n",
+ "fig.subplots_adjust(bottom=0.12)#(right=0.8)\n",
+ "clevs = np.array([-1, -0.5, 0, 0.5, 1])\n",
+ "cbar_ax = fig.add_axes([0.3, 0.0, 0.5, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both',\n",
+ " ticks=clevs)\n",
+ "cb.set_label('K day$^{-1}$', fontsize=14, labelpad=1)\n",
+ "cb.ax.tick_params(labelsize=13)\n",
+ "del cbar_ax, cb, cf0, clevs\n",
+ "\n",
+ "fig.savefig('figure4a_4f.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax\n",
+ "\n",
+ "del dQ_icon_vmean_shift, lons_icon_shift, dQ_mpi_vmean_shift, \\\n",
+ " lons_mpi_shift, dQ_ipsl_vmean_shift, lons_ipsl_shift\n",
+ "del vlim"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure5_tr_ml_po-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure5_tr_ml_po-checkpoint.ipynb
new file mode 100644
index 0000000..d490148
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure5_tr_ml_po-checkpoint.ipynb
@@ -0,0 +1,440 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: tropical, midlatitude and polar cloud impacts\n",
+ "\n",
+ "This script generates figure 5: maps of the impacts of tropical, midlatitude and polar cloud changes on the zonal wind response in ICON."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_ML = ['T1C2ML', 'T1C1ML', 'T2C2ML', 'T2C1ML']\n",
+ "runs_reg_PO = ['T1C2PO', 'T1C1PO', 'T2C2PO', 'T2C1PO']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_ML + runs_reg_PO\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud ML', 'cloud notML',\n",
+ " 'cloud PO', 'cloud notPO']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind at 850 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2ML\n",
+ "reading T1C1ML\n",
+ "reading T2C2ML\n",
+ "reading T2C1ML\n",
+ "reading T1C2PO\n",
+ "reading T1C1PO\n",
+ "reading T2C2PO\n",
+ "reading T2C1PO\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850 = {}\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculate DJF mean\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Calculate DJF mean')\n",
+ "u850_djf = {}\n",
+ "for run in runs_all:\n",
+ " u850_djf[run] = fct.calcMonthlyandSeasonMean(u850[run], months, seasons)[1]['DJF']\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF jet latitude in control simulation (T1C1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jetlat_sh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "jetlat_nh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "for i in range(lons.shape[0]):\n",
+ " jetlat_sh_T1C1[i], _, jetlat_nh_T1C1[i], _ = \\\n",
+ " fct.get_eddyjetlatint(u850_djf['T1C1'][:, i], lats)\n",
+ "del i"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_djf = np.full((len(response_all), len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "# total, SST, cloud\n",
+ "du850_djf[0, :, :], du850_djf[1, :, :], du850_djf[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, du850_djf[k*2+3, :, :], du850_djf[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'],\n",
+ " u850_djf[runs_all[4:][k*4]],\n",
+ " u850_djf[runs_all[4:][k*4+1]],\n",
+ " u850_djf[runs_all[4:][k*4+2]],\n",
+ " u850_djf[runs_all[4:][k*4+3]])\n",
+ "del k\n",
+ "del u850, u850_djf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal mean zonal wind."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath_bs = '../../ICON-NWP_lockedclouds/'\n",
+ "du850_mask_sm_bs = np.load(ipath_bs + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "del ipath_bs\n",
+ "\n",
+ "# only store masks for tropical, midlatitude and polar cloud impacts\n",
+ "response_sel = ['cloud TR', 'cloud ML', 'cloud PO']\n",
+ "du850_djf_mask = np.array([du850_mask_sm_bs[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_sel])\n",
+ "\n",
+ "del du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "du850_djf_shift, lons_shift = fct.shiftgrid_copy(90., du850_djf, lons, start=False)\n",
+ "du850_mask_shift, _ = fct.shiftgrid_copy(90., du850_djf_mask, lons, start=False)\n",
+ "jetlat_nh_shift = fct.shiftgrid_copy(90., jetlat_nh_T1C1, lons, start=False)[0]\n",
+ "\n",
+ "# find indices of border of North Atlantic/Europe box\n",
+ "# -> makes plotting faster (important if mask is plotted)\n",
+ "lonind_west = (np.abs(lons_shift--72)).argmin()\n",
+ "lonind_east = (np.abs(lons_shift-32)).argmin()\n",
+ "latind_sout = (np.abs(lats-28)).argmin()\n",
+ "latind_nort = (np.abs(lats-72)).argmin()\n",
+ "\n",
+ "lons_plot = lons_shift[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "jetlat_nh_plot = jetlat_nh_shift[lonind_west:lonind_east+1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of tropical, midlatitude and polar cloud impacts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x504 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response_plot = ['cloud TR', 'cloud ML', 'cloud PO']\n",
+ "\n",
+ "du850_plot = np.array([du850_djf_shift[response_all.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "mask_plot = np.array([du850_mask_shift[response_sel.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "\n",
+ "# box around region with jet exit strengthening\n",
+ "#lonwest = -4; loneast = 25; latsout = 52; latnort = 61\n",
+ "lonwest = -4; loneast = 26; latsout = 52; latnort = 62\n",
+ "\n",
+ "# plot\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 1, figsize=(5, 7),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "labs = ['(a)', '(b)', '(c)']\n",
+ "for r in range(du850_plot.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[r].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set yticks for longitudes\n",
+ " ax[r].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[r].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[r].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[r].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[r].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # jet latitude in control run\n",
+ " ax[r].plot(lons_plot, jetlat_nh_plot, marker='x',\n",
+ " color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " # plot different effects\n",
+ " cf0 = ax[r].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_plot[r, :, :],\n",
+ " vmin=-1.5, vmax=1.5, cmap=mymap2,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " # stippling for significance\n",
+ " ax[r].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_plot[r, :, :], 0),\n",
+ " hatch='.....', alpha=0.,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " ax[r].set_title(response_plot[r][0:8], fontsize=16)\n",
+ " # a), b) etc for subplots\n",
+ " ax[r].text(0.01, 1.02, labs[r], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[r].transAxes)\n",
+ "del r\n",
+ "# xaxis: longitudes\n",
+ "ax[2].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "ax[2].xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "#clevs = [-1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5]\n",
+ "fig.subplots_adjust(bottom=0.08)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.13, 0.0, 0.804, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')#,\n",
+ " #ticks=clevs)\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0#, clevs\n",
+ "\n",
+ "fig.savefig('figure5a_5c.pdf', dpi=200, bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, du850_plot, mask_plot\n",
+ "del lonwest, loneast, latsout, latnort"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure6_tropical_cloudimpacts-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure6_tropical_cloudimpacts-checkpoint.ipynb
new file mode 100644
index 0000000..a00fdd4
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure6_tropical_cloudimpacts-checkpoint.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: tropical cloud impacts\n",
+ " \n",
+ "This script generates figure 6: maps of the impacts of western tropical Pacific, eastern tropical Pacific, tropical Atlantic, and Indian Ocean cloud changes on the zonal wind response in ICON."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind at 850 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2TA\n",
+ "reading T1C1TA\n",
+ "reading T2C2TA\n",
+ "reading T2C1TA\n",
+ "reading T1C2IO\n",
+ "reading T1C1IO\n",
+ "reading T2C2IO\n",
+ "reading T2C1IO\n",
+ "reading T1C2WP\n",
+ "reading T1C1WP\n",
+ "reading T2C2WP\n",
+ "reading T2C1WP\n",
+ "reading T1C2EP\n",
+ "reading T1C1EP\n",
+ "reading T2C2EP\n",
+ "reading T2C1EP\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850 = {}\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u850_djf = {}\n",
+ "for run in runs_all:\n",
+ " u850_djf[run] = fct.calcMonthlyandSeasonMean(u850[run], months, seasons)[1]['DJF']\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF jet latitude in control simulation (T1C1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jetlat_sh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "jetlat_nh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "for i in range(lons.shape[0]):\n",
+ " jetlat_sh_T1C1[i], _, jetlat_nh_T1C1[i], _ = \\\n",
+ " fct.get_eddyjetlatint(u850_djf['T1C1'][:, i], lats)\n",
+ "del i"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_djf = np.full((len(response_all), len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "# total, SST, cloud\n",
+ "du850_djf[0, :, :], du850_djf[1, :, :], du850_djf[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, du850_djf[k*2+3, :, :], du850_djf[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'],\n",
+ " u850_djf[runs_all[4:][k*4]],\n",
+ " u850_djf[runs_all[4:][k*4+1]],\n",
+ " u850_djf[runs_all[4:][k*4+2]],\n",
+ " u850_djf[runs_all[4:][k*4+3]])\n",
+ "del k\n",
+ "del u850, u850_djf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal mean zonal wind."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath_bs = '../../ICON-NWP_lockedclouds/'\n",
+ "du850_mask_sm_bs = np.load(ipath_bs + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "del ipath_bs\n",
+ "\n",
+ "# only store masks for tropical regions\n",
+ "response_sel = ['cloud WP', 'cloud EP', 'cloud IO', 'cloud TA']\n",
+ "du850_djf_mask = np.array([du850_mask_sm_bs[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_sel])\n",
+ "\n",
+ "del du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "du850_djf_shift, lons_shift = fct.shiftgrid_copy(90., du850_djf, lons, start=False)\n",
+ "du850_mask_shift, _ = fct.shiftgrid_copy(90., du850_djf_mask, lons, start=False)\n",
+ "jetlat_nh_shift = fct.shiftgrid_copy(90., jetlat_nh_T1C1, lons, start=False)[0]\n",
+ "\n",
+ "# find indices of border of North Atlantic/Europe box\n",
+ "# -> makes plotting faster (important if mask is plotted)\n",
+ "lonind_west = (np.abs(lons_shift--72)).argmin()\n",
+ "lonind_east = (np.abs(lons_shift-32)).argmin()\n",
+ "latind_sout = (np.abs(lats-28)).argmin()\n",
+ "latind_nort = (np.abs(lats-72)).argmin()\n",
+ "\n",
+ "lons_plot = lons_shift[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "jetlat_nh_plot = jetlat_nh_shift[lonind_west:lonind_east+1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of cloud impacts from the four tropical and the linearity IO+WP+EP+TA vs TR "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response_plot = ['cloud WP', 'cloud EP',\n",
+ " 'cloud IO', 'cloud TA']\n",
+ "\n",
+ "du850_plot = np.array([du850_djf_shift[response_all.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "mask_plot = np.array([du850_mask_shift[response_sel.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "vlim = 1.5\n",
+ "# box around region with jet exit strengthening\n",
+ "lonwest = -4; loneast = 26; latsout = 52; latnort = 62\n",
+ "\n",
+ "# plot\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 8),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for r in range(ax.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[r].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if r > 3: # last row\n",
+ " ax[r].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[r].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if r in [0, 2, 4, 6]:\n",
+ " ax[r].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[r].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[r].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[r].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[r].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # jet latitude in control run\n",
+ " ax[r].plot(lons_plot, jetlat_nh_plot, marker='x',\n",
+ " color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " # plot different effects\n",
+ " if r in range(len(response_plot)):\n",
+ " cf0 = ax[r].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_plot[r, :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " # stippling for significance\n",
+ " ax[r].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_plot[r, :, :], 0),\n",
+ " hatch='.....', alpha=0.,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree()) \n",
+ " ax[r].set_title(response_plot[r], fontsize=16)\n",
+ " # a), b) etc for subplots\n",
+ " ax[r].text(0.01, 1.02, labs[r], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[r].transAxes)\n",
+ "del r\n",
+ "\n",
+ "# IO + WP + EP + TA\n",
+ "ax[4].pcolormesh(lons_shift, lats,\n",
+ " (du850_djf_shift[response_all.index('cloud IO'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud WP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud EP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud TA'), :, :]),\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[4].set_title('cloud IO + cloud WP \\n+ cloud EP + cloud TA', fontsize=16)\n",
+ "cf2 = ax[5].pcolormesh(lons_shift, lats,\n",
+ " (du850_djf_shift[response_all.index('cloud IO'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud WP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud EP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud TA'), :, :]) - \\\n",
+ " du850_djf_shift[response_all.index('cloud TR'), :, :],\n",
+ " vmin=-1.5, vmax=1.5, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[5].set_title('(IO+WP+EP+TA) - TR', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.07)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.165, 0.0, 0.7, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0\n",
+ "\n",
+ "# colorbar for differences\n",
+ "clevs = [-1.5, -0.7, 0, 0.7, 1.5]\n",
+ "cbar_ax = fig.add_axes([0.98, 0.07, 0.013, 0.23]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf2, cax=cbar_ax, orientation='vertical', extend='both', ticks=clevs)\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf2, clevs\n",
+ "\n",
+ "fig.savefig('figure6a_6f.pdf', dpi=200, bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, du850_plot, mask_plot\n",
+ "del vlim, lonwest, loneast, latsout, latnort"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure_S1_free_vs_locked_watervapor-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure_S1_free_vs_locked_watervapor-checkpoint.ipynb
new file mode 100644
index 0000000..6052b14
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure_S1_free_vs_locked_watervapor-checkpoint.ipynb
@@ -0,0 +1,392 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cloud locking vs. cloud and water vapor locking\n",
+ "\n",
+ "This script generates figure S1: total response and cloud impact from ICON simulations with locked clouds and interactive water vapor and from ICON simulations with locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated \n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read ICON data with locked clouds and interactive water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon_cld = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon_cld[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read ICON data with locked clouds and locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedcloudsandwatervapor/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.uwind.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " #lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " #lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# locked clouds\n",
+ "u850_icon_cld_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_cld_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon_cld[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# locked clouds and locked water vapor\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del u850_icon_cld, u850_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# locked clouds\n",
+ "du850_icon_cld = np.full((len(response_cld), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "du850_icon_cld[0, :, :], du850_icon_cld[1, :, :], du850_icon_cld[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_icon_cld_djf['T1C1'], u850_icon_cld_djf['T2C2'],\n",
+ " u850_icon_cld_djf['T1C2'], u850_icon_cld_djf['T2C1'])\n",
+ "\n",
+ "# locked clouds and locked water vapor\n",
+ "du850_icon = np.full((len(response_cldvap), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "du850_icon[0, :, :], du850_icon[1, :, :], du850_icon[2, :, :], \\\n",
+ "du850_icon[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_icon_djf['T1C1W1'], u850_icon_djf['T2C2W2'],\n",
+ " u850_icon_djf['T1C2W2'], u850_icon_djf['T2C1W1'],\n",
+ " u850_icon_djf['T1C2W1'], u850_icon_djf['T1C1W2'],\n",
+ " u850_icon_djf['T2C2W1'], u850_icon_djf['T2C1W2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot total response and cloud impact for both simulation setups and add differences between the two setups\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x324 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes\n",
+ "du850_icon_cld_shift, lons_shift = fct.shiftgrid_copy(90., du850_icon_cld,\n",
+ " lons, start=False)\n",
+ "du850_icon_shift, _ = fct.shiftgrid_copy(90., du850_icon, lons, start=False)\n",
+ "\n",
+ "vlim = 3\n",
+ "vlim_diff = 1\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(2, 3, figsize=(10, 4.5),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "for i in range(ax.shape[0]):\n",
+ " for k in range(ax.shape[1]):\n",
+ " ax[i, k].coastlines(rasterized=True)\n",
+ " ax[i, k].set_aspect('auto')\n",
+ " ax[i, k].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[i, k].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for longitudes and latitudes\n",
+ " # xaxis: longitudes\n",
+ " ax[1, k].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[1, k].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " del k\n",
+ " # yaxis: latitudes\n",
+ " ax[i, 0].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i, 0].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del i\n",
+ "# ICON (locked clouds)\n",
+ "cf = ax[0, 0].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('total'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 0].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# ICON (locked clouds and locked water vapor)\n",
+ "ax[0, 1].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_shift[response_cldvap.index('total'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 1].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_shift[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# difference\n",
+ "cf2 = ax[0, 2].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('total'), :, :] - \\\n",
+ " du850_icon_shift[response_cldvap.index('total'), :, :],\n",
+ " vmin=-vlim_diff, vmax=vlim_diff, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 2].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('cloud'), :, :] - \\\n",
+ " du850_icon_shift[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim_diff, vmax=vlim_diff, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "\n",
+ "ax[0, 0].set_title('a) locked clouds', fontsize=16)\n",
+ "ax[0, 1].set_title('b) locked clouds and\\nwater vapor', fontsize=16)\n",
+ "ax[0, 0].set_title('interactive\\nwater vapor', fontsize=16)\n",
+ "ax[0, 1].set_title('locked\\nwater vapor', fontsize=16)\n",
+ "ax[0, 2].set_title('interactive - locked', fontsize=16)#difference (a - b)', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.1)\n",
+ "cbar_ax = fig.add_axes([0.066, 0.0, 0.59, 0.027]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf\n",
+ "cbar_ax = fig.add_axes([0.69, 0.0, 0.277, 0.027]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf2, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf2\n",
+ "\n",
+ "for r, res in enumerate(['total', 'cloud']): # response_cldvap):\n",
+ " ax[r, 0].text(-0.2, 0.5, res, va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=18, transform=ax[r, 0].transAxes)\n",
+ "del r, res\n",
+ "\n",
+ "fig.savefig('figure_S1.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "\n",
+ "del lons_shift, du850_icon_cld_shift, du850_icon_shift, vlim, vlim_diff"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure_S2_schematics-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure_S2_schematics-checkpoint.ipynb
new file mode 100644
index 0000000..486a2fe
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure_S2_schematics-checkpoint.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Schematics\n",
+ "\n",
+ "This script generates figure S2: schematic of the regions, for which the cloud impact is determined."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import matplotlib.colors as mcolors\n",
+ "from matplotlib.colors import LinearSegmentedColormap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate colormap with two colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sample the colormaps that you want to use. Use 128 from each so we get 256\n",
+ "# colors in total\n",
+ "colors1 = np.array([166/255, 166/255, 166/255, 1])\n",
+ "colors2 = np.array([178/255, 34/255, 34/255, 1])\n",
+ "\n",
+ "# combine two colors and build a new colormap\n",
+ "colors = np.vstack((colors1, colors2))\n",
+ "mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)\n",
+ "del colors1, colors2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create latitude and longitude vectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lats = np.arange(-90, 91, 1)\n",
+ "lons = np.arange(-180, 180, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define regions for which the cloud impact is determined and generate masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tropics\n",
+ "lat_nort_TR = 30 # northern boundary: 30°N\n",
+ "lat_sout_TR = -30 # southern boundary: 30°S\n",
+ "lon_west_TR = -180 # western boundary: 180°W\n",
+ "lon_east_TR = 180 # eastern boundary: 180°E\n",
+ "\n",
+ "# midlatitudes\n",
+ "lat_nort_ML_NH = 60 # northern boundary in NH: 60°N\n",
+ "lat_sout_ML_NH = 30 # southern boundary in NH: 30°N\n",
+ "lat_nort_ML_SH = -30 # northern boundary in SH: 30°S\n",
+ "lat_sout_ML_SH = -60 # southern boundary in SH: 60°S\n",
+ "lon_west_ML = -180 # western boundary: 180°W\n",
+ "lon_east_ML = 180 # eastern boundary: 180°E\n",
+ "\n",
+ "# tropical Atlantic\n",
+ "lon_west_TA = -70 # western boundary: 70°W\n",
+ "lon_east_TA = 40 # eastern boundary: 40°E\n",
+ "\n",
+ "# Indian Ocean\n",
+ "lon_west_IO = 40 # western boundary: 40°E\n",
+ "lon_east_IO = 120 # eastern boundary: 120°E\n",
+ "\n",
+ "# western tropical Pacific\n",
+ "lon_west_WP = 120 # western boundary: 120°E\n",
+ "lon_east_WP = -150 # eastern boundary: 150°W\n",
+ "\n",
+ "# eastern tropical Pacific\n",
+ "lon_west_EP = -150 # western boundary: 150°W\n",
+ "lon_east_EP = -70 # eastern boundary: 70°W\n",
+ "\n",
+ "# extended North Atlantic\n",
+ "lat_nort_NAe = 60 # northern boundary: 60°N\n",
+ "lat_sout_NAe = 30 # southern boundary: 30°N\n",
+ "lon_west_NAe = -90 # western boundary: 270°E/90°W\n",
+ "lon_east_NAe = 30 # eastern boundary: 30°E\n",
+ "\n",
+ "\n",
+ "# create array with 1 over tropics and 0 everywhere else\n",
+ "# and array with 1 over midlatitudes and 0 everywhere else\n",
+ "mask_lat_TR = np.logical_and(lats >= lat_sout_TR, lats <= lat_nort_TR)\n",
+ "mask_lat_ML = np.logical_or(np.logical_and(lats >= lat_sout_ML_SH, lats <= lat_nort_ML_SH),\n",
+ " np.logical_and(lats >= lat_sout_ML_NH, lats <= lat_nort_ML_NH))\n",
+ "mask_lat_PO = np.logical_or(lats <= lat_sout_ML_SH, lats >= lat_nort_ML_NH)\n",
+ "mask_lon = np.logical_and(lons >= lon_west_TR, lons <= lon_east_TR)\n",
+ "mask_TR = np.logical_and(mask_lon[None,:], mask_lat_TR[:,None]) * 1\n",
+ "mask_ML = np.logical_and(mask_lon[None,:], mask_lat_ML[:,None]) * 1\n",
+ "mask_PO = np.logical_and(mask_lon[None,:], mask_lat_PO[:,None]) * 1\n",
+ "del mask_lat_ML, mask_lat_PO, mask_lon\n",
+ "\n",
+ "# create array with 1 over tropical Atlantic and 0 everywhere else\n",
+ "mask_lon_TA = np.logical_and(lons >= lon_west_TA, lons <= lon_east_TA)\n",
+ "mask_TA = np.logical_and(mask_lon_TA[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_TA\n",
+ "\n",
+ "# create array with 1 over Indian Ocean and 0 everywhere else\n",
+ "mask_lon_IO = np.logical_and(lons >= lon_west_IO, lons <= lon_east_IO)\n",
+ "mask_IO = np.logical_and(mask_lon_IO[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IO\n",
+ "\n",
+ "# create array with 1 over western tropical Pacific and 0 everywhere else\n",
+ "mask_lon_WP = (lons >= lon_west_WP) + (lons <= lon_east_WP) #np.logical_and(lons >= lon_west_WP, lons <= lon_east_WP)\n",
+ "mask_WP = np.logical_and(mask_lon_WP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_WP\n",
+ "\n",
+ "# create array with 1 over eastern tropical Pacific and 0 everywhere else\n",
+ "mask_lon_EP = np.logical_and(lons >= lon_west_EP, lons <= lon_east_EP)\n",
+ "mask_EP = np.logical_and(mask_lon_EP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_EP\n",
+ "\n",
+ "# create array with 1 over NAext and 0 everywhere else\n",
+ "mask_lat_NAe = np.logical_and(lats >= lat_sout_NAe, lats <= lat_nort_NAe)\n",
+ "mask_lon_NAe = np.logical_and(lons >= lon_west_NAe, lons <= lon_east_NAe)\n",
+ "mask_NAe = np.logical_and(mask_lon_NAe[None,:], mask_lat_NAe[:,None]) * 1\n",
+ "del mask_lat_NAe, mask_lon_NAe\n",
+ "\n",
+ "# create array with 1 over IOWPEP and 0 everywhere else\n",
+ "mask_lon_IOWPEP = (lons >= lon_west_IO) + (lons <= lon_east_EP)\n",
+ "mask_IOWPEP = np.logical_and(mask_lon_IOWPEP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IOWPEP\n",
+ "\n",
+ "# create array with 1 over IOWP and 0 everywhere else\n",
+ "mask_lon_IOWP = (lons >= lon_west_IO) + (lons <= lon_east_WP)\n",
+ "mask_IOWP = np.logical_and(mask_lon_IOWP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IOWP\n",
+ "\n",
+ "# create array with 1 over WPEP and 0 everywhere else\n",
+ "mask_lon_WPEP = (lons >= lon_west_WP) + (lons <= lon_east_EP)\n",
+ "mask_WPEP = np.logical_and(mask_lon_WPEP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_WPEP\n",
+ "\n",
+ "del mask_lat_TR"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x576 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(4, 2, figsize=(7, 8),#figsize(10), \n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].coastlines(rasterized=True)\n",
+ " ax[i].set_aspect('auto')\n",
+ " ax[i].tick_params(labelsize=14)\n",
+ "del i\n",
+ "\n",
+ "# TR\n",
+ "ax[0].pcolormesh(lons, lats, mask_TR, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[0].set_title('Tropics (TR)', fontsize=15)\n",
+ "\n",
+ "# ML\n",
+ "ax[1].pcolormesh(lons, lats, mask_ML, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1].set_title('Midlatitudes (ML)', fontsize=15)\n",
+ "\n",
+ "# PO\n",
+ "ax[2].pcolormesh(lons, lats, mask_PO, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[2].set_title('Polar Region (PO)', fontsize=15)\n",
+ "\n",
+ "# NAe\n",
+ "ax[3].pcolormesh(lons, lats, mask_NAe, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[3].set_title('North Atlantic (NA)', fontsize=15)\n",
+ "\n",
+ "# WP\n",
+ "ax[4].pcolormesh(lons, lats, mask_WP, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[4].set_title('Western Pacific (WP)', fontsize=15)\n",
+ "\n",
+ "# EP\n",
+ "ax[5].pcolormesh(lons, lats, mask_EP, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[5].set_title('Eastern Pacific (EP)', fontsize=15)\n",
+ "\n",
+ "# TA\n",
+ "ax[6].pcolormesh(lons, lats, mask_TA, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[6].set_title('Tropical Atlantic (TA)', fontsize=15)\n",
+ "\n",
+ "# IO\n",
+ "ax[7].pcolormesh(lons, lats, mask_IO, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[7].set_title('Indian Ocean (IO)', fontsize=15)\n",
+ "\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# TR\n",
+ "ax[0].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[0].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# ML\n",
+ "ax[1].set_yticks([lat_sout_ML_SH, lat_nort_ML_SH, \n",
+ " lat_sout_ML_NH, lat_nort_ML_NH], crs=ccrs.PlateCarree())\n",
+ "ax[1].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# PO\n",
+ "ax[2].set_yticks([-90, lat_sout_ML_SH, \n",
+ " lat_nort_ML_NH, 90], crs=ccrs.PlateCarree())\n",
+ "ax[2].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# NAe\n",
+ "ax[3].set_yticks([lat_sout_NAe, lat_nort_NAe], crs=ccrs.PlateCarree())\n",
+ "ax[3].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[3].set_xticks([lon_west_NAe, lon_east_NAe], crs=ccrs.PlateCarree())\n",
+ "ax[3].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# WP\n",
+ "ax[4].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[4].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[4].set_xticks([lon_west_WP, lon_east_WP], crs=ccrs.PlateCarree())\n",
+ "ax[4].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# EP\n",
+ "ax[5].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[5].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[5].set_xticks([lon_west_EP, lon_east_EP], crs=ccrs.PlateCarree())\n",
+ "ax[5].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# TA\n",
+ "ax[6].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[6].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[6].set_xticks([lon_west_TA, lon_east_TA], crs=ccrs.PlateCarree())\n",
+ "ax[6].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# IO\n",
+ "ax[7].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[7].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[7].set_xticks([lon_west_IO, lon_east_IO], crs=ccrs.PlateCarree())\n",
+ "ax[7].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.0, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=14,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.savefig('figure_S2.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure_S3_cloudcover-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure_S3_cloudcover-checkpoint.ipynb
new file mode 100644
index 0000000..21b55f5
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure_S3_cloudcover-checkpoint.ipynb
@@ -0,0 +1,387 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cloud cover\n",
+ "\n",
+ "This script generates figure S3: cloud impacts on the cloud cover response in ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated\n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean cloud cover (MPI-ESM, IPSL-CM5A with locked clouds and locked water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "clc_mpi = {}; clc_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.clc.nc'\n",
+ " ncfile = nc.Dataset('../../MPI-ESM/' + ifile, 'r')\n",
+ " lats_mpi = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_mpi = np.array(ncfile.variables['plev'][:].data)\n",
+ " # multiply with 100 to get cloud cover in %\n",
+ " clc_mpi[run] = np.nanmean(np.array(ncfile.variables['aclcac'][:].data)*100, axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.clc.nc'\n",
+ " ncfile = nc.Dataset('../../IPSL-CM5A/' + ifile, 'r')\n",
+ " lats_ipsl = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_ipsl = np.array(ncfile.variables['presnivs'][:].data)\n",
+ " # multiply with 100 to get cloud cover in %\n",
+ " clc_ipsl[run] = np.nanmean(np.array(ncfile.variables['rneb'][:].data)*100, axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean cloud cover (ICON with locked clouds and interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "clc_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_icon = np.array(ncfile.variables['lev'][:].data)\n",
+ " clc_icon[run] = np.nanmean(np.array(ncfile.variables['clc'][:].data), axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clc_mpi_djf = {}; clc_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " clc_mpi_djf[run] = fct.calcMonthlyandSeasonMean(clc_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " clc_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(clc_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "clc_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " clc_icon_djf[run] = fct.calcMonthlyandSeasonMean(clc_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del clc_mpi, clc_ipsl, clc_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dclc_mpi = np.full((len(response_cldvap), len(levs_mpi), len(lats_mpi)),\n",
+ " np.nan, dtype=float)\n",
+ "dclc_ipsl = np.full((len(response_cldvap), len(levs_ipsl), len(lats_ipsl)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "dclc_mpi[0, :, :], dclc_mpi[1, :, :], dclc_mpi[2, :, :], \\\n",
+ "dclc_mpi[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(clc_mpi_djf['T1C1W1'], clc_mpi_djf['T2C2W2'],\n",
+ " clc_mpi_djf['T1C2W2'], clc_mpi_djf['T2C1W1'],\n",
+ " clc_mpi_djf['T1C2W1'], clc_mpi_djf['T1C1W2'],\n",
+ " clc_mpi_djf['T2C2W1'], clc_mpi_djf['T2C1W2'])\n",
+ "dclc_ipsl[0, :, :], dclc_ipsl[1, :, :], dclc_ipsl[2, :, :], \\\n",
+ "dclc_ipsl[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(clc_ipsl_djf['T1C1W1'], clc_ipsl_djf['T2C2W2'],\n",
+ " clc_ipsl_djf['T1C2W2'], clc_ipsl_djf['T2C1W1'],\n",
+ " clc_ipsl_djf['T1C2W1'], clc_ipsl_djf['T1C1W2'],\n",
+ " clc_ipsl_djf['T2C2W1'], clc_ipsl_djf['T2C1W2'])\n",
+ "\n",
+ "dclc_icon = np.full((len(response_cld), len(levs_icon), len(lats_icon)),\n",
+ " np.nan, dtype=float)\n",
+ "dclc_icon[0, :, :], dclc_icon[1, :, :], dclc_icon[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(clc_icon_djf['T1C1'], clc_icon_djf['T2C2'],\n",
+ " clc_icon_djf['T1C2'], clc_icon_djf['T2C1'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean tropopause in control simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../tropopause_T1C1_T1C1W1/'\n",
+ "\n",
+ "# ICON\n",
+ "ifile = 'ICON-NWP_AMIP_T1C1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_icon = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# MPI-ESM\n",
+ "ifile = 'MPI-ESM_T1C1W1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_mpi = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# IPSL-CM5A\n",
+ "ifile = 'IPSL-CM5A_T1C1W1_tropopause_DJF_remapcon_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_ipsl = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the cloud impact on the zonal-mean cloud cover response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x216 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "vlim = 5\n",
+ "levs = np.arange(10, 100, 5)\n",
+ "\n",
+ "fig, ax = plt.subplots(1, 3, figsize=(10, 3))#figsize(10))\n",
+ "# cloud cover response\n",
+ "cf0 = ax[0].pcolormesh(lats_icon, levs_icon/100,\n",
+ " dclc_icon[response_cld.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[1].pcolormesh(lats_mpi, levs_mpi/100,\n",
+ " dclc_mpi[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[2].pcolormesh(lats_ipsl, levs_ipsl/100,\n",
+ " dclc_ipsl[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "\n",
+ "# cloud cover in control simulation\n",
+ "ax[0].contour(lats_icon, levs_icon/100, clc_icon_djf['T1C1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "ax[1].contour(lats_mpi, levs_mpi/100, clc_mpi_djf['T1C1W1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "ax[2].contour(lats_ipsl, levs_ipsl/100, clc_ipsl_djf['T1C1W1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "\n",
+ "# tropopause in control simulation\n",
+ "ax[0].plot(lats_icon, tropo_icon/100, color='tab:green')\n",
+ "ax[1].plot(lats_mpi, tropo_mpi/100, color='tab:green')\n",
+ "ax[2].plot(lats_ipsl, tropo_ipsl/100, color='tab:green')\n",
+ "\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].tick_params(labelsize=13)\n",
+ " ax[i].set(xticks=np.arange(-90, 91, 30),\n",
+ " xticklabels=['90S', '60S', '30S', '0', '30N', '60N' ,'90N'],\n",
+ " xlim=(-90, 90))\n",
+ " ax[i].set_yticks(np.arange(0, 1100, 200))\n",
+ " ax[i].set_ylim(1000, 10)\n",
+ " ax[i].set_xlabel('Latitude [$^{\\circ}$]', fontsize=15)\n",
+ "del i\n",
+ "ax[0].set_ylabel('Pressure [hPa]', fontsize=15)\n",
+ "\n",
+ "# titles for models\n",
+ "ax[0].set_title('ICON', fontsize=16)\n",
+ "ax[1].set_title('MPI-ESM', fontsize=16)\n",
+ "ax[2].set_title('IPSL-CM5A', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar\n",
+ "fig.subplots_adjust(bottom=0.24)#(right=0.8)\n",
+ "clevs = np.arange(-vlim, vlim+1, 2)#np.array([-1, -0.5, 0, 0.5, 1])\n",
+ "cbar_ax = fig.add_axes([0.29, -0.03, 0.5, 0.037]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both',\n",
+ " ticks=clevs)\n",
+ "cb.set_label('$\\Delta$cloud cover [%]', fontsize=14, labelpad=1)\n",
+ "cb.ax.tick_params(labelsize=13)\n",
+ "del cbar_ax, cb, cf0, clevs\n",
+ "\n",
+ "fig.savefig('figure_S3.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/figure_S4_streamfunction-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/figure_S4_streamfunction-checkpoint.ipynb
new file mode 100644
index 0000000..77e087e
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/figure_S4_streamfunction-checkpoint.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stationary eddy stream function response\n",
+ "\n",
+ "This script generates figure S4: cloud impacts on stationary eddy stream function response at 300 hPa."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read stream function at 300 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2TA\n",
+ "reading T1C1TA\n",
+ "reading T2C2TA\n",
+ "reading T2C1TA\n",
+ "reading T1C2IO\n",
+ "reading T1C1IO\n",
+ "reading T2C2IO\n",
+ "reading T2C1IO\n",
+ "reading T1C2WP\n",
+ "reading T1C1WP\n",
+ "reading T2C2WP\n",
+ "reading T2C1WP\n",
+ "reading T1C2EP\n",
+ "reading T1C1EP\n",
+ "reading T2C2EP\n",
+ "reading T2C1EP\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "sf300 = {}\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.streamfct.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['lev'][:].data\n",
+ " sf = ncfile.variables['sf'][:].data\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # calculate stationary eddy stream function \n",
+ " # subtract monthly-mean zonal-mean stream function from monthly-mean\n",
+ " # lev-lat-lon stream function to get the stationary eddy stream function\n",
+ " sfstat = sf - np.nanmean(sf, axis=3)[:, :, :, None]\n",
+ " \n",
+ " # get stationary stream function at 300 hPa\n",
+ " levind300 = (np.abs(levs-300e2)).argmin() # index of 300 hPa level\n",
+ " sf300[run] = sfstat[:, levind300, :, :]\n",
+ " \n",
+ " del levs, sf, sfstat, levind300\n",
+ "del run\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sf300_djf = {}\n",
+ "for run in runs_all:\n",
+ " sf300_djf[run] = fct.calcMonthlyandSeasonMean(sf300[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del sf300"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dsf300 = np.full((len(response_all), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "# total, SST, cloud\n",
+ "dsf300[0, :, :], dsf300[1, :, :], dsf300[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(sf300_djf['T1C1'], sf300_djf['T2C2'],\n",
+ " sf300_djf['T1C2'], sf300_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, dsf300[k*2+3, :, :], dsf300[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(sf300_djf['T1C1'],\n",
+ " sf300_djf['T2C2'],\n",
+ " sf300_djf['T1C2'],\n",
+ " sf300_djf['T2C1'],\n",
+ " sf300_djf[runs_all[4:][k*4]],\n",
+ " sf300_djf[runs_all[4:][k*4+1]],\n",
+ " sf300_djf[runs_all[4:][k*4+2]],\n",
+ " sf300_djf[runs_all[4:][k*4+3]])\n",
+ "del k"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot response of stationary eddy stream function\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes\n",
+ "dsf300_shift, lons_shift = fct.shiftgrid_copy(90., dsf300, lons, start=False)\n",
+ "\n",
+ "response_plot = ['cloud WP', 'cloud EP',\n",
+ " 'cloud IO', 'cloud TA',\n",
+ " 'cloud TR', 'cloud']\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 7),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1) \n",
+ "for r in range(ax.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if r > 3: # last row\n",
+ " ax[r].set_xticks([-120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[r].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if r in [0, 2, 4]:\n",
+ " ax[r].set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # cloud impacts\n",
+ " cf0 = ax[r].pcolormesh(lons_shift, lats,\n",
+ " dsf300_shift[response_all.index(response_plot[r]), :, :]/1e6,\n",
+ " vmin=-6, vmax=6, cmap='seismic',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " ax[r].set_title(response_plot[r], fontsize=16)\n",
+ "del r\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.08)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.174, 0.0, 0.7, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta \\psi_{300}$ [10$^6$ m$^{2}$ s$^{-1}$]',\n",
+ " fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['a)', 'b)', 'c)', 'd)', 'e)', 'f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i, labs\n",
+ "\n",
+ "fig.savefig('figure_S4.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, dsf300_shift, lons_shift"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/.ipynb_checkpoints/interpolate_cmip5_data_to_common_grid-checkpoint.ipynb b/pythonscripts/.ipynb_checkpoints/interpolate_cmip5_data_to_common_grid-checkpoint.ipynb
new file mode 100644
index 0000000..fb47236
--- /dev/null
+++ b/pythonscripts/.ipynb_checkpoints/interpolate_cmip5_data_to_common_grid-checkpoint.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Interpolate the CMIP5 data to a common grid\n",
+ "\n",
+ "Interpolate the zonal wind at 850 hPa coupled and atmosphere CMIP5 models (historical, RCP8.5, amip, amip4K, amipFuture to a common grid with 96 grid points in latitude and 192 grid points in longitude.\n",
+ "\n",
+ "Save the interpolated data as .npy files."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import glob\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "runs_cmip = ['historical', 'rcp85']\n",
+ "runs_amip = ['amip', 'amip4K', 'amipFuture']\n",
+ "\n",
+ "# time period of simulation\n",
+ "timeint_cmip = ['197501-200412', '207001-209912']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate latitude and logitude arrays on which the data will be interpolated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lats = np.arange(-89.0625, 90, 1.875)\n",
+ "lons = np.arange(0, 360, 1.875)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpolate data from coupled models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ACCESS1-0\n",
+ " historical\n",
+ " rcp85\n",
+ "ACCESS1-3\n",
+ " historical\n",
+ " rcp85\n",
+ "bcc-csm1-1-m\n",
+ " historical\n",
+ " rcp85\n",
+ "bcc-csm1-1\n",
+ " historical\n",
+ " rcp85\n",
+ "BNU-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "CanESM2\n",
+ " historical\n",
+ " rcp85\n",
+ "CCSM4\n",
+ " historical\n",
+ " rcp85\n",
+ "CESM1-BGC\n",
+ " historical\n",
+ " rcp85\n",
+ "CESM1-CAM5\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CESM\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CM\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CMS\n",
+ " historical\n",
+ " rcp85\n",
+ "CNRM-CM5\n",
+ " historical\n",
+ " rcp85\n",
+ "CSIRO-Mk3-6-0\n",
+ " historical\n",
+ " rcp85\n",
+ "EC-EARTH\n",
+ " historical\n",
+ " rcp85\n",
+ "FGOALS-g2\n",
+ " historical\n",
+ " rcp85\n",
+ "FIO-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-CM3\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-ESM2G\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-ESM2M\n",
+ " historical\n",
+ " rcp85\n",
+ "GISS-E2-H\n",
+ " historical\n",
+ " rcp85\n",
+ "GISS-E2-R\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-AO\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-CC\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-ES\n",
+ " historical\n",
+ " rcp85\n",
+ "inmcm4\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5A-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5A-MR\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5B-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC5\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC-ESM-CHEM\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "MPI-ESM-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "MPI-ESM-MR\n",
+ " historical\n",
+ " rcp85\n",
+ "MRI-CGCM3\n",
+ " historical\n",
+ " rcp85\n",
+ "NorESM1-ME\n",
+ " historical\n",
+ " rcp85\n",
+ "NorESM1-M\n",
+ " historical\n",
+ " rcp85\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "for model in models_cmip:\n",
+ " print(model)\n",
+ " for r, run in enumerate(runs_cmip):\n",
+ " print(' ', run)\n",
+ " # 1) read data of shape (time,lev,lat,lon)\n",
+ " # uwind\n",
+ " ifile = glob.glob(ipath + run + '/ua_Amon_' + model + \\\n",
+ " '*' + timeint_cmip[r] +'.absTime.nc')[0]\n",
+ " ncfile = nc.Dataset(ifile, 'r')\n",
+ " lats_cmip = ncfile.variables['lat'][:].data\n",
+ " lons_cmip = ncfile.variables['lon'][:].data\n",
+ " levs_cmip = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs_cmip-85000)).argmin() # index of 850hPa level\n",
+ " u850_cmip = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_cmip[u850_cmip > 1000] = np.nan\n",
+ "\n",
+ " del levs_cmip, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # 2) interpolate data to new grid\n",
+ " u850_int = fct.interpolate2d(u850_cmip, lats_cmip,\n",
+ " lons_cmip, lats, lons)\n",
+ " \n",
+ " # 3) save data to npy file\n",
+ " np.save(ipath + model + '_u850_' + run + '.npy', u850_int)\n",
+ " \n",
+ " del lats_cmip, lons_cmip, u850_cmip, u850_int\n",
+ " del r, run\n",
+ "del model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpolate data from atmosphere models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "bcc-csm1-1\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CanAM4\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CCSM4\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CNRM-CM5\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "HadGEM2-A\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "IPSL-CM5A-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "IPSL-CM5B-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MIROC5\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MPI-ESM-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MPI-ESM-MR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MRI-CGCM3\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "for model in models_amip:\n",
+ " print(model)\n",
+ " print(' amip')\n",
+ " # amip\n",
+ " ifile_amip = glob.glob(ipath + 'amip/ua_Amon_' + model + \\\n",
+ " '*197901-200812*.nc')[0]\n",
+ " ncfile = nc.Dataset(ifile_amip, 'r')\n",
+ " lats_amip = ncfile.variables['lat'][:].data\n",
+ " lons_amip = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_amip = uwind[:, levind850, :, :]\n",
+ "\n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amip[u850_amip > 100] = np.nan\n",
+ "\n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amip, ncfile\n",
+ "\n",
+ " # interpolate data to new grid\n",
+ " u850_amip_int = fct.interpolate2d(u850_amip, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amip.npy', u850_amip_int)\n",
+ "\n",
+ " del u850_amip, u850_amip_int\n",
+ "\n",
+ " ##########################################################################\n",
+ " # amip4K\n",
+ " print(' amip4K')\n",
+ " ifile_amip4k = glob.glob(ipath + 'amip4K/ua_Amon_' + model + \\\n",
+ " '*197901-200812*.nc')[0] \n",
+ " ncfile = nc.Dataset(ifile_amip4k, 'r')\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_amip4k = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amip4k[u850_amip4k > 100] = np.nan\n",
+ " \n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amip4k, ncfile\n",
+ " \n",
+ " # interpolate data to new grid\n",
+ " u850_amip4k_int = fct.interpolate2d(u850_amip4k, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amip4k.npy', u850_amip4k_int)\n",
+ "\n",
+ " del u850_amip4k, u850_amip4k_int\n",
+ " \n",
+ " ##########################################################################\n",
+ " # amipFuture simulations\n",
+ " print(' amipFuture')\n",
+ " ifile_amipfut = glob.glob(ipath + 'amipFuture/ua_Amon_' + model +\\\n",
+ " '*197901-200812*.nc')[0] \n",
+ " ncfile = nc.Dataset(ifile_amipfut, 'r')\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850_amipfut = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amipfut[u850_amipfut > 100] = np.nan\n",
+ " \n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amipfut, ncfile\n",
+ " \n",
+ " # interpolate data to new grid\n",
+ " u850_amipfut_int = fct.interpolate2d(u850_amipfut, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amipfut.npy', u850_amipfut_int)\n",
+ "\n",
+ " del u850_amipfut, u850_amipfut_int\n",
+ " \n",
+ " del lats_amip, lons_amip\n",
+ "del model\n",
+ "del ipath"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/BlueYellowRed.rgb b/pythonscripts/BlueYellowRed.rgb
new file mode 100644
index 0000000..1239e84
--- /dev/null
+++ b/pythonscripts/BlueYellowRed.rgb
@@ -0,0 +1,256 @@
+ncolors=254
+# R G B
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+ 255 255 200 # red BlueDarkRed18
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diff --git a/pythonscripts/calculate_significance_bootstrapping.ipynb b/pythonscripts/calculate_significance_bootstrapping.ipynb
new file mode 100644
index 0000000..afe9638
--- /dev/null
+++ b/pythonscripts/calculate_significance_bootstrapping.ipynb
@@ -0,0 +1,1092 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Get masks with significant responses based on bootstrapping.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Functions for calculation of significance based on bootstrapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# calculate bootstrap distribution of annual-mean or seasonal-mean\n",
+ "# time series with 1000 samplings with replacement\n",
+ "def calc_bootstrap_dist(data_ym, lats, lons, nreps):\n",
+ " # decide which years to use\n",
+ " ntime = data_ym.shape[0] # number of years in simulation\n",
+ " #nreps = 1000 # number of resamples \n",
+ " time = np.arange(ntime) # artificial time vector\n",
+ " time_bs = np.random.choice(time, (ntime, nreps))\n",
+ "\n",
+ " # find u850 profiles according to time_bs\n",
+ " data_bs = np.full((ntime, nreps, lats.size, lons.size), np.nan, dtype=float)\n",
+ " for l in range(nreps): # number of resamples\n",
+ " for k in range(ntime): # number of years\n",
+ " data_bs[k, l, :, :] = data_ym[time_bs[k, l], :, :]\n",
+ " del k\n",
+ " del l\n",
+ "\n",
+ " # mean over years for each resampling\n",
+ " if np.isnan(data_bs).any() == False:\n",
+ " print(\"No NaN's. Calculation worked.\")\n",
+ " u850_bs_mean = np.mean(data_bs, axis=0)\n",
+ " else:\n",
+ " print(\"Bootstrap array contains NaN's. Exit function.\")\n",
+ " return\n",
+ "\n",
+ " return u850_bs_mean"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_ML = ['T1C2ML', 'T1C1ML', 'T2C2ML', 'T2C1ML']\n",
+ "runs_reg_PO = ['T1C2PO', 'T1C1PO', 'T2C2PO', 'T2C1PO']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_ML + runs_reg_PO + \\\n",
+ " runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR', 'cloud ML', 'cloud notML',\n",
+ " 'cloud PO', 'cloud notPO',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Number of samplings / reputations in bootstrap function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "nreps=1000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind and do bootstrap calculations for ICON (locked clouds, interactive water vapor)\n",
+ "\n",
+ "Get the seasonal-mean zonal-mean zonal wind with Climate Data Operators (cdo): cdo seasmean -selvar,u ifile ofile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "###############\n",
+ "T1C1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1TR\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1ML\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1PO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1TA\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1IO\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1WP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1EP\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850_tmym_bs = {}; u850_smym_bs = {}\n",
+ "for run in runs_all:\n",
+ " # time-mean\n",
+ " print('###############')\n",
+ " print(run)\n",
+ " # read zonal wind for each month\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset('../../ICON-NWP_lockedclouds/' + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['lev'][:].data\n",
+ " uwind = ncfile.variables['u'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850 = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " # calculate yearly-mean u850 for each year\n",
+ " u850_tmym = np.full((int(u850.shape[0]/12), lats.size, lons.size), np.nan,\n",
+ " dtype=float)\n",
+ " for i in range(u850_tmym.shape[0]):\n",
+ " u850_tmym[i,:,:] = np.nanmean(u850[i*12:i*12+12,:,:], axis=0)\n",
+ " del i\n",
+ " del u850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### time-mean #####')\n",
+ " u850_tmym_bs[run] = calc_bootstrap_dist(u850_tmym, lats, lons, nreps)\n",
+ " del u850_tmym\n",
+ " \n",
+ " ##########################################################################\n",
+ " # seasonal-mean\n",
+ " # read seasonal-mean zonal wind for each year\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.uwind.seasmean.nc'\n",
+ " ncfile = nc.Dataset('../../ICON-NWP_lockedclouds/' + ifile, 'r')\n",
+ " levs_sm = ncfile.variables['lev'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " levind850 = (np.abs(levs_sm-85000)).argmin() # index of 850 hPa level\n",
+ " # DJF: do not read last timestep, because it only contains the value from\n",
+ " # Dec of the last year (note: first value only contains values from\n",
+ " # Jan and Feb of first year)\n",
+ " # other seasons: read all available time steps\n",
+ " u850_smym = {'DJF': u_wind_sm[0:-1:4, levind850, :, :],\n",
+ " 'MAM': u_wind_sm[1::4, levind850, :, :],\n",
+ " 'JJA': u_wind_sm[2::4, levind850, :, :],\n",
+ " 'SON': u_wind_sm[3::4, levind850, :, :],\n",
+ " }\n",
+ " del levs_sm, u_wind_sm, levind850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### seasonal-mean #####')\n",
+ " u850_smym_bs1 = {}\n",
+ " for s in seasons:\n",
+ " u850_smym_bs1[s] = calc_bootstrap_dist(u850_smym[s], lats, lons, nreps)\n",
+ " u850_smym_bs[run] = u850_smym_bs1.copy()\n",
+ " del s, u850_smym_bs1\n",
+ " del u850_smym\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses and store significance masks for ICON (locked clouds, interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculate response\n",
+ " T1C2TR T1C1TR T2C2TR T2C1TR\n",
+ "Calculate percentiles\n",
+ " cloud TR cloud notTR\n",
+ "Calculate response\n",
+ " T1C2ML T1C1ML T2C2ML T2C1ML\n",
+ "Calculate percentiles\n",
+ " cloud ML cloud notML\n",
+ "Calculate response\n",
+ " T1C2PO T1C1PO T2C2PO T2C1PO\n",
+ "Calculate percentiles\n",
+ " cloud PO cloud notPO\n",
+ "Calculate response\n",
+ " T1C2TA T1C1TA T2C2TA T2C1TA\n",
+ "Calculate percentiles\n",
+ " cloud TA cloud notTA\n",
+ "Calculate response\n",
+ " T1C2IO T1C1IO T2C2IO T2C1IO\n",
+ "Calculate percentiles\n",
+ " cloud IO cloud notIO\n",
+ "Calculate response\n",
+ " T1C2WP T1C1WP T2C2WP T2C1WP\n",
+ "Calculate percentiles\n",
+ " cloud WP cloud notWP\n",
+ "Calculate response\n",
+ " T1C2EP T1C1EP T2C2EP T2C1EP\n",
+ "Calculate percentiles\n",
+ " cloud EP cloud notEP\n"
+ ]
+ }
+ ],
+ "source": [
+ "# calculate total response, SST impact and cloud impact\n",
+ "# time-mean\n",
+ "du850_tmym_glo = np.full((3, nreps, len(lats), len(lons)), np.nan, dtype=float)\n",
+ "du850_tmym_glo[0, :, :, :], du850_tmym_glo[1, :, :, :], \\\n",
+ "du850_tmym_glo[2, :, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_tmym_bs['T1C1'], u850_tmym_bs['T2C2'],\n",
+ " u850_tmym_bs['T1C2'], u850_tmym_bs['T2C1'])\n",
+ "\n",
+ "# seasonal-mean\n",
+ "du850_smym_glo = np.full((len(seasons), 3, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "for s, seas in enumerate(seasons):\n",
+ " du850_smym_glo[s, 0, :, :, :], du850_smym_glo[s, 1, :, :, :], \\\n",
+ " du850_smym_glo[s, 2, :, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_smym_bs['T1C1'][seas],\n",
+ " u850_smym_bs['T2C2'][seas],\n",
+ " u850_smym_bs['T1C2'][seas],\n",
+ " u850_smym_bs['T2C1'][seas])\n",
+ "del s, seas\n",
+ "\n",
+ "# calculate percentiles of bootstrap distributions\n",
+ "lp = 5; up = 95 # 5th-95th percentile range\n",
+ "# set axis to calculate percentiles over nreps=1000\n",
+ "# time-mean dimensions: 3, nreps, lats, lons\n",
+ "du850_tmym_perc = np.percentile(du850_tmym_glo, [lp, up], axis=1)\n",
+ "# seasonal-mean dimensions: seasons, 3, nreps, lats, lons\n",
+ "du850_smym_perc = np.percentile(du850_smym_glo, [lp, up], axis=2)\n",
+ "# new dimensions:\n",
+ "# time-mean: 2, 3, lats, lons\n",
+ "# seasonal-mean: 2, seasons, 3, lats, lons\n",
+ "del lp, up\n",
+ "\n",
+ "# create mask based on percentiles\n",
+ "# true if significant: both percentile values have the same sig and, thus, 0\n",
+ "# is not included\n",
+ "# true if sign is the same and response is \n",
+ "du850_mask_tm_bs = {'total': np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :]),\n",
+ " 'SST': np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_tmym_perc[0, 2, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 2, :, :])}\n",
+ "du850_mask_sm_bs = {'total': np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :]),\n",
+ " 'SST': np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_smym_perc[0, :, 2, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 2, :, :])}\n",
+ "\n",
+ "del du850_tmym_glo, du850_smym_glo, du850_tmym_perc, du850_smym_perc\n",
+ "\n",
+ "##############################################################################\n",
+ "##############################################################################\n",
+ "# regional cloud impacts\n",
+ "for r in range(int(len(runs_reg)/4)):\n",
+ " # calculate u850 response for bootstrap distribtions\n",
+ " print('Calculate response')\n",
+ " print(' ', runs_reg[r*4], runs_reg[r*4+1], runs_reg[r*4+2], runs_reg[r*4+3])\n",
+ " # time-mean response\n",
+ " du850_tmym_reg = np.full((2, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " _, _, du850_tmym_reg[0, :, :, :], du850_tmym_reg[1, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_tmym_bs['T1C1'],\n",
+ " u850_tmym_bs['T2C2'],\n",
+ " u850_tmym_bs['T1C2'],\n",
+ " u850_tmym_bs['T2C1'],\n",
+ " u850_tmym_bs[runs_reg[r*4]],\n",
+ " u850_tmym_bs[runs_reg[r*4+1]],\n",
+ " u850_tmym_bs[runs_reg[r*4+2]],\n",
+ " u850_tmym_bs[runs_reg[r*4+3]])\n",
+ " \n",
+ " # seasonal-mean response\n",
+ " du850_smym_reg = np.full((len(seasons), 2, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " for s, seas in enumerate(seasons):\n",
+ " _, _, du850_smym_reg[s, 0, :, :, :], du850_smym_reg[s, 1, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_smym_bs['T1C1'][seas],\n",
+ " u850_smym_bs['T2C2'][seas],\n",
+ " u850_smym_bs['T1C2'][seas],\n",
+ " u850_smym_bs['T2C1'][seas],\n",
+ " u850_smym_bs[runs_reg[r*4]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+1]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+2]][seas],\n",
+ " u850_smym_bs[runs_reg[r*4+3]][seas])\n",
+ " del s, seas\n",
+ " \n",
+ " ##############################################################################\n",
+ " # calculate percentiles of bootstrap distributions\n",
+ " print('Calculate percentiles')\n",
+ " lp = 5; up = 95 # 5th-95th percentile range\n",
+ " # set axis to calculate percentiles over nreps=1000\n",
+ " # time-mean dimensions: 2, nreps, lats, lons\n",
+ " du850_tmym_perc = np.percentile(du850_tmym_reg, [lp, up], axis=1)\n",
+ " # seasonal-mean dimensions: seasons, 2, nreps, lats, lons\n",
+ " du850_smym_perc = np.percentile(du850_smym_reg, [lp, up], axis=2)\n",
+ " # new dimensions:\n",
+ " # time-mean: 2(perc), 2(response), lats, lons\n",
+ " # seasonal-mean: 2(perc), seasons, 2(response), lats, lons\n",
+ " del lp, up\n",
+ "\n",
+ " ##############################################################################\n",
+ " # create mask based on percentiles\n",
+ " # true if significant: both percentile values have the same sig and, thus, 0\n",
+ " # is not included\n",
+ " # true if sign is the same and response is\n",
+ " print(' ', response_all[r*2+3], response_all[r*2+4])\n",
+ " du850_mask_tm_bs.update({response_all[r*2+3]: \\\n",
+ " np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :])})\n",
+ " du850_mask_tm_bs.update({response_all[r*2+4]: \\\n",
+ " np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :])})\n",
+ " du850_mask_sm_bs.update({response_all[r*2+3]: \\\n",
+ " np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :])})\n",
+ " du850_mask_sm_bs.update({response_all[r*2+4]: \\\n",
+ " np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :])})\n",
+ " \n",
+ " del du850_tmym_reg, du850_smym_reg, du850_tmym_perc, du850_smym_perc\n",
+ "del r\n",
+ "\n",
+ "del u850_tmym_bs, u850_smym_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Save masks as numpy arrays"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.save('../../ICON-NWP_lockedclouds/du850_mask_tm_bs.npy', du850_mask_tm_bs)\n",
+ "np.save('../../ICON-NWP_lockedclouds/du850_mask_sm_bs.npy', du850_mask_sm_bs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Delete masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del du850_mask_tm_bs, du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind, do bootstrap calculations, and store masks for MPI-ESM and IPSL-CM5A (locked clouds and locked water vapor)\n",
+ "\n",
+ "Get the seasonal-mean zonal-mean zonal wind with Climate Data Operators (cdo): cdo seasmean -selvar,u ifile ofile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MPI-ESM\n",
+ "###############\n",
+ "T1C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "##############################\n",
+ "##############################\n",
+ "IPSL-CM5A\n",
+ "###############\n",
+ "T1C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T1C2W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C2W1\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "###############\n",
+ "T2C1W2\n",
+ "##### time-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "##### seasonal-mean #####\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "No NaN's. Calculation worked.\n",
+ "##############################\n",
+ "##############################\n",
+ "###########\n",
+ "## Done! ##\n"
+ ]
+ }
+ ],
+ "source": [
+ "runs = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "models = ['MPI-ESM', 'IPSL-CM5A']\n",
+ "\n",
+ "for m, model in enumerate(models):\n",
+ " u850_tmym_bs = {}\n",
+ " u850_smym_bs = {}\n",
+ "\n",
+ " print(model)\n",
+ " for run in runs:\n",
+ " print('###############')\n",
+ " print(run)\n",
+ "\n",
+ " # time-mean\n",
+ " # read zonal wind for each month\n",
+ " if model == 'MPI-ESM':\n",
+ " ifile = model + '_' + run + '_3d_mm.uwind.nc'\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " ifile = model + '_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " ncfile = nc.Dataset('../../' + model + '/' + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " # pressure levels and zonal wind are named differently\n",
+ " # in the two models\n",
+ " if model == 'MPI-ESM':\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['u'][:].data\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " levs = ncfile.variables['presnivs'][:].data\n",
+ " uwind = ncfile.variables['vitu'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850 = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " # calculate yearly-mean u850 for each year\n",
+ " u850_tmym = np.full((int(u850.shape[0]/12), lats.size, lons.size), np.nan,\n",
+ " dtype=float)\n",
+ " for i in range(u850_tmym.shape[0]):\n",
+ " u850_tmym[i,:,:] = np.nanmean(u850[i*12:i*12+12,:,:], axis=0)\n",
+ " del i\n",
+ " del u850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### time-mean #####')\n",
+ " u850_tmym_bs[run] = calc_bootstrap_dist(u850_tmym, lats, lons, nreps)\n",
+ " del u850_tmym\n",
+ "\n",
+ " ##########################################################################\n",
+ " # seasonal-mean\n",
+ " # read seasonal-mean zonal wind for each year\n",
+ " if model == 'MPI-ESM':\n",
+ " ifile = model + '_' + run + '_3d_mm.uwind.seasmean.nc'\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " ifile = model + '_' + run + '_3d_mm.remapcon.uwind.seasmean.nc'\n",
+ " ncfile = nc.Dataset('../../' + model + '/' + ifile, 'r')\n",
+ " # pressure levels and zonal wind are named differently\n",
+ " # in the two models\n",
+ " if model == 'MPI-ESM':\n",
+ " levs_sm = ncfile.variables['plev'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['u'][:].data)\n",
+ " elif model == 'IPSL-CM5A':\n",
+ " levs_sm = ncfile.variables['presnivs'][:].data\n",
+ " u_wind_sm = np.squeeze(ncfile.variables['vitu'][:].data)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "\n",
+ " levind850 = (np.abs(levs_sm-85000)).argmin() # index of 850 hPa level\n",
+ " # DJF: do not read last timestep, because it only contains the value from\n",
+ " # Dec of the last year (note: first value only contains values from\n",
+ " # Jan and Feb of first year)\n",
+ " # other seasons: read all available time steps\n",
+ " u850_smym = {'DJF': u_wind_sm[0:-1:4, levind850, :, :],\n",
+ " 'MAM': u_wind_sm[1::4, levind850, :, :],\n",
+ " 'JJA': u_wind_sm[2::4, levind850, :, :],\n",
+ " 'SON': u_wind_sm[3::4, levind850, :, :],\n",
+ " }\n",
+ " del levs_sm, u_wind_sm, levind850\n",
+ "\n",
+ " # do bootstrap calculations\n",
+ " print('##### seasonal-mean #####')\n",
+ " u850_smym_bs1 = {}\n",
+ " for s in seasons:\n",
+ " u850_smym_bs1[s] = calc_bootstrap_dist(u850_smym[s], lats, lons, nreps)\n",
+ " u850_smym_bs[run] = u850_smym_bs1.copy()\n",
+ " del s, u850_smym_bs1\n",
+ " del u850_smym\n",
+ " del run\n",
+ " \n",
+ " ##########################################################################\n",
+ " ##########################################################################\n",
+ " # Calculate responses and store significance masks\n",
+ " \n",
+ " # calculate responses\n",
+ " # time-mean\n",
+ " du850_tmym = np.full((4, nreps, len(lats), len(lons)), np.nan, dtype=float)\n",
+ "\n",
+ " du850_tmym[0, :, :, :], du850_tmym[1, :, :, :], du850_tmym[2, :, :, :], \\\n",
+ " du850_tmym[3, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_tmym_bs['T1C1W1'], u850_tmym_bs['T2C2W2'],\n",
+ " u850_tmym_bs['T1C2W2'], u850_tmym_bs['T2C1W1'],\n",
+ " u850_tmym_bs['T1C2W1'], u850_tmym_bs['T1C1W2'],\n",
+ " u850_tmym_bs['T2C2W1'], u850_tmym_bs['T2C1W2'])\n",
+ "\n",
+ " # seasonal-mean\n",
+ " du850_smym = np.full((len(seasons), 4, nreps, len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ " for s, seas in enumerate(seasons):\n",
+ " du850_smym[s, 0, :, :, :], du850_smym[s, 1, :, :, :], \\\n",
+ " du850_smym[s, 2, :, :, :], du850_smym[s, 3, :, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_smym_bs['T1C1W1'][seas],\n",
+ " u850_smym_bs['T2C2W2'][seas],\n",
+ " u850_smym_bs['T1C2W2'][seas],\n",
+ " u850_smym_bs['T2C1W1'][seas],\n",
+ " u850_smym_bs['T1C2W1'][seas],\n",
+ " u850_smym_bs['T1C1W2'][seas],\n",
+ " u850_smym_bs['T2C2W1'][seas],\n",
+ " u850_smym_bs['T2C1W2'][seas])\n",
+ " del s, seas\n",
+ "\n",
+ " # calculate percentiles of bootstrap distributions\n",
+ " lp = 5; up = 95 # 5th-95th percentile range\n",
+ " # set axis to calculate percentiles over nreps=1000\n",
+ " # time-mean dimensions: 3, nreps, lats, lons\n",
+ " du850_tmym_perc = np.percentile(du850_tmym, [lp, up], axis=1)\n",
+ " # seasonal-mean dimensions: seasons, 3, nreps, lats, lons\n",
+ " du850_smym_perc = np.percentile(du850_smym, [lp, up], axis=2)\n",
+ " # new dimensions:\n",
+ " # time-mean: 2, 3, lats, lons\n",
+ " # seasonal-mean: 2, seasons, 3, lats, lons\n",
+ " del lp, up\n",
+ "\n",
+ " # create mask based on percentiles\n",
+ " # true if significant: both percentile values have the same sig and, thus, 0\n",
+ " # is not included\n",
+ " # true if sign is the same and response is \n",
+ " du850_mask_tm_bs = {'total': np.sign(du850_tmym_perc[0, 0, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 0, :, :]),\n",
+ " 'SST': np.sign(du850_tmym_perc[0, 1, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_tmym_perc[0, 2, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 2, :, :]),\n",
+ " 'water vapor' : np.sign(du850_tmym_perc[0, 3, :, :]) == \\\n",
+ " np.sign(du850_tmym_perc[1, 3, :, :])}\n",
+ " du850_mask_sm_bs = {'total': np.sign(du850_smym_perc[0, :, 0, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 0, :, :]),\n",
+ " 'SST': np.sign(du850_smym_perc[0, :, 1, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 1, :, :]),\n",
+ " 'cloud': np.sign(du850_smym_perc[0, :, 2, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 2, :, :]),\n",
+ " 'water vapor': np.sign(du850_smym_perc[0, :, 3, :, :]) == \\\n",
+ " np.sign(du850_smym_perc[1, :, 3, :, :])}\n",
+ "\n",
+ " ##############################################################################\n",
+ " # save masks as numpy arrays\n",
+ " np.save('../../' + model + '/' + model + '_du850_mask_tm_bs.npy', du850_mask_tm_bs)\n",
+ " np.save('../../' + model + '/' + model + '_du850_mask_sm_bs.npy', du850_mask_sm_bs)\n",
+ "\n",
+ " del u850_tmym_bs, u850_smym_bs\n",
+ " del du850_tmym, du850_smym, du850_tmym_perc, du850_smym_perc\n",
+ " del du850_mask_tm_bs, du850_mask_sm_bs\n",
+ " del lats, lons\n",
+ " \n",
+ " print('##############################')\n",
+ " print('##############################')\n",
+ "\n",
+ "del model, models\n",
+ "print('###########')\n",
+ "print('## Done! ##')\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure1_robust_response.ipynb b/pythonscripts/figure1_robust_response.ipynb
new file mode 100644
index 0000000..09183b9
--- /dev/null
+++ b/pythonscripts/figure1_robust_response.ipynb
@@ -0,0 +1,793 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Robust zonal wind response\n",
+ "\n",
+ "This script generates figure 1: maps of zonal wind response during DJF for\n",
+ "- coupled CMIP5 models (RCP8.5)\n",
+ "- atmosphere CMIP5 models (amipFuture and amip4K)\n",
+ "- ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "sims_cmip = ['historical', 'rcp85']\n",
+ "sims_amip = ['amip', 'amip4K', 'amipFuture']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)\n",
+ "\n",
+ "The zonal wind fields were extracted from the data in the archive with Climate Data Operators (cdo; https://www.mpimet.mpg.de/cdo):\n",
+ "\n",
+ "cdo selvar,u file.nc file.uwind.nc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T1C1W1\n",
+ "reading T2C2W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON simulations with locked clouds and interactive water vapor\n",
+ "runs_cld = ['T1C1', 'T2C2']\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " u850_icon[run], lats, lons = fct.read_var_onelevel(ipath + ifile,\n",
+ " 'u', 'lev', 850)\n",
+ " del ifile\n",
+ "del run, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM and IPSL-CM5A simulations with locked clouds and locked water vapor\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2']\n",
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (CMIP5 models)\n",
+ "\n",
+ "Note: All simulations were interpolated to the same grid and stored in numpy arrays with the jupyter notebook \"interpolate_cmip5_data_to_common_grid.ipynb\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "ntime = 360 # length of cmip/amip simulations in months (30 years)\n",
+ "\n",
+ "# coupled models: historical and RCP8.5 simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_hist = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_rcp85 = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_cmip):\n",
+ " u850_hist[:, m, :, :] = np.load(ipath + model + '_u850_historical.npy')\n",
+ " u850_rcp85[:, m, :, :] = np.load(ipath + model + '_u850_rcp85.npy')\n",
+ "del m, model\n",
+ "\n",
+ "# atmosphere models: amip, amip4K and amipFuture simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_amip = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amip4k = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amipfut = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_amip):\n",
+ " u850_amip[:, m, :, :] = np.load(ipath + model + '_u850_amip.npy')\n",
+ " u850_amip4k[:, m, :, :] = np.load(ipath + model + '_u850_amip4k.npy')\n",
+ " u850_amipfut[:, m, :, :] = np.load(ipath + model + '_u850_amipfut.npy')\n",
+ "del m, model\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean for all simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:64: RuntimeWarning: Mean of empty slice\n",
+ " monthly_mean[month] = np.nanmean(monthly_data[month], axis=0)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:71: RuntimeWarning: Mean of empty slice\n",
+ " seasons_dict[season] ], axis=0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM and IPSL-CM5A\n",
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# coupled CMIP5 models\n",
+ "u850_cmip_djf = np.full((len(sims_cmip), len(models_cmip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_cmip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_hist, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_cmip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_rcp85, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# atmosphere CMIP5 models\n",
+ "u850_amip_djf = np.full((len(sims_amip), len(models_amip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_amip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip4k, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[2, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amipfut, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# model mean for historical and amip simulations\n",
+ "u850_cmip_djf_mm = np.nanmean(u850_cmip_djf, axis=1)\n",
+ "u850_amip_djf_mm = np.nanmean(u850_amip_djf, axis=1)\n",
+ "\n",
+ "# delete variables with time information\n",
+ "del u850_icon, u850_mpi, u850_ipsl\n",
+ "del u850_hist, u850_rcp85, u850_amip, u850_amip4k, u850_amipfut"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ICON\n",
+ "du850_icon = u850_icon_djf['T2C2'] - u850_icon_djf['T1C1']\n",
+ "# MPI-ESM\n",
+ "du850_mpi = u850_mpi_djf['T2C2W2'] - u850_mpi_djf['T1C1W1']\n",
+ "# IPSL-CM5A\n",
+ "du850_ipsl = u850_ipsl_djf['T2C2W2'] - u850_ipsl_djf['T1C1W1']\n",
+ "\n",
+ "# CMIP5\n",
+ "du850_rcp85 = u850_cmip_djf[1, :, :, :] - u850_cmip_djf[0, :, :, :]\n",
+ "du850_amip4k = u850_amip_djf[1, :, :, :] - u850_amip_djf[0, :, :, :]\n",
+ "du850_amipfut = u850_amip_djf[2, :, :, :] - u850_amip_djf[0, :, :, :]\n",
+ "\n",
+ "# model mean\n",
+ "du850_rcp85_mm = np.nanmean(du850_rcp85, axis=0)\n",
+ "du850_amip4k_mm = np.nanmean(du850_amip4k, axis=0)\n",
+ "du850_amipfut_mm = np.nanmean(du850_amipfut, axis=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Determine where responses are robust (CMIP simulations) and where ICON, MPI-ESM and IPSL-CM5A disagree with the robust amip4K response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:58: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:60: RuntimeWarning: invalid value encountered in greater\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:63: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:65: RuntimeWarning: invalid value encountered in greater\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:68: RuntimeWarning: invalid value encountered in less\n",
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:70: RuntimeWarning: invalid value encountered in greater\n"
+ ]
+ }
+ ],
+ "source": [
+ "# find indices, where 9 or more AMIP models agree on response\n",
+ "# 9 or more models should agree on the sign: set threshold to 7 (9-2)\n",
+ "# (because we sum over all models and positive and negative signs might cancel\n",
+ "# each other out)\n",
+ "# 9 models agree on sign: sum = 7 or sum = -7\n",
+ "mask_thd = 7\n",
+ "mask_model_amip4k = np.sign(du850_amip4k)\n",
+ "mask_model_amipfut = np.sign(du850_amipfut)\n",
+ "\n",
+ "# calculate sum over mask_model arrays along axis of models\n",
+ "# -> how many models agree on the sign\n",
+ "mask_sum_amip4k = np.nansum(mask_model_amip4k, axis=0)\n",
+ "mask_sum_amipfut = np.nansum(mask_model_amipfut, axis=0)\n",
+ "\n",
+ "# apply threshold to mask_sum arrays\n",
+ "mask_u850_amip4k = np.logical_or(mask_sum_amip4k >= mask_thd,\n",
+ " mask_sum_amip4k <= -1*mask_thd) * 1\n",
+ "mask_u850_amipfut = np.logical_or(mask_sum_amipfut >= mask_thd,\n",
+ " mask_sum_amipfut <= -1*mask_thd) * 1\n",
+ "\n",
+ "del mask_model_amip4k, mask_model_amipfut\n",
+ "del mask_sum_amip4k, mask_sum_amipfut\n",
+ "del mask_thd\n",
+ "\n",
+ "##############################################################################\n",
+ "# find indices, where 30 or more coupled CMIP5 models agree on response\n",
+ "# 30 or more models should agree on the sign: set threshold to 23 (30-7)\n",
+ "# (37 models in total)\n",
+ "# (because we sum over all models and positive and negative signs might cancel\n",
+ "# each other out)\n",
+ "# 30 models agree on sign: sum = 23 or sum = -23\n",
+ "mask_thd = 23\n",
+ "mask_model_rcp85 = np.sign(du850_rcp85)\n",
+ "\n",
+ "# calculate sum over mask_model arrays along axis of models\n",
+ "# -> how many models agree on the sign\n",
+ "mask_sum_rcp85 = np.nansum(mask_model_rcp85, axis=0)\n",
+ "\n",
+ "# apply threshold to mask_sum arrays\n",
+ "mask_u850_rcp85 = np.logical_or(mask_sum_rcp85 >= mask_thd,\n",
+ " mask_sum_rcp85 <= -1*mask_thd) * 1\n",
+ "\n",
+ "del mask_model_rcp85, mask_sum_rcp85, mask_thd\n",
+ "\n",
+ "##############################################################################\n",
+ "# find indices, where sign of robust response in amip4K simulations does not\n",
+ "# agree with ICON, MPI-ESM and IPSL-CM5A\n",
+ "mask_model_icon = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "mask_model_mpi = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "mask_model_ipsl = np.full((len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "for m in range(len(models_amip)):\n",
+ " mask_model_icon[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_icon * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_icon * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ " mask_model_mpi[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_mpi * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_mpi * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ " mask_model_ipsl[m, :, :] = \\\n",
+ " np.logical_or(np.logical_and(du850_ipsl * mask_u850_amip4k > 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k < 0),\n",
+ " np.logical_and(du850_ipsl * mask_u850_amip4k < 0,\n",
+ " du850_amip4k[m, :, :] * mask_u850_amip4k > 0)) * 1\n",
+ "del m\n",
+ "\n",
+ "mask_thd = 9 # icon should agree with 9 or more models\n",
+ "mask_u850_icon_na = (np.nansum(mask_model_icon, axis=0) >= mask_thd) * 1\n",
+ "mask_u850_mpi_na = (np.nansum(mask_model_mpi, axis=0) >= mask_thd) * 1\n",
+ "mask_u850_ipsl_na = (np.nansum(mask_model_ipsl, axis=0) >= mask_thd) * 1\n",
+ "\n",
+ "del mask_model_icon, mask_model_mpi, mask_model_ipsl\n",
+ "del mask_thd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude in model-mean historical, model-mean amip, ICON, MPI-ESM, and IPSL-CM5A control simulations for the Northern Hemisphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# latitudes in Northern Hemisphere\n",
+ "latind0 = (np.abs(lats-0)).argmin() + 1\n",
+ "lats_NH = lats[latind0:]\n",
+ "\n",
+ "# shift longitudes from 0deg...360deg to -270deg...90deg for visualization reasons\n",
+ "# and look at NH\n",
+ "u850_cmip_mm_shift, lons_shift = fct.shiftgrid_copy(90.,\n",
+ " u850_cmip_djf_mm[0, latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_amip_mm_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_amip_djf_mm[0, latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_icon_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_icon_djf['T1C1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_mpi_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_mpi_djf['T1C1W1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "u850_ipsl_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_ipsl_djf['T1C1W1'][latind0:, :],\n",
+ " lons, start=False)\n",
+ "\n",
+ "jetlat_hist_mm_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_amip_mm_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_icon_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_mpi_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_ipsl_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "for lo in range(lons_shift.size):\n",
+ " # historical simulation\n",
+ " jetlat_hist_mm_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_mm_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ " # amip simulation\n",
+ " jetlat_amip_mm_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_mm_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ " # ICON\n",
+ " jetlat_icon_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_shift[:, lo], lats_NH)\n",
+ " # MPI-ESM\n",
+ " jetlat_mpi_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_shift[:, lo], lats_NH)\n",
+ " # IPSL-CM5A\n",
+ " jetlat_ipsl_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ "del lo\n",
+ "\n",
+ "del u850_cmip_mm_shift, u850_amip_mm_shift, \\\n",
+ " u850_icon_shift, u850_mpi_shift, u850_ipsl_shift\n",
+ "del lons_shift"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot of u850 response\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant in the CMIP5 models, and the hatching for the regions, in which the response in ICON, MPI-ESM and IPSL-CM5A does not agree with the robust amip4K response)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "lons_plot = fct.shiftgrid_copy(90., du850_icon, lons, start=False)[1]\n",
+ "\n",
+ "# North Atlantic region\n",
+ "lonind_west = (np.abs(lons_plot--90)).argmin() # find index of 90°W\n",
+ "lonind_east = (np.abs(lons_plot-35)).argmin() # find index of 35°E\n",
+ "latind_sout = (np.abs(lats-20)).argmin() # find index of 20°N\n",
+ "latind_nort = (np.abs(lats-80)).argmin() # find index of 80°N\n",
+ "\n",
+ "lons_plot = lons_plot[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "# MPI-ESM uses slightly different latitudes\n",
+ "latind_sout_mpi = (np.abs(lats_mpi-20)).argmin() # find index of 20°N\n",
+ "latind_nort_mpi = (np.abs(lats_mpi-80)).argmin() # find index of 80°N\n",
+ "lats_mpi_plot = lats_mpi[latind_sout_mpi:latind_nort_mpi+1]\n",
+ "\n",
+ "# shift zonal wind fields and masks\n",
+ "du850_rcp85_plot = fct.shiftgrid_copy(90., du850_rcp85_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_rcp85_plot = fct.shiftgrid_copy(90., mask_u850_rcp85, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "du850_amipfut_plot = fct.shiftgrid_copy(90., du850_amipfut_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_amipfut_plot = fct.shiftgrid_copy(90., mask_u850_amipfut, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "du850_amip4k_plot = fct.shiftgrid_copy(90., du850_amip4k_mm, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_amip4k_plot = fct.shiftgrid_copy(90., mask_u850_amip4k, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_icon_plot = fct.shiftgrid_copy(90., du850_icon, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_icon_plot = fct.shiftgrid_copy(90., mask_u850_icon_na, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_mpi_plot = fct.shiftgrid_copy(90., du850_mpi, lons,\n",
+ " start=False)[0][latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_mpi_plot = fct.shiftgrid_copy(90., mask_u850_mpi_na, lons,\n",
+ " start=False)[0][latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_ipsl_plot = fct.shiftgrid_copy(90., du850_ipsl, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_ipsl_plot = fct.shiftgrid_copy(90., mask_u850_ipsl_na, lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of u850 response in DJF for RCP8.5, amipFuture, amip4K, ICON, MPI-ESM and IPSL-CM5A"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 12 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(2, 3, figsize=(12, 6),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].coastlines(rasterized=True)\n",
+ " ax[i].set_aspect('auto')\n",
+ " ax[i].tick_params(labelsize=14) \n",
+ " # extended North Atlantic region\n",
+ " ax[i].set_extent([-90, 30, 25, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if i > 2: # last row\n",
+ " #ax[i].set_xticks([0, 90, 180, 270], crs=ccrs.PlateCarree())\n",
+ " ax[i].set_xticks([-90, -60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[i].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if i in [0, 3]:\n",
+ " #ax[i].set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n",
+ " ax[i].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del i\n",
+ "\n",
+ "# plot contour for 8, 10 and 12 m/s in CTL\n",
+ "levp = [8, 10, 12]\n",
+ "ax[0].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_cmip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[1].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_amip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[2].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_amip_djf_mm[0,:,:], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[3].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_icon_djf['T1C1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[4].contour(lons_plot, lats_mpi_plot,\n",
+ " fct.shiftgrid_copy(90., u850_mpi_djf['T1C1W1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "ax[5].contour(lons_plot, lats_plot,\n",
+ " fct.shiftgrid_copy(90., u850_ipsl_djf['T1C1W1'], lons,\n",
+ " start=False)[0][latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1],\n",
+ " levels=levp, colors='lightgrey', transform=ccrs.PlateCarree())\n",
+ "del levp\n",
+ "\n",
+ "# cmip5 coupled models\n",
+ "cf = ax[0].pcolormesh(lons_plot, lats_plot, du850_rcp85_plot,\n",
+ " vmin=-1.5, vmax=1.5, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[0].plot(lons_plot, jetlat_hist_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[0].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_rcp85_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-1.5, 2, 1.5)\n",
+ "cb = fig.colorbar(cf, ax=ax[0], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[0].set_title('CMIP5 RCP8.5\\n(37 models)', fontsize=16)\n",
+ "# amipfuture\n",
+ "cf = ax[1].pcolormesh(lons_plot, lats_plot, du850_amipfut_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[1].plot(lons_plot, jetlat_amip_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[1].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_amipfut_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[1], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[1].set_title('CMIP5 AmipFuture\\n(11 models)', fontsize=16)\n",
+ "# amip4k\n",
+ "cf = ax[2].pcolormesh(lons_plot, lats_plot, du850_amip4k_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[2].plot(lons_plot, jetlat_amip_mm_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# stippling, where models agree on response\n",
+ "ax[2].pcolor(lons_plot, lats_plot, np.ma.masked_values(mask_amip4k_plot, 0),\n",
+ " hatch='....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[2], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[2].set_title('CMIP5 Amip4K\\n(11 models)', fontsize=16)\n",
+ "# ICON (locked clouds)\n",
+ "cf = ax[3].pcolormesh(lons_plot, lats_plot, du850_icon_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[3].plot(lons_plot, jetlat_icon_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[3].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(mask_icon_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[3], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[3].set_title('ICON', fontsize=16)\n",
+ "# MPI-ESM\n",
+ "cf = ax[4].pcolormesh(lons_plot, lats_mpi_plot, du850_mpi_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[4].plot(lons_plot, jetlat_mpi_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[4].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(mask_mpi_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[4], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[4].set_title('MPI-ESM', fontsize=16)\n",
+ "# IPSL-CM5A\n",
+ "cf = ax[5].pcolormesh(lons_plot, lats_plot, du850_ipsl_plot,\n",
+ " vmin=-4, vmax=4, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# jet latitude in control run\n",
+ "ax[5].plot(lons_plot, jetlat_ipsl_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "# hatching, where response does not agree with robust amip4K response\n",
+ "ax[5].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(mask_ipsl_plot, 0),\n",
+ " hatch='///', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "clevs = np.arange(-4, 5, 2)\n",
+ "cb = fig.colorbar(cf, ax=ax[5], aspect=15, extend='both', ticks=clevs)\n",
+ "cb.ax.tick_params(labelsize=12)\n",
+ "del clevs, cb, cf\n",
+ "ax[5].set_title('IPSL-CM5A', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ " \n",
+ "fig.savefig('figure1a_1f.pdf', dpi=400, bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/figure1a_1f.pdf b/pythonscripts/figure1a_1f.pdf
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diff --git a/pythonscripts/figure2_jetresponse.ipynb b/pythonscripts/figure2_jetresponse.ipynb
new file mode 100644
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+++ b/pythonscripts/figure2_jetresponse.ipynb
@@ -0,0 +1,738 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Jet response\n",
+ "\n",
+ "This script generates figure 2: jet shift vs. jet strengthening over the North Atlantic and over Europe for\n",
+ "- coupled CMIP5 models (RCP8.5)\n",
+ "- atmosphere CMIP5 models (amipFuture and amip4K)\n",
+ "- ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "# inset small plot in large plot\n",
+ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
+ "import cartopy.mpl.geoaxes\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "sims_cmip = ['historical', 'rcp85']\n",
+ "sims_amip = ['amip', 'amip4K', 'amipFuture']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T1C1W1\n",
+ "reading T2C2W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON simulations with locked clouds and interactive water vapor\n",
+ "runs_cld = ['T1C1', 'T2C2']\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM and IPSL-CM5A simulations with locked clouds and locked water vapor\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2']\n",
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (CMIP5 models)¶\n",
+ "\n",
+ "Note: All simulations were interpolated to the same grid and stored in numpy arrays with the jupyter notebook \"interpolate_cmip5_data_to_common_grid.ipynb\"."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "ntime = 360 # length of cmip/amip simulations in months (30 years)\n",
+ "\n",
+ "# coupled models: historical and RCP8.5 simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_hist = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_rcp85 = np.full((ntime, len(models_cmip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_cmip):\n",
+ " u850_hist[:, m, :, :] = np.load(ipath + model + '_u850_historical.npy')\n",
+ " u850_rcp85[:, m, :, :] = np.load(ipath + model + '_u850_rcp85.npy')\n",
+ "del m, model\n",
+ "\n",
+ "# atmosphere models: amip, amip4K and amipFuture simulations\n",
+ "# create arrays with dimensions (ntime, number of models, lats, lons)\n",
+ "u850_amip = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amip4k = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "u850_amipfut = np.full((ntime, len(models_amip), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "for m, model in enumerate(models_amip):\n",
+ " u850_amip[:, m, :, :] = np.load(ipath + model + '_u850_amip.npy')\n",
+ " u850_amip4k[:, m, :, :] = np.load(ipath + model + '_u850_amip4k.npy')\n",
+ " u850_amipfut[:, m, :, :] = np.load(ipath + model + '_u850_amipfut.npy')\n",
+ "del m, model\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean for all simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:64: RuntimeWarning: Mean of empty slice\n",
+ " monthly_mean[month] = np.nanmean(monthly_data[month], axis=0)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:71: RuntimeWarning: Mean of empty slice\n",
+ " seasons_dict[season] ], axis=0)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM and IPSL-CM5A\n",
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# coupled CMIP5 models\n",
+ "u850_cmip_djf = np.full((len(sims_cmip), len(models_cmip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_cmip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_hist, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_cmip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_rcp85, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# atmosphere CMIP5 models\n",
+ "u850_amip_djf = np.full((len(sims_amip), len(models_amip), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "u850_amip_djf[0, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[1, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amip4k, months,\n",
+ " seasons)[1]['DJF']\n",
+ "u850_amip_djf[2, :, :, :] = fct.calcMonthlyandSeasonMean(u850_amipfut, months,\n",
+ " seasons)[1]['DJF']\n",
+ "\n",
+ "# model mean for historical and amip simulations\n",
+ "u850_cmip_djf_mm = np.nanmean(u850_cmip_djf, axis=1)\n",
+ "u850_amip_djf_mm = np.nanmean(u850_amip_djf, axis=1)\n",
+ "\n",
+ "# delete variables with time information\n",
+ "del u850_icon, u850_mpi, u850_ipsl\n",
+ "del u850_hist, u850_rcp85, u850_amip, u850_amip4k, u850_amipfut"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate zonal-mean u850 over the North Atlantic (60W-0E) and over Europe (0E-25E)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:354: RuntimeWarning: Mean of empty slice\n",
+ " var_zm = np.nanmean(var[:, lonind_west:lonind_east+1], axis=1)\n",
+ "/mnt/lustre02/work/bb1018/b380490/outputdata/ERL2021_data/pythonscripts/helper_functions.py:362: RuntimeWarning: Mean of empty slice\n",
+ " var_zm = np.nanmean(var1[:, lonind_west:lonind_east+1], axis=1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# boundaries of regions\n",
+ "box_west = [0, -60]\n",
+ "box_east = [25, 0]\n",
+ "\n",
+ "# coupled models\n",
+ "u850_cmip_box = np.full((len(box_west), len(sims_cmip), len(models_cmip),\n",
+ " len(lats)), np.nan, dtype=float)\n",
+ "u850_cmip_mm_box = np.full((len(box_west), len(sims_cmip), len(lats)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_cmip)):\n",
+ " for m in range(len(models_cmip)):\n",
+ " u850_cmip_box[b, s, m, :] = fct.calcBoxZonalmean(u850_cmip_djf[s, m, :, :],\n",
+ " lats, lons, box_west[b], box_east[b])\n",
+ " del m\n",
+ " u850_cmip_mm_box[b, s, :] = fct.calcBoxZonalmean(u850_cmip_djf_mm[s, :, :],\n",
+ " lats, lons, box_west[b], box_east[b])\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# amip models\n",
+ "u850_amip_box = np.full((len(box_west), len(sims_amip), len(models_amip),\n",
+ " len(lats)), np.nan, dtype=float)\n",
+ "u850_amip_mm_box = np.full((len(box_west), len(sims_amip), len(lats)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_amip)):\n",
+ " for m in range(len(models_amip)):\n",
+ " u850_amip_box[b, s, m, :] = fct.calcBoxZonalmean(u850_amip_djf[s, m, :, :],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " del m\n",
+ " u850_amip_mm_box[b, s, :] = fct.calcBoxZonalmean(u850_amip_djf_mm[s, :, :],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# ICON\n",
+ "u850_icon_box = {}\n",
+ "for run in runs_cld:\n",
+ " u850icon = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " u850icon[b, :] = fct.calcBoxZonalmean(u850_icon_djf[run],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " u850_icon_box[run] = u850icon.copy()\n",
+ " del u850icon, b\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM, IPSL-CM5A\n",
+ "u850_mpi_box = {}; u850_ipsl_box = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850mpi = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " u850ipsl = np.full((len(box_west), len(lats)), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " u850mpi[b, :] = fct.calcBoxZonalmean(u850_mpi_djf[run],\n",
+ " lats_mpi, lons_mpi,\n",
+ " box_west[b], box_east[b])\n",
+ " u850ipsl[b, :] = fct.calcBoxZonalmean(u850_ipsl_djf[run],\n",
+ " lats, lons,\n",
+ " box_west[b], box_east[b])\n",
+ " u850_mpi_box[run] = u850mpi.copy()\n",
+ " u850_ipsl_box[run] = u850ipsl.copy()\n",
+ " del u850mpi, u850ipsl, b\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude and jet strength based on the zonal-mean u850 profiles for the two regions"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Northern Hemisphere\n",
+ "latind0 = (np.abs(lats-0)).argmin() + 1\n",
+ "lats_NH = lats[latind0:]\n",
+ "\n",
+ "# coupled models\n",
+ "jetlat_cmip_box = np.full((len(box_west), len(sims_cmip),\n",
+ " len(models_cmip)), np.nan, dtype=float)\n",
+ "jetint_cmip_box = np.full((len(box_west), len(sims_cmip),\n",
+ " len(models_cmip)), np.nan, dtype=float)\n",
+ "jetlat_cmip_mm_box = np.full((len(box_west), len(sims_cmip)), np.nan,\n",
+ " dtype=float)\n",
+ "jetint_cmip_mm_box = np.full((len(box_west), len(sims_cmip)), np.nan,\n",
+ " dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_cmip)):\n",
+ " for m in range(len(models_cmip)):\n",
+ " jetlat_cmip_box[b, s, m], jetint_cmip_box[b, s, m] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_box[b, s, m, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del m\n",
+ " jetlat_cmip_mm_box[b, s], jetint_cmip_mm_box[b, s] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_cmip_mm_box[b, s, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# amip models\n",
+ "jetlat_amip_box = np.full((len(box_west), len(sims_amip),\n",
+ " len(models_amip)), np.nan, dtype=float)\n",
+ "jetint_amip_box = np.full((len(box_west), len(sims_amip),\n",
+ " len(models_amip)), np.nan, dtype=float)\n",
+ "jetlat_amip_mm_box = np.full((len(box_west), len(sims_amip)),\n",
+ " np.nan, dtype=float)\n",
+ "jetint_amip_mm_box = np.full((len(box_west), len(sims_amip)),\n",
+ " np.nan, dtype=float)\n",
+ "for b in range(len(box_west)):\n",
+ " for s in range(len(sims_amip)):\n",
+ " for m in range(len(models_amip)):\n",
+ " jetlat_amip_box[b, s, m], jetint_amip_box[b, s, m] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_box[b, s, m, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del m\n",
+ " jetlat_amip_mm_box[b, s], jetint_amip_mm_box[b, s] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_amip_mm_box[b, s, latind0:],\n",
+ " lats_NH, -999)\n",
+ " del s\n",
+ "del b\n",
+ "\n",
+ "# ICON\n",
+ "jetlat_icon_box = {}; jetint_icon_box = {}\n",
+ "for run in runs_cld:\n",
+ " jlat = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jint = np.full(len(box_west), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " jlat[b], jint[b] = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_box[run][b, latind0:], lats_NH)\n",
+ " jetlat_icon_box[run] = jlat.copy()\n",
+ " jetint_icon_box[run] = jint.copy()\n",
+ " del jlat, jint, b\n",
+ "del run\n",
+ "\n",
+ "# MPI-ESM, IPSL-CM5A\n",
+ "jetlat_mpi_box = {}; jetint_mpi_box = {}\n",
+ "jetlat_ipsl_box = {}; jetint_ipsl_box = {}\n",
+ "for run in runs_cldvap:\n",
+ " jlatmpi = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jintmpi = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jlatipsl = np.full(len(box_west), np.nan, dtype=float)\n",
+ " jintipsl = np.full(len(box_west), np.nan, dtype=float)\n",
+ " for b in range(len(box_west)):\n",
+ " jlatmpi[b], jintmpi[b] = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_box[run][b, latind0:], lats_NH)\n",
+ " jlatipsl[b], jintipsl[b] = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_box[run][b, latind0:],\n",
+ " lats_NH, -999)\n",
+ " jetlat_mpi_box[run] = jlatmpi.copy()\n",
+ " jetint_mpi_box[run] = jintmpi.copy()\n",
+ " jetlat_ipsl_box[run] = jlatipsl.copy()\n",
+ " jetint_ipsl_box[run] = jintipsl.copy()\n",
+ " del jlatmpi, jintmpi, jlatipsl, jintipsl, b\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet responses"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# coupled models\n",
+ "djetlat_cmip_box = jetlat_cmip_box[:, 1, :] - jetlat_cmip_box[:, 0, :]\n",
+ "djetint_cmip_box = jetint_cmip_box[:, 1, :] - jetint_cmip_box[:, 0, :]\n",
+ "djetlat_cmip_mm_box = jetlat_cmip_mm_box[:, 1] - jetlat_cmip_mm_box[:, 0]\n",
+ "djetint_cmip_mm_box = jetint_cmip_mm_box[:, 1] - jetint_cmip_mm_box[:, 0]\n",
+ "\n",
+ "# amip models\n",
+ "djetlat_amip4k_box = jetlat_amip_box[:, 1, :] - jetlat_amip_box[:, 0, :]\n",
+ "djetint_amip4k_box = jetint_amip_box[:, 1, :] - jetint_amip_box[:, 0, :]\n",
+ "djetlat_amip4k_mm_box = jetlat_amip_mm_box[:, 1] - jetlat_amip_mm_box[:, 0]\n",
+ "djetint_amip4k_mm_box = jetint_amip_mm_box[:, 1] - jetint_amip_mm_box[:, 0]\n",
+ "\n",
+ "djetlat_amipfut_box = jetlat_amip_box[:, 2, :] - jetlat_amip_box[:, 0, :]\n",
+ "djetint_amipfut_box = jetint_amip_box[:, 2, :] - jetint_amip_box[:, 0, :]\n",
+ "djetlat_amipfut_mm_box = jetlat_amip_mm_box[:, 2] - jetlat_amip_mm_box[:, 0]\n",
+ "djetint_amipfut_mm_box = jetint_amip_mm_box[:, 2] - jetint_amip_mm_box[:, 0]\n",
+ "\n",
+ "# ICON\n",
+ "djetlat_icon_box = jetlat_icon_box['T2C2'] - jetlat_icon_box['T1C1']\n",
+ "djetint_icon_box = jetint_icon_box['T2C2'] - jetint_icon_box['T1C1']\n",
+ "\n",
+ "# MPI-ESM\n",
+ "djetlat_mpi_box = jetlat_mpi_box['T2C2W2'] - jetlat_mpi_box['T1C1W1']\n",
+ "djetint_mpi_box = jetint_mpi_box['T2C2W2'] - jetint_mpi_box['T1C1W1']\n",
+ "\n",
+ "# IPSL-CM5A\n",
+ "djetlat_ipsl_box = jetlat_ipsl_box['T2C2W2'] - jetlat_ipsl_box['T1C1W1']\n",
+ "djetint_ipsl_box = jetint_ipsl_box['T2C2W2'] - jetint_ipsl_box['T1C1W1']\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot jet shift vs. jet strengthening over the North Atlantic and over Europe\n",
+ "\n",
+ "Add maps in which the region is highlighted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "color_cmip = 'dimgrey'\n",
+ "color_mean = 'lightseagreen'\n",
+ "color_icon = 'tab:orange'\n",
+ "color_mpi = 'tab:red'\n",
+ "color_ipsl = 'tab:purple'\n",
+ "ms = 7.5 # markersize\n",
+ "ylab = ['Europe', 'North Atlantic']\n",
+ "\n",
+ "fig, ax = plt.subplots(len(box_west), 3, figsize=(10, 6))\n",
+ "for b in range(len(box_west)):\n",
+ " # coupled models\n",
+ " ax[b, 0].plot(djetlat_cmip_box[b, :], djetint_cmip_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 0].plot(djetlat_cmip_mm_box[b], djetint_cmip_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # amip models\n",
+ " # amipFuture\n",
+ " ax[b, 1].plot(djetlat_amipfut_box[b, :], djetint_amipfut_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 1].plot(djetlat_amipfut_mm_box[b], djetint_amipfut_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # amip4K\n",
+ " ax[b, 2].plot(djetlat_amip4k_box[b, :], djetint_amip4k_box[b, :],\n",
+ " linestyle='none', marker='o', color=color_cmip, \n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_amip4k_mm_box[b], djetint_amip4k_mm_box[b],\n",
+ " linestyle='none', marker='o', color=color_mean,\n",
+ " markersize=ms)\n",
+ " # ICON, MPI-ESM, IPSL-CM5A\n",
+ " ax[b, 2].plot(djetlat_icon_box[b], djetint_icon_box[b],\n",
+ " linestyle='none', marker='o', color=color_icon,\n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_mpi_box[b], djetint_mpi_box[b],\n",
+ " linestyle='none', marker='o', color=color_mpi,\n",
+ " markersize=ms)\n",
+ " ax[b, 2].plot(djetlat_ipsl_box[b], djetint_ipsl_box[b],\n",
+ " linestyle='none', marker='o', color=color_ipsl,\n",
+ " markersize=ms)\n",
+ " \n",
+ " # ylabel\n",
+ " ax[b, 0].set_ylabel(ylab[b] + '\\n$\\Delta$u$_{jet}$ [m s$^{-1}$]', fontsize=15)\n",
+ " ax[b, 0].yaxis.set_label_coords(-0.05, 0.5)\n",
+ "del b\n",
+ "ax[0, 0].set_title('RCP8.5', fontsize=16)\n",
+ "ax[0, 1].set_title('AmipFuture', fontsize=16)\n",
+ "ax[0, 2].set_title('Amip4K', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# plot maps for Europe and North Atlantic as small inserted plot\n",
+ "# Europe\n",
+ "inset_ax = inset_axes(ax[0, 0], \n",
+ " width=\"50%\",\n",
+ " height=\"50%\",\n",
+ " #loc=\"upper right\",\n",
+ " bbox_to_anchor=(0.15, 0.1, 1, 1),\n",
+ " bbox_transform=ax[0, 0].transAxes,\n",
+ " axes_class=cartopy.mpl.geoaxes.GeoAxes,\n",
+ " axes_kwargs=dict(map_projection=ccrs.PlateCarree(central_longitude=-90.)))\n",
+ "inset_ax.coastlines()\n",
+ "inset_ax.set_aspect(1.5)\n",
+ "inset_ax.tick_params(labelsize=12)\n",
+ "# extended North Atlantic region\n",
+ "inset_ax.set_extent([-80, 30, 30, 70], ccrs.PlateCarree())\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# xaxis: longitudes\n",
+ "inset_ax.set_xticks([0, 25], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "inset_ax.xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "# mark west and east boundaries of Europe\n",
+ "lonwest = 0; loneast = 25; latsout=30.8; latnort=69.2\n",
+ "# left vertical line\n",
+ "inset_ax.plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# right vertical line\n",
+ "inset_ax.plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# upper horizontal line\n",
+ "inset_ax.plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "inset_ax.plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "del inset_ax\n",
+ "\n",
+ "# North Atlantic\n",
+ "inset_ax = inset_axes(ax[1, 0], \n",
+ " width=\"50%\",\n",
+ " height=\"50%\",\n",
+ " #loc=\"upper right\",\n",
+ " bbox_to_anchor=(0.15, 0.1, 1, 1),\n",
+ " bbox_transform=ax[1, 0].transAxes,\n",
+ " axes_class=cartopy.mpl.geoaxes.GeoAxes, \n",
+ " axes_kwargs=dict(map_projection=ccrs.PlateCarree(central_longitude=-90.)))\n",
+ "inset_ax.coastlines()\n",
+ "inset_ax.set_aspect(1.7)\n",
+ "inset_ax.tick_params(labelsize=12)\n",
+ "# extended North Atlantic region\n",
+ "inset_ax.set_extent([-80, 30, 30, 70], ccrs.PlateCarree())\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# xaxis: longitudes\n",
+ "inset_ax.set_xticks([-60, 0], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "inset_ax.xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "# mark west and east boundaries of Europe\n",
+ "lonwest = -60; loneast = 0; latsout=30.8; latnort=69.2\n",
+ "# left vertical line\n",
+ "inset_ax.plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# right vertical line\n",
+ "inset_ax.plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# upper horizontal line\n",
+ "inset_ax.plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "inset_ax.plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='tab:red', transform=ccrs.PlateCarree())\n",
+ "del inset_ax\n",
+ "\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].tick_params(labelsize=14)\n",
+ " ax[i].spines['right'].set_color('none')\n",
+ " ax[i].spines['top'].set_color('none')\n",
+ " ax[i].spines['left'].set_position('zero')\n",
+ " ax[i].spines['bottom'].set_position('zero')\n",
+ " # x-ticks\n",
+ " ax[i].xaxis.set_ticks([-5, -2.5, 2.5, 5, 7.5, 10])#, 15])\n",
+ " ax[i].xaxis.set_ticklabels([-5, -2.5, 2.5, 5, 7.5, 10])#, 15])\n",
+ " ax[i].set_xlim(-2.5, 7.5)#7)\n",
+ " # y-ticks\n",
+ " ax[i].yaxis.set_ticks([-1, 1, 3, 5])\n",
+ " ax[i].yaxis.set_ticklabels([-1, 1, 3, 5])\n",
+ " ax[i].set_ylim(-1.6, 5)\n",
+ " # labels\n",
+ " ax[i].set_xlabel(r'$\\Delta \\varphi_{jet}$ [$^{\\circ}$]', fontsize=16)\n",
+ " ax[i].xaxis.set_label_coords(0.5, -0.05)\n",
+ " # hide x labels and tick labels for top plots and y ticks for right plots.\n",
+ " ax[i].label_outer()\n",
+ "del i\n",
+ "\n",
+ "# text for colors\n",
+ "fig.text(0.88, 0.82, 'CMIP models', color=color_cmip, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.78, 'CMIP model mean', color=color_mean, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.74, 'ICON', color=color_icon, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.70, 'MPI-ESM', color=color_mpi, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "fig.text(0.88, 0.66, 'IPSL-CM5A', color=color_ipsl, ha='left',\n",
+ " va='center', fontsize=14)\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.savefig('figure2a_2f.pdf', bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax\n",
+ "\n",
+ "del color_cmip, color_mean, color_icon\n",
+ "del color_mpi, color_ipsl, ms\n",
+ "del ylab"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/figure2a_2f.pdf b/pythonscripts/figure2a_2f.pdf
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diff --git a/pythonscripts/figure3_du850_icon_mpi_ipsl_total_vs_cloud.ipynb b/pythonscripts/figure3_du850_icon_mpi_ipsl_total_vs_cloud.ipynb
new file mode 100644
index 0000000..f9cad0b
--- /dev/null
+++ b/pythonscripts/figure3_du850_icon_mpi_ipsl_total_vs_cloud.ipynb
@@ -0,0 +1,635 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: total response vs. cloud impact\n",
+ "\n",
+ "This script generates figure 3: maps of total zonal wind response vs. cloud impact in ICON, MPI-ESM and IPSL-CM5A.\n",
+ "\n",
+ "Note: for ICON, we investigate simulations with locked clouds and interactive water vapor. For MPI-ESM and IPSL-CM5A, we investigate simulations with both locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated\n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (MPI-ESM, IPSL-CM5A with locked clouds and locked water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850_mpi = {}; u850_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.uwind.nc'\n",
+ " u850_mpi[run], lats_mpi, lons_mpi = fct.read_var_onelevel('../../MPI-ESM/' + ifile,\n",
+ " 'u', 'plev', 850)\n",
+ " del ifile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.uwind.nc'\n",
+ " u850_ipsl[run], lats_ipsl, lons_ipsl = fct.read_var_onelevel('../../IPSL-CM5A/' + ifile,\n",
+ " 'vitu', 'presnivs', 850)\n",
+ " del ifile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON with locked clouds and interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons_icon = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u850_mpi_djf = {}; u850_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_mpi_djf[run] = fct.calcMonthlyandSeasonMean(u850_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " u850_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(u850_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months,\n",
+ " seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del u850_mpi, u850_ipsl, u850_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_mpi = np.full((len(response_cldvap), len(lats_mpi),\n",
+ " len(lons_mpi)), np.nan, dtype=float)\n",
+ "du850_ipsl = np.full((len(response_cldvap), len(lats_ipsl),\n",
+ " len(lons_ipsl)), np.nan, dtype=float)\n",
+ "\n",
+ "du850_mpi[0, :, :], du850_mpi[1, :, :], du850_mpi[2, :, :], \\\n",
+ "du850_mpi[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_mpi_djf['T1C1W1'], u850_mpi_djf['T2C2W2'],\n",
+ " u850_mpi_djf['T1C2W2'], u850_mpi_djf['T2C1W1'],\n",
+ " u850_mpi_djf['T1C2W1'], u850_mpi_djf['T1C1W2'],\n",
+ " u850_mpi_djf['T2C2W1'], u850_mpi_djf['T2C1W2'])\n",
+ "du850_ipsl[0, :, :], du850_ipsl[1, :, :], du850_ipsl[2, :, :], \\\n",
+ "du850_ipsl[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_ipsl_djf['T1C1W1'], u850_ipsl_djf['T2C2W2'],\n",
+ " u850_ipsl_djf['T1C2W2'], u850_ipsl_djf['T2C1W1'],\n",
+ " u850_ipsl_djf['T1C2W1'], u850_ipsl_djf['T1C1W2'],\n",
+ " u850_ipsl_djf['T2C2W1'], u850_ipsl_djf['T2C1W2'])\n",
+ "\n",
+ "du850_icon = np.full((len(response_cld), len(lats_icon),\n",
+ " len(lons_icon)), np.nan, dtype=float)\n",
+ "du850_icon[0, :, :], du850_icon[1, :, :], du850_icon[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_icon_djf['T1C1'], u850_icon_djf['T2C2'],\n",
+ " u850_icon_djf['T1C2'], u850_icon_djf['T2C1'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal-mean zonal wind.\n",
+ "\n",
+ "The seasonal-mean zonal wind is calculated with cdo (Climate Data Operators):\n",
+ "\n",
+ "* select zonal wind: cdo selvar,u ICON-NWP_AMIP_run_3d_mm.nc ICON-NWP_AMIP_run_3d_mm.uwind.nc\n",
+ "* calculate the seasonal mean: cdo seasmean ICON-NWP_AMIP_run_3d_mm.uwind.nc ICON-NWP_AMIP_run_3d_mm.uwind.seasmean.nc\n",
+ "\n",
+ "\"run\" can be any of the simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# ICON\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "mask_read = np.load(ipath + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_icon = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cld])\n",
+ "del mask_read, ipath\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "mask_read = np.load('../../MPI-ESM/MPI-ESM_du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_mpi = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cldvap])\n",
+ "# shift latitudes to go from South to North and not from North to South\n",
+ "du850_mask_mpi = du850_mask_mpi[:, ::-1, :]\n",
+ "del mask_read\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "mask_read = np.load('../../IPSL-CM5A/IPSL-CM5A_du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "du850_mask_ipsl = np.array([mask_read[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_cldvap])\n",
+ "del mask_read"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate jet latitude in the control simulations for the Northern Hemisphere"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# latitudes in Northern Hemisphere\n",
+ "latind0 = (np.abs(lats_icon-0)).argmin() + 1\n",
+ "lats_NH = lats_icon[latind0:]\n",
+ "\n",
+ "# shift longitudes from 0deg...360deg to -270deg...90deg for visualization reasons\n",
+ "# and look at NH\n",
+ "u850_icon_shift, lons_shift = fct.shiftgrid_copy(90.,\n",
+ " u850_icon_djf['T1C1'][latind0:, :],\n",
+ " lons_icon, start=False)\n",
+ "u850_mpi_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_mpi_djf['T1C1W1'][latind0:, :],\n",
+ " lons_mpi, start=False)\n",
+ "u850_ipsl_shift, _ = fct.shiftgrid_copy(90.,\n",
+ " u850_ipsl_djf['T1C1W1'][latind0:, :],\n",
+ " lons_ipsl, start=False)\n",
+ "\n",
+ "jetlat_icon_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_mpi_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "jetlat_ipsl_nh = np.full(lons_shift.size, np.nan, dtype=float)\n",
+ "for lo in range(lons_shift.size):\n",
+ " # ICON\n",
+ " jetlat_icon_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_icon_shift[:, lo], lats_NH)\n",
+ " # MPI-ESM\n",
+ " jetlat_mpi_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH(u850_mpi_shift[:, lo], lats_NH)\n",
+ " # IPSL-CM5A\n",
+ " jetlat_ipsl_nh[lo], _ = \\\n",
+ " fct.get_eddyjetlatint_NH_nan(u850_ipsl_shift[:, lo],\n",
+ " lats_NH, lons_shift[lo])\n",
+ "del lo\n",
+ "\n",
+ "del u850_icon_shift, u850_mpi_shift, u850_ipsl_shift, lons_shift"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot of total response and cloud impact\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "lons_plot = fct.shiftgrid_copy(90., du850_icon, lons_icon, start=False)[1]\n",
+ "\n",
+ "# North Atlantic region\n",
+ "lonind_west = (np.abs(lons_plot--90)).argmin() # find index of 90°W\n",
+ "lonind_east = (np.abs(lons_plot-35)).argmin() # find index of 35°E\n",
+ "latind_sout = (np.abs(lats_icon-20)).argmin() # find index of 20°N\n",
+ "latind_nort = (np.abs(lats_icon-80)).argmin() # find index of 80°N\n",
+ "\n",
+ "lons_plot = lons_plot[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats_icon[latind_sout:latind_nort+1]\n",
+ "\n",
+ "# MPI-ESM uses slightly different latitudes\n",
+ "latind_sout_mpi = (np.abs(lats_mpi-20)).argmin() # find index of 20°N\n",
+ "latind_nort_mpi = (np.abs(lats_mpi-80)).argmin() # find index of 80°N\n",
+ "lats_mpi_plot = lats_mpi[latind_sout_mpi:latind_nort_mpi+1]\n",
+ "\n",
+ "# shift zonal wind fields and masks\n",
+ "du850_icon_plot = fct.shiftgrid_copy(90., du850_icon, lons_icon,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_icon_plot = fct.shiftgrid_copy(90., du850_mask_icon, lons_icon,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_mpi_plot = fct.shiftgrid_copy(90., du850_mpi, lons_mpi,\n",
+ " start=False)[0][:,\n",
+ " latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_mpi_plot = fct.shiftgrid_copy(90., du850_mask_mpi, lons_mpi,\n",
+ " start=False)[0][:,\n",
+ " latind_sout_mpi:latind_nort_mpi+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "\n",
+ "du850_ipsl_plot = fct.shiftgrid_copy(90., du850_ipsl, lons_ipsl,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]\n",
+ "mask_ipsl_plot = fct.shiftgrid_copy(90., du850_mask_ipsl, lons_ipsl,\n",
+ " start=False)[0][:,\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the total response and cloud impact on u850"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mods = ['ICON', 'MPI-ESM', 'IPSL-CM5A']\n",
+ "vlim_tot = 4\n",
+ "vlim_cld = 2\n",
+ "# boundaries of region with robust response\n",
+ "lonwest = -4; loneast = 25; latsout = 50; latnort = 59\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10,7),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "for i in range(ax.shape[0]):\n",
+ " for k in range(ax.shape[1]):\n",
+ " ax[i, k].coastlines(rasterized=True)\n",
+ " ax[i, k].set_aspect('auto')\n",
+ " ax[i, k].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[i, k].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if i == 2: # last row\n",
+ " ax[i, k].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[i, k].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if k == 0: # first column\n",
+ " ax[i, k].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i, k].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[i, k].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[i, k].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[i, k].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[i, k].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " del k\n",
+ "del i\n",
+ "del lonwest, loneast, latsout, latnort\n",
+ "# total response\n",
+ "# ICON\n",
+ "k = response_cldvap.index('total') # 0\n",
+ "cf = ax[0, 0].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_icon_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[0, 0].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_icon_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# MPI-ESM\n",
+ "ax[1, 0].pcolormesh(lons_plot, lats_mpi_plot,\n",
+ " du850_mpi_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[1, 0].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(1*mask_mpi_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# IPSL-CM5A\n",
+ "ax[2, 0].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_ipsl_plot[k, :, :],\n",
+ " vmin=-vlim_tot, vmax=vlim_tot, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[2, 0].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_ipsl_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "del k\n",
+ "##################################################\n",
+ "# cloud impact\n",
+ "k = response_cldvap.index('cloud') # 2\n",
+ "# ICON\n",
+ "cf1 = ax[0, 1].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_icon_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[0, 1].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_icon_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# MPI-ESM\n",
+ "ax[1, 1].pcolormesh(lons_plot, lats_mpi_plot,\n",
+ " du850_mpi_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[1, 1].pcolor(lons_plot, lats_mpi_plot,\n",
+ " np.ma.masked_values(1*mask_mpi_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "# IPSL-CM5A\n",
+ "ax[2, 1].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_ipsl_plot[k, :, :],\n",
+ " vmin=-vlim_cld, vmax=vlim_cld, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# stippling for significant response\n",
+ "ax[2, 1].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_ipsl_plot[k, :, :], 0),\n",
+ " hatch='.....', alpha=0., rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "del k\n",
+ "##################################################\n",
+ "# jet latitude in control simulation\n",
+ "for k in range(ax.shape[1]):\n",
+ " ax[0, k].plot(lons_plot, jetlat_icon_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " ax[1, k].plot(lons_plot, jetlat_mpi_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " ax[2, k].plot(lons_plot, jetlat_ipsl_nh[lonind_west:lonind_east+1],\n",
+ " marker='x', color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ "\n",
+ "# titles\n",
+ "ax[0, 0].set_title('total', fontsize=16)\n",
+ "ax[0, 1].set_title('cloud', fontsize=16)\n",
+ "\n",
+ "# labels for models\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(-0.15, 0.51, mods[i], va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=16, transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.1)\n",
+ "cbar_ax = fig.add_axes([0.09, 0.0, 0.423, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf\n",
+ "\n",
+ "cbar_ax = fig.add_axes([0.545, 0.0, 0.423, 0.023]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf1, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf1\n",
+ "\n",
+ "fig.savefig('figure3a_3f.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "\n",
+ "del mods, vlim_tot, vlim_cld"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure3a_3f.pdf b/pythonscripts/figure3a_3f.pdf
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diff --git a/pythonscripts/figure4_cloudheating_change.ipynb b/pythonscripts/figure4_cloudheating_change.ipynb
new file mode 100644
index 0000000..745548b
--- /dev/null
+++ b/pythonscripts/figure4_cloudheating_change.ipynb
@@ -0,0 +1,506 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Changes in atmospheric cloud radiative heating.\n",
+ "\n",
+ "This script generates figure 4: \n",
+ "zonal-mean changes and maps of upper-tropospheric changes in atmospheric cloud-radiative heating during DJF for ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify the months of the year (needed for DJF mean)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read data (ICON, MPI-ESM, IPSL-CM5A)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:50: RuntimeWarning: Mean of empty slice\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LMD: change order of levels to go from TOA to surface\n",
+ "False -5.538915423676372 5.984612123575062\n",
+ "True -8.794642752036452 10.795256146229804\n",
+ "False -14.69999528490007 12.625148193910718\n"
+ ]
+ }
+ ],
+ "source": [
+ "# ICON\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "ifile = 'ICON-NWP_prp_T1C1_vs_T1C2_3d_40PL_mm.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_icon = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_icon = np.array(ncfile.variables['lev'][:].data)\n",
+ "dQsw = np.array(ncfile.variables['dQ_c_srad'][:].data) # shortwave\n",
+ "dQlw = np.array(ncfile.variables['dQ_c_trad'][:].data) # longwave\n",
+ "ncfile.close()\n",
+ "# add shortwave and longwave components\n",
+ "dQ_icon = (dQsw + dQlw) * 86400 # K/s -> K/day\n",
+ "del ipath, ifile, ncfile, dQsw, dQlw\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_icon_djf = np.nanmean(np.array([dQ_icon[months.index('Jan'), :, :, :],\n",
+ " dQ_icon[months.index('Feb'), :, :, :],\n",
+ " dQ_icon[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_icon\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_icon[0] > levs_icon[1]:\n",
+ " print('ICON: change order of levels to go from TOA to surface')\n",
+ " levs_icon = levs_icon[::-1]\n",
+ " dQ_icon_djf = dQ_icon_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "ipath = '../../MPI-ESM/'\n",
+ "ifile = 'MPI-ESM_prp_T1C1W1_vs_T1C2W1_3d_mm.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_mpi = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_mpi = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_mpi = np.array(ncfile.variables['plev_2'][:].data)\n",
+ "dQsw = np.array(ncfile.variables['dQ_cld_srad'][:].data) # shortwave\n",
+ "dQlw = np.array(ncfile.variables['dQ_cld_trad'][:].data) # longwave\n",
+ "ncfile.close()\n",
+ "# add shortwave and longwave components\n",
+ "dQ_mpi = (dQsw + dQlw) * 86400 # K/s -> K/day\n",
+ "del ipath, ifile, ncfile, dQsw, dQlw\n",
+ "\n",
+ "# set missing values (-9e33) to NaN\n",
+ "dQ_mpi[dQ_mpi <= -8e33] = np.nan\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_mpi_djf = np.nanmean(np.array([dQ_mpi[months.index('Jan'), :, :, :],\n",
+ " dQ_mpi[months.index('Feb'), :, :, :],\n",
+ " dQ_mpi[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_mpi\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_mpi[0] > levs_mpi[1]:\n",
+ " print('ECHAM: change order of levels to go from TOA to surface')\n",
+ " levs_mpi = levs_mpi[::-1]\n",
+ " dQ_mpi_djf = dQ_mpi_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "ipath = '../../IPSL-CM5A/'\n",
+ "ifile = 'IPSL-CM5A_prp_T1C1W1_vs_T1C2W1_3d_mm.remapcon.ymonmean.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "lats_ipsl = np.array(ncfile.variables['lat'][:].data)\n",
+ "lons_ipsl = np.array(ncfile.variables['lon'][:].data)\n",
+ "levs_ipsl = np.array(ncfile.variables['presnivs'][:].data)\n",
+ "dtswr = np.array(ncfile.variables['dtswr'][:].data)\n",
+ "dtlwr = np.array(ncfile.variables['dtlwr'][:].data)\n",
+ "dtswr_prpc = np.array(ncfile.variables['dtswr_prpc'][:].data)\n",
+ "dtlwr_prpc = np.array(ncfile.variables['dtlwr_prpc'][:].data)\n",
+ "ncfile.close()\n",
+ "# get change in cloud-radiative heating in K/day\n",
+ "# multiply with 86400 to get K/s -> K/day\n",
+ "dQ_ipsl = 86400 * ((dtswr_prpc + dtlwr_prpc) - (dtswr + dtlwr))\n",
+ "del ifile, ncfile, dtswr, dtlwr, dtswr_prpc, dtlwr_prpc\n",
+ "\n",
+ "# set missing values (9.9e36) to NaN\n",
+ "dQ_ipsl[dQ_ipsl >= 9e36] = np.nan\n",
+ "\n",
+ "# get DJF data\n",
+ "dQ_ipsl_djf = np.nanmean(np.array([dQ_ipsl[months.index('Jan'), :, :, :],\n",
+ " dQ_ipsl[months.index('Feb'), :, :, :],\n",
+ " dQ_ipsl[months.index('Dec'), :, :, :]]),\n",
+ " axis=0)\n",
+ "del dQ_ipsl\n",
+ "\n",
+ "# levels must go from TOA to surface\n",
+ "if levs_ipsl[0] > levs_ipsl[1]:\n",
+ " print('LMD: change order of levels to go from TOA to surface')\n",
+ " levs_ipsl = levs_ipsl[::-1]\n",
+ " dQ_ipsl_djf = dQ_ipsl_djf[::-1, :, :]\n",
+ "\n",
+ "##############################################################################\n",
+ "# check that all missing values are replaced by NaN's\n",
+ "print(np.isnan(dQ_icon_djf).any(), np.nanmin(dQ_icon_djf), np.nanmax(dQ_icon_djf))\n",
+ "print(np.isnan(dQ_mpi_djf).any(), np.nanmin(dQ_mpi_djf), np.nanmax(dQ_mpi_djf))\n",
+ "print(np.isnan(dQ_ipsl_djf).any(), np.nanmin(dQ_ipsl_djf), np.nanmax(dQ_ipsl_djf))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read tropopause height (with interpolated missing values) and calculate vertical mean dQ for a 200hPa thick layer below tropopause.\n",
+ "\n",
+ "NOTE: pressure value that is closer to the ground must be the second plev value that is given to the function get_verticalmean_overp_tropo (due to order of levels in levs array)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../tropopause_T1C1_T1C1W1/'\n",
+ "\n",
+ "# ICON\n",
+ "ifile = 'ICON-NWP_AMIP_T1C1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_icon.size, lons_icon.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_icon.size, lons_icon.size), np.nan, dtype=int)\n",
+ "for la in range(lats_icon.size):\n",
+ " for lo in range(lons_icon.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_icon-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_icon-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_icon_vmean = fct.get_verticalmean_overp_tropo(dQ_icon_djf, levs_icon,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_icon = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "##############################################################################\n",
+ "# MPI-ESM\n",
+ "ifile = 'MPI-ESM_T1C1W1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_mpi.size, lons_mpi.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_mpi.size, lons_mpi.size), np.nan, dtype=int)\n",
+ "for la in range(lats_mpi.size):\n",
+ " for lo in range(lons_mpi.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_mpi-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_mpi-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_mpi_vmean = fct.get_verticalmean_overp_tropo(dQ_mpi_djf, levs_mpi,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_mpi = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "##############################################################################\n",
+ "# IPSL-CM5A\n",
+ "ifile = 'IPSL-CM5A_T1C1W1_tropopause_DJF_remapcon_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo = ncfile.variables['ptrop'][:]\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# find index of tropopause height for each grid point\n",
+ "tropoind1 = np.full((lats_ipsl.size, lons_ipsl.size), np.nan, dtype=int)\n",
+ "# index 300 hPa below tropopause\n",
+ "tropoind2 = np.full((lats_ipsl.size, lons_ipsl.size), np.nan, dtype=int)\n",
+ "for la in range(lats_ipsl.size):\n",
+ " for lo in range(lons_ipsl.size):\n",
+ " tropoind1[la, lo] = np.argmin(np.abs(levs_ipsl-tropo[la,lo]))\n",
+ " tropoind2[la, lo] = np.argmin(np.abs(levs_ipsl-(tropo[la, lo]+20000)))\n",
+ " del lo\n",
+ "del la\n",
+ "\n",
+ "# calculate vertical-mean dQ\n",
+ "dQ_ipsl_vmean = fct.get_verticalmean_overp_tropo(dQ_ipsl_djf, levs_ipsl,\n",
+ " tropoind1, tropoind2)\n",
+ "\n",
+ "# get zonal-mean tropopause\n",
+ "tropo_ipsl = np.nanmean(tropo, axis=1)\n",
+ "\n",
+ "# delete temporary variables\n",
+ "del tropo, tropoind1, tropoind2\n",
+ "\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get zonal-mean changes in atmospheric cloud-radiative heating."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/sw/rhel6-x64/conda/anaconda3-bleeding_edge/lib/python3.6/site-packages/ipykernel_launcher.py:2: RuntimeWarning: Mean of empty slice\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "dQ_icon_djf_zm = np.nanmean(dQ_icon_djf, axis=2)\n",
+ "dQ_mpi_djf_zm = np.nanmean(dQ_mpi_djf, axis=2)\n",
+ "dQ_ipsl_djf_zm = np.nanmean(dQ_ipsl_djf, axis=2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot zonal-mean and maps of upper-tropospheric changes in atmospheric cloud-radiative heating in ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes from 0deg...360deg to -90deg...270deg for visualization reasons\n",
+ "dQ_icon_vmean_shift, lons_icon_shift = fct.shiftgrid_copy(90., dQ_icon_vmean, lons_icon, start=False)\n",
+ "dQ_mpi_vmean_shift, lons_mpi_shift = fct.shiftgrid_copy(90., dQ_mpi_vmean, lons_mpi, start=False)\n",
+ "dQ_ipsl_vmean_shift, lons_ipsl_shift = fct.shiftgrid_copy(90., dQ_ipsl_vmean, lons_ipsl, start=False)\n",
+ "\n",
+ "# limit for colorbar\n",
+ "vlim = 1\n",
+ "\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 7))#figsize(10))\n",
+ "# zonal-mean change\n",
+ "cf0 = ax[0, 0].pcolormesh(lats_icon, levs_icon/100, dQ_icon_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[1, 0].pcolormesh(lats_mpi, levs_mpi/100, dQ_mpi_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[2, 0].pcolormesh(lats_ipsl, levs_ipsl/100, dQ_ipsl_djf_zm,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "# zonal-mean tropopause\n",
+ "ax[0, 0].plot(lats_icon, tropo_icon/100, color='tab:green')\n",
+ "ax[1, 0].plot(lats_mpi, tropo_mpi/100, color='tab:green')\n",
+ "ax[2, 0].plot(lats_ipsl, tropo_ipsl/100, color='tab:green')\n",
+ "\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].tick_params(labelsize=13)\n",
+ " ax[i, 0].set(xticks=np.arange(-90, 91, 30),\n",
+ " xticklabels=['90S', '60S', '30S', '0', '30N', '60N' ,'90N'],\n",
+ " xlim=(-90, 90))\n",
+ " ax[i, 0].set_yticks(np.arange(0, 1100, 200))\n",
+ " ax[i, 0].set_ylim(1000, 10)\n",
+ " ax[i, 0].set_ylabel('Pressure [hPa]', fontsize=15)\n",
+ "del i\n",
+ "ax[2, 0].set_xlabel('Latitude [$^{\\circ}$]', fontsize=15)\n",
+ "# text for models\n",
+ "mods = ['ICON', 'MPI-ESM', 'IPSL-CM5A']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(-0.25, 0.51, mods[i], va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=17, transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "###################################################################\n",
+ "# maps\n",
+ "ax1 = plt.subplot(3, 2, 2, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax1.pcolormesh(lons_icon_shift, lats_icon, dQ_icon_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "ax2 = plt.subplot(3, 2, 4, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax2.pcolormesh(lons_icon_shift, lats_icon, dQ_mpi_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "ax3 = plt.subplot(3, 2, 6, projection=ccrs.PlateCarree(central_longitude=-90))\n",
+ "ax3.pcolormesh(lons_icon_shift, lats_icon, dQ_ipsl_vmean_shift,\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ "\n",
+ "for axis in [ax1, ax2, ax3]:\n",
+ " axis.coastlines()\n",
+ " axis.set_aspect('auto')\n",
+ " axis.tick_params(labelsize=13)\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " axis.set_xticks([-150, -70, 40, 120], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " axis.xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " axis.set_yticks([-60, -30, 0, 30, 60],\n",
+ " crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " axis.yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del axis\n",
+ "\n",
+ "# add lines for tropical regions in ICON\n",
+ "latnort = 30 # northern boundary: 30°N\n",
+ "latsout = -30 # southern boundary: 30°S\n",
+ "lon1 = -150 # 150°W\n",
+ "lon2 = -70 # 70°W\n",
+ "lon3 = 40 # 40°E\n",
+ "lon4 = 120 # 120°E\n",
+ "# upper horizontal line\n",
+ "ax1.plot([90, -269.999], [latnort, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "# lower horizontal line\n",
+ "ax1.plot([90, -269.999], [latsout, latsout],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "# vertical lines\n",
+ "ax1.plot([lon1, lon1], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon2, lon2], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon3, lon3], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "ax1.plot([lon4, lon4], [latsout, latnort],\n",
+ " linewidth=2, color='tab:green', transform=ccrs.PlateCarree())\n",
+ "del latnort, latsout, lon1, lon2, lon3, lon4\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs1 = ['(a)', '(b)', '(c)']\n",
+ "labs2 = ['(d)', '(e)', '(f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i, 0].text(0.01, 1.02, labs1[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i, 0].transAxes)\n",
+ "del i\n",
+ "for i, axis in enumerate([ax1, ax2, ax3]):\n",
+ " axis.text(0.01, 1.02, labs2[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=axis.transAxes)\n",
+ "del i, axis\n",
+ "del labs1, labs2\n",
+ "\n",
+ "del ax1, ax2, ax3\n",
+ "\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar\n",
+ "fig.subplots_adjust(bottom=0.12)#(right=0.8)\n",
+ "clevs = np.array([-1, -0.5, 0, 0.5, 1])\n",
+ "cbar_ax = fig.add_axes([0.3, 0.0, 0.5, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both',\n",
+ " ticks=clevs)\n",
+ "cb.set_label('K day$^{-1}$', fontsize=14, labelpad=1)\n",
+ "cb.ax.tick_params(labelsize=13)\n",
+ "del cbar_ax, cb, cf0, clevs\n",
+ "\n",
+ "fig.savefig('figure4a_4f.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax\n",
+ "\n",
+ "del dQ_icon_vmean_shift, lons_icon_shift, dQ_mpi_vmean_shift, \\\n",
+ " lons_mpi_shift, dQ_ipsl_vmean_shift, lons_ipsl_shift\n",
+ "del vlim"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pythonscripts/figure4a_4f.pdf b/pythonscripts/figure4a_4f.pdf
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diff --git a/pythonscripts/figure5_tr_ml_po.ipynb b/pythonscripts/figure5_tr_ml_po.ipynb
new file mode 100644
index 0000000..d490148
--- /dev/null
+++ b/pythonscripts/figure5_tr_ml_po.ipynb
@@ -0,0 +1,440 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: tropical, midlatitude and polar cloud impacts\n",
+ "\n",
+ "This script generates figure 5: maps of the impacts of tropical, midlatitude and polar cloud changes on the zonal wind response in ICON."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_ML = ['T1C2ML', 'T1C1ML', 'T2C2ML', 'T2C1ML']\n",
+ "runs_reg_PO = ['T1C2PO', 'T1C1PO', 'T2C2PO', 'T2C1PO']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_ML + runs_reg_PO\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud ML', 'cloud notML',\n",
+ " 'cloud PO', 'cloud notPO']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind at 850 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2ML\n",
+ "reading T1C1ML\n",
+ "reading T2C2ML\n",
+ "reading T2C1ML\n",
+ "reading T1C2PO\n",
+ "reading T1C1PO\n",
+ "reading T2C2PO\n",
+ "reading T2C1PO\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850 = {}\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Calculate DJF mean\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Calculate DJF mean')\n",
+ "u850_djf = {}\n",
+ "for run in runs_all:\n",
+ " u850_djf[run] = fct.calcMonthlyandSeasonMean(u850[run], months, seasons)[1]['DJF']\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF jet latitude in control simulation (T1C1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jetlat_sh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "jetlat_nh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "for i in range(lons.shape[0]):\n",
+ " jetlat_sh_T1C1[i], _, jetlat_nh_T1C1[i], _ = \\\n",
+ " fct.get_eddyjetlatint(u850_djf['T1C1'][:, i], lats)\n",
+ "del i"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_djf = np.full((len(response_all), len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "# total, SST, cloud\n",
+ "du850_djf[0, :, :], du850_djf[1, :, :], du850_djf[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, du850_djf[k*2+3, :, :], du850_djf[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'],\n",
+ " u850_djf[runs_all[4:][k*4]],\n",
+ " u850_djf[runs_all[4:][k*4+1]],\n",
+ " u850_djf[runs_all[4:][k*4+2]],\n",
+ " u850_djf[runs_all[4:][k*4+3]])\n",
+ "del k\n",
+ "del u850, u850_djf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal mean zonal wind."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath_bs = '../../ICON-NWP_lockedclouds/'\n",
+ "du850_mask_sm_bs = np.load(ipath_bs + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "del ipath_bs\n",
+ "\n",
+ "# only store masks for tropical, midlatitude and polar cloud impacts\n",
+ "response_sel = ['cloud TR', 'cloud ML', 'cloud PO']\n",
+ "du850_djf_mask = np.array([du850_mask_sm_bs[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_sel])\n",
+ "\n",
+ "del du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "du850_djf_shift, lons_shift = fct.shiftgrid_copy(90., du850_djf, lons, start=False)\n",
+ "du850_mask_shift, _ = fct.shiftgrid_copy(90., du850_djf_mask, lons, start=False)\n",
+ "jetlat_nh_shift = fct.shiftgrid_copy(90., jetlat_nh_T1C1, lons, start=False)[0]\n",
+ "\n",
+ "# find indices of border of North Atlantic/Europe box\n",
+ "# -> makes plotting faster (important if mask is plotted)\n",
+ "lonind_west = (np.abs(lons_shift--72)).argmin()\n",
+ "lonind_east = (np.abs(lons_shift-32)).argmin()\n",
+ "latind_sout = (np.abs(lats-28)).argmin()\n",
+ "latind_nort = (np.abs(lats-72)).argmin()\n",
+ "\n",
+ "lons_plot = lons_shift[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "jetlat_nh_plot = jetlat_nh_shift[lonind_west:lonind_east+1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of tropical, midlatitude and polar cloud impacts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 360x504 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response_plot = ['cloud TR', 'cloud ML', 'cloud PO']\n",
+ "\n",
+ "du850_plot = np.array([du850_djf_shift[response_all.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "mask_plot = np.array([du850_mask_shift[response_sel.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "\n",
+ "# box around region with jet exit strengthening\n",
+ "#lonwest = -4; loneast = 25; latsout = 52; latnort = 61\n",
+ "lonwest = -4; loneast = 26; latsout = 52; latnort = 62\n",
+ "\n",
+ "# plot\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 1, figsize=(5, 7),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "labs = ['(a)', '(b)', '(c)']\n",
+ "for r in range(du850_plot.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[r].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set yticks for longitudes\n",
+ " ax[r].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[r].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[r].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[r].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[r].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # jet latitude in control run\n",
+ " ax[r].plot(lons_plot, jetlat_nh_plot, marker='x',\n",
+ " color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " # plot different effects\n",
+ " cf0 = ax[r].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_plot[r, :, :],\n",
+ " vmin=-1.5, vmax=1.5, cmap=mymap2,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " # stippling for significance\n",
+ " ax[r].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_plot[r, :, :], 0),\n",
+ " hatch='.....', alpha=0.,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " ax[r].set_title(response_plot[r][0:8], fontsize=16)\n",
+ " # a), b) etc for subplots\n",
+ " ax[r].text(0.01, 1.02, labs[r], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[r].transAxes)\n",
+ "del r\n",
+ "# xaxis: longitudes\n",
+ "ax[2].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ "lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ "ax[2].xaxis.set_major_formatter(lon_formatter)\n",
+ "del lon_formatter\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "#clevs = [-1.5, -1.0, -0.5, 0, 0.5, 1.0, 1.5]\n",
+ "fig.subplots_adjust(bottom=0.08)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.13, 0.0, 0.804, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')#,\n",
+ " #ticks=clevs)\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0#, clevs\n",
+ "\n",
+ "fig.savefig('figure5a_5c.pdf', dpi=200, bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, du850_plot, mask_plot\n",
+ "del lonwest, loneast, latsout, latnort"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure5a_5c.pdf b/pythonscripts/figure5a_5c.pdf
new file mode 100644
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diff --git a/pythonscripts/figure6_tropical_cloudimpacts.ipynb b/pythonscripts/figure6_tropical_cloudimpacts.ipynb
new file mode 100644
index 0000000..a00fdd4
--- /dev/null
+++ b/pythonscripts/figure6_tropical_cloudimpacts.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zonal wind response: tropical cloud impacts\n",
+ " \n",
+ "This script generates figure 6: maps of the impacts of western tropical Pacific, eastern tropical Pacific, tropical Atlantic, and Indian Ocean cloud changes on the zonal wind response in ICON."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal wind at 850 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2TA\n",
+ "reading T1C1TA\n",
+ "reading T2C2TA\n",
+ "reading T2C1TA\n",
+ "reading T1C2IO\n",
+ "reading T1C1IO\n",
+ "reading T2C2IO\n",
+ "reading T2C1IO\n",
+ "reading T1C2WP\n",
+ "reading T1C1WP\n",
+ "reading T2C2WP\n",
+ "reading T2C1WP\n",
+ "reading T1C2EP\n",
+ "reading T1C1EP\n",
+ "reading T2C2EP\n",
+ "reading T2C1EP\n"
+ ]
+ }
+ ],
+ "source": [
+ "u850 = {}\n",
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "u850_djf = {}\n",
+ "for run in runs_all:\n",
+ " u850_djf[run] = fct.calcMonthlyandSeasonMean(u850[run], months, seasons)[1]['DJF']\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF jet latitude in control simulation (T1C1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "jetlat_sh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "jetlat_nh_T1C1 = np.full(lons.shape, np.nan, dtype=float)\n",
+ "for i in range(lons.shape[0]):\n",
+ " jetlat_sh_T1C1[i], _, jetlat_nh_T1C1[i], _ = \\\n",
+ " fct.get_eddyjetlatint(u850_djf['T1C1'][:, i], lats)\n",
+ "del i"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate responses (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "du850_djf = np.full((len(response_all), len(lats), len(lons)), np.nan,\n",
+ " dtype=float)\n",
+ "# total, SST, cloud\n",
+ "du850_djf[0, :, :], du850_djf[1, :, :], du850_djf[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, du850_djf[k*2+3, :, :], du850_djf[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_djf['T1C1'], u850_djf['T2C2'],\n",
+ " u850_djf['T1C2'], u850_djf['T2C1'],\n",
+ " u850_djf[runs_all[4:][k*4]],\n",
+ " u850_djf[runs_all[4:][k*4+1]],\n",
+ " u850_djf[runs_all[4:][k*4+2]],\n",
+ " u850_djf[runs_all[4:][k*4+3]])\n",
+ "del k\n",
+ "del u850, u850_djf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read masks for significant responses\n",
+ "\n",
+ "These masks are generated with the script \"calculate_significance_bootstrapping.ipynb\" based on time series of the seasonal mean zonal wind."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath_bs = '../../ICON-NWP_lockedclouds/'\n",
+ "du850_mask_sm_bs = np.load(ipath_bs + 'du850_mask_sm_bs.npy',\n",
+ " allow_pickle='TRUE').item()\n",
+ "del ipath_bs\n",
+ "\n",
+ "# only store masks for tropical regions\n",
+ "response_sel = ['cloud WP', 'cloud EP', 'cloud IO', 'cloud TA']\n",
+ "du850_djf_mask = np.array([du850_mask_sm_bs[r][seasons.index('DJF'), :, :] \\\n",
+ " for r in response_sel])\n",
+ "\n",
+ "del du850_mask_sm_bs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Prepare plot\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons and select the North Atlantic region (otherwise it is very slow to add the dots for the regions, in which the response is significant)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# shift longitudes\n",
+ "du850_djf_shift, lons_shift = fct.shiftgrid_copy(90., du850_djf, lons, start=False)\n",
+ "du850_mask_shift, _ = fct.shiftgrid_copy(90., du850_djf_mask, lons, start=False)\n",
+ "jetlat_nh_shift = fct.shiftgrid_copy(90., jetlat_nh_T1C1, lons, start=False)[0]\n",
+ "\n",
+ "# find indices of border of North Atlantic/Europe box\n",
+ "# -> makes plotting faster (important if mask is plotted)\n",
+ "lonind_west = (np.abs(lons_shift--72)).argmin()\n",
+ "lonind_east = (np.abs(lons_shift-32)).argmin()\n",
+ "latind_sout = (np.abs(lats-28)).argmin()\n",
+ "latind_nort = (np.abs(lats-72)).argmin()\n",
+ "\n",
+ "lons_plot = lons_shift[lonind_west:lonind_east+1]\n",
+ "lats_plot = lats[latind_sout:latind_nort+1]\n",
+ "\n",
+ "jetlat_nh_plot = jetlat_nh_shift[lonind_west:lonind_east+1]\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps of cloud impacts from the four tropical and the linearity IO+WP+EP+TA vs TR "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x576 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "response_plot = ['cloud WP', 'cloud EP',\n",
+ " 'cloud IO', 'cloud TA']\n",
+ "\n",
+ "du850_plot = np.array([du850_djf_shift[response_all.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "mask_plot = np.array([du850_mask_shift[response_sel.index(r),\n",
+ " latind_sout:latind_nort+1,\n",
+ " lonind_west:lonind_east+1] \\\n",
+ " for r in response_plot])\n",
+ "vlim = 1.5\n",
+ "# box around region with jet exit strengthening\n",
+ "lonwest = -4; loneast = 26; latsout = 52; latnort = 62\n",
+ "\n",
+ "# plot\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 8),#figsize(10),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)']\n",
+ "for r in range(ax.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[r].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if r > 3: # last row\n",
+ " ax[r].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[r].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if r in [0, 2, 4, 6]:\n",
+ " ax[r].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # draw box around region, for which we determine the area-mean response\n",
+ " # left vertical line\n",
+ " ax[r].plot([lonwest, lonwest], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # right vertical line\n",
+ " ax[r].plot([loneast, loneast], [latsout, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # upper horizontal line\n",
+ " ax[r].plot([loneast, lonwest], [latnort, latnort],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # lower horizontal line\n",
+ " ax[r].plot([lonwest, loneast], [latsout, latsout],\n",
+ " linewidth=2, color='k', transform=ccrs.PlateCarree())\n",
+ " # jet latitude in control run\n",
+ " ax[r].plot(lons_plot, jetlat_nh_plot, marker='x',\n",
+ " color='k', linestyle='none', markeredgewidth=2,\n",
+ " markersize=2, transform=ccrs.PlateCarree())\n",
+ " # plot different effects\n",
+ " if r in range(len(response_plot)):\n",
+ " cf0 = ax[r].pcolormesh(lons_plot, lats_plot,\n",
+ " du850_plot[r, :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " # stippling for significance\n",
+ " ax[r].pcolor(lons_plot, lats_plot,\n",
+ " np.ma.masked_values(1*mask_plot[r, :, :], 0),\n",
+ " hatch='.....', alpha=0.,\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree()) \n",
+ " ax[r].set_title(response_plot[r], fontsize=16)\n",
+ " # a), b) etc for subplots\n",
+ " ax[r].text(0.01, 1.02, labs[r], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[r].transAxes)\n",
+ "del r\n",
+ "\n",
+ "# IO + WP + EP + TA\n",
+ "ax[4].pcolormesh(lons_shift, lats,\n",
+ " (du850_djf_shift[response_all.index('cloud IO'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud WP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud EP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud TA'), :, :]),\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[4].set_title('cloud IO + cloud WP \\n+ cloud EP + cloud TA', fontsize=16)\n",
+ "cf2 = ax[5].pcolormesh(lons_shift, lats,\n",
+ " (du850_djf_shift[response_all.index('cloud IO'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud WP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud EP'), :, :] + \\\n",
+ " du850_djf_shift[response_all.index('cloud TA'), :, :]) - \\\n",
+ " du850_djf_shift[response_all.index('cloud TR'), :, :],\n",
+ " vmin=-1.5, vmax=1.5, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[5].set_title('(IO+WP+EP+TA) - TR', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.07)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.165, 0.0, 0.7, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0\n",
+ "\n",
+ "# colorbar for differences\n",
+ "clevs = [-1.5, -0.7, 0, 0.7, 1.5]\n",
+ "cbar_ax = fig.add_axes([0.98, 0.07, 0.013, 0.23]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf2, cax=cbar_ax, orientation='vertical', extend='both', ticks=clevs)\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf2, clevs\n",
+ "\n",
+ "fig.savefig('figure6a_6f.pdf', dpi=200, bbox_inches='tight')\n",
+ "\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, du850_plot, mask_plot\n",
+ "del vlim, lonwest, loneast, latsout, latnort"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
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+}
diff --git a/pythonscripts/figure6a_6f.pdf b/pythonscripts/figure6a_6f.pdf
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diff --git a/pythonscripts/figure_S1_free_vs_locked_watervapor.ipynb b/pythonscripts/figure_S1_free_vs_locked_watervapor.ipynb
new file mode 100644
index 0000000..6052b14
--- /dev/null
+++ b/pythonscripts/figure_S1_free_vs_locked_watervapor.ipynb
@@ -0,0 +1,392 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cloud locking vs. cloud and water vapor locking\n",
+ "\n",
+ "This script generates figure S1: total response and cloud impact from ICON simulations with locked clouds and interactive water vapor and from ICON simulations with locked clouds and locked water vapor."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load own colorbar"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mymap, mymap2 = fct.generate_mymap()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated \n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read ICON data with locked clouds and interactive water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "u850_icon_cld = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon_cld[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read ICON data with locked clouds and locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedcloudsandwatervapor/'\n",
+ "u850_icon = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.uwind.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " #lats = np.array(ncfile.variables['lat'][:].data)\n",
+ " #lons = np.array(ncfile.variables['lon'][:].data)\n",
+ " levs = np.array(ncfile.variables['lev'][:].data)\n",
+ " uwind = np.array(ncfile.variables['u'][:].data)\n",
+ " ncfile.close() \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_icon[run] = uwind[:, levind850, :, :]\n",
+ " del levs, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# locked clouds\n",
+ "u850_icon_cld_djf = {}\n",
+ "for run in runs_cld:\n",
+ " u850_icon_cld_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon_cld[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "# locked clouds and locked water vapor\n",
+ "u850_icon_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " u850_icon_djf[run] = fct.calcMonthlyandSeasonMean(u850_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del u850_icon_cld, u850_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# locked clouds\n",
+ "du850_icon_cld = np.full((len(response_cld), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "du850_icon_cld[0, :, :], du850_icon_cld[1, :, :], du850_icon_cld[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(u850_icon_cld_djf['T1C1'], u850_icon_cld_djf['T2C2'],\n",
+ " u850_icon_cld_djf['T1C2'], u850_icon_cld_djf['T2C1'])\n",
+ "\n",
+ "# locked clouds and locked water vapor\n",
+ "du850_icon = np.full((len(response_cldvap), len(lats),\n",
+ " len(lons)), np.nan, dtype=float)\n",
+ "du850_icon[0, :, :], du850_icon[1, :, :], du850_icon[2, :, :], \\\n",
+ "du850_icon[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(u850_icon_djf['T1C1W1'], u850_icon_djf['T2C2W2'],\n",
+ " u850_icon_djf['T1C2W2'], u850_icon_djf['T2C1W1'],\n",
+ " u850_icon_djf['T1C2W1'], u850_icon_djf['T1C1W2'],\n",
+ " u850_icon_djf['T2C2W1'], u850_icon_djf['T2C1W2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot total response and cloud impact for both simulation setups and add differences between the two setups\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x324 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes\n",
+ "du850_icon_cld_shift, lons_shift = fct.shiftgrid_copy(90., du850_icon_cld,\n",
+ " lons, start=False)\n",
+ "du850_icon_shift, _ = fct.shiftgrid_copy(90., du850_icon, lons, start=False)\n",
+ "\n",
+ "vlim = 3\n",
+ "vlim_diff = 1\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(2, 3, figsize=(10, 4.5),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "for i in range(ax.shape[0]):\n",
+ " for k in range(ax.shape[1]):\n",
+ " ax[i, k].coastlines(rasterized=True)\n",
+ " ax[i, k].set_aspect('auto')\n",
+ " ax[i, k].tick_params(labelsize=14)\n",
+ " # extended North Atlantic region\n",
+ " ax[i, k].set_extent([-70, 30, 30, 70], ccrs.PlateCarree())\n",
+ " # set xticks and yticks for longitudes and latitudes\n",
+ " # xaxis: longitudes\n",
+ " ax[1, k].set_xticks([-60, -30, 0, 30], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[1, k].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " del k\n",
+ " # yaxis: latitudes\n",
+ " ax[i, 0].set_yticks([30, 50, 70], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[i, 0].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ "del i\n",
+ "# ICON (locked clouds)\n",
+ "cf = ax[0, 0].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('total'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 0].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# ICON (locked clouds and locked water vapor)\n",
+ "ax[0, 1].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_shift[response_cldvap.index('total'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 1].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_shift[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap=mymap2,\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "# difference\n",
+ "cf2 = ax[0, 2].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('total'), :, :] - \\\n",
+ " du850_icon_shift[response_cldvap.index('total'), :, :],\n",
+ " vmin=-vlim_diff, vmax=vlim_diff, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1, 2].pcolormesh(lons_shift, lats,\n",
+ " du850_icon_cld_shift[response_cld.index('cloud'), :, :] - \\\n",
+ " du850_icon_shift[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim_diff, vmax=vlim_diff, cmap='RdBu_r',\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "\n",
+ "ax[0, 0].set_title('a) locked clouds', fontsize=16)\n",
+ "ax[0, 1].set_title('b) locked clouds and\\nwater vapor', fontsize=16)\n",
+ "ax[0, 0].set_title('interactive\\nwater vapor', fontsize=16)\n",
+ "ax[0, 1].set_title('locked\\nwater vapor', fontsize=16)\n",
+ "ax[0, 2].set_title('interactive - locked', fontsize=16)#difference (a - b)', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.1)\n",
+ "cbar_ax = fig.add_axes([0.066, 0.0, 0.59, 0.027]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf\n",
+ "cbar_ax = fig.add_axes([0.69, 0.0, 0.277, 0.027]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf2, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta$u$_{850}$ [m s$^{-1}$]', fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf2\n",
+ "\n",
+ "for r, res in enumerate(['total', 'cloud']): # response_cldvap):\n",
+ " ax[r, 0].text(-0.2, 0.5, res, va='bottom', ha='center',\n",
+ " rotation='vertical', rotation_mode='anchor',\n",
+ " fontsize=18, transform=ax[r, 0].transAxes)\n",
+ "del r, res\n",
+ "\n",
+ "fig.savefig('figure_S1.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "\n",
+ "del lons_shift, du850_icon_cld_shift, du850_icon_shift, vlim, vlim_diff"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure_S2.pdf b/pythonscripts/figure_S2.pdf
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diff --git a/pythonscripts/figure_S2_schematics.ipynb b/pythonscripts/figure_S2_schematics.ipynb
new file mode 100644
index 0000000..486a2fe
--- /dev/null
+++ b/pythonscripts/figure_S2_schematics.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Schematics\n",
+ "\n",
+ "This script generates figure S2: schematic of the regions, for which the cloud impact is determined."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import matplotlib.colors as mcolors\n",
+ "from matplotlib.colors import LinearSegmentedColormap"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate colormap with two colors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# sample the colormaps that you want to use. Use 128 from each so we get 256\n",
+ "# colors in total\n",
+ "colors1 = np.array([166/255, 166/255, 166/255, 1])\n",
+ "colors2 = np.array([178/255, 34/255, 34/255, 1])\n",
+ "\n",
+ "# combine two colors and build a new colormap\n",
+ "colors = np.vstack((colors1, colors2))\n",
+ "mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)\n",
+ "del colors1, colors2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Create latitude and longitude vectors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lats = np.arange(-90, 91, 1)\n",
+ "lons = np.arange(-180, 180, 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define regions for which the cloud impact is determined and generate masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# tropics\n",
+ "lat_nort_TR = 30 # northern boundary: 30°N\n",
+ "lat_sout_TR = -30 # southern boundary: 30°S\n",
+ "lon_west_TR = -180 # western boundary: 180°W\n",
+ "lon_east_TR = 180 # eastern boundary: 180°E\n",
+ "\n",
+ "# midlatitudes\n",
+ "lat_nort_ML_NH = 60 # northern boundary in NH: 60°N\n",
+ "lat_sout_ML_NH = 30 # southern boundary in NH: 30°N\n",
+ "lat_nort_ML_SH = -30 # northern boundary in SH: 30°S\n",
+ "lat_sout_ML_SH = -60 # southern boundary in SH: 60°S\n",
+ "lon_west_ML = -180 # western boundary: 180°W\n",
+ "lon_east_ML = 180 # eastern boundary: 180°E\n",
+ "\n",
+ "# tropical Atlantic\n",
+ "lon_west_TA = -70 # western boundary: 70°W\n",
+ "lon_east_TA = 40 # eastern boundary: 40°E\n",
+ "\n",
+ "# Indian Ocean\n",
+ "lon_west_IO = 40 # western boundary: 40°E\n",
+ "lon_east_IO = 120 # eastern boundary: 120°E\n",
+ "\n",
+ "# western tropical Pacific\n",
+ "lon_west_WP = 120 # western boundary: 120°E\n",
+ "lon_east_WP = -150 # eastern boundary: 150°W\n",
+ "\n",
+ "# eastern tropical Pacific\n",
+ "lon_west_EP = -150 # western boundary: 150°W\n",
+ "lon_east_EP = -70 # eastern boundary: 70°W\n",
+ "\n",
+ "# extended North Atlantic\n",
+ "lat_nort_NAe = 60 # northern boundary: 60°N\n",
+ "lat_sout_NAe = 30 # southern boundary: 30°N\n",
+ "lon_west_NAe = -90 # western boundary: 270°E/90°W\n",
+ "lon_east_NAe = 30 # eastern boundary: 30°E\n",
+ "\n",
+ "\n",
+ "# create array with 1 over tropics and 0 everywhere else\n",
+ "# and array with 1 over midlatitudes and 0 everywhere else\n",
+ "mask_lat_TR = np.logical_and(lats >= lat_sout_TR, lats <= lat_nort_TR)\n",
+ "mask_lat_ML = np.logical_or(np.logical_and(lats >= lat_sout_ML_SH, lats <= lat_nort_ML_SH),\n",
+ " np.logical_and(lats >= lat_sout_ML_NH, lats <= lat_nort_ML_NH))\n",
+ "mask_lat_PO = np.logical_or(lats <= lat_sout_ML_SH, lats >= lat_nort_ML_NH)\n",
+ "mask_lon = np.logical_and(lons >= lon_west_TR, lons <= lon_east_TR)\n",
+ "mask_TR = np.logical_and(mask_lon[None,:], mask_lat_TR[:,None]) * 1\n",
+ "mask_ML = np.logical_and(mask_lon[None,:], mask_lat_ML[:,None]) * 1\n",
+ "mask_PO = np.logical_and(mask_lon[None,:], mask_lat_PO[:,None]) * 1\n",
+ "del mask_lat_ML, mask_lat_PO, mask_lon\n",
+ "\n",
+ "# create array with 1 over tropical Atlantic and 0 everywhere else\n",
+ "mask_lon_TA = np.logical_and(lons >= lon_west_TA, lons <= lon_east_TA)\n",
+ "mask_TA = np.logical_and(mask_lon_TA[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_TA\n",
+ "\n",
+ "# create array with 1 over Indian Ocean and 0 everywhere else\n",
+ "mask_lon_IO = np.logical_and(lons >= lon_west_IO, lons <= lon_east_IO)\n",
+ "mask_IO = np.logical_and(mask_lon_IO[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IO\n",
+ "\n",
+ "# create array with 1 over western tropical Pacific and 0 everywhere else\n",
+ "mask_lon_WP = (lons >= lon_west_WP) + (lons <= lon_east_WP) #np.logical_and(lons >= lon_west_WP, lons <= lon_east_WP)\n",
+ "mask_WP = np.logical_and(mask_lon_WP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_WP\n",
+ "\n",
+ "# create array with 1 over eastern tropical Pacific and 0 everywhere else\n",
+ "mask_lon_EP = np.logical_and(lons >= lon_west_EP, lons <= lon_east_EP)\n",
+ "mask_EP = np.logical_and(mask_lon_EP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_EP\n",
+ "\n",
+ "# create array with 1 over NAext and 0 everywhere else\n",
+ "mask_lat_NAe = np.logical_and(lats >= lat_sout_NAe, lats <= lat_nort_NAe)\n",
+ "mask_lon_NAe = np.logical_and(lons >= lon_west_NAe, lons <= lon_east_NAe)\n",
+ "mask_NAe = np.logical_and(mask_lon_NAe[None,:], mask_lat_NAe[:,None]) * 1\n",
+ "del mask_lat_NAe, mask_lon_NAe\n",
+ "\n",
+ "# create array with 1 over IOWPEP and 0 everywhere else\n",
+ "mask_lon_IOWPEP = (lons >= lon_west_IO) + (lons <= lon_east_EP)\n",
+ "mask_IOWPEP = np.logical_and(mask_lon_IOWPEP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IOWPEP\n",
+ "\n",
+ "# create array with 1 over IOWP and 0 everywhere else\n",
+ "mask_lon_IOWP = (lons >= lon_west_IO) + (lons <= lon_east_WP)\n",
+ "mask_IOWP = np.logical_and(mask_lon_IOWP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_IOWP\n",
+ "\n",
+ "# create array with 1 over WPEP and 0 everywhere else\n",
+ "mask_lon_WPEP = (lons >= lon_west_WP) + (lons <= lon_east_EP)\n",
+ "mask_WPEP = np.logical_and(mask_lon_WPEP[None,:], mask_lat_TR[:,None]) * 1\n",
+ "del mask_lon_WPEP\n",
+ "\n",
+ "del mask_lat_TR"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 504x576 with 8 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(4, 2, figsize=(7, 8),#figsize(10), \n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1)\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].coastlines(rasterized=True)\n",
+ " ax[i].set_aspect('auto')\n",
+ " ax[i].tick_params(labelsize=14)\n",
+ "del i\n",
+ "\n",
+ "# TR\n",
+ "ax[0].pcolormesh(lons, lats, mask_TR, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[0].set_title('Tropics (TR)', fontsize=15)\n",
+ "\n",
+ "# ML\n",
+ "ax[1].pcolormesh(lons, lats, mask_ML, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[1].set_title('Midlatitudes (ML)', fontsize=15)\n",
+ "\n",
+ "# PO\n",
+ "ax[2].pcolormesh(lons, lats, mask_PO, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[2].set_title('Polar Region (PO)', fontsize=15)\n",
+ "\n",
+ "# NAe\n",
+ "ax[3].pcolormesh(lons, lats, mask_NAe, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[3].set_title('North Atlantic (NA)', fontsize=15)\n",
+ "\n",
+ "# WP\n",
+ "ax[4].pcolormesh(lons, lats, mask_WP, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[4].set_title('Western Pacific (WP)', fontsize=15)\n",
+ "\n",
+ "# EP\n",
+ "ax[5].pcolormesh(lons, lats, mask_EP, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[5].set_title('Eastern Pacific (EP)', fontsize=15)\n",
+ "\n",
+ "# TA\n",
+ "ax[6].pcolormesh(lons, lats, mask_TA, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[6].set_title('Tropical Atlantic (TA)', fontsize=15)\n",
+ "\n",
+ "# IO\n",
+ "ax[7].pcolormesh(lons, lats, mask_IO, vmin=0, vmax=1,\n",
+ " cmap=LinearSegmentedColormap.from_list(mymap, colors=colors,\n",
+ " N=2),\n",
+ " rasterized=True, transform=ccrs.PlateCarree())\n",
+ "ax[7].set_title('Indian Ocean (IO)', fontsize=15)\n",
+ "\n",
+ "# set xticks and yticks for latitudes and longitudes\n",
+ "# TR\n",
+ "ax[0].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[0].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# ML\n",
+ "ax[1].set_yticks([lat_sout_ML_SH, lat_nort_ML_SH, \n",
+ " lat_sout_ML_NH, lat_nort_ML_NH], crs=ccrs.PlateCarree())\n",
+ "ax[1].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# PO\n",
+ "ax[2].set_yticks([-90, lat_sout_ML_SH, \n",
+ " lat_nort_ML_NH, 90], crs=ccrs.PlateCarree())\n",
+ "ax[2].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "# NAe\n",
+ "ax[3].set_yticks([lat_sout_NAe, lat_nort_NAe], crs=ccrs.PlateCarree())\n",
+ "ax[3].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[3].set_xticks([lon_west_NAe, lon_east_NAe], crs=ccrs.PlateCarree())\n",
+ "ax[3].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# WP\n",
+ "ax[4].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[4].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[4].set_xticks([lon_west_WP, lon_east_WP], crs=ccrs.PlateCarree())\n",
+ "ax[4].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# EP\n",
+ "ax[5].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[5].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[5].set_xticks([lon_west_EP, lon_east_EP], crs=ccrs.PlateCarree())\n",
+ "ax[5].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# TA\n",
+ "ax[6].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[6].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[6].set_xticks([lon_west_TA, lon_east_TA], crs=ccrs.PlateCarree())\n",
+ "ax[6].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "# IO\n",
+ "ax[7].set_yticks([lat_sout_TR, lat_nort_TR], crs=ccrs.PlateCarree())\n",
+ "ax[7].yaxis.set_major_formatter(LatitudeFormatter(degree_symbol=''))\n",
+ "ax[7].set_xticks([lon_west_IO, lon_east_IO], crs=ccrs.PlateCarree())\n",
+ "ax[7].xaxis.set_major_formatter(LongitudeFormatter(degree_symbol='',\n",
+ " dateline_direction_label=True))\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.0, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=14,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i\n",
+ "\n",
+ "fig.savefig('figure_S2.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure_S3.pdf b/pythonscripts/figure_S3.pdf
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diff --git a/pythonscripts/figure_S3_cloudcover.ipynb b/pythonscripts/figure_S3_cloudcover.ipynb
new file mode 100644
index 0000000..21b55f5
--- /dev/null
+++ b/pythonscripts/figure_S3_cloudcover.ipynb
@@ -0,0 +1,387 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cloud cover\n",
+ "\n",
+ "This script generates figure S3: cloud impacts on the cloud cover response in ICON, MPI-ESM and IPSL-CM5A."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated\n",
+ "\n",
+ "* xx_cld: locked clouds, interactive water vapor\n",
+ "* xx_cldvap: locked clouds, locked water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "runs_cld = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "runs_cldvap = ['T1C1W1', 'T2C2W2', 'T1C2W1', 'T1C1W2',\n",
+ " 'T1C2W2', 'T2C1W1', 'T2C2W1', 'T2C1W2']\n",
+ "\n",
+ "response_cld = ['total', 'SST', 'cloud']\n",
+ "response_cldvap = ['total', 'SST', 'cloud', 'water vapor']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean cloud cover (MPI-ESM, IPSL-CM5A with locked clouds and locked water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1W1\n",
+ "reading T2C2W2\n",
+ "reading T1C2W1\n",
+ "reading T1C1W2\n",
+ "reading T1C2W2\n",
+ "reading T2C1W1\n",
+ "reading T2C2W1\n",
+ "reading T2C1W2\n"
+ ]
+ }
+ ],
+ "source": [
+ "clc_mpi = {}; clc_ipsl = {}\n",
+ "for run in runs_cldvap:\n",
+ " print('reading ' + run)\n",
+ " # MPI-ESM\n",
+ " #print(' MPI-ESM')\n",
+ " ifile = 'MPI-ESM_' + run + '_3d_mm.clc.nc'\n",
+ " ncfile = nc.Dataset('../../MPI-ESM/' + ifile, 'r')\n",
+ " lats_mpi = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_mpi = np.array(ncfile.variables['plev'][:].data)\n",
+ " # multiply with 100 to get cloud cover in %\n",
+ " clc_mpi[run] = np.nanmean(np.array(ncfile.variables['aclcac'][:].data)*100, axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # IPSL-CM5A\n",
+ " #print(' IPSL-CM5A')\n",
+ " ifile = 'IPSL-CM5A_' + run + '_3d_mm.remapcon.clc.nc'\n",
+ " ncfile = nc.Dataset('../../IPSL-CM5A/' + ifile, 'r')\n",
+ " lats_ipsl = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_ipsl = np.array(ncfile.variables['presnivs'][:].data)\n",
+ " # multiply with 100 to get cloud cover in %\n",
+ " clc_ipsl[run] = np.nanmean(np.array(ncfile.variables['rneb'][:].data)*100, axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "del run"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean cloud cover (ICON with locked clouds and interactive water vapor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "clc_icon = {}\n",
+ "for run in runs_cld:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats_icon = np.array(ncfile.variables['lat'][:].data)\n",
+ " levs_icon = np.array(ncfile.variables['lev'][:].data)\n",
+ " clc_icon[run] = np.nanmean(np.array(ncfile.variables['clc'][:].data), axis=3)\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ "del run, ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clc_mpi_djf = {}; clc_ipsl_djf = {}\n",
+ "for run in runs_cldvap:\n",
+ " clc_mpi_djf[run] = fct.calcMonthlyandSeasonMean(clc_mpi[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ " clc_ipsl_djf[run] = fct.calcMonthlyandSeasonMean(clc_ipsl[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "clc_icon_djf = {}\n",
+ "for run in runs_cld:\n",
+ " clc_icon_djf[run] = fct.calcMonthlyandSeasonMean(clc_icon[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del clc_mpi, clc_ipsl, clc_icon"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF responses and decompose the total response into contributions from changes in SST, clouds and water vapor"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dclc_mpi = np.full((len(response_cldvap), len(levs_mpi), len(lats_mpi)),\n",
+ " np.nan, dtype=float)\n",
+ "dclc_ipsl = np.full((len(response_cldvap), len(levs_ipsl), len(lats_ipsl)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "dclc_mpi[0, :, :], dclc_mpi[1, :, :], dclc_mpi[2, :, :], \\\n",
+ "dclc_mpi[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(clc_mpi_djf['T1C1W1'], clc_mpi_djf['T2C2W2'],\n",
+ " clc_mpi_djf['T1C2W2'], clc_mpi_djf['T2C1W1'],\n",
+ " clc_mpi_djf['T1C2W1'], clc_mpi_djf['T1C1W2'],\n",
+ " clc_mpi_djf['T2C2W1'], clc_mpi_djf['T2C1W2'])\n",
+ "dclc_ipsl[0, :, :], dclc_ipsl[1, :, :], dclc_ipsl[2, :, :], \\\n",
+ "dclc_ipsl[3, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(clc_ipsl_djf['T1C1W1'], clc_ipsl_djf['T2C2W2'],\n",
+ " clc_ipsl_djf['T1C2W2'], clc_ipsl_djf['T2C1W1'],\n",
+ " clc_ipsl_djf['T1C2W1'], clc_ipsl_djf['T1C1W2'],\n",
+ " clc_ipsl_djf['T2C2W1'], clc_ipsl_djf['T2C1W2'])\n",
+ "\n",
+ "dclc_icon = np.full((len(response_cld), len(levs_icon), len(lats_icon)),\n",
+ " np.nan, dtype=float)\n",
+ "dclc_icon[0, :, :], dclc_icon[1, :, :], dclc_icon[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(clc_icon_djf['T1C1'], clc_icon_djf['T2C2'],\n",
+ " clc_icon_djf['T1C2'], clc_icon_djf['T2C1'])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read zonal-mean tropopause in control simulations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ipath = '../../tropopause_T1C1_T1C1W1/'\n",
+ "\n",
+ "# ICON\n",
+ "ifile = 'ICON-NWP_AMIP_T1C1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_icon = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# MPI-ESM\n",
+ "ifile = 'MPI-ESM_T1C1W1_tropopause_DJF_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_mpi = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "# IPSL-CM5A\n",
+ "ifile = 'IPSL-CM5A_T1C1W1_tropopause_DJF_remapcon_timemean.fillmiss.nc'\n",
+ "ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ "tropo_ipsl = np.nanmean(ncfile.variables['ptrop'][:], axis=1)\n",
+ "ncfile.close()\n",
+ "del ifile, ncfile\n",
+ "\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot the cloud impact on the zonal-mean cloud cover response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 720x216 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "vlim = 5\n",
+ "levs = np.arange(10, 100, 5)\n",
+ "\n",
+ "fig, ax = plt.subplots(1, 3, figsize=(10, 3))#figsize(10))\n",
+ "# cloud cover response\n",
+ "cf0 = ax[0].pcolormesh(lats_icon, levs_icon/100,\n",
+ " dclc_icon[response_cld.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[1].pcolormesh(lats_mpi, levs_mpi/100,\n",
+ " dclc_mpi[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "ax[2].pcolormesh(lats_ipsl, levs_ipsl/100,\n",
+ " dclc_ipsl[response_cldvap.index('cloud'), :, :],\n",
+ " vmin=-vlim, vmax=vlim, cmap='RdBu_r')\n",
+ "\n",
+ "# cloud cover in control simulation\n",
+ "ax[0].contour(lats_icon, levs_icon/100, clc_icon_djf['T1C1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "ax[1].contour(lats_mpi, levs_mpi/100, clc_mpi_djf['T1C1W1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "ax[2].contour(lats_ipsl, levs_ipsl/100, clc_ipsl_djf['T1C1W1'],\n",
+ " levels=levs, colors='dimgrey', linewidths=1)\n",
+ "\n",
+ "# tropopause in control simulation\n",
+ "ax[0].plot(lats_icon, tropo_icon/100, color='tab:green')\n",
+ "ax[1].plot(lats_mpi, tropo_mpi/100, color='tab:green')\n",
+ "ax[2].plot(lats_ipsl, tropo_ipsl/100, color='tab:green')\n",
+ "\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].tick_params(labelsize=13)\n",
+ " ax[i].set(xticks=np.arange(-90, 91, 30),\n",
+ " xticklabels=['90S', '60S', '30S', '0', '30N', '60N' ,'90N'],\n",
+ " xlim=(-90, 90))\n",
+ " ax[i].set_yticks(np.arange(0, 1100, 200))\n",
+ " ax[i].set_ylim(1000, 10)\n",
+ " ax[i].set_xlabel('Latitude [$^{\\circ}$]', fontsize=15)\n",
+ "del i\n",
+ "ax[0].set_ylabel('Pressure [hPa]', fontsize=15)\n",
+ "\n",
+ "# titles for models\n",
+ "ax[0].set_title('ICON', fontsize=16)\n",
+ "ax[1].set_title('MPI-ESM', fontsize=16)\n",
+ "ax[2].set_title('IPSL-CM5A', fontsize=16)\n",
+ "\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar\n",
+ "fig.subplots_adjust(bottom=0.24)#(right=0.8)\n",
+ "clevs = np.arange(-vlim, vlim+1, 2)#np.array([-1, -0.5, 0, 0.5, 1])\n",
+ "cbar_ax = fig.add_axes([0.29, -0.03, 0.5, 0.037]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both',\n",
+ " ticks=clevs)\n",
+ "cb.set_label('$\\Delta$cloud cover [%]', fontsize=14, labelpad=1)\n",
+ "cb.ax.tick_params(labelsize=13)\n",
+ "del cbar_ax, cb, cf0, clevs\n",
+ "\n",
+ "fig.savefig('figure_S3.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/figure_S4.pdf b/pythonscripts/figure_S4.pdf
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diff --git a/pythonscripts/figure_S4_streamfunction.ipynb b/pythonscripts/figure_S4_streamfunction.ipynb
new file mode 100644
index 0000000..77e087e
--- /dev/null
+++ b/pythonscripts/figure_S4_streamfunction.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Stationary eddy stream function response\n",
+ "\n",
+ "This script generates figure S4: cloud impacts on stationary eddy stream function response at 300 hPa."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import cartopy.crs as ccrs\n",
+ "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify months and seasons of the year"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
+ " 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
+ "seasons = ['DJF', 'MAM', 'JJA', 'SON']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify simulations that are analyzed and impacts that are calculated (total response, SST impact, global cloud impact, regional cloud impacts)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# simulations with global cloud changes\n",
+ "runs_glo = ['T1C1', 'T2C2', 'T2C1', 'T1C2']\n",
+ "\n",
+ "# simulations with regional cloud changes\n",
+ "runs_reg_TR = ['T1C2TR', 'T1C1TR', 'T2C2TR', 'T2C1TR']\n",
+ "runs_reg_TA = ['T1C2TA', 'T1C1TA', 'T2C2TA', 'T2C1TA']\n",
+ "runs_reg_IO = ['T1C2IO', 'T1C1IO', 'T2C2IO', 'T2C1IO']\n",
+ "runs_reg_WP = ['T1C2WP', 'T1C1WP', 'T2C2WP', 'T2C1WP']\n",
+ "runs_reg_EP = ['T1C2EP', 'T1C1EP', 'T2C2EP', 'T2C1EP']\n",
+ "\n",
+ "runs_reg = runs_reg_TR + runs_reg_TA + runs_reg_IO + runs_reg_WP + runs_reg_EP\n",
+ "runs_all = runs_glo + runs_reg\n",
+ "\n",
+ "# responses\n",
+ "response_all = ['total', 'SST', 'cloud',\n",
+ " 'cloud TR', 'cloud notTR',\n",
+ " 'cloud TA', 'cloud notTA', 'cloud IO', 'cloud notIO',\n",
+ " 'cloud WP', 'cloud notWP', 'cloud EP', 'cloud notEP']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Read stream function at 300 hPa"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "reading T1C1\n",
+ "reading T2C2\n",
+ "reading T2C1\n",
+ "reading T1C2\n",
+ "reading T1C2TR\n",
+ "reading T1C1TR\n",
+ "reading T2C2TR\n",
+ "reading T2C1TR\n",
+ "reading T1C2TA\n",
+ "reading T1C1TA\n",
+ "reading T2C2TA\n",
+ "reading T2C1TA\n",
+ "reading T1C2IO\n",
+ "reading T1C1IO\n",
+ "reading T2C2IO\n",
+ "reading T2C1IO\n",
+ "reading T1C2WP\n",
+ "reading T1C1WP\n",
+ "reading T2C2WP\n",
+ "reading T2C1WP\n",
+ "reading T1C2EP\n",
+ "reading T1C1EP\n",
+ "reading T2C2EP\n",
+ "reading T2C1EP\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../ICON-NWP_lockedclouds/'\n",
+ "sf300 = {}\n",
+ "for run in runs_all:\n",
+ " print('reading ' + run)\n",
+ " ifile = 'ICON-NWP_AMIP_' + run + '_3d_mm.streamfct.nc'\n",
+ " ncfile = nc.Dataset(ipath + ifile, 'r')\n",
+ " lats = ncfile.variables['lat'][:].data\n",
+ " lons = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['lev'][:].data\n",
+ " sf = ncfile.variables['sf'][:].data\n",
+ " ncfile.close()\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # calculate stationary eddy stream function \n",
+ " # subtract monthly-mean zonal-mean stream function from monthly-mean\n",
+ " # lev-lat-lon stream function to get the stationary eddy stream function\n",
+ " sfstat = sf - np.nanmean(sf, axis=3)[:, :, :, None]\n",
+ " \n",
+ " # get stationary stream function at 300 hPa\n",
+ " levind300 = (np.abs(levs-300e2)).argmin() # index of 300 hPa level\n",
+ " sf300[run] = sfstat[:, levind300, :, :]\n",
+ " \n",
+ " del levs, sf, sfstat, levind300\n",
+ "del run\n",
+ "del ipath"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate DJF mean"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sf300_djf = {}\n",
+ "for run in runs_all:\n",
+ " sf300_djf[run] = fct.calcMonthlyandSeasonMean(sf300[run],\n",
+ " months, seasons)[1]['DJF']\n",
+ "del run\n",
+ "\n",
+ "del sf300"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Calculate response"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dsf300 = np.full((len(response_all), len(lats), len(lons)),\n",
+ " np.nan, dtype=float)\n",
+ "\n",
+ "# total, SST, cloud\n",
+ "dsf300[0, :, :], dsf300[1, :, :], dsf300[2, :, :] = \\\n",
+ " fct.calc_impacts_timmean(sf300_djf['T1C1'], sf300_djf['T2C2'],\n",
+ " sf300_djf['T1C2'], sf300_djf['T2C1'])\n",
+ "# regional cloud impacts\n",
+ "for k in range(int(len(runs_reg)/4)):\n",
+ " _, _, dsf300[k*2+3, :, :], dsf300[k*2+4, :, :] = \\\n",
+ " fct.calc_3impacts_timmean(sf300_djf['T1C1'],\n",
+ " sf300_djf['T2C2'],\n",
+ " sf300_djf['T1C2'],\n",
+ " sf300_djf['T2C1'],\n",
+ " sf300_djf[runs_all[4:][k*4]],\n",
+ " sf300_djf[runs_all[4:][k*4+1]],\n",
+ " sf300_djf[runs_all[4:][k*4+2]],\n",
+ " sf300_djf[runs_all[4:][k*4+3]])\n",
+ "del k"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Plot response of stationary eddy stream function\n",
+ "\n",
+ "Shift the longitudes from 0deg...360deg to -90deg...270deg for visualization reasons."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x504 with 7 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# shift longitudes\n",
+ "dsf300_shift, lons_shift = fct.shiftgrid_copy(90., dsf300, lons, start=False)\n",
+ "\n",
+ "response_plot = ['cloud WP', 'cloud EP',\n",
+ " 'cloud IO', 'cloud TA',\n",
+ " 'cloud TR', 'cloud']\n",
+ "\n",
+ "proj = ccrs.PlateCarree(central_longitude=-90)\n",
+ "fig, ax = plt.subplots(3, 2, figsize=(10, 7),\n",
+ " subplot_kw=dict(projection=proj))\n",
+ "ax = ax.reshape(-1) \n",
+ "for r in range(ax.shape[0]): # loop over responses\n",
+ " ax[r].coastlines(rasterized=True)\n",
+ " ax[r].set_aspect('auto')\n",
+ " ax[r].tick_params(labelsize=14)\n",
+ " # set xticks and yticks for latitudes and longitudes\n",
+ " # xaxis: longitudes\n",
+ " if r > 3: # last row\n",
+ " ax[r].set_xticks([-120, -60, 0, 60, 120, 180], crs=ccrs.PlateCarree())\n",
+ " lon_formatter = LongitudeFormatter(#zero_direction_label=True,\n",
+ " degree_symbol='',\n",
+ " dateline_direction_label=True)\n",
+ " ax[r].xaxis.set_major_formatter(lon_formatter)\n",
+ " del lon_formatter\n",
+ " # yaxis: latitudes\n",
+ " if r in [0, 2, 4]:\n",
+ " ax[r].set_yticks([-90, -60, -30, 0, 30, 60, 90], crs=ccrs.PlateCarree())\n",
+ " lat_formatter = LatitudeFormatter(degree_symbol='')\n",
+ " ax[r].yaxis.set_major_formatter(lat_formatter)\n",
+ " del lat_formatter\n",
+ " # cloud impacts\n",
+ " cf0 = ax[r].pcolormesh(lons_shift, lats,\n",
+ " dsf300_shift[response_all.index(response_plot[r]), :, :]/1e6,\n",
+ " vmin=-6, vmax=6, cmap='seismic',\n",
+ " rasterized=True,\n",
+ " transform=ccrs.PlateCarree())\n",
+ " ax[r].set_title(response_plot[r], fontsize=16)\n",
+ "del r\n",
+ "fig.canvas.draw()\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "# colorbar for response\n",
+ "fig.subplots_adjust(bottom=0.08)#(right=0.8)\n",
+ "cbar_ax = fig.add_axes([0.174, 0.0, 0.7, 0.02]) # left,bottom,width,height\n",
+ "cb = fig.colorbar(cf0, cax=cbar_ax, orientation='horizontal', extend='both')\n",
+ "cb.set_label('$\\Delta \\psi_{300}$ [10$^6$ m$^{2}$ s$^{-1}$]',\n",
+ " fontsize=15, labelpad=5)\n",
+ "cb.ax.tick_params(labelsize=14)\n",
+ "del cbar_ax, cb, cf0\n",
+ "\n",
+ "# a), b) etc for subplots\n",
+ "labs = ['a)', 'b)', 'c)', 'd)', 'e)', 'f)']\n",
+ "for i in range(ax.shape[0]):\n",
+ " ax[i].text(0.01, 1.02, labs[i], va='bottom', ha='left',\n",
+ " rotation_mode='anchor', fontsize=15,\n",
+ " transform=ax[i].transAxes)\n",
+ "del i, labs\n",
+ "\n",
+ "fig.savefig('figure_S4.pdf', bbox_inches='tight')\n",
+ "plt.show(fig)\n",
+ "plt.close(fig)\n",
+ "del fig, ax, proj\n",
+ "del response_plot, dsf300_shift, lons_shift"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/pythonscripts/helper_functions.py b/pythonscripts/helper_functions.py
new file mode 100644
index 0000000..edbfb7a
--- /dev/null
+++ b/pythonscripts/helper_functions.py
@@ -0,0 +1,587 @@
+import numpy as np
+import numpy.ma as ma
+import matplotlib.colors as mcolors
+import pandas as pd
+import netCDF4 as nc
+from scipy.optimize import curve_fit
+
+# create new colormap based on ncl colormap BlueYellowRed.rgb
+def generate_mymap():
+ cmap_name='BlueYellowRed'
+ colortab=pd.read_csv(cmap_name + '.rgb',
+ delim_whitespace=True, skiprows=2)
+ colortab=colortab.values[:,0:3]/255
+ colortab_new = np.full((colortab.shape[0], 4), np.nan, dtype=float)
+ colortab_new[:, 0:3] = colortab
+ colortab_new[:,3] = 1
+ mymap = mcolors.LinearSegmentedColormap.from_list(cmap_name, colortab_new, N=254)
+ del colortab_new
+ # create another colormap with white space at 3 center points of colormap
+ colortab_new2 = np.full((colortab.shape[0]+2, 4), np.nan, dtype=float)
+ colortab_new2[0:126, 0:3] = colortab[0:126, :]
+ colortab_new2[126:129, 0:3] = np.array([[1, 1, 1], [1,1,1], [1,1,1]]) # change center of colorbar to white
+ colortab_new2[129:, 0:3] = colortab[127:, :]
+ colortab_new2[:,3] = 1
+ mymap2 = mcolors.LinearSegmentedColormap.from_list(cmap_name, colortab_new2, N=254)
+ del colortab_new2
+ del colortab, cmap_name
+
+ return mymap, mymap2
+
+######################################################################
+######################################################################
+# calculate monthly mean and seasonal mean data
+def calcMonthlyandSeasonMean(data, months, seasons):
+ """ Calculate the monthly mean and season mean of a given time series.
+
+ Use a time series of monthly means of a variable to calculate the mean
+ over all Januarys, Februarys etc. and for the seasons for the whole
+ time series.
+
+ Args:
+ data: numpy ndarray with monthly data.
+ months: List with abbreviations for all 12 months.
+ seasons: List with abbreviations for the 4 seasons.
+
+ Returns:
+ monthly_mean: Mean over all months for whole time series.
+ season_mean: Mean over seasons for whole time series.
+ """
+
+ # dictionary to be able to identify which month belongs to which season
+ seasons_dict = {'DJF': ['Dec', 'Jan', 'Feb'],
+ 'MAM': ['Mar', 'Apr', 'May'],
+ 'JJA': ['Jun', 'Jul', 'Aug'],
+ 'SON': ['Sep', 'Oct', 'Nov']
+ }
+
+ monthly_data = {}
+ monthly_mean = {}
+ for n, month in enumerate(months):
+ monthly_data[month] = data[n::12, :]
+ # mean over every twelth month for every n -> one mean for all
+ # 12 months
+ monthly_mean[month] = np.nanmean(monthly_data[month], axis=0)
+
+ # the season mean considers all DJF months, even JF at beginning of time
+ # series and D at its end
+ season_mean = {}
+ for season in seasons:
+ season_mean[season] = np.nanmean([ monthly_mean[month] for month in \
+ seasons_dict[season] ], axis=0)
+
+ return monthly_mean, season_mean
+
+######################################################################
+######################################################################
+# jet latitude and jet strength
+def func_fit_quadratic(x, p0, p1, p2):
+ return p0+p1*x+p2*x**2
+
+def get_eddyjetlatint(u, lat):
+ # calculate latitude of eddy-driven jet
+ # make sure that lat is ordered from SP to NP; otherwise
+ # np.arange does not work to create latint
+ if lat[0] > lat[1]:
+ lat = lat[::-1]
+ u = u[::-1]
+
+ # Southern hemisphere
+ # search for jet between 25S and 70S
+ indlat_sh = np.squeeze(np.array(np.where((lat<-25.0) & (lat>-70.0))))
+ # find index of maximum value of u within the defined latitude range
+ maxlat = np.argmax(u[indlat_sh]) + indlat_sh[0]
+ # do quadratic fit around the maximum
+ # interpolate latitude and u-wind for a more precise result
+ latint=np.arange(lat[maxlat-1], lat[maxlat+1], 0.01)
+ uint = np.interp(latint, lat[indlat_sh], u[indlat_sh])
+ p,_ = curve_fit(func_fit_quadratic, latint, uint)
+ ufit = func_fit_quadratic(latint, p[0], p[1], p[2])
+ jetlat_sh = latint[np.argmax(ufit)]
+ # get strength of jet (maximum of u850 at jet latitude)
+ jetint_sh = ufit[np.argmax(ufit)]
+
+ # delete variables that are not returned by the function, so that you can
+ # use the same names for the northern hemisphere
+ del indlat_sh, maxlat, latint, uint, p, ufit
+
+ # Northern hemisphere
+ # search for jet between 25N and 70N
+ indlat_nh = np.squeeze(np.array(np.where((lat>25.0) & (lat<70.0))))
+ # find index of maximum value of u within the defined latitude range
+ maxlat = np.argmax(u[indlat_nh]) + indlat_nh[0]
+ # do a quadratic fit around the maximum
+ # interpolate latitude and u-wind for a more precise result
+ latint=np.arange(lat[maxlat-1], lat[maxlat+1], 0.01)
+ uint = np.interp(latint, lat[indlat_nh], u[indlat_nh])
+ p,_ = curve_fit(func_fit_quadratic, latint, uint)
+ ufit = func_fit_quadratic(latint, p[0], p[1], p[2])
+ jetlat_nh = latint[np.argmax(ufit)]
+
+ # get strength of jet (maximum of u850 at jet latitude)
+ jetint_nh = ufit[np.argmax(ufit)] # = ufit.max()
+
+ del indlat_nh, maxlat, latint, uint, p, ufit
+
+ return jetlat_sh, jetint_sh, jetlat_nh, jetint_nh
+
+# same function as above, but only for northern hemisphere
+def get_eddyjetlatint_NH(u, lat):
+ # calculate latitude of eddy-driven jet
+ # make sure that lat is ordered from SP to NP; otherwise
+ # np.arange does not work to create latint
+ if lat[0] > lat[1]:
+ lat = lat[::-1]
+ u = u[::-1]
+ # Northern hemisphere
+ # search for jet between 25N and 70N
+ indlat_nh = np.squeeze(np.array(np.where((lat>25.0) & (lat<70.0))))
+ # find index of maximum value of u within the defined latitude range
+ maxlat = np.argmax(u[indlat_nh]) + indlat_nh[0]
+ # do a quadratic fit around the maximum
+ # interpolate latitude and u-wind for a more precise result
+ latint=np.arange(lat[maxlat-1], lat[maxlat+1], 0.01)
+ uint = np.interp(latint, lat[indlat_nh], u[indlat_nh])
+ p,_ = curve_fit(func_fit_quadratic, latint, uint)
+ ufit = func_fit_quadratic(latint, p[0], p[1], p[2])
+ jetlat_nh = latint[np.argmax(ufit)]
+
+ # get strength of jet (maximum of u850 at jet latitude)
+ jetint_nh = ufit[np.argmax(ufit)] # = ufit.max()
+
+ return jetlat_nh, jetint_nh
+
+######################################################################
+######################################################################
+# adapt function for detection of jet latitude and jet strength in
+# Northern Hemisphere so that it can treat NaN's.
+def get_eddyjetlatint_NH_nan(u, lat, lo):
+ # calculate latitude of eddy-driven jet
+ # make sure that lat is ordered from SP to NP; otherwise
+ # np.arange does not work to create latint
+ if lat[0] > lat[1]:
+ lat = lat[::-1]
+ u = u[::-1]
+ # Northern Hemisphere
+ if lo > 0 and lo < 40: # avoid diagnosing the jet in the Mediterranean sea
+ # and Black Sea
+ indlat_nh = np.squeeze(np.array(np.where((lat>45.0) & (lat<62.0))))
+ else:
+ indlat_nh = np.squeeze(np.array(np.where((lat>25.0) & (lat<62.0))))
+ # find index of maximum value of u within the defined latitude range
+ maxlat = np.nanargmax(u[indlat_nh]) + indlat_nh[0]
+ # do a quadratic fit around the maximum
+ # interpolate latitude and u-wind for a more precise result
+ try:
+ latint=np.arange(lat[maxlat-1], lat[maxlat+1], 0.01)
+ uint = np.interp(latint, lat[indlat_nh], u[indlat_nh])
+ p,_ = curve_fit(func_fit_quadratic, latint, uint)
+ ufit = func_fit_quadratic(latint, p[0], p[1], p[2])
+ jetlat_nh = latint[np.nanargmax(ufit)]
+
+ # get strength of jet (maximum of u850 at jet latitude)
+ jetint_nh = ufit[np.nanargmax(ufit)] # = ufit.max()
+
+ except ValueError:
+ print("Value error in function get_eddyjetlatint_NH_nan!\n" + \
+ " Set jetlat and jetint to NaN for longitude " + str(lo))
+ jetlat_nh = np.nan
+ jetint_nh = np.nan
+
+ return jetlat_nh, jetint_nh
+
+######################################################################
+######################################################################
+# get total response, SST impact and cloud impact from cloud locking
+# simulations
+def calc_impacts_timmean(var_T1C1, var_T2C2, var_T1C2, var_T2C1):
+ """ Decompose climate warming response of a given variable into total
+ response, SST impact and cloud impact.
+
+ Calculate the global warming response of a time-mean variable based on
+ cloud-locking method. Decompose
+ the (total) response into a contribution of changes in cloud-radiative
+ properties and a contribution of changes in SSTs (SST increase). You need
+ four simulations. SST and clouds are taken either from control climate
+ (T1, C1) or from global warming simulation (T2, C2).
+
+ Args:
+ var_T1C1: lat-lon array with time-mean data of T1C1 simulation.
+ var_T2C2: lat-lon array with time-mean data of T2C2 simulation.
+ var_T1C2: lat-lon array with time-mean data of T1C2 simulation.
+ var_T2C1: lat-lon array with time-mean data of T2C1 simulation.
+
+ Returns:
+ tot: total response of variable to global warming.
+ sst: SST impact on global warming response.
+ cld: cloud impact on global warming response.
+ """
+ # calculate climate change response of time mean variable
+ # total response of circulation
+ tot = var_T2C2 - var_T1C1
+ # response to SST increase (mean over both climates)
+ sst = np.nanmean(np.array([(var_T2C1 - var_T1C1),
+ (var_T2C2 - var_T1C2)]), axis=0)
+ # response to clouds (mean over both climates)
+ cld = np.nanmean(np.array([(var_T1C2 - var_T1C1),
+ (var_T2C2 - var_T2C1)]), axis=0)
+ return tot, sst, cld
+
+
+######################################################################
+######################################################################
+# get total response, SST impact, cloud impact and water vapor impact
+# from cloud- and water vapor-locking simulations
+def calc_3impacts_timmean(var_T1C1W1, var_T2C2W2, var_T1C2W2, var_T2C1W1,
+ var_T1C2W1, var_T1C1W2, var_T2C2W1, var_T2C1W2):
+ """ Decompose climate warming response of a given variable into total
+ response, SST impact, cloud-radiative impact and water vapor (or clouds
+ in different region) radiative impact.
+
+ Calculate the global warming response of a time-mean variable based on
+ cloud-locking method. Decompose the (total) response into a contribution
+ of changes in cloud-radiative properties, changes in water vapor (or
+ clouds in a different region) and changes in SSTs (SST increase). You need
+ eight simulations. SST, clouds and water vapor (clouds in different region)
+ are taken either from control climate (T1, C1, W1) or from global warming
+ simulation (T2, C2, W2).
+
+ Args:
+ var_T1C1W1: lat-lon array with time-mean data of T1C1W1 simulation.
+ var_T2C2W2: lat-lon array with time-mean data of T2C2W2 simulation.
+ var_T1C2W2: lat-lon array with time-mean data of T1C2W2 simulation.
+ var_T2C1W1: lat-lon array with time-mean data of T2C1W1 simulation.
+ var_T1C2W1: lat-lon array with time-mean data of T1C2W1 simulation.
+ var_T1C1W2: lat-lon array with time-mean data of T1C1W2 simulation.
+ var_T2C2W1: lat-lon array with time-mean data of T2C2W1 simulation.
+ var_T2C1W2: lat-lon array with time-mean data of T2C1W2 simulation.
+
+ Returns:
+ tot: total response of variable to global warming.
+ sst: SST impact on global warming response.
+ cld: cloud-radiative impact on global warming response.
+ vap: water vapor radiative impact or impact from clouds in a different
+ region than in cld on global warming response.
+ """
+ # calculate climate change response of time mean variable
+ # total response of circulation
+ tot = var_T2C2W2 - var_T1C1W1
+ # response to SST increase (mean over both climates)
+ sst = np.nanmean(np.array([(var_T2C1W1 - var_T1C1W1),
+ (var_T2C2W2 - var_T1C2W2)]), axis=0)
+ # response to clouds (mean over both climates)
+ cld = np.nanmean(np.array([(var_T1C2W1 - var_T1C1W1),
+ (var_T1C2W2 - var_T1C1W2),
+ (var_T2C2W1 - var_T2C1W1),
+ (var_T2C2W2 - var_T2C1W2)]), axis=0)
+ # response in water vapor or clouds in a different region than in cld
+ # (mean over both climates)
+ vap = np.nanmean(np.array([(var_T1C1W2 - var_T1C1W1),
+ (var_T1C2W2 - var_T1C2W1),
+ (var_T2C1W2 - var_T2C1W1),
+ (var_T2C2W2 - var_T2C2W1)]), axis=0)
+
+ return tot, sst, cld, vap
+
+######################################################################
+######################################################################
+# copy of basemap function shiftgrid, because basemap could not be installed
+def shiftgrid_copy(lon0,datain,lonsin,start=True,cyclic=360.0):
+ if np.fabs(lonsin[-1]-lonsin[0]-cyclic) > 1.e-4:
+ # Use all data instead of raise ValueError, 'cyclic point not included'
+ start_idx = 0
+ else:
+ # If cyclic, remove the duplicate point
+ start_idx = 1
+ if lon0 < lonsin[0] or lon0 > lonsin[-1]:
+ raise ValueError('lon0 outside of range of lonsin')
+ i0 = np.argmin(np.fabs(lonsin-lon0))
+ i0_shift = len(lonsin)-i0
+ if ma.isMA(datain):
+ dataout = ma.zeros(datain.shape,datain.dtype)
+ else:
+ dataout = np.zeros(datain.shape,datain.dtype)
+ if ma.isMA(lonsin):
+ lonsout = ma.zeros(lonsin.shape,lonsin.dtype)
+ else:
+ lonsout = np.zeros(lonsin.shape,lonsin.dtype)
+ if start:
+ lonsout[0:i0_shift] = lonsin[i0:]
+ else:
+ lonsout[0:i0_shift] = lonsin[i0:]-cyclic
+ dataout[...,0:i0_shift] = datain[...,i0:]
+ if start:
+ lonsout[i0_shift:] = lonsin[start_idx:i0+start_idx]+cyclic
+ else:
+ lonsout[i0_shift:] = lonsin[start_idx:i0+start_idx]
+ dataout[...,i0_shift:] = datain[...,start_idx:i0+start_idx]
+ return dataout,lonsout
+
+
+######################################################################
+######################################################################
+# calcualte the zonal mean over a given region
+def calcBoxZonalmean(var, lats, lons, lonwest, loneast):
+ """ Calculate the zonal-mean of a given variable for a
+ region specified by lonwest and loneast.
+
+ Calculate the zonal-mean of a given variable for a region
+ specified by lonwest and loneast.
+
+ Args:
+ var: lat-lon array.
+ lats: array with latitudes.
+ lons: array with longitudes.
+ lonwest: western longitude.
+ loneast: eastern longitude.
+
+ Returns:
+ var_zm: time-mean zonal-mean variable for the given region.
+ """
+
+ # check that longitudes range from 0 to 360
+ if lons.min() < 0:
+ print('longitudes range from ' + str(lons.min()) + ' to ' + \
+ str(lons.max()))
+ print('Leave function calcOceanbasinMean')
+ return
+
+ if lonwest >= 0: # keep lons from 0 to 360
+ # index of lonwest and loneast in longitude vector
+ lonind_west = (np.abs(lons-lonwest)).argmin()
+ lonind_east = (np.abs(lons-loneast)).argmin()
+
+ var_zm = np.nanmean(var[:, lonind_west:lonind_east+1], axis=1)
+ del lonind_west, lonind_east
+
+ elif lonwest < 0: # shift longitudes from 0...360 deg lon to -180...180 deg
+ var1, lons1 = shiftgrid_copy(180., var, lons, start=False)
+ lonind_west = (np.abs(lons1-lonwest)).argmin()
+ lonind_east = (np.abs(lons1-loneast)).argmin()
+
+ var_zm = np.nanmean(var1[:, lonind_west:lonind_east+1], axis=1)
+ del lonind_west, lonind_east
+
+ return var_zm
+
+######################################################################
+######################################################################
+
+# calculate vertical means based on level indices
+def get_verticalmean_overp_tropo(data, plev, tropoind1, tropoind2):
+ # input data must have shape: levs, lats, lons
+
+ # levels must go from TOA to surface
+ if plev[0] > plev[1]:
+ print('change order of levels to go from TOA to surface')
+ plev = plev[::-1]
+ data = data[::-1, :, :]
+
+ nlev = plev.size
+ dplev = np.full(nlev, np.nan, dtype=float)
+ dplev[1:nlev-1] = 0.5*(np.abs(plev[2:nlev]-plev[0:nlev-2]))
+ dplev[0] = np.abs(plev[1]-plev[0])
+ dplev[nlev-1] = np.abs(plev[nlev-1]-plev[nlev-2])
+
+ out = np.full(data[0,:,:].shape, np.nan, dtype=float)
+ for la in range(data[0,:,0].shape[0]): # latitudes
+ for lo in range(data[0,0,:].shape[0]): # longitudes
+ out[la,lo] = np.nansum(data[tropoind1[la,lo]:tropoind2[la,lo]+1,
+ la, lo] * \
+ dplev[tropoind1[la,lo]:tropoind2[la,lo]+1],
+ axis=0) / \
+ np.nansum(dplev[tropoind1[la,lo]:tropoind2[la,lo]+1])
+ del lo
+ del la
+ return out
+
+
+######################################################################
+######################################################################
+# read a specified variable for a certain pressure level
+def read_var_onelevel(ifile, var, varlev, level, printmessages=False):
+ ncfile = nc.Dataset(ifile, 'r')
+ lats = ncfile.variables['lat'][:].data
+ lons = ncfile.variables['lon'][:].data
+ levs = ncfile.variables[varlev][:].data
+ var_nc = ncfile.variables[var][:].data
+ ncfile.close()
+ del ncfile
+
+ # shift longitudes, so that they range from 0 to 360 and not from -180 to 180
+ if lons.min() < 0 or lons.max() < 190:
+ if printmessages:
+ print(' shift longitudes')
+ var_nc, lons = obm.shiftgrid_copy(0., var_nc, lons, start=False)
+ lons = lons + 360
+
+ # check that latitudes are from south to north
+ if lats[0] > lats[1]:
+ if printmessages:
+ print(' shift latitudes')
+ lats = lats[::-1]
+ var_nc = var_nc[:, :, ::-1, :]
+
+ # get variable for specified pressure level
+ levind = (np.abs(levs-level*1e2)).argmin() # index of level
+ var_lev = var_nc[:, levind, :, :]
+ del levs, var_nc, levind
+
+ return var_lev, lats, lons
+
+##############################################################################
+##############################################################################
+# read zonal wind at 850hPa from file that contains only this variable
+#def read_u850(ifile, var, printmessages=False):
+# ncfile = nc.Dataset(ifile, 'r')
+# lats = ncfile.variables['lat'][:].data
+# lons = ncfile.variables['lon'][:].data
+# var_nc = np.squeeze(ncfile.variables[var][:].data)
+# ncfile.close()
+# del ncfile
+#
+# # Set high values to NaN's.
+# var_nc[var_nc > 1000] = np.nan
+# if printmessages:
+# print(' ', np.nanmax(var_nc), np.nanmin(var_nc),
+# " contains NaN's:", np.isnan(var_nc).any())
+#
+# # check latitudes and longitudes
+# # longitudes should range from 0 to 360 and not from -180 to 180
+# if lons.min() < 0 or lons.max() < 190:
+# if printmessages:
+# print(' shift longitudes')
+# var_nc, lons = obm.shiftgrid_copy(0., var_nc, lons, start=False)
+# lons = lons + 360
+#
+# # latitudes should be from south to north
+# if lats[0] > lats[1]:
+# if printmessages:
+# print(' shift latitudes')
+# lats = lats[::-1]
+# var_nc = var_nc[:, ::-1, :]
+#
+# return var_nc, lats, lons
+
+
+##############################################################################
+##############################################################################
+# horizontal interpolation of the data
+
+# this is a copy of the mpl_toolkits.basemap.interp function
+def interp_copy(datain,xin,yin,xout,yout,checkbounds=False,masked=False,order=1):
+ # xin and yin must be monotonically increasing.
+ if xin[-1]-xin[0] < 0 or yin[-1]-yin[0] < 0:
+ raise ValueError('xin and yin must be increasing!')
+ if xout.shape != yout.shape:
+ raise ValueError('xout and yout must have same shape!')
+ # check that xout,yout are
+ # within region defined by xin,yin.
+ if checkbounds:
+ if xout.min() < xin.min() or \
+ xout.max() > xin.max() or \
+ yout.min() < yin.min() or \
+ yout.max() > yin.max():
+ raise ValueError('yout or xout outside range of yin or xin')
+ # compute grid coordinates of output grid.
+ delx = xin[1:]-xin[0:-1]
+ dely = yin[1:]-yin[0:-1]
+ if max(delx)-min(delx) < 1.e-4 and max(dely)-min(dely) < 1.e-4:
+ # regular input grid.
+ xcoords = (len(xin)-1)*(xout-xin[0])/(xin[-1]-xin[0])
+ ycoords = (len(yin)-1)*(yout-yin[0])/(yin[-1]-yin[0])
+ else:
+ # irregular (but still rectilinear) input grid.
+ xoutflat = xout.flatten(); youtflat = yout.flatten()
+ ix = (np.searchsorted(xin,xoutflat)-1).tolist()
+ iy = (np.searchsorted(yin,youtflat)-1).tolist()
+ xoutflat = xoutflat.tolist(); xin = xin.tolist()
+ youtflat = youtflat.tolist(); yin = yin.tolist()
+ xcoords = []; ycoords = []
+ for n,i in enumerate(ix):
+ if i < 0:
+ xcoords.append(-1) # outside of range on xin (lower end)
+ elif i >= len(xin)-1:
+ xcoords.append(len(xin)) # outside range on upper end.
+ else:
+ xcoords.append(float(i)+(xoutflat[n]-xin[i])/(xin[i+1]-xin[i]))
+ for m,j in enumerate(iy):
+ if j < 0:
+ ycoords.append(-1) # outside of range of yin (on lower end)
+ elif j >= len(yin)-1:
+ ycoords.append(len(yin)) # outside range on upper end
+ else:
+ ycoords.append(float(j)+(youtflat[m]-yin[j])/(yin[j+1]-yin[j]))
+ xcoords = np.reshape(xcoords,xout.shape)
+ ycoords = np.reshape(ycoords,yout.shape)
+ # data outside range xin,yin will be clipped to
+ # values on boundary.
+ if masked:
+ xmask = np.logical_or(np.less(xcoords,0),np.greater(xcoords,len(xin)-1))
+ ymask = np.logical_or(np.less(ycoords,0),np.greater(ycoords,len(yin)-1))
+ xymask = np.logical_or(xmask,ymask)
+ xcoords = np.clip(xcoords,0,len(xin)-1)
+ ycoords = np.clip(ycoords,0,len(yin)-1)
+ # interpolate to output grid using bilinear interpolation.
+ if order == 1:
+ xi = xcoords.astype(np.int32)
+ yi = ycoords.astype(np.int32)
+ xip1 = xi+1
+ yip1 = yi+1
+ xip1 = np.clip(xip1,0,len(xin)-1)
+ yip1 = np.clip(yip1,0,len(yin)-1)
+ delx = xcoords-xi.astype(np.float32)
+ dely = ycoords-yi.astype(np.float32)
+ dataout = (1.-delx)*(1.-dely)*datain[yi,xi] + \
+ delx*dely*datain[yip1,xip1] + \
+ (1.-delx)*dely*datain[yip1,xi] + \
+ delx*(1.-dely)*datain[yi,xip1]
+ elif order == 0:
+ xcoordsi = np.around(xcoords).astype(np.int32)
+ ycoordsi = np.around(ycoords).astype(np.int32)
+ dataout = datain[ycoordsi,xcoordsi]
+ elif order == 3:
+ try:
+ from scipy.ndimage import map_coordinates
+ except ImportError:
+ raise ValueError('scipy.ndimage must be installed if order=3')
+ coords = [ycoords,xcoords]
+ dataout = map_coordinates(datain,coords,order=3,mode='nearest')
+ else:
+ raise ValueError('order keyword must be 0, 1 or 3')
+ if masked:
+ newmask = ma.mask_or(ma.getmask(dataout), xymask)
+ dataout = ma.masked_array(dataout, mask=newmask)
+ if not isinstance(masked, bool):
+ dataout = dataout.filled(masked)
+ return dataout
+
+def interpolate2d(datao, lato, lono, lati, loni):
+ # datao is input of assumed dimension (ntim, nlato, nlono)
+ # lato, lono is old input grid
+ # o means "old"
+
+ # dimensions of time
+ ntim = datao.shape[0]
+
+ # dimensions of new latitude and longitude
+ nlati = lati.size
+ nloni = loni.size
+
+ # lato, lono must be increasing
+ if (not(np.all(np.diff(lato)>0))):
+ raise ValueError()
+ if (not(np.all(np.diff(lono)>0))):
+ raise ValueError()
+
+ # prepare interpolated data array
+ datai = np.full((ntim, nlati, nloni), np.nan, dtype=float)
+
+ # interpolate lat-lon dimension
+ x, y = np.meshgrid(loni, lati)
+ for t in range(ntim):
+ datai[t,:,:] = interp_copy(datao[t,:,:], lono, lato, x, y,
+ checkbounds=False, masked=False, order=1)
+ del t
+
+ return datai
\ No newline at end of file
diff --git a/pythonscripts/interpolate_cmip5_data_to_common_grid.ipynb b/pythonscripts/interpolate_cmip5_data_to_common_grid.ipynb
new file mode 100644
index 0000000..fb47236
--- /dev/null
+++ b/pythonscripts/interpolate_cmip5_data_to_common_grid.ipynb
@@ -0,0 +1,444 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Interpolate the CMIP5 data to a common grid\n",
+ "\n",
+ "Interpolate the zonal wind at 850 hPa coupled and atmosphere CMIP5 models (historical, RCP8.5, amip, amip4K, amipFuture to a common grid with 96 grid points in latitude and 192 grid points in longitude.\n",
+ "\n",
+ "Save the interpolated data as .npy files."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import netCDF4 as nc\n",
+ "import glob\n",
+ "\n",
+ "import helper_functions as fct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specify CMIP5 models and simulations that are analyzed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# models\n",
+ "models_amip = ['bcc-csm1-1', 'CanAM4', 'CCSM4', 'CNRM-CM5', 'HadGEM2-A',\n",
+ " 'IPSL-CM5A-LR', 'IPSL-CM5B-LR', 'MIROC5', 'MPI-ESM-LR',\n",
+ " 'MPI-ESM-MR', 'MRI-CGCM3']\n",
+ "models_cmip = ['ACCESS1-0', 'ACCESS1-3', 'bcc-csm1-1-m', 'bcc-csm1-1',\n",
+ " 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC',\n",
+ " 'CESM1-CAM5', 'CMCC-CESM', 'CMCC-CM', 'CMCC-CMS',\n",
+ " 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'EC-EARTH', 'FGOALS-g2',\n",
+ " 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M',\n",
+ " 'GISS-E2-H', 'GISS-E2-R', 'HadGEM2-AO', 'HadGEM2-CC',\n",
+ " 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR',\n",
+ " 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM-CHEM', 'MIROC-ESM',\n",
+ " 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-ME',\n",
+ " 'NorESM1-M']\n",
+ "\n",
+ "# simulations\n",
+ "runs_cmip = ['historical', 'rcp85']\n",
+ "runs_amip = ['amip', 'amip4K', 'amipFuture']\n",
+ "\n",
+ "# time period of simulation\n",
+ "timeint_cmip = ['197501-200412', '207001-209912']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Generate latitude and logitude arrays on which the data will be interpolated"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "lats = np.arange(-89.0625, 90, 1.875)\n",
+ "lons = np.arange(0, 360, 1.875)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpolate data from coupled models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "ACCESS1-0\n",
+ " historical\n",
+ " rcp85\n",
+ "ACCESS1-3\n",
+ " historical\n",
+ " rcp85\n",
+ "bcc-csm1-1-m\n",
+ " historical\n",
+ " rcp85\n",
+ "bcc-csm1-1\n",
+ " historical\n",
+ " rcp85\n",
+ "BNU-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "CanESM2\n",
+ " historical\n",
+ " rcp85\n",
+ "CCSM4\n",
+ " historical\n",
+ " rcp85\n",
+ "CESM1-BGC\n",
+ " historical\n",
+ " rcp85\n",
+ "CESM1-CAM5\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CESM\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CM\n",
+ " historical\n",
+ " rcp85\n",
+ "CMCC-CMS\n",
+ " historical\n",
+ " rcp85\n",
+ "CNRM-CM5\n",
+ " historical\n",
+ " rcp85\n",
+ "CSIRO-Mk3-6-0\n",
+ " historical\n",
+ " rcp85\n",
+ "EC-EARTH\n",
+ " historical\n",
+ " rcp85\n",
+ "FGOALS-g2\n",
+ " historical\n",
+ " rcp85\n",
+ "FIO-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-CM3\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-ESM2G\n",
+ " historical\n",
+ " rcp85\n",
+ "GFDL-ESM2M\n",
+ " historical\n",
+ " rcp85\n",
+ "GISS-E2-H\n",
+ " historical\n",
+ " rcp85\n",
+ "GISS-E2-R\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-AO\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-CC\n",
+ " historical\n",
+ " rcp85\n",
+ "HadGEM2-ES\n",
+ " historical\n",
+ " rcp85\n",
+ "inmcm4\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5A-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5A-MR\n",
+ " historical\n",
+ " rcp85\n",
+ "IPSL-CM5B-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC5\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC-ESM-CHEM\n",
+ " historical\n",
+ " rcp85\n",
+ "MIROC-ESM\n",
+ " historical\n",
+ " rcp85\n",
+ "MPI-ESM-LR\n",
+ " historical\n",
+ " rcp85\n",
+ "MPI-ESM-MR\n",
+ " historical\n",
+ " rcp85\n",
+ "MRI-CGCM3\n",
+ " historical\n",
+ " rcp85\n",
+ "NorESM1-ME\n",
+ " historical\n",
+ " rcp85\n",
+ "NorESM1-M\n",
+ " historical\n",
+ " rcp85\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "for model in models_cmip:\n",
+ " print(model)\n",
+ " for r, run in enumerate(runs_cmip):\n",
+ " print(' ', run)\n",
+ " # 1) read data of shape (time,lev,lat,lon)\n",
+ " # uwind\n",
+ " ifile = glob.glob(ipath + run + '/ua_Amon_' + model + \\\n",
+ " '*' + timeint_cmip[r] +'.absTime.nc')[0]\n",
+ " ncfile = nc.Dataset(ifile, 'r')\n",
+ " lats_cmip = ncfile.variables['lat'][:].data\n",
+ " lons_cmip = ncfile.variables['lon'][:].data\n",
+ " levs_cmip = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs_cmip-85000)).argmin() # index of 850hPa level\n",
+ " u850_cmip = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_cmip[u850_cmip > 1000] = np.nan\n",
+ "\n",
+ " del levs_cmip, uwind, levind850\n",
+ " del ifile, ncfile\n",
+ " \n",
+ " # 2) interpolate data to new grid\n",
+ " u850_int = fct.interpolate2d(u850_cmip, lats_cmip,\n",
+ " lons_cmip, lats, lons)\n",
+ " \n",
+ " # 3) save data to npy file\n",
+ " np.save(ipath + model + '_u850_' + run + '.npy', u850_int)\n",
+ " \n",
+ " del lats_cmip, lons_cmip, u850_cmip, u850_int\n",
+ " del r, run\n",
+ "del model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Interpolate data from atmosphere models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "bcc-csm1-1\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CanAM4\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CCSM4\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "CNRM-CM5\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "HadGEM2-A\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "IPSL-CM5A-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "IPSL-CM5B-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MIROC5\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MPI-ESM-LR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MPI-ESM-MR\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n",
+ "MRI-CGCM3\n",
+ " amip\n",
+ " amip4K\n",
+ " amipFuture\n"
+ ]
+ }
+ ],
+ "source": [
+ "ipath = '../../cmip5/'\n",
+ "for model in models_amip:\n",
+ " print(model)\n",
+ " print(' amip')\n",
+ " # amip\n",
+ " ifile_amip = glob.glob(ipath + 'amip/ua_Amon_' + model + \\\n",
+ " '*197901-200812*.nc')[0]\n",
+ " ncfile = nc.Dataset(ifile_amip, 'r')\n",
+ " lats_amip = ncfile.variables['lat'][:].data\n",
+ " lons_amip = ncfile.variables['lon'][:].data\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ "\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_amip = uwind[:, levind850, :, :]\n",
+ "\n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amip[u850_amip > 100] = np.nan\n",
+ "\n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amip, ncfile\n",
+ "\n",
+ " # interpolate data to new grid\n",
+ " u850_amip_int = fct.interpolate2d(u850_amip, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amip.npy', u850_amip_int)\n",
+ "\n",
+ " del u850_amip, u850_amip_int\n",
+ "\n",
+ " ##########################################################################\n",
+ " # amip4K\n",
+ " print(' amip4K')\n",
+ " ifile_amip4k = glob.glob(ipath + 'amip4K/ua_Amon_' + model + \\\n",
+ " '*197901-200812*.nc')[0] \n",
+ " ncfile = nc.Dataset(ifile_amip4k, 'r')\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " \n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # index of 850 hPa level\n",
+ " u850_amip4k = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amip4k[u850_amip4k > 100] = np.nan\n",
+ " \n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amip4k, ncfile\n",
+ " \n",
+ " # interpolate data to new grid\n",
+ " u850_amip4k_int = fct.interpolate2d(u850_amip4k, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amip4k.npy', u850_amip4k_int)\n",
+ "\n",
+ " del u850_amip4k, u850_amip4k_int\n",
+ " \n",
+ " ##########################################################################\n",
+ " # amipFuture simulations\n",
+ " print(' amipFuture')\n",
+ " ifile_amipfut = glob.glob(ipath + 'amipFuture/ua_Amon_' + model +\\\n",
+ " '*197901-200812*.nc')[0] \n",
+ " ncfile = nc.Dataset(ifile_amipfut, 'r')\n",
+ " levs = ncfile.variables['plev'][:].data\n",
+ " uwind = ncfile.variables['ua'][:].data\n",
+ " ncfile.close()\n",
+ " # get zonal wind at 850 hPa\n",
+ " levind850 = (np.abs(levs-85000)).argmin() # find index of 850 hPa level\n",
+ " u850_amipfut = uwind[:, levind850, :, :]\n",
+ " \n",
+ " # In some models, orography is masked with very high values.\n",
+ " # Set these values to NaN's.\n",
+ " u850_amipfut[u850_amipfut > 100] = np.nan\n",
+ " \n",
+ " del levs, uwind, levind850\n",
+ " del ifile_amipfut, ncfile\n",
+ " \n",
+ " # interpolate data to new grid\n",
+ " u850_amipfut_int = fct.interpolate2d(u850_amipfut, lats_amip,\n",
+ " lons_amip, lats, lons)\n",
+ "\n",
+ " # save data to npy file\n",
+ " np.save(ipath + model + '_u850_amipfut.npy', u850_amipfut_int)\n",
+ "\n",
+ " del u850_amipfut, u850_amipfut_int\n",
+ " \n",
+ " del lats_amip, lons_amip\n",
+ "del model\n",
+ "del ipath"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 bleeding edge (using the module anaconda3/bleeding_edge)",
+ "language": "python",
+ "name": "anaconda3_bleeding"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.6.10"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/runscripts_ICON/exp.nanw0005.run b/runscripts_ICON/exp.nanw0005.run
new file mode 100755
index 0000000..7d1523c
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0005.run
@@ -0,0 +1,762 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0005.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0005/LOG.exp.nanw0005.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0005/LOG.exp.nanw0005.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0005.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0005"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2010-01-01T00:00:00Z" #"2000-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2025-01-01T00:00:00Z" #"2010-01-01T00:00:00Z" #"2000-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .false.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+!&fixvar_nml
+! lfixvar_record = .true.
+!/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','t_seasfc'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2026}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0006.run b/runscripts_ICON/exp.nanw0006.run
new file mode 100755
index 0000000..a9e17b9
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0006.run
@@ -0,0 +1,762 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0006.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0006/LOG.exp.nanw0006.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0006/LOG.exp.nanw0006.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0006.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0006"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2010-01-01T00:00:00Z" #"2000-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2025-01-01T00:00:00Z" #"2010-01-01T00:00:00Z" #"2000-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .false.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+!&fixvar_nml
+! lfixvar_record = .true.
+!/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','t_seasfc'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2026}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0007.run b/runscripts_ICON/exp.nanw0007.run
new file mode 100755
index 0000000..05410c8
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0007.run
@@ -0,0 +1,778 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0007.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0007/LOG.exp.nanw0007.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0007/LOG.exp.nanw0007.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0007.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0007"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="1999-02-01T00:00:00Z" #"2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0008.run b/runscripts_ICON/exp.nanw0008.run
new file mode 100755
index 0000000..6363a42
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0008.run
@@ -0,0 +1,778 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0008.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0008/LOG.exp.nanw0008.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0008/LOG.exp.nanw0008.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0008.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0008"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = 1
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0009.run b/runscripts_ICON/exp.nanw0009.run
new file mode 100755
index 0000000..4cf1ca0
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0009.run
@@ -0,0 +1,778 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0009.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0009/LOG.exp.nanw0009.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0009/LOG.exp.nanw0009.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0009.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0009"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = 1
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0010.run b/runscripts_ICON/exp.nanw0010.run
new file mode 100755
index 0000000..2089d51
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0010.run
@@ -0,0 +1,778 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0010.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0010/LOG.exp.nanw0010.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0010/LOG.exp.nanw0010.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0010.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0010"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0015.run b/runscripts_ICON/exp.nanw0015.run
new file mode 100755
index 0000000..efbc877
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0015.run
@@ -0,0 +1,781 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0015.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0015/LOG.exp.nanw0015.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0015/LOG.exp.nanw0015.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=00:30:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0015.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0015"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1988-01-01T00:00:00Z" #"2008-01-01T00:00:00Z" #"2007-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="1988-02-01T00:00:00Z" #"2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_q_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 12
+ fixvar_apply_q_yoffset = 13
+
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0016.run b/runscripts_ICON/exp.nanw0016.run
new file mode 100755
index 0000000..8e1a6b1
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0016.run
@@ -0,0 +1,792 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0016.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0016/LOG.exp.nanw0016.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0016/LOG.exp.nanw0016.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:30:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0016.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0016"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_q_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = -6
+ fixvar_apply_q_yoffset = -5
+
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0017.run b/runscripts_ICON/exp.nanw0017.run
new file mode 100755
index 0000000..7b3e37c
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0017.run
@@ -0,0 +1,791 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0017.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0017/LOG.exp.nanw0017.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0017/LOG.exp.nanw0017.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:30:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0017.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0017"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_q_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = -6
+ fixvar_apply_q_yoffset = -5
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0018.run b/runscripts_ICON/exp.nanw0018.run
new file mode 100755
index 0000000..0b5950b
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0018.run
@@ -0,0 +1,781 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0018.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0018/LOG.exp.nanw0018.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0018/LOG.exp.nanw0018.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=03:30:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0018.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0018"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2009-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.0250001
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_q_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = -6
+ fixvar_apply_q_yoffset = -5
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0019.run b/runscripts_ICON/exp.nanw0019.run
new file mode 100755
index 0000000..26500b8
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0019.run
@@ -0,0 +1,780 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0019.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0019/LOG.exp.nanw0019.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0019/LOG.exp.nanw0019.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0019.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0019"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_q_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = -6
+ fixvar_apply_q_yoffset = -5
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0020.run b/runscripts_ICON/exp.nanw0020.run
new file mode 100755
index 0000000..df90803
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0020.run
@@ -0,0 +1,791 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0020.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0020/LOG.exp.nanw0020.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0020/LOG.exp.nanw0020.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0020.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0020"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_q_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = -6 !3 !12 !21
+ fixvar_apply_q_yoffset = -5 !4 !13 !22
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0021.run b/runscripts_ICON/exp.nanw0021.run
new file mode 100755
index 0000000..62a4dbd
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0021.run
@@ -0,0 +1,791 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0021.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0021/LOG.exp.nanw0021.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0021/LOG.exp.nanw0021.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0021.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0021"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2007-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"2003-01-01T00:00:00Z" #"1998-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_q_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = -6 !3 !12 !21
+ fixvar_apply_q_yoffset = -5 !4 !13 !22
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0022.run b/runscripts_ICON/exp.nanw0022.run
new file mode 100755
index 0000000..078d844
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0022.run
@@ -0,0 +1,781 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0022.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0022/LOG.exp.nanw0022.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0022/LOG.exp.nanw0022.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0022.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0022"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #""1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z"
+end_date="2010-01-01T00:00:00Z" #"2006-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1988-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .true.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0006'
+ fixvar_apply_q_expid = 'nanw0006'
+ fixvar_apply_c_yoffset = -6 !3 !12 !21
+ fixvar_apply_q_yoffset = -5 !4 !13 !22
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+ p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0006_201001.nc
+
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0023.run b/runscripts_ICON/exp.nanw0023.run
new file mode 100755
index 0000000..4334cda
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0023.run
@@ -0,0 +1,844 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0023.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0023/LOG.exp.nanw0023.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0023/LOG.exp.nanw0023.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=05:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0023.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0023"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/work/bm0834/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc"
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2009-01-01T00:00:00Z"
+end_date="2014-01-01T00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/work/bm0834/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+ lprp = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = -9
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+&prp_nml
+ lprp_c = .true.
+ prp_c_expid = 'nanw0006'
+ prp_c_yoffset = -9
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT1H" !"${output_interval}"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+! output_filename = '${EXPNAME}-2d_ll' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr'
+! remap = 1
+! reg_lon_def = ${reg_lon_def_reg}
+! reg_lat_def = ${reg_lat_def_reg}
+!/
+!&output_nml
+! filetype = 4
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "${output_interval}"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+! include_last = .false.
+! output_filename = '${EXPNAME}-3d_ll' ! file name base
+! filename_format = "<output_filename>_PL_<datetime2>"
+! pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv'!,'lwflxall','lwflxclr','swflxall','swflxclr'
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+! remap = 1
+! reg_lon_def = ${reg_lon_def_reg}
+! reg_lat_def = ${reg_lat_def_reg}
+!/
+! Output for PRP
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll_prp' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'dR_c_trad_toa','dR_c_srad_toa','dR_c_trad_sfc','dR_c_srad_sfc'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_prp' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'dR_c_trad','dR_c_srad','dQ_c_trad','dQ_c_srad'
+ p_levels = 50,100,200,300,500,700,1000,1500,2100,2800,3500,4500,6000,7500,9000,11000,13000,15500,18000,20000,23000,25000,30000,37000,45000,50000,55000,60000,68000,75000,82000,85000,87000,90000,92000,95000,97000,98000,99000,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_prp' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres','dR_c_trad','dR_c_srad','dQ_c_trad','dQ_c_srad'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2016}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0005/nanw0005-fixvarfields_ML_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+# link prp input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}0${mon}01T000000Z.nc prp_nanw0006_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_${year}${mon}01T000000Z.nc prp_nanw0006_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0006/nanw0006-fixvarfields_ML_20100101T000000Z.nc prp_nanw0006_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0031.run b/runscripts_ICON/exp.nanw0031.run
new file mode 100755
index 0000000..bb4645d
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0031.run
@@ -0,0 +1,796 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0031.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0031/LOG.exp.nanw0031.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0031/LOG.exp.nanw0031.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0031.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0031"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1985-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0031_fixvar_input/fixvarfields_TROP4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0031_fixvar_input/fixvarfields_TROP4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0031_fixvar_input/fixvarfields_TROP4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0032.run b/runscripts_ICON/exp.nanw0032.run
new file mode 100755
index 0000000..db22277
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0032.run
@@ -0,0 +1,796 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0032.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0032/LOG.exp.nanw0032.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0032/LOG.exp.nanw0032.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0032.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0032"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1988-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+# use Aiko'S icon bnary for the time being
+ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0032_fixvar_input/fixvarfields_TROPCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0032_fixvar_input/fixvarfields_TROPCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0032_fixvar_input/fixvarfields_TROPCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0033.run b/runscripts_ICON/exp.nanw0033.run
new file mode 100755
index 0000000..2788ca5
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0033.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0033.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0033/LOG.exp.nanw0033.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0033/LOG.exp.nanw0033.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0033.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0033"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2001-07-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2001-12-01T00:00:00Z" #"2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0033_fixvar_input/fixvarfields_TROP4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0033_fixvar_input/fixvarfields_TROP4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0033_fixvar_input/fixvarfields_TROP4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0034.run b/runscripts_ICON/exp.nanw0034.run
new file mode 100755
index 0000000..96eb445
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0034.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0034.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0034/LOG.exp.nanw0034.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0034/LOG.exp.nanw0034.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0034.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0034"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0034_fixvar_input/fixvarfields_TROPCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0034_fixvar_input/fixvarfields_TROPCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0034_fixvar_input/fixvarfields_TROPCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0044.run b/runscripts_ICON/exp.nanw0044.run
new file mode 100755
index 0000000..6e41458
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0044.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0044.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0044/LOG.exp.nanw0044.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0044/LOG.exp.nanw0044.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0044.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0044"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0045.run b/runscripts_ICON/exp.nanw0045.run
new file mode 100755
index 0000000..7a776ba
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0045.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0045.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0045/LOG.exp.nanw0045.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0045/LOG.exp.nanw0045.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0045.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0045"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1998-01-01T00:00:00Z" #"1997-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1998-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0046.run b/runscripts_ICON/exp.nanw0046.run
new file mode 100755
index 0000000..75a61d9
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0046.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0046.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0046/LOG.exp.nanw0046.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0046/LOG.exp.nanw0046.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0046.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0046"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0044_fixvar_input/fixvarfields_ML4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0047.run b/runscripts_ICON/exp.nanw0047.run
new file mode 100755
index 0000000..09878fe
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0047.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0047.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0047/LOG.exp.nanw0047.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0047/LOG.exp.nanw0047.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0047.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0047"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1996-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0045_fixvar_input/fixvarfields_MLCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0048.run b/runscripts_ICON/exp.nanw0048.run
new file mode 100755
index 0000000..7159ad0
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0048.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0048.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0048/LOG.exp.nanw0048.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0048/LOG.exp.nanw0048.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0048.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0048"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2005-01-01T00:00:00Z" #"2004-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"2005-01-01T00:00:00Z" #"2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0049.run b/runscripts_ICON/exp.nanw0049.run
new file mode 100755
index 0000000..d0c2830
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0049.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0049.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0049/LOG.exp.nanw0049.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0049/LOG.exp.nanw0049.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0049.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0049"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0050.run b/runscripts_ICON/exp.nanw0050.run
new file mode 100755
index 0000000..1d4b842
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0050.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0050.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0050/LOG.exp.nanw0050.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0050/LOG.exp.nanw0050.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0050.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0050"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1998-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0048_fixvar_input/fixvarfields_PO4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0051.run b/runscripts_ICON/exp.nanw0051.run
new file mode 100755
index 0000000..a50078b
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0051.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0051.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0051/LOG.exp.nanw0051.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0051/LOG.exp.nanw0051.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0051.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0051"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0049_fixvar_input/fixvarfields_POCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0052.run b/runscripts_ICON/exp.nanw0052.run
new file mode 100755
index 0000000..9ae9880
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0052.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0052.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0052/LOG.exp.nanw0052.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0052/LOG.exp.nanw0052.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0052.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0052"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0053.run b/runscripts_ICON/exp.nanw0053.run
new file mode 100755
index 0000000..da3ffcc
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0053.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0053.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0053/LOG.exp.nanw0053.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0053/LOG.exp.nanw0053.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0053.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0053"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2006-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0054.run b/runscripts_ICON/exp.nanw0054.run
new file mode 100755
index 0000000..c78b1dd
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0054.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0054.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0054/LOG.exp.nanw0054.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0054/LOG.exp.nanw0054.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0054.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0054"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0052_fixvar_input/fixvarfields_NAext4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0055.run b/runscripts_ICON/exp.nanw0055.run
new file mode 100755
index 0000000..99fbe57
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0055.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0055.run
+#SBATCH --partition=compute,compute2
+#SBATCH --workdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0055/LOG.exp.nanw0055.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0055/LOG.exp.nanw0055.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0055.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0055"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0053_fixvar_input/fixvarfields_NAextCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0056.run b/runscripts_ICON/exp.nanw0056.run
new file mode 100755
index 0000000..52f4190
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0056.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0056.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0056/LOG.exp.nanw0056.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0056/LOG.exp.nanw0056.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0056.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0056"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0057.run b/runscripts_ICON/exp.nanw0057.run
new file mode 100755
index 0000000..4db0f67
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0057.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0057.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0057/LOG.exp.nanw0057.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0057/LOG.exp.nanw0057.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0057.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0057"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0058.run b/runscripts_ICON/exp.nanw0058.run
new file mode 100755
index 0000000..aa290d3
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0058.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0058.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0058/LOG.exp.nanw0058.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0058/LOG.exp.nanw0058.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0058.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0058"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0056_fixvar_input/fixvarfields_TAAF4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0059.run b/runscripts_ICON/exp.nanw0059.run
new file mode 100755
index 0000000..f442943
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0059.run
@@ -0,0 +1,798 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0059.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0059/LOG.exp.nanw0059.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0059/LOG.exp.nanw0059.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0059.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0059"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1992-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0057_fixvar_input/fixvarfields_TAAFCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0060.run b/runscripts_ICON/exp.nanw0060.run
new file mode 100755
index 0000000..f0cdef2
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0060.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0060.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0060/LOG.exp.nanw0060.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0060/LOG.exp.nanw0060.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0060.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0060"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0061.run b/runscripts_ICON/exp.nanw0061.run
new file mode 100755
index 0000000..e374cec
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0061.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0061.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0061/LOG.exp.nanw0061.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0061/LOG.exp.nanw0061.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0061.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0061"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0062.run b/runscripts_ICON/exp.nanw0062.run
new file mode 100755
index 0000000..d15004c
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0062.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0062.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0062/LOG.exp.nanw0062.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0062/LOG.exp.nanw0062.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0062.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0062"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0060_fixvar_input/fixvarfields_IO4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0063.run b/runscripts_ICON/exp.nanw0063.run
new file mode 100755
index 0000000..ad3d91f
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0063.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0063.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0063/LOG.exp.nanw0063.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0063/LOG.exp.nanw0063.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0063.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0063"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0061_fixvar_input/fixvarfields_IOCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0064.run b/runscripts_ICON/exp.nanw0064.run
new file mode 100755
index 0000000..0788a2f
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0064.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0064.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0064/LOG.exp.nanw0064.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0064/LOG.exp.nanw0064.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0064.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0064"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="2007-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0065.run b/runscripts_ICON/exp.nanw0065.run
new file mode 100755
index 0000000..6f824f2
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0065.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0065.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0065/LOG.exp.nanw0065.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0065/LOG.exp.nanw0065.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0065.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0065"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0066.run b/runscripts_ICON/exp.nanw0066.run
new file mode 100755
index 0000000..a820728
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0066.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0066.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0066/LOG.exp.nanw0066.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0066/LOG.exp.nanw0066.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0066.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0066"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0064_fixvar_input/fixvarfields_WP4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0067.run b/runscripts_ICON/exp.nanw0067.run
new file mode 100755
index 0000000..4929f1a
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0067.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0067.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0067/LOG.exp.nanw0067.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0067/LOG.exp.nanw0067.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0067.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0067"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.SST4K.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0065_fixvar_input/fixvarfields_WPCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0068.run b/runscripts_ICON/exp.nanw0068.run
new file mode 100755
index 0000000..47b3417
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0068.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0068.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0068/LOG.exp.nanw0068.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0068/LOG.exp.nanw0068.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0068.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0068"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0068_fixvar_input/fixvarfields_EP4K_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0068_fixvar_input/fixvarfields_EP4K_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0068_fixvar_input/fixvarfields_EP4K_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0069.run b/runscripts_ICON/exp.nanw0069.run
new file mode 100755
index 0000000..40d8221
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0069.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0069.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0069/LOG.exp.nanw0069.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0069/LOG.exp.nanw0069.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0069.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0069"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1993-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&lnd_nml
+ ntiles = 3 !!! operational since March 2015
+ nlev_snow = 3 !!! effective only if lmulti_snow = .true.
+ lmulti_snow = .false. !!! for the time being, until numerical stability issues and coupling with snow analysis are solved
+ itype_heatcond = 2
+ idiag_snowfrac = 20 !! ** operational since 1.12.15 **
+ lsnowtile = .true. !! ** operational since 1.12.15 **
+ lseaice = .true.
+ llake = .true.
+ frlake_thrhld = 0.5
+ itype_lndtbl = 3 ! minimizes moist/cold bias in lower tropical troposphere
+ itype_root = 2
+ sstice_mode = 4 ! Jennifer
+ sst_td_filename = "SST_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+ ci_td_filename = "CI_<year>_<month>_iconR2B04-grid.nc" ! Jennifer
+/
+&radiation_nml
+ irad_o3 = 7
+ irad_aero = 6
+ albedo_type = 2 ! Modis albedo
+ vmr_co2 = 390.e-06 ! values representative for 2012
+ vmr_ch4 = 1800.e-09
+ vmr_n2o = 322.0e-09
+ vmr_o2 = 0.20946
+ vmr_cfc11 = 240.e-12
+ vmr_cfc12 = 532.e-12
+/
+&nonhydrostatic_nml
+ iadv_rhotheta = 2
+ ivctype = 2
+ itime_scheme = 4
+ exner_expol = 0.333
+ vwind_offctr = 0.2
+ damp_height = 44000.
+ rayleigh_coeff = 1.0 ! R3B7: 1.0 for forecasts, 5.0 in assimilation cycle; 0.5/2.5 for R2B6 (i.e. ensemble) runs
+ lhdiff_rcf = .true.
+ divdamp_order = 24 ! setting for forecast runs; use '2' for assimilation cycle
+ divdamp_type = 32 ! ** new setting for assimilation and forecast runs **
+ divdamp_fac = 0.004 ! use 0.032 in conjunction with divdamp_order = 2 in assimilation cycle
+ divdamp_trans_start = 12500. ! use 2500. in assimilation cycle
+ divdamp_trans_end = 17500. ! use 5000. in assimilation cycle
+ l_open_ubc = .false.
+ igradp_method = 3
+ l_zdiffu_t = .true.
+ thslp_zdiffu = 0.02
+ thhgtd_zdiffu = 125.
+ htop_moist_proc= 22500.
+ hbot_qvsubstep = 19000. ! use 22500. with R2B6
+/
+&sleve_nml
+ min_lay_thckn = 20.
+ max_lay_thckn = 400. ! maximum layer thickness below htop_thcknlimit
+ htop_thcknlimit = 14000. ! this implies that the upcoming COSMO-EU nest will have 60 levels
+ top_height = 75000.
+ stretch_fac = 0.9
+ decay_scale_1 = 4000.
+ decay_scale_2 = 2500.
+ decay_exp = 1.2
+ flat_height = 16000.
+/
+&dynamics_nml
+ iequations = 3
+ idiv_method = 1
+ divavg_cntrwgt = 0.50
+ lcoriolis = .TRUE.
+/
+&transport_nml
+ ivadv_tracer = 3,3,3,3,3
+ itype_hlimit = 3,4,4,4,4
+ ihadv_tracer = 52,2,2,2,2
+ iadv_tke = 0
+/
+&diffusion_nml
+ hdiff_order = 5
+ itype_vn_diffu = 1
+ itype_t_diffu = 2
+ hdiff_efdt_ratio = 24.0
+ hdiff_smag_fac = 0.025
+ lhdiff_vn = .TRUE.
+ lhdiff_temp = .TRUE.
+/
+&interpol_nml
+ nudge_zone_width = 8
+ lsq_high_ord = 3
+ l_intp_c2l = .true.
+ l_mono_c2l = .true.
+ support_baryctr_intp = .false.
+/
+&extpar_nml
+ itopo = 1
+ n_iter_smooth_topo = 1 !
+ hgtdiff_max_smooth_topo = 0. ! ** should be changed to 750.,750 with next Extpar update! **
+/
+&io_nml
+ itype_pres_msl = 5 ! New extrapolation method to circumvent Ninjo problem with surface inversions
+ itype_rh = 1 ! RH w.r.t. water
+/
+&fixvar_nml
+ lfixvar_record = .false. !.true.
+ lfixvar_apply = .true.
+ lfixvar_apply_q = .false.
+ lfixvar_apply_c = .true.
+ lfixvar_apply_xcdnc = .false.
+ fixvar_apply_c_expid = 'nanw0005'
+ fixvar_apply_c_yoffset = 1 !11 !21
+ fixvar_read_dt = 2160.0 ! 36 min
+/
+!&output_nml
+! filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+! output_start = "${start_date}"
+! output_end = "${end_date}"
+! output_interval = "PT36M"
+! file_interval = "${file_interval}"
+! include_last = .false.
+! output_filename = '${EXPNAME}-fixvarfields' ! file name base
+! filename_format = "<output_filename>_ML_<datetime2>"
+! ml_varlist = 'xtom','xqm_vap','xqm_liq','xqm_ice','xcld_frc'!,'xcdnc'
+! remap = 0
+!/
+! OUTPUT: Regular grid, model levels, all domains
+&output_nml
+ filetype = 4 ! output format: 2=GRIB2, 4=NETCDFv2
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ output_filename = '${EXPNAME}-2d_ll' ! file name base
+ filename_format = "<output_filename>_ML_<datetime2>"
+ ml_varlist = 'pres_msl','pres_sfc','t_2m','t_g','tot_prec','tqv','tqc','tqi','clct','clcl','clcm','clch','shfl_s','lhfl_s','qhfl_s','sod_t','sob_t','sou_s','sob_s','thb_t','thu_s','thb_s','swsfcclr','swtoaclr','lwsfcclr','lwtoaclr','tqv_dia','tqc_dia','tqi_dia'
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'u','v','w','temp','rh','qv','qc','qi','clc','geopot','pv','tot_qv_dia','tot_qc_dia','tot_qi_dia'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+! p_levels = 1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,85000,92500,100000
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+&output_nml
+ filetype = 4
+ output_start = "${start_date}"
+ output_end = "${end_date}"
+ output_interval = "PT1H" !"${output_interval}"
+ file_interval = "${file_interval}"
+ include_last = .false.
+ mode = 2 ! 1: forecast mode (relative t-axis), 2: climate mode (absolute t-axis)
+ include_last = .false.
+ output_filename = '${EXPNAME}-3d_ll_heatingrates' ! file name base
+ filename_format = "<output_filename>_PL_<datetime2>"
+ pl_varlist = 'lwflxall','lwflxclr','swflxall','swflxclr','ddt_temp_radsw','ddt_temp_radlw','ddt_temp_radswcs','ddt_temp_radlwcs'
+ p_levels = 100000,99000,98000,97000,95000,92500,90000,87000,85000,82000,75000,70000,68000,60000,53000,50000,45000,40000,37000,30000,25000,20000,18000,15000,13000,11000,10000,9000,7500,7000,6000,5000,4500,3500,3000,2800,2100,2000,1500,1000,700,500,300,200,100,50
+ remap = 1
+ reg_lon_def = ${reg_lon_def_reg}
+ reg_lat_def = ${reg_lat_def_reg}
+/
+EOF
+
+#!/bin/ksh
+#=============================================================================
+#
+# This section of the run script prepares and starts the model integration.
+#
+# bindir and START must be defined as environment variables or
+# they must be substituted with appropriate values.
+#
+# Marco Giorgetta, MPI-M, 2010-04-21
+#
+#-----------------------------------------------------------------------------
+#
+# directories definition
+# this part is shifted up
+#
+#-----------------------------------------------------------------------------
+# set up the model lists if they do not exist
+# this works for subngle model runs
+# for coupled runs the lists should be declared explicilty
+if [ x$namelist_list = x ]; then
+# minrank_list=( 0 )
+# maxrank_list=( 65535 )
+# incrank_list=( 1 )
+ minrank_list[0]=0
+ maxrank_list[0]=65535
+ incrank_list[0]=1
+ if [ x$atmo_namelist != x ]; then
+ # this is the atmo model
+ namelist_list[0]="$atmo_namelist"
+ modelname_list[0]="atmo"
+ modeltype_list[0]=1
+ run_atmo="true"
+ elif [ x$ocean_namelist != x ]; then
+ # this is the ocean model
+ namelist_list[0]="$ocean_namelist"
+ modelname_list[0]="ocean"
+ modeltype_list[0]=2
+ elif [ x$testbed_namelist != x ]; then
+ # this is the testbed model
+ namelist_list[0]="$testbed_namelist"
+ modelname_list[0]="testbed"
+ modeltype_list[0]=99
+ else
+ check_error 1 "No namelist is defined"
+ fi
+fi
+
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# set some default values and derive some run parameteres
+restart=${restart:=".false."}
+restartSemaphoreFilename='isRestartRun.sem'
+#AUTOMATIC_RESTART_SETUP:
+if [ -f ${restartSemaphoreFilename} ]; then
+ restart=.true.
+ # do not delete switch-file, to enable restart after unintended abort
+ #[[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+#END AUTOMATIC_RESTART_SETUP
+#
+# wait 5min to let GPFS finish the write operations
+if [ "x$restart" != 'x.false.' -a "x$submit" != 'x' ]; then
+ if [ x$(df -T ${EXPDIR} | cut -d ' ' -f 2) = gpfs ]; then
+ sleep 10;
+ fi
+fi
+# fill some checks
+
+run_atmo=${run_atmo="false"}
+if [ x$atmo_namelist != x ]; then
+ run_atmo="true"
+fi
+run_jsbach=${run_jsbach="false"}
+run_ocean=${run_ocean="false"}
+
+#-----------------------------------------------------------------------------
+
+# link grid, extpar, init and rrtm files
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/grids/icon_grid_0010_R02B04_G.nc iconR2B04_DOM01.nc
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/icon_extpar_0010_R02B04_G.nc extpar_iconR2B04_DOM01.nc
+
+ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/ifs2icon_R2B04_DOM01.nc ifs2icon_R2B04_DOM01.nc
+
+for year in {1970..2010}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc SST_${year}_0${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon0${mon}.ymonmean.remapdis.nc CI_${year}_0${mon}_iconR2B04-grid.nc
+done
+for mon in 10 11 12; do
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sst_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc SST_${year}_${mon}_iconR2B04-grid.nc
+ ln -sf /work/bb1018/b380490/inputdata/icon-2.1.00/realistic/bc_sic_1979_2008_icongrid_0010_R02B04_mon${mon}.ymonmean.remapdis.nc CI_${year}_${mon}_iconR2B04-grid.nc
+done
+done
+
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_lw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/rrtmg_sw.nc ./
+ln -sf /work/bb1018/b380490/icon-2.1.00/data/ECHAM6_CldOptProps.nc ./
+
+# link fixvar input fields
+for year in {2000..2009}; do
+for mon in 1 2 3 4 5 6 7 8 9; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0069_fixvar_input/fixvarfields_EPCTL_${year}0${mon}01T000000Z.nc fixvar_nanw0005_${year}0${mon}.nc
+done
+for mon in 10 11 12; do
+ ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0069_fixvar_input/fixvarfields_EPCTL_${year}${mon}01T000000Z.nc fixvar_nanw0005_${year}${mon}.nc
+done
+done
+ln -sf /scratch/b/b380490/experiments/icon-2.1.00/nanw0069_fixvar_input/fixvarfields_EPCTL_20100101T000000Z.nc fixvar_nanw0005_201001.nc
+
+#-----------------------------------------------------------------------------
+# print_required_files
+copy_required_files
+link_required_files
+
+
+#-----------------------------------------------------------------------------
+# get restart files
+
+if [ x$restart_atmo_from != "x" ] ; then
+ rm -f restart_atm_DOM01.nc
+# ln -s ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} ${EXPDIR}/restart_atm_DOM01.nc
+ cp ${ICONDIR}/experiments/${restart_from_folder}/${restart_atmo_from} cp_restart_atm.nc
+ ln -s cp_restart_atm.nc restart_atm_DOM01.nc
+ restart=".true."
+fi
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+#
+# create ICON master namelist
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+#-----------------------------------------------------------------------------
+# create master_namelist
+master_namelist=icon_master.namelist
+if [ x$end_date = x ]; then
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&time_nml
+ ini_datetime_string = "$start_date"
+ dt_restart = $dt_restart
+ is_relative_time = .false.
+/
+EOF
+else
+if [ x$calendar = x ]; then
+ calendar='proleptic gregorian'
+ calendar_type=1
+else
+ calendar=$calendar
+ calendar_type=$calendar_type
+fi
+cat > $master_namelist << EOF
+&master_nml
+ lrestart = $restart
+/
+&master_time_control_nml
+ calendar = "$calendar"
+ checkpointTimeIntval = "$checkpoint_interval"
+ restartTimeIntval = "$restart_interval"
+ experimentStartDate = "$start_date"
+ experimentStopDate = "$end_date"
+/
+&time_nml
+ is_relative_time = .false.
+/
+EOF
+fi
+#-----------------------------------------------------------------------------
+
+
+#-----------------------------------------------------------------------------
+# add model component to master_namelist
+add_component_to_master_namelist()
+{
+
+ model_namelist_filename="$1"
+ model_name=$2
+ model_type=$3
+ model_min_rank=$4
+ model_max_rank=$5
+ model_inc_rank=$6
+
+cat >> $master_namelist << EOF
+&master_model_nml
+ model_name="$model_name"
+ model_namelist_filename="$model_namelist_filename"
+ model_type=$model_type
+ model_min_rank=$model_min_rank
+ model_max_rank=$model_max_rank
+ model_inc_rank=$model_inc_rank
+/
+EOF
+
+}
+#-----------------------------------------------------------------------------
+
+no_of_models=${#namelist_list[*]}
+echo "no_of_models=$no_of_models"
+
+j=0
+while [ $j -lt ${no_of_models} ]
+do
+ add_component_to_master_namelist "${namelist_list[$j]}" "${modelname_list[$j]}" ${modeltype_list[$j]} ${minrank_list[$j]} ${maxrank_list[$j]} ${incrank_list[$j]}
+ j=`expr ${j} + 1`
+done
+
+#-----------------------------------------------------------------------------
+#
+# get model
+#
+export MODEL=${bindir}/icon
+#
+ls -l ${MODEL}
+check_error $? "${MODEL} does not exist?"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+#
+# start experiment
+#
+
+rm -f finish.status
+date
+${START} ${MODEL}
+date
+if [ -r finish.status ] ; then
+ check_error 0 "${START} ${MODEL}"
+else
+ check_error -1 "${START} ${MODEL}"
+fi
+#
+#-----------------------------------------------------------------------------
+#
+finish_status=`cat finish.status`
+echo $finish_status
+echo "============================"
+echo "Script run successfully: $finish_status"
+echo "============================"
+#-----------------------------------------------------------------------------
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+# Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ] ; then
+ echo "restart next experiment..."
+ this_script="${RUNSCRIPTDIR}/${job_name}"
+ echo 'this_script: ' "$this_script"
+ touch ${restartSemaphoreFilename}
+ cd ${RUNSCRIPTDIR}
+ ${submit} $this_script
+else
+ [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+# automatic call/submission of post processing if available
+if [ "x${autoPostProcessing}" = "xtrue" ]; then
+ # check if there is a postprocessing is available
+ cd ${RUNSCRIPTDIR}
+ targetPostProcessingScript="./post.${EXPNAME}.run"
+ [[ -x $targetPostProcessingScript ]] && ${submit} ${targetPostProcessingScript}
+ cd -
+fi
+
+#-----------------------------------------------------------------------------
+# check if we test the restart mechanism
+get_last_1_restart()
+{
+ model_restart_param=$1
+ restart_list=$(ls *restart_*${model_restart_param}*_*T*Z.nc)
+
+ last_restart=""
+ last_1_restart=""
+ for restart_file in $restart_list
+ do
+ last_1_restart=$last_restart
+ last_restart=$restart_file
+
+ echo $restart_file $last_restart $last_1_restart
+ done
+}
+
+
+restart_atmo_from=""
+restart_ocean_from=""
+if [ x$test_restart = "xtrue" ] ; then
+ # follows a restart run in the same script
+ # set up the restart parameters
+ restart_from_folder=${EXPNAME}
+ # get the previous from last rstart file for atmo
+ get_last_1_restart "atm"
+ if [ x$last_1_restart != x ] ; then
+ restart_atmo_from=$last_1_restart
+ fi
+ get_last_1_restart "oce"
+ if [ x$last_1_restart != x ] ; then
+ restart_ocean_from=$last_1_restart
+ fi
+
+ EXPNAME=${EXPNAME}_restart
+ test_restart="false"
+fi
+
+#-----------------------------------------------------------------------------
+
+cd $RUNSCRIPTDIR
+
+#-----------------------------------------------------------------------------
+
+
+# exit 0
+#
+# vim:ft=sh
+#-----------------------------------------------------------------------------
diff --git a/runscripts_ICON/exp.nanw0070.run b/runscripts_ICON/exp.nanw0070.run
new file mode 100755
index 0000000..2255c2b
--- /dev/null
+++ b/runscripts_ICON/exp.nanw0070.run
@@ -0,0 +1,800 @@
+#!/bin/ksh
+#=============================================================================
+# =====================================
+# mistral batch job parameters
+#-----------------------------------------------------------------------------
+#SBATCH --account=bb1018
+#SBATCH --job-name=exp.nanw0070.run
+#SBATCH --partition=compute,compute2
+#SBATCH --chdir=/work/bb1018/b380490/icon-2.1.00/run
+#SBATCH --nodes=8
+#SBATCH --threads-per-core=2
+#SBATCH --output=/pf/b/b380490/RUN/icon-2.1.00/nanw0070/LOG.exp.nanw0070.run.%j.o
+#SBATCH --error=/pf/b/b380490/RUN/icon-2.1.00/nanw0070/LOG.exp.nanw0070.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=04:00:00
+#SBATCH --mail-user=b380490
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+#=============================================================================
+#
+# ICON run script. Created by ./config/make_target_runscript
+# target machine is bullx
+# target use_compiler is intel
+# with mpi=yes
+# with openmp=yes
+# memory_model=large
+# submit with sbatch -N ${SLURM_JOB_NUM_NODES:-1}
+#
+#=============================================================================
+set -x
+. /work/bb1018/b380490/icon-2.1.00/run/add_run_routines
+#-----------------------------------------------------------------------------
+# target parameters
+# ----------------------------
+site="dkrz.de"
+target="bullx"
+compiler="intel"
+loadmodule="intel/16.0 ncl/6.2.1-gccsys cdo/default svn/1.8.13 fca/2.5.2431 mxm/3.4.3082 bullxmpi_mlx/bullxmpi_mlx-1.2.9.2"
+with_mpi="yes"
+with_openmp="yes"
+job_name="exp.nanw0070.run"
+submit="sbatch -N ${SLURM_JOB_NUM_NODES:-1}"
+#-----------------------------------------------------------------------------
+# openmp environment variables
+# ----------------------------
+export OMP_NUM_THREADS=4
+export ICON_THREADS=4
+export OMP_SCHEDULE=dynamic,1
+export OMP_DYNAMIC="false"
+export OMP_STACKSIZE=200M
+#-----------------------------------------------------------------------------
+# MPI variables
+# ----------------------------
+mpi_root=/opt/mpi/bullxmpi_mlx/1.2.9.2
+no_of_nodes=${SLURM_JOB_NUM_NODES:=1}
+mpi_procs_pernode=$((${SLURM_JOB_CPUS_PER_NODE%%\(*} / 2 / OMP_NUM_THREADS))
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+START="srun --cpu-freq=HighM1 --kill-on-bad-exit=1 --nodes=${SLURM_JOB_NUM_NODES:-1} --cpu_bind=verbose,cores --distribution=block:block --ntasks=$((no_of_nodes * mpi_procs_pernode)) --ntasks-per-node=${mpi_procs_pernode} --cpus-per-task=$((2 * OMP_NUM_THREADS)) --propagate=STACK"
+#-----------------------------------------------------------------------------
+# load ../setting if exists
+if [ -a ../setting ]
+then
+ echo "Load Setting"
+ . ../setting
+fi
+#-----------------------------------------------------------------------------
+export EXPNAME="nanw0070"
+basedir="/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME}"
+#bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/build/x86_64-unknown-linux-gnu/bin" # binaries
+bindir="/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/build/x86_64-unknown-linux-gnu/bin" # binaries
+# use Aiko'S icon binary for the time being
+#bindir="/pf/b/b380459/icon-hdcp2s6-2.1.00/build/x86_64-unknown-linux-gnu/bin" # binaries
+
+#BUILDDIR=build/x86_64-unknown-linux-gnu
+#-----------------------------------------------------------------------------
+#=============================================================================
+# load profile
+if [ -a /etc/profile ] ; then
+. /etc/profile
+#=============================================================================
+#=============================================================================
+# load modules
+module purge
+module load "$loadmodule"
+module list
+#=============================================================================
+fi
+#=============================================================================
+export LD_LIBRARY_PATH=/sw/rhel6-x64/netcdf/netcdf_c-4.3.2-parallel-bullxmpi-intel14/lib:$LD_LIBRARY_PATH
+#=============================================================================
+nproma=16
+cdo="cdo"
+cdo_diff="cdo diffn"
+icon_data_rootFolder="/pool/data/ICON"
+icon_data_poolFolder="/pool/data/ICON"
+icon_data_buildbotFolder="/pool/data/ICON/buildbot_data"
+icon_data_buildbotFolder_aes="/pool/data/ICON/buildbot_data/aes"
+icon_data_buildbotFolder_oes="/pool/data/ICON/buildbot_data/oes"
+export KMP_AFFINITY="verbose,granularity=core,compact,1,1"
+export KMP_LIBRARY="turnaround"
+export KMP_KMP_SETTINGS="1"
+export OMP_WAIT_POLICY="active"
+export OMPI_MCA_pml="cm"
+export OMPI_MCA_mtl="mxm"
+export OMPI_MCA_coll="^ghc,fca" # Feb 14, 2019
+export MXM_RDMA_PORTS="mlx5_0:1"
+export OMPI_MCA_coll_fca_enable="1"
+export OMPI_MCA_coll_fca_priority="95"
+export MALLOC_TRIM_THRESHOLD_="-1"
+ulimit -s 2097152
+
+# this can not be done in use_mpi_startrun since it depends on the
+# environment at time of script execution
+#case " $loadmodule " in
+# *\ mxm\ *)
+# START+=" --export=$(env | sed '/()=/d;/=/{;s/=.*//;b;};d' | tr '\n' ',')LD_PRELOAD=${LD_PRELOAD+$LD_PRELOAD:}${MXM_HOME}/lib/libmxm.so"
+# ;;
+#esac
+#!/bin/ksh
+#=============================================================================
+#
+# recommended Namelist settings for pre-operational runs (except for output_nml
+# which may be adapted according to personal needs)
+#
+# Date: 2013.10.01 Guenther Zangl, Daniel Reinert
+#
+#=============================================================================
+#=============================================================================
+#
+# This section of the run script containes the specifications of the experiment.
+# The specifications are passed by namelist to the program.
+# For a complete list see Namelist_overview.pdf
+#
+# EXPNAME and NPROMA must be defined in as environment variables or must
+# they must be substituted with appropriate values.
+#
+# DWD, 2010-08-31
+#
+#-----------------------------------------------------------------------------
+#
+# Basic specifications of the simulation
+# --------------------------------------
+#
+# These variables are set in the header section of the completed run script:
+#
+# EXPNAME = experiment name
+# NPROMA = array blocking length / inner loop length
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# The following values must be set here as shell variables so that they can be used
+# also in the executing section of the completed run script
+#-----------------------------------------------------------------------------
+# the namelist filename
+atmo_namelist=NAMELIST_${EXPNAME}
+#
+#-----------------------------------------------------------------------------
+# global timing
+start_date="1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" #"1986-01-01T00:00:00Z" #"1979-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+end_date="2009-01-01T00:00:00Z" #"1999-01-01T00:00:00Z" #"1989-01-01T00:00:00Z" # "mydate_nmlT00:00:00Z"
+
+# restart intervals
+checkpoint_interval="P6M"
+restart_interval="P1Y"
+
+# output intervals
+output_interval="PT6H"
+file_interval="P1M"
+
+# regular grid: nlat=96, nlon=192, npts=18432, dlat=1.875 deg, dlon=1.875 deg
+reg_lat_def_reg=-89.0625,1.875,89.0625
+reg_lon_def_reg=0.,1.875,358.125
+
+#
+#-----------------------------------------------------------------------------
+# model timing
+dtime=720 # 360 sec for R2B6, 120 sec for R3B7
+
+#
+#-----------------------------------------------------------------------------
+# model parameters
+model_equations=3 # equation system
+# 1=hydrost. atm. T
+# 1=hydrost. atm. theta dp
+# 3=non-hydrost. atm.,
+# 0=shallow water model
+# -1=hydrost. ocean
+#-----------------------------------------------------------------------------
+# the grid parameters
+atmo_dyn_grids="iconR2B04_DOM01.nc"
+#-----------------------------------------------------------------------------
+
+# directories definition
+#
+#ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_prp/ # ${ICON_BASE_PATH}
+ICONDIR=/work/bb1018/b380490/icon-hdcp2s6-2.1.00_aiko_20190319/
+# use Aiko'S icon bnary for the time being
+#ICONDIR=/pf/b/b380459/icon-hdcp2s6-2.1.00/ # ${ICON_BASE_PATH}
+RUNSCRIPTDIR=/pf/b/b380490/RUN/icon-2.1.00/${EXPNAME} # ${ICONDIR}/run
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/scratch/b/b380490/experiments/icon-2.1.00/${EXPNAME} # ${ICONDIR}/experiments/${EXPNAME}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ; then
+ mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+check_error $? "${EXPDIR} does not exist?"
+
+cd ${EXPDIR}
+
+#-----------------------------------------------------------------------------
+#
+# write ICON namelist parameters
+# ------------------------
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+#
+# ------------------------
+# reconstrcuct the grid parameters in namelist form
+dynamics_grid_filename=""
+for gridfile in ${atmo_dyn_grids}; do
+ dynamics_grid_filename="${dynamics_grid_filename} '${gridfile}',"
+done
+
+# ------------------------
+
+cat > ${atmo_namelist} << EOF
+¶llel_nml
+ nproma = 8 ! optimal setting 8 for CRAY; use 16 or 24 for IBM
+ p_test_run = .false.
+ l_test_openmp = .false.
+ l_log_checks = .false.
+ num_io_procs = 1 ! asynchronous output for values >= 1
+ itype_comm = 1
+ iorder_sendrecv = 3 ! best value for CRAY (slightly faster than option 1)
+/
+&grid_nml
+ dynamics_grid_filename = ${dynamics_grid_filename} !"iconR2B04_DOM01.nc"
+ radiation_grid_filename = ""
+ dynamics_parent_grid_id = 0
+ lredgrid_phys = .false.
+/
+&initicon_nml
+ init_mode = 2 ! operation mode 2: IFS
+ zpbl1 = 500.
+ zpbl2 = 1000.
+ l_sst_in = .true. ! Jennifer
+/
+&run_nml
+ num_lev = 47 ! 60
+ dtime = ${dtime} ! timestep in seconds
+ ldynamics = .TRUE. ! dynamics
+ ltransport = .true.
+ iforcing = 3 ! NWP forcing
+ ltestcase = .false. ! false: run with real data
+ msg_level = 7 ! print maximum wind speeds every 5 time steps
+ ltimer = .false. ! set .TRUE. for timer output
+ timers_level = 1 ! can be increased up to 10 for detailed timer output
+ output = "nml"
+/
+&nwp_phy_nml
+ inwp_gscp = 1
+ inwp_convection = 1
+ inwp_radiation = 1
+ inwp_cldcover = 1
+ inwp_turb = 1
+ inwp_satad = 1
+ inwp_sso = 1
+ inwp_gwd = 1
+ inwp_surface = 1
+ icapdcycl = 3 ! apply CAPE modification to improve diurnalcycle over tropical land (optimizes NWP scores)
+ latm_above_top = .false. ! the second entry refers to the nested domain (if present)
+ efdt_min_raylfric = 7200.
+ ldetrain_conv_prec = .false. ! ** new for v2.0.15 **; set .true. to activate detrainment of rain and snow; should be used in R3B08 EU-nest only (not for R2B07)!
+ itype_z0 = 2
+ icpl_aero_conv = 1
+ icpl_aero_gscp = 1
+ icpl_o3_tp = 1
+ ! resolution-dependent settings - please choose the appropriate one
+ ! dt_rad = 2160. (R2B6) / 1440. (R3B7) ** should be an integer multiple of dt_conv **
+ ! dt_conv = 720. (R2B6) / 360. (R3B7) / 180. (R3B8 - EU-nest)
+ ! dt_sso = 1440. (R2B6) / 720. (R3B7)
+ ! dt_gwd = 1440. (R2B6) / 720. (R3B7)
+ lfixvar = .true.
+/
+&nwp_tuning_nml
+ tune_zceff_min = 0.075 ! ** resolution-independent since rev. 25646 **
+ itune_albedo = 1 ! somewhat reduced albedo (w.r.t. MODIS data) over Sahara in order to reduce cold bias
+/
+&turbdiff_nml
+ tkhmin = 0.75 ! new default since rev. 16527
+ tkmmin = 0.75 ! "
+ pat_len = 750.
+ c_diff = 0.2
+ rat_sea = 7.5 ! ** new value since for v2.0.15; previously 8.0 **
+ ltkesso = .true.
+ frcsmot = 0.2 ! these 2 switches together apply vertical smoothing of the TKE source terms
+ imode_frcsmot = 2 ! in the tropics (only), which reduces the moist bias in the tropical lower troposphere
+ ! use horizontal shear production terms with 1/SQRT(Ri) scaling to prevent unwanted side effects:
+ itype_sher = 3
+ ltkeshs = .true.
+ a_hshr = 2.0
+ icldm_turb = 1 ! ** new recommendation for v2.0.15 in conjunction with evaporation fix for grid-scale rain **
+/
+&