diff --git a/README.md b/README.md
index 5dfc5aabeac97d894aa3be8937b71a77aef515a9..eaeb88276d7bf09e89708bcb08df5a74b39bc1be 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,2 @@
 # hoerner-voigt-waterbelt-framework-2025
-
-
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://gitlab.phaidra.org/climate/hoerner-voigt-waterbelt-framework-JGRA2025.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.phaidra.org/climate/hoerner-voigt-waterbelt-framework-JGRA2025/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Code repository for the manuscript **Making sense of bifurcation diagrams: a new framework to understand the role of clouds and bare sea ice for waterbelt states**, submitted to JGR Atmospheres in 2025.
\ No newline at end of file
diff --git a/pythonscripts/Fig12_CCF.ipynb b/pythonscripts/Fig12_CCF.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..50d25ddde89ebd1a6b667d48f662d52405eddbad
--- /dev/null
+++ b/pythonscripts/Fig12_CCF.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cf8e5e42-5da9-4c15-9569-609e45d856eb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ICON_tools' from '../../../snowball-waterbelt-continents/python_packages/ICON_tools.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import integrate\n",
+    "import sys, importlib, os\n",
+    "sys.path.append(\"../../../snowball-waterbelt-continents/python_packages\")\n",
+    "import ICON_tools\n",
+    "import pandas as pd\n",
+    "from climlab.utils.thermo import EIS\n",
+    "\n",
+    "importlib.reload(ICON_tools)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2e9d11e1-30aa-47d0-8154-23a8ec716a70",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "### set global fonts for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6658f42a-ea10-432b-ae06-02ea06a5f08f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "BIGGER_SIZE = 14\n",
+    "\n",
+    "plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
+    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
+    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
+    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
+    "plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "91350e6c-ffab-43e6-af35-a57d49b8bda2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "path_WB_ESM = \"/jetfs/scratch/jhoerner/experiments/ape_5500_55_0S\"\n",
+    "path_WB_A = \"/jetfs/scratch/jhoerner/experiments/ape_ia_5500_90_0S\"\n",
+    "colorlist = [\"C2\",\"C4\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fee611ef-aa04-4590-a2a3-16f59f2372c8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### load files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "2ff2d95e-f954-4e30-bec1-72acc304292a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DS_mean_ESM = xr.open_dataset(path_WB_ESM +\"/ape_5500_55_0S_oc_ic_sn.mean.nc\")\n",
+    "DS_mean_A = xr.open_dataset(path_WB_A +\"/ape_ia_5500_90_0S_oc_ic_sn.mean.nc\")\n",
+    "DS_std_ESM = xr.open_dataset(path_WB_ESM +\"/ape_5500_55_0S_oc_ic_sn.std.nc\")\n",
+    "DS_std_A = xr.open_dataset(path_WB_A +\"/ape_ia_5500_90_0S_oc_ic_sn.std.nc\")\n",
+    "DS_min_ESM = xr.open_dataset(path_WB_ESM +\"/ape_5500_55_0S_oc_ic_sn.min.nc\")\n",
+    "DS_min_A = xr.open_dataset(path_WB_A +\"/ape_ia_5500_90_0S_oc_ic_sn.min.nc\")\n",
+    "DS_max_ESM = xr.open_dataset(path_WB_ESM +\"/ape_5500_55_0S_oc_ic_sn.max.nc\")\n",
+    "DS_max_A = xr.open_dataset(path_WB_A +\"/ape_ia_5500_90_0S_oc_ic_sn.max.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42705ca-31fc-4589-8aa7-58c6fc729244",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2741edeb-ae16-42d2-a591-a49962a859ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, sharey=True, figsize=(12, 4))\n",
+    "plt.subplots_adjust(bottom=0.3, left=0.2, top=0.9, right=0.9, wspace=0.1, hspace=0.04)\n",
+    "bar_width = 0.4\n",
+    "\n",
+    "var_ESM = ICON_tools.get_cre(DS_mean_ESM, cretype=\"toa\", radtype=\"net\")\n",
+    "var_A = ICON_tools.get_cre(DS_mean_A, cretype=\"toa\", radtype=\"net\")\n",
+    "\n",
+    "ax[0].barh(var_ESM.oc_ic_sn_category- bar_width/2, var_ESM, height=bar_width, color=\"C4\", label='ICON-ESM')\n",
+    "ax[0].barh(var_A.oc_ic_sn_category + bar_width/2, var_A, height=bar_width, color=\"C2\", label='ICON-A')\n",
+    "ax[0].vlines(0,-1,5,color=\"black\", lw=1)\n",
+    "\n",
+    "\n",
+    "var_ESM = DS_mean_ESM.cllvi.squeeze()\n",
+    "var_A = DS_mean_A.cllvi.squeeze()\n",
+    "var_ESM2 = DS_mean_ESM.clivi.squeeze()\n",
+    "var_A2 = DS_mean_A.clivi.squeeze()\n",
+    "\n",
+    "ax[1].barh(var_ESM.oc_ic_sn_category- bar_width/2, var_ESM+var_ESM2, height=bar_width, color=\"C4\", label='ICON-ESM')\n",
+    "ax[1].barh(var_A.oc_ic_sn_category + bar_width/2, var_A+var_A2, height=bar_width, color=\"C2\", label='ICON-A')\n",
+    "\n",
+    "ax[1].barh(var_ESM.oc_ic_sn_category- bar_width/2, var_ESM, height=bar_width, color=\"C4\", label='ICON-ESM', hatch='///')\n",
+    "ax[1].barh(var_A.oc_ic_sn_category + bar_width/2, var_A, height=bar_width, color=\"C2\", label='ICON-A', hatch='///')\n",
+    "ax[1].vlines(0,-1,5,color=\"black\", lw=1)\n",
+    "\n",
+    "ax[1].annotate(\"liquid\", [0.02,-0.25], color=\"black\", rotation=0, bbox=dict(boxstyle=\"round\", fc=\"C4\", ec=\"C4\", pad=0))\n",
+    "ax[1].annotate(\"solid\", [0.08,-0.25], color=\"black\", rotation=0)\n",
+    "\n",
+    "\n",
+    "ax[0].set_ylim(-0.5, 3.5)\n",
+    "ax[0].set_yticks([])\n",
+    "ax[1].set_yticks([])\n",
+    "\n",
+    "ax[0].spines['top'].set_visible(False)\n",
+    "ax[0].spines['right'].set_visible(False)\n",
+    "ax[0].spines['left'].set_visible(False)\n",
+    "ax[1].spines['top'].set_visible(False)\n",
+    "ax[1].spines['right'].set_visible(False)\n",
+    "ax[1].spines['left'].set_visible(False)\n",
+    "\n",
+    "ax[1].set_xlabel(r\"cloud condensate [kgm$^{-2}$]\")\n",
+    "ax[0].set_xlabel(r\"TOA net CRE [Wm$^{-2}$]\")\n",
+    "labels=[\"ocean\", \"ocean-ice\\ntransition\", \"bare ice\", \"snow-covered ice\"]\n",
+    "\n",
+    "ax[0].annotate(labels[3], [-43, 3.2], ha=\"left\", va=\"center\")\n",
+    "ax[0].annotate(labels[2], [-43, 2.2], ha=\"left\", va=\"center\")\n",
+    "ax[0].annotate(labels[1], [-43, 1.2], ha=\"left\", va=\"center\")\n",
+    "ax[0].annotate(labels[0], [-43, 0.2], ha=\"left\", va=\"center\")\n",
+    "\n",
+    "\n",
+    "ax[1].annotate(\"ICON-A-WBF\", [0.9, 0.9], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[1].annotate(\"ICON-ESM-WBF\", [0.9, 0.83], color=\"C4\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "\n",
+    "fig.patch.set_facecolor(\"None\")\n",
+    "ax[0].set_facecolor(\"None\")\n",
+    "ax[1].set_facecolor(\"None\")\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig12_oc_ic_sn_panel.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a2727f-e39f-40ca-887d-abbb37852222",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "baseenv - Python 3.7",
+   "language": "python",
+   "name": "baseenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pythonscripts/Fig2_3_4_5_7-EBM.ipynb b/pythonscripts/Fig2_3_4_5_7-EBM.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..93cb0bd940af1b6924969c14b2ad22dedc4ccaf3
--- /dev/null
+++ b/pythonscripts/Fig2_3_4_5_7-EBM.ipynb
@@ -0,0 +1,1447 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "16692aa4-65e6-471e-8775-e26895d79a0e",
+   "metadata": {},
+   "source": [
+    "# Budyko-Sellers EBM to study waterbelt states\n",
+    "\n",
+    "The model includes the extension for the Jormungand mechanism and a shortwave cloud feedback.\n",
+    "\n",
+    "The code can caclulate the shortwave and longwave feedbacks from the ice-line perspective, and it can decompose the shortwave feedback into its contributions. The code can also compute the bifurcation diagram.\n",
+    "\n",
+    "The model setup is defined by calling the function set_parameters. This sets the model parameters as global variables that are used by the other functions.\n",
+    "\n",
+    "The notebook implements the equations derived in the associated qmd/pdf file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "09a5b03c-7d8d-4863-87c7-6b95011b9154",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import xarray as xr\n",
+    "import sys\n",
+    "sys.path.append(\"/jetfs/home/jhoerner/projects/snowball-waterbelt-continents/python_packages\")\n",
+    "import ICON_tools"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9499ecc4-7c1c-4276-9051-f2a035f7482e",
+   "metadata": {},
+   "source": [
+    "### set global fonts for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9bbfd606-77d6-4482-985c-33f4dd9a50d0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "BIGGER_SIZE = 14\n",
+    "\n",
+    "plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
+    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
+    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
+    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
+    "plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cee42dbb-e7a0-4b69-9663-56e14bc9e6f4",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Define model and required functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ed2b6b9-aa14-464a-a74f-f5b8cc6f382a",
+   "metadata": {},
+   "source": [
+    "Meridional coordinate is sine of latitude."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "34306f3c-09e4-4224-80ec-a2f46ae5e700",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x = np.sin(np.deg2rad(np.linspace(0, 90, 360)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91d6d66e-ea9f-4a3d-9935-99d429230440",
+   "metadata": {},
+   "source": [
+    "Function to set model parameters. Parameters will be set to default values unless their values are given as input paramters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8aca592-598e-4b71-af82-3fe1778a3a34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def set_parameters(**kwargs):\n",
+    "    \"\"\"\n",
+    "    Declare EBM model parameters and set them to default values if they are not included in the function parameters.\n",
+    "\n",
+    "    Parameters:\n",
+    "        **kwargs: Key-value pairs of variables to set.\n",
+    "\n",
+    "    Returns:\n",
+    "        None\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # set default parameters\n",
+    "    params = {\n",
+    "        \"Q\":  1285,    # insolation in Wm-2\n",
+    "        \"B\":  1.5,     # longwave feedback in temperature perspective in Wm-2K-1 \n",
+    "        \"C\":  1.5*1.5, # 1.5*B; constant for meridional energy transport\n",
+    "        \"Ti\": 0,       # temperature at which ice forms\n",
+    "        \"A0\": 210,     # reference radiative forcing\n",
+    "        \"A\":  210,     # A0\n",
+    "        \"alpha_or\": 0.35,      # reference TOA albedo of ocean regions (without shortwave cloud feedback)\n",
+    "        \"alpha_ocs\": 0.25,     # clear-sky TOA albedo of ocean regions\n",
+    "        \"alpha_is\": 0.8,       # TOA albedo of snow-covered ice regions\n",
+    "        \"alpha_ii\": 0.5,       # TOA albedo of bare ice regions\n",
+    "        \"xs\":       0.35,      # snow-line latitude (a.k.a. Jormungand latitude)\n",
+    "        \"delta_xs\": 0.04,      # transition width from snow-covered to bare ice\n",
+    "        \"dalpha_oc\": 0.05,     # change in TOA ocean albedo due to shortwave cloud feedback when ice line moves equatorward of latitude xc\n",
+    "        \"xc\":       0.33,      # latitude at which the cloud feedback is triggered\n",
+    "        \"delta_xc\": 0.015,     # transition width for cloud feedback\n",
+    "    }\n",
+    "\n",
+    "    # check for invalid keys in kwargs\n",
+    "    invalid_keys = [key for key in kwargs if key not in params]\n",
+    "    if invalid_keys:\n",
+    "        raise ValueError(f\"set_parameters: invalid keys in kwargs: {', '.join(invalid_keys)}\")\n",
+    "    \n",
+    "    # update defaults with provided values\n",
+    "    params.update(kwargs)\n",
+    "\n",
+    "    # Create individual global variables\n",
+    "    for key, value in params.items():\n",
+    "        globals()[key] = value\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b956ec75-fec7-4386-bd77-49eb41d0aa58",
+   "metadata": {},
+   "source": [
+    "Functions to calculate insolation, TOA albedo of ocean and ice regions, TOA albedo at ice latitude, shortwave and longwave feedbacks, bifurcation diagram."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e46043cd-279f-49d3-b37a-6ecef1b4a90d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def insolation_profile(x):\n",
+    "    \"\"\"\n",
+    "    Calculates s(x) as a function of sine of latitude.\n",
+    "    \"\"\"\n",
+    "    return 1-0.241*(3*np.power(x,2)-1)\n",
+    "\n",
+    "def insolation_profile_ddx(x):\n",
+    "    \"\"\"\n",
+    "    Calculates ds/dx (x) as a function of sine of latitude.\n",
+    "    \"\"\"\n",
+    "    return -0.241*6*x\n",
+    "\n",
+    "def alpha_o(x):\n",
+    "    \"\"\"\n",
+    "    Calculates TOA albedo of ocean region as a function of sine of latitude, when the ice line is at x.\n",
+    "    Allows for shortwave cloud feedback.\n",
+    "    \"\"\"\n",
+    "    return alpha_or + 0.5*dalpha_oc*(1-np.tanh((x-xc)/delta_xc))\n",
+    "\n",
+    "def alpha_o_ddxi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates derivative of TOA albedo of ocean region as a function of sine of latitude.\n",
+    "    Allows for shortwave cloud feedback.\n",
+    "    \"\"\"\n",
+    "    return -0.5*dalpha_oc*(1-np.power(np.tanh((x-xc)/delta_xc),2))/delta_xc\n",
+    "\n",
+    "def alpha_i(x):\n",
+    "    \"\"\"\n",
+    "    Calculates TOA albedo of ice region as a function of sine of latitude.\n",
+    "    \"\"\"\n",
+    "    return alpha_ii + 0.5*(alpha_is-alpha_ii)*(1+np.tanh((x-xs)/delta_xs))\n",
+    "\n",
+    "def alpha_i_ddxi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates derivative of TOA albedo of ice region as a function of sine of latitude.\n",
+    "    \"\"\"\n",
+    "    return 0.5*(alpha_is-alpha_ii)*(1-np.power(np.tanh((x-xs)/delta_xs),2))/delta_xs\n",
+    "\n",
+    "def alpha_xi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates TOA albedo at ice-line latitude xi, given by the arithmetic mean of TOA albedo of ocean and ice regions.\n",
+    "    \"\"\"\n",
+    "    return 0.5*(alpha_o(x) + alpha_i(x))\n",
+    "\n",
+    "def alpha_xi_ddxi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates derivative of TOA albedo at ice-line latitude xi w.r.t. xi.\n",
+    "    \"\"\"\n",
+    "    return 0.5*(alpha_i_ddxi(x)+alpha_o_ddxi(x))\n",
+    "\n",
+    "def tmean_xi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates global-mean temperature as a function of sine of ice-line latitude xi.\n",
+    "    \"\"\"\n",
+    "    return 1/C * ( -0.25*Q*(1-alpha_xi(x))*insolation_profile(x) + A + B*Ti + C*Ti )\n",
+    "\n",
+    "def tmean_ddxi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates derivative of global-mean temperature w.r.t. xi.\n",
+    "    \"\"\"\n",
+    "    return 0.25 * Q/C * ( (1-alpha_xi(x)) * insolation_profile_ddx(x) - alpha_xi_ddxi(x) * insolation_profile(x) )\n",
+    "\n",
+    "def lambdasw_xi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates shortwave feedback from the ice-line perspective as a function of the ice-line xi.\n",
+    "    \"\"\"\n",
+    "    _term1 = insolation_profile(x) * (alpha_o(x)-alpha_i(x))\n",
+    "    _term2 = - (1.241*x-0.241*np.power(x,3)) * 0.5*dalpha_oc/delta_xc * (1-np.power(np.tanh((x-xc)/delta_xc),2))\n",
+    "    return -0.25*Q * ( _term1 +_term2 )\n",
+    "\n",
+    "def lambdasw_xi_masking(x):\n",
+    "    \"\"\"\n",
+    "    Calculates cloud-masking contribution to shortwave feedback from the ice-line perspective as a function of the ice-line xi.\n",
+    "    \"\"\"\n",
+    "    _term1 = insolation_profile(x) * (alpha_o(x) - alpha_i(x))\n",
+    "    _term2 = insolation_profile(x) * (alpha_ocs  - alpha_i(x))\n",
+    "    return -0.25*Q * ( _term1 - _term2 )\n",
+    "\n",
+    "def lambdasw_xi_cloudfeedback(x):\n",
+    "    \"\"\"\n",
+    "    Calculates cloud-feedback contribution to shortwave feedback from the ice-line perspective as a function of the ice-line xi.\n",
+    "    \"\"\"\n",
+    "    return 0.25*Q * (1.241*x-0.241*np.power(x,3)) * 0.5*dalpha_oc/delta_xc * (1-np.power(np.tanh((x-xc)/delta_xc),2))\n",
+    "\n",
+    "def lambdalw_xi(x):\n",
+    "    \"\"\"\n",
+    "    Calculates longwave feedback from the ice-line perspective as a function of the sine of the ice-line latitude xi.\n",
+    "    \"\"\"\n",
+    "    return B * tmean_ddxi(x)\n",
+    "\n",
+    "def alpha_p(x):\n",
+    "    \"\"\"\n",
+    "    Calculates global-mean TOA albedo as a function of the sine of the ice-line latitude xi.\n",
+    "\n",
+    "    Input: \n",
+    "      - x, array of the sine of latitudes\n",
+    "    Output:\n",
+    "      - array of global-mean TOA albedo for each value of ice-line position\n",
+    "    \"\"\"\n",
+    "    import scipy.integrate as integrate\n",
+    "    _alpha_p = np.zeros(np.size(x))    \n",
+    "    for i, xi in enumerate(x):\n",
+    "        _ocean = integrate.quad(lambda x: alpha_o(xi)*insolation_profile(x), 0, xi)[0]\n",
+    "        _ice   = integrate.quad(lambda x: alpha_i(x)*insolation_profile(x), xi, 1)[0]\n",
+    "        _alpha_p[i] = _ocean + _ice\n",
+    "    return _alpha_p\n",
+    "\n",
+    "def solve_bifurcation_diagram(x):\n",
+    "    \"\"\"\n",
+    "    Solve for the bifurcation diagram.\n",
+    "\n",
+    "    Input:\n",
+    "      - x, array of the sine of ic-line latitudes\n",
+    "\n",
+    "    Returns dA_xi: change in longwave forcing required to reach equilibrium for each ice-line latitude\n",
+    "    \"\"\"\n",
+    "    A_xi  = 1/(1+(C/B)) * (Q/4 * (insolation_profile(x)*(1-alpha_xi(x)) + C/B * (1-alpha_p(x))) - (B+C)*Ti)\n",
+    "    return A0 - A_xi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3551f54b-3555-4d9e-bbe7-6a94c3e07b25",
+   "metadata": {},
+   "source": [
+    "Functions to diagnose the BASIR width "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d2825eb4-399a-4a02-8052-f4454b2bee52",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_BASIRwidth(x, _xs):\n",
+    "    return np.clip(np.rad2deg(np.arcsin(_xs) - np.arcsin(x)), a_min=0, a_max=None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1521ae0-da06-454b-bf13-df3d03181ae1",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Run model and analyze"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f5b6e93-ce2d-40f4-b4c8-90d5fd53c95e",
+   "metadata": {},
+   "source": [
+    "Make overview plots for different setups of the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "21c7e5b5-3b9f-4f9d-8c7b-d540e2f68217",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_plots_nice():\n",
+    "    plt.xlabel(\"sine of latitude\")\n",
+    "    plt.xlim(1,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "51c7d1ca-1824-4a7b-aeb8-d06f69da7cf2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def make_summary_plot(figtitle, figname, return_bifurcation=False):\n",
+    "\n",
+    "    plt.figure(figsize=(24,12))\n",
+    "\n",
+    "    ax=plt.subplot(2,4,1)\n",
+    "    plt.plot(x, insolation_profile(x))\n",
+    "    plt.ylabel(\"s(x)\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"Insolation profile\")\n",
+    "    plt.text(0, 1.1, figtitle, transform=ax.transAxes, fontsize=16, weight=\"bold\")\n",
+    "\n",
+    "    plt.subplot(2,4,2)\n",
+    "    plt.plot(x, alpha_i(x), label=\"TOA albedo of ice-covered regions\")\n",
+    "    plt.plot(x, alpha_o(x), label=\"TOA albedo of ocean regions\")\n",
+    "    plt.plot(x, alpha_p(x), label=\"global-mean TOA albedo\")\n",
+    "    plt.ylabel(\"TOA albedo\")\n",
+    "    plt.legend()\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"TOA albedos\")\n",
+    "\n",
+    "    plt.subplot(2,4,3)\n",
+    "    plt.plot(x, alpha_xi(x))\n",
+    "    plt.ylabel(\"alpha(xi)\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"TOA albedo at ice line\")\n",
+    "\n",
+    "    plt.subplot(2,4,4)\n",
+    "    plt.plot(x, tmean_xi(x))\n",
+    "    plt.ylabel(\"tmean(xi) in deg C\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"global-mean temperature for ice-line latitude\")\n",
+    "\n",
+    "    plt.subplot(2,4,5)\n",
+    "    plt.plot(x, lambdasw_xi(x), label=\"SW feedback\")\n",
+    "    plt.plot(x, -lambdalw_xi(x), label=\"LW feedback\")\n",
+    "    plt.ylabel(\"Wm-2\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"Feedbacks in the ice-line perspective\")\n",
+    "    plt.legend()\n",
+    "\n",
+    "    plt.subplot(2,4,6)\n",
+    "    plt.plot(x, -lambdasw_xi(x)/lambdalw_xi(x))\n",
+    "    plt.plot(x, 1+0*x, color=\"black\")\n",
+    "    plt.ylabel(\"[-]\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"|SW/LW| feedback\")\n",
+    "    plt.ylim(0, 2.5)\n",
+    "\n",
+    "    plt.subplot(2,4,7)\n",
+    "    plt.plot(x, lambdasw_xi(x)+lambdalw_xi(x))\n",
+    "    plt.plot(x, 0*x, color=\"black\")\n",
+    "    plt.ylabel(\"Wm-2\")\n",
+    "    make_plots_nice()\n",
+    "    plt.title(\"SW+LW feedback\")\n",
+    "    plt.ylim(-200,200)\n",
+    "\n",
+    "    plt.subplot(2,4,8)\n",
+    "    dA = solve_bifurcation_diagram(x)\n",
+    "    plt.plot(dA, x)\n",
+    "    plt.xlabel(\"radiative forcing dA in Wm-2\")\n",
+    "    plt.ylabel(\"sine of ice-line latitude\")\n",
+    "    plt.title(\"Bifurcation diagram\")\n",
+    "    plt.ylim(0,1)\n",
+    "\n",
+    "    plt.savefig(figname)\n",
+    "    plt.close()\n",
+    "    if return_bifurcation:\n",
+    "        return dA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cdc89e7c-9496-438e-b0fa-dd42b32fc633",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:42: RuntimeWarning: invalid value encountered in true_divide\n",
+      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:42: RuntimeWarning: divide by zero encountered in true_divide\n"
+     ]
+    }
+   ],
+   "source": [
+    "# no shortwave feedback\n",
+    "set_parameters(alpha_or=0.35, alpha_is=0.35, alpha_ii=0.35, dalpha_oc=0.0)\n",
+    "make_summary_plot(figtitle=\"EBM without shortwave feedback\", figname=\"summaryplot_noswfeedback.pdf\")\n",
+    "\n",
+    "# original Budyko-Sellers model\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.8, dalpha_oc=0)\n",
+    "dA_BS = make_summary_plot(figtitle=\"EBM: classical Budyko-Sellers model\", figname=\"summaryplot_budykosellers.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0)\n",
+    "dA_Jor = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version, larger BASIR\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0, xs=0.4)\n",
+    "dA_Jor_BASIR = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism, large BASIR\", figname=\"summaryplot_withjormungand-basir.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version, smaller BASIR\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0, xs=0.3)\n",
+    "dA_Jor_BASIR2 = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism, small BASIR\", figname=\"summaryplot_withjormungand-basir2.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version and shortwave cloud feedback\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.05)\n",
+    "dA_Jor_cf = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism and shortwave cloud feedback\", figname=\"summaryplot_withjormungand-and-swcloudfeedback.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version and negative shortwave cloud feedback\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=-0.05)\n",
+    "dA_Jor_negcf = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism and neagtive shortwave cloud feedback\", figname=\"summaryplot_withjormungand-and-negswcloudfeedback.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "# Jormungand version clear sky\n",
+    "set_parameters(alpha_or=alpha_ocs, alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0)\n",
+    "dA_Jor_cs = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand_cs.pdf\", return_bifurcation=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "857efdda-8c1e-4627-8357-faa75e724e4d",
+   "metadata": {},
+   "source": [
+    "Now we decompose the shortwave feedback for the version with the Jormungand mechanism and a cloud feedback."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "590752c6-0dc1-4786-9093-ea74cce78438",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Now we convert from d/dxi to d/dphi so that the feedbacks have units W m-2 deglat-1 instead of Wm-2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2dbeba1f-cec9-4d59-bd4b-c0326cdc3852",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def convert_ddxi2ddphi(fld):\n",
+    "    \"\"\"\n",
+    "    fld is an arbitrary function that is the derivative w.r.t. xi, we convert it to a derivative w.r.t. phii.\n",
+    "    The conversion is based on dxi = d(sin(phii)) = cos(phii) dphii.\n",
+    "    The factor 1/90 is needed because xi goes from 0..1 and phii from 0..90.\n",
+    "    \"\"\"\n",
+    "    phi_rad = np.arcsin(x)\n",
+    "    return fld*np.cos(phi_rad)/90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "51c94a4c-f1ff-46dc-9439-364ae80d668b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# full model with jormungand and positive shortwave cloud feedback\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.05)\n",
+    "swfdbck  = convert_ddxi2ddphi(lambdasw_xi(x))               # total shortwave feedback\n",
+    "cldmskng = convert_ddxi2ddphi(lambdasw_xi_masking(x))       # cloud masking\n",
+    "cldfdbck = convert_ddxi2ddphi(lambdasw_xi_cloudfeedback(x)) # cloud feedback\n",
+    "lwfdbck  = convert_ddxi2ddphi(lambdalw_xi(x))               # longwave feedback\n",
+    "\n",
+    "# compute the total shortwave feedback in clear-sky --> clear-sky ice-albedo feedback\n",
+    "# to this end, we set the TOA ocean albedo to the clear-sky value and disable the cloud feedback\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.0)\n",
+    "swfdbckcs = convert_ddxi2ddphi(lambdasw_xi(x))\n",
+    "\n",
+    "# compute the Jormungand contribution to the total shortwave feedback in clear-sky\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.8, dalpha_oc=0.0)\n",
+    "jorfdbckcs = swfdbckcs - convert_ddxi2ddphi(lambdasw_xi(x))\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "plt.plot(x, swfdbck, label = \"total SW feedback\")\n",
+    "plt.plot(x, swfdbckcs, label = \"clear-sky ice-albedo feedback\")\n",
+    "plt.plot(x, jorfdbckcs, label = \"Jormungand mechanism\")\n",
+    "plt.plot(x, cldfdbck, label = \"cloud feedback\")\n",
+    "plt.plot(x, cldmskng, label = \"cloud masking\")\n",
+    "plt.plot(x, swfdbckcs + cldfdbck + cldmskng, label = \"test: should be same\\nas total SW feedback!\")\n",
+    "plt.legend(loc=2)\n",
+    "plt.title(\"decomposition of shortwave feedback (with phii)\")\n",
+    "plt.xlabel(\"sine of ice-line latitude\")\n",
+    "plt.ylabel(\"Wm-2 deglat-1\")\n",
+    "plt.xlim(1,0)\n",
+    "plt.ylim(-2,4)\n",
+    "plt.savefig(\"summaryplot_withjormungand-and-swcloudfeedback_decomposoed-swfeedback-phii.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "39aa4228-b4c6-4872-84b3-1557de3c01d9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# full model with jormungand and negative shortwave cloud feedback\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=-0.05)\n",
+    "swfdbck2  = convert_ddxi2ddphi(lambdasw_xi(x))               # total shortwave feedback\n",
+    "cldmskng2 = convert_ddxi2ddphi(lambdasw_xi_masking(x))       # cloud masking\n",
+    "cldfdbck2 = convert_ddxi2ddphi(lambdasw_xi_cloudfeedback(x)) # cloud feedback\n",
+    "lwfdbck2  = convert_ddxi2ddphi(lambdalw_xi(x))               # longwave feedback\n",
+    "\n",
+    "# compute the total shortwave feedback in clear-sky --> clear-sky ice-albedo feedback\n",
+    "# to this end, we set the TOA ocean albedo to the clear-sky value and disable the cloud feedback\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.0)\n",
+    "swfdbckcs2 = convert_ddxi2ddphi(lambdasw_xi(x))\n",
+    "\n",
+    "# compute the Jormungand contribution to the total shortwave feedback in clear-sky\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.8, dalpha_oc=0.0)\n",
+    "jorfdbckcs2 = swfdbckcs2 - convert_ddxi2ddphi(lambdasw_xi(x))\n",
+    "\n",
+    "plt.figure(figsize=(6,4))\n",
+    "plt.plot(x, swfdbck2, label = \"total SW feedback\")\n",
+    "plt.plot(x, swfdbckcs2, label = \"clear-sky ice-albedo feedback\")\n",
+    "plt.plot(x, jorfdbckcs2, label = \"Jormungand mechanism\")\n",
+    "plt.plot(x, cldfdbck2, label = \"cloud feedback\")\n",
+    "plt.plot(x, cldmskng2, label = \"cloud masking\")\n",
+    "plt.plot(x, swfdbckcs2 + cldfdbck2 + cldmskng2, label = \"test: should be same\\nas total SW feedback!\")\n",
+    "plt.legend(loc=2)\n",
+    "plt.title(\"decomposition of shortwave feedback (with phii)\")\n",
+    "plt.xlabel(\"sine of ice-line latitude\")\n",
+    "plt.ylabel(\"Wm-2 deglat-1\")\n",
+    "plt.xlim(1,0)\n",
+    "plt.ylim(-2,4)\n",
+    "plt.savefig(\"summaryplot_withjormungand-and-negswcloudfeedback_decomposoed-swfeedback-phii.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ae3c8cf-0b72-432a-8a0e-64c8da906c6d",
+   "metadata": {},
+   "source": [
+    "### Fig. 2: Bifurcation diagrams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3dfca104-5933-451e-a798-ce44348d1c94",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 5))\n",
+    "\n",
+    "\n",
+    "ax.plot(dA_BS, x, c=\"black\", label=\"original Budyko-Sellers\", zorder=3)\n",
+    "ax.plot(dA_Jor, x, c=\"C0\", label=\"Jormungand\", zorder=2)\n",
+    "ax.plot(dA_Jor_BASIR, x, c=\"C0\", label=\"Jormungand & large BASIR\", zorder=2, ls=\"--\")\n",
+    "ax.plot(dA_Jor_BASIR2, x, c=\"C0\", label=\"Jormungand & small BASIR\", zorder=2, ls=\":\")\n",
+    "ax.plot(dA_Jor_cs, x, c=\"C2\", label=\"Jormungand & clear sky \", zorder=1, ls=\"-\")\n",
+    "ax.plot(dA_Jor_cf, x, c=\"C1\", label=\"Jormungand &\\npositive cloud feedback\", zorder=1)\n",
+    "ax.plot(dA_Jor_negcf, x, c=\"C1\", label=\"Jormungand &\\nnegative cloud feedback \", zorder=1, ls=\"--\")\n",
+    "\n",
+    "ax.set_yticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax.set_yticklabels([0,10,20,30,45,60,90])\n",
+    "ax.set_ylim(0, 1)\n",
+    "\n",
+    "ax.set_xlim(30, 105)\n",
+    "\n",
+    "ax2 = ax.twinx()\n",
+    "ax2.set_ylabel(r\"sine of ice-line latitude $x_{i}$ []\")\n",
+    "\n",
+    "ax.set_ylabel(r\"ice-line latitude $\\varphi_{i}$ [deglat]\")\n",
+    "ax.set_xlabel(r\"$\\Delta$A [Wm$^{-2}$]\")\n",
+    "\n",
+    "ax.spines['left'].set_position(('outward', 5))\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "ax.spines['bottom'].set_position(('outward',5))\n",
+    "ax2.spines['right'].set_position(('outward',5))\n",
+    "ax2.spines['left'].set_color('none')\n",
+    "ax2.spines['top'].set_color('none')\n",
+    "ax2.spines['bottom'].set_color('none')\n",
+    "\n",
+    "ax.legend(frameon=False, bbox_to_anchor=(0.5, -0.2), loc=\"upper center\", ncol=2, columnspacing=0.5)\n",
+    "plt.tight_layout()\n",
+    "plt.subplots_adjust(bottom=0.4)  # Values >1 push the plot down\n",
+    "\n",
+    "plt.savefig(\"plots/Fig2_EBM_bifurcation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c580054d-b0b7-4960-9de5-a77ab34837f6",
+   "metadata": {},
+   "source": [
+    "### Fig. 3: BASIR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fecf2de5-04e6-44c2-a877-2f7d7a9d2483",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "\n",
+    "ax.plot(x, calc_BASIRwidth(x, _xs=0.35), c=\"C0\", label=\"Jormungand\")\n",
+    "ax.plot(x, calc_BASIRwidth(x, _xs=0.4), c=\"C0\", label=\"Jormungand & large BASIR\",ls=\"--\")\n",
+    "ax.plot(x, calc_BASIRwidth(x, _xs=0.3), c=\"C0\", label=\"Jormungand & small BASIR\",ls=\":\")\n",
+    "\n",
+    "ax.set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax.set_xticklabels([0,10,20,30,45,60,90])\n",
+    "ax.set_xlim(1, 0)\n",
+    "ax.set_ylim(0, 25)\n",
+    "\n",
+    "\n",
+    "ax.set_xlabel(r\"ice-line latitude $\\varphi_{i}$ [deglat]\")\n",
+    "ax.set_ylabel(r\"BASIR width [deglat]\")\n",
+    "\n",
+    "ax.spines['right'].set_color('none')\n",
+    "ax.spines['top'].set_color('none')\n",
+    "ax.spines['bottom'].set_position(('outward', 5))\n",
+    "ax.spines['left'].set_position(('outward', 5))\n",
+    "\n",
+    "ax.legend(frameon=False)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig3_BASIR.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2025b461-a426-46f9-bb49-40445bb24648",
+   "metadata": {},
+   "source": [
+    "### Fig. 4: Feedback factors\n",
+    "for the \"EBM: with Jormungand mechanism and shortwave cloud feedback\" parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f3d465af-4d0a-4c2d-889a-0c3310fd6e72",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 2, figsize=(12, 8), sharex=True, sharey=False)\n",
+    "# positive feedback\n",
+    "# left plot: net, SW, LW feedback\n",
+    "ax[0, 0].plot(x, swfdbck, label = r\"shortwave feedback $\\lambda_{sw}$\", color=\"C1\")\n",
+    "ax[0, 0].plot(x, lwfdbck, label = r\"longwave feedback $\\lambda_{lw}$\", color=\"red\")\n",
+    "ax[0, 0].plot(x, swfdbck+lwfdbck, label = r\"total feedback $\\lambda$\", color=\"black\")\n",
+    "ax[0, 0].legend(frameon=False, loc=3)\n",
+    "\n",
+    "# right plot: decomposition of SW feedback\n",
+    "ax[0, 1].plot(x, cldfdbck, label = r\"cloud feedback $\\lambda_{cld}$\", c=\"C2\")\n",
+    "ax[0, 1].plot(x, cldmskng, label = r\"cloud masking $\\lambda_{mask}$\", c=\"C4\")\n",
+    "ax[0, 1].plot(x, swfdbckcs, label = r\"clear-sky ice-albedo feedback $\\lambda_{ice}^{clr}$\", c=\"C0\")\n",
+    "ax[0, 1].plot(x, jorfdbckcs, label = r\"Jormungand mechanism\", c=\"C5\", ls=\"--\")\n",
+    "ax[0, 1].legend(frameon=False, loc=\"lower left\")\n",
+    "\n",
+    "# negative feedback\n",
+    "# left plot: net, SW, LW feedback\n",
+    "ax[1, 0].plot(x, swfdbck2, label = r\"shortwave feedback $\\lambda_{sw}$\", color=\"C1\")\n",
+    "ax[1, 0].plot(x, lwfdbck2, label = r\"longwave feedback $\\lambda_{lw}$\", color=\"red\")\n",
+    "ax[1, 0].plot(x, swfdbck2+lwfdbck2, label = r\"total feedback $\\lambda$\", color=\"black\")\n",
+    "#ax[1, 0].legend(frameon=False, loc=3)\n",
+    "\n",
+    "# right plot: decomposition of SW feedback\n",
+    "ax[1, 1].plot(x, cldfdbck2, label = r\"cloud feedback $\\lambda_{cld}$\", c=\"C2\")\n",
+    "ax[1, 1].plot(x, cldmskng2, label = r\"cloud masking $\\lambda_{mask}$\", c=\"C4\")\n",
+    "ax[1, 1].plot(x, swfdbckcs2, label = r\"clear-sky ice-albedo feedback $\\lambda_{ice}^{clr}$\", c=\"C0\")\n",
+    "ax[1, 1].plot(x, jorfdbckcs2, label = r\"Jormungand mechanism\", c=\"C5\", ls=\"--\")\n",
+    "#ax[1, 1].legend(frameon=False, loc=\"lower left\", bbox_to_anchor=(-0.02, -0.02))\n",
+    "\n",
+    "\n",
+    "# change looks\n",
+    "ax[0, 0].set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax[0, 0].set_xticklabels([0,10,20,30,45,60,90])\n",
+    "ax[0, 0].set_xlim(1, 0)\n",
+    "\n",
+    "ax[0, 0].xaxis.set_tick_params(labelbottom=True)\n",
+    "ax[0, 1].xaxis.set_tick_params(labelbottom=True) \n",
+    "\n",
+    "ax[1, 0].set_xlabel(r\"ice-line latitude $\\varphi_{i}$ [deglat]\")\n",
+    "ax[1, 1].set_xlabel(r\"ice-line latitude $\\varphi_{i}$ [deglat]\")\n",
+    "ax[1, 0].xaxis.set_label_coords(0.5, 0.)\n",
+    "ax[1, 1].xaxis.set_label_coords(0.5, 0.)\n",
+    "ax[0, 0].set_ylabel(r\"feedback [Wm$^{-2}$ deglat$^{-1}$]\")\n",
+    "ax[1, 0].set_ylabel(r\"feedback [Wm$^{-2}$ deglat$^{-1}$]\")\n",
+    "ax[0, 0].set_xlim(1, 0)\n",
+    "ax[0, 0].set_ylim(-5.2, 5.2)\n",
+    "ax[0, 1].set_ylim(-2.5, 2.5)\n",
+    "ax[1, 0].set_ylim(-8.2, 8.2)\n",
+    "ax[1, 1].set_ylim(-2.5, 2.5)\n",
+    "\n",
+    "ax[0, 0].spines['right'].set_color('none')\n",
+    "ax[0, 0].spines['top'].set_color('none')\n",
+    "ax[0, 1].spines['right'].set_color('none')\n",
+    "ax[0, 1].spines['top'].set_color('none')\n",
+    "ax[0, 0].spines['left'].set_position(('outward', 5))\n",
+    "ax[0, 1].spines['left'].set_position(('outward', 5))\n",
+    "ax[0, 0].spines['bottom'].set_position(('data', 0))\n",
+    "ax[0, 1].spines['bottom'].set_position(('data', 0))\n",
+    "\n",
+    "ax[1, 0].spines['right'].set_color('none')\n",
+    "ax[1, 0].spines['top'].set_color('none')\n",
+    "ax[1, 1].spines['right'].set_color('none')\n",
+    "ax[1, 1].spines['top'].set_color('none')\n",
+    "ax[1, 0].spines['left'].set_position(('outward', 5))\n",
+    "ax[1, 1].spines['left'].set_position(('outward', 5))\n",
+    "ax[1, 0].spines['bottom'].set_position(('data', 0))\n",
+    "ax[1, 1].spines['bottom'].set_position(('data', 0))\n",
+    "\n",
+    "ax[0, 0].annotate(\"a)\", xycoords=\"axes fraction\", xy=(0.01, 0.99), fontweight=\"bold\", va=\"top\", fontsize=MEDIUM_SIZE)\n",
+    "ax[0, 1].annotate(\"b)\", xycoords=\"axes fraction\", xy=(0.01, 0.99), fontweight=\"bold\", va=\"top\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1, 0].annotate(\"c)\", xycoords=\"axes fraction\", xy=(0.01, 0.99), fontweight=\"bold\", va=\"top\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1, 1].annotate(\"d)\", xycoords=\"axes fraction\", xy=(0.01, 0.99), fontweight=\"bold\", va=\"top\", fontsize=MEDIUM_SIZE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "fig.text(0.55, 0.98, \"positive cloud feedback\", fontsize=14, fontweight=\"bold\", ha=\"center\", va=\"top\")\n",
+    "fig.text(0.55, 0.49, \"negative cloud feedback\", fontsize=14, fontweight=\"bold\", ha=\"center\", va=\"top\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "fig.subplots_adjust(hspace=0.2, top=0.95)\n",
+    "plt.savefig(\"plots/Fig4_EBM_feedbacks.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6cacde7-94e2-4edb-8251-6d51575324f8",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## combined bifurcation diagrams GCM & EBM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "8a9e1b68-2714-4b19-8cec-09d4cdce564f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# load ICON-A data (Braun et al., 2022)\n",
+    "datapath = \"/jetfs/scratch/jhoerner/data_braunetal2022/\"\n",
+    "\n",
+    "explist_icon_noWBF = [\"mlo_aqua_1500ppmv_hice_unlim_damped\", \"mlo_aqua_1875ppmv_hice_unlim\", \"mlo_aqua_2250ppmv_hice_unlim\", \"mlo_aqua_3907ppmv_13lat_hice_unlim_damped\", \n",
+    "                      \"mlo_aqua_4063ppmv_13lat_hice_unlim_damped\", \"mlo_aqua_4375ppmv_13lat_hice_unlim_damped\", \"mlo_aqua_5000ppmv_13lat_hice_unlim_damped\"]\n",
+    "noWBF_DSlistgmym = np.empty([len(explist_icon_noWBF)], dtype=\"object\")\n",
+    "for i, exp in enumerate(explist_icon_noWBF):\n",
+    "    noWBF_DSlistgmym[i] = xr.open_dataset(datapath + \"ICON/\" + exp +\"_atm_2d_ml.ym.gm.nc\").squeeze().groupby('time.year').mean(dim='time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "3915adc0-b659-4020-9000-2ae608f75bf9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
+      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
+      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
+      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
+      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
+      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
+      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
+      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ICON-A-WBF data\n",
+    "data_path = \"/jetfs/scratch/jhoerner/postprocessing\"\n",
+    "Aexplist, Anexp = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \n",
+    "                                                     \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
+    "ADSlistgm, ADSlistzm = ICON_tools.load_ds_2d(data_path, Aexplist, True)\n",
+    "\n",
+    "ADSlistgmym = np.empty([Anexp], dtype=\"object\")\n",
+    "\n",
+    "for i in range(Anexp):\n",
+    "    # fillna\n",
+    "    ADSlistgm[i] = ADSlistgm[i].where(ADSlistgm[i]['sic'] < 1e36)\n",
+    "    ADSlistgmym[i] = xr.decode_cf(ADSlistgm[i]).groupby('time.year').mean(dim='time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "187e4eb1-dd90-4a0b-99eb-20e52fae3e0d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_5000_55_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_5000_55_0S\n",
+      "ape_5500_55_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_5500_55_0S\n",
+      "ape_6000_90_0S_merged: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_90_0S_merged\n",
+      "ape_6000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_22_0S\n",
+      "ape_6000_13_0S_snowcap: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_13_0S_snowcap\n",
+      "ape_7000_13_0S_snowcap: directory is /jetfs/scratch/jhoerner/postprocessing/ape_7000_13_0S_snowcap\n",
+      "ape_7000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_7000_22_0S\n",
+      "ape_8000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_8000_22_0S\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ICON-ESM data\n",
+    "ESMexplist, ESMnexp = ICON_tools.get_explist(data_path, [\"ape_5000_55_0S\", \"ape_5500_55_0S\",\"ape_6000_90_0S_merged\", \"ape_6000_22_0S\", \"ape_6000_13_0S_snowcap\" ,\"ape_7000_13_0S_snowcap\", \"ape_7000_22_0S\", \"ape_8000_22_0S\"]) \n",
+    "ESMDSlistgm, ESMDSlistzm = ICON_tools.load_ds_2d(data_path, ESMexplist, True)\n",
+    "\n",
+    "ESMDSlistgmym = np.empty([ESMnexp], dtype=\"object\")\n",
+    "\n",
+    "for i in range(ESMnexp):\n",
+    "    ESMDSlistgm[i] = ESMDSlistgm[i].where(ESMDSlistgm[i]['sic'] < 1e36)\n",
+    "    ESMDSlistgmym[i] = xr.decode_cf(ESMDSlistgm[i]).groupby('time.year').mean(dim='time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5759e4e3-8033-4081-b550-bfd1756325ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# EBM\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0)\n",
+    "dA_Jor = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=-0.05)\n",
+    "dA_Jor_negcf = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0)\n",
+    "dA_Jor_nocld = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand_nocld.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "set_parameters(alpha_or=0.25, alpha_is=0.8, alpha_ii=0.5, dalpha_oc=-0.05)\n",
+    "dA_Jor_nocld_negcf = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand_nocld.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.0, xs=0.4)\n",
+    "dA_Jor_BASIR = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand_BASIR_cf.pdf\", return_bifurcation=True)\n",
+    "\n",
+    "set_parameters(alpha_is=0.8, alpha_ii=0.5, dalpha_oc=0.05, xs=0.4, xc=0.375)\n",
+    "dA_Jor_BASIR_cf = make_summary_plot(figtitle=\"EBM: with Jormungand mechanism\", figname=\"summaryplot_withjormungand_BASIR_cf.pdf\", return_bifurcation=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ee51025-006a-4095-a359-546ac2343937",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Fig. 5: ICON-A (Braun et al., 2022) & ICON-A-WBF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e35831dd-e000-46ac-966d-393ea1a8f3d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Vn7ZeuG84fUL6MGfrHIpMRRxIP8D3h77novYXAdA1qCt/nPqDrKIsUgtS+WLvF+Xb9gnpg6uLK1/u+5IScwkrTqxgd+ruGs+jtvJL8wnwDMDT1ZOYlBh+OfZL+bJNCZs4mHEQk9lEM/dmuLm4nZa4urm48b/z/kdBaQGP/fnYWd+TqrRs1pIewT14d8e7lJhK2JG8g7Wxa212bkb549QfXLHkCjYlbuKxwY/xzrh3TrtKJ5oWadgphBBCODCtNfP+PMaNn2wizN+Ln+4ZwaV9Io0Oq0bFpmLmbJ3DyAUjGb1wNOmF6czqNwuACW0tzdFGLBjBtJ+mVbl9oFcgb417i/l75jNywUg+3v0xb417i+ZeNfcT69i8I48OfpQH1z3ImG/H4OPmQ5BX0Bn9oso8MOABOjXvxDU/X8OIBSN4fevrVSYWL416ifi8eMYuHMu9q+9lZp+ZDG05FICLO1xMl6AuTPxuIrctv42JURPLt3N3dWfO6Dn8eORHRnw9gt+O/8a4tuNqPI/a+u/g//L2jrcZ/OVg3tv1XvnrC5arLPevuZ+hXw/lksWXMCBsABd3uPi07d1d3ZkzZg5phWk88dcTdU6qXhz5IjtTdjJiwQje2v4WE6Mm4uFS9Wtt70xmE29tf4u7Vt5FC+8WfHPRN1zT9Rq5KtXEqYqXlx2RUmqLtXNwRY59UkIIIapjN99a7KH+KS418+SPu1mwOZYJ3cN4/ao++Hra71Upe5Vfks+wr4fx82U/08qvldHhOL0H1j5Au4B2zOwz0+hQ6iSjMINH/niE9fHrubTDpTw+5HG83LyMDkucO9XWP03mCtXERRPZEL8BsHRGffKvJxmzcAyDvxzMxT9czNs73i7vvKm15pPdnzD5+8kM+GIA4xeNZ87WOadd3v/vn/8len40MSkx5WUns08SPT+6xlje2fEO0fOj2ZWyq9p1fjn6C5csvuS0slt/v7XKso9iPio/xwFfDGDQl4MY9vUw7lpxF4l5/7RT/u+f/6Xv530Z9OWg8ttvx36rMV4hhBD2J6+olJs+3cyCzbHMHNOB967rL8lUHayJXUNBaQH5Jfm8uuVVOjXvRGQz+7+y54h2p+4mNjsWszbzZ9yfrD65mrGtxxodVp0cSD/AVT9fxebEzTw59ElmD58tyZQo1+Q+ebOKsrjul+voHdqbLyZ9QWSzSBLzEvl0z6fE5sTSJagLL2x6gb/i/uK5Ec/Rs0VPjmcd54m/nuBI1hHeHPtm+b4CPAN4c/ubfDDhjInvq6W1ZsmRJQR4BrDkyBJ6hfSqcr3+Yf05lnWM9MJ0gryCKDWXcjDjIF6uXqeV7UzZedovPG+OfZOhLYdSZCri2b+f5fmNz/PG2DfKl9/Y40b+3e/f9XjlhBBC2IvM/GJu+GQzu05l8vLUXkwbcPYR6MSZVseu5rE/HkOj6RHcg5dHvSzNthpJakEq966+l6yiLMJ8w3h8yON0C+5mdFi19lfcX9y/9n583Xz57MLP6Nmip9EhCTvT5BKq+Xvm4+Puw4sjXyzv/BruG84jgx4B4ET2Cb458A1fXPgF0SGWq00dm3fk9TGvM+n7SWxM2MjgiMEAXNLhEn45+gubEzczMHxgrY6/NWkrqQWpPDXsKV7c9CIPD3wYd1f3M9YL8w2jVbNWbE3ayvi249mXto8OgR2I8I04rUxrXeVcFZ6unoxvO56XN79cr9dJCCGEfUrNLeLaDzdyLDWPd67tzwU9w2veSJzh6WFP8/Swp40Oo0kY3Xq0w849tfDAQp7f+DwdAzvy1ri3ThtuX4gyTabJX5mNCRs5v+351U60tjFhI2E+YeXJVJlw33B6hfQqbzYIlnkfbu11K29uf7Pybqq15MgSzmt1Xnln1IoTEFbWP6w/W5O2ApZErF9oP/qG9j2trFdIr/K5IioqKC1g2fFl9GpR9RUwIYQQjieroITr523iRHoeH98wUJIpIRqJ1pp3d7zL7L9nM6zlMOZfOF+SKVGtJpdQZRZlEuIdUu3yjMKMapeHeIecMU/FlZ2vJCEvgT9O/VHjsQtKC/j9xO9Maj8Jdxd3xrcdf9YZ1AeED/gneUreSr+wfvQL63da2YCw0/tDz1o9i2FfDWPYV8PYEL+BG3recNry+Xvmly8fuWBkjTELIYSwD/nFlj5Th5JzeO+6/ozoJEM01yQ2PZ+oR5ZSaqrbqHSVXfX+BhZsOmmjqP6x4UgaQ55fafP91sW3W2KZ+u76Kpct3h7Hv+ZtPMcRGU9rzZxtc3hn5ztc0uES3hj7xmkTNgtRWZNLqAI9A0kpSKl2eXOv5tUuTylIOWMmdQ9XD27vdTtv7XjrtPKtSVvLB36YsngKACtPrsRVuTIqchQAk9tN5s+4P0kvTCchN+G0wSLAcoXqYMZBsoqy2JWyiz4hfWgf0J6U/BSyirLYnryd/mH9Tzvu3DFzWT99PVuu28Jjgx/jxt9uPG3ywBk9ZrB++nrWT1/PH1fXnAQKIYQwntmsuXfBDrafzOCNq/syukvNk9cK0VBT+kby+c2DjQ7jnNJa8/Lml/l498dM6zyN2cNn4+bS5HrIiDpqcgnV4IjBrDy5sto5FAaFDyIxL/G00fsAEvMS2ZWyq7z/VEVTOk4hpziHFSdXlJf1D+vPpms3senaTSyeshiAJYeXkF+az/hF4xn9zWjuX3s/peZSfjn6CxHNIsrX33TtJgBa+7UmxDuERQcXEeEbgY+7DwC9Q3qz6OAi8kvyqx3UwtXFlfPbno+rcmVb0rY6v05CCCHsx5yVh/h9bxL/ndydC6MjjA5HCKc1Z9scvtj3Bdd1u47HhzxebRcRISpqcn8lM3rMIK84j//++V/ic+MBSMpL4uXNL3Mg/QBRAVFM6zKNR/54hJ0pOzGZTRzOOMx/Vv+HIRFDyifoq8jNxY27+tzFx7s/rva4SXlJbEzcyNtj32bRJYtYdMkivrvkO27qedNZm/31C+vHZ3s/o19YvzPKegT3qHbITq01q06uIrs4m/YB7Wv78gghhLAzK/cl8cbKQ1zZvxU3DY8yOhybWbgllps/3Vz+fPQrq7nry63lz4e+sJI98VkAHE7O5bqPNtL76d8Z++oaft4VX77eqv1JTJr7Bz3/bxlDX1jJ68sPVnvMX2MSGP7iKg4k5lS5/Pc9iVxo3deol1ez5kDyGeuYzZo3Vx5i+Iur6D97Ofd9s4PswhKg6iZ8w19cxZ+HLC1FCktM3L9wJ72eWsb5/1vLrlOZZ32Noh5ZyucbjjP6ldX0ePI3Xvv9ACfS8rj8nb/o+X/LmPnlNopLLT8QZ+WXcNOnm+k3ezm9nlrGTZ9uJiGroHxf326JZeTLq+jx5G+MeGkVi7fHVXnM53/Zx9R315NdWHJGc8CoR5byxd8nGP3KaqKfWsYTi3dTNp+pyax59ue99H3md0a8tIr564/bpLnluTR/z3w+3v0xV3W5iocGPiSjPopaa3LXMAM8A/h80ue8uf1Npi+dTkFpAaE+oUxqN4k2/m0AeGzwY3yy+xMe/eNRkvOTCfQMZFK7SczsW/0EdJPaTWJezDyyirKqXP7T0Z/o0rwLwyKHnVZ+bbdr+WzPZxzKOESn5p3O2G5A2AB+PfYr/UIrJFSh/Xi18FUu63jZGevfs+oeXJQLCkXLZi15dsSzdGzesVavjRBCCPuSklPEQ4t20T3Cn2cv6+lUX/CGtAtm9s97MZs1KblFlJg0205kAnAyLZ+8olK6hfuTX1zKv+Zt5D/jO/PpjQPZn5jDv+ZtpEuYH53C/PB2d+N/V/Wmc6gfB5Isy7q39Gdij9MHEFi4JZa3Vx/my1sGE9XizP4wO2IzuX/hTt65rh/DO7QgOaeI3KLSM9ZbtPUUi7ad4utbhxDczIP7Fu7g/37cw+tX9anxnOesOMTJ9DzWPTSG/GITN3yyqcZt1h5M5ad7RpCQVchFb/zJ1hMZzLmqL4G+7lz+znqW7Ixnav9WmLXmyv6teHt6P0xa89CinTz54x4+vH4A+cWlPP3TXn68ezgdQpqRnF1IZkHJaccxmzWP/RBDXGYBn988GG8P1yrjWbU/mR/vHkFuUSkXv/kn47qFMrpLKF9vOsmagyn8MmskPu5u3PXV1iq3t1c/HfmJV7e8yoS2E3h00KNO9b8mGl+TSaiWTV1W/jjUJ5TZw2dXu66LcuHm6Ju5Ofrmatd5bsRzZ2zzw6U/VLv+LdG3cEv0LWeUh/qEsv367dVuN63LNKZ1mXZaWXRINDEzYs5Yt+I51iZmIYQQ9u2xH2LIKSrl66v74OlW9RdcR9Um2Idmnm7sTcjmaGoeozq3YG98NoeTc9l2MoNB7YJwcVGs3JdMq+be5XNt9YwM4IKeESyNSeDeMD+Gdggu32e3CH8u7t2SjUfTT0uoPv7rGAu3nGLBbUOICPCuMp5vNsdy5YDWjOxkGZgqPKDqFiCLd8Rxy4h2tAm2NMN/6IKuTHx9Ha9MrXlU3aUx8cy+tCeBPh4E+sANw9rxxspDZ93mjvPa4+fljp+XO53DmzGyU0j5sUd3DmFPfBZT+7eiua/Hac1BZ47pyDUf/F3+XCk4mJhDZKA3of5ehPr/c34lZs09C7ZjMmnmzRiIh1v1DZjuHN2BAG93ArzdGdo+mL0J2YzuEsrSXQncODyq/PW987yO/HU4rcbXxB7EpMTwf+v/j0Hhg3hh5Au4ujjX/5pofE0moRJCCCEcyer9ySzfm8QjF3alc5if0eE0isHtgvj7aBrH0/IY3C4Yfy93Nh5LY9uJTAa3syRKcZkF7IjNJPqpf340NJk1l/WNBGD7yQxe+m0/B5NyKSk1U2QyM7lSP7MP1h3l3+M6VZtMASRkFTCmFoN9JGUXEtn8n/1EBnpTatak5hbXYtsiWgZW2LZ59fGUadHMs/yxl5srLZp5/PPc3ZWUnCIACopNPPPzXtYdTCHLevUpt6gUk1nj4+HGW9P78eG6ozz03S4GtG3Ofyd3p2NoMwBOpOWxLyGbH2cOP2syBRBSMR53V/KLTJZzyymkZYXXNyKw6oTU3qQWpHLvmnsJ9QnltfNew8PVo+aNhKhEEiohhBDCzhSXmnnm5720D/HlpuHtjA6n0QxuH8zKfUnEphcwc0xH/L3dWLw9nm0nM5gxrC0AEQFeDG4XzBe3VD3a3KwFO7h+aFs+vXEQXu6uPP3THjLyTk9uPrtpMDM+2URIM89qB/WICPDmRFp+jTGH+XsRl/FP36T4zALcXBQtmnmQlF1IQYmpfJnJrEmvEEuonyfxmQXlCXJ85j/7aagP/zjK0ZRcfpg5jFA/L/bEZzH5jT+tfZwU53UO4bzOIRSWmHh12QEe/X4X395h6YbQMaQZ/xralhs+2cRXtw6hQ0izOh8/1M+ThKzC8ucJmYVnWds+mLWZR9Y9QnZRNp9P+pxAr0CjQxIOqskNSiGEEELYu8U74jiWmsfjk7vVeMXAkQ1uF8SGI2kUlpqICPBmYFQQaw+mkJlfQo+WAQCM6xbGsdQ8vt92ihKTmRKTmZ2xmRxOtgwskVdUSqCPB17uruyIzWTJjvgzjtM5rBnzbxzEEz/uYfnepCpjuWpga77dGstfh1MxmzWJWYUcTs49Y71Lerdk3p/HiE239PN6ZdkBLuoVgZurC+1CfCkqNbFqfxIlJjNvrjpEcYVBGSb3iuCdNUfIyi8hIauA+euP2+BVpPx18HJ3xd/Lncz8Yuau+KcpYUpOEb/vSSS/uBQPVxd8PN3O6CN0aZ9IHpzYles+2siJtLw6H39yr5Z88tcxErMKySoo4b21Rxp8To3ty31fsjFxI48MeoSuQV2NDkc4MLlCJYQQQtgRk1nz3toj9GjpX6smaI6sfUgzfDzdGBgVBICflzttgnwI8vXA1cXyhb+Zpxuf3TyIZ3/ey7NL92HWmm7h/jx+UTcAZk/pyXNL9/F/P+5mcPtgJveKILvSgAsA3Vv68/ENA7jp0824ufY+47Xt0zqQV6b2ZvbPe4lNz6eFnyfPXNqzvFlcmWkDWpOUXcS09zdQVGpmVKcWPH1JTwD8vdyZfWlPHv4uBrNZc/t57Qmv0Ffp3nGd+e8PMYx4eRVh/l5c2b8Vn/x13Cav5U0j2vHvr7fTb/Zywvy9uGVkO363Jo9aaz768xj3L9wJCrpH+PPclJ5n7GNq/1aUmMxM/3AjC24bUqfjXzOwNcdS8rhg7jqaebpxw7Ao/j6aVv4+2pujWUeZs3UO57U6j8s7XW50OMLBqbLhLh2VUmqL1npApWLHPikhhBDVsZtvZ41V/6w5kMwNn2zmjWv6cknvlg3dnRCGWH0gmcd/2M1fj4w1OpQzaK25bflt7Endw5LLltDCu4XRIQnHUG3947ztCCqpOA9EcnYhDy3aycDnVtDjyd8Y+9oa/rf8IPnFluFRtda8v/YIo19ZTZfHf2XYCyt56bf9FJX+0y76/oU7iXpkKTtiM8vLjqfmEfXI0hpjeX35QaIeWcr2kxm2PUkhhBAOb/H2OAK83bmg0rDfQtizwhITq/cnU2oyk5hVyNwVh5jQI8zosKq0KnYVfyf8zcy+MyWZEjbR5Jr8ZeYXc9k76+nftjnf3zmM1kE+xGcW8MG6o5xIy6dbhD9PLdnD2oMpvDatD71bBXA0NY8Hv93JoaRcPprxz4+RgT7uvPb7AT6/ueqOslXRWvP99lME+rjz/bY4+rZp3hinKYQQwgGVmMws35vEJX1aOnXfKeF8tIbXVxzk7q+24eXuypiuodw3vrPRYZ3BZDYxZ+scOgR0OGNaGiHqy7BPa6VUoFJqkVJqv1Jqn1JqqFIqSCm1XCl1yHpv82zjoz+O0czTjTlX9aF1kGUeh5aB3jx1SQ+6RfhzLDWPz/8+wZyr+9K/bXPcXF3oHObHu9f1Z93BFNYfTi3f1xX9WrEvIYe/j9Z+noVNx9JJzi7iqYt78NOu+PIZzoUQQpwbRtU/tbHrVBZ5xSZGWedCEsJReHu4suTuEex55gK2PjGeV6/sjZ+Xu9FhnWHFyRUczz7OXX3uwt3F/uITjqnGhEopta6Wt9/reOy5wG9a665Ab2Af8AiwUmvdCVhpfW5Tfx5OZWLPcFyq6ST51+FUIgK86dM68LTyloHe9GkTyB8VEipvd1dmjunAq8sO1Pr43207xfndwpjcyzJs68p9VY82JIQQotEYUv/UxqZj6QAMahdkxOGFcGpaa+bFzCPKP4pxbcYZHY5wIrVp8jcQuKOGdRSWCqpWlFIBwCjgBgCtdTFQrJS6FBhtXW0+sAZ4uLb7rY3M/GJC/TyrXZ6RV0xINctD/TzPmNti+uA2fLjuKKsPJNMu2Pesxy4oNvFLTCKvTeuNu6sLF/aM4LttcdXOiSGEEMK2jKx/auNoSi4hfp4EN6u+nhJC1M/u1N3sS9/Hk0OfxNXF1ehwhBOpTUK1Xms9v6aVlFLT63DcdkAK8IlSqjewFZgFhGmtE6zrJAJV9mZUSt0G3GZ9WqfehIE+HiRbZxWvSnNfj/JZxytLzikqbyZYxtPNlXvGdeJ/vx/kzWv6lpdvOpbODZ9sAiyzqC+/7zyW7UnE1UWVD9U6pU9Lrpu3kbTcIqk8hRDi3DCs/qmNk+n5tK1UzwghbGPJkSV4unpyQdQFRocinEyNTf601rW6Jqq1nlCH47oB/YB3tdZ9gTwqNa/QlvHcqxx+Vmv9gdZ6gHW42tSq1qnOiI4t+H1PImZz1SPbDusQTHxWwWmj94FlNvMdJzMZ3uHM+vPK/q3ILizhtz2J5WWD2gWx95kL2PvMBSy/7zzA0twvv7iUYS+uYsCzK5j51TZKTJofq5iEUAghRKMwrP6pjayCEpr7eth6t0I0eWZt5vcTvzO69Wj8PPyMDkc4mToNSqGUeqCa8vvqeNxTwCmt9Ubr80VYKrgkpVSEdZ8RQHId91ujW0a2I6ewlPu/3cmpjHwAErMKmf3zXvYlZNM+pBnXDm7DvQu2s+1kBiaz5mBSDnd+sZXhHYMZ0enMhMrN1YX/nN/5rLOCJ2YV8tfhVObNGMgvs0ZYbyO547wOfL/9lK1PUwghRNUMq39qo8RkxsNVRvcTwtYOZhwkvTCdUa1GGR2KcEJ1/dR+spryx+uyE611IhCrlOpiLRoH7AWWADOsZTOAH+sYX40CfTz4/q5huLkopry9nh5P/sb0j/7Gz8uNKGsfqGcu6clVA9vwn2920P3J35jx8SaGtA/m3ev6V7vfS3q3PGvfrO+3n6J7S39GdQ4h1M+r/Hbj8Cj2J+RwIDHH1qfaaBZvj2P4i6to98hShr+4isXb44wOSYhzYunRpUxYNIFe83sxYdEElh6ted45YV+MrH9qw0UpTNW0oBBC1N/GBMtvKEMihhgciXBGytKyoYaVlCqb5von4CJOnym4PfCE1rptnQ6sVB/gI8ADOArciCXBWwi0AU4A07TW6TXsp1FmqhdVW7w9jke/j6Gg5J9Jjr3dXXnh8mim9I00MDIhGtfSo0t5av1TFJoKy8u8XL14athTTG4/2cDImpxqZ6qv9Q7suP6Z9v4GABbePrQhuxFCVPLoH4+yOXEzK65cYXQownFVW//UdmLfedZ7L+DjCuUaSALuqWtEWusdQOWKCCy/Fgo79cqyA6clUwAFJSZeWXZAEirh1OZum3taMgVQaCpk7ra5klA5GHuufyICvNhyPMPoMIRwOoczD9MxsKPRYQgnVauESmvdDkAp9ZnW+vrGDUnYs/jMgjqVC+EsEvMS61QuRH10Dffnxx3xZBWUEOAtk44KYStxuXH0CeljdBjCSdX2ChUAWuvrlVJhwCAsw8WqCss+rnZD4TRaBnoTV0Xy1DLQ24BohDh3wn3DSchLqLJcCFvp1SoAgG0nMhjTNdTgaIRwDmZtJrc4lwDPAKNDEU6qrqP8TQGOAM8A72Np6vc+8C+bRybs0oMTu+DtfvpkeN7urjw4sUs1WwjhHGb1m4WXq9dpZV6uXszqN8ugiIQz6t+2OV7uLqw9mGJ0KEI4jbySPDRahksXjaauo/w9C9xYNneH9f42LBMjiiZgSt9IXrg8mshAbxSWSYtlQArRFExuP5mnhj1FhG8ECkWEb4QMSCFszsvdlREdQ/h1d4KM9ieEjbi5WBpkmbSphjWFqJ86NfkD2mitv61UNh/LrPJVzlElbGvx9jheWXaA+MwCWgZ68+DELuc8mZnSN1ISKNEkTW4/WRIo0eiu6BfJin1JrDuUwpgu0uxPiIbydLVMa1NYWljDmkLUT12vUCVb+1ABHFdKDQU6AK5n2UbYSNmQ5XGZBWggLrOAR7+PkXmghBDCiYzrFkaLZh588tdxo0MRwim4KBeauTcjsyjT6FCEk6prQvUhMML6+HVgNbATeMeWQYmqnW3IciGEEM7Bw82Fm0e0Z93BFHbGZhodjhBOIbJZJKdyThkdhnBSdUqotNYvaa2/sz7+DOgM9NdaP9EYwYnTyZDlQgjRNFw3pA0B3u68vGw/WktfKiEaqq1/W45mHTU6DOGk6nqF6jRa65Na6322CkacXXVDk8uQ5UII4Vz8vNy5b3xn/jqcxrI9SUaHI4TD6xXSi7jcOFILUo0ORTihGhMqpdTY2tzORbBNnQxZLoQQTce1g9vQNdyPp3/aQ1ZBidHhCOHQeof0BmBL0haDIxHOqDaj/M2rxToaaN/AWEQNykbWM3qUPyGEEI3PzdWFl67oxeXvrufpJXv431V9jA5JCIfVs0VPAj0DWX1yNRdEXWB0OMLJ1JhQaa3bnYtARO3IkOVCCNF09G4dyN1jOjJ35SHO6xLCpX3k81+I+nBzcWN069GsOLGCIlNR+VDqQthCneahUko9U82iIuAU8JvWWhp7CyHs2tKjS5m7bS6JeYmE+4Yzq98smV9K2K27x3Zk/ZFUHv5uF51C/eje0t/okIRwSBe1v4jFhxfz+/HfubjDxUaHI5xIXQel6Aw8DIwBOlrvHwb6AncCR5VSch1VCGG3lh5dylPrnyIhLwGNJiEvgafWP8XSo0uNDk2IKrm7uvD2tf0I9Pbgts+3kJZbZHRIQjikQeGDiPKPYuGBhUaHIpxMXRMqF+BqrfVIrfV0rfVIYBpg0loPAe4CXrR1kEIIYStzt82l0FR4WlmhqZC52+YaFJEQNQv18+K9f/UnJaeIGz/dTF5RqdEhCeFwlFJc3fVqdqTsYEuiDE4hbKeuCdVEYEmlsp+BC62Pv0AGpxBC2LHEvMQ6lQthL/q0DuTt6f3YE5/NHV9spbjUbHRIQjicKzpdQQvvFry38z2jQxFOpK4J1REsTfsqusNaDtACyG9oUEII0VjCfcPrVC6EPTm/exgvXB7NH4dSuevLrRSVmowOSQiH4uXmxU09b2Jj4kb+jPvT6HCEk6hrQnUL8IBSKlYp9bdSKhZ4ELjZurwL8IQtAxRCCFua1W8WXq5ep5V5uXoxq98sgyISom6mDWjN7Et7sGJfMnd8vpXCEkmqhKiLq7tcTZR/FC9uepFiU7HR4QgnoLTWddtAKXdgKBABJAAbtNaGzTiolNqitR5QqbhuJyWEaFJklD+HpowOoIzR9c9XG0/y2A8xDOsQzHv/6o+/l/u5OrQQDu/PuD+5c8Wd3N3nbm7vfbvR4QjHUG39U+eEyt4YXaEJIYQ4pyShquD7bad4aNEuOoQ045MbB9Iy0PtcHl4Ih/bA2gdYeXIlX0/+mq5BXY0OR9i/auufujb5Qyk1Xik1Tyn1k/X5AKXU2IZEJ4QQQoi6u7xfKz69cRBxmQVc9s5f7I3PNjokIRzG44MfJ9AzkEf/eJQik0xHIOqvTgmVUuoe4F3gEDDKWlwAPGvjuIQQQghRCyM6teDbO4aiUEx7fwO/7ZYRK4WojUCvQJ4Z9gyHMw/zwsYXjA5HOLC6XqG6Fzhfa/0iUDZe634sg1EIIYQQwgDdIvz5YeYw2of4cscXW3nx1/2UmmRYdSFqMrLVSG6NvpXvDn3Hdwe/Mzoc4aDqmlD5AbHWx2XtxN0BGSJFCCGEMFBEgDff3jGU6YPb8N7aI1z/8SZSc6UZkxA1mdlnJkMjhvLcxufYlrTN6HCEA6prQrUOeKRS2b+B1fU5uFLKVSm1XSn1s/X5p0qpY0qpHdZbn/rsVwghhDgbZ61/PN1cef6yaF6Z2outJzK46I0/2XAkzeiwhLBrri6uvDzqZSKbRXLPqns4mnXU6JCEg6lrQnUPcJlS6jjgp5Q6AEwD7qvn8WcB+yqVPai17mO97ajnfoUQQoizcer658oBrfnuzmF4e7gy/aO/eeGXfTIJsBBnEegVyDvnv4Obixt3rbiLlPwUo0MSDqROCZXWOgEYiCWJmg7MAAZprevcA1Yp1QqYDHxU122FEEKI+moq9U/PyACW/nsE1wxqw/vrjjLl7fUcTMoxOiwh7FZrv9a8M+4d0gvTufn3m0ktSDU6JOEgapyHSin1TG12pLV+sk4HVmoR8AKWflkPaK0vUkp9imXS4CJgJfCI1vqMBuBKqduA26xPW2itoyqHU5dYhBBCOIwGz0PVFOufFXuTePi7XeQUlfLQxC7cOLwdri52M6WXEHZlS+IW7lp5Fy19W/LRxI9o4d3C6JCEfWjQPFStK9w6YelDNQ7oCIy1Pu9Up2iUughI1lpvrbToUaArlqtgQcDDVW2vtf5Aaz3AOqGi/HwghBCiVppq/XN+9zCW/WcUozqF8OzSfTJnlRBnMSB8AG+Pe5v4vHhu/f1W0gqkH6I4uxqvUJ22slILgG+11t9VKLscuFJrfU0d9vMC8C+gFPAC/IHvtdbXVVhnNNZfDmvYl+Ez1QshhDhnGnRZpanXP1prft6VwNM/7SEjv4TbRrVn1rhOeLm7Gh2aEHZnU8ImZq6cSZhvGO+d/x6t/FoZHZIwVrX1T10TqiwgSGttqlDmBqRprQPqFVmFikspFaG1TlBKKeB1oFBrXXlUwcrbO1yFJoQQot5s1k6tKdc/mfnFPP/LPhZuOUXbYB+emxLNiE7SrEmIyrYnb+fulXfj4erBO+PeoVtwN6NDEsZpUJO/ig4DMyuV3QkcqWtE1fhSKRUDxAAtgGdttF8hhBDibJpU/RPo48HLU3vz1S2DAbhu3kbu+Hwrsen5BkcmhH3pG9qXzy78DDcXN25cdiMbEzYaHZKwQ3W9QtUX+AFwA+KASCzNJi7XWhsyE5oj/0IohBCizuxmJAVnqX8KS0x89MdR3l59BJPW3DGqPXeM7oCPh5vRoQlhNxLzErlzxZ0czz7O44Mf54rOVxgdkjj3bNPkD0Ap5Q4MAVoCCcAGrXVJg8JrAGep0IQQQtSKJFSNJCGrgBd+2c+SnfFEBHjx6KRuXNwrAksrSCFEVlEWD659kA0JG7iqy1U8POhh3F3cjQ5LnDu2S6jsjbNVaEIIIc7Kbr7dO2v9s/l4Ok8t2cOe+Gz6tgnkkQu6Mrh9sNFhCWEXSs2lzNk6h/l759M/rD+vnfcawd7y/9FE1L8PlVJqdq2OoNTTdYlICCGEEPZnYFQQS+4ewUtXRBOfWcBVH/zNTZ9uZn+iDLMuhJuLGw8MfIAXRr7A7tTdXL30anal7DI6LGGw2kzsmwP0ouZfBbdqrZvbKrDactZfCIUQQlRJrlCdQwXFJj5df5x31hwmt6iUy/u24j/jO9GquY/RoQlhuD1pe7hv9X0k5yfz737/ZkaPGbiouo73JhxI/Zv8KaXMWCqImiqxQq31Of+EbQoVmhBCiHKSUBkgM7+Yd9cc4ZP1x0HDdUPacsfo9oT6eRkdmhCGyirK4ukNT7P8xHKGtxzOcyOekyaAzkv6UAkhhHAKklAZKD6zgDkrDvLdtjjcXBTXDm7LHee1J9RfEivRdGmt+fbgt7y06SX8Pf15fsTzDG051OiwhO1JQiWEEMIpSEJlB46n5vHW6sP8sN2SWE0f3IY7z+sgiZVo0g5mHOTBtQ9yNOso07tOZ1a/Wfi4S/NYJyIJlRBCCKcgCZUdOZGWx1urDvP99jhcXRTTB7XhjvM6EB4giZVomgpKC3hj2xt8ue9LWvm1Yvbw2fQP6290WMI2JKESQgjhFCShskMn0/J5a/UhvtsWh4uCy/pGctuoDnQMbWZ0aEIYYkviFp746wnicuO4rvt13NP3HrzdvI0OSzSMJFRCCCGcgiRUdiw2PZ8P1h1l4ZZYik1mxncL4/bzOtC/7TkfBFgIw+WX5DNn2xy+3v81bf3b8uSQJxkUMcjosET92TahUkp10FofaVBINiIVmhBCNCmSUDmAtNwi5q8/zvwNJ8gqKGFQVBB3jG7PmC6hKGU3b6EQ58TGhI08tf4pTuWe4uL2F3P/gPtlJEDHZPOEaqbW+m2l1GogCYix3nZprY/XN8r6kApNCCGaFLv5Ni71T83yikpZsDmWeX8cJT6rkC5hftw0IopL+0Ti5e5qdHhCnDOFpYV8GPMhH+/+GB83H/7T/z9c3ulymbfKsTRekz+lVDCWiX97AT211rc2aId1P75UaEII0XRIQuWASkxmftoZzwfrjrI/MYcgXw+mD2rDv4a2JUxGBhRNyNHMo8z+ezZbkrbQJ6QPTwx9gs7NOxsdlqgd2yRUSikfYDrQBsuVqaXn+opUFTFJhSaEEE2HJFQOTGvNhqNpfPzncVbuT8JVKSZFR3Dj8Cj6tpF+VqJp0Fqz5MgSXt3yKtnF2VzZ+Upm9plJcy/5H7BzNkuofgXaAeuBEGAU8BnwH611aQODrBep0IQQokmRhMpJnEjLY/76E3y7JZacolL6tA7kxuFRTIqOwN1VmkEJ55dZmMk7O99h4YGF+Lj7cGfvO7m669W4u7gbHZqoms0SqnRgpNZ6j/W5D/AxcERr/d+GRlkfUqEJIUSTIgmVk8ktKmXRllg+XX+c42n5hPh5ctWA1lwzuA2RgTLMtHB+hzMO88qWV1gfv54o/ygeHPggo1qNMjoscSabJVQ3Aa8CucAOYCdwDJittY5sWIz1IxWaEEI0KZJQOSmzWbPmYDJf/n2SVQeSUcCYLqFcN6QtozqH4OpiN2+9EDantWbdqXW8suUVTmSfYHjkcP7T7z90CepidGjiHzZLqBYB/we4YhmEorf1Ng4oAA4BB7XWVzUk2rqQCk0IIZoUu/lWLfVP4zmVkc+CTbEs2BxLam4RkYHeTB/chmkDWhPi52l0eEI0mhJTCV/t/4r3d71PbnEuk9tP5u6+dxPZzJDrFuJ0NkuorgNeAB4EftZa5yqlLgXeBAYC3YAuWuv3GxZv7UmFJoQQTYokVE1IcamZ5XuT+HLjCdYfScPNRTGxZzjXDGzDsA7BuMhVK+GksoqymLd7Hl/t+wqTNnF1l6u5tdetBHkFGR1aU2a7YdOVUhcBTwF9gVKgCLhFa72wAQHWm1RoQgjRpNjNN2ipf86tIym5fLXxJIu2niKroITIQG+uHNCKqf1b0aq5j9HhCdEoEvMSeW/ne/xw+Ae83byZ0WMGM7rPwMdd/uYNYPt5qJRSIUAL4LjWuqCegTWYVGhCCNGkSELVxBWWmPh9bxILN8fy5+FUlIIRHVtw5YDWTOgeJhMGC6d0NOsob257kxUnVxDkFcQNPW7gqi5XSWJ1bjXexL5GkwpNCCGaFEmoRLnY9HwWbT3Foq2niMssIMDbnSl9WjJtYGt6tAwwOjwhbG5Xyi7e2fEOf8X/RZBXEDf2uJFpXaZJYnVuSEIlhBDCKUhCJc5gNmv+OpLKwi2nWLY7kWKTme4R/lzeL5JL+rQk1M/L6BCFsKkdyTt4d+e7rI9fT5BXEDf1vIkrO18piVXjkoRKCCGEU5CESpxVZn4xP+6IZ9HWU8TEZeGiYGSnEC7rG8mEHmH4eLgZHaIQNrM9eTvv7niXDQkbJLFqfJJQCSGEcAqSUIlaO5ycww/b41i8PZ64zAJ8PFy5oEc4l/WLZFiHFjK3lXAa25K28e7Od/k74W8CPAO4tuu1TO82nQBPafpqQzYd5W88cDUQqrW+WCk1APDXWq9qWIz1IxWaEEI0KXbzDVjqH8dhNms2H0/nh+1xLI1JIKewlFA/Ty7t05LL+raie0t/o0MUwiZ2JO/go5iPWHtqLd5u3lzZ+Uqu7349Yb5hRofmDGw2D9U9wCzgI+BRrXWAUqoH8KHWeliDw6wHqdCEEKJJkYRKNEhhiYlV+5P5YXscaw4kU2LSdA33Y0rfSC7p3ZKWgd5GhyhEgx3MOMjHuz/mt2O/oZTikg6XcGOPG4kKiDI6NEdms4TqCDBOa31cKZWhtW6ulHIFkrXWwTYItM6kQhNCiCZFEiphMxl5xfwck8AP206x7WQmAP3bNueiXhFMjo4g1F8GsxCO7VTOKT7d8ymLDy+m2FTM+LbjuaHHDUSHRBsdmiOyWUKVDERorU1KqXStdZBSygs4prWOsEGgdSYVmhBCNCmSUIlGcSItj593JfDTznj2J+agFAyKCuLi3i25sGc4wc08jQ5RiHpLLUjly31fsmD/AnJLcukb2pfru1/PmNZjcHWRudtqyWYJ1SJgu9b6uQoJ1UNAH631dBsEWmdSoQkhRJMiCZVodIeTc/hpZwI/74rnSEoeri6KYR2CuahXBBN7hBPo42F0iELUS15JHj8c+oEv9n1BXG4crZq14rru1zGl4xR83X2NDs/e2SyhigB+AloAkcBRIAe4SGud2MAg60UqNCGEaFIkoRLnjNaa/Yk5/Lwrnp93JXAiLR83F8XITi24qFdLxvcIw9/L3egwhaizUnMpq2NX89mez9iRsgM/dz+mdp7K9G7TCfcNNzo8e2XTUf4UMBBoC8QCm7TW5gaF1wBSoQkhRJMiCZUwhNaa3XHZ5clVXGYBHm4unNc5hAt7hjOuaxgBPpJcCcezM2Unn+35jBUnV6BQTGg7gendptM7pDeWr/3Cqv4JlVLqmdocQWv9ZB2Dsgmp0IQQokmxm9pd6p+mS2vNtpOZ/Lwrnl9jEknMLsTNRTGsYwsu6BHOhB5htJA+V8LBxOXG8eW+L/nh0A/kluTSLagbV3e9mgvbXYi3m4x+SQMTqk8qPPUCrgA2AyeANsAg4Dut9TW1jsYykMU6wBNwAxZprf9PKdUOWAAEA1uBf2mti2vYl1RoQgjRdDQooZL6R9ia2azZeSqT3/Yk8tvuRE6k5eOiYEBUEBf0COeCnuEyFLtwKPkl+fx89Ge+3v81hzMP4+/hz+WdLmdal2m09mttdHhGslkfqgXAt1rr7yqUXQ5cWceESgG+WutcpZQ78CeW+a3uA77XWi9QSr0H7NRav1vDvqRCE0KIpqOhCZXUP6LRlPW5+nV3Ist2J3IgKQeA3q0CuKBnBBf0DKddC+n4LxyD1potSVtYsH8BK0+uxKzNjGo1imu6XsPQlkNxUS5Gh3iu2SyhygKCtNamCmVuQJrWOqBekSnlg6VCuxNYCoRrrUuVUkOBp7TWE2vYXio0IYRoOmzW5E/qH9HYjqbk8tseS3K181QWAF3D/ZjYI5wLo8PpEuYnfVSEQ0jKS+Lbg9+y6OAi0grTaOPXhqu7Xs2lHS/F38Pf6PDOFZslVFuB+VrrNyqU3QPcqLXuV6eILBMCbwU6Am8DrwB/a607Wpe3Bn7VWvesYT9SoQkhRNPR4G+fUv8II8RlFrBsdyK/7Ulk8/F0tIY2QT6c3y2M87uHMjAqCHfXJveLv3AwJaYSlp9Yztf7v2ZHyg68XL24oN0FTO08lV4tejn7DwQ2S6j6Aj9gaXceh2Xo9FLgcq31tnpFplSgdZ9PAJ/WpkJTSt0G3GZ92kJrHVVpFanQhBDCOdnyClUgUv8IA6TkFLF8bxIr9iXx5+FUikvNBHi7M6ZLCOd3D2NU5xAZjl3YvX1p+1h4cCG/HP2F/NJ8OjXvxNROU7mow0XOetXKpsOmuwNDgJZAArBBa13SoOiUehIoAB5GmlwIIYSonk1//pT6Rxgtv7iUdQdTWbEviVX7k0nPK8bdVTGkfTDndwtjXLdQWjX3MTpMIaqVV5LHr8d+ZdHBRexJ24OXqxcToiZwZecrnW3oddslVLaglAoBSrTWmUopb+B34CVgBpYRA8s6Be/SWr9Tw76kQhNCiKajoYNSSP0j7JbJrNl+MoPl+5JYsTeJIyl5AHSP8Of87mGM7xZGz0h/Z/qCKpzM3rS9fHfwO5YeW0peSR4dAzsytfNULmp/EQGe9RpuwZ7YrMlftXNS1WUeKqVUL2A+4Aq4AAu11s8opdpjGbY2CNgOXKe1LqphX1KhCSFE09HQhErqH+EwjqbksmJfEiv2JrPlRDpmDeH+XozrFsr53cMY1iEYTzdXo8MU4gz5JfnlV612p+3G09WTCW0nMLXzVPqG9nXUHwVsllB9UqkoHDgP+EFrfW39YmsYqdCEEKJJsZtaWOofcS6l5xWzen8yK/YlsfZgCvnFJnw9XBnVOYTzu4UxpmsoQb4eRocpxBn2pe3ju0Pf8fPRn8kryaNDQAemdp7KxR0udrSrVo3X5E8pdQFwjdZ6RoN2VP/jS4UmhBBNhyRUoskrLDGx4WgaK6wDWyRlF1kmE24bxPndQzm/WxjtQ5oZHaYQp8kvyWfZ8WV8e/BbYlJj8HDxYEKU5apVv9B+jnDVqlETKhcgo77zUDWUVGhCCNGk2E2NK/WPsAdaa3bHZZf3u9qbkA1A+xBfxncLY3z3MPq2aY6ri9386wjB/vT9LDq4iKVHl5Jbkku7gHZM7TSVSzpcQqBXoNHhVcdmTf7aVyryAaYDl9Q0X0djkQpNCCGaFLv5Vij1j7BHpzLyWbnP0jTw76NplJg0Qb4ejO0ayvjuYYzs1AIfDzejwxQC+Oeq1aJDi9iVsgsPFw/GR43nys5X2uNVK5slVGYslUXZDvOxdN69V2u9tSER1pdUaEII0aTYTe0q9Y+wd9mFJaw9kMKKfUms3p9MdmEpnm4ujOjYgvO7W4ZkD/XzMjpMIQA4kH6ARQcX8fPRn0+7anVpx0vtpa+VfQ2bbktSoQkhRJMiCZUQ9VBiMrP5WDrL9yWxfG8SpzIKAOjTOpDx3S1NAzuFNrO3KwKiCSq/anVwEbtSd9lTXyubXaF6QGv9ahXl92mt/1fP4BpEKjQhhGhS7ObbntQ/wlFprTmQlMPyPZZBLXaeygKgbbAP53cL4/xuYQyMao6bq4vBkYqm7kD6Ab49+G15X6sOAR2Y3m06F3e4GG8373Mdjs0SqmyttX8V5ela66B6BtcgUqEJIUSTIgmVEDaWmFXIyv2WK1frD6dRbDIT5OvBxB5hTIqOYGj7YEmuhKHKrlp9vf9r9qXvw9/Dnys6X8E1Xa4holnEuQqjYQmVUmqs9eFPwEWVdtgeeEJr3bYhEdaXVGhCCNGkSEIlRCPKLSpl3cEUft2dyMp9SeQXm2ju486E7uFM6hXBsA7BuEtyJQyitWZb8ja+3PclK0+uRKEY22YsN/a4keiQ6MY+fIMTqmPWh22AkxUWaSAJeEFrvaQhEdaXVGhCCNGkSEIlxDlSWGJi7cEUfolJYOW+ZHKLSgnwdueCHuFc3i+SQe2CpM+VMExCbgJfH/ia7w5+R3ZxNsMjh3NHrzvoE9qnsQ5psyZ/n2mtr7dJSDYiFZoQjkdrTalZy6+coj7s5tub1D+iKSksMfHHoVR+iUng9z2J5BWbaB3kzeV9W3FFv1a0CfYxOkTRROWV5LFg/wLm75lPRlEGwyOH8/DAh2kX0M7Wh6p/QqWUGqW1Xmd9PLa69bTWq+odXgNIhSaEYzGZNQ9/t4ttJzL46tYhhAfIkL2iTiShEsJg+cWlLNuTyHdb4/jrSCpaw5guIdw8oj3DOwbLVSthiPySfL458A0f7PqAQlMhN/S4gVujb8XH3WbJfoMSqt1lk/ZWaPpXmdZaV57095yQCk0Ix1GWTMVlFDC0QzA/bI/ja0mqRN3YzTc1qX+EgPjMAr7dcorP/z5Bam4RXcP9uHVke6b0jcTVxW7+XUUTklqQyutbX2fJkSV0DOzInDFzaOtvk6EeZB4qIYSxKiZT824YgI+HG++uOcLCLbGSVIm6sJtvaFL/CPGPwhITS3bGM++PYxxIyqFruB/PXdaT/m0NGQRaCNbHr+fhdQ/j4eLBF5O+sMVogNXWP3XqwKCUeqCa8vvqGpEQoumoKpkCuHN0B6YNaM01H/5NYlahwVEKIYSoLy93V6YNaM1v947k7en9yCks5cr3NjDvz+oaNwnRuIa1HMbHEz8mpySH17a+1qjHqmuP8CerKX+8oYEIIZzXJ38dY9Ox9NOSqTJ3ju7AiI4tuPurbQZFJ4QQwlaUUkzuFcFv945kQvdwZv+8l+V7k4wOSzRRnZp34rxW57EzZWejHqdWCZVSaqx1QApXpdSYsufW2y1ATqNGKYRwaBf3bombi+KTv46fsWxHbCa/xCRw5+gO5z4wIYQQjcLPy503p/clxM+TxdvjjA5HNFGbEzez7tQ6egb3bNTjuNW8CgDzrPdewMcVysvmobrHlkEJIZxLmL8XX982hGs++BuAmWM6ApZk6uZPN/Py1F6M6xZmZIhCCCFsSGvND9vjSMstol0LX6PDEU2I1pr96fv5ZM8n/HrsV9oFtOPhQQ836jFrlVBprduBfc5DJYRwDJWTquEdW0gyJYQQTqag2MSKfUl88tcxtp3MZHC7IGmBIBqdyWxiT9oe1sevZ9nxZRzOPIyXqxe3Rt/KjT1vxM/Dr1GPX+dR/pRSYcAgoAUVRrvQWn9c7UaNSEZZEsKxJGUXcs0Hf5OSW8Scq/pIMiXqSkb5E8LOnEzL568jqfx1OJXV+5PJKzYRGejNv8d15Ip+rXCTSdyFjeUW57I7bTe7U3ezK2UXW5O2kl2cjULRK6QXl3S4hIlREwnwDLDlYW0zbLpSagrwBXAI6AHsAXoCf2qtxzQsxvqRCk0Ix5OSU0R8ZgG9WwcaHYpwPJJQCWGgjLxidsdnsetUFjGnsth1KpN46yitYf6ejO4cyqV9WzK4XbDMQyUarMRcQmxOLIcyDnE48zCHMw5zOPMwJ7JPoK0ft23929InpA/DI4czJGIIzb2aN1Y41f5B17YPVZlngRu11t8qpTK01n2VUjdiSa6EEKJWQvw8CfHzNDoMIYQQVTCZNUnZhZxMz+dwci6HknI4lJzLwaRcUnOLyteLCvahf1QQt7dtzvCOwXQIaYZSkkSJuikyFZGQm0Bcbhwnsk9wMuckJ7JPcCL7BPG58Zi0CQAX5UIbvzZ0DOzIpPaT6NWiFz1b9LT1Vah6qesVqmyttb/1cYbWurlSygVI1FqHNlaQNcQkvxAKIUTTYTff1qT+EY4sK7+Ek+n5xGbkE5ueb31cQGx6PnEZBRSbzOXr+nq40jHMj86hzegU1oweLQPo2TKAAB93A89AOAKzNpNemE5yfjIp+Skk5SeRkGdJnuJz44nPjSelIOW0bbzdvGnr35Y2fm1o69+WqIAoOgZ2pH1Ae7zcvAw6E8CGV6iSlVJhWusk4LhSaiiQCrg2JDohhBBCCNFwWmtyikpJzi4iOafwtPtTGQXEZliSp5zC0tO2C/Rxp02QD90j/JnQI4w2QT60bu5Dh9BmtAzwkitP4gwFpQUk5yefcUvKTyp/nFKQQqn59L81N+VGuG84kc0iGRE5gpbNWhLZLJKWzVrSxq8NLbxbONzfW10Tqg+BEcB3wOvAasAMNO70w0IIIYQQTZjWmsz8EpJz/kmQkqz3KWVlOUUkZRdSWGI+Y3tvd1daBnrRJsiH/m2b0ybIh1bNfWgd5E3rIB/8veRqk4BScykZhRmkFaaRVpD2z32FxykFlitNOcVnTkPr4+ZDqE8oYT5hDAgbQIhPSPnzUJ9QQn1CCfEOwdXFua7F1HmUv9M2VqoN4Ku13lfH7VoDnwFhWJpHfKC1nquUCgK+AaKA48A0rXVGDfuSJhdCCNF0NOhnS6l/hL0xmzXp+cXlCVJK9unJUXJOkSVpyi2iuPTMRMnP040Qf09C/TwJ9fMizN9yH+pv6asa5u9FqJ8nzTzdHO5Xf2EbxaZi0gvTT0+QKiRK6YXp5c8zizLLB3uoyNPVk2CvYIK9gwnxtiZJvv8kSaHelvtmHs0MOMNzpv6j/CmlxtbmCFrrVbWORqkIIEJrvU0p5QdsBaYANwDpWusXlVKPAM211mediUsqNCEax8m0fNoE+xgdhhCVNTShkvpHnBOlJjNpecX/NLmrnCDlFJKUXURqbhGl5jP/bAK83S1Jkr8nYX5e1qSpQsJkXebjUdfGRsIZFJQW1CpBSitMq/JKEliuJgV7B5cnSmfcewcT5BVEsFcwvu6+kpA3sA/VvFqso4H2tY1Ga50AJFgf5yil9gGRwKXAaOtq84E1QONObSyEOMPbqw/zyrIDPDixCzPHdDQ6HCFsRuof0VAlJrO1id0/CVJKdqWEKaeItNwiqsiTCPL1sCZDXnQK8yO0wlWkUGuyFOLniZe7czWJEmentSa/NL/aZnaV7/NL86vcj5+HX3lC1Ll5Z0tC5F11wuTt5n2Oz9J51ZhQaa3bNWYASqkooC+wEQizVnYAiViaZAghzqG3Vx/mzVWHAMrvJakSzkjqH1GRyaxJzS0iMauQxOxCkspvRdarSpZkKT2v+IxtXRQENytrdudJdGQAoX6ehFgTpbKEqUUzTzzcZJLbpkJrTXZxdvXN7QrTSC/452pSoamwyv0EegaWJ0E9g3tWmyAFeQXh4epxjs9SQN0HpbAppVQzLANc3Ku1zq54KVFrrZVSVTadUErdBtxmfdqi0QMVookoS6bKOjQXlpglqRJOSeqfpiWvqNSSJFmTpdMfF5GUVUhKbhGmSpeUXF0UIc08CfP3pFVzH/q1bX76FSVrX6VgXw/cXCVRairM2kxGYcZpo9qlFKSUDw1eliClF6ZTYi45Y3sX5WJJkqzJUGv/1tU2u2vu1Rx3FxkwxN41aFCKBh1YKXfgZ2CZ1vp/1rIDwGitdYK1nfsarXWXGvYjbdiFsIHKyVRFXu4u3DO2kyRVwh40uBG/1D/OpWz0u9iMfMuw4OVzKxUQn1lAYnbhGUOEA/h5uRHu70V4gBdh/l6E+3sRFmC5tzz2pIWvJy4uTb7fSJOSX5L/zxDgBWcOCV7dUOAKRbB3MC28W1R55ei0JMmzudONctdE1H9QisagLD8FzsfSAfjeCuWvAGkVOgUHaa0fqmFfUqEJ0UAn0/IZ9crqGtdb9+AYGahCGK2hg1JI/eOA8opKy5OkignTKWsSlVt05pxKrZp7ExnoTUSAtyVhCvAsT5zCA7xkMIcmKr8kn7jcuNNvOXHlE83mlJw5gIOvu+9pI9mF+oQS4hNy2lDgwd7BciXJ+dldQjUC+AOIwTKPFcBjWNqxLwTaACewDFubXsO+pEITwgbkCpVoTLE5scSkxDCp/aSG7qqhCZXUP3YsNbeIA4k5HEnJ5UhyLkdS8jiSkktC1ul9S3w8XGltnUOpVXMfWjW3zKXUurkPrYK8ZU6lJsxkNpGQl8CJ7BNVJk0ZRafPhuDt5l0+qWxL35aE+4b/MxS49ebr7mvQ2Qg7Y18JlS1JhSaE7VSVVEky1XQVlBaQnJ9MW/+2DdpPbHYsN/9+M6XmUq7tdi03R9/ckN3ZTfsrqX/qT2vNyfR8dsRmsjc+m70J2exPzCElp6h8nWaebnQI8aVDSDPah/gS1cLXkjA19ybI10OGcG7iSs2lnMg+wcGMgxzNOsqxrGMcyzrGiewTFJn++Ttyc3GjpW/L8qSplV8rIptFlt+CvILkb0nUVoOGTT99T0qNB64GQrXWFyulBgD+dZmHSghhn8qSprKkSpKppiu/JJ+7Vt7F3rS9vDX2LQZFDKrXfsqSqVt73cqoyFHc/LslmWpgUiUcTKnJTExcFuuPpLH1RAY7YjPLR8vzcHWhU1gzRnUKoVuEH13D/ekU1oxQP0/5oisAy8S0+9P3E5Maw/70/RxIP8CRzCMUmy1/Qy7KhchmkbQLaMfQiKG0C2hHG/82tPZrTYh3iPRXEo2uTgmVUuoeYBbwETDVWlwAvAEMs21oQggjlCVPryw7IMlUE1WWTLX2a81dve/iwXUP8sqoV+qcVFVMpq7sfCUA8ybMk6SqicgqKGH53iR+35PIhqNp5QNDdAxtxriuofRt05w+rQPpFNYMdxkhT1SQVZTFlsQtbE7azM7knezP2F8+CESwVzBdgrowvdt0OjfvTOfmnWkX0E6GCxeGqlOTP6XUEWCc1vq4UipDa91cKeUKJGutgxstyrPHJE0uhGgEJ9PyZQCKJqhiMvX0sKdxUS5sTtzMA2sfqFNSFZsTy83LTk+myiTlJXHz7zdzRacruLHnjXUN0W4uWUj9cyazWfPn4VQ+23CCtQeTKTFpWgZ4cV6XEIZ3bMHQ9sEEN/M0OkxhZ8zazO7U3ayJXcOfcX+yP30/Go2XqxfRIdFEt4imV4te9GzRkzBfmSJOGMZmTf78gFjr47JKwx04c5Y7IYRDk2Sq6akqmQIYGD6QV897tU5J1d60vRSbihnRcsQZy0J9QhkSMYRVJ1fVJ6ESdkhrzZoDKbzw6z4OJuXSopkHNwyLYnKvlvRuFSBN90SVDmYcZMnhJfxy7BdSClJwUS70CenDXX3uYlD4IKJbROPuKgOMCPtX14RqHfAI8FyFsn8DNY+3LIQQwq4tObKEk9knee/898qTqTIDwwdyZecreW7jc/w45cca9zUxaiJJeUnctOwmPp74MRHNIgDLF+/Xt77OzpSdfDj+w0Y5D3Fu5ReX8tj3MSzeEU+7Fr68flVvJkVH4Okm/VbEmczazOqTq5m/dz7bk7fjptwY0WoEE9pOYGTkSAK9Ao0OUYg6q2uTvwjgJyyzw0cCR4Ec4CKtdWKjRFhzTNLkQogaSPO9piE2J5bWfq3rvX2JuYSH1z1MYWkhc8bMOa1Pwu/Hf+f5jc/z/vj36RJ01vluT/PZns/4ev/XfDzxY8J9w3l96+tsSNjAh+M/rO8XJ7u51CH1D5SYzNz4yWb+OpLKveM6c+foDni4SX8oUbWtSVt5adNL7EvfR2SzSKZ3nc7FHS6muVdzo0MTojZsN2y6dVLEgUBbLM3/Nmmtz5y45hyRCk2Is3t79WFeWXaAByd2kQEmnNhHuz5i7va5zOo7i1t63VLv/VSVVNU3mSpTllQNazmMXam7GpJMgSRUduXzv0/wxOLdvHxFL6YNrH8yL5xbibmEOVvn8Nnezwj3Deffff/Nhe0uxM1FJlcWDsU2CZVSygO4AegDNKu4TGt9ff1iaxip0ISoXsV5pWQIdOf10a6PeH/X+xSaCvFy9eL2XrfbLKma1H4Sr25+td7JVJnP937OihMrmDtmbkOb9EhCZUeuen8D2YWl/PLvEdJPSlSpyFTEvavv5c+4P7mqy1XcP+B+vN28jQ5LiPqwWUL1NdAbS7O//IrLtNZP1ze6hpAKTYiqySS9TUPFZKqMLZOqTYmbmDdhXoOSKRuzm2/tUv/AhXP/ICLAi49vGGh0KMJO/ffP/7LkyBKeHPrkGSN+CuFgbDbK3wVAO611ZoPCEUI0qqqSKYDCEjNvrjoEIEmVE6gqmQIoNBXy/q73AeqdVLm7uPPKqFfIKc6RTuKiWl3D/Vh9IJnCEhNe7jIIhTjdX3F/seTIEm7vdbskU8Kp1bXn6ElAJpAQwo6dTMvnlWUHzkimyhSWmHll2QFOpuVXuVw4hticWOZun3tGMlWm0FTI3O1zic2JrXJ5bbi6uEoyJc5q2oDWZOaX8PFfx4wORdihT/Z8QoRvBLf3ut3oUIRoVHVNqD4DflRKXaOUGlvx1hjBCSHqrk2wDw9O7IKXe9X/3l7uLjw4sYuM+gcNSjaM1tqvNbP6zsLL1avK5V6uXszqO6tBo/4JUZOhHYKZ2COMOcsPsfl4utHhCDuSX5LPlsQtTGo3SeaSEk6vrgnV3UAY8Dwwr8LtIxvHJYRogJljOnLP2E5nJFXSh+ofH+36iEnfT+KjXY778XVLr1u4vdftZyRVtuhDJURtvXh5L1o19+bWz7ZwODnX6HCEnYjLjcOkTfbU/1KIRlOnhEpr3a6aW/vGClAIUT+VkypJpv5R1vcI4P1d7ztVUiXJlDjXmvt68OmNg3BzUVzz4d/sS8g2OiRhB8zWGXXcXeTqlHB+MvueEE6sLKkCJJmyqjyQQ9kADs6QVAGSTAlDtAn2YcFtQ3BViqve38DGo2lGhyQM5uNuaVaeXSwJtnB+NQ6brpQapbVeZ31cbV8prfUqG8dWKzJsrRA1O5mWL32mqH5UPHCOKzuxObFNoc+UDJtux05l5HP9x5s4mZbP05f24NrBbY0OSRjEZDYx5KshTO08lYcHPWx0OELYQv3noVJK7dZa97Q+rm4YH21Usz+p0IQQtRGbE8uk7yfVuN4vl//SFJISRyYJlZ3LKihh1oLtrDmQwtUDW/N/F/fA20OGVG+KbvztRrKLs/nuku+MDkUIW6i2/qmxyV9ZMmV9LH2ohBCGW3p0KRMWTaDX/F5MWDSBpUeX1riNjIonxLkR4O3OvBkDmTmmA99sieXit/6UflVN1OjWozmYcZDYbMcdUVWI2qjNFapaDYkuTf6EEOfC0qNLeWr9U6c12/Ny9eKpYU8xuf3kGrevqtmfMzT3a0LkCpUD+eNQCvct3ElWQQmPT+7Gv4a0RSm7eQtFI0vMS2TidxO5qedNzOo3y+hwhGioBjX5q81sfdLkTwhxTkxYNIGEvIQzyiN8I/h96u+12kfFpEqSKYdjN9/Gpf6pndTcIh74didrDqQwslMLXryiF5GB3kaHJc6Re1bdw87knfx2xW/lA1UI4aAa1OSvumZ+0uRPCHHOJeYl1qm8KjIqnhDnTotmnnw8YyCzL+3B1hMZTHx9HV9tPElNP+gK53Bzz5vJKMrgmwPfGB2KEI2m3sOmK6Vq7rQghBA2Fu4bXqfy6tzS6xZ+ufwXSaaEOAdcXBT/GhrFsntHER0ZwGM/xPCveZs4kZZndGiikfUJ7cOwlsP4ZPcn5JfkGx2OEI2iIfNQjbRZFEIIUUuz+p05sISXq1e92ufLABRCnFutg3z48pbBPDulJ9tPZjDh9XW8ufIQRaUmo0MTjWhmn5lkFGXwwa4PjA5FiEbRkITKbtqxCyGajsntJ/PUsKeI8I1AoYjwjaj1gBRCCOO5uCiuG9KWlfeP5vxuYby2/CAXzv2Dvw6nGh2aaCS9QnpxaYdLmb93PkezjhodjhA2V+OgFNVuqNR0rfVXNo6nPnFIp2AhhGg67ObHPKl/bGPtwRSe/HE3J9LyubRPS/47uRuhflVPbyAcV1pBGhcvvpiuQV35aMJHuKiG/KYvhCHqPyhFdewhmRJCCCGEYzuvcwjL7h3FrHGd+DUmkXGvruWDdUekGaCTCfYO5v7+97M5cTNf7//a6HCEsKk6J1RKqfFKqXlKqZ+szwfUdq6qCvv4WCmVrJTaXaEsSCm1XCl1yHrfvK6xCSGEEGcj9Y998nJ35T/jO7PsP6MY1C6I53/Zz4TX17FsT6KMBuhELu90OSMjR/L61tel6Z9wKnVKqJRS9wDvAoeAUdbiAuDZOh73U+CCSmWPACu11p2AldbnQgghhC19itQ/dqtdC1/m3TCQz24ahKebC7d/vpXpH25kb3y20aEJG1BK8fSwp/Fy8+LhdQ9TWFpY80ZCOIA69aFSSh0BxmmtjyulMrTWzZVSrkCy1jq4TgdWKgr4WWvd0/r8ADBaa52glIoA1mitu9RiP9KGXQghmo4G96GS+scxlJrMfL3pJP9bfpDMghKuGtCa+8Z3JtRf+lc5unWn1jFz5UymdJzCM8OeQSm76RopxNnYrA+VHxBrfVxWabgDxfUIqrIwrXWC9XEiEFbdikqp25RSW5RSW4AWNji2EEKIpkvqHzvk5urCv4ZGseaBMdw8vB2Ltp7ivFfW8OqyA2QXlhgdnmiAUa1GcXuv21l8eDHfHfrO6HCEaLC6XqFaBGzXWj+nlErXWgcppR4C+mitp9fpwGf+QpiptQ6ssDxDa11jO3b5hVAIIZqUxrhCJfWPAziemsdryw/y0854mvu4M3NMR64b0hYvd1ejQxP1YDKbuGvlXWxO3Mz8C+YTHRJtdEhC1MRmV6juAS5TSh0H/KzNJKYB99U/tnJJ1qYWWO+TbbBPIYQQoiZS/ziAqBa+vHlNX36+ZwQ9IwN4duk+xr22lkVbT2EySx7raFxdXHlp5EuE+oRy96q7OZVzyuiQhKi3OiVU1iYRA7EkUdOBGcAgrXWiDWJZYt0f1vsfbbBPIYQQoiZS/ziQnpEBfH7zYL68ZTBBvh488O1OLpy7jl9iEjBLYuVQAr0Ceef8dyg1l3LnijvJKsoyOiQh6qWuTf7eABZorddXKBsGTNNa31uH/XwNjMbS/jwJ+D9gMbAQaAOcsO4zvRb7kiYXQgjRdDSoyZ/UP87FbNb8sjuB/y0/yNGUPLqG+3Hv+Z2Y0D0cFxcZ6MBRbE3ayq2/30p0i2g+mPABnq6eRockRFWq/VCpa0KVAkRqrYsrlHkCsVrr0AaFWE9SoQkhRJNiN9+Spf6xHyaz5qed8byx8hBHU/PoHuHPrPM7MaF7mIwg5yB+PfYrD617iHFtxvHKea/g7uJudEhCVGazhCoZaKO1LqxQ5gOc1FobMtqRVGhCCNGk2M23Y6l/7E+pycwSa2J1PC2fnpH+3DuuM+O6hUpi5QC+3PclL256kQujLuSFkS/g6iIDjgi7YrOE6jvgGPCQ1tqslHIBXgQ6aa0va3CY9SAVmhBCNCl2861Y6h/7VWoys3hHPG+uOsSJtHx6tQpg1rhOjO0qiZW9+2T3J/xv6/+4pMMlzB4+GxdV1/HThGg0NkuoWgE/AxFY2pm3ARKAi7XWhgzPIhWaEEI0KXbzbVjqH/tXYjLzw/Y43lx1iNj0ArqG+3Hn6A5Mjo7AzVW+qNur93a+x9s73mZq56k8MeQJSaqEvbBNQgVgvSo1CGiNZZLfTVprc4PCawCp0IQQokmRhErUWYnJzE8743l3zREOJefSJsiH20a1Z2r/VjKPlR3SWvPm9jf5MOZDLulwCU8Pexo3FzejwxKi/gmVUmqU1nqd9fHY6tbTWq+qd3gNIBWaEEI0KZJQiXozmzUr9iXx9poj7IzNJMTPk5tHtOPawW3w85JBEOyJ1poPdn3AWzveYlybcbw86mU8XD2MDks0bQ1KqHZXmE3+WDWraa11+/rHV39SoQkhRJMiCZVoMK01G46k8c6aI/x5OBV/LzeuHxrFjcOjCG4mQ3bbk7KBKoZGDGXOmDn4uPsYHZJoumzX5M/eSIUmhBBNiiRUwqZ2xmby7pojLNubiKebC1P7t+Km4e1oH9LM6NCE1eLDi/m/9f9HzxY9eXPsmwR5BRkdkmiaJKESQgjhFCShEo3icHIOH6w7yuLt8ZSYzYzrGsYtI9sxuF2QjAxoB1aeWMnDfzxMqE8o74x7h6iAKKNDEk2PJFRCCCGcgt18s5X6xzml5BTx+YbjfP73CTLyS+gZ6c8tI9ozuVcE7jIyoKF2puzknpX3YMbMG2PeoF9YP6NDEk2LJFRCCCGcgiRU4pwoLDHx/bY4PvrzKEdT8gj39+KG4VFcM7ANAT4ygIVRYrNjuWvlXcTlxvHciOe4sN2FRockmg5JqIQQQjgFSajEOWU2a9YcTOajP46x/kgaPh6uTBvQmhnDomjXwtfo8JqkzMJMZq2exbbkbdwafSsz+8zE1UWGvxeNThIqIYQQTkESKmGYPfFZzPvzGD/tjKfEpDmvcwgzhrVldOdQXFzs5k+zSSg2FfP8xuf57tB3jIwcyYujXsTfw9/osIRzk4RKCCGEU7Cbb61S/zRdydmFfLXpJF9uPElKThFtgnz415C2TBvQWpoDnkNaa749+C0vbHyBSL9I5o6ZS4fADkaHJZyXJFRCCCGcgiRUwm4Ul5pZtieRzzYcZ/PxDLzcXbisbyT/GhJF95ZyteRc2Za0jfvW3EdBaQGzh89mQtQEo0MSzkkSKiGEEE5BEiphl/bEZ/H5hhMs3hFHYYmZQVFBXD+sLRN7hMvogOdAYl4i96+5n12pu7im6zU8MOABPFw9jA5LOBdJqIQQQjgFSaiEXcvML2bhllg+23CCUxkFhPp5ctXA1lw1sDWtmvsYHZ5TKzGVMHfbXObvnU+3oG68dt5rtPZvbXRYwnlIQiWEEMIpSEIlHILJrFm9P5kvNp5g7cEUAEZ3DuGaQW0Y2zUUN7lq1WhWn1zN4389jlmbeWrYU0yMmmh0SMI5SEIlhBDCKUhCJRxObHo+C7fE8s3mWJJzigj392Ka9apVZKC30eE5pfjceB5c9yC7UnZxeafLeXjgw/i4yxVC0SCSUAkhhHAKklAJh1ViMrNyXzJfbzrJukMpKGB0l1CmD2rDmK6huMrQ6zZVYi7hnR3vMC9mHq38WvHCyBfoHdLb6LCE45KESgghhFOwm2+cUv+IhohNz2fB5pMs3HKKlJwiIgK8uGpga64cIFetbG1r0lYe++MxkvKTuK3Xbdza61bcXWR4e1FnklAJIYRwCpJQCadiuWqVxJcbT/LHoVSUghEdW3DlgNZM6B6Gl7ur0SE6hZziHF7c9CJLjiwhukU0z414jnYB7YwOSzgWSaiEEEI4BUmohNOKTc9n0dZTLNp6irjMAvy93JjSN5JpA1rTo6U/StnNn7/DWnZ8Gc9seIbC0kJm9p3J9d2vx83FzeiwhGOQhEoIIYRTsJtvlFL/iMZiNmvWH0lj4ZZYftuTSHGpmW4R/kwb0IopfSJp7ivzKzVEakEqz/79LCtPrqRHcA+eGf4MnZt3NjosYf8koRJCCOEUJKESTUpWfglLdsaxcMspYuKy8HB1YXz3MK4c0IqRnUJkIIt60lrz+4nfeX7j82QXZ3Nb9G3cEn0L7q7St0pUSxIqIYQQTsFuvj1K/SPOtb3x2Xy7NZbF2+PIyC8h3N+LS/u25PK+regS7md0eA4pozCDlza/xNKjS+nUvBOzh82mR4seRocl7JMkVEI4u5Np+bQJdpw5NmJzYmntJzPYizqThEo0eUWlJlbuS2bR1lOsPZiCyazpHuHP5f0iuaRPS0L9vIwO0eGsiV3D7A2zSS1MZXrX6dzd92583X2NDkvYF0mohHBmb68+zCvLDvDgxC7MHNPR6HBq9NGuj5i7fS6z+s7ill63GB2OcCySUAlRQWpuET/tjOeH7XHsOpWFi4KRnUK4vF8kE7qH4+0howTWVnZxNnO2zmHRwUWEeIfw4KAHmdh2ogwGIso4TkKllLoAmAu4Ah9prV+sYX2bVmiLt8fxyrIDxGcW0DLQmwcndmFK38j67k6IRvf26sO8ueoQhSVmvNxduGdsJ7tOqj7a9RHv73qfQlMhXq5e3N7rdkmqRF002jcbo+sfIRrqcHIOP2yPY/H2eOIyC/D1cOWCnhFc3i+SIe2Dpb9VLe1K2cWzfz/LvvR9DGs5jMcGP0Zb/7ZGhyWM5xgJlVLKFTgIjAdOAZuBa7TWe8+yjc0qtMXb43j0+xgKSkzlZd7urrxwebQkVcIuVUymythzUlUxmSojSZWoo0b5Rmh0/SOELZnNmo3H0vlh+yl+jUkkp6hU+lvVUam5lG8OfMNb29+i2FTMzdE3c3P0zXi6ehodmjCOwyRUQ4GntNYTrc8fBdBav3CWbWxWoQ1/cRVxmQVnlEcGevPXI2Prs0shGk1VyVQZe0yqqkqmykhSJeqgsRIqQ+sfIRpLYYmJ5XuT+GF73Bn9ra4d3FaaBNYgJT+FV7e8yi/HfqG1X2seG/wYIyJHGB2WMIbDJFRTgQu01rdYn/8LGKy1vrvSercBt1mfemmte9ri+FGPLDVT9Yulj7842cUWxxDCFqIeWdoeOFKLVTscf3Hy0caOpybR86NrHW/MjBjD4xVNj9H1jxBCCMflkEmC1voDrfUA682WldnJOpYLYQhrkvQokF/NKvnAo/aQTAFYk6Qa45VkSti7Rqx/hBBCOCh7S6jigIrjKLeylp0rj3HmF758a7kQduX4i5NfBGZT9d/sbOtyuxEzI+as8VqXC2EUo+sfIYQQDsreEqrNQCelVDullAdwNbDkXB38+IuTvwJuBU5gaQd/ArjVWi6E3akiqbLLZKpMFUmVJFPCXhha/wghhHBcdtWHCkApNQmYg2XY2o+11s8ZG5EQ9i/qkaWPAC9gaeZn98lJ9Pzo8nglmRL2QuofIYQQ9WF3CZUtKKV+A1oYHYedaQGkGh2EnXPo10i5untoU0lxhSL7Ph8XwjGTaHAU5/o1auzjNcb+bblPW+wrVWt9gS2CaQyV6h/7/h+sHUc/B0ePH+Qc7IGjxw9yDrZQbf3jlAmVOFM1w/uKCpztNbL387GH+M51DI19vMbYvy33aQ/v+bnkDOfr6Ofg6PGDnIM9cPT4Qc6hsdlbHyohhBBCCCGEcBiSUAkhhBBCCCFEPUlC1XR8YHQADsDZXiN7Px97iO9cx9DYx2uM/dtyn/bwnp9LznC+jn4Ojh4/yDnYA0ePH+QcGpX0oRJCCCGEEEKIepIrVEIIIYQQQghRT5JQCSGEEEIIIUQ9SULloJRSrZVSq5VSe5VSe5RSs6zlQUqp5UqpQ9b75tZypZR6Qyl1WCm1SynVr8K+ZljXP6SUmmHUOdmaUspLKbVJKbXT+ho9bS1vp5TaaH0tvlFKeVjLPa3PD1uXR1XY16PW8gNKqYkGnVJZLK5Kqe1KqZ+tz+3mfJRSx5VSMUqpHUqpLdYyp/2bVEp1sZ5r2S1bKXWvM58zgFLqY6VUslJqd4Uym52zUqq/9e/osHVbdW7PsG7q+lljj85yDp8qpY5V+BvvY3CoZ1Xbz0d7VsU5ONp7UOt6wF5Vcw5PKaXiKrwPk4yO82yUUoFKqUVKqf1KqX1KqaGO9D5UE7/9vgdaa7k54A2IAPpZH/sBB4HuwMvAI9byR4CXrI8nAb8CChgCbLSWBwFHrffNrY+bG31+NnqNFNDM+tgd2Gg994XA1dby94A7rY/vAt6zPr4a+Mb6uDuwE/AE2gFHAFcDz+s+4CvgZ+tzuzkf4DjQolJZk/ibBFyBRKCts58zMAroB+xujPcZ2GRdV1m3vdDoc67h9ajTZ4093s5yDp8CU42Orw7nUavPR3u+VXEOjvYe1LoesNdbNefwFPCA0bHV4RzmA7dYH3sAgY70PlQTv92+B3KFykFprRO01tusj3OAfUAkcCmWP0Ks91Osjy8FPtMWfwOBSqkIYCKwXGudrrXOAJYDVc4C7Wis55prfepuvWlgLLDIWl75NSp77RYB46y/jF8KLNBaF2mtjwGHgUGNfwZnUkq1AiYDH1mfK+z/fJrK3+Q44IjW+gROfs5a63VAeqVim5yzdZm/1vpvbalJP6uwL7tUj88au3OWc3AYdfx8tEuVz8GJVPf5IBqBUioAyw9f8wC01sVa60wc5H04S/x2SxIqJ6AsTbn6YvlFMUxrnWBdlAiEWR9HArEVNjtlLauu3ClYm07sAJKxfGE7AmRqrUutq1Q83/LXwro8CwjGvl6jOcBDgNn6PBj7Oh8N/K6U2qqUus1a1lT+Jq8GvrY+birnXJGtzjnS+rhyuV2r42eNXap8DlrrjdZFz1mba76ulPI0LsIazaH2n4/2ag6nn0MZR3kPoG71gL2q6hwA7ra+Dx/bc3M5LK1PUoBPrM1HP1JK+eI470N18YOdvgeSUDk4pVQz4DvgXq11dsVl1l93HeoXRlvTWpu01n2AVliuwnQ1NqL6U0pdBCRrrbcaHctZjNBa9wMuBGYqpUZVXOisf5PWfhmXAN9WXuas53w2TfScHf6zpvI5KKV6Ao9iOZeBWJpnPmxchNVzkM/HszrLOTjEe1CBM9QDVZ3Du0AHoA+QALxmXHg1csPSLPtdrXVfIA9LE79ydv4+VBe/3b4HklA5MKWUO5Zk6kut9ffW4iRrkxms98nW8jigdYXNW1nLqit3KtZLxauBoViaHLlZF1U83/LXwro8AEjDfl6j4cAlSqnjwAIsTVnmYkfno7WOs94nAz9g+WLZFP4mLwS2aa2TrM+bwjlXZqtzjrM+rlzuEGr5WWPXKpzDBdbm5VprXQR8gkHNnWuhrp+P9uiMc1BKfeFA7wFQ53rALlV1DlrrJOuPDmbgQ+z7fTgFnKpwlXkRlgTFUd6HKuO35/dAEioHZW0bPg/Yp7X+X4VFS4Cy0bJmAD9WKL9eWQwBsqyXfZcBE5RSza2XTidYyxyeUipEKRVofewNjMfS12w1MNW6WuXXqOy1mwqssv6CswS4WllGzWsHdMLSaf6c0lo/qrVupbWOwtK8bJXW+lrs5HyUUr5KKb+yx1j+lnbTNP4mr+Gf5n7QNM65Mpucs3VZtlJqiPVz7voK+7JL9fissTvVnMP+Cl++FJb+Frur24eR6vH5aHeqOYfrHOU9gHrVA3anunMoex+sLsOO3wetdSIQq5TqYi0aB+zFQd6H6uK36/dA28HIGHKr+w0YgeVS7S5gh/U2CUub8ZXAIWAFEGRdXwFvY2nXHwMMqLCvm7AMTHAYuNHoc7Pha9QL2G59jXYDT1rL22NJIA5jaaLlaS33sj4/bF3evsK+/mt97Q5gByOOAaP5ZwQouzgfaxw7rbc9wH+t5U79Nwn4YrnyF1ChzNnP+WsszS1KsPySeLMtzxkYYP2fPQK8BSijz7mG16NOnzX2eDvLOayyvm+7gS+wjgRoz7fafD7a+63SOTjMe1DXesAeb2c5h8+t78MuLIlJhNGx1nAefYAt1ngXYxlN1ZHeh6rit9v3QFmDFkIIIYQQQghRR9LkTwghhBBCCCHqSRIqIYQQQgghhKgnSaiEEEIIIYQQop4koRJCCCGEEEKIepKESgghhBBCCCHqSRIqIYQQQghhN5RSg5RSG5RS65RSXyul3I2OSYizkYRKCCGEEELYk1hgrNZ6FHAcuNTYcIQ4O0mohHAASimtlMpTSj1ndCyVKaVWKaUKlVJ/Gh2LEEKI+rOXukZrnaC1LrA+LQbMZcukzhH2SBIqIRpAKTVdKbVFKZWrlEpQSv2qlBpRYfkNSqkYpVS+UipRKfWuUiqwnofrrbX+r20itx2t9VjgDqPjEEKIpkoptUYplaGU8qzP8kp6a63/q5R6VCn1a6X9HKqm7OoGhF8tpVRbYALwU1mZ1DnCHklCJUQ9KaXuA+YAzwNhQBvgHaxNE5RS9wMvAQ8CAcAQoC2wXCnlYUDIQgghnIxSKgroBewDLqlm+UhAV7X8LNYBw5RSrtb9RADuQN9KZR2t69Y17nBrolf5Fm5d7g98DtygtS6p6/6FOJckoRKiHpRSAcAzwEyt9fda6zytdYnW+iet9YPWiuBp4B6t9W/WZceBaUAUcJ0NYjhu/QVxr/WXx0+UUl41Lauw/EGl1C5r8455Sqkw6xW2HKXUCqVUc+u6DyulFlU69lyl1BsNPQchhBANdj3wA/ApMKOa5X+fZXl1NmNJoPpYn48EVgMHKpUd0VrHQ93qFq11otZ6dBW3RKWUG7AAeFprfaAOMQthCEmohKifoYAXlkqsKsOsy7+vWKi1zgV+AcbbKI5rgYlAB6Az8HgtlwFcYY2jM3Ax8CvwGBCC5bPh39b1FgCTlFJ+ANZfJqcBX9noHIQQQtTf9cDXwCJgrFIqrIrlX1pvE6tYXiWtdTGwERhlLRoF/AH8Wams8tWp2tYtZ3MNMBh4wnrV6qraxCyEUSShEqJ+goFUrXVpNctbnGV5AtDCRsPCvqW1jtVapwPPYamEarMM4E2tdZLWOg5LJblRa71da12IJVHsC6C1PgFsAy6zbjcWyNda/12PeIUQQtiItc+uL7Da+lm/EpheaXlbYKHWeitwpOLyWljLP8nTSCx1xR+VytZW2qZWdcvZaK0/11oHV7hq9U0dYhbinJOESoj6ScOSFLlVszz1LMsjrMttMSxsbIXHJ4CWtVwGkFThcUEVz5tVeP4V/yRk05GrU0IIYQ9mYEmWTNbnX3F6s74ZwO9a69RqltdkHTBCKRUEhGitDwHrsfStCgJ6cuYVqrrULUI4BUmohKifDUARMKWG5ZdXLFRKNQMuBFaebVjYOmhd4XEbIL6Wy+rqW2C0UqoVlitVklAJIYSBlFLenNn8egnQUSnVu8Ly86yjzCYC/wF6K6V61/IwG7AMqnQr8BeA1jobS31yKxCvtT5mkxMSwoFJQiVEPWits4AngbeVUlOUUj5KKXel1IVKqZety58G3lRKXWBdFgUsBE5hGbkIqHpY2DqYqZRqZf2l8L/AN7VcVida6xRgDfAJcExrva+++xJCCGETU4B0YKdSyss68JAJSz/d663LTUB3LINI9AG6YWmGd31tDmD90W8LcJ91uzJ/WsvqPLqfEM6ouuZKQogaaK1fs/7i9ziWzr45wFYs/ZXQWr+slEoDXsUyMEQ2sBi4VmtdBDYZFvYr4Hcszfl+BJ6t5bL6+Ar4DHiogfsRQgjRcDOwjBpbUMWyJKA38InW+mTFBUqpt4A3lFIPn6UfcEVrsQzEVHEi3T+Au5GESggAlNba6BiEaJKs/auWAK9prVfWsG4hliaEb2itn7CWHQdu0VqvqGL9apfZmlJqOZY5tjZprcc19vGEEEI0jqrqGnsjdY6wR3KFSgjjVBwW9gng3epGMtJae1VVbg+01rYaAl4IIYSB7LmuKSN1jrBHklAJYRCt9edU6EslhBBCCCEcjzT5E0IIIYQQQoh6klH+hBBCCCGEEKKeJKESQgghhBBCiHqShEoIIYQQQggh6kkSKiGEEEIIIYSoJ0mohBBCCCGEEKKeJKESQgghhBBCiHqShEoIIYQQQggh6kkSKiGEEEIIIYSop/8HDSAM+jPpjBMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=False)\n",
+    "\n",
+    "\n",
+    "###########################################\n",
+    "# GCM bifurcation\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    if ADSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ADSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp))*1.00,(1-ADSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "\n",
+    "color=\"C0\"\n",
+    "for i, exp in enumerate(explist_icon_noWBF):\n",
+    "    if noWBF_DSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif noWBF_DSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"mlo_aqua_3750ppmv_13lat_hice_unlim_damped\", \"mlo_aqua_5000ppmv_13lat_hice_unlim_damped\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A_noWBF = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp)[:-4]),(1-noWBF_DSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "# EBM\n",
+    "ax[1].plot(dA_Jor, x, c=\"C2\")\n",
+    "ax[1].plot(dA_Jor_nocld, x, c=\"C0\")\n",
+    "\n",
+    "# axes\n",
+    "yticks_deg = [0, 10, 20, 30, 45, 60, 90]\n",
+    "for axi in ax:\n",
+    "    \n",
+    "    axi.set_yticks(1-ICON_tools.icelatosic(yticks_deg))\n",
+    "    axi.set_ylim(0, 1)\n",
+    "    axi.set_yticklabels(yticks_deg)\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['bottom'].set_position(('outward', 5))\n",
+    "    axi.spines['top'].set_visible(False)\n",
+    "    axi.spines['right'].set_visible(False)\n",
+    "\n",
+    "ax[0].set_ylabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_xscale('log')\n",
+    "ax[0].set_xlabel(\"CO$_2$ [ppmv]\")\n",
+    "ax[0].set_xticks([2e3, 3e3, 4e3, 5e3, 6e3, 7e3, 8e3, 9e3, 10e3])\n",
+    "ax[0].set_xticklabels([\"2000\", \"3000\", \"4000\", \"5000\", \"\", \"7000\", \"\", \"\", \"10000\"])\n",
+    "ax[1].set_xlabel(r\"$\\Delta$A [Wm$^{-2}$]\")\n",
+    "\n",
+    "\n",
+    "# legends and annotations\n",
+    "#ax[0].annotate(\"ICON-A-WBF\", [0.99, 0.95], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\")\n",
+    "#ax[0].annotate(\"ICON-A\", [0.99, 0.88], color=\"C0\", xycoords=\"axes fraction\", ha=\"right\")\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-A\", [0.01, 0.9], color=\"C0\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "ax[1].annotate(\"strong cloud masking\", [0.2, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\")\n",
+    "ax[1].annotate(\"weak cloud masking\", [0.2, 0.9], color=\"C0\", xycoords=\"axes fraction\", ha=\"left\")\n",
+    "\n",
+    "ax[0].annotate(\"a) GCM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1].annotate(\"b) EBM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig5_ICON-A-WBF_bifurcation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b973d7e-0740-48ce-abf9-63182ba5f167",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "### Fig. 5 optional: ICON-A (Braun et al., 2022) & ICON-A-WBF, both with negative cloud feedback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "9a872890-b7ab-44b6-b7ee-417040b01e82",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=False)\n",
+    "\n",
+    "\n",
+    "###########################################\n",
+    "# GCM bifurcation\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    if ADSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ADSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp))*1.00,(1-ADSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "\n",
+    "color=\"C0\"\n",
+    "for i, exp in enumerate(explist_icon_noWBF):\n",
+    "    if noWBF_DSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif noWBF_DSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"mlo_aqua_3750ppmv_13lat_hice_unlim_damped\", \"mlo_aqua_5000ppmv_13lat_hice_unlim_damped\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A_noWBF = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp)[:-4]),(1-noWBF_DSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "# EBM\n",
+    "ax[1].plot(dA_Jor_negcf, x, c=\"C2\")\n",
+    "ax[1].plot(dA_Jor_nocld_negcf, x, c=\"C0\")\n",
+    "\n",
+    "# axes\n",
+    "yticks_deg = [0, 10, 20, 30, 45, 60, 90]\n",
+    "for axi in ax:\n",
+    "    \n",
+    "    axi.set_yticks(1-ICON_tools.icelatosic(yticks_deg))\n",
+    "    axi.set_ylim(0, 1)\n",
+    "    axi.set_yticklabels(yticks_deg)\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['bottom'].set_position(('outward', 5))\n",
+    "    axi.spines['top'].set_visible(False)\n",
+    "    axi.spines['right'].set_visible(False)\n",
+    "\n",
+    "ax[0].set_ylabel(\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_xscale('log')\n",
+    "ax[0].set_xlabel(\"CO$_2$ [ppmv]\")\n",
+    "ax[0].set_xticks([2e3, 3e3, 4e3, 5e3, 6e3, 7e3, 8e3, 9e3, 10e3])\n",
+    "ax[0].set_xticklabels([\"2000\", \"3000\", \"4000\", \"5000\", \"\", \"7000\", \"\", \"\", \"10000\"])\n",
+    "ax[1].set_xlabel(r\"$\\Delta$A [Wm$^{-2}$]\")\n",
+    "\n",
+    "\n",
+    "# legends and annotations\n",
+    "#ax[0].annotate(\"ICON-A-WBF\", [0.99, 0.95], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\")\n",
+    "#ax[0].annotate(\"ICON-A\", [0.99, 0.88], color=\"C0\", xycoords=\"axes fraction\", ha=\"right\")\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-A\", [0.01, 0.9], color=\"C0\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "ax[1].annotate(\"strong cloud masking & negative cloud feedback\", [0.2, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\")\n",
+    "ax[1].annotate(\"weak cloud masking & negative cloud feedback\", [0.2, 0.9], color=\"C0\", xycoords=\"axes fraction\", ha=\"left\")\n",
+    "\n",
+    "ax[0].annotate(\"a) GCM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1].annotate(\"b) EBM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig(\"plots/Fig5opt_ICON-A-WBF_bifurcation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fc92ac9-88e9-4ed7-aa5c-aac85d58a0b1",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Fig. 7: ICON-A-WBF & ICON-ESM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "23b8503b-25c2-4a7f-ad04-60ca8825be5d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=False)\n",
+    "\n",
+    "\n",
+    "###########################################\n",
+    "# GCM bifurcation\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    if ADSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ADSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp))*1.00,(1-ADSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "\n",
+    "color=\"C4\"\n",
+    "for i, exp in enumerate(ESMexplist):\n",
+    "    if ESMDSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ESMDSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_7000_22_0S\", \"ape_7000_13_0S_snowcap\", \"ape_8000_22_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_ESM = ax[0].scatter(float(ICON_tools.find_co2_expname(exp)),(1-ESMDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "# EBM\n",
+    "ax[1].plot(dA_Jor, x, c=\"C2\")\n",
+    "ax[1].plot(dA_Jor_BASIR, x, c=\"C1\")\n",
+    "ax[1].plot(dA_Jor_BASIR_cf, x, c=\"C4\")\n",
+    "\n",
+    "# axes\n",
+    "yticks_deg = [0, 10, 20, 30, 45, 60, 90]\n",
+    "for axi in ax:\n",
+    "    \n",
+    "    axi.set_yticks(1-ICON_tools.icelatosic(yticks_deg))\n",
+    "    axi.set_ylim(0, 1)\n",
+    "    axi.set_yticklabels(yticks_deg)\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['bottom'].set_position(('outward', 5))\n",
+    "    axi.spines['top'].set_visible(False)\n",
+    "    axi.spines['right'].set_visible(False)\n",
+    "    \n",
+    "ax[0].set_ylabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_xscale('log')\n",
+    "ax[0].set_xlabel(\"CO$_2$ [ppmv]\")\n",
+    "ax[0].set_xticks([5e3, 6e3, 7e3, 8e3, 9e3, 10e3])\n",
+    "ax[0].set_xticklabels([\"5000\", \"6000\", \"7000\", \"8000\", \"9000\", \"10000\"])\n",
+    "ax[1].set_xlabel(r\"$\\Delta$A [Wm$^{-2}$]\")\n",
+    "\n",
+    "\n",
+    "# legends and annotations\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.99, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-ESM-WBF\", [0.99, 0.9], color=\"C4\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "\n",
+    "ax[1].annotate(\"small BASIR\", [1, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[1].annotate(\"large BASIR\", [1, 0.9], color=\"C1\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[1].annotate(\"large BASIR & positive cloud feedback\", [1, 0.83], color=\"C4\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "\n",
+    "ax[0].annotate(\"a) GCM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1].annotate(\"b) EBM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig7_ICON-A-ESM_bifurcation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1839eea5-20ec-4c32-9317-f51c429b3369",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "### Fig. 7 optional: ICON-A-WBF (with negative cloud feedback) & ICON-ESM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "3bd731cb-109a-4886-a6ca-6ac28b2d0b8e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Bd/L6jterLqZWC48s5PyO5zOjywwAfjvxW63zDo8YzraUbYAjERsWPoyh4UMrlQ0KG1SpC1+ZwtJCfj3+K4NCa24BE+3PsexjfHXwK766+Cs23bCJt6e9TQefDozvMJ47B97JjK4z2HzDZubPPnMNz09Hf+KpsU+x8fqNeJu8uW/FfdzQ9wbWXruWm/rdxH0r7iOrKKt8/sVHF/PP8/7J6mtWY7Vb+XjfxwAcyTrCsxuf5fkJz7PqqlXkleSRWpBaa6wvbX2J/af389lFn7Hu2nX8dfhfa/ybfWj1Q0T4RLDy6pX8d9J/eW37a2xK2lTvvrDarMxdNZeLu13MuuvWMT1mOsvjl9f5mT3pe+gV1Iu1165lZteZPLTmIfam72XxnMX8e/y/+demf1FgLQDAy8OL58Y/x/rr1vO/Kf/jm9hvWJHgSOQWxC0grySPZVcuY+21a3li7BNYjJUP6E/knuDWX2/lml7XoPYFM++fOzAUeWLvmMOdz01m9JBB7E3fS5eALqy9Zi239r+Vp9Y/Rdmo0o+sfYSBoQNZd+067ht8Hz8f/bnObRsQOoBfjv3C94e/r3ffVbT6xGq+uvgr5s+ez9LjS/n91O+AIzF/b897vDLpFdZcs4Zh4cN4eM3DAGQWZfLX1X9l7rC5rL12LV38u7ArdVf5MjWa2wfezsqrVrLgsgUk5yfz1s63APj3hH8T5RPF65NfZ/MNm6t1Szy/0/kczz5OfE58edniY4vLD9hf3vYy8TnxzLtkHovnLCa1IJW3d71d47b9evxX3tr5Fv8a/y82Xr+R1ye/TqA5sNp87+5+l91pu/nuku+YN3see9L38M7udxq0/57b9ByeRk9WXrWSZ857hh8P/1jjfJ5Gz/JujfMumccvV/yCXdv544o/0iu4FyuuWsF709/js/2f8ftJx3fw5YEvWZWwio8u/IiVV63E3+zPcxufAxx/j//c+E/+Nf5frLxqJVnFWaQUpFRa56qEVUyPmc6669Yxs+tM5q6ai9VuBaCTXyc+uegTNly/gXsH38tj6x4jrSCtwftt/+n93L38bh4b9ViDkymQhEoIIVqMrOIswrzCap2eWZRZ6/QwrzCyirMqlV3V6yqS8pNYe2JtvesuLC1kafxSZnabiclgYlrMNBYeWVjr/CMiR5xJnlK3MSxiGMMihlUqGxFReUjkuavmMu7LcYz7chwbTm3glgG3VJr+yb5PyqdP+HpCvTGLtsOojFjtVo5kHcFqt9LBtwOd/DvV+ZlLu19Kj6AeeBg82HBqAzF+MVzS/RI8DB7M7DaTrgFdK50UuKzHZXQJ6ILFw8KMLjOIzYgFYGn8UiZ1nMSwiGGYjCbuH3p/rYMj27WdH+N+5NFRjxLhE4HRYGRI+JDy6xfLJOcnszNtJ38Z/hfMRjN9gvswp+ecOv+myuxK20WpvZQ/9PsDJoOJ6V2m19jSW1EH3w5c3vNyjAYjF3a9kOT8ZO4ZfA+eRk/GdRiHyWgiITcBgJGRI+kV1AuDMtA7uDczu85ka/JWAEwGE1nFWSTkJGA0GOkf0h9fzzM32j2SfYTbf72diyMv59gnmvxVfpQEZxPY0cKUSaMI9PUHHNcOXdnrSowGI7N7zCatMI3TRadJyktiX/o+7h96PyajiWERw5jUaVKt25WQk8D/bfg/PpzxIR/s+YAfDv8AOFoohn42lNyS3Fo/e/uA2/H39CfKN4qRkSM5mHEQgG8PfcsdA++gW2A3PAwe3DnoTmIzYjmVd4q1J9fSPbA7U2Om4mHw4Ia+NxDiFVK+zM7+nRkXPQ5PoyfBlmBu6n8TW1O21vndlPHy8OKCzhew+NhiwNHj4Fj2MSZ1moTWmvmH5/PwyIcJMAfgY/LhjoF3sOT4khqX9f3h77l1wK0MCB2AUorO/p3LW9gqWnR0EXcPvpsQrxCCLcHcO/hefj5SdwILYLPbWB6/nPuH3o+3yZueQT2Z3WN2g7YTYG/6XjKLM7l38L2YjCY6+XXiyl5X8suxXwDHd/DAsAeI9InE0+jJfYPvY1n8MkrtpSyNX8r5Hc9nROQIPI2e/HHIH8tbFsv0C+nH9C7TMRlM3NT/JoptxexO2w04WuHCvcMxKAMXdr2Qzn6d2ZO+p0H7bXvqdh5Y+QD/Gv8vzu90foO3F6TLnxBCtBiB5kDSCtNqnR5kCap1elphGh18O1Qq8zR6cvegu3lj5xv8v4n/r7x8W8o27l1+LwDRPtH8eNmPrEhYgVEZmdhhIgCzus7izmV3klGUQXFpMZcuuLT885tv2MzwiOE8tf4psouz2Z22mxcmvIC3yZu0gjSyi7PZkbqDP/T9Q6V4Xr3gVcZGj8Vmt7EqcRW3LrmVBZctKO9OdHP/m+Uaqnaqs39nHh75MG/teou41XGcF30eD418iHDv8Fo/UzagCTi610X5RlWaHu0TXamlqWK3NYuHhYJSR4tNWkFapWV5eXjVeLYfHCc1im3FdPKrO9lLLUglwNNxYFwej280+07vq/Nz4PhbDvcOr3QQGe1T/WC5oooH/WUtShW312w0l7dQ7U7bzSvbXyEuMw6r3UqJrYTpXaYDcHH3i0nOT+bhNQ+TW5LLxd0u5oFhD5S3NC86ugjPIm8KP4/CU9kJmlnAvRdfy+1Ll1WKp+K6vTy8ACiwFpBZnEmAOaC8DCDSO5LkguQat+v7w98zqdMkRkSO4J1p73DzkpsB6OjXkd5BvfHz9Kt1n1T7vp3bn5SXxPObn+elLS+VT9doUgtSSS1IJdL7zG9BKUWET0T5+/TCdF7Y/ALbU7aTX5qPXdvx9/SvNYaqZnadyUtbX+Lewfey+OhiJneejJeHF6cLT1NYWsg1P19TKSabrnl0wOT85Hp/g+D4LVX87UT5RpFaWHvra5nM4kxKdWmlfRHlE1XHJyo7lX+KtII0xn15ppugTdsYFjEMcHwHf171ZwwV2nUMysDpwtPV/h69Td7V/h4rficGZSDCO6K8FWrhkYV8uu9TTuWdAqCgtKD8ZGN9++3b2G8ZETGiwd3kK5KESgghWojRUaNZkbCCewffW2MXolGRo3h247PsSdtTqdtfcn4yu9N2c/egu6t95rIel/Hh3g9ZnnCmy9DwiOHVRp5aGLeQgtICps2bBjgq81J7KYuPLubGfjdWm7+TXyfCvMKYd2geUT5ReJu8ARgcNph5h+ZRYC2odVALo8HI1JipPLPhGbanbC8/mBPt26xus5jVbRZ5JXk8s+EZXt72Mv+e8O9arx+qWB7uHV6tW1xSfhLndTiv3vWGeYVxLOdY+fui0qJqrb1lgixBmI3m8kFiahPuHU52STb51vzypCopP6k8QfTy8KLIVlQ+f3phevnrUK9QUgtS0VqXJ1VJ+Ul09OtY77Y0xCNrHuG6Ptfx1tS3MBvNvLD5BTKLMwFHC9W9Q+7l3iH3cjLvJPctv48uAV2Y03MOhcVFdI8fRZrpBMsHfMT/LnuZLlGNiynMK4zs4mwKSwvLk6rakilwHISXDUPf0a8jb099m9t/vR0/Tz/mDpvbpO2P9InkzkF3cnG3i6tNi8+J57eC38rfa61JyT/T3ey17a+hUHx/6fcEmANYkbCCf236V/n0qi0pVY2NHktmUSYHMw6y+NhiHh7p6GoYZAnCYrTww6U/VEoW6tqGxNzEeucL8wrjVP4pegQ5BotIzksm3KvCb7C05t9gkDkID+VBckEy3QK6OT6bX/v3VC0+70g6+HZg0ZxFtcb/zHnP1Hg9XJhXWKWBRwpLC6v9PVb8TuzaTkpBCmHeYZzKO8XT65/m/envMzhsMEaDkSsXXlne3bS+/fbkmCf5cO+HvLD5BR4Z9UiDtxeky58QQrQYN/e/mfySfB5f93j52bWU/BRe3PIisRmxdAnowtW9r+bRtY+yK20XNruNuMw4/rLqL4yJGlPjBfQeBg/uG3IfH+79sNb1puSnsCl5E/+b/D/mzZ7HvNnzmD97PrcNuK3OLkrDIobx6f5Py886VizrH9K/1ouptdasTFhJTklOeWUt2rdj2cfYlLSJElsJZqMZs4e5/KRCiFcIp/JOYdf2Wj8/ocME4nPiWXR0EaX2UpYcW8LRrKOc37H+bjvTYqaxOnE1O1N3YrVZeXPXm6BrntegDFzW4zL+35b/R2pBKja7jZ2pOymxlVSaL9InkiFhQ3hl2ysU24qJzYjl+8Pflx/E9wnuw9oTa8kuzia9MJ3P939e/tkhYUMwGox8ceALrHYry+OXszd9b73b0VAFpQUEmAMwG83sSdtT3gUNKB+0w2a34WvyxcPg4Uggli7jdEI+ngU+XNPtOjr3COWtw6/X+Z3UJNo3mv4h/Xlr51tYbVZ2pu5kdeLqWuef0nkKvx7/lRUJK8pj6hXci8TcxFr/v9Tn6l5X88GeD4jLjAMgtyS3fNCGiR0ncjjzMCsSVlBqL+Wrg19xuvB0+Wfzrfl4m7zxNfmSkp/Cx3s/rrTsEEsIJ/JO1Lpuk8HE9Jjp/Gfrf8gpySn/n21QBq7odQUvbnmxfH0p+Snl1xxVNafnHD7e9zH7Tu9Da01CTkJ5nVHRRV0v4t3d75JRlEFmUSZv736bWd1mAdA7qDdxWXEczDhIsa2YN3e+Wf45o8HIlJgpvLXzLQpLCzmSdYQFRxZUW35tBoYOxMfkwwd7PqCotAib3cbhzMPlv+Orel3Fa9tfK485oyiDlQkrAeff44nVbE/ZjtVm5X87/leeEJXZf3o/y+OXU2ov5bP9n+Fp8GRQ2CAKSwtRqPLBZn44/ANxWXEN3m/eJm/emvYW21K28fK2lxu8vSAJlRBCtBgB5gA+m/kZHgYPrl90PaO/GM0dS+/Az+RHZ//OAPx99N+Z03MOj619jNFfjuae5fcwMnIkL19Q+z//mV1n1nlt1k9Hf6J3UG/GdRhHqFdo+eOGvjdwOPMwhzMP1/i5EREjyCjKYFh4hYQqfBgZRRkMjxhebf4HVj7AqC9GMebLMby+43WeHf9s+ZlT0b6V2Ep4ZdsrTPh6ApO+nURGUUZ5C8T0GEcL5vivx3P1T1fX+PlASyBvTHmDT/Z9woSvJ/Dh3g95Y8ob5QdWdekR1IPHRj/GQ2se4oLvLsDbw5tgS3C166LKPDjiQXoG9eS6n69j/NfjeXnbyzUmFi9MfIFT+aeY/O1k/rzqz9w/5P7yA+hLul9C7+DezJg/g7uW3VU+EAyAyWjilUmvsODIAsZ/NZ4lx5dUGqWsuR4f/Tj/2/k/Rn8xmrd3v12+f8HRSvG33/7G2K/GMvvH2QwNG8rxJQUkfW/EbrQxcHokN146m1cmv8LpotM88fsTjU6qnp/wPLvSdjH+6/G8seMNZnSZgaeh5n09JHwIz094nrd3vc3Yr8Zyy5JbGBkxkv9O+i+PrHmkSTeQnRIzhdsG3MZDax5izJdjuHzB5aw7uQ5wtBT9Z9J/eHnby0z4egJHs4/SL7Rf+W/h3sH3sv/0fsZ+NZb7V9zP1JiplZZ9+8DbeXf3u4z7cly1ZKvMzG4z2Zi0kWkx0/AwnOko9pfhf6Gzf2duWHwDY74cw53L7uR4zvEalzGjywzuGnQXj655lNFfjmbuqrlkF2dXm+/uwXfTP6Q/Vyy8gjkL59A3uC93D3b0ZOgS0IV7Bt/DnUvvZNb3s6q1Fv199N8pKC3ggm8v4B/r/sFlPS5ryO4FHAnZG1PeIDYjlgvnX8iEbybw9Pqny695u7HfjUzqNIm7l93N6C9Gc8OiG8qvc+oR1IPHRz/OI2sf4YLvLsDf7E+Ed+VWuws6X8CS40s476vz+Pnoz7x8wcuYDCa6B3bn5v43c+PiG5n07SQOZx0uH+mzofvN39Of96a/x7qT6xo1qJOqmvW1NkqprVrrEVWKW/dGCSGEqE3dfWrOIal/2qYCawHjvhrHz5f/7LJudq1RSkY6H728FL+0SIr7JnPfPVdgMZtdvp4HVz9I14Cule5Z11LYtZ2p303l+QnPMypqlLvDEe5Xa/0jLVRCCCGEaNd+S/yNwtJCCqwFvLT1JXoG9aw2yEt7cvDYET5+9je800MJuqiQv8693mXJ1N70vSTmJGLXdtadXMeqhFVM7jTZJct2hd9P/k5OSQ4lthLe2/0eGl3r9aBClJFBKYQQQgjRrq1KXMXf1/4djaZ/SH9enPhivQMMtFVb9u1h7dvHMWkLg28N4vxRjR/xrC7phen8edWfyS7OJsIngn+M+Qd9Q/q6dB3NsSttF4+sfQSrzUr3wO68esGrTb5eS7Qf0uVPCCFEa9JijnKl/hFtzbqd29j8fhI2o5ULH+jHwB61j2QoRDtUa/0jLVRCCCGEEO3cxl272PJeMjaTlTl/G073Tp3dHZIQrYZcQyWEEEII0QJ8+vf1JB7IOOfr3X8sjt/fj8dqKmbOg5JMNdSpw1l88dTGWqfnZhTxztzV2O3nruH6bP2GDqxP4vv/t63W6Ud3pPHJY7/zztzVpCXkunTdFbdp809HWfZh/TfIbs46mkJaqIQQook+/ft6LvhDHzr1DSY/u5hNC44Sv/c01mIbPoFmeowIZ9j0GExmI1prdixLYP/aU+RlFePla6LXqEhGXdwVo8lxbmvFx/s5uDGZKx8ZQURXfwCyUgv44smN3P92zRdtH1ifxKrPDmD0NFYqv/H/xuATaOZUXBYbvo8j41Q+yqAIivRh/NU9iejiz4H1Saz89ACDJ3di/NU9yz97dGcav7y9hz5jIplyS79q6/ziqY2MuqQrPUc4hrJNisvi+5e2M/2O/pXKfnp9F3f8dwKxm1IqxejtZ2LotM4MON8xglpOeiGf/WMDHuYz2xAQ6sW1T8ioWkKcbWmZGfz8+i48tJmL/ziI7h07V/u/UvVvtkxJUSkfPfI70T0CuOSBIZWm1fe/58Dvp5jzkOP2Cp/+fT0FuSUog8JkNhLTL5gJ1/bC01L7YWrspmQ2LThKUYGVoEgfLrp7AL5B5/Zap+iegdzwf2PK31esEwD8gi3c/Wr990JrC36ff5gJ1/Si25Dab9HRlklCJYQQzVSUb2X+C9uI7B7AFQ8Pxz/Ui9yMInYuSyA7rZDQjr6s/eYwCftOM/XWfoTH+JGZUsDKTw6QkZTPrPvOjCBl9vFg08IjzJ5b/Q7ytYnsFlB+YFJRSWEpi/63m/Ov70WP4RHYS+2cisvC6HGmc4J/mBdx21IYd0V3DEZHeezGZAIjvGtdX3SPAE4dzipPnk7FZREU6V2tLLKbf/kyK8aYlpDLD//ZTkTXAMI6+5Uv987/TiifXwjRcHabvUl/Oza7jfdfW4xPYTgDb/WnX48z94VryN/s0R1pGD0UiQcyyc8uxifAMRJgQ/73VDXrvkHlJ6d+em0X25fEM+ay7jXOW1JUyspPDjDrj4Po1CeY1ITc8hNTwj1yM4oJjvZxdxhu47aESikVCLwPDMBxEe9tQCzwDdAFOA5crbXOdE+EQgjRMDuXJ2CyGJl2az+UwXHNql+whQnX9AIgK6WAvatPcMXDZ1qeQqJ9ufDugXz+5AZOHMygYx/HGc0+Y6I4tCWFk4cy6dCr/puS1iUrtQCAXiMjATB4GuncL6TSPD7+npjMRhL2Z9BlYChF+VaSjmbTe3QkRbklNS43umcg25cmlL8/dTibodNj2Lm8cll0z8AaPx/W2Y+gSG8yk/MrHZydK1L/iNYg5VgOa789RGZyAR4mA92HhnHeVT3Lk5L/3bOSidf2YteKROx2zU3PjWP7r/HsWpEICkZf0o1Vnx/khmfGEBjujc1qZ+OCI8RtS8VWaqfbkDDibAfxT4rG+/xcLhg1vdZYavubPbgxiQETOhC/7zSHNqUwdLqjq2BD/vfUxifATOd+waSfyKt1HqUUyqjwD/FCGRQRXfzrXe7mn46Wt5bF7z1NQLgXU27uS2hHx/ZkJOWz+stY0k/k4RNoZuxl3eg62NHacnxPOuvnx5GXWYynxcjgKZ0ZOr0zJ2MzWfbRfm55/jyWfbSP3MwiFr25G2VQjJzZhR7Dw/nsHxu493+TOLIjjR1LE7j672dGTdy5PIGTh7KYdd+gGr+f8Vf1xKNK74My+9aeZNeKRPIyi/ENMjPttv7V/p/arHbW/xBH3LZUAHoMD2fc5T0wmgzVWgnB8Zsq+70U5VlZ8ekBTh7KJCjCm879a/7+bFY77z+4Fm3XfPPsZrz9PfnDs+PIzypmzTeHOHU4C5PZyOApnRg8uRMA2q7ZvjSe/etOUVxYSsfewUy6oTcWHxMAsRuT2LjwKNZiG0OmVO9+arPa+fW9vTV+j9uWHGf/ulMU5lrxDTIz5tLudBt6ptWsIfstIymfn9/YxZjLupX/hutTbzqvlFrTwMfSBq3xjFeBJVrrPsBg4ADwKLBCa90TWOF8L4QQLdqJg5l0GxpWnkxVmx6biU+QuTyZKuMXbCGyawCJB84ct3t4Ghh+YQybFhxtdlyB4d4oAyz/eD/xe09TlG+tcb7eYyKJ3ZgMwOEtKXQdHIrRo/bB9KJ6BpKRlE9RvhVt16Ql5NBzRDglhaXlZclHsmpNqFKO55CVWkh4TP0HQWeJ1D+ixVMGGH9VT25/aTxXPDycE7GZ7F19stI8R3emceWjI7j+qdHE7zvNzhWJzP7zEG7851hOHqp8PmDDD0fISi3kmn+M4sZ/jiX5VAaF633IiTrFzddcUmcsNf3N5pwu5OShLHqNiqTXqEhiNyWVT2vo/56a5GUWEb/vNAFhXrXOY/BQhHb05df39zZq2cd2pdN9WDi3/3cCvUZGsvitPdhsdmw2O4ve3E2nfsHc9v/GM/Ganiz7cD+ZyfkArPrsIJNu6MNdr57PtU+OpmOf6ie7pt3aH78gC7PuG8Tdr57PsBkxlaZ3GRRKVkoBWSkF5WWHt6TQa6SjVb/q95OfVcyWRcdr3I64bals+fkYU27px52vTGTmfYMw+1RvI9n6y3FSjuVwzeOjuPYfo0g9nsPWX2peZlWrv47F6GHg1hfGM/mmvhz4/VSN8xlNhvJujdf8YxR/eHYc2q5Z9OZuQjv6csvz53Hpn4eye2UiCftOA7B71QmO7Urn8r8N49bnx2P28WDNV7EAZJzK57evDjHt1n7c+vx4ivKt5GUVV1pnbd8jQECYN3MeHM6dL09k5MVdWfbRPvKzixu839IScvnptZ1MvKZXg5MpaFgL1UjgnnrmUTgqqAZRSgUAE4FbALTWJUCJUupSYJJztk+A34BHGrpcIYRwh6I8a3lXl5qnl9Q63TvAk6K8yi1BAyZ0YOeyhPKzb/VJPpbDe39ZU/7e4uPBH54dh6eXB3MeHM6OX+NZ9flBCnJKiBkQwgU39sHb37N8/m5Dwlj33WGKC0uJ3ZTMeVf0IN5Z8dXEP8QLvyALpw5n4RdsISDMGw9PI5HdA8rLbDZNRJeAajFqu8ZabGPgpI7Vtu2DB9eVvx5xUZfys92uJPWPaC0qJi/+oV70n9CBk4cyGTylU3n58Atjys/qx21Lpe/YKEKifQEYeXFXDm1OAUBrzb51J7n2iVFYfExorUnITMSbUK6/czIGQ/Xz6/X9zR7alExIB1+Co33w9PJgw/dxpCXkEtbZr8H/eypa/PYeFGAtttGhdxCjLula675Z+/UhQjv64h/qxcJXdzJ77hAsPiY2LjhCqdXO+Ct71vi5sBg/egwPB2DI1E7sXJ5AytEccK53+IwYlEHRsU8wXQaGcHhLCqMu6YbBqMhIyie0oy8WH1P5Pm8Mk6eRroNDObw1hZGzupKVUkBmcgFdBodW+34Ahl/UhaUf7GPs5dW7Pe7//RRDp8eUt8wFhtfcRfvQ5mQmXNOrfJ+PnNWV376IZfTsbnXGardrjm5P49onR2EyGwnp4EvvsVEkHc5q0LamxOdQmFvCyFmO7zAgzIt+46M5vDWFzv1D2Lf2JBOu7VV+zduoi7vy6WPrsdvsHNmRSpeBIUT3dCSto2d3Y89vJyotv7bvMbpnYHk5QM8REWxbEk/KsRy6DQmrd7+disviwO9JTLu1Hx16N66HSEMSqvVa60/qm0kpdX0j1tsVSAM+UkoNBrYBc4EIrXXZKY5kIKKWdd0F3OV8G9qI9QohhMtZfE3lZ8Bqnu5Z6/SC7BL8uwVUKjOaDIyY2YVNC48y/Y7+5eWnDmfx0xu7AEfr1vVPjQYgsqt/jddQAQRH+ZQPLJGZnM+yD/ez7ttDTL9jQPk8Hp5GYgaGsnXxcYryrET1CKyUUP32xUFinQdmwy+MYcRFXYjuGcipuCz8gixE93TEH9X9TFlEF/9K1zRUjLEgp4SlH+xl449HKx0s3P7S+HNxDZXUP6JVyEopYN28w6TG51JaYkPbNGExlbsmVRyEoSCrmPAKXZf8KkwrzLVSWmLn239tBaC0tBQfaxgYNDHR0TWuv76/2YMbk+k3PtoZh5noXoEc3JhU3n2qIf97Kpp5z0A69Q3m5KFMln2wj8I8K2bv6omLtdjGgd+TuOnf4/AJMFNcYGXBKzu49M9DST6SzZBptZ+I8Q06c2JLGRS+Qeby/81+QeZKvQz8QizkO1tGLrx7IFsXH2fjj0cI6eDL2Mu7E1nl/3ZD9BwZwfr5cYyc1ZVDW1LoOiQUk6eRgpySSt8PAFpT2+CAeRlFdbbglcnPLsEv5MzvwC/EUmddVaYwtwS7XVf6ffkFW0iq4zMV5Z4uIj+7pNKJPm3XRPUILJ/+y9t7Kt08WxkUBTlW8rOKK63XZDZWS2Dr+h4Pbkxi5/JEck8XAY7fS1krZn37bd+ak0T3DGp0MgUNSKi01lMasiCtde2db2te7zDgAa31JqXUq1TpXqG11kqpGn9KWut3gXfBcWPFRqxXCCFcrmOfII7tTGPUrK41dvvr2DuI1V/FknIsp1K3v9yMIpKPZTNiZpdqn+k7LortSxM4uiOtvCy6Z2CzRowKivShz9go9q09WW1an9GR/PjKjvIzihVNuqEPk27oU6ksumcg+9aeJC/YQp9xUc6yAGI3JpEXbKm1ux+At78n3YeGs3fNyRrPvp5lUv+IVuG3L2MJ6+TL9Nv742nxYNeKRI5sT608U4V/N94BnpW6RuVmFpW/9vI14WEycN2To/ENMvP8E1/ike3DfS/OaFAsVf9mk45kk51ayPYl8excngiAtaiUjFP5nHdFj2onRur631NVh15B9Bkbxfr5ccy8d1C16Vpr7Fpjtzn+RMde3oOigoPMe2ErHiYjMbVc6wOQl3lm/2i7Ji/zzEAauZnFaLsu/x+em1FMoLNFLqKLv+M6J5udPatO8Ot7e7n53+dVX0E9tx3v1C+Ywo+tpCXmcnhLCuOvcrSkVf1+6uMbbCE7rbDe+XwCPMk9XVTeapmbUVS+vSazEWuJrXzeiomWl58nBoMiL7OIoEjHQBN5GUU0lF+wBf8QCzf+c2yt8U/+Q5/yBKsi7wBzeVdLAGuJrVq3ztq+x5zThaz6/CCX/nkokd0CMBgUXz+7ufx26/Xtt0nX92H70njWfXu40si3DdGoU4FKqQdrKf9ro9YKJ4ATWutNzvfzcFRwKUqpKOcyo4DUWj4vhBAtxpCpnSkptLH8k/3knHb8s87LLGbdd4dJP5FHYIQ3AyZ0YNmH+0g+mo3drjl9Ko8l7+yhU5/g8iF2KzIYDYy6uCvbl8Y3Oa7M5Hx2LEsgz3lglZtRxOEtKUR2rX7tUnSvQGbPHcKgCzpWm1aT6J6BpCXmcSoui6jujjO1IdG+5KQXceJQJtE1VJRlivKsHN2ZRnCUW0aEkvpHtArWolI8vTwwmY1kJuezd03dyUiP4REcXJ9ERlI+1hIbWxcfL5+mDIp+46NZ991hNu3Yg19aJL4DSkk/kl/7Aiuo+jd7cGMSnfoGcd1To7nm8ZFc8/hIrntyNKUlduL3ZTTqf09NBk/pROKBDNJPVL+fkafFg879Qlj9VSwFOSXYSu107B1ETnoRnl7GOu/5lBafy5EdqdhtdnatTMToYSCimz8RXf0xeRrYvjQem83OydhMju9Op+fICGyldmI3JVNcWIrRaMDTy6PWxMnb35Oc9NoP2I1GA92Hh7N+fhzFBVY69XW0hFT8fgpyHF3A8zKLy685qqrfedHsXJ5AanwOWmuyUgvK656Keo6MYNsvxynMLaEwr4Sti47Ta7Sj8T20oy8ZSfmkJeZSarWx5edj5Z8zGBTdhoax+edjWEtsZJzK56DzOtuGCO/ij8liZPuv8ZSW2Bx13sk8Uo47ulf2nxDNxgVHy2MuzC3h6E7HycPuw8I4vuc0p+KysJXa2bzwKLrKV1rb91habEeh8PJ1tGgdWH+KjFNnfuP17TeTxcglDwx2DPn/Q1yDtxcaP8rfk8BLNZT/A/hvQxeitU5WSiUqpXprrWOBKcB+5+Nm4Hnn84JGxidEsy06uohXt79Kcn4ykT6RzB02l1ndZrk7LNGCWXxMXPHwcDYuPMq8F7ZR6rwPVc+REeXXHEy8thc7liWw7KP95DvvQ9VzRASjZtd+nUCvkRFs/zWe4vzSOteffDSbd+aurlR22V+G4hNgJuVYDruWJ1BcWIrZy4OYQaGcN6dHtWUopejUp3piV5vACG+8fE14+ZnKu+UogyK8iz8nDmQQ2b1yd5iKMZo8DXTsHcT4q3s1eH2uIvWPaC3Ou7IHqz6PZfvSBMI6+dJjeDgnY2sfeDJmQAiDJnfkx5d3oBSMnNmF2I3J5aMCjp3TnS2LjrPlo6OAAa+ECDJ7FNQ6elttf7OlVhtHtqUy5ZZ+1a4N7T06ktgNSYy/uleD//fUxMvPk95jotiy6DgX3T2w2vRpt/Vj/fw4vn52M6UlNqK6BTDnoWH8/l0cKz89wLTb+tewVOg6OJS4rams+PgAAeFeXHTPQIzO1rSZ9w1izVeH2L4kHp9AM1Nv6UdQpA+2UjuHNiWz9ptD2O2aoAjvWpc/bEYMa785xPrvjzDioi50H1b9nky9Rkbww3+2M+D8DpVa8sq+n/kvbqUwz4pvoJn+EzvU+P30GB5OUb6VZR/uJy+rGP8QC1Nv6QdVZh0xswslRTa+/udmALoPDy/vEREY4c3ImV1Z+MpOPDwNjLmsO/vWnhl4YuK1vVjx6QE+engdQRHe9B0XVefvryKDQXHx/YP5fd5hPv3HBuyldgIjvMuv3Sob7e+n13Y56kM/Ez1GRNBtSBgh0b6cf20vln2wD2uJY5Q/38DKv7PavsfgaB+GTO3E/Be3oZSi95jI8hN+Dd1vZm8Ts+cO4cf/7sBgPFrv9WZllK6a9tU0k1Jld5T8CbiYyrl5N+AJrXVMtQ/WvcwhOIat9QSOArfiaDH7FugMxOMYtrbO2xYrpbZqrUdUKT53t6QWbcqio4t4ev3TFNnONG1bjBaeHve0JFVCtAz1dKppwAKk/hHtQEZSPl8/s4l73phU6cD9+Ye+RVtKeeyfjbn0vfXb/NNRstMKa02GhGiAWuufhrZQfeB8tgAfVijXQArwQGMj0lrvBKpWROA4WyiEW7y6/dVKyRRAka2IV7e/KgmVEG2E1D+irTq6I42YASFYS2xs+OEIXQaFVkqm0rMy8c0NxthHbrEmhCs1KKHSWncFUEp9qrW+6eyGJNqDQ5uS2bDgCHkZxfgGmxl7aXd6jW74eP9nS3J+zX2EaysXQgghWop9a0+y4pP9KIMiumcg51/fu9L0XQcPojAQ06PGQSyFEE3UqGuotNY3KaUigFE4hotVFaZ9WOsHhajg0KZkVn1xkNISx03Y8jKKWfXFQQC3J1WRPpEk5VcfGDTSx/3JnhBCCFGXS/40pM7pp9OyAU86RofXOV9bNOqShl0LI0RTNHaUv8uAI8AzwDs4uvq9A/zB5ZGJNmvDgiPlyVSZ0hI7GxYccVNEZ8wdNheL0VKpzGK0MHfYXDdFJIQQQrhGXr5jRLPQwMbfZ0cIUbvGjvL3LHCr1vo7pVSm1nqoUupWQK7wEw2Wl1HzTeVqKz+Xyq6TklH+hBBCtDUG5TiPbtf2euYUQjRGYxOqzlrr76qUfYLjrvI13qNKiKp8g801Jk++wfXfzO5cmNVtliRQQggh2hyzxUQ+kJWTS8fwKHeHI0Sb0aguf0Cq8xoqgONKqbFAd8Do2rBEWzb20u54eFb+6Xl4Ghh7aXc3RSSEEEK0fVHRoQDEnzhVz5xCiMZobEL1HjDe+fplYBWwC3jTlUGJtq3X6EguuKFPeYuUb7CZC27o4/YBKYQQQoi2rG83x4nLxOPpbo5EiLalsaP8vVDh9adKqd8AH631AVcHJtq2XqMjJYESQgghzqGIkBBy/dLgiM3doQjRpjS2haoSrXWCJFNCCCGEEK2Dd1fwTg8lNeO0u0MRos2ot4VKKTW5IQvSWq9sfjhCCCGEEOJsGTupL5t3p7Nw8WruuHGOu8MRok1oSJe/DxowjwbkjmlCCCGEEC3YyH6DWBH8NfnbTJRcY8XTZHJ3SEK0evV2+dNad23AQ5IpIYQQQohWoM+UMHwKg/h6wRJ3hyJEm9CoQSmUUs/UMqkYOAEs0VqnNDsq0eYtOrpIbp4rhBBCuMHsCy7gxRXfULjGm9PTMgkJCHJ3SEK0ao0dlKIX8AhwAdDD+fwIMBS4FziqlLrQpRGKNmfR0UU8vf5pkvKT0GiS8pN4ev3TLDq6yN2hCSGEEG2ewWDg/Gv6Yi7x4eP3f3F3OEK0eo1NqAzAtVrrCVrr67XWE4CrAZvWegxwH/C8q4MUbcur21+lyFZUqazIVsSr2191U0RCCCFE+zJ28BDsg9OwHI5k4UoZV0yI5mhsQjUDWFil7GfgIufrz5HBKUQ9kvOTG1UuhBBCCNe769bLyfM7Tdz3BRyKP+bucIRotRqbUB3B0bWvonuc5QChQEFzgxJtW6RPzTf0ra1cCCGEEK7nZbEw657BgOKH17eQmZvt7pCEaJUam1DdATyolEpUSm1USiUCDwG3O6f3Bp5wZYCi7Zk7bC4Wo6VSmcVoYe6wuW6KSAghhGifBnTvRf9rAvDOC+adl36mqLjY3SEJ0eoorXXjPqCUCRgLRAFJwAattfUsxNbQeLZqrUdUKW7cRolzTkb5E0I0kXJ3AGWk/hFtyWfzF5KzzJe8jkn8+ZGrMJs83R2SEC1NrfVPoxOqlkYqNCGEaFckoRLiLHn/y+8pXhNIXueT/PmhaySpEqKyWuufxnb5Qyk1TSn1gVLqJ+f7EUqpyc2JTgghhBBCuNcd18/Bc1wWvgkdeOVf88gtyHN3SEK0Co1KqJRSDwBvAYeBic7iQuBZF8clhBBCCCHOsTtvmoP3lBy8k8J5458/kXw6zd0hCdHiNarLn1LqCDBFa31cKZWptQ5SShmBVK11yFmLsu6YpMuFEEK0H9LlT4hz4IdfV5D4o40iSx4z7unH4N593B2SEO7msi5/fkCi83VZpWECSpoQlBBCCCGEaIEunzGFIbcHYbB5sOq1Y/y4bIW7QxKixWpsQrUGeLRK2Z+AVU1ZuVLKqJTaoZT62fn+Y6XUMaXUTudjSFOWK4QQQtRF6h8h6jdxxEjmPDKUIr9sTs5XvPb2V5RY3TawsxAtVmMTqgeAy5VSxwE/pVQscDXw1yaufy5woErZQ1rrIc7HziYuVwghhKiL1D9CNEC3Dp3409OXUdQzGePOCP775Hxi44+5OywhWpRGJVRa6yRgJI4k6nrgZmCU1jq5sStWSnUEZgHvN/azQgghRFNJ/SNE43hbLPztb9cTekkx5hx/Fr94gK9/Xkxrv/WOEK5S76AUSqlnGrIgrfWTjVqxUvOAf+O4LutBrfXFSqmPcdw0uBhYATyqta52y26l1F3AXc63oVrrLlXDaUwsQgghWo1mD0oh9Y8QTXco4Tjfv7URv8xw8jslccNdU+kQFuHusIQ4F5o1KEWnCo+eOK6hmgL0ACY73/dsVDRKXYxjZMBtVSY9BvTB0QoWDDxS0+e11u9qrUc4R1dKb8y6hRBCtF9S/wjRPL06d+Fvz1yJccxpzCdC+eb/tvDVwsXY7XZ3hyaE2zR22PSvge+01vMrlM0BrtJaX9eI5fwb+ANQClgAf+B7rfWNFeaZhPPMYT3LkmFrhRCi/WhWC5XUP0K4zs7YAyz5eCd+mRHkRJxizq3j6N2lm7vDEuJsqbX+aWxClQ0Ea61tFco8gNNa64AmRVah4lJKRWmtk5RSCngZKNJaVx1VsOrnpUITQoj2w2X3oZL6R4jmK7XZ+Ozbn8le64lBGzAOy+bmG2bh6+3j7tCEcDWX3YcqDri/Stm9wJHGRlSLL5RSe4A9QCjwrIuWK4QQQtRF6h8hmsDDaOTW6y7l0n8MoLBTKmwL5c2//8oPy5ZJN0DRbjS2hWoo8APgAZwEOuDoNjFHa739rERYf0xyhlAIIdoPl7VQNZfUP0JUt2LDBrbOP4FvXgg5YUlMu3YwI/oPcHdYQriCa7r8ASilTMAYIBpIAjZord12lzep0IQQol2RhEqIFq7YWsKX8xaT+bsHnqUWCrumcPl14+nROcbdoQnRHK5LqFoaqdCEEKJdkYRKiFYiLfM0X369FHYHA2AfcJrrrp1GZEiYmyMTokmanlAppf6ptX6i3jUo9X9a66eaEFyzSIUmhBDtiiRUQrQyR08k8v2Xa7EcDcdqLMFzSB7XXDWV0MBgd4cmRGM0K6HKBQbVtRCnbVrroMbH1jxSoQkhRLsiCZUQrdTOgwdYMm8bfieiKTEW4jksj6uvnEZYgCRWolVoVkJlx1FB1FeJFWmtvRsfW/NIhSaEEO2KJFRCtHJb9u5h5Q978D0ZSbFHAaYhuVx1+VTpCihaOrmGSgghRJsgCZUQbcSWPXtY9cNefE5FYDUUY+ifzWVzJtI5KtrdoQlRE0mohBBCtAmSUAnRxuw4sJ+lC7bhfTwSrTQl3dKZfukwBvXq4+7QhKhIEiohhBBtgiRUQrRR+44d4pcFmzEdCsVo9yA3OokxF/Vk0ojRKNVi/vRF+yUJlRBCiDahxRxVSf0jxNlxKj2VH374jeJdPphLvcgJTKH7+CBmT78Ai6fZ3eGJ9ksSKiGEEG2CJFRCtBO5+fl8//NKUjYV41MQTJFnHj5DSrjkkgl0CItwd3ii/XFtQqWU6q61PtKskFxEKjQhhGhXJKESop2x2Wz8snYtu1edICAlGpsqpahLKmOn92H8kOHSHVCcKy5PqO7XWv9PKbUKSAH2OB+7tdbHmxplU0iFJoQQ7UqLOXKS+keIc2/P4ViWLd6G4VAQJpuZnMBkOo3x49LpF+Dn7evu8ETbdva6/CmlQnDc+HcQMEBrfWezFtj49UuFJoQQ7YckVEIIsnJy+HHxb6RuseKTH0SJsRDVO4cLpg9jcJ/e7g5PtE2uSaiUUt7A9UBnHC1Ti851i1QNMUmFJoQQ7YckVEKIcna7ndVbtrBl5WEsCeEYtQd5Qal0HOXLxdPOJ8DXz90hirbDZQnVL0BXYD0QBkwEPgX+orUubWaQTSIVmhBCtCuSUAkhanQyPZlFS9aRuUPjmx9CibEIW/cMxkzpw3mD5For0WwuS6gygAla633O997Ah8ARrfXjzY2yKaRCE0KIdqXFHBFJ/SNEy2S321m7bRtbVh3GdDwED7uJHL80Qgd7MHP6eXQIj3R3iKJ1cllCdRvwEpAH7AR2AceAf2qtOzQvxqaRCk0IIdoVSaiEEA2WkZ3Nol/Xcmp7Pr5ZYdixURCVRq/REcyYdB7eFou7QxSth8sSqnnAU4ARxyAUg52PKUAhcBg4pLW+pjnRNoZUaEII0a5IQiWEaJJdsQf4bcVOSmK98C72p9ijEHu3LEZM7MGEYSMwGozuDlG0bC5LqG4E/g08BPystc5TSl0KvA6MBPoCvbXW7zQv3oaTCk0IIdoVSaiEEM1SUmpl5YaN7P49AXNCCB52T/K8T+Pdr5QLLhjGgO4ySqCokeuGTVdKXQw8DQwFSoFi4A6t9bfNCLDJpEITQoh2RRIqIYTLZOfm8svKdcRvycY3PRyAnMAUwgaamTFlLJ0io9wcoWhBXH8fKqVUGBAKHNdaFzYxsGaTCk0IIdoVSaiEEGdF/KlTLF+xibS9Jfhlh6GxkxeaRtRgb6ZPHktUSLi7QxTudfZu7OtuUqEJIUS7IgmVEOKs2x0Xy5pVO8g/6IFvfjB2ZSM3PJlOQwOYMWkc4YGh7g5RnHuSUAkhhGgTJKESQpwzdrud7fv3sWHtfopjzXgV+VNqsJIXlUyXwSHMOH8coQHB7g5TnBuSUAkhhGgTJKESQriF3WZn8649bF4XizXOgqXEl1JlpSAylc6Dg5h+/ljCgkLcHaY4eyShEkII0SZIQiWEcDu7zc7GXbvYsj4W62ELXsX+2FQpeeGpRA30YfLEkcSEd3R3mMK1XDrK3zTgWiBca32JUmoE4K+1Xtm8GJtGKjQhhGhXJKESQrQodpudDbt2sm3jIYoPm/EuDMCOjezQJML6m5k4YRh9O/Z0d5ii+Vx2H6oHgLnA+8BjWusApVR/4D2t9bhmh9kEUqEJIUS7IgmVEKLF0lqzc/9BNv6+n7xYhXd+IBo7WYFJ+Pc2MGZMf0b2GYxSLeZfmWg4lyVUR4ApWuvjSqlMrXWQUsoIpGqt3dJpVCo0IYRoV1rMUYjUP0KIumitOXQknnVrd5Fx0Ip3tmPwilzv03h2K2bAyC6cP2wUZpOnmyMVDeSyhCoViNJa25RSGVrrYKWUBTimtXbLnc+kQhNCiHZFEiohRKt0MimV39Zu5eS+HLxSQzBoI4WmPKydMug6OJTJY8cQ7i/DsbdgLkuo5gE7tNbPVUioHgaGaK2vd0GgjSYVmhBCtCuSUAkhWr2snFxWr9/KkZ2pGBL9MNksWA3F5IYnE9bPi/FjB9O/Yx/pGtiyuCyhigJ+AkKBDsBRIBe4WGud3Mwgm0QqNCHah/f/uobigtJq5WZvD+7470Q3RCTcpMUcXUj9I4RwhVKrjY3bdrNry1GKj5gwF/lix05m4Em8e9gYNqon4/uPxtMoXQPdrNb6x6MxS9FaJymlRgIjgRggEdistbY3Lz4hhKhbcUEp9789uVr5/+5xywCjQgghhEt4mIyMHzOU8WOGOq67OpzAhvV7KTnoh2VrIAe2WtngNR97TDbdh0Qwadhoov2j3R22qKDehEop9UwdkwcCM5VSaK2fdF1YQgghhBBCtC9KKXr3iqF3rxgA0lKyWPv7Tqx7zBhju5N50MgX8zeTEZFAaF8LY0YMYGTHEdJ65WYNaaHqVOG1BbgC2ALEA52BUcD8xqzUOZDFGsDsjGGe1voppVRX4GsgBNgG/EFrXdKYZQshhBC1kfpHCNGahEUEMmfOJJgDhXklbN16kP1bCzAf743xhIktK7JZEPQ2Ht0K6T8shondz6OTf6d6lytcq96ESmt9a9lrpdTXwHVa6/kVyuYAVzVyvcXAZK11nlLKBKxTSv0C/BV4WWv9tVLqbeB24K1GLlsIIYSojdQ/QohWycvXkwmTBjFh0iBsVjtHDySzdVMsxgM9MWwxk7HFztv+i8mKOkGngUGM6zOCkZEj8fLwcnfobV6jrqECLgJuqFK2EPioMQvRjpEw8pxvTc6HBiYDZaMFfgI8jVRoQgghXETqHyFEW2A0Geg5KJqeg6LRWpOWkMvOzUcw7bQRHdsTYuE3rwQ+C1mCT0/N0P59mdBpPF39u8rIgWdBYxOqOOB+4LUKZfcCRxq7YucNgbcBPYD/OZeRpbUuG8brBI6RBIUQArO3R40DUJi9G/tvTLR3Uv8IIdoSpRThMf5MjxkKV0F2WiFxO5PZu9VAcEIU6oQiZV0WLwV/Tnb0Cbr3jWJMx9GMjhpNiFeIu8NvExo7bPpQ4AccidhJHBVOKTBHa729SQEoFehc5hPAx1rrHs7yTsAvWusBNXzmLuAu59tQrXWXKrPIsLVCCNE2uezUqtQ/Qoi2rijfSvyedPZvS+TUwVywKoo9CjgatJujITvw6aIY03E0Y6LGMCximHQPrJtr7kMF4OxzPgaIBpKADVpra7OiU+pJoBB4BIjUWpcqpcYCT2utZ9TzWbkPiBBCtB8u7asi9Y8Qor0otdpIPJBJ3NYUjuxKxVasKTUVcyRoJ4dDtpMaeIwhkYMZGz2WMVFj6BvcF6PB6O6wWxLXJVSuoJQKA6xa6yyllBewFHgBuBmYX+Gi4N1a6zfrWZZUaEII0X40K6GS+kcIIcBmtZNwIIO4bSkc25WOtciGNpdyImo/G/1/4bTPKfw9/Rkd5Wi9Ghs1VkYPdFVCVdc9qRpzHyql1CAcF/0aAQPwrdb6GaVUNxzD1gYDO4AbtdbF9SxLKjQhhGg/mptQSf0jhBAVlFptJOzL4PCWFI7uSsNeqvGMsJMeE8dqr4WcLEkEoINvB0dyFT2W0ZGjCbQEujfwc89lCVXV0fwigfOBH7TWVUf/OyekQhNCiHalxQxPJfWPEKKtKcq3cmhzCgfWnyI9MQ+zlwedxvqS3uMQm7LWsyV5C3nWPBSKviF9yxOsoeFDMRvN7g7/bDt7Xf6UUhfiuDfVzc1aUNPXLxWaEEK0H5JQCSHEOZByLIftS+M5ujMNDw8DAyd1ZOhFnYjNO8DGpI1sOLWB3Wm7KdWlmI1mhoUPY0y0o3tg7+DeGJTB3Zvgamc1oTIAmVrrgGYtqOnrlwpNCCHaD0mohBDiHMpMzmfbknhiNybjF2Jh1n2DCOngC0CBtYCtKVvZcGoDG5M2EpcVB0CQOYjRUaMZGz2WsVFjifKNcucmuIrLuvx1q1LkjeNGiLNrGl72XJAKTQgh2hVJqIQQwg1OxWWx9L29lJbaue7J0fgEVO/il1qQyqakTeUJVlphGgBd/LuUJ1ijIkfh5+l3rsN3BZclVHYclUXZAgtwXLz7Z631tuZE2FRSoYm27v2/rqG4oLRaudnbgzv+O9ENEQnhVpJQCSGEm5w+lcfXz2zmvCt7MGRq5zrn1VpzJOsIG5I2sOHUBrambKWwtBCjMjIgdEB569XAsIGYDKZztAXNUmv949GYpWit21xnSCFauuKCUu5/e3K18v/ds9IN0QghhBCiPbLZ7BzenAKAt79nvfMrpegR1IMeQT34Q78/YLVZ2Zm2s7z16t3d7/L2rrfx9vBmZOTI8gSra0BXlGox584apFEJlVLqQa31SzWU/1Vr/V/XhSWEEEIIIYRwt/zsYmI3JbN/3SmyUwvpPTqSniMiGr0ck9HEyMiRjIwcyZ/4E9nF2WxJ3sKGUxvYkLSB1SdWAxDhHcH4DuOZ2HEiY6LG4G3ydvUmuVyjEirgSaBaQgX8A5CESgghhBBCiFasKM9KSnwOJw9mkngwg/TEPACiugdw3hU96Do4zCXrCTAHMDVmKlNjpgJwIvcEG5M28vvJ31lyfAnzD8/HZDAxImIEEzpOYGLHicT4x7hk3a7WoIRKKVXW38iolLqAyn0IuwG5rg5MCCGEEEIIcXbYSu1kpxWSlVxARnI+6Qm5pMbnkptRBIDBqIjsFsDo2d3oNjSM4CifsxpPR7+OXOl3JVf2uhKrzcqO1B2sObGGtSfX8uKWF3lxy4t09uvMxI4Tmdx5MsPCh2E0GM9qTA3V0BaqD5zPFuDDCuUaSAEecGVQQgghhBBCiOYpKSwl53QROemF5J4uIud0ITnpRWSlFJCdVoi2nxlHxz/Mi4iu/gw4vwNhMX5EdPHH09LYzmyuYTKaGBU1ilFRo3hw5IMk5iay7uQ61pxYw3eHvuPzA58TYglhasxUpsdMZ3jEcLcmV40d5e9TrfVNZzGeRpNRlkRbJ6P8CVFJi7lSWeofIYS7aK0pLiglP6uYvKxi8p2Pstd5mcXkZRRVO37wMBvxD7EQFOFNYKQ3QZE+BEV6Exjh7bbkqbEKrAWsObmGpceXsvbEWopsRQRbgrmk2yVc2etKugR0OVurbvqw6UqpiVrrNc7X1Ycac9Jau2XIManQhBCiXZGESgjRJpWW2CjILaEw10phpeczrytOt9uq/7vx8jPhE2jGJ9CMb5AF/xAL/qFe+IVY8A+1YPExtboR9OpSYC1g3cl1/HLsF35L/I1SXcqoyFFc1esqpsZMxcPg0iSxWQnV3rKb9iqljtUym9ZaV73p7zkhFZoQQrQrLeZIQOofIURt7DY7xQWlFOVbKz/nnUmSCqokTNZiW43LMpoMePt54uVnwsvfEy8/T7z9THj5eZ5JngLN+ASYMZra7x2O0grS+DHuR+Yfns/JvJPE+MfwwNAHmB4z3VVJpGtu7NsSSYUmhBDtiiRUQohzQmtNqdVOcb6VovxSisuSogKrI0HKd7wuzi+luEJZcYGVkqKakyMAZVB4+ToSIi9nYuTt54mXf1mZs9zX8WwyG9tUq9LZZrPbWH1iNW/sfIPDmYe5sMuFPDf+OTyN9d87qx6uubGv3IdKCCGEEEK0Jna7pqTwTCtRcb61SiJUVlb1vRV7ae3nSAwGhdnHA4uPCbO3Cd9AMyHRvpXKzN7O1z4eWLxNznIPlEESpLPFaDAyufNkzu94Ph/s/YDXd7xOkCWIv4/++1lbZ2MHpcjRWvvXUJ6htQ52aWQNj0nOEAohRPvRYo5CpP4R4tyyWe3liZDj2Vq9W11ZYlThubiwtM6/TJPZWCEJciQ+Zh8TFh+PKklRhdfeHtJy1Ercvexu4nPiWXLFkuYuqnktVHIfKiGEEEII4Qp2u6Y431o+wEJRnpXiglqSorJWpIJSSmu5xghAKRzJjzMxsviaCAj3xuLtgdnX5EySak6WjB7t97qjtiy9MJ13dr3DhlMbuLLXlWd1XXIfKiGEEEII0WylVht5GcXkZhSRm1FEQXZxtYEXCnNLKMqzUlsHKYOHciREzlYgvxALYZ19HUmQtzMR8jmTIJmdZZ4W6UYn4GTeSTYnbWZp/FI2nNqARnNN72v464i/ntX1Niih0lp3hZZ5HyrR+rw7d3WNI9mYzEbuevV8N0QkhBBCiIayWe2kHM/m5KEsTp/ML0+gCnNKqs3r6eWBl58Jbz9PAsO9ieoeUGXgBRMWX8/yRMnDZJBudKJBMosyic2M5VDGIQ5kHGBbyjaS8pMAiPKJ4tYBtzK7+2y6BnQ967E0alAKrfVNSqkIYBQQSoWuf1rrD2v9oBAV1DYsaG3lQgghhGgZln6wj8NbUhxvFASEeeEXbKHLwBD8gi34hVjwC3I8t/dhvEXzldhKOJF3gsScROJz4knITSAhJ4EjWUdILUwtny/UK5Sh4UO5pf8tDI8YTs+gnhjUufvtNXaUv8uAz4HDQH9gHzAAWEflroBCCCGEEKKNyUzOBxxDfw+b3pmYgaGEdfLFw9Po5shEa2PXdjKLMkkuSCY5v/ojKT+J1IJUdIURRfxMfnT278zoqNH0Du5Nz6Ce9A7qTYhXiBu3pJEJFfAscKvW+julVKbWeqhS6lYcyZUQQgghhGjDZtw5gK2LjpN0NJttS+LZtiQepcA/zIvgKB+Cox2PkA6+BEX6YJDrmtoVu7aTW5JLZlEmmcWZnC48TVphGumF6eWPtII0Thee5nTRaWy6cu8kT4MnkT6RRPpEMjpqNNG+0XT260xn/87E+MUQYA5okV1CG5tQddZaf1el7BMgGXjQNSEJIYQQQoiWKDDcm6m39gOgIKeE5CPZpJ3IJfNUPhlJ+Rzfcxptd7QoeHgaCO3oR3iMH1E9AukyMERasloRq81Kdkk2OSU55BTnOJ6drzOLMx1JkzNxKnudVZxVLUkCMCgDIZYQQr1CCfEKoU9wn/LXkd6RRPpGEukdSbAluEUmTPVpbEKVqpSK0FqnAMeVUmOBdED+OoQQQggh2hFvf0+6DQ2j29Cw8jKb1U5mSgHpJ3JJi88lLSGX/b+fYveqE3h6edBnTCTnXdVTWq7OkRJbSbWEKLs4u1JyVNPr3JJcCksLa12uQhFgDiDQHEiwJZgY/xiGhA8hyBxEkMX5MAeVJ01B5iCMhrabLjQ2oXoPGA/MB14GVgF24D8ujku0YSazsdZR/oQQQgjRehlNBkI7+hLa0Zc+Y6IAx32nTsZmsvDVnexedYIB53cgKNLHzZG2bKX2UvKt+RRYC8iz5pFvzSffmk+eNa/GsvLXJXmVkqMiW1Gd6/H28Mbf7I+/p+PR2a9zpfe1vQ4wB+BhaGwa0XYpXduNABryYaU6Az5a6wON/Fwn4FMgAse9rN7VWr+qlAoGvgG6AMeBq7XWmfUsS+5UL4QQ7UezTmtL/SPEuaO15vTJfI7sSOXItlQykwsI6ejL5X8bhtmr7R2M2+w2CksLa0x0GvK+LCEqKC2os3WoIovRgrfJG1+TLz4mH3w9fc8kQHUkRP5mf/w8/TAZTGd5r7QptdY/9SZUSqnJDVmD1nplg6NRKgqI0lpvV0r5AduAy4BbgAyt9fNKqUeBIK31I/UsSyo0IYRoP5qbUEn9I8RZVJRv5cTBTBL3nybhQAZ5GcWgILpHIP0nRNNzRITbbsCrtcZqt1JYWkhhaWF54lJoLaTIVlReXmgtPPO64nx1PayFlNir34erJh4GjzMJkPO57HXF5KgsQapW5nztbfKWhOjcqvWH25DTAx80YB4NdGtoNFrrJCDJ+TpXKXUA6ABcCkxyzvYJ8BtQZ4UmhBBCNJTUP0K4lt1mJ/loDgn7T5O4P4PUhFzQjhv6duwTxIiLguk6OAxvf886l2Oz2yi2FVd/lFYvKyotqj3Jsdad+NQ0YEJdPA2eeJm88PKo/Ai2BFd67+3hXf7ax7NyolQ1EfI01r0vROtTb0KltT6rtxdWSnUBhgKbgAhnZQeOkQMjzua6hRBCtF9S/whRndVupcRWQlFpkePZVuW5tIjCvBIy46zkx0HRMQ90sQGURocXUDokm8KodAqDMjmsCykpLaFos+OzNSVHZUlTqS5tUrwKVTnZMXnhZXS8DjQHninzaNjD28O7fH6L0dKmB1IQruPWDqxKKV8cA1z8WWudU3GYRK21VkrV2HVCKXUXcJfzbehZD1QIIUSbIvWPaMnKuqY1pLWmKeVVE6SK72trwbFYfemZPpxupwcTkdsVA54UeuSSELSD+KD9nAiIxepRhMXDgrnAjGexJxajBU/jmWdfT1+CjcHVyi0eVd47n81Gc7VpZqO5UgJkNppb5TDbom1p1qAUzVqxUibgZ+BXrfV/nWWxwCStdZKzn/tvWuve9SxH+rCLdmHXikSyUwsYf00vGW5WtGfN/vFL/SMaSmtNib3u1pqK75ud/NjPTNfN+Cl5KI+6ExUPM2aDGbPR7HhtrOHhYcbTbqb0mDeF+03kH1WgwSfSSFhfL6L7+REe44/FZCmf30N5SHIj2rJmXUPlcsrx1/YBcKCsMnNaCNwMPO98XuCG8IRocbYvjWf/2lP4BJpZ+ekBJt/UV5IqIZpA6p/WT2tNka2IAmtB+fU0Rbai8ufi0uJq7wttheWJSmFpYaXPVV1G2fxlCVJzeBg86myN8TZ5Vy431N1aU1vyU/UzzR3OOj+7mN0rE9m75hQlhaX4BpkZNj2S3qMjCY6W4c6FqMotLVRKqfHAWmAPjvtYAfwdRz/2b4HOQDyOYWsz6lmWnCEUbVpZMnXZX4di9jax6M1d+AZZJKkSrUpibiJ70vYws9vM5i6quaP8Sf1zDmmtKSgtILckl7ySPMc9dEoLKLSeGTmtwFpQaSS1iu9rm9aU1puy5MbiYSnvNlaWkJS9tnicmW4xWmpvvTE2oHXH4Nnqrr/JTitkx9J4Dm5Ixm6z031YOP0nRNOhV5DbRuYTogVp+rDpLZ1UaKItq5hM+QZZALAW2ySpagcKrAWkF6bT2b+zu0NptsScRG5fejul9lJu6HsDtw+8vTmLazE/+PZS/xTbisksyiSrOMvxKMoipyTHkSRZ88qf80qqvLbmkm/Nx67t9a+EM/fTqWlwgLIR1MqmVxxRrWICZPFwJEFeRi/MHuZKCZTchLR2pSU2ti2JZ/vSeAD6jo1iyLTOBIZ7uzkyIVoU13X5U0pNA64FwrXWlyilRgD+jbkPlRCifvvWnmTfmpNc/rdh5ckUgMlsZNZ9g/n5jV2s+/YwE6/t5cYoxdmQb83n3uX3EpsRy/+m/I8RkVWP2VuPsmTqzkF3MrHDRG5f6kimmplUiWbQWpNrzSU1P5XUglRSClJIK0wjoyiDzKJMsouzySzOJKsoi8zizDpvMOqhPPD19MXX5Iufpx++nr508O3geG3yxdfTFz+To7xsPh+TT3lSVJYgyWhq7nMyNpOVnx8kJ62QniMjOO+KHvgEmt0dlhCtSqMSKqXUA8Bc4H3gSmdxIfAaMM61oQnRvvmFWLAW28jPKqmUUIHjxol5WcV0GxrmpujE2VKWTHUP7M69g+/lb6v/xn/O/0+rTKoqJlNX9boKgA+mfyBJ1VmmteZ00WlO5J4gITeBhJwETuSdICU/hdQCRxJVZCuq9jk/kx+BlkCCzEGEWELoEdiDQHMgQZYgAs2BlR4B5gB8PX2xGC0yCEErpbVm5/JENnwfh3+YF7P/PIROfYLdHZYQrVKjuvwppY4AU7TWx5VSmVrrIKWUEUjVWoectSjrjqlddLkQ7dPx3ems/OwAs+4bTERXfwByM4r48eUdDJjYgaHTWn93MHFGxWTqiTFPYFAGNiZt5JE1j7S6pCoxN5Hbf62cTJVJyU/h9qW3c0XPK7h1wK2NXXSLOXpvCfVPVlEWsZmxHMw4SGxGLIezDpOQk0BBaUH5PAZlIMonigjvCMK9w8sfEd4RhHmHlb83G6VVor2w2zUrPz1A7MZkug8NY/LNffG0SJdIIerhsi5/fkCi83VZpWECSpoQlBCiHl0GhTL5D31Z9OYuZt03GO8AT0mm2qiakimAMVFjeGHiC62upWr/6f2U2EoYHz2+2rRw73DGRI1hZcLKpiRU7dqJ3BNsSd7C1pStbE3eyqn8U+XTwr3C6Rnck+ERw+nk14nOfp3p5NeJDr4dMBlNboxatCR2u2blJweI3ZTMyIu7MnJWF2llFKKZGttCNQ/YobV+TimVobUOVko9DAzRWl9/1qKsOya3nyEU4mwra6kymY0MOL+jJFNt0Of7P+eT/Z/w8+U/19hS8Mq2V1h7ci3zZ893Q3RN8+m+T/nq4Fd8OONDonyjAEc3o5e3vcyGpA28N+09Ai2BjV1siznyO1f1z7HsYyw5toSl8UuJy4oDIMgcxIjIEQwKHUTv4N70Du5NsEW6a4n6/T4/jp3LEhg9uysjZnZ1dzhCtCauGeXPebPDn3DcHb4DcBTIBS7WWic3M8gmkYRKtBcJ+0+Tl1FMv/HR7g7F7RJzE+nk18ndYbiU1Wblb6v/hkbz3/P/W6lF4Zdjv/Dilhd5Z9o79ApqXYOQVEyqIn0im5tMQTtKqHam7uTd3e+y9uRaFIphEcOYFjONMVFj6BbQTVoVRKMd3ZHGL+/sYcD5HTj/ujrvWy2EqM51w6Y7b4o4EojB0f1vs9YNHBP1LJCESoj25f3d7/PqjleZO3Qudwy6w93huFRNSVVrTqbKlCVV46LHsTt9d3OSKWgHCVVhaSEvbnmReYfmEWwJ5ro+1zGn5xzCvcObu2jRjhXlWfniqY34h1qY8+BwjCaDu0MSorVxWQuVJ3ALMATwrThNa31T02JrHkmoWrfstEICwrzcHYZoJd7f/T7v7H6HIlsRFqOFuwfd3aaTqmkx03h528utOpkq89n+z1gev5xXL3i1OckUtPGEymqzcu/ye9mcvJmb+9/MfUPuw8tD/keK5vvti4Ps/z2Jax4fSUgH3/o/IISoymUJ1VfAYBzd/goqTtNa/19To2sOSahar22/HGfjgqOMubQbwy/q4u5wRAtXMZkq09aTqp2pO3l/xvutPplysTadUH2y7xNe2voS/zzvn1zW47LmLEqIcjnphXz+5EYGTOwg9y4UoulcNsrfhUBXrXVWs8IR7d62X46z9ZfjAOXPklSJ2tSUTAEU2Yp4Z/c7AG0qqTIZTfx30n/Jt+YTYA5wdzjiHPr1+K8MChskyZRwqZ0rElEKhs2QAY2EOBsa24E2AZAbVYhmKUumSkscl96VltjZ+stxtjkTKyEqSsxN5NUdr9Z4I1JwJFWv7niVxNzEGqe3Vh4GD0mm2qE8ax7hXnKtlHAdm9XOoU3JdB8WXu0m8UII12hsQvUpsEApdZ1SanLFx9kITrQ9VZOpMpJUidp08uvE3KFzsRhrPhCwGC3MHTq3zY36J9qnPkF92JayjaLSmk8gCNFYCftPU1xQSu8xke4ORYg2q7EJ1R+BCOBfwAcVHu+7OC7RBmWnFbJxwdFqyVSZ0hI7GxccJTut8BxHJlq6Owbdwd2D7q6WVLXVa6hE+3VV76vILM7k3d3vujsU0UYk7M/Aw2ykY+8gd4ciRJvVqIRKa921lke3sxWgaDsCwrwYc2k3PDxr/tl5eBoYc2k3GfVP1KhqUiXJlGiLRkaO5PIel/P+nvdZf2q9u8MRbUBSXBbRPQIwesgw6UKcLfLXJc6p4Rd1QQ9No9RQUqm81FCCHpomA1OIOpUlVYAkU6LNenTUo3QP7M5fVv2Fnak73R2OaMVsNjuZyQWEdpRh0oU4m+od5U8pNVFrvcb5utZrpbTWK10ZmGi8Q5uS2bDgCHkZxfgGmxl7aXd6jW5Zfabf3/0+H5jeoU+H8Qw/OQOT3ROroYRtHX7loGkdHruz5SBZ1OmOQXcwo+sMuWZKtFneJm/enfYut/16G/csv4fXJ7/OyMiR7g5LtELZqYXYbZrgKB93hyJEm9aQFqo3K7z+oJaHXEPlZoc2JbPqi4PkZRQDkJdRzKovDnJoU7KbIzuj4mhtOzsuZ1uHXwHY1uFXdnZc3mZHaxOuJ8mUaOvCvMP4YMYHRHhHcNfSu/j+8PfuDkm0QvnZjmMC32AZ3U+Is6nehEprPaDCa7mGqoXasOBIjSPnbVhwxE0RVVd1tLadHZfzxdBn2NlxOSCjtQkhREXh3uF8PvNzRkeN5qn1T/Hsxmdl9D/RKNZCGwCeXo297agQojEa0uWvQUOiS5c/98rLKKKmGzg7yluOsu58ZTdpzbWcBmSAASGEqImfpx9vTHmD17a/xkf7PmJbyjb+38T/R4+gHu4OTbQCJcWlAJjMRjdHIkTb1pBTFh80YB4NSCuVGxVYcvAuqn4T0AJLjhuiqVvVpEqSKSGEqJ2HwYO/jvgro6NG8/d1f+faRdfy8MiHuarXVShV/USaEGXKRvazldZ8uxIhhGs0pMtfbd38pMtfC7K+4wKsVUbOsxpKWN9xgZsiqpuM1iaEEI1zXofzmD97PsMjhvPPjf/kzmV3yjWnok5lLVPWIpubIxGibWvysOlKqUWuDEQ0T36XU6zu9jW5nhloNLmeGazu9jX5XU65O7Ra3THoDhbPWSzJlBBCNFCoVyhvTX2LJ8Y8wd70vcxZMIdP9n2CzS4HzKI6i48JgMI8q5sjEaJta85VihNcFoVotrnD5vJ00dPEhW0rL7MYLTw97Gm3xdQQMgCFEEI0jkEZuLr31UzsOJHnNj7HS1tfYsmxJfxjzD/oH9rf3eGJFiQw3BuArJQCN0ciRNvWnBv7SsftFmRWt1k8Pe5ponyiUCiifKJ4etzTzOo2y92hCSGEOAsifSJ5bfJr/L+J/4+k/CSuW3QdT69/moyiDHeHJloIi68Ji6+JrOR8d4ciRJumtNZN+6BS12utv3RxPE2JY6vWekSV4qZtlBBCiJauxZzMa0n1T25JLm/vepsvD3yJl8mL+4fczzW9r8HDIMNlt3cLX91BQU4J1z4x2t2hCNHa1Vr/NLmFqiUkU0IIIYRwDK/+0MiHmD97Pv1D+vP85ue5cuGV/Jb4G009cSrahuheQZw+mU9hbkn9MwshmqTRCZVSappS6gOl1E/O9yMaeq+qCsv4UCmVqpTaW6EsWCm1TCl12Pkc1NjYhBBCiLq09fqnW2A33p32Lq9c8Ao2beOBlQ9wy5Jb2JW2y92hCTfp2Nvxc048KF1BhThbGpVQKaUeAN4CDgMTncWFwLONXO/HwIVVyh4FVmitewIrnO+FEEIIV/qYNl7/KKWY0nkK31/6PU+MeYL4nHhuXHwjf1n1F45lH3N3eOIcC+/ij3eAJ3FbU90dihBtVmNbqP4MTNVaPw+U3SXuINC7MQvRWq8Bqp4quRT4xPn6E+CyRsYmhBBC1Kk91T8mg4mre1/N4jmLuW/Ifaw/tZ7LF1zOMxueISU/xd3hiXPEYFD0HBFB/L7TFOXL8OlCnA2NTaj8gLK7CJZ1yjYBruiYG6G1TnK+TgYiaptRKXWXUmqrUmorEOqCdQshhGi/2nT9423y5t7B97J4zmKu7n01Pxz+gYu+v4jnNj5Hcn6yu8MT50Dv0ZHYSzWxG+X7FuJsaGxCtYbqXSH+BKxyTTgO2nEFba1X0Wqt39Vaj3COrpTuynULIYRov9py/RPiFcLfR/+dn+f8zOzus5l3aB4zv58piVU7ENbZj6geAexakYjdZq//A0KIRmlsQvUAcLlS6jjgp5SKBa4G/uqCWFKUUlEAzmfp7CuEEOJcaFf1TwffDjw97ml+nvMzl/a4tDyxenbjs5JYtWFDpnYmN6OII9vT3B2KEG1OoxIqZ5eIkTiSqOuBm4FRWmtX/Ade6FwezucFLlimEEIIUZ92Wf908O3AU2OfKk+s5h+az8zvZ/L0+qc5nn3c3eEJF+syKJTgaB82/XQUm7RSCeFSjbqxr1LqNeBrrfX6CmXjgKu11n9uxHK+Aibh6H+eAjwF/Ah8C3QG4p3LrHeMz5Z0Y0UhhBBnXbNu7Cv1T+1O5Z3igz0f8GPcj1jtVqbGTOW2AbcxIHSAu0MTLnJ8dzqL3tzNxGt7MXBSR3eHI0RrU2v909iEKg3ooLUuqVBmBhK11uHNCrGJ2lqFJoQQok7NSqhcqa3WP+mF6Xx54Eu+jv2a3JJcRkaO5LYBt3Fe9Hko1WJ2v2gCrTU//ncHmcn5XP/UGCy+JneHJERrUus/wMZeQ6Vr+IyxCcsRQgghRAsU6hXKn4b9iWVXLuPBEQ8SnxPPvcvv5aqfrmLR0UVY7TL0dmullGLCNb0ozi/l93mH3R2OEG1GY1uo5gPHgIe11nallAF4Huiptb78LMVYX0xt8gyhEEKIGrWYJpL2Uv9YbVZ+PvozH+37iGPZxwj3Due6PtdxZc8rCbQEujs80QQbFxxh2y/xXPKnwXTuF+LucIRoLVzW5a8j8DMQhaOfeWcgCbhEa32imUE2SXup0IQQQgCSULmNXdtZe2Itnx/4nI1JGzEbzVzc7WJu6HsDPYN6ujs80QilVhvfPreFksJSrvnHKLz8PN0dkhCtgWsSKgBnq9QooBOOm/xu1lq7bbiY9lahCSFEOycJVQsQlxnHFwe/4KcjP1FsK2Z01Ghu7HsjEztOxKDkKoDWIC0xl/kvbKND7yAuvn8QytBi/rSEaKmanlAppSZqrdc4X0+ubT6t9comh9cM7blCE0KIdqjFHPVJ/QNZRVnMOzyPrw9+TUpBCp38OnFN72u4tPul0h2wFdjz2wnWfH2IsZd3Z9iMGHeHI0RL16yEaq/WeoDz9bFaZtNa625Nj6/ppEITQoh2RRKqFshqt7IiYQVf7P+CnWk78TR4cmHXC7m699UMCh0kowO2UFprlr6/j7jtqcy6dxBdBoW6OyQhWjLXdflraaRCE0KIdqXFHJlL/VOz2IxYvjv0HT8d+YmC0gJ6B/Xm6t5Xc3G3i/E2ebs7PFGFtcTGDy9tJyulgCseHk5IB193hyRESyUJlRBCiDZBEqpWIt+az6Kji/g29ltiM2PxMflwcbeLubr31fQK6uXu8EQFeZnFfPf8FgwGxZyHhuMXbHF3SEK0RJJQCSGEaBMkoWpltNbsStvFd4e+Y8mxJZTYSxgYOpDLe17ORV0uwtdTWkRagrSEXH7873Z8As1c/rdhMvKfENVJQiWEEKJNkISqFcsqymLhkYX8EPcDcVlxWIwWpneZzuU9Lmd4xHC51srNTh3OZOFruwiO8uGyvwzF08vD3SEJ0ZJIQiWEEKJNaDFH3FL/NJ3Wmr3pe/k+7nt+OfYL+dZ8YvxjuKzHZVza/VLCvMPcHWK7dXxPOr+8tYewGD8ueWAwZm+Tu0MSoqWQhEoIIUSbIAlVG1NgLWBZ/DK+P/w921O3Y1RGxncYz6U9LuX8jufjaZSuZ+fa0Z1p/PreXkI6+DL7T0Ow+EpSJQSSUAkhhGgjJKFqw45nH+fHuB9ZcGQB6YXp+Hv6M6PLDC7pfglDwoZIl8BzKH7vaX55ew+BEV5c8qch+ASY3R2SEO4mCZUQQog2ocUcUUv9c/aU2kvZlLSJn47+xMqElRSWFtLRtyMXd7+YS7pdQmf/zu4OsV1IPJDB4rf34OVr4pIHBhMU6ePukIRwJ0mohBBCtAmSULUz+dZ8lscv56ejP7E5aTMazaCwQczuNpsZXWYQaAl0d4htWmp8Dj+/sQu7XXPx/YOJ7Bbg7pCEcBdJqIQQQrQJklC1Y8n5ySw+tpifjvxEXFYcHgYPJnaYyCXdL2Fix4lyvdVZkp1WwE+v7SIvq5gpN/Wl58gId4ckhDtIQiWEEKJNkIRKoLUmNjOWn478xOJji0kvTMfP048pnadwUZeLGBU1Cg+DDPntSoW5Jfzyzh6S4rIZOr0zYy7rjsHQYv4chTgXJKESQgjRJrSYIzipf1qGsuutFh9bzMqEleRZ8wgyBzE1ZioXdb2IYeHDMBqM7g6zTbCV2ln77WH2rTlJ5/7BTLutPxYfGQFQtBuSUAkhhGgTJKEStSq2FbPu5Dp+PfYrv534jcLSQkK9QpnRZQYXdrmQQWGDMCiDu8Ns9fatPcmarw/hF2zhonsHEhLt6+6QhDgXJKESQgjRJkhCJRqkwFrAmpNrWHJsCWtPrKXEXkKUT1R5ctUvpJ8Mw94MSXFZ/PLuXqxFpUy4phd9x0XJ/hRtnSRUQggh2oQWc8Qm9U/rkVeSx6rEVSw5voT1J9dTqkvp5NeJC7tcyNSYqfQN7ivJQBPkZxez7MP9nIzNpOfICCZd3xtPL7l2TbRZklAJIYRoE1rMUa/UP61TdnE2KxJW8MuxX9icvBm7ttPBtwNTOk9hWsw06RbYSHa7Zvuv8Wz+6Rh+wWam3zGAiC7+7g5LiLNBEiohhBBtgiRUwmUyizJZlbiK5fHL2ZC0gVJ7KWFeYUzuPJlpMdMYHjFcRgtsoKS4LJZ+uI+CrBJGze7K0GmdMRglMRVtiiRUQggh2gRJqMRZkVuSy5oTa1gev5x1J9dRZCsi0BzIBZ0uYGrMVMZEjZH7XNWjKN/Kb18c5Mj2NCK6+jPl5r4ERfq4OywhXEUSKiGEEG2CJFTirCssLeT3k7+zPGE5qxNXk2fNw8fkw8SOE5kWM43zos/D2+Tt7jBbJK01cVtTWf11LKXFdkbP7sbgqZ3knlWiLZCEqrEScxPp5NepuYsRQgjhWi3mqEwSqvahxFbCpqRNLE9YzsqElWQVZ2E2mhkbNZZJnSZxfqfzCfUKdXeYLU5+djGrv4zl2K50Irv5M+XmfgRGSBIqWjVJqBrj/d3v8+qOV5k7dC53DLqjOYsSQgjhWpJQCbcptZeyPWU7KxJW8Fvib5zKP4VCMTBsIBd0uoBJHSfRPbC7jBjopLXm0OYU1n5ziNISOyNmxjB0WgxGk1xbJVql1pNQKaUuBF4FjMD7Wuvn65nfpRXa2/O+InutJz7FgeSbswiYUMI9V17X1MUJIYRwrbN2pOru+ke0LlprDmUe4rfE3/gt8Tf2nt4LQCe/TkzqNIkLOl3A0PChMqgFjtaqtd8c5sj2VIIivTn/ut506B3k7rCEaKzWkVAppYzAIWAacALYAlyntd5fx2dcVqG9Pe8rilcG4WE/c9FpqaEE8+RMSaqEEKJlOCsJlbvrH9H6pRaklidXm5I2UWIvwd/TnwkdJzCp0yTGR4/H19PX3WG6Vfze06z5Opac9CJ6j4nkvCt64OUnA32IVqPVJFRjgae11jOc7x8D0Fr/u47PuKRCe3/3+5z+IAjf4upnTPLMmYTcnind/4QQwv3OVkLltvpHtD0F1gI2nNrAqsRVrDmxhsziTDwMHoyKHMWkTpO4steVmAwmd4fpFtYSG9sWH2fHsgRMZiPj5vSg77golAxaIVq+VpNQXQlcqLW+w/n+D8BorfUfq8x3F3CX861Faz2gOesd+MnAbsCRuze8gqphX2k074z9M0D3PTfvOdqcdQkhhGh53FX/CCGEaP1a5VWBWut3tdYjnI9mV2bOJOmxPM/MGrNLZ/ljkkwJIUT75ur6RwghROvX0hKqk0DFsco7OsvOuj0373k+LnT7N1ZDSaVyq6GEuNDt3+y5eU+dFycLIYRo1dxW/wghhGjdWlpCtQXoqZTqqpTyBK4FFp6rlb//5L+u2xu55utczwyt0eR6Zui9kWu+fv/Jf8mIFEII0ba5tf4RQgjRerWoa6gAlFIzgVdwDFv7odb6uXMdw8BPBj4K/BtHNz9pmRJCiHagJdQ/QgghWp8Wl1C5glJqCdC825YbiMROsmsiEnUIBdLdHUQrIvvLob3sh7a2na7YnnSt9YWuCOZscEn94x5t7bd2Nsg+qpvsn/rJPqpbS98/tdY/bTKhcoVahsMVLib7uXFkfzm0l/3Q1razrW1PWyLfTf1kH9VN9k/9ZB/VrTXvn5Z2DZUQQgghhBBCtBqSUAkhhBBCCCFEE0lCVbt33R1AOyH7uXFkfzm0l/3Q1razrW1PWyLfTf1kH9VN9k/9ZB/VrdXuH7mGSgghhBBCCCGaSFqohBBCCCGEEKKJJKESQgghhBBCiCZqVwmVUuq4UmqPUmqnUmqrsyxYKbVMKXXY+RzkLFdKqdeUUnFKqd1KqWEVlnOzc/7DSqmb3bU9LZVSKlApNU8pdVApdUApNVb2c82UUr2dv8eyR45S6s/tcX8ppf6ilNqnlNqrlPpKKWVRSnVVSm1ybu83SilP57xm5/s45/QuFZbzmLM8Vik1w20bVAul1FznNu5TSv3ZWdaqvm+l1IdKqVSl1N4KZS7bBqXUcOf/6jjnZ9W53L72QCllVErtUEr97Hz/sVLqWIX/RUPcHKJbqUYcL7RXteyjp5VSJyv8jma6O053UY04FmqvatlHrfM3pLVuNw/gOBBapexF4FHn60eBF5yvZwK/AAoYA2xylgcDR53PQc7XQe7etpb0AD4B7nC+9gQCZT83aL8ZgWQgpr3tL6ADcAzwcr7/FrjF+Xyts+xt4F7n6/uAt52vrwW+cb7uB+wCzEBX4AhgdPf2VdjOAcBewBvwAJYDPVrb9w1MBIYBeyuUuWwbgM3OeZXzsxe5+7traw/gr8CXwM/O9x8DV7o7rpbyoBHHC+31Ucs+ehp40N2xtYQHjTgWaq+PWvZRq/wNtasWqlpciuMLxfl8WYXyT7XDRiBQKRUFzACWaa0ztNaZwDKgxrsmt0dKqQAcB1sfAGitS7TWWch+bogpwBGtdTztc395AF5KKQ8cCUcSMBmY55xedT+U7Z95wBRnK8alwNda62Kt9TEgDhh1bsJvkL44EooCrXUpsBqYQyv7vrXWa4CMKsUu2QbnNH+t9UbtqGU/rbAs4QJKqY7ALOB9d8fSytT2GxeikiYcC7U7deyjVqm9JVQaWKqU2qaUustZFqG1TnK+TgYinK87AIkVPnvCWVZbuXDoCqQBHzm7k7yvlPJB9nNDXAt85XzdrvaX1vok8BKQgCORyga2AVnOxAMqb1P59jqnZwMhtPz9sBeYoJQKUUp542i96UTb+L5dtQ0dnK+rlgvXeQV4GLBXKX/O2S3zZaWU+dyH1aI05nihvappHwH80fk7+rAdd2lr7LFQe1TbPoJW+BtqbwnVeK31MOAi4H6l1MSKE51nQ2Uc+ebxwNEV6C2t9VAgH0ezdjnZz9Upx7VBs4Hvqk5rD/vL+Q/zUhz/YKMBH1pfC1u9tNYHgBeApcASYCdgqzJPq/++28I2tFVKqYuBVK31tiqTHgP6ACNxdMN85FzH1sLI8UL9atpHbwHdgSE4To79x33huZUcC9Wvtn3UKn9D7Sqhcp4FR2udCvyAoytQirOLCc7nVOfsJ3GcOS7T0VlWW7lwOAGc0Fpvcr6fh+MPRvZz3S4CtmutU5zv29v+mgoc01qnaa2twPfAeTi6h3k456m4TeXb65weAJymFewHrfUHWuvhWuuJQCZwiLbxfbtqG046X1ctF65xHjBbKXUc+BqYrJT6XGud5OyWWQx8RMvqKnvONfJ4oV2qaR9prVO01jattR14j/b7O2rssVB7VOM+aq2/oXaTUCmlfJRSfmWvgek4ut8sBMpGl7oZWOB8vRC4STmMAbKdzbS/AtOVUkHOs+rTnWUC0FonA4lKqd7OoinAfmQ/1+c6znT3g/a3vxKAMUopb+e1UGW/m1XAlc55qu6Hsv1zJbDSebZvIXCtcowC2BXoiWOAgxZDKRXufO6M4/qpL2kb37dLtsE5LUcpNcb5W7ipwrJEM2mtH9Nad9Rad8HRzXil1vrGCgd5Csd1HXtrX0rb1oTjhXantn1U9jtyupx2+jtqwrFQu1PbPmq1vyF9Fke8aEkPoBuO0b92AfuAx53lIcAK4DCOEbeCneUK+B+OUcL2ACMqLOs2HBe7xwG3unvbWtoDRzPtVmA38COOEbxkP9e+v3xwtK4EVChrd/sL+D/gII5/np/hGKmvG46EKA5Hd0izc16L832cc3q3Cst53Ll/YmmBo8MBa3FUrLuAKa3x+8aR/CcBVhxnGW935TYAI5y/gyPAG4By9/fWFh/AJM6M8rfS+f3sBT4HfN0dnxv3S6OOF9rjo4599Jnzd7QbR/IQ5e5Y3biPhtDAY6H2+qhlH7XK35BybpAQQgghhBBCiEZqN13+hBBCCCGEEMLVJKESQgghhBBCiCaShEoIIYQQQgghmkgSKiGEEEIIIYRoIkmohBBCCCGEEKKJJKESQgghhBAthlJqlFJqg1JqjVLqK6WUyd0xCVEXSaiEEEIIIURLkghM1lpPBI4Dl7o3HCHqJgmVEK2AUkorpfKVUs+5O5aqlFIrlVJFSql17o5FCCFE07WUukZrnaS1LnS+LQHsZdOkzhEtkSRUQjSDUup6pdRWpVSeUipJKfWLUmp8hem3KKX2KKUKlFLJSqm3lFKBTVzdYK31466J3HW01pOBe9wdhxBCtFdKqd+UUplKKXNTplcxWGv9uFLqMaXUL1WWc7iWsmubEX6tlFIxwHTgp7IyqXNESyQJlRBNpJT6K/AK8C8gAugMvImza4JS6m/AC8BDQAAwBogBlimlPN0QshBCiDZGKdUFGAQcAGbXMn0CoGuaXoc1wDillNG5nCjABAytUtbDOW9j4450JnpVH5HO6f7AZ8AtWmtrY5cvxLkkCZUQTaCUCgCeAe7XWn+vtc7XWlu11j9prR9yVgT/BzygtV7inHYcuBroAtzoghiOO88g7neeefxIKWWpb1qF6Q8ppXY7u3d8oJSKcLaw5SqlliulgpzzPqKUmldl3a8qpV5r7jYIIYRotpuAH4CPgZtrmb6xjum12YIjgRrifD8BWAXEVik7orU+BY2rW7TWyVrrSTU8kpVSHsDXwP9prWMbEbMQbiEJlRBNMxaw4KjEajLOOf37ioVa6zxgMTDNRXHcAMwAugO9gH80cBrAFc44egGXAL8AfwfCcPxv+JNzvq+BmUopPwDnmcmrgS9dtA1CCCGa7ibgK2AeMFkpFVHD9C+cjxk1TK+R1roE2ARMdBZNBNYC66qUVW2damjdUpfrgNHAE85Wq2saErMQ7iIJlRBNEwKka61La5keWsf0JCDURcPCvqG1TtRaZwDP4aiEGjIN4HWtdYrW+iSOSnKT1nqH1roIR6I4FEBrHQ9sBy53fm4yUKC13tiEeIUQQriI85pdH2CV83/9CuD6KtNjgG+11tuAIxWnN8BqziRPE3DUFWurlK2u8pkG1S110Vp/prUOqdBq9U0jYhbinJOESoimOY0jKfKoZXp6HdOjnNNdMSxsYoXX8UB0A6cBpFR4XVjDe98K77/kTEJ2PdI6JYQQLcHNOJIlm/P9l1Tu1nczsFRrnV7L9PqsAcYrpYKBMK31YWA9jmurgoEBVG+hakzdIkSbIAmVEE2zASgGLqtn+pyKhUopX+AiYEVdw8I2QqcKrzsDpxo4rbG+AyYppTriaKmShEoIIdxIKeVF9e7XC4EeSqnBFaaf7xxlNhn4CzBYKTW4gavZgGNQpTuB3wG01jk46pM7gVNa62Mu2SAhWjFJqIRoAq11NvAk8D+l1GVKKW+llEkpdZFS6kXn9P8DXldKXeic1gX4FjiBY+QioOZhYRvhfqVUR+eZwseBbxo4rVG01mnAb8BHwDGt9YGmLksIIYRLXAZkALuUUhbnwEM2HNfp3uScbgP64RhEYgjQF0c3vJsasgLnSb+twF+dnyuzzlnW6NH9hGiLauuuJISoh9b6P84zfv/AcbFvLrANx/VKaK1fVEqdBl7CMTBEDvAjcIPWuhhcMizsl8BSHN35FgDPNnBaU3wJfAo83MzlCCGEaL6bcYwaW1jDtBRgMPCR1jqh4gSl1BvAa0qpR+q4Drii1TgGYqp4I921wB+RhEoIAJTW2t0xCNEuOa+vWgj8R2u9op55i3B0IXxNa/2Es+w4cIfWenkN89c6zdWUUstw3GNrs9Z6ytlenxBCiLOjprqmpZE6R7RE0kIlhPtUHBb2CeCt2kYy0lpbaipvCbTWrhoCXgghhBu15LqmjNQ5oiWShEoIN9Faf0aFa6mEEEIIIUTrI13+hBBCCCGEEKKJZJQ/IYQQQgghhGgiSaiEEEIIIYQQookkoRJCCCGEEEKIJpKESgghhBBCCCGaSBIqIYQQQgghhGgiSaiEEEIIIYQQookkoRJCCCGEEEKIJpKESgghhBBCCCGa6P8Do5782bUOniMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharey=False)\n",
+    "\n",
+    "\n",
+    "###########################################\n",
+    "# GCM bifurcation\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    if ADSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ADSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_A = ax[0].scatter(float(ICON_tools.find_co2_expname_vscicona(exp))*1.00,(1-ADSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "\n",
+    "color=\"C4\"\n",
+    "for i, exp in enumerate(ESMexplist):\n",
+    "    if ESMDSlistgmym[i][\"sic\"][0]<0.4:\n",
+    "        marker = \"o\"\n",
+    "    elif ESMDSlistgmym[i][\"sic\"][0]<0.7:\n",
+    "        marker = \"s\"\n",
+    "    else:\n",
+    "        marker = \"D\"\n",
+    "    if exp in [\"ape_7000_22_0S\", \"ape_7000_13_0S_snowcap\", \"ape_8000_22_0S\"]: # unstable simulations\n",
+    "        facecolor = \"none\"\n",
+    "    else:\n",
+    "        facecolor = color\n",
+    "    l_ESM = ax[0].scatter(float(ICON_tools.find_co2_expname(exp)),(1-ESMDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=marker, facecolor=facecolor)\n",
+    "\n",
+    "# EBM\n",
+    "ax[1].plot(dA_Jor_negcf, x, c=\"C2\")\n",
+    "ax[1].plot(dA_Jor_BASIR_cf, x, c=\"C4\")\n",
+    "\n",
+    "# axes\n",
+    "yticks_deg = [0, 10, 20, 30, 45, 60, 90]\n",
+    "for axi in ax:\n",
+    "    \n",
+    "    axi.set_yticks(1-ICON_tools.icelatosic(yticks_deg))\n",
+    "    axi.set_ylim(0, 1)\n",
+    "    axi.set_yticklabels(yticks_deg)\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['bottom'].set_position(('outward', 5))\n",
+    "    axi.spines['top'].set_visible(False)\n",
+    "    axi.spines['right'].set_visible(False)\n",
+    "    \n",
+    "ax[0].set_ylabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_xscale('log')\n",
+    "ax[0].set_xlabel(\"CO$_2$ [ppmv]\")\n",
+    "ax[0].set_xticks([5e3, 6e3, 7e3, 8e3, 9e3, 10e3])\n",
+    "ax[0].set_xticklabels([\"5000\", \"6000\", \"7000\", \"8000\", \"9000\", \"10000\"])\n",
+    "ax[1].set_xlabel(r\"$\\Delta$A [Wm$^{-2}$]\")\n",
+    "\n",
+    "\n",
+    "# legends and annotations\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.99, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-ESM-WBF\", [0.99, 0.9], color=\"C4\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "\n",
+    "ax[1].annotate(\"strong cloud masking & negative cloud feedback\", [0.99, 0.97], color=\"C2\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "ax[1].annotate(\"large BASIR & positive cloud feedback\", [0.99, 0.9], color=\"C4\", xycoords=\"axes fraction\", ha=\"right\", va=\"top\")\n",
+    "\n",
+    "ax[0].annotate(\"a) GCM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "ax[1].annotate(\"b) EBM\", xycoords=\"axes fraction\", xy=(0.01, 0.995), fontweight=\"bold\", va=\"bottom\", fontsize=MEDIUM_SIZE)\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "#plt.savefig(\"plots/Fig7opt_ICON-A-ESM_bifurcation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5987d39c-2166-4923-a8c5-02c177884da0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "baseenv - Python 3.7",
+   "language": "python",
+   "name": "baseenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pythonscripts/Fig6_10_13-APRP_zm.ipynb b/pythonscripts/Fig6_10_13-APRP_zm.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4915d690ba523a2bb00c3dbe45d8c5ae2e0ee734
--- /dev/null
+++ b/pythonscripts/Fig6_10_13-APRP_zm.ipynb
@@ -0,0 +1,1051 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "71c92334-eb97-4155-9084-3c6bc049dc48",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ICON_tools' from '../../../snowball-waterbelt-continents/python_packages/ICON_tools.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import sys, importlib\n",
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib as mlp\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as colors\n",
+    "import matplotlib.tri as tri\n",
+    "from matplotlib import cm\n",
+    "import matplotlib.transforms as mtrans\n",
+    "from matplotlib.text import TextPath\n",
+    "from matplotlib.patches import PathPatch\n",
+    "import numpy as np\n",
+    "\n",
+    "sys.path.append(\"/jetfs/home/jhoerner/projects/aprp/code\")\n",
+    "sys.path.append(\"../../../snowball-waterbelt-continents/python_packages\")\n",
+    "import aprp\n",
+    "importlib.reload(aprp)\n",
+    "import ICON_tools\n",
+    "importlib.reload(ICON_tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67199bbf-2b12-45f2-843f-49693fd3998e",
+   "metadata": {},
+   "source": [
+    "### set global fonts for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "cbfc2566-b25e-420f-b883-2af1a5838500",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "BIGGER_SIZE = 14\n",
+    "\n",
+    "plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
+    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
+    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
+    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
+    "plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "89e8724e-8f7d-48a9-bae3-7e7bc6da4796",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DS_grid = xr.open_dataset(\"/jetfs/scratch/jhoerner/inputdata/grids/icon_grid_0005_R02B04_G.nc\")\n",
+    "cell_area = DS_grid.cell_area\n",
+    "cell_area = cell_area.rename({\"cell\" : \"ncells\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0c8ca60-bf89-4957-aadc-525fda35ec19",
+   "metadata": {},
+   "source": [
+    "### load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c1daa2d7-06e8-4d22-9a0a-9805881d29f3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "path_data = \"/jetfs/scratch/jhoerner/aprp/\"\n",
+    "\n",
+    "explist = [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \n",
+    "           \"ape_5000_55_0S\" , \"ape_5500_55_0S\",\n",
+    "           \"mlo_aqua_1500ppmv_hice_unlim_damped\"]\n",
+    "\n",
+    "\n",
+    "DS_parms=[]\n",
+    "for exp in explist:\n",
+    "    DS_parms.append(xr.open_dataset(path_data + exp + \"/\" + exp + \"_aprp_parms_binned_sic45.nc\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe14b7d-e766-41de-8ce8-77bc8a5ba1ef",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### calc APRP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fd03285c-1ac7-4257-8df0-b04f962ad562",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RSDT = DS_parms[0].rsdt\n",
+    "RSDT[:,:,:]=RSDT[40,:,:]\n",
+    "RSDT_zm = DS_parms[-1].rsdt\n",
+    "RSDT_zm = RSDT_zm[40,:,:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "46681dbc-c244-4758-9a58-ba0640fd5a43",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'nbins' ()>\n",
+      "array(2.)\n",
+      "<xarray.DataArray 'nbins' ()>\n",
+      "array(2.)\n",
+      "<xarray.DataArray 'nbins' ()>\n",
+      "array(2.)\n",
+      "<xarray.DataArray 'nbins' ()>\n",
+      "array(3.)\n",
+      "<xarray.DataArray 'nbins' ()>\n",
+      "array(2.)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, exp in enumerate(explist):\n",
+    "    DS_parms[i] = xr.where(DS_parms[i].nbins<=1, np.nan, DS_parms[i])\n",
+    "    print(DS_parms[i].nbins.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "40821144-7ef9-47cc-9d33-7cb8f018c888",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate APRP for ape_ia_5000_13_0S\n",
+      "Using RSDT from parameter file.\n",
+      "calculate APRP for ape_ia_5500_90_0S\n",
+      "Using RSDT from parameter file.\n",
+      "calculate APRP for ape_5000_55_0S\n",
+      "Using RSDT from parameter file.\n",
+      "calculate APRP for ape_5500_55_0S\n",
+      "Using RSDT from parameter file.\n",
+      "calculate APRP for mlo_aqua_1500ppmv_hice_unlim_damped\n",
+      "Using RSDT from parameter file.\n"
+     ]
+    }
+   ],
+   "source": [
+    "DS_forcing=[]\n",
+    "for i, exp in enumerate(explist):\n",
+    "    print(\"calculate APRP for \" + exp)\n",
+    "    if \"mlo_aqua\" in exp:\n",
+    "        DS_forcing.append(aprp.APRP_pp_forcing(DS_parms[i], flag='', simtype=\"ICONzm\", calc_TOA_net=True))\n",
+    "    else:\n",
+    "        DS_forcing.append(aprp.APRP_pp_forcing(DS_parms[i], flag='', simtype=\"ICON\", calc_TOA_net=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61742fa5-ec5e-4ca0-8099-7f69f11fc613",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### global mean yearly mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e3ac7da4-d2a6-4e54-99b3-1ebd4f2781f4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate mean for ape_ia_5000_13_0S\n",
+      "calculate mean for ape_ia_5500_90_0S\n",
+      "calculate mean for ape_5000_55_0S\n",
+      "calculate mean for ape_5500_55_0S\n",
+      "calculate mean for mlo_aqua_1500ppmv_hice_unlim_damped\n"
+     ]
+    }
+   ],
+   "source": [
+    "DS_mean=[]\n",
+    "for i, exp in enumerate(explist):\n",
+    "    print(\"calculate mean for \" + exp)\n",
+    "    if \"mlo_aqua\" in exp:\n",
+    "        inds = []\n",
+    "        inds = ~DS_forcing[i].cld.isnull().any(dim=[\"month\", \"lat\"])\n",
+    "        weights = np.cos(np.deg2rad(DS_forcing[i].lat))\n",
+    "        DS_temp = []\n",
+    "        DS_temp = ICON_tools.weighted_mean_dim(DS_forcing[i], weights=weights, dim=\"lat\")\n",
+    "        DS_temp = DS_temp.mean(dim=\"month\")\n",
+    "\n",
+    "    else:\n",
+    "        inds = []\n",
+    "        inds = ~DS_forcing[i].cld.isnull().any(dim=[\"month\", \"ncells\"])\n",
+    "        DS_temp = []\n",
+    "        DS_temp = DS_forcing[i].weighted(weights=cell_area).mean(dim=\"ncells\").mean(dim=\"month\")\n",
+    "\n",
+    "    bindiff = (ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[1:] - ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[:-1])[0]\n",
+    "    DS_mean.append(DS_temp.where(inds)/bindiff)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "415649e1-cd72-4692-b4d4-24c84b8cdf34",
+   "metadata": {},
+   "source": [
+    "## test for differences between total feedback and sum of all feedbacks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "35667ffd-243a-4242-83e5-cbd4049d36b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6,4))\n",
+    "colESM = \"C4\"\n",
+    "colA = \"C2\"\n",
+    "colBraun22 = \"C0\"\n",
+    "for i, exp in enumerate(explist):\n",
+    "\n",
+    "    latcenter = ICON_tools.sictoicelat(DS_mean[i].var_bin_left)-1\n",
+    "    latcenter_x = 1-ICON_tools.icelatosic(latcenter)\n",
+    "    if \"_ia_\" in exp:\n",
+    "        col = colA\n",
+    "        label = \"ICON-A-WBF\"\n",
+    "    elif \"mlo_aqua\" in exp:\n",
+    "        col = colBraun22\n",
+    "        label = \"ICON-A\"\n",
+    "    else:\n",
+    "        col = colESM\n",
+    "        label = \"ICON-ESM\"\n",
+    "    marker = \"x\"\n",
+    "    line = \"-\"\n",
+    "\n",
+    "    # feedbacks\n",
+    "    diff = DS_mean[i].sw_toa - (DS_mean[i].sfc_alb + DS_mean[i].cld + DS_mean[i].noncld)\n",
+    "    ax.plot(latcenter_x, diff, label=label, marker=marker, ls=line, color=col)\n",
+    "    \n",
+    "ax.set_xlabel(\"ice-line latitude [deglat]\")\n",
+    "ax.set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax.set_xticklabels([0,10,20,30,45,60,90])\n",
+    "ax.hlines(0, 1, 0, color=\"black\", lw=1)\n",
+    "ax.set_xlim(1, 0)\n",
+    "ax.set_ylabel(r\"APRP error [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "\n",
+    "plt.ylim(-0.12, 0.12)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"aprp_test.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a36d10bf-c4df-4b37-8128-43a0e45ed016",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## paper plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d03c72ca-a5f7-4b17-9568-e7ea1732df20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_5000_55_0S\n",
+      "ape_5500_55_0S\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x1152 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 1, figsize=(6, 16), sharex=True)\n",
+    "colESM = \"C4\"\n",
+    "colA = \"C2\"\n",
+    "colBraun22 = \"C0\"\n",
+    "\n",
+    "for i, exp in enumerate(explist):\n",
+    "\n",
+    "    latcenter = ICON_tools.sictoicelat(DS_mean[i].var_bin_left)-1\n",
+    "    latcenter_x = 1-ICON_tools.icelatosic(latcenter)\n",
+    "    if \"_ia_\" in exp:\n",
+    "        col = colA\n",
+    "        label = \"ICON-A-WBF\"\n",
+    "    elif \"mlo_aqua\" in exp:\n",
+    "        continue\n",
+    "    else:\n",
+    "        col = colESM\n",
+    "        label = \"ICON-ESM-WBF\"\n",
+    "        print(exp)\n",
+    "    marker = \"x\"\n",
+    "    line = \"-\"\n",
+    "\n",
+    "    # feedbacks\n",
+    "\n",
+    "    # aprp\n",
+    "    l = ax[0].plot(latcenter_x, DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[1].plot(latcenter_x, DS_mean[i].cld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[2].plot(latcenter_x, DS_mean[i].sfc_alb - DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[3].plot(latcenter_x, DS_mean[i].noncld, label=label, marker=marker, ls=line, color=col)\n",
+    "\n",
+    "    if exp.find(\"_ia_\")==-1:\n",
+    "        l_ESM = l[0]\n",
+    "    else:\n",
+    "        l_A = l[0]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "for axi in ax:\n",
+    "    axi.set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "    axi.set_xticklabels([0,10,20,30,45,60,90])\n",
+    "    axi.hlines(0, 1, 0, color=\"black\", lw=1)\n",
+    "    axi.set_xlim(1, 0)\n",
+    "    axi.spines['right'].set_color('none')\n",
+    "    axi.spines['top'].set_color('none')\n",
+    "    axi.spines['bottom'].set_position(('data', 0))\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.xaxis.set_tick_params(labelbottom=True)\n",
+    "\n",
+    "ax[2].xaxis.set_ticks_position('top')\n",
+    "\n",
+    "ax[3].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_ylabel(r\"$\\lambda_{ice}^{clr}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[1].set_ylabel(r\"$\\lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[2].set_ylabel(r\"$\\lambda_{mask}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[3].set_ylabel(r\"$\\lambda_{atm}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "\n",
+    "ax[0].set_ylim(0, 4.7)\n",
+    "ax[1].set_ylim(-1, 1)\n",
+    "ax[2].set_ylim(-1.75, 0)\n",
+    "ax[3].set_ylim(0, 0.5)\n",
+    "\n",
+    "ax[0].annotate(\"a)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1].annotate(\"b)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[2].annotate(\"c)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[3].annotate(\"d)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[0].annotate(\"ICON-ESM-WBF\", [0.01, 0.8], color=colESM, xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.73], color=colA, xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig10_ESM-A_aprp.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9c9af0cd-7c30-4d71-b839-803e1aa43308",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S\n",
+      "ICON-A-WBF\n",
+      "ape_ia_5500_90_0S\n",
+      "ICON-A-WBF\n",
+      "mlo_aqua_1500ppmv_hice_unlim_damped\n",
+      "ICON-A\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x1152 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 1, figsize=(6, 16), sharex=True)\n",
+    "\n",
+    "\n",
+    "for i, exp in enumerate(explist):\n",
+    "    #latcenter_x = 1-DS_mean[i].var_bin_left\n",
+    "\n",
+    "    latcenter = ICON_tools.sictoicelat(DS_mean[i].var_bin_left)-1\n",
+    "    latcenter_x = 1-ICON_tools.icelatosic(latcenter)\n",
+    "    if \"_ia_\" in exp:\n",
+    "        col = colA\n",
+    "        label = \"ICON-A-WBF\"\n",
+    "        print(exp)\n",
+    "    elif \"mlo_aqua\" in exp:\n",
+    "        col = colBraun22\n",
+    "        label = \"ICON-A\"\n",
+    "        print(exp)\n",
+    "    else:\n",
+    "        continue\n",
+    "    print(label)\n",
+    "    marker = \"x\"\n",
+    "    line = \"-\"\n",
+    "\n",
+    "\n",
+    "    # feedbacks\n",
+    "\n",
+    "    # aprp\n",
+    "    l = ax[0].plot(latcenter_x, DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[1].plot(latcenter_x, DS_mean[i].cld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[2].plot(latcenter_x, DS_mean[i].sfc_alb - DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[3].plot(latcenter_x, DS_mean[i].noncld, label=label, marker=marker, ls=line, color=col)\n",
+    "\n",
+    "    if exp.find(\"_ia_\")==-1:\n",
+    "        l_ESM = l[0]\n",
+    "    else:\n",
+    "        l_A = l[0]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "for axi in ax:\n",
+    "    axi.set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "    axi.set_xticklabels([0,10,20,30,45,60,90])\n",
+    "    axi.hlines(0, 1, 0, color=\"black\", lw=1)\n",
+    "    axi.set_xlim(1, 0)\n",
+    "    axi.spines['right'].set_color('none')\n",
+    "    axi.spines['top'].set_color('none')\n",
+    "    axi.spines['bottom'].set_position(('data', 0))\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.xaxis.set_tick_params(labelbottom=True)\n",
+    "\n",
+    "ax[2].xaxis.set_ticks_position('top')\n",
+    "\n",
+    "ax[3].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_ylabel(r\"$\\lambda_{ice}^{clr}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[1].set_ylabel(r\"$\\lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[2].set_ylabel(r\"$\\lambda_{mask}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "ax[3].set_ylabel(r\"$\\lambda_{atm}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "\n",
+    "ax[0].set_ylim(0, 4.7)\n",
+    "ax[1].set_ylim(-1, 1)\n",
+    "ax[2].set_ylim(-1.75, 0)\n",
+    "ax[3].set_ylim(0, 0.5)\n",
+    "\n",
+    "ax[0].annotate(\"a)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1].annotate(\"b)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[2].annotate(\"c)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[3].annotate(\"d)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[0].annotate(\"ICON-A\", [0.01, 0.8], color=colBraun22, xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.73], color=colA, xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig6_A-braun22_aprp.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdae6357-f19a-464b-a3f9-0debd665a551",
+   "metadata": {},
+   "source": [
+    "## zonal mean yearly mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "341c3bfc-ac33-4571-ac7e-d5ccff0a5986",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_zonal_mean(variable, **kwargs):\n",
+    "    \"\"\"Compute a zonal-mean (along `clat`) for multi-dimensional input.\"\"\"\n",
+    "    counts_per_bin, bin_edges = np.histogram(variable.clat, **hist_opts)\n",
+    "\n",
+    "    def _compute_varsum(var, **kwargs):\n",
+    "        \"\"\"Helper function to compute histogram for a single timestep.\"\"\"\n",
+    "        varsum_per_bin, _ = np.histogram(variable.clat, weights=var, **kwargs)\n",
+    "        return varsum_per_bin\n",
+    "\n",
+    "    # For more information see:\n",
+    "    # https://docs.xarray.dev/en/stable/generated/xarray.apply_ufunc.html\n",
+    "    varsum = xr.apply_ufunc(\n",
+    "        _compute_varsum,  # function to map\n",
+    "        variable,  # variables to loop over\n",
+    "        kwargs=hist_opts,  # keyword arguments passed to the function\n",
+    "        input_core_dims=[[\"ncells\"]],  # dimensions that should not be kept\n",
+    "        # Description of the output dataset\n",
+    "        dask=\"parallelized\",\n",
+    "        vectorize=True,\n",
+    "        output_core_dims=[(\"lat\",)],\n",
+    "        dask_gufunc_kwargs={\n",
+    "            \"output_sizes\": {\"lat\": hist_opts[\"bins\"]},\n",
+    "        },\n",
+    "        output_dtypes=[\"f8\"],\n",
+    "    )\n",
+    "\n",
+    "    return varsum / counts_per_bin, bin_edges"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "33fddff9-bd1f-4ae8-8221-0cecda6f8d19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#hist_opts = dict(bins=90, range=(-np.pi / 2, np.pi / 2))\n",
+    "\n",
+    "#DS_zonmean=[]\n",
+    "#for i, exp in enumerate(explist):\n",
+    "#    inds = []\n",
+    "#    inds = ~DS_forcing[i].cld.isnull().any(dim=[\"month\", \"ncells\"])\n",
+    "#    DS_temp, edges = calc_zonal_mean(DS_forcing[i], **hist_opts)\n",
+    "#    bindiff = (ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[1:] - ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[:-1])[0]\n",
+    "#    DS_zonmean.append(DS_temp.where(inds).mean(dim=\"month\")/bindiff)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "75305cca-5d57-46e2-a56d-7cc64c9742ad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S\n",
+      "(90,)\n",
+      "(45,)\n",
+      "(45,)\n",
+      "ape_ia_5500_90_0S\n",
+      "(90,)\n",
+      "(45,)\n",
+      "(45,)\n",
+      "ape_5000_55_0S\n",
+      "(90,)\n",
+      "(45,)\n",
+      "(45,)\n",
+      "ape_5500_55_0S\n",
+      "(90,)\n",
+      "(45,)\n",
+      "(45,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "hist_opts = dict(bins=90, range=(-np.pi / 2, np.pi / 2))\n",
+    "DS_zonmean = []\n",
+    "for i, exp in enumerate(explist):\n",
+    "\n",
+    "    if \"mlo_aqua\" in exp:\n",
+    "        continue\n",
+    "    print(exp)\n",
+    "    inds = []\n",
+    "    DS_temp = []\n",
+    "    edges = []\n",
+    "    inds = ~DS_forcing[i].cld.isnull().any(dim=[\"month\", \"ncells\"])\n",
+    "    DS_temp, edges = calc_zonal_mean(DS_forcing[i], **hist_opts)\n",
+    "    bindiff = (ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[1:] - ICON_tools.sictoicelat(DS_parms[1].var_bin_left).values[:-1])[0]\n",
+    "\n",
+    "    DS_temp[\"lat\"] = (edges[:-1] + edges[1:])/2\n",
+    "    print(np.shape(DS_temp.lat))\n",
+    "\n",
+    "    # Split the dataset into northern and southern hemispheres\n",
+    "    northern_hemisphere = DS_temp.where(inds).sel(lat=slice(0, np.pi/2))\n",
+    "    southern_hemisphere = DS_temp.where(inds).sel(lat=slice(-np.pi/2, 0))\n",
+    "\n",
+    "    # Reverse the southern hemisphere latitude for symmetry\n",
+    "    southern_hemisphere = southern_hemisphere.sel(lat=slice(None, None, -1))\n",
+    "    print(np.shape(southern_hemisphere.lat))\n",
+    "\n",
+    "    # Ensure latitudes align for averaging\n",
+    "    northern_hemisphere['lat'] = abs(northern_hemisphere['lat'])\n",
+    "    southern_hemisphere['lat'] = abs(northern_hemisphere['lat'])\n",
+    "\n",
+    "    # Average the two hemispheres\n",
+    "    folded_ds = (northern_hemisphere + southern_hemisphere) / 2\n",
+    "    DS_zonmean.append(folded_ds.mean(dim=\"month\")/bindiff)\n",
+    "    print(np.shape(folded_ds.lat))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "03080358-7e40-4353-9f04-b3647ba68c9d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x576 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2, 1, figsize=(6,8), sharex=True, sharey=True)\n",
+    "\n",
+    "exp_ICONA = [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\"]\n",
+    "exp_ICONESM = [\"ape_5000_55_0S\"]\n",
+    "# Find indices of the target strings\n",
+    "indA = [explist.index(target) for target in exp_ICONA]\n",
+    "indESM = [explist.index(target) for target in exp_ICONESM]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "DS = xr.concat([DS_zonmean[i] for i in indA], dim=\"datasets\").mean(dim=\"datasets\", skipna=True)\n",
+    "bincenters = ICON_tools.icelatosic(ICON_tools.sictoicelat(DS.var_bin_left)+1)\n",
+    "\n",
+    "levels_diff = np.linspace(-6.5, 6.5, 27)\n",
+    "cmap_diff = plt.colormaps['seismic']\n",
+    "norm_diff = colors.BoundaryNorm(levels_diff, ncolors=cmap_diff.N, clip=True)\n",
+    "\n",
+    "im0 = ax[0].pcolormesh(1-bincenters, 1-ICON_tools.icelatosic(np.rad2deg(DS.lat)), DS.cld.T, norm=norm_diff, cmap=cmap_diff, linewidth=0,rasterized=True)\n",
+    "ax[0].plot([1, 0], [1, 0], color=\"black\", lw=1)\n",
+    "#plt.colorbar(im0, ax=ax[0], label=r\"$\\Lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "\n",
+    "DS = xr.concat([DS_zonmean[i] for i in indESM], dim=\"datasets\").mean(dim=\"datasets\", skipna=True)\n",
+    "\n",
+    "im1 = ax[1].pcolormesh(1-bincenters, 1-ICON_tools.icelatosic(np.rad2deg(DS.lat)), DS.cld.T, norm=norm_diff, cmap=cmap_diff, linewidth=0,rasterized=True)\n",
+    "ax[1].plot([1, 0], [1, 0], color=\"black\", lw=1)\n",
+    "#plt.colorbar(im1, ax=ax[1], label=r\"$\\lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "\n",
+    "\n",
+    "ax[0].set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax[0].set_xticklabels([0,10,20,30,45,60,90])\n",
+    "ax[0].set_yticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "ax[0].set_yticklabels([0,10,20,30,45,60,90])\n",
+    "\n",
+    "plt.xlim(1, 0)\n",
+    "plt.ylim(0, 1)\n",
+    "\n",
+    "ax[1].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_ylabel(\"latitude [deglat]\")\n",
+    "ax[1].set_ylabel(\"latitude [deglat]\")\n",
+    "ax[0].set_aspect(\"equal\")\n",
+    "ax[1].set_aspect(\"equal\")\n",
+    "ax[0].annotate(\"a) ICON-A-WBF\", (0.02, 1.02), xycoords='axes fraction', va='bottom', weight='bold', fontsize=MEDIUM_SIZE)\n",
+    "ax[1].annotate(\"b) ICON-ESM-WBF\", (0.02, 1.02), xycoords='axes fraction', va='bottom', weight='bold', fontsize=MEDIUM_SIZE)\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "\n",
+    "#cbar = fig.colorbar(cm.ScalarMappable(norm=norm_diff, cmap=cmap_diff), ax=ax, orientation=\"vertical\", fraction=0.04, pad=0.02)  \n",
+    "#cbar.set_label(r\"$\\lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "#pos = cbar.ax.get_position()\n",
+    "#cbar.ax.set_position([pos.x0, pos.y0 -0.5, pos.width, pos.height+1])  \n",
+    "x1 = ax[0].get_position().x1+0.02\n",
+    "y1 = ax[1].get_position().y0\n",
+    "y2 = ax[0].get_position().y1\n",
+    "ax_cb = fig.add_axes([x1, y1, 0.03, y2-y1])\n",
+    "\n",
+    "cbar_diff = fig.colorbar(im0, cax=ax_cb, label=r\"$\\lambda_{cld}$ [Wm$^{-2}$deglat$^{-1}$]\")\n",
+    "\n",
+    "\n",
+    "plt.savefig(\"plots/Fig13_ESM-A_aprp_cld_zm.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "4447bfc7-30ad-4bde-a791-dde90f5d4cf8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(10,4), sharex=True, sharey=True)\n",
+    "\n",
+    "\n",
+    "DS = DS_zonmean[1]\n",
+    "ax[0].set_title(explist[1])\n",
+    "\n",
+    "levels_diff = np.linspace(-22, 22, 27)\n",
+    "cmap_diff = plt.colormaps['seismic']\n",
+    "norm_diff = colors.BoundaryNorm(levels_diff, ncolors=cmap_diff.N, clip=True)\n",
+    "\n",
+    "im0 = ax[0].pcolormesh(ICON_tools.sictoicelat(DS.var_bin_left)+1, np.rad2deg(DS.lat), DS.sfc_alb_nocld.T, norm=norm_diff, cmap=cmap_diff, linewidth=0,rasterized=True)\n",
+    "ax[0].plot([90, 0], [90, 0], color=\"black\", lw=1)\n",
+    "plt.colorbar(im0, ax=ax[0])\n",
+    "\n",
+    "DS = DS_zonmean[8]\n",
+    "ax[1].set_title(explist[8])\n",
+    "\n",
+    "im1 = ax[1].pcolormesh(ICON_tools.sictoicelat(DS.var_bin_left)+1, np.rad2deg(DS.lat), DS.sfc_alb_nocld.T, norm=norm_diff, cmap=cmap_diff, linewidth=0,rasterized=True)\n",
+    "ax[1].plot([90, 0], [90, 0], color=\"black\", lw=1)\n",
+    "plt.colorbar(im1, ax=ax[1])\n",
+    "\n",
+    "\n",
+    "plt.xlim(60, 0)\n",
+    "plt.ylim(0, 60)\n",
+    "\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a33796a7-e200-4dfa-b861-ed08eadeaf8e",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "3c76e2e4-361c-4135-bfdf-552faea5c133",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S\n",
+      "ICON-A-WBF\n",
+      "C0\n",
+      "ape_ia_5500_90_0S\n",
+      "ICON-A-WBF\n",
+      "C1\n",
+      "ape_ia_6000_90_0S\n",
+      "ICON-A-WBF\n",
+      "C2\n",
+      "ape_ia_6000_90_0S_cltlim_dtime10\n",
+      "ICON-A-WBF\n",
+      "C3\n",
+      "ape_ia_6500_90_0S_cltlim_dtime10\n",
+      "ICON-A-WBF\n",
+      "C4\n",
+      "ape_ia_8000_13_0S\n",
+      "ICON-A-WBF\n",
+      "C5\n",
+      "mlo_aqua_1500ppmv_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C11\n",
+      "mlo_aqua_1875ppmv_hice_unlim\n",
+      "ICON-A\n",
+      "C12\n",
+      "mlo_aqua_3750ppmv_13lat_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C13\n",
+      "mlo_aqua_3907ppmv_13lat_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C14\n",
+      "mlo_aqua_4063ppmv_13lat_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C15\n",
+      "mlo_aqua_4375ppmv_13lat_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C16\n",
+      "mlo_aqua_5000ppmv_13lat_hice_unlim_damped\n",
+      "ICON-A\n",
+      "C17\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x153777ddd390>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(4, 2, figsize=(10, 10), sharex=True)\n",
+    "colESM = \"C4\"\n",
+    "colA = \"C2\"\n",
+    "\n",
+    "for i, exp in enumerate(explist):\n",
+    "    #latcenter_x = 1-DS_mean[i].var_bin_left\n",
+    "\n",
+    "    latcenter = ICON_tools.sictoicelat(DS_mean[i].var_bin_left)-1\n",
+    "    latcenter_x = 1-ICON_tools.icelatosic(latcenter)\n",
+    "    if \"_ia_\" in exp:\n",
+    "        col = colA\n",
+    "        label = \"ICON-A-WBF\"\n",
+    "        line=\"-\"\n",
+    "        print(exp)\n",
+    "    elif \"mlo_aqua\" in exp:\n",
+    "        col = colBraun22\n",
+    "        label = \"ICON-A\"\n",
+    "        line=\"--\"\n",
+    "        print(exp)\n",
+    "    else:\n",
+    "        continue\n",
+    "    print(label)\n",
+    "    marker = \"x\"\n",
+    "    label=exp\n",
+    "    col = \"C\" + str(i)\n",
+    "    print(col)\n",
+    "    \n",
+    "    # feedbacks\n",
+    "    ax[0, 0].plot(latcenter_x, DS_mean[i].sw_toa, label=label, marker=marker, ls=line)\n",
+    "    ax[1, 0].plot(latcenter_x, DS_mean[i].lw_toa, label=label, marker=marker, ls=line)\n",
+    "    ax[2, 0].plot(latcenter_x, DS_mean[i].net_toa, label=label, marker=marker, ls=line)\n",
+    "\n",
+    "    # aprp\n",
+    "    l = ax[0, 1].plot(latcenter_x, DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line)\n",
+    "    ax[1, 1].plot(latcenter_x, DS_mean[i].cld, label=label, marker=marker, ls=line, color=col)\n",
+    "    ax[2, 1].plot(latcenter_x, DS_mean[i].sfc_alb - DS_mean[i].sfc_alb_nocld, label=label, marker=marker, ls=line)\n",
+    "    ax[3, 1].plot(latcenter_x, DS_mean[i].noncld, label=label, marker=marker, ls=line)\n",
+    "\n",
+    "    if exp.find(\"_ia_\")==-1:\n",
+    "        l_ESM = l[0]\n",
+    "    else:\n",
+    "        l_A = l[0]\n",
+    "\n",
+    "\n",
+    "for axi in ax.flatten():\n",
+    "    axi.set_xlabel(\"ice-line latitude [deglat]\")\n",
+    "    axi.set_xticks([np.sin(np.radians(0)),np.sin(np.radians(10)),np.sin(np.radians(20)),np.sin(np.radians(30)),np.sin(np.radians(45)),np.sin(np.radians(60)),np.sin(np.radians(90))])\n",
+    "    axi.set_xticklabels([0,10,20,30,45,60,90])\n",
+    "    axi.hlines(0, 1, 0, color=\"black\", lw=1)\n",
+    "    axi.set_xlim(1, 0)\n",
+    "\n",
+    "ax[0, 0].set_ylabel(r\"$\\Lambda\\;_{SW}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "ax[1, 0].set_ylabel(r\"$\\Lambda\\;_{LW}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "ax[2, 0].set_ylabel(r\"$\\Lambda\\;_{net}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "\n",
+    "ax[0, 1].set_ylabel(r\"$\\Lambda\\;_{ice}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "ax[1, 1].set_ylabel(r\"$\\Lambda\\;_{cld}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "ax[2, 1].set_ylabel(r\"$\\Lambda\\;_{cmask}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "ax[3, 1].set_ylabel(r\"$\\Lambda\\;_{cs}$ [Wm$^{-2}$($°$icelat)$^{-1}$]\")\n",
+    "\n",
+    "ax[0, 0].set_ylim(0, 3.5)\n",
+    "ax[1, 0].set_ylim(-3.5, 0)\n",
+    "ax[2, 0].set_ylim(-0.65, 0.65)\n",
+    "\n",
+    "ax[0, 1].set_ylim(0, 4.7)\n",
+    "ax[1, 1].set_ylim(-1, 1)\n",
+    "ax[2, 1].set_ylim(-1.75, 0)\n",
+    "ax[3, 1].set_ylim(0, 0.5)\n",
+    "\n",
+    "\n",
+    "ax[0, 0].annotate(\"a) SW fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1, 0].annotate(\"b) LW fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[2, 0].annotate(\"c) net fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[0, 1].annotate(\"d) SW clear-sky albedo fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1, 1].annotate(\"e) SW cloud fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[2, 1].annotate(\"f) SW cloud masking\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[3, 1].annotate(\"g) SW noncloud fb\", (0.02, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[3, 0].set_visible(False)\n",
+    "\n",
+    "plt.subplots_adjust(left=0.1, right=0.9, \n",
+    "                    top=0.9, bottom=0.1, \n",
+    "                    wspace=0.5, hspace=0.15)\n",
+    "plt.legend()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "45d0b051-6c24-43c9-ab46-4536b695c38e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(array([], dtype=int64),), (array([], dtype=int64),)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Create a NumPy array of strings\n",
+    "arr = [\"apple\", \"banana\", \"cherry\", \"date\", \"elderberry\"]\n",
+    "\n",
+    "# Strings to find\n",
+    "target_strings = [\"banana\", \"date\"]\n",
+    "\n",
+    "# Find indices of the target strings\n",
+    "indices = [np.where(arr == target) for target in target_strings]\n",
+    "\n",
+    "print(indices)  # Output: [1, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed2036c4-9991-4a8f-9be1-196d261decff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "baseenv - Python 3.7",
+   "language": "python",
+   "name": "baseenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pythonscripts/Fig8_11-BASIR_HC_CRE.ipynb b/pythonscripts/Fig8_11-BASIR_HC_CRE.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..1e29f4d395a69e496322c8b218280959c7be9d86
--- /dev/null
+++ b/pythonscripts/Fig8_11-BASIR_HC_CRE.ipynb
@@ -0,0 +1,488 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "098afdbb-2eb5-44be-82a5-025167b03611",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ICON_tools' from '../../../snowball-waterbelt-continents/python_packages/ICON_tools.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "from os import path\n",
+    "import sys, importlib\n",
+    "\n",
+    "sys.path.append(\"../../../snowball-waterbelt-continents/python_packages\")\n",
+    "import ICON_tools\n",
+    "\n",
+    "importlib.reload(ICON_tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d3bc209-12fd-4048-bf27-13a69586f2f6",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### set global fonts for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "905cb03e-877c-4709-a7b5-ec418f0eafd8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "BIGGER_SIZE = 14\n",
+    "\n",
+    "plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
+    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
+    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
+    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
+    "plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "bc3607e0-e714-4a74-8c56-8771b49304a3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
+      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
+      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
+      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
+      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
+      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
+      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
+      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n",
+      "ape_5000_55_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_5000_55_0S\n",
+      "ape_5500_55_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_5500_55_0S\n",
+      "ape_6000_90_0S_merged: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_90_0S_merged\n",
+      "ape_6000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_22_0S\n",
+      "ape_6000_13_0S_snowcap: directory is /jetfs/scratch/jhoerner/postprocessing/ape_6000_13_0S_snowcap\n",
+      "ape_7000_13_0S_snowcap: directory is /jetfs/scratch/jhoerner/postprocessing/ape_7000_13_0S_snowcap\n",
+      "ape_7000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_7000_22_0S\n",
+      "ape_8000_22_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_8000_22_0S\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = \"/jetfs/scratch/jhoerner/postprocessing\"\n",
+    "Aexplist, Anexp = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
+    "ADSlistgm, _ = ICON_tools.load_ds_2d(data_path, Aexplist, True)\n",
+    "\n",
+    "ESMexplist, ESMnexp = ICON_tools.get_explist(data_path, [\"ape_5000_55_0S\", \"ape_5500_55_0S\",\"ape_6000_90_0S_merged\", \"ape_6000_22_0S\", \"ape_6000_13_0S_snowcap\" ,\"ape_7000_13_0S_snowcap\", \"ape_7000_22_0S\", \"ape_8000_22_0S\" ]) \n",
+    "ESMDSlistgm, _ = ICON_tools.load_ds_2d(data_path, ESMexplist, True)\n",
+    "\n",
+    "ADSlistpsi = np.empty([Anexp], dtype=\"object\")\n",
+    "ESMDSlistpsi = np.empty([ESMnexp], dtype=\"object\")\n",
+    "ADSlistgmym = np.empty([Anexp], dtype=\"object\")\n",
+    "ESMDSlistgmym = np.empty([ESMnexp], dtype=\"object\")\n",
+    "\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    mask = ADSlistgm[i].where(ADSlistgm[i]['sic'] < 1e36, drop=True).squeeze().time.drop_vars(['lon', 'lat', 'clon', 'clat'], errors=\"ignore\") # datasets that were postprocessed with cdo version 1.9.9 instead of 2.1.0 have the variables 'lon' 'lat' instead of 'clon' 'clat' \n",
+    "    ADSlistpsi[i] = xr.open_dataset(data_path + \"/\" + exp + \"/mastrfu/\" + exp + \"_mastrfu.mm.nc\").sel(time=mask.time)\n",
+    "    ADSlistgm[i] = ADSlistgm[i].where(ADSlistgm[i]['sic'] < 1e36)\n",
+    "    ADSlistgmym[i] = xr.decode_cf(ADSlistgm[i]).groupby('time.year').mean(dim='time')\n",
+    "\n",
+    "for i, exp in enumerate(ESMexplist):\n",
+    "    mask = ESMDSlistgm[i].where(ESMDSlistgm[i]['sic'] < 1e36, drop=True).squeeze().time.drop_vars(['lon', 'lat', 'clon', 'clat'], errors=\"ignore\")\n",
+    "    ESMDSlistpsi[i] = xr.open_dataset(data_path + \"/\" + exp + \"/mastrfu/\" + exp + \"_mastrfu.mm.nc\").sel(time=mask.time)\n",
+    "    ESMDSlistgm[i] = ESMDSlistgm[i].where(ESMDSlistgm[i]['sic'] < 1e36)\n",
+    "    ESMDSlistgmym[i] = xr.decode_cf(ESMDSlistgm[i]).groupby('time.year').mean(dim='time')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "548688b2-8156-4d47-aac6-92ba385c2e05",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Psi: width of Hadley cell, in 500hPa height, where psi changes sign the first time after the ITCZ"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "509430a8-0add-4324-b818-9b2f10b59553",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_HCw_psi(DS, plev=50000):\n",
+    "    DS500 = DS.sel(plev=plev).squeeze().groupby(\"time.year\").mean()\n",
+    "    # create empty dataset\n",
+    "    fillval = np.full(len(DS500[\"year\"]), np.nan)\n",
+    "    DS_HC = xr.Dataset(\n",
+    "        {\n",
+    "            \"HC_S\": ([\"year\"], fillval.copy(), {\"name\": \"Southern edge of the Hadley cell\", \"unit\": \"° latitude\"}),\n",
+    "            \"HC_N\": ([\"year\"], fillval.copy(), {\"name\": \"Southern edge of the Hadley cell\", \"unit\": \"° latitude\"})\n",
+    "        },\n",
+    "        coords={\"year\": DS500[\"year\"]}\n",
+    "    )\n",
+    "    # check sign changes\n",
+    "    signs = np.sign(DS500.mastrfu)\n",
+    "    sign_diff = signs.copy(data=np.diff(signs, axis=signs.get_axis_num(\"lat\"), prepend=np.nan))  # calculate the difference between the signs to find the zero points. Append a NaN at the beginning so it has the same shape\n",
+    "    sign_changes = xr.where((sign_diff != 0) & (~np.isnan(sign_diff)), True, False)\n",
+    "\n",
+    "    # get psi value on both sides of sign change\n",
+    "    vals1 = DS500.mastrfu.where(sign_changes, drop=False)\n",
+    "    vals2 = DS500.mastrfu.where(sign_changes.shift(lat=-1, fill_value=False))\n",
+    "\n",
+    "    # loop through all time steps and calculate zero points\n",
+    "    print(\"loop over years\")\n",
+    "    for i, itime in enumerate(vals1[\"year\"]):\n",
+    "        y1 = vals1.sel(year=itime).dropna(dim=\"lat\").copy()\n",
+    "        y2 = vals2.sel(year=itime).dropna(dim=\"lat\").copy()\n",
+    "        zero_crossing_points = (y1.lat - y1 * (y2.lat.values - y1.lat.values) / (y2.values - y1.values)).values\n",
+    "        inds_HC = np.argsort(np.abs(zero_crossing_points))[1:3]  # the edges of the Hadley cell are the 2nd and 3rd closest sign change to the equator (closest one is the ITCZ)\n",
+    "        vals_HC = zero_crossing_points[inds_HC]\n",
+    "\n",
+    "        DS_HC[\"HC_S\"].loc[itime] = vals_HC.min()\n",
+    "        DS_HC[\"HC_N\"].loc[itime] = vals_HC.max()\n",
+    "\n",
+    "    DS_HC[\"HC_width\"] = DS_HC[\"HC_N\"] - DS_HC[\"HC_S\"]\n",
+    "    return DS_HC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3894855a-3066-4152-bac6-614954f3b7fe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5000_13_0S\n",
+      "loop over years\n",
+      "ape_ia_5500_90_0S\n",
+      "loop over years\n",
+      "ape_ia_6000_90_0S\n",
+      "loop over years\n",
+      "ape_ia_6000_90_0S_cltlim_dtime10\n",
+      "loop over years\n",
+      "ape_ia_6500_90_0S_cltlim_dtime10\n",
+      "loop over years\n",
+      "ape_ia_8000_13_0S\n",
+      "loop over years\n",
+      "ape_ia_9000_13_0S\n",
+      "loop over years\n",
+      "ape_ia_10000_13_0S\n",
+      "loop over years\n",
+      "ape_5000_55_0S\n",
+      "loop over years\n",
+      "ape_5500_55_0S\n",
+      "loop over years\n",
+      "ape_6000_90_0S_merged\n",
+      "loop over years\n",
+      "ape_6000_22_0S\n",
+      "loop over years\n",
+      "ape_6000_13_0S_snowcap\n",
+      "loop over years\n",
+      "ape_7000_13_0S_snowcap\n",
+      "loop over years\n",
+      "ape_7000_22_0S\n",
+      "loop over years\n",
+      "ape_8000_22_0S\n",
+      "loop over years\n"
+     ]
+    }
+   ],
+   "source": [
+    "ADSlistHCwpsi = np.empty([Anexp], dtype=\"object\")\n",
+    "ESMDSlistHCwpsi = np.empty([ESMnexp], dtype=\"object\")\n",
+    "\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    print(exp)\n",
+    "    ADSlistHCwpsi[i] = calc_HCw_psi(ADSlistpsi[i])\n",
+    "\n",
+    "for i, exp in enumerate(ESMexplist):\n",
+    "    print(exp)\n",
+    "    ESMDSlistHCwpsi[i] = calc_HCw_psi(ESMDSlistpsi[i])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae7ffcf8-4642-4a1f-bfab-e185b2b749e6",
+   "metadata": {},
+   "source": [
+    "## paper: combined plot of BASIR and Hadley cell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b14b0b6b-5717-43cd-bc06-0258468debc6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharex=True)\n",
+    "#plt.subplots_adjust(wspace=0.25, hspace=0.04, left=0.05, bottom=0.2, top=0.98)\n",
+    "alpha=0.8\n",
+    "xfac= 1\n",
+    "omitlast=True\n",
+    "omitfirst=True\n",
+    "\n",
+    "if omitlast:\n",
+    "    lastind=-2\n",
+    "else:\n",
+    "    lastind=None\n",
+    "if omitfirst:\n",
+    "    firstind=1\n",
+    "else:\n",
+    "    firstind=0\n",
+    "\n",
+    "\n",
+    "\n",
+    "j=0\n",
+    "explist=Aexplist\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(explist): # simulations\n",
+    "    x = xfac*(ADSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "    y2 = ICON_tools.sictoicelat(ADSlistgmym[i][\"snowfrac\"].squeeze()[firstind:lastind]) - ICON_tools.sictoicelat(ADSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "\n",
+    "\n",
+    "    l_A, = ax[0].plot(x, y2, color=color,  lw=1, label=exp, alpha=alpha)   \n",
+    "    \n",
+    "    HCepsi = ADSlistHCwpsi[i].sel(year=ADSlistgmym[i].year).HC_width.squeeze()[firstind:lastind] / 2\n",
+    "\n",
+    "    l_A, = ax[1].plot(x, ICON_tools.icelatosic(HCepsi), color=\"C2\", ls=\"-\",  lw=1, label=exp) \n",
+    "\n",
+    "\n",
+    "j=1\n",
+    "explist=ESMexplist\n",
+    "color=\"C4\"\n",
+    "for i, exp in enumerate(explist): # simulations\n",
+    "    x = xfac*(ESMDSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "    y2 = ICON_tools.sictoicelat(ESMDSlistgmym[i][\"snowfrac\"].squeeze()[firstind:lastind]) - ICON_tools.sictoicelat(ESMDSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "\n",
+    "    l_ESM, = ax[0].plot(x, y2, color=color,   lw=1, label=exp,alpha=alpha)\n",
+    "\n",
+    "    HCepsi = ESMDSlistHCwpsi[i].sel(year=ESMDSlistgmym[i].year).HC_width.squeeze()[firstind:lastind] / 2\n",
+    "\n",
+    "    l_ESM, = ax[1].plot(x, ICON_tools.icelatosic(HCepsi), color=\"C4\", ls=\"-\",  lw=1, label=exp) \n",
+    "\n",
+    "\n",
+    "\n",
+    "ax[0].set_xlim(-0.01, 1.01)\n",
+    "ax[0].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[1].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "\n",
+    "xticks = [90, 60, 45, 30, 20, 10, ICON_tools.sictoicelat(0.99)]\n",
+    "ax[0].set_xticks(xfac*(ICON_tools.icelatosic(xticks)))\n",
+    "ax[0].set_xticklabels(int(x) for x in xticks)\n",
+    "ax[0].set_xlim(0, 0.99)\n",
+    "\n",
+    "yticks = np.arange(0, 100, 2)\n",
+    "ax[1].set_yticks(ICON_tools.icelatosic(yticks))\n",
+    "ax[1].set_yticklabels(yticks)\n",
+    "ax[1].set_ylim(0.62, 0.45)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax[0].set_ylabel(r\"BASIR width [deglat]\")\n",
+    "ax[1].set_ylabel(r\"Hadley cell edge [deglat]\")\n",
+    "#ax.tick_params(axis='y', which='major', pad=1)\n",
+    "ax[0].annotate(\"a)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1].annotate(\"b)\", (0.01, 0.98), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.9], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-ESM-WBF\", [0.01, 0.83], color=\"C4\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "\n",
+    "for axi in ax:\n",
+    "    axi.spines['right'].set_color('none')\n",
+    "    axi.spines['top'].set_color('none')\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['bottom'].set_position(('outward', 5))\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig8_basir_hc.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "bfa4d366-e5a2-40cf-af80-eb6c391fe1a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 2, figsize=(12, 4), sharex=True)\n",
+    "alpha=0.8\n",
+    "xfac= 1\n",
+    "omitlast=True\n",
+    "omitfirst=True\n",
+    "\n",
+    "if omitlast:\n",
+    "    lastind=-1\n",
+    "else:\n",
+    "    lastind=None\n",
+    "if omitfirst:\n",
+    "    firstind=1\n",
+    "else:\n",
+    "    firstind=0\n",
+    "\n",
+    "\n",
+    "\n",
+    "j=0\n",
+    "explist=Aexplist\n",
+    "color=\"C2\"\n",
+    "for i, exp in enumerate(explist): # simulations\n",
+    "    x = xfac*(ADSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "    y2 = ICON_tools.get_cre(ADSlistgmym[i], \"toa\", \"lw\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    y1 = ICON_tools.get_cre(ADSlistgmym[i], \"toa\", \"sw\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    y0 = ICON_tools.get_cre(ADSlistgmym[i], \"toa\", \"net\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    l_A, = ax[0].plot(x, y0, color=color, lw=1, label=\"ICON-A-WBF\", alpha=alpha)\n",
+    "    _, = ax[1].plot(x, y1, color=color, lw=1, label=\"ICON-A-WBF\", alpha=alpha)\n",
+    "\n",
+    "j=1\n",
+    "explist=ESMexplist\n",
+    "color=\"C4\"\n",
+    "for i, exp in enumerate(explist): # simulations\n",
+    "    x = xfac*(ESMDSlistgmym[i][\"sic\"].squeeze()[firstind:lastind])\n",
+    "    y2 = ICON_tools.get_cre(ESMDSlistgmym[i], \"toa\", \"lw\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    y1 = ICON_tools.get_cre(ESMDSlistgmym[i], \"toa\", \"sw\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    y0 = ICON_tools.get_cre(ESMDSlistgmym[i], \"toa\", \"net\").squeeze()[firstind:lastind]\n",
+    "\n",
+    "    l_ESM, = ax[0].plot(x, y0, color=color, lw=1, label=\"ICON-ESM\", alpha=alpha)\n",
+    "    _, = ax[1].plot(x, y1, color=color, lw=1, label=\"ICON-ESM\", alpha=alpha)\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax[1].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "ax[0].set_xlabel(r\"ice-line latitude $\\varphi_i$ [deglat]\")\n",
+    "\n",
+    "ax[0].set_ylim(-33, 0)\n",
+    "ax[1].set_ylim(-60, 0)\n",
+    "\n",
+    "xticks = [90, 60, 45, 30, 20, 10, ICON_tools.sictoicelat(0.99)]\n",
+    "ax[0].set_xticks(xfac*(ICON_tools.icelatosic(xticks)))\n",
+    "ax[0].set_xticklabels(int(x) for x in xticks)\n",
+    "ax[0].set_xlim(0, 0.99)\n",
+    "\n",
+    "\n",
+    "ax[0].set_ylabel(r\"TOA net CRE [Wm$^{-2}$]\")\n",
+    "ax[1].set_ylabel(r\"TOA SW CRE [Wm$^{-2}$]\")\n",
+    "\n",
+    "\n",
+    "ax[0].annotate(\"a)\", (0.01, 1.13), xycoords='axes fraction', va='top', weight='bold')\n",
+    "ax[1].annotate(\"b)\", (0.01, 1.13), xycoords='axes fraction', va='top', weight='bold')\n",
+    "\n",
+    "ax[0].annotate(\"ICON-A-WBF\", [0.01, 0.7], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax[0].annotate(\"ICON-ESM-WBF\", [0.01, 0.63], color=\"C4\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "\n",
+    "for axi in ax:\n",
+    "    axi.spines['right'].set_color('none')\n",
+    "    axi.spines['bottom'].set_color('none')\n",
+    "    axi.spines['left'].set_position(('outward', 5))\n",
+    "    axi.spines['top'].set_position(('data', 0))\n",
+    "    axi.xaxis.set_ticks_position('top')\n",
+    "    axi.xaxis.set_label_position('top')\n",
+    "    \n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\"plots/Fig11_cre.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f83253f-2ea8-4b1c-9455-afdaa479cd02",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "baseenv - Python 3.7",
+   "language": "python",
+   "name": "baseenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pythonscripts/Fig9-waterbelt_zm.ipynb b/pythonscripts/Fig9-waterbelt_zm.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..dad213eeffa0a465714c7ff4b8bb58037e8ad8ff
--- /dev/null
+++ b/pythonscripts/Fig9-waterbelt_zm.ipynb
@@ -0,0 +1,360 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1a84b8d-b713-4245-a461-76d932a01adb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'ICON_tools' from '../../../snowball-waterbelt-continents/python_packages/ICON_tools.py'>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import integrate\n",
+    "import sys, importlib, os\n",
+    "sys.path.append(\"../../../snowball-waterbelt-continents/python_packages\")\n",
+    "import ICON_tools\n",
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "from mpl_toolkits.axes_grid1 import AxesGrid\n",
+    "\n",
+    "importlib.reload(ICON_tools)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc2fdbfc-299d-42cb-816b-dd7c6212b72c",
+   "metadata": {},
+   "source": [
+    "### set global fonts for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "35b7cdf9-5d2a-409f-91a3-2d64bdade5c4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "SMALL_SIZE = 10\n",
+    "MEDIUM_SIZE = 12\n",
+    "BIGGER_SIZE = 14\n",
+    "\n",
+    "plt.rc('font', size=MEDIUM_SIZE)          # controls default text sizes\n",
+    "plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title\n",
+    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
+    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
+    "plt.rc('legend', fontsize=MEDIUM_SIZE)    # legend fontsize\n",
+    "plt.rc('figure', titlesize=MEDIUM_SIZE)  # fontsize of the figure title"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d5e94955-f651-44cb-9ebc-fa331f5c970f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "path_WB_ESM = \"/jetfs/scratch/jhoerner/experiments/ape_5500_55_0S\"\n",
+    "path_WB_pp_ESM = \"/jetfs/scratch/jhoerner/postprocessing/ape_5500_55_0S\"\n",
+    "path_WB_A = \"/jetfs/scratch/jhoerner/experiments/ape_ia_5500_90_0S\"\n",
+    "path_WB_pp_A = \"/jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\"\n",
+    "colorlist = [\"C2\", \"C4\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0ef1d8d9-917d-4a02-8c12-7aa757abfdb8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DS_WB_ESM2d = xr.open_dataset(path_WB_ESM + \"/ape_5500_55_0S_2d_merged.nc\").squeeze()\n",
+    "DS_WB_ESM2d = DS_WB_ESM2d.drop_vars([\"clat_bnds\", \"clon_bnds\"]).squeeze()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4ef40154-ff21-4e4e-a10f-b997d34113e8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DS_WB_A2d = xr.open_dataset(path_WB_A +\"/ape_ia_5500_90_0S_2d_merged.nc\")\n",
+    "DS_WB_A2d = DS_WB_A2d.drop_vars([\"clat_bnds\", \"clon_bnds\"]).squeeze()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f4cb1856-aa2d-4358-966f-39518fcb6260",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "DS_grid = xr.open_dataset(\"/jetfs/scratch/jhoerner/inputdata/grids/icon_grid_0005_R02B04_G.nc\")\n",
+    "cell_area = DS_grid.cell_area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "8bfb7b15-7249-4240-8195-f9df3aa1d0f6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
+      "ape_5500_55_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_5500_55_0S\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_path = \"/jetfs/scratch/jhoerner/postprocessing\"\n",
+    "Aexplist, Anexp = ICON_tools.get_explist(data_path, [\"ape_ia_5500_90_0S\"]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
+    "ADSlistgm, ADSlistzm = ICON_tools.load_ds_2d(data_path, Aexplist, True)\n",
+    "\n",
+    "ADSlistgmym = np.empty([Anexp], dtype=\"object\")\n",
+    "ADSlistzmym = np.empty([Anexp], dtype=\"object\")\n",
+    "ADSlistpsi = np.empty([Anexp], dtype=\"object\")\n",
+    "\n",
+    "for i, exp in enumerate(Aexplist):\n",
+    "    # fillna\n",
+    "    ADSlistgm[i] = ADSlistgm[i].where(ADSlistgm[i]['sic'] < 1e36)\n",
+    "    ADSlistgmym[i] = xr.decode_cf(ADSlistgm[i]).groupby('time.year').mean(dim='time')\n",
+    "    ADSlistzm[i] = ADSlistzm[i].where(ADSlistzm[i]['sic'] < 1e36)\n",
+    "    ADSlistpsi[i] = xr.open_dataset(data_path + \"/\" + exp + \"/mastrfu/\" + exp + \"_mastrfu.mm.nc\")\n",
+    "\n",
+    "\n",
+    "ESMexplist, ESMnexp = ICON_tools.get_explist(data_path, [\"ape_5500_55_0S\"]) \n",
+    "ESMDSlistgm, ESMDSlistzm = ICON_tools.load_ds_2d(data_path, ESMexplist, True)\n",
+    "\n",
+    "ESMDSlistgmym = np.empty([ESMnexp], dtype=\"object\")\n",
+    "ESMDSlistzmym = np.empty([ESMnexp], dtype=\"object\")\n",
+    "ESMDSlistpsi = np.empty([ESMnexp], dtype=\"object\")\n",
+    "\n",
+    "for i, exp in enumerate(ESMexplist):\n",
+    "    # fillna\n",
+    "    ESMDSlistgm[i] = ESMDSlistgm[i].where(ESMDSlistgm[i]['sic'] < 1e36)\n",
+    "    ESMDSlistgmym[i] = xr.decode_cf(ESMDSlistgm[i]).groupby('time.year').mean(dim='time')\n",
+    "    ESMDSlistzm[i] = ESMDSlistzm[i].where(ESMDSlistzm[i]['sic'] < 1e36)\n",
+    "    ESMDSlistpsi[i] = xr.open_dataset(data_path + \"/\" + exp + \"/mastrfu/\" + exp + \"_mastrfu.mm.nc\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d5146dbb-5448-43d0-9a16-39c1cf39e73e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ESMDSlistzm[0] = ESMDSlistzm[0].squeeze().sel(time=DS_WB_ESM2d.time)\n",
+    "ADSlistzm[0] = ADSlistzm[0].squeeze().sel(time=DS_WB_A2d.time)\n",
+    "ESMDSlistgm[0] = ESMDSlistgm[0].squeeze().sel(time=DS_WB_ESM2d.time)\n",
+    "ADSlistgm[0] = ADSlistgm[0].squeeze().sel(time=DS_WB_A2d.time)\n",
+    "ESMDSlistpsi[0] = ESMDSlistpsi[0].squeeze().sel(time=DS_WB_ESM2d.time)\n",
+    "ADSlistpsi[0] = ADSlistpsi[0].squeeze().sel(time=DS_WB_A2d.time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "17acae47-323d-4e4d-b3a4-9a8559f99974",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# time mean \n",
+    "Aym_gm = ADSlistgm[0].mean(dim=\"time\")\n",
+    "ESMym_gm = ESMDSlistgm[0].mean(dim=\"time\")\n",
+    "Aym_zm = ADSlistzm[0].mean(dim=\"time\")\n",
+    "ESMym_zm = ESMDSlistzm[0].mean(dim=\"time\")\n",
+    "Apsiym_zm = ADSlistpsi[0].mean(dim=\"time\")\n",
+    "ESMpsiym_zm = ESMDSlistpsi[0].mean(dim=\"time\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "d976cb15-5245-47aa-a20f-f1858aebbaab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calc_HCe_wap(DS, plev=50000):\n",
+    "    DS500 = DS.sel(plev=plev).squeeze()\n",
+    "    # check sign changes\n",
+    "    signs = np.sign(DS500.mastrfu)\n",
+    "    sign_diff = signs.copy(data=np.diff(signs, axis=signs.get_axis_num(\"lat\"), prepend=np.nan))  # calculate the difference between the signs to find the zero points. Append a NaN at the beginning so it has the same shape\n",
+    "    sign_changes = xr.where((sign_diff != 0) & (~np.isnan(sign_diff)), True, False)\n",
+    "\n",
+    "    # get psi value on both sides of sign change\n",
+    "    vals1 = DS500.mastrfu.where(sign_changes, drop=False)\n",
+    "    vals2 = DS500.mastrfu.where(sign_changes.shift(lat=-1, fill_value=False))\n",
+    "\n",
+    "    y1 = vals1.dropna(dim=\"lat\").copy()\n",
+    "    y2 = vals2.dropna(dim=\"lat\").copy()\n",
+    "    zero_crossing_points = (y1.lat - y1 * (y2.lat.values - y1.lat.values) / (y2.values - y1.values)).values\n",
+    "    inds_HC = np.argsort(np.abs(zero_crossing_points))[1:3]  # the edges of the Hadley cell are the 2nd and 3rd closest sign change to the equator (closest one is the ITCZ)\n",
+    "    vals_HC = zero_crossing_points[inds_HC]\n",
+    "\n",
+    "    return vals_HC.min() , vals_HC.max() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3d2dfec-0d86-4df3-94a7-7c1c76d3ba98",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## composite plot of time mean zonal means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e6762c1a-fe63-4324-9c29-b5099d62e393",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x144 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, 1, figsize=(6, 2), sharex=True)\n",
+    "colESM = \"C4\"\n",
+    "colA = \"C2\"\n",
+    "density = 1000 # rhoh20 [kgm^-3]\n",
+    "spy = 60*60*24*360 # seconds per year\n",
+    "\n",
+    "axind_alb=2\n",
+    "axind_pe=0\n",
+    "axind_cre=1\n",
+    "\n",
+    "# ice & snow lat\n",
+    "Aicelat = ICON_tools.sictoicelat(Aym_gm.sic)\n",
+    "Asnowlat = ICON_tools.sictoicelat(Aym_gm.snowfrac)\n",
+    "ESMicelat = ICON_tools.sictoicelat(ESMym_gm.sic)\n",
+    "ESMsnowlat = ICON_tools.sictoicelat(ESMym_gm.snowfrac)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "# P-E\n",
+    "l_A = ax.plot(Aym_zm.lat, spy*(Aym_zm.pr + Aym_zm.evspsbl)/density, color=colA, label=\"ICON-A-WBF\")\n",
+    "l_ESM = ax.plot(ESMym_zm.lat, spy*(ESMym_zm.pr + ESMym_zm.evspsbl)/density, color=colESM, label=\"ICON-ESM\")\n",
+    "\n",
+    "# HC edge\n",
+    "AHCe = calc_HCe_wap(Apsiym_zm, plev=50000)\n",
+    "ESMHCe = calc_HCe_wap(ESMpsiym_zm, plev=50000)\n",
+    "ax.scatter([AHCe[0], AHCe[1]], [1, 1], color=colA, marker=\"x\")\n",
+    "ax.scatter([ESMHCe[0], ESMHCe[1]], [1, 1], color=colESM, marker=\"x\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "ax.vlines([-Aicelat, Aicelat], ymin=-0.7, ymax=0.5, colors=colA, lw=1, clip_on=False)\n",
+    "ax.vlines([-Asnowlat, Asnowlat], ymin=-0.7, ymax=0.5, colors=colA, lw=1, ls=\"--\", clip_on=False)\n",
+    "ax.vlines([-ESMicelat, ESMicelat], ymin=-0.7, ymax=0.5, colors=colESM, lw=1, clip_on=False)\n",
+    "ax.vlines([-ESMsnowlat, ESMsnowlat], ymin=-0.7, ymax=0.5, colors=colESM, lw=1, ls=\"--\", clip_on=False)\n",
+    "\n",
+    "ax.annotate(r\"$\\varphi_i$\", [(Aicelat+ESMicelat)/2, -0.75], ha=\"center\", va=\"top\")\n",
+    "ax.annotate(r\"$\\varphi_s$\", [(Asnowlat+ESMsnowlat)/2, -0.75], ha=\"center\", va=\"top\")\n",
+    "ax.annotate(r\"$\\varphi_i$\", [(-Aicelat-ESMicelat)/2, -0.75], ha=\"center\", va=\"top\")\n",
+    "ax.annotate(r\"$\\varphi_s$\", [(-Asnowlat-ESMsnowlat)/2, -0.75], ha=\"center\", va=\"top\")\n",
+    "\n",
+    "\n",
+    "ax.set_ylim(-1, 2.5)\n",
+    "\n",
+    "plt.xlim(-30, 30)\n",
+    "\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.spines['left'].set_position(('outward', 5))\n",
+    "ax.spines['bottom'].set_position(('data', 0))\n",
+    "ax.xaxis.set_label_coords(0.5, 0.)\n",
+    "\n",
+    "\n",
+    "ax.set_ylabel(\"P-E [my$^{-1}$]\")\n",
+    "ax.set_xlabel(\"latitude [deglat]\")\n",
+    "\n",
+    "ax.annotate(\"ICON-A-WBF\", [0.01, 0.95], color=\"C2\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "ax.annotate(\"ICON-ESM-WBF\", [0.01, 0.81], color=\"C4\", xycoords=\"axes fraction\", ha=\"left\", va=\"top\")\n",
+    "\n",
+    "plt.savefig(\"plots/Fig9_WB_PE.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "221777f8-dfbd-4f64-a0e4-990000e0a4c2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "baseenv - Python 3.7",
+   "language": "python",
+   "name": "baseenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pythonscripts/ICON_tools.py b/pythonscripts/ICON_tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..897e0ac0342282d4cd864930d036ec7e6fc29785
--- /dev/null
+++ b/pythonscripts/ICON_tools.py
@@ -0,0 +1,714 @@
+import pandas as pd
+import xarray as xr
+import numpy as np
+from os import path
+import cftime
+
+def convert_climatetime(timein):
+    """
+    Converts the time variable of the ICON climatology dataset to a datetime object.
+    The time variable is in the format YYYYMMDDHHMMSS. The function converts it to a datetime object.
+    
+    Parameters
+    ----------
+    timein : array_lgetike
+        time variable of the ICON climatology dataset
+    
+    Returns
+    ----------
+    time : array_like
+        datetime object
+    """
+    date=pd.to_datetime(timein,format="%Y%m%d")
+    hours=((timein%1)*24).round(6)
+    time=date+pd.to_timedelta(hours,unit="hours")
+    return time
+
+
+def load_experiment(expname): #loads the dataset of a simulation
+    """
+    Loads the dataset of a simulation.
+
+    Parameters
+    ----------
+    expname : string
+        name of the experiment 
+
+    Returns
+    ----------
+    expname : string
+        name of the experiment 
+    DS : xarray dataset
+        dataset of the simulation
+    """
+
+    fname = expname +"_atm_2d_ml.ym.gm.nc" #filename of global yearly mean
+    dpath = "/work/bb1092/pp_JH/" +expname +"/" #simulation path
+    DS = xr.open_dataset(dpath +fname, decode_times=False) #loading of dataset
+    print(dpath +fname)
+    return expname, DS # returns the name of the experiment & the actual dataset
+
+
+def get_explist(data_path, explist):
+    """
+    Checks if all experiment folders exists and returns the number of experiments.  
+
+    Parameters
+    ----------
+    data_path : string
+        base path where the experiment folders are stored
+    explist : array_like
+        array of experiment names (names of the folders)
+
+    Returns
+    ----------
+    explist : array 
+    len(explist): integer
+    """
+    for exp in explist:
+        if not path.isdir(data_path +"/" +exp):
+            print("ERROR: " +exp +": directory " +data_path +"/" +exp +" does not exist!") 
+            return None
+        else:
+            print(exp +": directory is " +data_path +"/" +exp)
+    return explist, len(explist)
+
+
+def get_explist_icona(data_path, explist):
+    """
+    Checks if all experiment files exists and returns the number of experiments.  
+
+    Parameters
+    ----------
+    data_path : string
+        base path where the experiment folders are stored
+    explist : array_like
+        array of experiment names 
+
+    Returns
+    -------
+    explist : array 
+    len(explist): integer
+    """
+    for exp in explist:
+        fpath = data_path +"/" +exp +"_atm_2d_ml.mm.gm.nc"
+        if not path.isfile(fpath):
+            print("ERROR: " +fpath +" does not exist!") 
+            return None
+        else:
+            print(exp +" is in " +data_path +"/")
+    return explist, len(explist)
+
+
+def load_ds_2d(data_path, explist, timesdecode=False, droplast=True ): 
+    """
+    Loads the 2d monthly mean zonal & global mean data  
+
+    Parameters
+    ----------
+    data_path : string
+        base path where the experiment folders are stored
+    explist : array_like
+        array of experiment names (names of the folders)
+
+    Returns
+    -------
+    DSlistgm: array of 2d global mean datasets 
+    DSlistzm: array of 2d zonal mean datasets
+    """
+    DSlistgm=np.empty(len(explist),dtype="object")
+    DSlistzm=np.empty(len(explist),dtype="object")
+    for i, exp in enumerate(explist):
+        DS=xr.open_dataset(data_path +"/" +exp +"/" +exp + "_atm_2d_ml.mm.gm.nc", decode_times=timesdecode)
+        if timesdecode==False:
+            DS=DS.assign_coords(time=(DS.time/360))
+        if droplast:
+            DS=DS.isel(time=slice(0,-1))
+        DSlistgm[i]=DS
+
+        DS=xr.open_dataset(data_path +"/" +exp +"/" +exp + "_atm_2d_ml.mm.zm.nc", decode_times=timesdecode)
+        if timesdecode==False:
+            DS=DS.assign_coords(time=(DS.time/360))
+        if droplast:
+            DS=DS.isel(time=slice(0,-1))
+        DSlistzm[i]=DS
+    return DSlistgm, DSlistzm
+
+
+def load_ds_3d(data_path, explist, timesdecode=False, droplast=True): 
+    """
+    Loads the 3d monthly mean zonal  data  
+
+    Parameters
+    ----------
+    data_path : string
+        base path where the experiment folders are stored
+    explist : array_like
+        array of experiment names (names of the folders)
+
+    Returns
+    -------
+    DSlistzmml: array of 3d zonal mean datasets on model levels
+    DSlistzmpl: array of 3d zonal mean datasets on pressure levels
+    """
+    DSlistzmml = np.empty(len(explist), dtype="object")
+    DSlistzmpl = np.empty(len(explist), dtype="object")
+    for i, exp in enumerate(explist):
+        DSml = xr.open_mfdataset(data_path+"/"+exp+"/"+exp+"_atm_3d_ml*.ps.mm.zm.nc", decode_times = timesdecode)
+        DSpl = xr.open_mfdataset(data_path+"/"+exp+"/"+exp+"_atm_3d_pl*.ps.mm.zm.nc", decode_times = timesdecode)
+        if timesdecode==False:
+            DSml=DSml.assign_coords(time=(DSml.time/360))
+            DSpl=DSpl.assign_coords(time=(DSpl.time/360))
+
+        if droplast:
+            DSml=DSml.isel(time=slice(0,-1))
+            DSpl=DSpl.isel(time=slice(0,-1))
+            
+        DSlistzmml[i]=DSml
+        DSlistzmpl[i]=DSpl
+    return  DSlistzmml, DSlistzmpl
+
+def load_ds_2d_icona(data_path, explist, timesdecode=False, droplast=True ): 
+    """
+    Loads the 2d monthly mean zonal & global mean data from ICON-A simulations from mistral.
+
+    Parameters
+    ----------
+    data_path : string
+        base path where the experiment folders are stored
+    explist : array_like
+        array of experiment names (names of the folders)
+
+    Returns
+    -------
+    DSlistgm: array of 2d global mean datasets 
+    DSlistzm: array of 2d zonal mean datasets
+    """
+    DSlistgm=np.empty(len(explist),dtype="object")
+    DSlistzm=np.empty(len(explist),dtype="object")
+    for i, exp in enumerate(explist):
+        DS=xr.open_dataset(data_path +"/" +exp + "_atm_2d_ml.mm.gm.nc", decode_times=timesdecode)
+        if timesdecode==False:
+            DS=DS.assign_coords(time=(DS.time/360))
+        if droplast:
+            DS=DS.isel(time=slice(0,-1))
+        DSlistgm[i]=DS
+
+        DS=xr.open_dataset(data_path +"/"  +exp + "_atm_2d_ml.mm.zm.nc", decode_times=timesdecode)
+        if timesdecode==False:
+            DS=DS.assign_coords(time=(DS.time/360))
+        if droplast:
+            DS=DS.isel(time=slice(0,-1))
+        DSlistzm[i]=DS
+    return DSlistgm, DSlistzm
+
+
+
+def sictoicelat(sic):
+    """
+    Extremely sophisticated trigonometric calculations to get the ice-edge latitude from a fractional ice cover, with the assumption that the ice border is parallel to meridians and symmetric with the respect to the equator.
+
+    Parameters
+    ----------
+    sic : array_like
+        array of fractional sea-ice cover (between 0 and 1)
+
+    Returns
+    -------
+    icelat: array of ice-edge latitudes in degree
+    """
+    return np.rad2deg(np.arcsin(1-sic))
+
+
+def icelatosic(icelat):
+    """
+    Extremely sophisticated trigonometric calculations to get the fractional ice cover from an ice-edge latitude , with the assumption that the ice border is parallel to meridians and symmetric with the respect to the equator.
+
+    Parameters
+    ----------
+    icelat : array_like
+        array of ice-edge latitudes in degree
+
+    Returns
+    -------
+    sic: array of fractional sea-ice cover (between 0 and 1) 
+    """
+    return 1-np.sin(np.deg2rad(icelat))
+
+
+def get_albedo(dataset, albtype):
+    """
+    Calculate albedo at TOA/surface with/without clouds from ICON data.
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the radiative variables from an ICON simulation.
+    albtype : string
+        Where albedo should be calculated: toa, toacs, surf, surfcs
+    Returns
+    -------
+    da_albedo: Dataarray that contains the albedo values. NaN if there is no incoming solar radiation.
+    """
+    if albtype=="toa":
+        return (dataset["rsut"]/dataset["rsdt"]).persist()
+    elif albtype=="toacs":
+        return (dataset["rsutcs"]/dataset["rsdt"]).persist()
+    elif albtype=="surf":
+        return (dataset["rsus"]/dataset["rsds"]).persist()
+    elif albtype=="surfcs":
+        return (dataset["rsuscs"]/dataset["rsdscs"]).persist()
+    else:
+        print("ERROR: albtype has to be one of: toa, toacs, surf, surfcs")
+        exit()
+
+
+def get_cre(dataset, cretype, radtype):
+    """
+    Calculate CRE at TOA/surface with/without clouds from ICON data.
+
+    Parameters
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the radiative variables from an ICON simulation.
+    cretype : string
+        Where CRE should be calculated: toa, surf, atm.
+    radtype : string
+        What radiation should be used: sw, lw, net.
+
+    Returns
+    -------
+    da_cre: Dataarray that contains the CRE values. Positive downward in W/m^2.
+    """
+    da_cre = None
+    if cretype=="toa":
+        CRESW = dataset["rsutcs"]-dataset["rsut"]
+
+        if radtype=="net":
+            CRELW = dataset["rlutcs"]-dataset["rlut"]
+            da_cre = CRESW+CRELW
+        elif radtype=="sw":
+            da_cre = CRESW
+        elif radtype=="lw":
+            CRELW = dataset["rlutcs"]-dataset["rlut"]
+            da_cre = CRELW
+        else: 
+            print("ERROR: radtyp has to be one of: sw, lw, net")
+            exit()
+    elif cretype=="surf":
+        CRESW = dataset["rsds"]-dataset["rsus"] - (dataset["rsdscs"]-dataset["rsuscs"])
+
+        if radtype=="net":
+            CRELW = dataset["rlds"]-dataset["rldscs"]
+            da_cre = CRESW+CRELW
+        elif radtype=="sw":
+            da_cre = CRESW
+        elif radtype=="lw":
+            CRELW = dataset["rlds"]-dataset["rldscs"]
+            da_cre = CRELW
+        else: 
+            print("ERROR: radtyp has to be one of: sw, lw, net")
+            exit()
+    elif cretype=="atm":
+        CRESW_toa = dataset["rsutcs"]-dataset["rsut"]
+        CRELW_toa = dataset["rlutcs"]-dataset["rlut"]
+
+        CRESW_surf = dataset["rsds"]-dataset["rsus"] - (dataset["rsdscs"]-dataset["rsuscs"])
+        CRELW_surf = dataset["rlds"]-dataset["rldscs"]
+
+        if radtype=="net":
+            da_cre = CRESW_toa+CRELW_toa - (CRESW_surf+CRELW_surf)
+        elif radtype=="sw":
+            da_cre = CRESW - CRESW_surf
+        elif radtype=="lw":
+            da_cre = CRELW - CRELW_surf
+        else: 
+            print("ERROR: radtyp has to be one of: sw, lw, net")
+            exit()
+
+    else:
+        print("ERROR: cretype has to be one of: toa, surf, atm")
+        exit()
+
+    da_cre.attrs["units"]="Wm-2"
+    return da_cre
+
+
+def get_cre_CAM(dataset, cretype, radtype):
+    """
+    Calculate CRE at TOA/surface with/without clouds from ICON data.
+
+    Parameters
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the radiative variables from an ICON simulation.
+    cretype : string
+        Where CRE should be calculated: toa, surf, atm.
+    radtype : string
+        What radiation should be used: sw, lw, net.
+
+    Returns
+    -------
+    da_cre: Dataarray that contains the CRE values. Positive downward in W/m^2.
+    """
+    da_cre = None
+    if cretype=="toa":
+        CRESW = (dataset["FSNT"] - dataset["FSNTC"]).squeeze()
+
+        if radtype=="net":
+            CRELW = (dataset["FLNTC"] - dataset["FLNT"]).squeeze()
+            da_cre = CRESW+CRELW
+        elif radtype=="sw":
+            da_cre = CRESW
+        elif radtype=="lw":
+            CRELW = (dataset["FLNTC"] - dataset["FLNT"]).squeeze()
+            da_cre = CRELW
+        else: 
+            print("ERROR: radtyp has to be one of: sw, lw, net")
+            exit()
+    else:
+        print("ERROR: cretype has to be one of: toa")
+        exit()
+
+    da_cre.attrs["units"]="Wm-2"
+    return da_cre
+
+def get_toaeb(dataset, ebtype="as"):
+    """
+    Calculate the TOA energy balance from ICON data.
+
+    Parameters
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the radiative variables from an ICON simulation.
+    ebtype : string
+        All sky or clear sky toa eb (as or cs)
+
+    Returns
+    -------
+    da_toaeb: Dataarray that contains the toa eb values. Positive downward in W/m^2.
+    """
+    if ebtype=="as":
+        return dataset["rsdt"]-dataset["rsut"]-dataset["rlut"]
+    elif ebtype=="cs":
+        return dataset["rsdt"]-dataset["rsutcs"]-dataset["rlutcs"]
+    else:
+        print("ERROR: ebtype has to be one of: as, cs")
+        exit()
+
+
+def get_eb(dataset, location="TOA", ebtype="as"):
+    """
+    Calculate the TOA energy balance from ICON data.
+
+    Parameters
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the energy fluxes from an ICON simulation.
+    location : string
+        TOA, SURF or ATM. Where the energy balance should be calculated. 
+    ebtype : string
+        All sky or clear sky eb (as or cs)
+
+    Returns
+    -------
+    da_toaeb: Dataarray that contains the eb values. Positive downward in W/m^2.
+    """
+
+    if ebtype=="as":
+        toaeb = dataset["rsdt"]-dataset["rsut"]-dataset["rlut"]
+
+        surfeb=dataset["rsds"] + dataset["rlds"] + dataset["hfss"] + dataset["hfls"] 
+        - dataset["rsus"] - dataset["rlus"]
+    elif ebtype=="cs":
+        toaeb = dataset["rsdt"]-dataset["rsutcs"]-dataset["rlutcs"]
+
+        #surfeb=dataset["rsdscs"] + dataset["rldscs"] + dataset["hfss"] + dataset["hfls"] 
+        #- dataset["rsuscs"] - dataset["rluscs"]
+    else:
+        print("ERROR: ebtype has to be one of: as, cs")
+        exit()
+
+    if location=="SURF":
+        return surfeb
+    elif location=="TOA":
+        return toaeb
+    elif location=="ATM":
+        return toaeb - surfeb
+    else:
+        print("ERROR: location has to be one of: TOA, SURF, ATM")
+        exit()
+
+    
+
+    if ebtype=="as":
+        return dataset["rsdt"]-dataset["rsut"]-dataset["rlut"]
+    elif ebtype=="cs":
+        return dataset["rsdt"]-dataset["rsutcs"]-dataset["rlutcs"]
+    else:
+        print("ERROR: ebtype has to be one of: as, cs")
+        exit()
+
+
+def get_evap_ice(dataset):
+    """
+    Calculate the fraction of the evaporative flux that consists of solid ice/snow sublimating. evspsbl in ICON consists of both surface ice and water sublimating/evaporating in kg/(m^2s). 
+
+    Parameters
+    ----------
+    dataset : xarray dataset
+        Dataset that contains the evaporative flux evspsbl in kg/m^2s from an ICON simulation.
+
+    Returns
+    -------
+    evap_icefrac: Dataarray that containts the fraction of ice sublimating. Water evaporating is 1-evap_icefrac.
+    """
+
+    # from ICON
+    alv   = 2.5008e6     # [J/kg]   latent heat for vaporisation
+    als   = 2.8345e6      # [J/kg]   latent heat for sublimation
+
+    L_global = (dataset["hfls"] / dataset["evspsbl"] ) # global mean latent heat of vaporisation/sublimation
+
+    evap_icefrac=(L_global-alv) / (als - alv)
+    return evap_icefrac
+
+
+
+def lighten_color(color, amount=0.5):
+    """
+    Lightens the given color by multiplying (1-luminosity) by the given amount.
+    Input can be matplotlib color string, hex string, or RGB tuple.
+
+    Examples:
+    >> lighten_color('g', 0.3)
+    >> lighten_color('#F034A3', 0.6)
+    >> lighten_color((.3,.55,.1), 0.5)
+    """
+    import matplotlib.colors as mc
+    import colorsys
+    try:
+        c = mc.cnames[color]
+    except:
+        c = color
+    c = colorsys.rgb_to_hls(*mc.to_rgb(c))
+    newcolor=np.asarray(colorsys.hls_to_rgb(c[0], 1 - amount * (1 - c[1]), c[2]))
+
+    # fix values out of range
+    newcolor[(newcolor>1.0)] = 1.0
+    newcolor[(newcolor<0.0)] = 0.0
+    return newcolor
+
+def minimal_legend(axis, **kwargs):
+    """
+    Creates a minimalistic legend for a plot. The legend frame and legend handles are removed and the text color is set to the color of the handle.
+
+    Parameters
+    ----------
+    axis : matplotlib axis
+        axis object of the plot
+
+    Returns
+    -------
+    legend: matplotlib legend
+        legend object of the plot
+    """
+    # make legend pretty
+    legend = axis.legend(**kwargs)
+    legend.get_frame().set_visible(False)
+    handles, labels = legend.legendHandles, legend.get_texts()
+    for handle, label in zip(handles, labels):
+        label.set_color(handle.get_color())  
+        handle.set_visible(False)  
+    return legend
+
+def scale_color_co2(color, co2, co2min=4000, co2max=10000, lummin=0.5, lummax=1.5):
+    """
+    Scales the color by the given co2 value. The luminosity is scaled linearly between lummin and lummax.
+    Input can be matplotlib color string, hex string, or RGB tuple.
+
+    Parameters
+    ----------
+    color : string
+        matplotlib color string, hex string, or RGB tuple
+    co2 : float
+        co2 value
+    co2min : float
+        minimum co2 value
+    co2max : float
+        maximum co2 value
+    lummin : float
+        minimum luminosity
+    lummax : float
+        maximum luminosity  
+
+    Returns
+    -------
+    newcolor: RGB tuple
+    """
+    a = (lummax-lummin) / (float(co2max)-float(co2min))
+    b = lummin -float(co2min) * a
+    return lighten_color(color, amount=a*float(co2)+b)
+
+
+def calc_spline(x, y, xnew, smoothing=1, degree=1): 
+    """
+    Calculate 1d spline interpolation to a new array. Dublicates in x are removed. If xnew is larger than x, ynew values outside are set to NaN.
+
+    Parameters
+    ----------
+    x : array_like 
+        1d array that contains the original x values (where the spline interpolation takes place)
+    y : array_like 
+        1d array that contains the original y values
+    xnew : array_like 
+        1d array that contains the new x values where the values should be interpolated to
+    smoothing : integer 
+        smoothing factor of the spline calculation (see documentation of scipy.interpolate.UnivariateSpline), default 1
+    degree : integer 
+        degree of the spline interpolation, 1 (default) is linear 
+
+    Returns
+    -------
+    ynew: the input array y interpolated to xnew, same shape as xnew
+    """
+    from scipy.interpolate import UnivariateSpline # import module 
+
+
+    _, ind1=np.unique(x,return_index=True) # remove dublicate x values
+    ind2=np.squeeze(np.argwhere(~np.isnan(x.values))) # remove nans
+    ind = np.intersect1d(ind1,ind2)
+
+    data=[]
+    data1=[]
+
+    data = np.vstack([x[ind].values,y[ind].values])
+    data1=data[:,data[0].argsort()] # sort data for ascending x
+
+    f = UnivariateSpline(data1[0], data1[1], s=smoothing, k=degree, ext=1) # make spline interpolation
+
+    xrange=[min(x.values),max(x.values)]
+    ynew = f(xnew) 
+    ynew[(xnew>xrange[1]) | (xnew<xrange[0])]=np.nan # set values outside of original xbounds to nan
+
+    return ynew
+
+
+def get_forcing_co2(co2_fac, alpha=5.35):    
+    """
+    Calculate estimated radiative forcing from a change in atmospheric co2 (see e.g Myhre et al., 1998). dF=alpha*ln(CO2/CO2_0)
+
+    Parameters
+    ----------
+    co2_fac: float 
+        Relative change in CO2 content, co2_fac=co2/co2_0
+    alpha : float 
+        factor that relates CO2 to radiative forcing, in W/m^2       
+
+    Returns
+    -------
+    dF: change in radiative forcing in W/m^2
+    """
+    return alpha*np.log(co2_fac)
+
+
+def get_co2_forcing(dF, alpha=5.35):
+    """
+    Calculate estimated relative co2 change from a given radiative forcing. (see e.g Myhre et al., 1998). CO2_fac=CO2/CO2_0=exp(dF/alpha)
+
+    Parameters
+    ----------
+    dF: float 
+        Change in radiative forcing attributed to CO2, in W/m^2
+    alpha : float 
+        factor that relates CO2 to radiative forcing, in W/m^2         
+
+    Returns
+    -------
+    CO2_fac: relative change in CO2 content, CO2_fac=CO2/CO2_0
+    """
+    return np.exp(np.divide(dF,alpha))
+
+
+def find_co2_expname(expname):
+    startind=expname.find("_")+1
+    endind=expname[startind:].find("_")
+    return expname[startind:startind+endind]
+
+def find_co2_expname_vscicona(expname):
+    temp=expname.find("_")+1
+    startind = expname.find("_", temp+1)+1 # second occurance
+    endind=expname[startind:].find("_")
+    return expname[startind:startind+endind]
+
+
+def find_co2_expname_icona(expname):    
+    temp = expname.find("_")
+    startind = expname.find("_", temp+1)+1 # second occurance
+    endind=expname[startind:].find("ppmv")
+    return expname[startind:startind+endind]
+
+def weighted_mean_dim(DS, weights, dim="lat"):
+    """
+    Calculate the weighted mean of a dataset along a dimension.
+
+    Parameters
+    ----------
+    DS : xarray dataset
+        Dataset that contains the variables lat and lon.
+    weights : array like
+        array that contains the weights for the weighted mean.
+    dim : string
+        Dimension along which the weighted mean is calculated.
+
+    Returns
+    -------
+    weighted_mean: weighted mean of the dataset along the dimension dim    
+    """
+    return ((DS * weights).sum(dim=dim) / sum(weights)).squeeze()
+
+
+def mean_climatezones(DS, lat_trop, lat_subtrop, relative_global=False):
+    """
+    Calculate the mean of a dataset for the tropics, subtropics and extratropics. Tropics  are between -lat_trop and lat_trop, subtropics between lat_trop and lat_subtrop and extratropics between lat_subtrop and 90.
+
+    Parameters
+    ----------
+    DS : xarray dataset
+        Dataset that contains the coordinate lon.
+    lat_trop : float
+        Latitude of the border between tropics and subtropics.
+    lat_subtrop : float
+        Latitude of the border between subtropics and extratropics.
+    relative_global : boolean
+        Switch if the values should be calculated as a fake global mean (True), so that the values for different climatezones are comparable.
+    Returns
+    -------
+    DS_trop: mean of the dataset for the tropics
+    DS_subtrop: mean of the dataset for the subtropics
+    DS_extratrop: mean of the dataset for the extratropics
+    """
+
+    DS_trop = DS.where((DS.lat <= lat_trop) & (DS.lat >= -1*lat_trop), drop=True)
+    weight_trop = np.cos(np.deg2rad(DS_trop.lat))
+    print("tropics: "  + str(DS_trop.lat.count().values) + " points between "  + str(lat_trop) + "° and -" + str(lat_trop) + "°")
+    DS_subtrop = DS.where(((DS.lat <= lat_subtrop) & (DS.lat > lat_trop)) | ((DS.lat >= -1*lat_subtrop) & (DS.lat < -1*lat_trop)), drop=True)
+    weight_subtrop = np.cos(np.deg2rad(DS_subtrop.lat))
+    print("subtropics: " + str(DS_subtrop.lat.count().values) + " points between +-"  + str(lat_trop) + "° and +-" + str(lat_subtrop) + "°")
+    DS_extratrop = DS.where((DS.lat >= lat_subtrop) | (DS.lat <= -1*lat_subtrop), drop=True)
+    weight_extratrop = np.cos(np.deg2rad(DS_extratrop.lat))
+    print("extratropics: " + str(DS_extratrop.lat.count().values) + " points between +-"  + str(lat_subtrop) + "° and +-90°")
+
+    DS_trop = weighted_mean_dim(DS_trop, weight_trop)
+    DS_subtrop = weighted_mean_dim(DS_subtrop, weight_subtrop)
+    DS_extratrop = weighted_mean_dim(DS_extratrop, weight_extratrop)
+
+    if relative_global:
+        print("calculate values relative to global")
+        weight_global = sum(weight_trop) + sum(weight_subtrop) + sum(weight_extratrop)
+
+        DS_trop = DS_trop * sum(weight_trop) / weight_global
+        DS_subtrop = DS_subtrop * sum(weight_subtrop) / weight_global
+        DS_extratrop = DS_extratrop * sum(weight_extratrop) / weight_global
+        
+    return DS_trop, DS_subtrop, DS_extratrop
+
+    
\ No newline at end of file
diff --git a/pythonscripts/aprp.py b/pythonscripts/aprp.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c1704e5f1de11dc7664f74de93433995344fc6b
--- /dev/null
+++ b/pythonscripts/aprp.py
@@ -0,0 +1,739 @@
+"""
+APRP Functions for ICON and CESM output. 
+
+APRP_pp_parms_calc: Post-process monthly ICON 2d output to calculate the APRP parameters and save them to a new file.
+APRP_pp_parms_bin: Bin the APRP parameters according to a given variable, calculate the climatological monthly mean and save them to a new file.
+APRP_pp_forcing: Calculate the TOA anomalies from the binned parameters from APRP_pp_parms_bin.
+
+adapted from: 
+https://github.com/mzelinka/aprp
+https://zenodo.org/records/8206763 
+"""
+ 
+#IMPORT STUFF:
+#=====================
+import xarray as xr
+import numpy as np
+import os, glob
+import multiprocessing as mp
+
+def APRP_pp_parms_calc(exp, inpath, outpath, simtype="ICON"):
+    """
+    Post-process monthly ICON 2d output to calculate the APRP parameters and save them to a new file. 
+    This function is parallelized using multiprocessing. 
+
+    Inputs:
+    exp: experiment name
+    inpath: path to the monthly ICON 2d output files
+    outpath: path to save the APRP parameter files
+    simtype: Describes the input data expected. At the moment, has to be one of "ICON" "ICONzm" "CESM".
+
+    Output:
+    None, files are saved to the specified path:
+        DS_out: dataset with the APRP parameters. Dimensions: month, ncells
+    """
+
+    print('Post-processing ICON simulation: ', exp)
+    # add slash to path
+    inpath = os.path.join(inpath, '')
+    outpath = os.path.join(outpath, '')
+    print('input path: ', inpath)
+    print('save path: ', outpath)
+
+
+    # Check paths
+    if os.path.exists(inpath) == False:
+        print('Input path does not exist! Exiting...')
+        return
+    if os.path.exists(outpath) == False:
+        print('Creating output directory')
+        os.makedirs(outpath)
+    if os.path.exists(outpath +"parameters/") == False:
+        print('Creating output subdirectory')
+        os.makedirs(outpath +"parameters/")
+
+    if simtype == "ICON":
+        filelist = sorted(glob.glob(inpath + exp +"*atm_2d_ml*.nc"))
+    elif simtype == "ICONzm":
+        filelist = sorted(glob.glob(inpath + exp +"*atm_2d_ml.mm.zm.nc")) 
+    elif simtype == "CESM":
+        filelist = sorted(glob.glob(inpath + exp +".cam.h0.????-??.nc")) 
+    for file in filelist:
+        process = mp.Process(target=_calc_parameters, args=(file, outpath +"parameters/", simtype))
+        process.start()
+        #_calc_parameters(file, outpath +"parameters/", simtype)
+    
+def APRP_pp_parms_bin(exp, aprppath, datapath, var_bin_array, var, varunit, simtype="ICON"):
+    """
+    Bin the APRP parameters according to a given variable, calculate the yearly mean per month and save them to a new file. 
+    This is function is not (yet) parallelized. 
+    
+    Inputs:
+    exp: experiment name
+    aprppath: path to the APRP parameter files (base path, exluding /parameters/)
+    datapath: path to the 2d yearly mean postprocessed ICON files
+    var_bin_array: array with the bin edges (left and right)
+    var: variable name
+    varunit: variable unit
+    simtype: Describes the input data expected. At the moment, has to be one of "ICON" "ICONzm" "CESM".
+    
+    Output:
+    None, files are saved to the specified path:
+        DS_final: dataset with the binned APRP parameters. Dimensions: month, bin, ncells
+    """
+    # add slash to path
+    aprppath = os.path.join(aprppath, '') 
+    datapath = os.path.join(datapath, '')
+
+    # check paths
+    if os.path.exists(aprppath) == False:
+        print('APRP path does not exist! Exiting...')
+        return
+    if os.path.exists(datapath) == False:
+        print('Data path does not exist! Exiting...')
+        return
+    
+    # Load the data
+    print("load data...", flush=True)
+    if simtype == "ICON" or simtype == "ICONzm":
+        DS_bins = xr.open_dataset(datapath + exp + '_atm_2d_ml.ym.gm.nc') # yearly mean global mean data
+    elif simtype == "CESM":
+        DS_bins = xr.open_dataset(datapath + exp + '_2d.ym.gm.nc')
+    DS_data = xr.open_mfdataset(aprppath + "parameters/" + exp + '_aprp_parms*.nc', combine='by_coords', parallel=True) # aprp parameters
+
+    # group the yearly mean global mean data by the variable
+    DS_grouped = DS_bins.groupby_bins(DS_bins[var], var_bin_array, include_lowest=True, labels=var_bin_array[:-1])
+    groups=DS_grouped.groups
+
+    # create list to store the dataset for each bin
+    DS_list = list()
+
+    # loop over each bin
+    for i, key in enumerate(groups):
+        # get the years for each bin
+        years = DS_bins.time.dt.year[groups[key]] 
+        print("Bin " + str(i) + ": lower bin edge: " + str(key) +", " + str(len(groups[key])) + " year(s): " + str(years.values), flush=True)
+        # select the years for each bin and calculate the monthly mean
+        DS_temp = DS_data.sel(time=DS_data.time.dt.year.isin(years)).groupby("time.month").mean(skipna=True)
+        # add the bin edge as a coordinate
+        DS_temp = DS_temp.assign_coords(var_bin_left=key) 
+        # expand the dataset to include the bin edge as a dimension
+        DS_temp = DS_temp.expand_dims(dim="var_bin_left") 
+        # add the number of years per bin as an attribute
+        DS_temp['nbins'] = xr.DataArray([len(groups[key])], dims='var_bin_left', coords={'var_bin_left': [key]}, attrs={'description': 'number of years per bin'}) 
+        DS_list.append(DS_temp)
+
+    # create dummy dataset so that all bins are included, even if they are empty. Also add the right bin edge.
+    if simtype == "ICON": # model output
+        NCELLS = DS_list[0].ncells
+        MONTH = DS_list[0].month
+        CLON = DS_list[0].clon
+        CLAT = DS_list[0].clat
+        VAR_BIN_LEFT = var_bin_array[:-1]
+        VAR_BIN_RIGHT = var_bin_array[1:]
+
+        DS_varbins = xr.Dataset(
+            {
+            'var_bin_right':(('var_bin_left'), VAR_BIN_RIGHT, {'description': 'right edge of bin, included', 'variable': var, 'unit': varunit}),
+            },
+            coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH, 'ncells': NCELLS,  "clon": CLON, "clat": CLAT}
+        )
+    elif simtype== "ICONzm": # zonal mean
+        MONTH = DS_list[0].month
+        LAT = DS_list[0].lat
+        VAR_BIN_LEFT = var_bin_array[:-1]
+        VAR_BIN_RIGHT = var_bin_array[1:]
+
+        DS_varbins = xr.Dataset(
+            {
+            'var_bin_right':(('var_bin_left'), VAR_BIN_RIGHT, {'description': 'right edge of bin, included', 'variable': var, 'unit': varunit}),
+            },
+            coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH, 'lat': LAT}
+        )
+    elif simtype == "CESM": # model output
+        MONTH = DS_list[0].month
+        LON = DS_list[0].lon
+        LAT = DS_list[0].lat
+        VAR_BIN_LEFT = var_bin_array[:-1]
+        VAR_BIN_RIGHT = var_bin_array[1:]
+
+        DS_varbins = xr.Dataset(
+            {
+            'var_bin_right':(('var_bin_left'), VAR_BIN_RIGHT, {'description': 'right edge of bin, included', 'variable': var, 'unit': varunit}),
+            },
+            coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH,  "lat": LAT, "lon": LON}
+        )
+
+    # merge the datasets
+    print('Merging datasets...', flush=True)
+    DS_list.append(DS_varbins)
+    DS_final = xr.merge(DS_list).compute()
+
+    # add attributes
+    DS_final.var_bin_left.attrs['description'] = 'left edge of bin, not included (except for lowermost value)'
+    DS_final.var_bin_left.attrs['variable'] = var
+    DS_final.var_bin_left.attrs['unit'] = varunit
+
+    # save the dataset
+    filename = aprppath + exp + '_aprp_parms_binned_' + var + str(len(var_bin_array)-1) + '.nc'
+    if os.path.exists(filename) == True:
+        print('Warning: File already exists, overwriting: ' + filename, flush=True)
+        os.remove(filename)
+    else:
+        print('Saving file: ' + filename, flush=True)
+    DS_final.to_netcdf(filename, mode='w', format='NETCDF4')
+    print('Done!', flush=True)
+
+
+def APRP_pp_forcing(DS_parms, RSDT=None, flag='', simtype="ICON", calc_TOA_net=False):
+    """
+    Do the actual APRP with the binned parameters from APRP_pp_parms_bin. 
+
+    Inputs:
+    DS_parms: dataset with the binned APRP parameters. Dimensions: month, bin, ncells
+    RSDT: DataArray with the incoming solar radiation at TOA. If not provided, the value from the parameter file is used.
+    flag: flag to do forward, backward, or avg of forward / backward calcuations (the default)
+    simtype: Describes the input data expected. At the moment, has to be one of "ICON" "ICONzm" "CESM".
+    calc_TOA_net: calculate the TOA net anomalies. Default is False.
+
+    Output:
+    None, files are saved to the specified path:
+        DS: dataset with the TOA anomalies due to different features. Dimensions: month, bin, ncells
+    """
+
+    clt = DS_parms.clt.values
+    a_clr = DS_parms.a_clr.values
+    a_oc = DS_parms.a_oc.values
+    mu_clr = DS_parms.mu_clr.values
+    mu_cld = DS_parms.mu_cld.values
+    ga_clr = DS_parms.ga_clr.values
+    ga_cld = DS_parms.ga_cld.values
+    f_bare = DS_parms.f_bare.values
+    f_ice = DS_parms.f_ice.values
+
+    if calc_TOA_net:
+        rsdt = DS_parms.rsdt.values
+        rsut = DS_parms.rsut.values
+        rlut = DS_parms.rlut.values
+
+    if isinstance(RSDT, xr.DataArray):
+        print('Using RSDT from external input file.')
+        RSDT = RSDT.values
+    else:
+        print('Using RSDT from parameter file.')
+        RSDT = DS_parms.rsdt.values
+
+    A = _albedo(clt , a_clr, a_oc, mu_clr, mu_cld, ga_clr, ga_cld)
+    A_nocld = _albedo(0 , a_clr, a_oc, mu_clr, mu_cld, ga_clr, ga_cld)
+    A_bottom_up = _albedo_bottom_up(clt, f_ice, f_bare, mu_clr, mu_cld, ga_clr, ga_cld)
+
+    # reference state is the state with LOWER sic, perturbed state is with HIGHER sic.
+
+    # forward -> take state with lower sic, i.e lower var_bin_left value, and perturb with value from higher sic state
+    dA_amt_cld_fwd=     _albedo(clt[1:, :, :], a_clr[:-1, :, :], a_oc[:-1, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_a_clr_fwd=       _albedo(clt[:-1, :, :], a_clr[1:, :, :], a_oc[:-1, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_a_oc_fwd=        _albedo(clt[:-1, :, :], a_clr[:-1, :, :], a_oc[1:, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_abs_noncld_fwd=  _albedo(clt[:-1, :, :], a_clr[:-1, :, :], a_oc[:-1, :, :], mu_clr[1:, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_abs_cld_fwd=     _albedo(clt[:-1, :, :], a_clr[:-1, :, :], a_oc[:-1, :, :], mu_clr[:-1, :, :], mu_cld[1:, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_scat_noncld_fwd= _albedo(clt[:-1, :, :], a_clr[:-1, :, :], a_oc[:-1, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[1:, :, :], ga_cld[:-1, :, :])-A[:-1, :, :]
+    dA_scat_cld_fwd=    _albedo(clt[:-1, :, :], a_clr[:-1, :, :], a_oc[:-1, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[1:, :, :])-A[:-1, :, :]
+
+    dA_a_nocld_fwd=     _albedo(0, a_clr[1:, :, :], -999, mu_clr[:-1, :, :], -999, ga_clr[:-1, :, :], -999)-A_nocld[:-1, :, :] 
+
+    dA_sic_fwd=         _albedo_bottom_up(clt[:-1, :, :], f_ice[1:, :, :], f_bare[:-1, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A_bottom_up[:-1, :, :]
+    dA_bare_fwd=        _albedo_bottom_up(clt[:-1, :, :], f_ice[:-1, :, :], f_bare[1:, :, :], mu_clr[:-1, :, :], mu_cld[:-1, :, :], ga_clr[:-1, :, :], ga_cld[:-1, :, :])-A_bottom_up[:-1, :, :]
+
+
+    # backward -> take state with higher uq and perturb with value from lower state
+    dA_amt_cld_bwd=     A[1:, :, :]-_albedo(clt[:-1, :, :], a_clr[1:, :, :], a_oc[1:, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_a_clr_bwd=       A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[:-1, :, :], a_oc[1:, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_a_oc_bwd=        A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[1:, :, :], a_oc[:-1, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_abs_noncld_bwd=  A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[1:, :, :], a_oc[1:, :, :], mu_clr[:-1, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_abs_cld_bwd=     A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[1:, :, :], a_oc[1:, :, :], mu_clr[1:, :, :], mu_cld[:-1, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_scat_noncld_bwd= A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[1:, :, :], a_oc[1:, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[:-1, :, :], ga_cld[1:, :, :])
+    dA_scat_cld_bwd=    A[1:, :, :]-_albedo(clt[1:, :, :], a_clr[1:, :, :], a_oc[1:, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[:-1, :, :])
+
+    dA_a_nocld_bwd=     A_nocld[1:, :, :]-_albedo(0, a_clr[:-1, :, :], -999, mu_clr[1:, :, :], -999, ga_clr[1:, :, :], -999) 
+
+    dA_sic_bwd=         A_bottom_up[1:, :, :]-_albedo_bottom_up(clt[1: :, :], f_ice[:-1, :, :], f_bare[1:, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    dA_bare_bwd=        A_bottom_up[1:, :, :]-_albedo_bottom_up(clt[1: :, :], f_ice[1:, :, :], f_bare[:-1, :, :], mu_clr[1:, :, :], mu_cld[1:, :, :], ga_clr[1:, :, :], ga_cld[1:, :, :])
+    
+    if flag=='': # do forward and backward PRP (default)
+        dA_amt_cld =    0.5*(dA_amt_cld_fwd + dA_amt_cld_bwd)
+        dA_a_clr =      0.5*(dA_a_clr_fwd + dA_a_clr_bwd) 
+        dA_a_oc =       0.5*(dA_a_oc_fwd + dA_a_oc_bwd)
+        dA_abs_noncld = 0.5*(dA_abs_noncld_fwd + dA_abs_noncld_bwd)
+        dA_abs_cld =    0.5*(dA_abs_cld_fwd + dA_abs_cld_bwd)
+        dA_scat_noncld =0.5*(dA_scat_noncld_fwd + dA_scat_noncld_bwd)
+        dA_scat_cld =   0.5*(dA_scat_cld_fwd + dA_scat_cld_bwd)        
+
+        dA_a_nocld = 0.5*(dA_a_nocld_fwd + dA_a_nocld_bwd)   
+
+        dA_sic = 0.5*(dA_sic_fwd + dA_sic_bwd)
+        dA_bare = 0.5*(dA_bare_fwd + dA_bare_bwd)
+
+        RSDT = 0.5*(RSDT[:-1, :, :]+RSDT[1:, :, :])
+    elif flag=='forward': # do forward-only PRP  
+        dA_amt_cld = dA_amt_cld_fwd
+        dA_a_clr = dA_a_clr_fwd
+        dA_a_oc = dA_a_oc_fwd
+        dA_abs_noncld = dA_abs_noncld_fwd
+        dA_abs_cld = dA_abs_cld_fwd
+        dA_scat_noncld = dA_scat_noncld_fwd
+        dA_scat_cld = dA_scat_cld_fwd 
+
+        dA_a_nocld = dA_a_nocld_fwd
+
+        dA_sic = dA_sic_fwd
+        dA_bare = dA_bare_fwd
+
+        RSDT = RSDT[:-1, :, :]
+    elif flag=='backward': # do backward-only PRP
+        dA_amt_cld = dA_amt_cld_bwd
+        dA_a_clr = dA_a_clr_bwd
+        dA_a_oc = dA_a_oc_bwd
+        dA_abs_noncld = dA_abs_noncld_bwd
+        dA_abs_cld = dA_abs_cld_bwd
+        dA_scat_noncld = dA_scat_noncld_bwd
+        dA_scat_cld = dA_scat_cld_bwd
+
+        dA_a_nocld = dA_a_nocld_bwd
+
+        dA_sic = dA_sic_bwd
+        dA_bare = dA_bare_bwd
+    
+        RSDT = RSDT[1:, :, :]
+    # if the cld fraction is less than 2%, set fields to be zero
+    dA_amt_cld=xr.where(clt[:-1, :, :]<0.02,0.,dA_amt_cld)
+    dA_amt_cld=xr.where(clt[1:, :, :]<0.02,0.,dA_amt_cld)
+    dA_a_oc=xr.where(clt[:-1, :, :]<0.02,0.,dA_a_oc)
+    dA_a_oc=xr.where(clt[1:, :, :]<0.02,0.,dA_a_oc)
+    dA_abs_cld=xr.where(clt[:-1, :, :]<0.02,0.,dA_abs_cld)
+    dA_abs_cld=xr.where(clt[1:, :, :]<0.02,0.,dA_abs_cld)
+    dA_scat_cld=xr.where(clt[:-1, :, :]<0.02,0.,dA_scat_cld)
+    dA_scat_cld=xr.where(clt[1:, :, :]<0.02,0.,dA_scat_cld)
+
+
+    dA_a=dA_a_clr+dA_a_oc # 16.a total, surface albedo
+    dA_cld=dA_abs_cld+dA_scat_cld+dA_amt_cld # 16.b total, clouds
+    dA_noncld=dA_abs_noncld+dA_scat_noncld # 16.c total, clear-sky atmosphere
+    
+    ## TOA SW Anomalies due to Surface Albedo Anomalies
+    sfc_alb=-dA_a*RSDT # total
+    sfc_alb_clr=-dA_a_clr*RSDT # clear-sky   
+    sfc_alb_oc=-dA_a_oc*RSDT # overcast-sky
+
+    sfc_alb_nocld=-dA_a_nocld*RSDT # no clouds
+
+    ## TOA SW Anomalies due to Cloud Anomalies
+    cld=-dA_cld*RSDT # total
+    cld_amt=-dA_amt_cld*RSDT # amount of clouds    
+    cld_scat=-dA_scat_cld*RSDT # cloud scattering
+    cld_abs=-dA_abs_cld*RSDT # cloud absorption
+
+    ## TOA SW Anomalies due to Non-cloud Anomalies
+    noncld=-dA_noncld*RSDT # total
+    noncld_scat=-dA_scat_noncld*RSDT # non-cloud scattering  
+    noncld_abs=-dA_abs_noncld*RSDT # non-cloud absorption
+    
+    ## TOA SW Anomalies due to Sea-Ice Cover and Snow Thickness Anomalies
+    sic=-dA_sic*RSDT # sea-ice cover
+    bare=-dA_bare*RSDT # snow thickness on sea-ice
+
+    # set fields to zero when incoming solar radiation is zero
+    sfc_alb=xr.where(RSDT<0.1,0.,sfc_alb)
+    cld=xr.where(RSDT<0.1,0.,cld)
+    noncld=xr.where(RSDT<0.1,0.,noncld)
+    sfc_alb_clr=xr.where(RSDT<0.1,0.,sfc_alb_clr)
+    sfc_alb_oc=xr.where(RSDT<0.1,0.,sfc_alb_oc)
+    sfc_alb_nocld=xr.where(RSDT<0.1,0.,sfc_alb_nocld)
+    cld_amt=xr.where(RSDT<0.1,0.,cld_amt)
+    cld_scat=xr.where(RSDT<0.1,0.,cld_scat)
+    cld_abs=xr.where(RSDT<0.1,0.,cld_abs)
+    noncld_scat=xr.where(RSDT<0.1,0.,noncld_scat)
+    noncld_abs=xr.where(RSDT<0.1,0.,noncld_abs)
+    sic=xr.where(RSDT<0.1,0.,sic)
+    bare=xr.where(RSDT<0.1,0.,bare)
+
+    # calculate TOA net anomalies
+    if calc_TOA_net:
+        sw_toa = (rsdt - rsut)[1:, :, :] - (rsdt - rsut)[:-1, :, :]
+        lw_toa = (-rlut)[1:, :, :] - (-rlut)[:-1, :, :]
+        net_toa = (rsdt - rsut - rlut)[1:, :, :] - (rsdt - rsut - rlut)[:-1, :, :]
+
+    # store in a dataset:
+    if simtype=="ICON":
+        MONTH = DS_parms.month
+        NCELLS = DS_parms.ncells
+        CLON = DS_parms.clon
+        CLAT = DS_parms.clat
+        VAR_BIN_LEFT = DS_parms.var_bin_left[1:]
+        DS = xr.Dataset(
+        {
+            'sfc_alb':(('var_bin_left', 'month','ncells'),sfc_alb.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (total)', 'units': 'W/m^2'}),
+            'sfc_alb_clr':(('var_bin_left', 'month','ncells'),sfc_alb_clr.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (clear-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_oc':(('var_bin_left', 'month','ncells'),sfc_alb_oc.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (overcast-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_nocld':(('var_bin_left', 'month','ncells'),sfc_alb_nocld.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (no clouds)', 'units': 'W/m^2'}),
+            'cld':(('var_bin_left', 'month','ncells'),cld.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (total)', 'units': 'W/m^2'}),
+            'cld_amt':(('var_bin_left', 'month','ncells'),cld_amt.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (amount of clouds)', 'units': 'W/m^2'}),
+            'cld_scat':(('var_bin_left', 'month','ncells'),cld_scat.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud scattering)', 'units': 'W/m^2'}),
+            'cld_abs':(('var_bin_left', 'month','ncells'),cld_abs.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud absorption)', 'units': 'W/m^2'}),
+            'noncld':(('var_bin_left', 'month','ncells'),noncld.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (total)', 'units': 'W/m^2'}),
+            'noncld_scat':(('var_bin_left', 'month','ncells'),noncld_scat.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky scattering)', 'units': 'W/m^2'}),
+            'noncld_abs':(('var_bin_left', 'month','ncells'),noncld_abs.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky absorption)', 'units': 'W/m^2'}),
+            'sic':(('var_bin_left', 'month','ncells'),sic.data, {'long_name': 'TOA SW Anomalies due to Sea-Ice Cover Anomalies', 'units': 'W/m^2'}),
+            'bare':(('var_bin_left', 'month','ncells'),bare.data, {'long_name': 'TOA SW Anomalies due to Snow Thickness on Sea-Ice Anomalies', 'units': 'W/m^2'}),
+            'sw_toa':(('var_bin_left', 'month','ncells'),sw_toa.data, {'long_name': 'TOA net SW Anomalies', 'units': 'W/m^2'}),
+            'lw_toa':(('var_bin_left', 'month','ncells'),lw_toa.data, {'long_name': 'TOA net LW Anomalies', 'units': 'W/m^2'}),
+            'net_toa':(('var_bin_left', 'month','ncells'),net_toa.data, {'long_name': 'TOA net SW+LW Anomalies', 'units': 'W/m^2'})
+        },
+        coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH,'ncells': NCELLS, 'clon': CLON, 'clat': CLAT},
+        )
+    elif simtype=="ICONzm":
+        MONTH = DS_parms.month
+        LAT = DS_parms.lat
+        VAR_BIN_LEFT = DS_parms.var_bin_left[1:]
+        DS = xr.Dataset(
+        {
+            'sfc_alb':(('var_bin_left', 'month','lat'),sfc_alb.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (total)', 'units': 'W/m^2'}),
+            'sfc_alb_clr':(('var_bin_left', 'month','lat'),sfc_alb_clr.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (clear-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_oc':(('var_bin_left', 'month','lat'),sfc_alb_oc.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (overcast-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_nocld':(('var_bin_left', 'month','lat'),sfc_alb_nocld.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (no clouds)', 'units': 'W/m^2'}),
+            'cld':(('var_bin_left', 'month','lat'),cld.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (total)', 'units': 'W/m^2'}),
+            'cld_amt':(('var_bin_left', 'month','lat'),cld_amt.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (amount of clouds)', 'units': 'W/m^2'}),
+            'cld_scat':(('var_bin_left', 'month','lat'),cld_scat.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud scattering)', 'units': 'W/m^2'}),
+            'cld_abs':(('var_bin_left', 'month','lat'),cld_abs.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud absorption)', 'units': 'W/m^2'}),
+            'noncld':(('var_bin_left', 'month','lat'),noncld.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (total)', 'units': 'W/m^2'}),
+            'noncld_scat':(('var_bin_left', 'month','lat'),noncld_scat.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky scattering)', 'units': 'W/m^2'}),
+            'noncld_abs':(('var_bin_left', 'month','lat'),noncld_abs.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky absorption)', 'units': 'W/m^2'}),
+            'sic':(('var_bin_left', 'month','lat'),sic.data, {'long_name': 'TOA SW Anomalies due to Sea-Ice Cover Anomalies', 'units': 'W/m^2'}),
+            'bare':(('var_bin_left', 'month','lat'),bare.data, {'long_name': 'TOA SW Anomalies due to Snow Thickness on Sea-Ice Anomalies', 'units': 'W/m^2'}),
+            'sw_toa':(('var_bin_left', 'month','lat'),sw_toa.data, {'long_name': 'TOA net SW Anomalies', 'units': 'W/m^2'}),
+            'lw_toa':(('var_bin_left', 'month','lat'),lw_toa.data, {'long_name': 'TOA net LW Anomalies', 'units': 'W/m^2'}),
+            'net_toa':(('var_bin_left', 'month','lat'),net_toa.data, {'long_name': 'TOA net SW+LW Anomalies', 'units': 'W/m^2'})       
+            },
+        coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH,'lat': LAT},
+        )
+    elif simtype=="CESM":
+        MONTH = DS_parms.month
+        LON = DS_parms.lon
+        LAT = DS_parms.lat
+        VAR_BIN_LEFT = DS_parms.var_bin_left[1:]
+        DS = xr.Dataset(
+        {
+            'sfc_alb':(('var_bin_left', 'month','lat', 'lon'),sfc_alb.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (total)', 'units': 'W/m^2'}),
+            'sfc_alb_clr':(('var_bin_left', 'month','lat', 'lon'),sfc_alb_clr.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (clear-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_oc':(('var_bin_left', 'month','lat', 'lon'),sfc_alb_oc.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (overcast-sky)', 'units': 'W/m^2'}),
+            'sfc_alb_nocld':(('var_bin_left', 'month','lat', 'lon'),sfc_alb_nocld.data, {'long_name': 'TOA SW Anomalies due to Surface Albedo Anomalies (no clouds)', 'units': 'W/m^2'}),
+            'cld':(('var_bin_left', 'month','lat', 'lon'),cld.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (total)', 'units': 'W/m^2'}),
+            'cld_amt':(('var_bin_left', 'month','lat', 'lon'),cld_amt.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (amount of clouds)', 'units': 'W/m^2'}),
+            'cld_scat':(('var_bin_left', 'month','lat', 'lon'),cld_scat.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud scattering)', 'units': 'W/m^2'}),
+            'cld_abs':(('var_bin_left', 'month','lat', 'lon'),cld_abs.data, {'long_name': 'TOA SW Anomalies due to Cloud Anomalies (cloud absorption)', 'units': 'W/m^2'}),
+            'noncld':(('var_bin_left', 'month','lat', 'lon'),noncld.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (total)', 'units': 'W/m^2'}),
+            'noncld_scat':(('var_bin_left', 'month','lat', 'lon'),noncld_scat.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky scattering)', 'units': 'W/m^2'}),
+            'noncld_abs':(('var_bin_left', 'month','lat', 'lon'),noncld_abs.data, {'long_name': 'TOA SW Anomalies due to clear-sky Anomalies (clear-sky absorption)', 'units': 'W/m^2'}),
+            'sic':(('var_bin_left', 'month','lat', 'lon'),sic.data, {'long_name': 'TOA SW Anomalies due to Sea-Ice Cover Anomalies', 'units': 'W/m^2'}),
+            'bare':(('var_bin_left', 'month','lat', 'lon'),bare.data, {'long_name': 'TOA SW Anomalies due to Snow Thickness on Sea-Ice Anomalies', 'units': 'W/m^2'}),
+            'sw_toa':(('var_bin_left', 'month','lat', 'lon'),sw_toa.data, {'long_name': 'TOA net SW Anomalies', 'units': 'W/m^2'}),
+            'lw_toa':(('var_bin_left', 'month','lat', 'lon'),lw_toa.data, {'long_name': 'TOA net LW Anomalies', 'units': 'W/m^2'}),
+            'net_toa':(('var_bin_left', 'month','lat', 'lon'),net_toa.data, {'long_name': 'TOA net SW+LW Anomalies', 'units': 'W/m^2'})       
+        },
+        coords={'var_bin_left': VAR_BIN_LEFT, 'month': MONTH, "lon": LON, "lat": LAT},
+        )
+    #DS.lat.attrs["axis"] = "Y"
+    #DS.lon.attrs["axis"] = "X"
+    return DS
+
+
+
+###########################################################################
+def _albedo(c,a_clr,a_oc,mu_clr,mu_cld,ga_clr,ga_cld):
+    """
+    Calculate all-sky planetary albedo from cloud cover, clear-sky and overcast-sky albedos, transmittances and scatterings.
+    
+    Inputs:
+    c: cloud cover fraction
+    a_clr: clear-sky albedo
+    a_oc: overcast-sky albedo
+    mu_clr: clear-sky transmittance
+    mu_cld: cloud transmittance
+    ga_clr: clear-sky scattering parameter
+    ga_cld: cloud scattering parameter
+    
+    Output:
+    A: all-sky planetary albedo
+    """
+    mu_oc=mu_clr*mu_cld # Eq. 14
+    ga_oc=1-(1-ga_clr)*(1-ga_cld) # Eq. 13
+    A_clr=(mu_clr*ga_clr) + ((mu_clr*a_clr*(1-ga_clr)**2)/(1-(a_clr*ga_clr))) # Eq. 7
+    A_oc= (mu_oc*ga_oc)   + ((mu_oc*a_oc*(1-ga_oc)**2)/(1-(a_oc*ga_oc))) # Eq. 7
+    A=(1-c)*A_clr + c*A_oc # Eq. 15
+
+    return A 
+
+def _albedo_bottom_up(c,sic,f_bare,mu_clr,mu_cld,ga_clr,ga_cld,a_snow=0.79,a_bare=0.45,a_ocean=0.07):
+    """
+    Calculate all-sky planetary albedo from cloud cover, sea-ice cover, bare ice fraction, transmittances and scatterings.
+    This is similar to the albedo function, but uses a bottom-up approach to calculate the surface albedo from sea-ice cover and snow thickness on sea-ice.
+
+    Inputs:
+    c: cloud cover fraction
+    sic: fractional sea-ice cover
+    f_bare: fractional bare ice cover
+    mu_clr: clear-sky transmittance
+    mu_cld: cloud transmittance
+    ga_clr: clear-sky scattering parameter
+    ga_cld: cloud scattering parameter
+
+    Output:
+    A: all-sky planetary albedo
+    """
+    a_surf = (1-sic)*a_ocean + sic*(1-f_bare)*a_snow + sic*f_bare*a_bare
+    A = _albedo(c, a_surf, a_surf, mu_clr, mu_cld, ga_clr, ga_cld)
+    return A    
+
+def _albedo_bottom_up_alt(c,f_ice,f_snow,mu_clr,mu_cld,ga_clr,ga_cld,a_snow=0.79,a_bare=0.45,a_ocean=0.07):
+    """
+    Calculate all-sky planetary albedo from cloud cover, sea-ice cover, snow fraction, transmittances and scatterings using the alternative method.
+    Ocean, Ice and Snow are now 3 layers, each one covering the one below.
+    
+    Inputs:
+    c: cloud cover fraction
+    f_ice: fractional ice cover (sic)
+    f_snow: fractional snow cover
+    mu_clr: clear-sky transmittance
+    mu_cld: cloud transmittance
+    ga_clr: clear-sky scattering parameter
+    ga_cld: cloud scattering parameter
+
+    Output:
+    A: all-sky planetary albedo
+    """
+    a_surf1 = f_snow*a_snow + (f_ice-f_snow)*a_bare + (1-f_ice)*a_ocean
+    a_surf2 = f_snow*a_snow + (1-f_snow)*a_ocean
+
+    a_surf = xr.where(f_ice>f_snow, a_surf1, a_surf2)
+     
+    A = _albedo(c, a_surf, a_surf, mu_clr, mu_cld, ga_clr, ga_cld)
+    return A    
+
+def _parameters(SWupsfccs,SWdnsfccs,SWdn,SWupcs,SWupsfcoc,SWdnsfcoc,SWupoc):
+    """
+    Calculate the parameters (scattering ga, transmittance mu, and albedo a) for clear-sky and overcast conditions. Combine inputs from different states for the APRP method. 
+    
+    Inputs:
+    SWupsfccs: upward SW flux at sfc under clear-sky conditions
+    SWdnsfccs: downward SW flux at sfc under clear-sky conditions
+    SWdn: downward SW flux at TOA
+    SWupcs: upward SW flux at TOA under clear-sky conditions
+    SWupsfcoc: upward SW flux at sfc under overcast conditions
+    SWdnsfcoc: downward SW flux at sfc under overcast conditions
+    SWupoc: upward SW flux at TOA under overcast conditions
+
+    Outputs:
+    a_clr: surface albedo under clear-sky conditions
+    mu_clr: clear-sky transmittance
+    ga_clr: clear-sky scattering parameter
+    a_oc: surface albedo under overcast-sky conditions
+    mu_cld: cloud transmittance
+    ga_cld: cloud scattering parameter
+    """
+
+    # clear sky parameters
+    a_clr=SWupsfccs/SWdnsfccs # albedo
+    Q=SWdnsfccs/SWdn # ratio of incident sfc flux to TOA insolation
+    mu_clr=SWupcs/SWdn + Q*(1-a_clr) # Eq. 9
+    ga_clr=(mu_clr-Q)/(mu_clr-a_clr*Q) # Eq. 10
+
+    # overcast parameters
+    a_oc=SWupsfcoc/SWdnsfcoc # albedo
+    Q=SWdnsfcoc/SWdn # ratio of incident sfc flux to TOA insolation
+    mu_oc=SWupoc/SWdn + Q*(1-a_oc) # Eq. 9
+    ga_oc=(mu_oc-Q)/(mu_oc-a_oc*Q) # Eq. 10
+
+    # cloud parameters
+    mu_cld=mu_oc/mu_clr  # Eq. 14 sometimes this is greater than 1??
+    ga_cld=(ga_oc-1)/(1-ga_clr)+1  # Eq. 13
+
+    return (a_clr,mu_clr,ga_clr,a_oc,mu_cld,ga_cld) 
+
+def _parameters_bottom_up(sic, hs, hsmin=0.0341674):
+    """
+    Calculate the bottom up parameters for the surface albedo calculation.
+    Here, the snow effect is dependent on sea-ice cover, because if there is no ice, snow cover is irrelevant. 
+
+    Inputs:
+    sic: fractional sea-ice cover
+    hs: snow thickness on sea-ice (m)
+    hsmin: minimum snow thickness on sea-ice for ice to be considered snow-covered (m)
+
+    Outputs:
+    f_bare: fractional bare ice cover
+    """
+    f_bare = xr.where((sic==0) | (hs>hsmin*sic),0,1) # fraction of sea-ice that is bare, f_bare=0 if hs>hsmin*sic, f_bare=1 otherwise. 
+    #if sic=0, f_bare=0, which means that no sea-ice is considered snow covered.
+
+    return f_bare
+
+def _parameters_bottom_up_alt(sic, hs, hsmin=0.0341674):
+    """
+    Calculate the bottom up parameters for the surface albedo calculation using the alternative method. Snow fraction is now the fraction of the grid cell that is snow covered.
+    Here, it is the other way around, the sea-ice effect is dependent on snow cover, because if there is snow, change in sea-ice cover is irrelevant.
+
+    Inputs:
+    sic: fractional sea-ice cover
+    hs: snow thickness on sea-ice (m)
+    hsmin: minimum snow thickness on sea-ice for ice to be considered snow-covered (m)
+
+    Outputs:
+    f_snow: fractional snow cover
+    """
+    f_snow = xr.where((hs>hsmin*sic),sic,0) # fraction of GRID CELL that is snow covered, f_snow=sic if hs>hsmin*sic, f_snow=0 otherwise.
+    #if sic=0, f_snow=0, which means that no sea-ice is considered bare.
+
+    return f_snow
+
+def _APRP_get_parameters(DS, simtype):
+    """
+    Calculate the APRP parameters from the input dataset.
+    """
+    # Get stuff out of the dictionary
+    if simtype=="ICON" or simtype=="ICONzm":
+        clt = DS['clt']
+        rsdt = DS['rsdt']
+        rsut = DS['rsut']
+        rlut = DS['rlut']
+        rsutcs = DS['rsutcs']
+        rsds = DS['rsds']
+        rsus = DS['rsus']
+        rsdscs = DS['rsdscs']
+        rsuscs = DS['rsuscs']
+        sic = DS['sic']
+        hs = DS['hs_icecl'].squeeze()
+    elif simtype=="CESM": # change the CESM naming to ICON naming
+        clt = (DS['CLDTOT'])
+        rsdt = (DS['SOLIN'])
+        rsut = (DS['SOLIN'] - DS['FSNTOA'])
+        rlut = DS['FLNT']
+        rsutcs = (DS['SOLIN'] - DS['FSNTOAC'])
+        rsds = (DS['FSDS'])
+        rsus = (DS['FSDS'] - DS['FSNS'])
+        rsdscs = (DS['FSDSC'])
+        rsuscs = (DS['FSDSC'] - DS['FSNSC'])
+        sic = DS['ICEFRAC']
+        hs = DS['SNOWHICE']
+
+
+       
+
+    # Make sure the cld fractions are expressed as fraction and not percent
+    if np.max(clt)>1.:
+        clt=clt/100.
+
+    # Derive overcast conditions (Eq 3)
+    rsutoc=(1/clt)*(rsut-(1-clt)*rsutcs)
+    rsdsoc=(1/clt)*(rsds-(1-clt)*rsdscs)
+    rsusoc=(1/clt)*(rsus-(1-clt)*rsuscs)
+
+    # Mask these where values are unphysical 
+    rsdsoc=xr.where(rsdsoc > rsds,np.nan,rsdsoc)   
+    rsusoc=xr.where(rsusoc > rsus,np.nan,rsusoc)
+    rsutoc=xr.where(rsutoc < 0,np.nan,rsutoc)
+    rsdsoc=xr.where(rsdsoc < 0,np.nan,rsdsoc)
+    rsusoc=xr.where(rsusoc < 0,np.nan,rsusoc)
+
+    ## NOW THE FORMAL APRP CALCULATIONS:
+    a_clr,mu_clr,ga_clr,a_oc,mu_cld,ga_cld = \
+        _parameters(rsuscs,rsdscs,rsdt,rsutcs,rsusoc,rsdsoc,rsutoc) 
+
+    ## Bottom-up parameters
+    f_bare = _parameters_bottom_up(sic, hs)
+
+    # store in a dataset:
+    if simtype=="ICON":
+        TIME = DS.time
+        NCELLS = DS.ncells
+        CLON = DS.clon
+        CLAT = DS.clat
+        DS_out = xr.Dataset(
+        {
+            'clt':(('time','ncells'),clt.data, {'long_name': 'cloud cover fraction', 'units': ''}),
+            'a_clr':(('time','ncells'),a_clr.data, {'long_name': 'surface albedo under clear-sky conditions', 'units': ''}),
+            'a_oc':(('time','ncells'),a_oc.data, {'long_name': 'surface albedo under overcast conditions', 'units': ''}),
+            'mu_clr':(('time','ncells'),mu_clr.data, {'long_name': 'clear-sky transmittance', 'units': ''}),
+            'ga_clr':(('time','ncells'),ga_clr.data, {'long_name': 'clear-sky scattering parameter', 'units': ''}),
+            'mu_cld':(('time','ncells'),mu_cld.data, {'long_name': 'cloud transmittance', 'units': ''}),
+            'ga_cld':(('time','ncells'),ga_cld.data, {'long_name': 'cloud scattering parameter', 'units': ''}),
+            'f_bare':(('time','ncells'),f_bare.data, {'long_name': 'fraction of sea-ice which is bare', 'units': ''}),
+            'f_ice':(('time','ncells'),sic.data, {'long_name': 'fractional sea-ice cover (sic)', 'units': ''}),
+            'rsdt':(('time','ncells'),rsdt.data, {'long_name': 'TOA incoming solar radiation', 'units': 'W/m^2'}),
+            'rsut':(('time','ncells'),rsut.data, {'long_name': 'TOA outgoing shortwave radiation', 'units': 'W/m^2'}),
+            'rlut':(('time','ncells'),rlut.data, {'long_name': 'TOA outgoing longwave radiation', 'units': 'W/m^2'}),
+        },
+        coords={'time': TIME,'ncells': NCELLS, "clon": CLON, "clat": CLAT},
+        )
+    elif simtype=="ICONzm":
+        TIME = DS.time
+        LAT = DS.lat
+        DS_out = xr.Dataset(
+        {
+            'clt':(('time','lat'),clt.squeeze().data, {'long_name': 'cloud cover fraction', 'units': ''}),
+            'a_clr':(('time','lat'),a_clr.squeeze().data, {'long_name': 'surface albedo under clear-sky conditions', 'units': ''}),
+            'a_oc':(('time','lat'),a_oc.squeeze().data, {'long_name': 'surface albedo under overcast conditions', 'units': ''}),
+            'mu_clr':(('time','lat'),mu_clr.squeeze().data, {'long_name': 'clear-sky transmittance', 'units': ''}),
+            'ga_clr':(('time','lat'),ga_clr.squeeze().data, {'long_name': 'clear-sky scattering parameter', 'units': ''}),
+            'mu_cld':(('time','lat'),mu_cld.squeeze().data, {'long_name': 'cloud transmittance', 'units': ''}),
+            'ga_cld':(('time','lat'),ga_cld.squeeze().data, {'long_name': 'cloud scattering parameter', 'units': ''}),
+            'f_bare':(('time','lat'),f_bare.squeeze().data, {'long_name': 'fraction of sea-ice which is bare', 'units': ''}),
+            'f_ice':(('time','lat'),sic.squeeze().data, {'long_name': 'fractional sea-ice cover (sic)', 'units': ''}),
+            'rsdt':(('time','lat'),rsdt.squeeze().data, {'long_name': 'TOA incoming solar radiation', 'units': 'W/m^2'}),
+            'rsut':(('time','lat'),rsut.squeeze().data, {'long_name': 'TOA outgoing shortwave radiation', 'units': 'W/m^2'}),
+            'rlut':(('time','lat'),rlut.squeeze().data, {'long_name': 'TOA outgoing longwave radiation', 'units': 'W/m^2'}),
+        },
+        coords={'time': TIME,'lat': LAT},
+        )
+    elif simtype=="CESM":
+        TIME = DS.time
+        LON = DS.lon
+        LAT = DS.lat
+        DS_out = xr.Dataset(
+        {
+            'clt':(('time','lat', 'lon'),clt.data, {'long_name': 'cloud cover fraction', 'units': ''}),
+            'a_clr':(('time','lat', 'lon'),a_clr.data, {'long_name': 'surface albedo under clear-sky conditions', 'units': ''}),
+            'a_oc':(('time','lat', 'lon'),a_oc.data, {'long_name': 'surface albedo under overcast conditions', 'units': ''}),
+            'mu_clr':(('time','lat', 'lon'),mu_clr.data, {'long_name': 'clear-sky transmittance', 'units': ''}),
+            'ga_clr':(('time','lat', 'lon'),ga_clr.data, {'long_name': 'clear-sky scattering parameter', 'units': ''}),
+            'mu_cld':(('time','lat', 'lon'),mu_cld.data, {'long_name': 'cloud transmittance', 'units': ''}),
+            'ga_cld':(('time','lat', 'lon'),ga_cld.data, {'long_name': 'cloud scattering parameter', 'units': ''}),
+            'f_bare':(('time','lat', 'lon'),f_bare.data, {'long_name': 'fraction of sea-ice which is bare', 'units': ''}),
+            'f_ice':(('time','lat', 'lon'),sic.data, {'long_name': 'fractional sea-ice cover (sic)', 'units': ''}),
+            'rsdt':(('time','lat', 'lon'),rsdt.data, {'long_name': 'TOA incoming solar radiation', 'units': 'W/m^2'}),
+            'rsut':(('time','lat', 'lon'),rsut.data, {'long_name': 'TOA outgoing shortwave radiation', 'units': 'W/m^2'}),
+            'rlut':(('time','lat', 'lon'),rlut.data, {'long_name': 'TOA outgoing longwave radiation', 'units': 'W/m^2'}),
+        },
+        coords={'time': TIME, "lat": LAT, "lon": LON},
+        )
+
+    return DS_out
+
+def _calc_parameters(file, outpath, simtype):
+    """
+    Calculate the APRP parameters and save them to a new file.
+    """
+    DS_in = xr.open_dataset(file)
+    if simtype=="ICON" or simtype=="ICONzm":
+        DS = DS_in.where((DS_in['sic']<=1) & (DS_in['sic']>=0)) # mask out unphysical values
+    elif simtype=="CESM":
+        DS = DS_in.where((DS_in['ICEFRAC']<=1) & (DS_in['ICEFRAC']>=0))
+    DS_out = _APRP_get_parameters(DS, simtype)
+    filename = os.path.basename(file)
+    if simtype=="ICON" or simtype=="ICONzm":
+        filename = filename.replace("atm_2d_ml", "aprp_parms")
+    elif simtype=="CESM":
+        filename = filename.replace(".cam.h0", "_aprp_parms")
+    if os.path.exists(outpath + filename) == True:
+        print('Warning: File already exists, overwriting: ' + outpath + filename, flush=True)
+        DS_out.to_netcdf(outpath + filename, mode='w', format='NETCDF4')
+    else:
+        print('Saving file: ' + outpath  + filename)
+        DS_out.to_netcdf(outpath + filename, mode='w', format='NETCDF4')
diff --git a/runscripts/exp.ape_5000_55_0S.run b/runscripts/exp.ape_5000_55_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..f478090726b8a7d2a2def6350feb379ae72b1368
--- /dev/null
+++ b/runscripts/exp.ape_5000_55_0S.run
@@ -0,0 +1,668 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=mem_0384
+#SBATCH --job-name=ape_5000_55_0S
+#SBATCH --workdir=/gpfs/data/fs71767/jhoerner/experiments/ape_5000_55_0S
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5000_55_0S/logfiles/LOG.exp.ape_5000_55_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5000_55_0S/logfiles/LOG.exp.ape_5000_55_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=11:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=2
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_5000_55_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5000_55_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0450-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P5Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          10.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 5000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel/19.0.5.281 intel-mpi/2019.5-intel-19.0.5.281-77hdffd netcdf-fortran/4.4.5-intel-19.0.5.281-qye4cqn netcdf/4.7.0-intel-19.0.5.281-cgrpqof hdf5/1.10.5-intel-19.0.5.281-xmpf7vd intel-mkl/2019.6-intel-19.0.5.281-gfphznz libiconv/1.15-intel-19.0.5.281-a24zavx libxml2/2.9.9-intel-19.0.5.281-7lowqyy eccodes/2.13.0-gcc-9.1.0-fuvac77
+module list
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_5500_55_0S.run b/runscripts/exp.ape_5500_55_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..b6421e4c3b8168c3f5621081793b50c788cfbcf7
--- /dev/null
+++ b/runscripts/exp.ape_5500_55_0S.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=ape_5500_55_0S
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5500_55_0S/logfiles/LOG.exp.ape_5500_55_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5500_55_0S/logfiles/LOG.exp.ape_5500_55_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=2
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_5500_55_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_5500_55_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0900-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          12.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 5500e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_6000_13_0S_snowcap.run b/runscripts/exp.ape_6000_13_0S_snowcap.run
new file mode 100644
index 0000000000000000000000000000000000000000..2ffdf9a4da869f800c8073483101191172a95572
--- /dev/null
+++ b/runscripts/exp.ape_6000_13_0S_snowcap.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_6000_13_0S_snowcap
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_13_0S_snowcap/logfiles/LOG.exp.ape_6000_13_0S_snowcap.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_13_0S_snowcap/logfiles/LOG.exp.ape_6000_13_0S_snowcap.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_6000_13_0S_snowcap
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_13_0S_snowcap
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="1100-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          12.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 6000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_6000_22_0S.run b/runscripts/exp.ape_6000_22_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..7aa9ca64a3d028e0324da59e0fd35d8876f3d75e
--- /dev/null
+++ b/runscripts/exp.ape_6000_22_0S.run
@@ -0,0 +1,672 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_6000_22_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_22_0S/logfiles/LOG.exp.ape_6000_22_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_22_0S/logfiles/LOG.exp.ape_6000_22_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=11:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_6000_22_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_22_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0570-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          12.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 6000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_6000_90_0S.run b/runscripts/exp.ape_6000_90_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..d4f6f71c83d84cdfb90bd27d698ed3a66235e3f2
--- /dev/null
+++ b/runscripts/exp.ape_6000_90_0S.run
@@ -0,0 +1,632 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=mem_0096
+#SBATCH --job-name=ape_6000_90_0S
+#SBATCH --workdir=/gpfs/data/fs71767/jhoerner/experiments/ape_6000_90_0S
+#SBATCH --nodes=20
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S/logfiles/LOG.exp.ape_6000_90_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S/logfiles/LOG.exp.ape_6000_90_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=05:30:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM 
+
+# MPI variables
+# -------------
+no_of_nodes=20
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_6000_90_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0150-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P1Y"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          10.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = 0.015       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 6000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel/19.0.5.281 intel-mpi/2019.5-intel-19.0.5.281-77hdffd netcdf-fortran/4.4.5-intel-19.0.5.281-qye4cqn netcdf/4.7.0-intel-19.0.5.281-cgrpqof hdf5/1.10.5-intel-19.0.5.281-xmpf7vd intel-mkl/2019.6-intel-19.0.5.281-gfphznz libiconv/1.15-intel-19.0.5.281-a24zavx libxml2/2.9.9-intel-19.0.5.281-7lowqyy eccodes/2.13.0-gcc-9.1.0-fuvac77
+module list
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then
+  check_final_status 0 "${START} ${MODEL}"
+else
+  check_final_status -1 "${START} ${MODEL}"
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+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}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+else
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_6000_90_0S_dtime10.run b/runscripts/exp.ape_6000_90_0S_dtime10.run
new file mode 100644
index 0000000000000000000000000000000000000000..613dcd58fedddfb9a1d5fd322127837148da9a25
--- /dev/null
+++ b/runscripts/exp.ape_6000_90_0S_dtime10.run
@@ -0,0 +1,681 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_6000_90_0S_dtime10
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S_dtime10/logfiles/LOG.exp.ape_6000_90_0S_dtime10.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S_dtime10/logfiles/LOG.exp.ape_6000_90_0S_dtime10.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM 
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_6000_90_0S_dtime10
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_6000_90_0S_dtime10
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0250-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT10M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          0.1       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 6000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_7000_13_0S_snowcap.run b/runscripts/exp.ape_7000_13_0S_snowcap.run
new file mode 100644
index 0000000000000000000000000000000000000000..01ce808e1d77b1c0ef36aad8d2c238390f24dbd2
--- /dev/null
+++ b/runscripts/exp.ape_7000_13_0S_snowcap.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_7000_13_0S_snowcap
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_13_0S_snowcap/logfiles/LOG.exp.ape_7000_13_0S_snowcap.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_13_0S_snowcap/logfiles/LOG.exp.ape_7000_13_0S_snowcap.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_7000_13_0S_snowcap
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_13_0S_snowcap
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="1100-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          10.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 7000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_7000_22_0S.run b/runscripts/exp.ape_7000_22_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..393a3d0f54104976c91e328fda492185afcfc597
--- /dev/null
+++ b/runscripts/exp.ape_7000_22_0S.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_7000_22_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_22_0S/logfiles/LOG.exp.ape_7000_22_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_22_0S/logfiles/LOG.exp.ape_7000_22_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=11:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_7000_22_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_7000_22_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0500-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          10.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 7000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_8000_22_0S.run b/runscripts/exp.ape_8000_22_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..c3d573afef569434439211d52c7738cf74e057f1
--- /dev/null
+++ b/runscripts/exp.ape_8000_22_0S.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=ape_8000_22_0S
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_8000_22_0S/logfiles/LOG.exp.ape_8000_22_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_8000_22_0S/logfiles/LOG.exp.ape_8000_22_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=2
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=8
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_8000_22_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-esm-univie-run       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape/ape_8000_22_0S
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0600-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P10Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid 
+atmo_dyn_grids="iconR02B04-grid.nc"
+ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+
+#file with some precission definitions
+ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+
+# boundary conditions ozone
+ln -sf $INDIR/ozone/bc_ozone_ape.nc          ./bc_ozone.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+# land cover library
+ln -sf $basedir/externals/jsbach/data/lctlib_nlct21.def lctlib_nlct21.def
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+
+#-----------------------------------------------------------------------------
+# write ICON namelist parameters
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+
+
+# atmospheric dynamics and physics
+# ---------------------
+cat > $atmo_namelist << EOF
+!
+&parallel_nml
+ nproma                      = ${nproma}
+ num_io_procs                =                          2         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+ iorder_sendrecv             =                          1         ! sequence of MPI send/receive calls
+/
+
+&run_nml
+ ltestcase                   =                          .FALSE.   ! idealized testcase runs
+ num_lev                     =                         45         ! number of full levels (atm.) for each domain
+ modelTimeStep               =                          ${dtime}  ! apparently the same as dtime but as ISO8601 formatted string? 
+ ltransport                  =                          .TRUE.    ! main switch for large-scale tracer transport
+ iforcing                    =                          2         ! type of forcing (0:no forcing, 1:HS forcing, 2:ECHAM forcing, 3:NWP forcing 
+ msg_level                   =                          5         ! controls how much printout is written during runtime
+ output                      =                          "nml"     ! main switch for enabling/disabling components of the model output
+ activate_sync_timers        =                          .TRUE.    ! timer for monitoring communication routines
+ restart_filename            =           "${EXP}_restart_atm_<rsttime>.nc"
+/
+
+! grid_nml: horizontal grid --------------------------------------------------
+&grid_nml
+ dynamics_grid_filename      =                          ${atmo_dyn_grids}
+/
+
+! initcon_nml: initial conditions --------------------------------------------
+&initicon_nml
+ init_mode                   =                          2         ! 2: initialize from IFS analysis
+ ifs2icon_filename           =                          ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+
+! transport_nml: how to calculate which tracer
+&transport_nml
+ tracer_names                = 'hus','clw','cli'                  ! specific tracer names
+ ivadv_tracer                =    3 ,   3 ,   3                   ! method of vertical advection
+ itype_hlimit                =    3 ,   4 ,   4                   ! type of horizontal transport limiter
+ ihadv_tracer                =   52 ,   2 ,   2                   ! method of horzontal advection
+/
+
+! extpar_nml: external data --------------------------------------------------
+&extpar_nml
+ itopo                       =                          1         ! topography (0:analytical,1:ext. file)
+ itype_lwemiss               =                          0         ! 0: constant lw emmissivity
+/
+
+! io_nml: general switches for model I/O -------------------------------------
+&io_nml
+ output_nml_dict             = "${dict_file}"                     ! dictionary for model output variable names? default=''
+ netcdf_dict                 = "${dict_file}"
+/
+
+! sleve_nml: smooth level vertical coordinate (i.e terrain following) -------- 
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+
+! nonhydrostatic_nml: nonhydrostatic model -----------------------------------
+&nonhydrostatic_nml
+ ndyn_substeps               =                          5         ! number of dynamics steps per fast-physics step
+ vwind_offctr                =                          0.2       ! off-centering in vertical wind solver
+ damp_height                 =                      50000.        ! height at which Rayleigh damping of vertical wind starts
+ rayleigh_coeff              =                          10.       ! Rayleigh Coefficient
+ divdamp_fac                 =                          0.004     ! scaling factor of divergence damping
+ !htop_moist_proc             = 22500.                             ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+
+&interpol_nml                                                     ! for interpolation on lat lon grid
+ rbf_scale_mode_ll           =                          1         ! specifies how the RBF (Radial basis function interpolation) parameter is determined (1:lookuptable)
+/
+
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac} ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+
+! echam_phy_nml: ECHAM physics settings (iforcing=2) -------------------------
+&echam_phy_nml
+!
+! atmospheric phyiscs (""=never)
+ echam_phy_config(1)%dt_rad = "PT2H"                              ! radiation
+ echam_phy_config(1)%dt_vdf = $dtime                              ! vertical diffusion
+ echam_phy_config(1)%dt_cnv = $dtime                              ! cumulus convection
+ echam_phy_config(1)%dt_cld = $dtime                              ! cloud microphysics
+ echam_phy_config(1)%dt_gwd = $dtime                              ! atmospheric gravity wave drag
+ echam_phy_config(1)%dt_sso = $dtime                              ! sub grid scale orographic effects
+!
+! sea ice on mixed-layer ocean (""=never)
+ echam_phy_config(1)%dt_ice = $dtime
+!
+! atmospheric chemistry (""=never)
+ echam_phy_config(1)%dt_mox = ""                                  ! methane oxidation and water vapor photolysis
+ echam_phy_config(1)%dt_car = ""                                  ! Cariolle’s linearized ozone chemistry
+ echam_phy_config(1)%dt_art = ""                                  ! ICON-ART
+!
+ echam_phy_config(1)%ljsb   = ${ljsbach}                          ! JSBACH land surface model
+ echam_phy_config(1)%lamip  = .FALSE.                              ! AMIP boundary conditions
+ echam_phy_config(1)%lice   = .TRUE.                              ! Sea ice temperature calculations
+ echam_phy_config(1)%lmlo   = .TRUE.                             ! mixed layer ocean
+ echam_phy_config(1)%llake  = ${llake}                            ! lake model
+/
+
+! echam_rad_nml: ECHAM radiation settings (PSrad scheme) ----------------------
+/
+&echam_rad_nml
+ echam_rad_config(1)%isolrad          = 2 ! 2=preindustrial
+ echam_rad_config(1)%irad_h2o         = 1
+ echam_rad_config(1)%irad_co2         = 2
+ echam_rad_config(1)%irad_ch4         = 0
+ echam_rad_config(1)%irad_n2o         = 0
+ echam_rad_config(1)%irad_o3          = 4 !4=3D concentration, constant in time
+ echam_rad_config(1)%irad_o2          = 2
+ echam_rad_config(1)%irad_cfc11       = 0
+ echam_rad_config(1)%irad_cfc12       = 0
+ echam_rad_config(1)%irad_aero        = 0
+ echam_rad_config(1)%vmr_co2          = 8000e-6
+ echam_rad_config(1)%fsolrad          = 0.9442      ! instead of scale_ssi_preind
+ echam_rad_config(1)%l_orbvsop87      = .FALSE. ! FALSE = Kepler orbit
+ echam_rad_config(1)%cecc             = 0.0       ! eccentricity for Kepler orbit
+ echam_rad_config(1)%cobld            = 23.5    ! obliquity for Kepler orbit
+/
+&upatmo_nml
+/
+&echam_gwd_nml
+/
+&echam_sso_nml
+/
+&echam_vdf_nml
+/
+&echam_cnv_nml
+/
+&echam_cld_nml
+ echam_cld_config(:)% csecfrl  = 5e-5 ! [kgm^-3], default = 1.5e-5
+/
+&echam_cop_nml
+/
+&echam_cov_nml
+/
+&sea_ice_nml
+ i_ice_albedo = 3    ! 3=linear albedo interpolation for warm ice & snow
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+ albsm        = 0.66 ! warm snow on sea ice albedo
+ albs         = 0.79 ! cold snow on sea ice albedo
+ albim        = 0.38 ! warm sea-ice albedo
+ albi         = 0.45 ! warm sea-ice albedo
+/
+&echam_seaice_mlo_nml
+ lqflux               = .FALSE. ! default .TRUE.
+ hmin                 = 0.05    ! default 0.1
+ max_seaice_thickness = 99999.  ! default 5
+ qbot_mlo_nh          = 0.      ! default 10
+ qbot_mlo_sh          = 0.      ! default 10
+/
+EOF
+
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  use_lakes       = ${llake}
+  fract_filename  = "bc_land_frac.nc"
+  output_tiles    = ${output_tiles}     ! List of tiles to output
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.          ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.              ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'            ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_carbon_nml
+  active                 = ${lcarbon}
+  bc_filename            = 'bc_land_carbon.nc'
+  ic_filename            = 'ic_land_carbon.nc'
+  read_cpools            = .FALSE.
+  !fire_frac_wood_2_atmos = 0.2
+/
+&jsb_fuel_nml
+  active                 = ${lcarbon}
+  fuel_algorithm         = 1
+/
+&jsb_disturb_nml
+  active                  = .FALSE.
+  ic_filename             = 'ic_land_soil.nc'
+  bc_filename             = 'bc_land_phys.nc'
+  fire_algorithm          = 1
+  windbreak_algorithm     = 1
+  lburn_pasture           = .FALSE.
+/
+EOF
+
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+                    'ptp'     ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+cp -p $ICONFOLDER/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+START_debug="ddt --connect /opt/sw/vsc4/VSC/x86_64/glibc-2.17/skylake/intel/compilers_and_libraries_2019.5.281/linux/mpi/intel64/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#----------------------------------------------------------------------------
diff --git a/runscripts/exp.ape_ia_10000_13_0S.run b/runscripts/exp.ape_ia_10000_13_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..a9b8835f21236df6b568602c684c51ce5ee9c674
--- /dev/null
+++ b/runscripts/exp.ape_ia_10000_13_0S.run
@@ -0,0 +1,676 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_10000_13_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_10000_13_0S/logfiles/LOG.exp.ape_ia_10000_13_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_10000_13_0S/logfiles/LOG.exp.ape_ia_10000_13_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_10000_13_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0350-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 10000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_5000_13_0S.run b/runscripts/exp.ape_ia_5000_13_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..c29c9edab6f1fe0a0afdef8a5a01e8fca62eebbd
--- /dev/null
+++ b/runscripts/exp.ape_ia_5000_13_0S.run
@@ -0,0 +1,671 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_5000_13_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_5000_13_0S/logfiles/LOG.exp.ape_ia_5000_13_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_5000_13_0S/logfiles/LOG.exp.ape_ia_5000_13_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_5000_13_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0400-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 5000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_5500_90_0S.run b/runscripts/exp.ape_ia_5500_90_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..ce9d0e12ba05fac383b5a58dff8833f466231743
--- /dev/null
+++ b/runscripts/exp.ape_ia_5500_90_0S.run
@@ -0,0 +1,680 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_5500_90_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_5500_90_0S/logfiles/LOG.exp.ape_ia_5500_90_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_5500_90_0S/logfiles/LOG.exp.ape_ia_5500_90_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_5500_90_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0300-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 5500e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+# manually add to LD_LIBRARY_PATH as some modules don't do it automatically (temporary fix)
+#export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_6000_90_0S.run b/runscripts/exp.ape_ia_6000_90_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..0b758f771e69ac358798e3b4b66b21cda113fc12
--- /dev/null
+++ b/runscripts/exp.ape_ia_6000_90_0S.run
@@ -0,0 +1,671 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=ape_ia_6000_90_0S
+#SBATCH --nodes=2
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6000_90_0S/logfiles/LOG.exp.ape_ia_6000_90_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6000_90_0S/logfiles/LOG.exp.ape_ia_6000_90_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=15:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=2
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_6000_90_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0300-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P5Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 6000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_6000_90_0S_cltlim_dtime10.run b/runscripts/exp.ape_ia_6000_90_0S_cltlim_dtime10.run
new file mode 100644
index 0000000000000000000000000000000000000000..66adb89b179691f27ec9a4131c3ff3c794a5ffbd
--- /dev/null
+++ b/runscripts/exp.ape_ia_6000_90_0S_cltlim_dtime10.run
@@ -0,0 +1,680 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_6000_90_0S_cltlim_dtime10
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6000_90_0S_cltlim_dtime10/logfiles/LOG.exp.ape_ia_6000_90_0S_cltlim_dtime10.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6000_90_0S_cltlim_dtime10/logfiles/LOG.exp.ape_ia_6000_90_0S_cltlim_dtime10.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_6000_90_0S_cltlim_dtime10
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0220-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT10M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 0.1    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 6000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+# manually add to LD_LIBRARY_PATH as some modules don't do it automatically (temporary fix)
+#export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_6500_90_0S_cltlim_dtime10.run b/runscripts/exp.ape_ia_6500_90_0S_cltlim_dtime10.run
new file mode 100644
index 0000000000000000000000000000000000000000..ee58418eb816281886df0fe3572a2fd11bcc8dd7
--- /dev/null
+++ b/runscripts/exp.ape_ia_6500_90_0S_cltlim_dtime10.run
@@ -0,0 +1,679 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_6500_90_0S_cltlim_dtime10
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6500_90_0S_cltlim_dtime10/logfiles/LOG.exp.ape_ia_6500_90_0S_cltlim_dtime10.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_6500_90_0S_cltlim_dtime10/logfiles/LOG.exp.ape_ia_6500_90_0S_cltlim_dtime10.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_6500_90_0S_cltlim_dtime10
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0150-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT10M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 0.1    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 6500e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+
+## adjust modulepath (see https://wiki.vsc.ac.at/doku.php?id=doku:spack-transition)
+export MODULEPATH=/opt/sw/vsc4/VSC/Modules/TUWien:/opt/sw/vsc4/VSC/Modules/Intel/oneAPI:/opt/sw/vsc4/VSC/Modules/Parallel-Environment:/opt/sw/vsc4/VSC/Modules/Libraries:/opt/sw/vsc4/VSC/Modules/Compiler:/opt/sw/vsc4/VSC/Modules/Debugging-and-Profiling:/opt/sw/vsc4/VSC/Modules/Applications:/opt/sw/vsc4/VSC/Modules/p71545:/opt/sw/vsc4/VSC/Modules/p71782::/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-x86_64:/opt/sw/spack-0.19.0/var/spack/environments/skylake/modules/linux-almalinux8-skylake
+export LD_LIBRARY_PATH=:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+# manually add to LD_LIBRARY_PATH as some modules don't do it automatically (temporary fix)
+#export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib/64:/opt/sw/vsc4/VSC/x86_64/generic/arm/20.1_FORGE/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/eccodes-2.25.0-hu7dgod7gf74ga4g3nsmcmpuocs6exiw/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/expat-2.4.8-xmo5dgbu5wtzbbbkvlrv4swwwfug44le/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gdbm-1.19-jy4nm5ykjxpepom3ipbkze3zbyokjft3/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/gettext-0.21-pwxz72x7sx6qkqhbj4k3ubxxme26j3jc/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libbsd-0.11.5-cnqog4qv6nhpc5bvvsmcizf76cxr7jyd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libffi-3.4.2-r3phrdc7ytbzn3leleegmrcr76cpx2ky/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libmd-1.0.4-zaniib3sfp7klwc5bmeqkglijauckuhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libpng-1.6.37-nkdaweppa2jmfn4ppg5nek6f4xxmazhd/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/openjpeg-2.3.1-qidbr475aivuv3j54clna4yif5ppnux4/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-numpy-1.16.6-o5kmt5ebnburugxciqwn7vws6kpll273/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/py-setuptools-44.1.1-xz4fgahr6psfstqpmh6lpv4rzumxiywg/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/sqlite-3.39.2-xf4wugvtkqpwzp2pcn57qreu3jdrryqz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/readline-8.1.2-ufyd7l5fy7xsgxkgo324snjk2ltdflxz/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/ncurses-6.3-e42b4shzw4mv5dqaj72l5ji4l4qmmyym/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/bzip2-1.0.8-jv3bhggqmwgwhoyf4ptwwzyy37toaux6/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/util-linux-uuid-2.37.4-eic4im5vhjipymwciuywf63ifzwugjbi/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zstd-1.5.2-rqo23z7mor7xn22cd45agw5g2hbvtuvj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libxml2-2.10.1-3htfdmnkdmy5etoxzynnvx22vhfxr2mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/xz-5.2.5-7mgkxyje4sp5zdyq3z4dogzup4ulmrm5/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/libiconv-1.16-4rpaj4ypa7ib7s5z7tiqqmu7tfuai7gj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mkl-2022.1.0-cvhktedeellvvlgsjf3pap7vpx6f55z5/mkl/2022.1.0/lib/intel64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-tbb-2021.6.0-xb42jplakefi5zt667wrhottjpksgd7d/tbb/2021.6.0/env/../lib/intel64/gcc4.8:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-fortran-4.6.0-pnaropyoft7hicu7bfsugqa2aqcsggxj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/netcdf-c-4.8.1-hmrqrz22oi6fn4uorchs36xxm5ruffhr/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-skylake/intel-2021.5.0/hdf5-1.12.2-loke5pdbheud7c2wtvcefl6ao42ui7ia/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/zlib-1.2.12-pctnhmb36u364evebp7adp4qdmziy3mj/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/pkgconf-1.8.0-bkuyrr7xuzspbxljk66eussj4inp5oeh/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//libfabric/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib/release:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0//lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/x64:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/lib/oclfpga/host/linux64/lib:/gpfs/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/gcc-8.5.0/intel-oneapi-compilers-2022.1.0-kiyqwf7md4zpdhweoetajmd5hu2yme65/compiler/2022.1.0/linux/compiler/lib/intel64_lin::/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib:/opt/sw/slurm/x86_64/alma8.5/22-05-2-1/lib/slurm
+
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_8000_13_0S.run b/runscripts/exp.ape_ia_8000_13_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..75dffa6563599110df17f2a5a83c6f31db718103
--- /dev/null
+++ b/runscripts/exp.ape_ia_8000_13_0S.run
@@ -0,0 +1,671 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_8000_13_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_8000_13_0S/logfiles/LOG.exp.ape_ia_8000_13_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_8000_13_0S/logfiles/LOG.exp.ape_ia_8000_13_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_8000_13_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0400-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 8000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/runscripts/exp.ape_ia_9000_13_0S.run b/runscripts/exp.ape_ia_9000_13_0S.run
new file mode 100644
index 0000000000000000000000000000000000000000..ef5a9e2c9fb03830e036d0b31264ec3d24fa803f
--- /dev/null
+++ b/runscripts/exp.ape_ia_9000_13_0S.run
@@ -0,0 +1,671 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71767
+#SBATCH --partition=skylake_0096
+#SBATCH --job-name=ape_ia_9000_13_0S
+#SBATCH --nodes=5
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --output=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_9000_13_0S/logfiles/LOG.exp.ape_ia_9000_13_0S.run.%j.o
+#SBATCH --error=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/ape_ia_9000_13_0S/logfiles/LOG.exp.ape_ia_9000_13_0S.run.%j.o
+#SBATCH --exclusive
+#SBATCH --time=24:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+set -x # debugging command: enables a mode of the shell where all executed commands are printed to the terminal
+ulimit -s unlimited # unsets limits for RAM
+
+# MPI variables
+# -------------
+no_of_nodes=5
+mpi_procs_pernode=48
+((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
+#
+# blocking length
+# ---------------
+nproma=16
+
+
+
+#=============================================================================
+# Input variables:
+
+# SIMULATION NAME
+EXP=ape_ia_9000_13_0S
+
+ICONFOLDER=/home/fs71767/jhoerner/icon-a       # DIRECTORY OF ICON MODEL CODE
+RUNSCRIPTDIR=/home/fs71767/jhoerner/runscripts/snowball_ape_ia/${EXP}
+INDIR=/gpfs/data/fs71767/jhoerner/inputdata/aquaplanet    # directory with input data
+basedir=$ICONFOLDER # icon base directory
+
+. ${ICONFOLDER}/run/add_run_routines
+
+# experiment directory, with plenty of space, create if new
+EXPDIR=/gpfs/data/fs71767/jhoerner/experiments/${EXP}
+if [ ! -d ${EXPDIR} ] ;  then
+  mkdir -p ${EXPDIR}
+fi
+#
+ls -ld ${EXPDIR}
+if [ ! -d ${EXPDIR} ] ;  then
+    mkdir ${EXPDIR}
+fi
+ls -ld ${EXPDIR}
+
+cd $EXPDIR
+
+
+
+
+#=================================================================================
+# dictionary file for output variable names
+dict_file="dict.${EXP}"
+cat $ICONFOLDER/run/dictfiles/dict.iconam.mpim  > ${dict_file}
+
+# the namelist filename
+atmo_namelist=NAMELIST_${EXP}_atm
+lnd_namelist=NAMELIST_${EXP}_lnd
+
+
+#-----------------------------------------------------------------------------
+# global timing
+start_date="0001-01-01T00:00:00Z" # format of specified model time is YYYY-MM-DDTHH:MM:SSZ
+end_date="0400-01-01T00:00:00Z"
+
+
+# restart intervals
+restart_interval="P20Y"
+checkpoint_interval="P6M"
+
+# output intervals
+output_interval="P1M"
+file_interval="P1M"
+
+
+#-----------------------------------------------------------------------------
+# model timing
+dtime="PT6M" # 360 sec for R2B6, 120 sec for R3B7
+
+
+
+#================================================================================
+# Link the input files
+
+# range of years for yearly files
+# assume start_date and end_date have the format yyyy-...
+start_year=$(( ${start_date%%-*} - 1 ))
+end_year=$(( ${end_date%%-*} + 1 ))
+
+# grid
+atmo_dyn_grids="iconR02B04-grid.nc"
+#ln -sf $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids} #grid
+add_link_file $INDIR/grids/icon_grid_0005_R02B04_G.nc ${atmo_dyn_grids}
+
+#file with some precission definitions
+#ln -sf  $ICONFOLDER/data/lsdata.nc lsdata.nc
+
+# boundary conditions atmosphere
+#ln -sf $INDIR/sst_and_seaice/sic_R02B04_aqua.nc bc_sic.nc
+#ln -sf $INDIR/sst_and_seaice/sst_R02B04_aqua.nc bc_sst.nc
+add_link_file $INDIR/sst_and_seaice/sic_R02B04_aqua.nc ./bc_sic.nc
+add_link_file $INDIR/sst_and_seaice/sst_R02B04_aqua.nc ./bc_sst.nc
+
+# initial conditions atmosphere
+ifsfile="ifs2icon.nc"
+#ln -sf $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile} # initial conditions
+add_link_file $INDIR/ifs/ifs2icon_1979010100_R02B04_G_aqua.nc ${ifsfile}
+
+
+# boundary conditions ozone
+#ln -sf $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc          ./o3_icon_DOM01.nc
+add_link_file $INDIR/ozone/ape_o3_iconR2B04-ocean_aqua_planet.nc               ./o3_icon_DOM01.nc
+
+# aerosols
+# tropospheric anthropogenic aerosols, simple plumes
+# ln -sf $BASEDIR/data/MACv2.0-SP_v1.nc MACv2.0-SP_v1.nc
+# boundary conditions hitoric background aerosol (Kinne 1850)
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_lw_b16_coa.nc bc_aeropt_kinne_lw_b16_coa.nc
+# ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_coa.nc bc_aeropt_kinne_sw_b14_coa.nc
+
+#year=$start_year
+#while [[ $year -le $end_year ]]
+#do
+#  ln -sf $INDIR/aerosol/bc_aeropt_kinne_sw_b14_fin_2000.nc bc_aeropt_kinne_sw_b14_fin_${year}.nc
+#  (( year = year+1 ))
+#done
+
+# Cloud optical properties
+#ln -sf $ICONFOLDER/data/ECHAM6_CldOptProps.nc ECHAM6_CldOptProps.nc
+add_link_file $ICONFOLDER/data/rrtmg_lw.nc                              ./rrtmg_lw.nc
+add_link_file $ICONFOLDER/data/rrtmg_sw.nc                              ./rrtmg_sw.nc
+add_link_file $ICONFOLDER/data/ECHAM6_CldOptProps.nc                    ./ECHAM6_CldOptProps.nc
+
+# land
+# initial conditions land
+#ln -sf $INDIR/land/ic_land_soil_aqua.nc ic_land_soil.nc
+add_link_file $INDIR/land/ic_land_soil_aqua.nc ./ic_land_soil.nc
+
+
+# JSBACH settings --> land model (part of atmo model)
+run_jsbach=yes
+jsbach_usecase=jsbach_lite    # jsbach_lite or jsbach_pfts
+jsbach_with_lakes=yes
+jsbach_with_hd=no
+jsbach_with_carbon=no         # yes needs jsbach_pfts usecase
+jsbach_check_wbal=no          # check water balance
+# Some further processing for land configuration
+ljsbach=$([ "${run_jsbach:=no}" == yes ] && echo .TRUE. || echo .FALSE. )
+llake=$([ "${jsbach_with_lakes:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+lcarbon=$([ "${jsbach_with_carbon:=yes}" == yes ] && echo .TRUE. || echo .FALSE. )
+#
+# boundary conditions land 
+#ln -sf $INDIR/land/bc_land_sso_aqua.nc bc_land_sso.nc # subgrid scale orography
+#ln -sf $INDIR/land/bc_land_frac_aqua.nc bc_land_frac.nc
+#ln -sf $INDIR/land/bc_land_phys_aqua.nc bc_land_phys.nc
+#ln -sf $INDIR/land/bc_land_soil_aqua.nc bc_land_soil.nc
+add_link_file $INDIR/land/bc_land_sso_aqua.nc ./bc_land_sso.nc
+add_link_file $INDIR/land/bc_land_frac_aqua.nc ./bc_land_frac.nc
+add_link_file $INDIR/land/bc_land_phys_aqua.nc ./bc_land_phys.nc
+add_link_file $INDIR/land/bc_land_soil_aqua.nc ./bc_land_soil.nc
+
+# - lctlib file for JSBACH
+add_link_file ${ICONFOLDER}/externals/jsbach/data/lctlib_nlct21.def        ./lctlib_nlct21.def
+
+# print_required_files
+copy_required_files
+link_required_files
+
+
+
+# initialize diffusion parameter for automatic restart from crashes
+if [ -r hdiff_smag_fac_offset ]; then
+  diff_fac_offset=`awk '{ print }' hdiff_smag_fac_offset` #read the offset from file
+else
+  diff_fac_offset=0.0
+fi
+
+init_diff_fac=$(echo $diff_fac_offset + 0.015 | bc)
+
+
+# model parameters
+model_equations=3
+  # equation system
+#                     1=hydrost. atm. T
+#                     1=hydrost. atm. theta dp
+#                     3=non-hydrost. atm.,
+#                     0=shallow water model
+#                    -1=hydrost. ocean
+
+# calendar type:   'proleptic gregorian'->type 1 (default), '365 day year'->type 2, '360 day year' -> type 3
+calendar='360 day year'
+calendar_type=2 # Namelist overview seems to be wrong. 2 is 360 day year.
+		# In shared/mo_impl_constants.f90
+		#!------------------------!
+		#!  CALENDAR TYPES        !
+  		#!------------------------!
+
+  		#INTEGER,  PARAMETER :: julian_gregorian    = 0 !< historic Julian / Gregorian
+  		#INTEGER,  PARAMETER :: proleptic_gregorian = 1 !< proleptic Gregorian
+  		#INTEGER,  PARAMETER :: cly360              = 2 !< constant 30 dy/mo and 360 dy/yr
+
+
+#-----------------------------------------------------------------------------
+# automatic restart setup
+# set some default values and derive some run parameteres
+restart=${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
+
+
+#==============================================================================
+# create ICON master namelist
+# ------------------------
+
+# For a complete list see Namelist_overview and Namelist_overview.pdf
+cat > icon_master.namelist << EOF
+&master_nml
+ lrestart            = ${restart}
+/
+&master_model_nml
+  model_name="atmo"
+  model_type=1	!model types:
+		! 1 = atmsphere
+		! 2 = ocean
+		! 3 = radiation
+  		! 99 = dummy
+  model_namelist_filename="${atmo_namelist}"
+  model_type=1
+  model_min_rank=0
+  model_max_rank=65535
+  model_inc_rank=1
+/
+&jsb_model_nml
+ model_id = 1
+ model_name = "JSBACH"
+ model_shortname = "jsb"
+ model_description = 'JSBACH land surface model'
+ model_namelist_filename = "${lnd_namelist}"
+
+/
+&master_time_control_nml
+ calendar             = "$calendar"
+ restartTimeIntval    = "$restart_interval"
+ checkpointTimeIntval = "$checkpoint_interval"
+ experimentStartDate  = $start_date
+ experimentStopDate   = $end_date
+/
+&time_nml
+ calendar = $calendar_type
+ is_relative_time = .FALSE.
+/
+EOF
+
+#--------------------------------------------------------------------------------------------------
+
+# (3) Define the model configuration
+
+# atmospheric dynamics and physics
+# --------------------------------
+cat > ${atmo_namelist} << EOF
+!
+&parallel_nml
+ nproma           = ${nproma}
+ num_io_procs                =                          0         ! number of I/O processors
+ num_restart_procs           =                          0         ! number of restart processors
+/
+&grid_nml
+ dynamics_grid_filename      =  ${atmo_dyn_grids}
+/
+&run_nml
+ num_lev          = 45          ! number of full levels
+ modelTimeStep    = ${dtime}
+ ltestcase        = .FALSE.     ! run testcase
+ ldynamics        = .TRUE.      ! dynamics
+ ltransport       = .TRUE.      ! transport
+ ntracer          = 3           ! number of tracers; 3: hus, clw, cli; 4: hus, clw, cli, o3
+ iforcing         = 2           ! 0: none, 1: HS, 2: ECHAM, 3: NWP
+ output           = 'nml'
+ msg_level        = 5           ! level of details report during integration 
+ restart_filename = "${EXP}_restart_atm_<rsttime>.nc"
+ activate_sync_timers = .TRUE.
+/
+&extpar_nml
+ itopo            = 1           ! 1: read topography from the grid file
+ l_emiss          = .FALSE.
+/
+&initicon_nml
+ init_mode        = 2           ! 2: initialize from IFS analysis
+ ifs2icon_filename= ${ifsfile}! name of file with IFS initial conditions (link file into working directory!)
+/
+&io_nml
+ output_nml_dict  = "${dict_file}"
+ netcdf_dict      = "${dict_file}"
+/
+&nonhydrostatic_nml
+ ndyn_substeps    = 5           ! dtime/dt_dyn
+ damp_height      = 50000.      ! [m]
+ rayleigh_coeff   = 10.    	! default 0.1
+ vwind_offctr     = 0.2
+ divdamp_fac      = 0.004
+ !htop_moist_proc  = 75000.      ! [m] Height above which moist physics and advection of cloud and precipitation variables are turned off; def-val = 22500.0
+/
+&interpol_nml
+ rbf_scale_mode_ll = 1
+/
+&sleve_nml
+ min_lay_thckn    = 40.         ! [m]
+ top_height       = 72226.      ! [m]
+ stretch_fac      = 0.949
+ decay_scale_1    = 4000.       ! [m]
+ decay_scale_2    = 2500.       ! [m]
+ decay_exp        = 1.2
+ flat_height      = 16000.      ! [m]
+/
+&diffusion_nml
+ hdiff_smag_fac   = ${init_diff_fac}       ! scaling factor for smagorinsky diffusion; def-val = 0.015
+ hdiff_efdt_ratio = 36.0        ! ratio of e-folding time to time step (or 2* time step when using a 3 time level time stepping scheme) (for triangular NH model, values above 30 are recommended when using hdiff_order=5)
+ hdiff_w_efdt_ratio = 15.0      ! ratio of e-folding time to time step for diffusion on vertical wind speed
+ hdiff_min_efdt_ratio = 1.0     ! minimum value of hdiff_efdt_ratio (for upper sponge layer); def-val = 1.0
+ hdiff_tv_ratio = 1.0           ! Ratio of diffusion coefficients for temperature and normal wind: T : vn
+/
+&transport_nml
+!                   hus,clw,cli
+ ivadv_tracer     =   3,  3,  3
+ itype_hlimit     =   3,  4,  4
+ ihadv_tracer     =  52,  2,  2
+/
+&mpi_phy_nml
+!
+! domain 1
+! --------
+!
+! atmospheric phyiscs (""=never)
+ mpi_phy_config(1)%dt_rad = "PT2H"
+ mpi_phy_config(1)%dt_vdf = $dtime
+ mpi_phy_config(1)%dt_cnv = $dtime
+ mpi_phy_config(1)%dt_cld = $dtime
+ mpi_phy_config(1)%dt_gwd = $dtime
+ mpi_phy_config(1)%dt_sso = $dtime
+
+! atmospheric chemistry (""=never)
+ mpi_phy_config(1)%dt_mox = ""
+ mpi_phy_config(1)%dt_car = ""
+ mpi_phy_config(1)%dt_art = ""
+!
+! seaice on mixed-layer ocean
+ mpi_phy_config(1)%dt_ice = $dtime
+!
+! surface (.TRUE. or .FALSE.)
+ mpi_phy_config(1)%ljsb  = ${ljsbach}
+ mpi_phy_config(1)%lamip = .FALSE.
+ mpi_phy_config(1)%lice  = .TRUE.
+ mpi_phy_config(1)%lmlo  = .TRUE.
+ mpi_phy_config(1)%llake  = ${llake}
+!
+/
+&mpi_sso_nml
+/
+&radiation_nml
+ irad_h2o         = 1           ! 1: prognostic vapor, liquid and ice
+ irad_co2         = 2           ! 4: from greenhouse gas scenario
+ vmr_co2          = 9000e-6
+ irad_ch4         = 0           ! 4: from greenhouse gas scenario
+ irad_n2o         = 0           ! 4: from greenhouse gas scenario
+ irad_o3          = 4           ! 1: prognostic ozone; 8: prescribed transient monthly mean ozone
+ irad_o2          = 2           ! 2: horizontally and vertically constant
+ irad_cfc11       = 0           ! 4: from greenhouse gas scenario
+ irad_cfc12       = 0           ! 4: from greenhouse gas scenario
+ irad_aero        = 0          ! 0: no aerosol
+                                !13: only Kinnes tropospheric aerosols
+                                !14: only Stenchikovs volcanic aerosols
+                                !15: Kinne aerosol optics for troposphere
+                                !   +Stenchikov aerosol optics for stratosphere
+                                !18: Kinne background aerosols of 1865 (natural
+                                !    background + Stenchikovs volc. aerosols
+                                !    + simple plumes (anthropogenic)
+ ighg             = 0           ! 1: transient well mixed greenhouse gas concentrations
+ isolrad          = 2           ! 1: transient solar irradiance (at 1 AE)
+ scale_ssi_preind = 0.9442      ! requires isolrad = 2; scales ssi by specified value; default = 1
+ albsnow_warm     = 0.66
+ albsnow_cold     = 0.79
+ albice_warm      = 0.38
+ albice_cold      = 0.45
+/
+&psrad_nml
+ rad_perm         = 1           ! Integer for perturbing random number seeds
+/
+&psrad_orbit_nml
+ cecc = 0
+ cobld = 23.5
+ l_orbvsop87 = .FALSE.
+/
+&echam_conv_nml
+/
+&echam_cloud_nml
+ csecfrl = 5e-5 ! [kgm^-3], default = 5e-6
+/
+&gw_hines_nml
+/
+&sea_ice_nml
+ use_no_flux_gradients = .FALSE.
+ i_ice_therm  = 1    ! 1=0L-Semtner; 2=3L-Winton
+/
+&echam_seaice_mlo_nml
+ max_seaice_thickness = 9999.0 ! [m]
+ hmin = 0.05                ! [m]
+/
+EOF
+
+# land surface and soil
+# ---------------------
+cat > ${lnd_namelist} << EOF
+&jsb_model_nml
+  usecase         = "${jsbach_usecase}"
+  fract_filename  = "bc_land_frac.nc"
+  l_compat401     = .TRUE.              ! TRUE: overwrites some of the settings below
+/
+&jsb_seb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_rad_nml
+  use_alb_veg_simple = .TRUE.           ! Use TRUE for jsbach_lite, FALSE for jsbach_pfts
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_turb_nml
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_sse_nml
+  l_heat_cap_map  = .FALSE.
+  l_heat_cond_map = .FALSE.
+  l_heat_cap_dyn  = .TRUE.
+  l_heat_cond_dyn = .TRUE.
+  l_snow          = .TRUE.
+  l_dynsnow       = .TRUE.
+  l_freeze        = .FALSE.
+  l_supercool     = .FALSE.
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+&jsb_hydro_nml
+  bc_filename     = 'bc_land_soil.nc'
+  ic_filename     = 'ic_land_soil.nc'
+  bc_sso_filename = 'bc_land_sso.nc'
+/
+&jsb_assimi_nml
+  active          = .FALSE.             ! Use FALSE for jsbach_lite, TRUE for jsbach_pfts
+/
+&jsb_pheno_nml
+  scheme          = 'climatology'       ! scheme = logrop / climatology; use climatology for jsbach_lite
+  bc_filename     = 'bc_land_phys.nc'
+  ic_filename     = 'ic_land_soil.nc'
+/
+EOF
+if [[ ${jsbach_with_hd} = yes ]]; then
+  cat >> ${lnd_namelist} << EOF
+&jsb_hd_nml
+  active               = .TRUE.
+  routing_scheme       = 'full'
+  bc_filename          = 'bc_land_hd.nc'
+  diag_water_budget    = .TRUE.
+  debug_hd             = .FALSE.
+  enforce_water_budget = .TRUE.         ! True: stop in case of water conservation problem
+/
+EOF
+fi
+
+
+output_atm_2d=yes
+#
+if [[ "$output_atm_2d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_2d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ mode             = 1        ! 1=forecast mode, relative time axis
+ taxis_tunit      = 9        ! 9=number of days since start
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'ps'      , 'psl'     ,
+                    'rsdt'    ,
+                    'rsut'    , 'rsutcs'  , 'rlut'    , 'rlutcs'  ,
+                    'rsds'    , 'rsdscs'  , 'rlds'    , 'rldscs'  ,
+                    'rsus'    , 'rsuscs'  , 'rlus'    ,
+                    'ts'      ,
+                    'sic'     , 'sit'     , 'sic_icecl', 'qbot_icecl', 'qtop_icecl', 'ts_icecl', 't1_icecl', 't2_icecl', 'hs_icecl', 'fluxres_w_icecl', 'fluxres_i_icecl',
+                    'albedo'  ,
+                    'clt'     ,
+                    'prlr'    , 'prls'    , 'prcr'    , 'prcs'    ,
+                    'pr'      , 'prw'     , 'cllvi'   , 'clivi'   ,
+                    'hfls'    , 'hfss'    , 'evspsbl' ,
+                    'tauu'    , 'tauv'    ,
+                    'tauu_sso', 'tauv_sso', 'diss_sso',
+                    'sfcwind' , 'uas'     , 'vas'     ,
+                    'tas'     , 'dew2'    ,
+
+/
+EOF
+fi
+
+
+
+output_atm_3d=yes
+#
+if [[ "$output_atm_3d" == "yes" ]]; then
+  #
+  cat >> ${atmo_namelist} << EOF
+&output_nml
+ output_filename  = "${EXP}_atm_3d"
+ filename_format  = "<output_filename>_<levtype_l>_<datetime2>"
+ filetype         = 5
+ taxis_tunit       = 9
+ mode             = 1
+ remap            = 0
+ operation        = 'mean'
+ output_grid      = .TRUE.
+ output_start     = "${start_date}"
+ output_end       = "${end_date}"
+ output_interval  = "${output_interval}"
+ file_interval    = "${file_interval}"
+ include_last     = .FALSE.
+ ml_varlist       = 'pfull'   , 'zg'      , 'rho' ,
+                    'clw'     , 'cli'     ,
+                    'hus'     , 'cl'      ,
+                    'ta'      ,
+                    'ua'      , 'va'      , 'wap'     ,
+/
+EOF
+fi
+
+
+
+
+
+## setup for status check & restart
+final_status_file=${EXPDIR}/${EXP}.final_status
+
+## Copy icon executable to working directory
+#cp -p $ICONFOLDER/bin/icon ./icon.exe
+cp -p $ICONFOLDER/build/x86_64-unknown-linux-gnu/bin/icon ./icon.exe
+##
+
+## Start model
+date
+ulimit -s unlimited
+
+source /usr/share/Modules/init/ksh
+module purge
+module load intel-oneapi-compilers/2022.1.0-gcc-8.5.0-kiyqwf7
+module load intel-oneapi-mpi/2021.6.0-intel-2021.5.0-wpt4y32
+module load pkgconf/1.8.0-intel-2021.5.0-bkuyrr7
+module load zlib/1.2.12-intel-2021.5.0-pctnhmb
+module load hdf5/1.12.2-intel-2021.5.0-loke5pd
+module load netcdf-c/4.8.1-intel-2021.5.0-hmrqrz2
+module load netcdf-fortran/4.6.0-intel-2021.5.0-pnaropy
+module load intel-oneapi-mkl/2022.1.0-intel-2021.5.0-cvhkted --auto
+module load libiconv/1.16-intel-2021.5.0-4rpaj4y
+module load libxml2/2.10.1-intel-2021.5.0-3htfdmn --auto
+module load eccodes/2.25.0-intel-2021.5.0-hu7dgod --auto
+
+
+echo "loading arm"
+module load arm/20.1_FORGE
+
+ldd icon.exe
+
+START="/opt/sw/spack-0.19.0/opt/spack/linux-almalinux8-x86_64/intel-2021.5.0/intel-oneapi-mpi-2021.6.0-wpt4y32prmkcjdsos57rj6chrpa2yt2g/mpi/2021.6.0/bin/mpiexec -n $mpi_total_procs"
+MODEL=${EXPDIR}/icon.exe
+
+rm -f finish.status
+
+${START} ${MODEL}
+
+if [ -r finish.status ] ; then  # if finish.status exists, continue
+  echo "finish.status exists, continue"
+else # if it not exists, abort (or change diffusion parameter!)
+  echo "CRASH" > finish.status
+fi
+
+#-----------------------------------------------------------------------------
+finish_status=`cat finish.status`
+echo $finish_status
+
+#-----------------------------------------------------------------------------
+namelist_list=""
+#-----------------------------------------------------------------------------
+# check if we have to restart, ie resubmit
+#   Note: this is a different mechanism from checking the restart
+if [ $finish_status = "RESTART" ]; then # restart simulation
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+elif  [ $finish_status = "CRASH" ]; then # restart simulation after crash
+  echo "model crashed, changing diffusion parameter..."
+  echo "old diffusion parameter: ${init_diff_fac}" 
+  if (( $(echo "$init_diff_fac == 0.015" |bc ) )); then
+    echo 0.000001 > hdiff_smag_fac_offset
+    echo "new diffusion parameter: 0.015001"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015001"
+  else
+    if [ -r hdiff_smag_fac_offset ]; then
+      rm -f hdiff_smag_fac_offset
+    fi
+    echo "new diffusion parameter: 0.015"
+    log="- $(date '+%d-%m-%Y %H:%M:%S') crash, autorestart with hdiff_smag_fac=0.015"
+  fi
+
+
+  echo "restart next experiment..."
+  this_script="${RUNSCRIPTDIR}/exp.${EXP}.run"
+  echo 'this_script: ' "$this_script"
+  # note that if ${restartSemaphoreFilename} does not exist yet, then touch will create it
+  touch ${restartSemaphoreFilename}
+  cd ${RUNSCRIPTDIR}
+  sbatch exp.${EXP}.run
+  echo -e ${log} >> README.md
+  # abort the script, so SLURM will sent a mail
+  exit 100
+elif  [ $finish_status = "OK" ]; then # end simulation
+  [[ -f ${restartSemaphoreFilename} ]] && rm ${restartSemaphoreFilename}
+fi
+
+#-----------------------------------------------------------------------------
+
+cd ${RUNSCRIPTDIR}
+
+#-----------------------------------------------------------------------------
+#
+
+echo "============================"
+echo "Script run successfully: ${finish_status}"
+echo "============================"
+#-----------------------------------------------------------------------------
+
diff --git a/shellscripts/ICON-A_pp_2d.sh b/shellscripts/ICON-A_pp_2d.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0cd9cad133a3a247d78891dcf3c82e442229d29d
--- /dev/null
+++ b/shellscripts/ICON-A_pp_2d.sh
@@ -0,0 +1,78 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=ICON_pp_2d
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --time=00:30:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+
+# Script for initial post processing routines for ICON simulations on VSC
+# it will create monthyl, global & zonal means of 2D data 
+# additionally, the fractional snow cover for each grid point will be calculated from hs_icecl
+
+logfile="${PWD}/logfiles/LOG.pp2d.${1}";
+exec 2>&1 | tee -a $logfile;
+
+echo "$(date '+%Y-%m-%d %H:%M:%S') postprocessing 2d data for $1" | tee -a $logfile;
+
+if [ -z "$1" ]
+  then
+    echo "no experiment given!" | tee -a $logfile;
+    exit | tee -a $logfile;
+fi
+
+experiment=$1;
+
+# input & output directory
+datapath="/gpfs/data/fs71767/jhoerner/experiments/$experiment";
+outpath="/gpfs/data/fs71767/jhoerner/postprocessing/$experiment";
+
+# create output directory
+if [ ! -d "$outpath" ] 
+then
+    echo "creating directory ${outpath}" | tee -a $logfile;
+    mkdir $outpath | tee -a $logfile;
+fi
+
+
+# add trailing slash if needed
+length=${#datapath};
+last_char=${datapath:length-1:1};
+
+[[ $last_char != "/" ]] && datapath="$datapath/";
+
+echo "input path is $datapath" | tee -a $logfile;
+echo "output path is $outpath/" | tee -a $logfile;
+
+
+mergedfile_temp="${datapath}${experiment}_atm_2d_ml_merged_temp.nc";
+mergedfile="${datapath}${experiment}_atm_2d_ml_merged.nc";
+gmfile="${outpath}/${experiment}_atm_2d_ml.mm.gm.nc";
+gmymfile="${outpath}/${experiment}_atm_2d_ml.ym.gm.nc";
+zmfile="${outpath}/${experiment}_atm_2d_ml.mm.zm.nc";
+zmymfile="${outpath}/${experiment}_atm_2d_ml.ym.zm.nc";
+
+echo "merging files..." | tee -a $logfile;
+cdo -O mergetime "${datapath}${experiment}_atm_2d_ml_0*Z.nc" $mergedfile_temp | tee -a $logfile;
+
+# calculate snowfrac
+# the albedo scheme sees ice as snow covered if hs*rho_ref/rhos>0.01m <-> hs>rhos/rho_ref * 0.01m = 0.0341674m (see mo_ice_parameterizations.f90)
+echo "calculating fractional snowcover" | tee -a $logfile;
+cdo -O aexpr,'snowfrac=hs_icecl>0.0341674' $mergedfile_temp $mergedfile | tee -a $logfile;
+
+echo "creating global & monthly & yearly mean..." | tee -a $logfile;
+cdo -O monmean -fldmean  $mergedfile $gmfile | tee -a $logfile;
+cdo -O yearmean  $gmfile $gmymfile| tee -a $logfile;
+
+echo "creating zonal & monthly & yearly mean..." | tee -a $logfile;
+cdo -O zonmean -remapcon,r192x96 -monmean $mergedfile $zmfile | tee -a $logfile;
+cdo -O yearmean  $zmfile $zmymfile| tee -a $logfile;
+
+echo "deleting temporary files" | tee -a $logfile;
+rm $mergedfile | tee -a $logfile;
+rm $mergedfile_temp | tee -a $logfile;
+
+echo "done" | tee -a $logfile;
diff --git a/shellscripts/ICON-ESM_pp_2d.sh b/shellscripts/ICON-ESM_pp_2d.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4f9a825668ac815127509dacd84cf156a4ef38a3
--- /dev/null
+++ b/shellscripts/ICON-ESM_pp_2d.sh
@@ -0,0 +1,79 @@
+#! /bin/ksh
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=ICON_pp_2d
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --time=00:30:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+
+# Script for initial post processing routines for ICON simulations on VSC
+# it will create monthyl, global & zonal means of 2D data 
+# additionally, the fractional snow cover for each grid point will be calculated from hs_icecl
+
+logfile="${PWD}/logfiles/LOG.pp2d.${1}";
+exec 2>&1 | tee -a $logfile;
+
+echo "$(date '+%Y-%m-%d %H:%M:%S') postprocessing 2d data for $1" | tee -a $logfile;
+
+if [ -z "$1" ]
+  then
+    echo "no experiment given!" | tee -a $logfile;
+    exit | tee -a $logfile;
+fi
+
+
+experiment=$1;
+
+# input & output directory
+datapath="/gpfs/data/fs71767/jhoerner/experiments/ape_icon-esm/$experiment";
+outpath="/gpfs/data/fs71767/jhoerner/postprocessing/$experiment";
+
+# create output directory
+if [ ! -d "$outpath" ] 
+then
+    echo "creating directory ${outpath}" | tee -a $logfile;
+    mkdir $outpath | tee -a $logfile;
+fi
+
+
+# add trailing slash if needed
+length=${#datapath};
+last_char=${datapath:length-1:1};
+
+[[ $last_char != "/" ]] && datapath="$datapath/";
+
+echo "input path is $datapath" | tee -a $logfile;
+echo "output path is $outpath/" | tee -a $logfile;
+
+
+mergedfile_temp="${datapath}${experiment}_atm_2d_ml_merged_temp.nc";
+mergedfile="${datapath}${experiment}_atm_2d_ml_merged.nc";
+gmfile="${outpath}/${experiment}_atm_2d_ml.mm.gm.nc";
+gmymfile="${outpath}/${experiment}_atm_2d_ml.ym.gm.nc";
+zmfile="${outpath}/${experiment}_atm_2d_ml.mm.zm.nc";
+zmymfile="${outpath}/${experiment}_atm_2d_ml.ym.zm.nc";
+
+echo "merging files..." | tee -a $logfile;
+cdo -O mergetime "${datapath}${experiment}_atm_2d_ml_*Z.nc" $mergedfile_temp | tee -a $logfile;
+
+# calculate snowfrac
+# the albedo scheme sees ice as snow covered if hs*rho_ref/rhos>0.01m <-> hs>rhos/rho_ref * 0.01m = 0.0341674m (see mo_ice_parameterizations.f90)
+echo "calculating fractional snowcover" | tee -a $logfile;
+cdo -O aexpr,'snowfrac=hs_icecl>0.0341674' $mergedfile_temp $mergedfile | tee -a $logfile;
+
+echo "creating global & monthly & yearly mean..." | tee -a $logfile;
+cdo -O monmean -fldmean  $mergedfile $gmfile | tee -a $logfile;
+cdo -O yearmean  $gmfile $gmymfile| tee -a $logfile;
+
+echo "creating zonal & monthly & yearly mean..." | tee -a $logfile;
+cdo -O zonmean -remapcon,r192x96 -monmean $mergedfile $zmfile | tee -a $logfile;
+cdo -O yearmean  $zmfile $zmymfile| tee -a $logfile;
+
+echo "deleting temporary files" | tee -a $logfile;
+rm $mergedfile | tee -a $logfile;
+rm $mergedfile_temp | tee -a $logfile;
+
+echo "done" | tee -a $logfile;
diff --git a/shellscripts/mastrfu.sh b/shellscripts/mastrfu.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ccaf37732ecf9d5cde6fc6a63b65da4d45e6670b
--- /dev/null
+++ b/shellscripts/mastrfu.sh
@@ -0,0 +1,63 @@
+#! /bin/bash
+#=============================================================================
+#SBATCH --account=p71386
+#SBATCH --partition=skylake_0384
+#SBATCH --job-name=mastrfu
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=48
+#SBATCH --ntasks-per-core=1
+#SBATCH --time=05:00:00
+#SBATCH --mail-user=johannes.hoerner@univie.ac.at
+#SBATCH --output=/home/fs71767/jhoerner/projects/waterbelt-tropical-cloudfeedback/shellscripts/LOG.mastrfu_%j
+#SBATCH --error=/home/fs71767/jhoerner/projects/waterbelt-tropical-cloudfeedback/shellscripts/LOG.mastrfu_%j
+#SBATCH --mail-type=BEGIN,END,FAIL
+
+logfile="/home/fs71767/jhoerner/projects/waterbelt-tropical-cloudfeedback/shellscripts/LOG.mastrfu_${SLURM_JOB_ID}";
+exec > "$logfile" 2>&1;
+
+#explistA=("ape_ia_5000_13_0S" "ape_ia_5500_90_0S" "ape_ia_6000_90_0S" "ape_ia_6000_90_0S_cltlim_dtime10" "ape_ia_6500_90_0S_cltlim_dtime10" "ape_ia_8000_13_0S" "ape_ia_9000_13_0S" "ape_ia_10000_13_0S")
+#explistESM=("ape_4000_22_0S" "ape_5000_55_0S" "ape_5500_55_0S" "ape_6000_90_0S" "ape_6000_90_0S_dtime10" "ape_6000_22_0S" "ape_7000_22_0S" "ape_8000_22_0S")
+explistESM=("ape_7000_22_0S" "ape_8000_22_0S")
+
+basepath="/gpfs/data/fs71767/jhoerner/experiments/";
+pids=()
+
+for exp in "${explistESM[@]}"
+do
+    (
+    echo "processing experiment ${exp}" | tee -a $logfile;
+    exppathA="${basepath}${exp}/";
+    exppathESM="${basepath}ape_icon-esm/${exp}/";
+    outpath="/gpfs/data/fs71767/jhoerner/postprocessing/${exp}/mastrfu/";
+    plevs="100,200,300,500,700,1000,2000,3000,5000,7000,10000,15000,20000,25000,30000,40000,50000,60000,70000,80000,85000,90000,92500,95000,100000";
+    
+    if [ ! -d "$outpath" ]; then
+        mkdir -p "$outpath"
+    fi
+
+    for file2d in "${exppathESM}"*_atm_2d_ml*Z.nc; do
+        basename_file2d=$(basename "$file2d")
+        outputfile="${outpath}${basename_file2d/_2d_ml/_mastrfu}"
+
+        file3d="${file2d//_2d_ml/_3d_ml}"
+        if [ -f "$file3d" ]; then
+            echo "calculating mastrfu for $file2d" | tee -a "$logfile";
+            cdo -O mastrfu -invertlev -selvar,va -ap2pl,"${plevs}" -zonmean -remapcon,r192x96 -monmean -selvar,ps,pfull,va -merge "${file2d}" "${file3d}" "${outputfile}" | tee -a "$logfile";
+        else
+            echo "3D file not found for $file2d" | tee -a "$logfile";
+        fi
+    done
+    echo "merging files for ${exp}" | tee -a $logfile;
+    cdo -O mergetime "${outpath}${exp}_*_mastrfu*Z.nc" "${outpath}${exp}_mastrfu.mm.nc" | tee -a $logfile;
+    echo "delete temporary files for ${exp}" | tee -a $logfile;
+    rm "${outpath}${exp}_*_mastrfu*Z.nc" | tee -a $logfile;
+    ) &
+    pids+=($!)
+done
+
+for pid in "${pids[@]}"; do
+    wait $pid
+done
+
+echo "all done" | tee -a $logfile;
+