diff --git a/.gitignore b/.gitignore
index 4dcfb60f2d14375f6df26a7158c0b0ec90c139c3..fc3cfe49edfe7b24d744107230f56c553f391edf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1 @@
 runs/amip/logfiles/
-runs/slab4x-sun/logfiles/
-runs/slab4x-vap/logfiles/
-runs/slab4x/logfiles/
-runs/slabctr/logfiles/
-
-analysis/.ipynb_checkpoints/
-analysis/__pycache__/
diff --git a/README.md b/README.md
index e58bc6c92d05ff636d742608abe6bcf1860b4cf7..58ef92a9e7c89c368ad3080de5afbda2b3f24de0 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,27 @@
-# Climate Modelling Lab, MSc Meteorology, elective class, S2024 (280351 VU)
-
-General tools and tipps can be found at the [project wiki](https://gitlab.phaidra.org/climate/msc-climmodlab-s2024/-/wikis/home).
+# Climate Modelling Lab, MSc Meteorology S2025 (280351 VU)
 
 ## Recipe for lecturer: generate a tarball with the icon model code for distribution to students
 
-Get model code from branch climmodlab_s2023:
+Get model code, use branch climmodlab_s2023:
 
 ```
 git clone https://gitlab.phaidra.org/climate/icon-esm-univie.git
 git checkout origin/climmodlab_s2023
 ```
 
-Configure following the recipe for the students. Then create a distribution tarball:
+Configure following the recipe for the students below. Then create a distribution tarball:
 
 ```
 make dist
 ```
-This leads to the file `icon-2.6.0.tar.bz2`, which can then be distributed to the students.
+This leads to the file `icon-2.6.0.tar.bz2`, which can be distributed to the students.
 
 ## Recipe for students: how to get, configure and compile ICON on VSC4
 
 Get and untar the tarball and enter the model code directory:
 
 ```
-cp  /gpfs/data/fs72044/avoigt_teach/icon-2.6.0.tar.bz2 /gpfs/data/fs72044/iconAB
+cp  /gpfs/data/fs72044/avoigt_teach/icon-2.6.0.tar.bz2 /gpfs/data/fs72044/iconXY
 tar xfv icon-2.6.0.tar.bz2
 mv icon-2.6.0 icon-esm-univie
 cd icon-esm-univie/
@@ -69,50 +67,4 @@ The compilation leads to the binary `bin/icon`.
 
 A runscript for an AMIP-like simulation is available in `runs/amip/`, the run script is `exp.amip.run`. The script is written for user `avoigt_teach` and needs to be adapted accordingly for others users (e.g., replacing `avoigt_teach` where needed and adapting some of the directory names.)
 
-The simulation is AMIP-like because SSTs, sea ice etc. are prescribed as climatological averages and are the same for each year.
-
-## Simulations
-
-- amip simulation: to test basic running of model
-- slabctr: slab-ocean present-day control simulation, q-flux taken from https://phaidra.univie.ac.at/detail/o:1683142, sstclim_seb_atm_seb_2d_ml_1980-2008.ymonmean.seb_wtr.addc_3.1970-2069.nc, uses a ghost flux of 3Wm-2 to keep climate close to AMIP climate
-- slab4x: 4xCO2, restarted from 1990-01-01 of slabctr
-- slab4x-sun: 4xCO2 and sun dimmed by 3% via fsolrad=0.97 namelist switch, restarted from 1990-01-01 of slabctr
-- slab4x-vap: 4xCO2 and transparent stratospheric water vapor at klev=22 and above, restarted from 1990-01-01 of slabctr
-
-Output is on native triangular grid and model levels. To interpolate to lat-lon-p:
-
-```
-# 2d data: lat-lon interpolation
-cdo -P 16 -remapcon,r180x90 -setgrid,icon_grid_G.nc file.nc file.r180x90.nc
-```
-
-```
-# 2d daily-mean precip data interpolated to nearest neighbour for analysis of extreme precipitation
-cdo -P 16 -remapnn,r360x90 -setgrid,icon_grid_G.nc -selvar,pr file_dm.nc file_dm.pr.remapnn-r360x180.nc
-```
-
-For example:
-```
-for file in slab4x-vap_atm_2d_daily_ml_????????T000000Z.nc; do echo $file; cdo -remapnn,r360x180 -setgrid,icon_grid_G.nc -selvar,pr $file ${file}.pr.remapnn-r360x180.nc; done
-
-cdo mergetime slab4x-vap_atm_2d_daily_ml_*pr.remapnn-r360x180.nc ../output4students/slab4x-vap/slab4x-vap_atm_2d_daily_ml_199001-201701.pr.remapnn-r360x180.nc
-
-# clean up if needed: files with one month each
-rm  slab4x-vap_atm_2d_daily_ml_*.pr.remapnn-r360x180.nc
-```
-
-```
-# 3d data: lat-lon-p interpolation in one go
-plev="100000,95000,90000,85000,80000,75000,70000,65000,60000,55000,50000,45000,40000,35000,30000,25000,20000,15000,10000,5000,10"
-cdo -s -P 16 -remapcon,r180x90 -setgrid,icon_grid_G.nc -ap2pl,$plev file.nc file.r180x90.plev.nc
-```
-
-For example:
-```
-cdo -O -P 16 mergetime slabctr_atm_3d_ml_????????T000000Z.nc slabctr_atm_3d_ml_197901-201701.nc
-cdo -O -P 16 -ap2pl,$plev slabctr_atm_3d_ml_197901-201701.nc slabctr_atm_3d_ml_197901-201701.plev.nc
-cdo -O -s -P 16 -remapcon,r180x90 -setgrid,icon_grid_G.nc slabctr_atm_3d_ml_197901-201701.plev.nc slabctr_atm_3d_ml_197901-201701.plev.r180x90.nc
-```
-
-
-Interpolated output is on VSC4 in `/gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students`. The output is also available at the IMG Teachinghub in `/lehre/climmodlab_s2024/output4students/`.
+The simulation is AMIP-like because SSTs, sea ice etc. are prescribed as climatological averages and are the same for each year.
\ No newline at end of file
diff --git a/analysis/core.py b/analysis/core.py
deleted file mode 100644
index 5a7fb6630e5b1a7fa81399e2ab56c8e36706df3c..0000000000000000000000000000000000000000
--- a/analysis/core.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import xarray as xr
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-# load yearly mean dataset
-def load_yearmean_dataset(file: str, startdate: str):
-    ds = xr.load_dataset(file)
-    ds["time"] = pd.date_range(startdate, freq="ME", periods=ds.time.size)
-    return ds.groupby(ds.time.dt.year).mean()
-
-# compute global mean
-def globalmean(ds):
-    weights = np.cos(np.deg2rad(ds.lat))
-    weights.name = "weights"
-    ds_weighted = ds.weighted(weights)
-    return ds_weighted.mean(("lon", "lat"))
-
-def beautify_timeseries(ax, yaxis0):
-    """ Makes plots of time series  nicer in terms of axes and labeling"""
-    # adjust spines
-    ax = plt.gca()
-    ax.spines['top'].set_color('none')
-    ax.spines['right'].set_color('none')
-    ax.xaxis.set_ticks_position('bottom')
-    ax.spines['bottom'].set_position(('data',yaxis0))
\ No newline at end of file
diff --git a/analysis/dpr_4xco2_map.pdf b/analysis/dpr_4xco2_map.pdf
deleted file mode 100644
index e1ad158b8bb49181e1205d22774e35a12fefc4aa..0000000000000000000000000000000000000000
Binary files a/analysis/dpr_4xco2_map.pdf and /dev/null differ
diff --git a/analysis/dts_4xco2_map.pdf b/analysis/dts_4xco2_map.pdf
deleted file mode 100644
index 676eaa56a4a55777cc58d7469c7d6d41272e6098..0000000000000000000000000000000000000000
Binary files a/analysis/dts_4xco2_map.pdf and /dev/null differ
diff --git a/analysis/globalmean.ipynb b/analysis/globalmean.ipynb
deleted file mode 100644
index f262fe0f463e403b890288ca27f822c63edc93b9..0000000000000000000000000000000000000000
--- a/analysis/globalmean.ipynb
+++ /dev/null
@@ -1,229 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1d1abe75-9ec9-46a7-b938-7eefaf5e37ad",
-   "metadata": {},
-   "source": [
-    "# Evolution of global mean surface temperature and sea ice area"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "80f55624-7ed4-4e11-ab49-4ce095a98d71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dda27ec2-7f9f-44d5-9c31-cbf8188fe59e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import core as core"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "caf0443b-bb04-4952-b0c0-2432a95d991f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# path to model output\n",
-    "path=\"/home/voigta80/LEHRE/climmodlab_s2024/output4students/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "115178b5-2b01-4441-a373-eaf55555dc6f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slabctr = core.load_yearmean_dataset(file=path+\"/slabctr/slabctr_atm_2d_ml_197901-202901.r180x90.nc\", startdate=\"1979-01-01\")        # control run\n",
-    "slab4x  = core.load_yearmean_dataset(file=path+\"/slab4x/slab4x_atm_2d_ml_199002-203901.r180x90.nc\", startdate=\"1999-02-01\")          # 4xCO2\n",
-    "slabsun = core.load_yearmean_dataset(file=path+\"/slab4x-sun/slab4x-sun_atm_2d_ml_199002-203901.r180x90.nc\", startdate=\"1999-02-01\")  # 4xCO2 and dimmed sun\n",
-    "slabvap = core.load_yearmean_dataset(file=path+\"/slab4x-vap/slab4x-vap_atm_2d_ml_199002-203901.r180x90.nc\", startdate=\"1999-02-01\")  # 4xCO2 and transparent water vapor in the stratosphere"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "364a4154-b864-4d60-ba69-4ac5384e05cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slabctr_mean = core.globalmean(slabctr)\n",
-    "slab4x_mean  = core.globalmean(slab4x)\n",
-    "slabsun_mean = core.globalmean(slabsun)\n",
-    "slabvap_mean = core.globalmean(slabvap)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8dfda055-ea17-4a09-854f-617107fb7c6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# some data massaging to remove faulty years\n",
-    "# year 2029 of slabctr is not complete\n",
-    "slabctr_mean = slabctr_mean.where(slabctr_mean.year < 2029, drop=True)\n",
-    "# year 2026 of slabvap has missing values\n",
-    "slabvap_mean = xr.merge([slabvap_mean.where(slabvap_mean.year < 2026, drop=True), slabvap_mean.where(slabvap_mean.year > 2026, drop=True)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16822ce6-5606-4693-9f3b-7359bc89dd92",
-   "metadata": {},
-   "source": [
-    "Global mean surface temperature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0e30e8ce-54d8-4aa9-85b8-2d8e5f76bec6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Slab control : 287.910243809416\n",
-      "Slab 4x      : 294.4904180230014\n",
-      "Slab 4x-vapor: 293.87587189765486\n",
-      "Slab 4x-sun  : 290.28140743731694\n"
-     ]
-    }
-   ],
-   "source": [
-    "# print global mean surface temperatures averaged over 10 years to screen\n",
-    "print(\"Slab control :\", slabctr_mean[\"ts\"].isel(year=slice(-10,-1)).mean().values)\n",
-    "print(\"Slab 4x      :\", slab4x_mean[\"ts\"].isel(year=slice(-10,-1)).mean().values)\n",
-    "print(\"Slab 4x-vapor:\", slabvap_mean[\"ts\"].isel(year=slice(-10,-1)).mean().values)\n",
-    "print(\"Slab 4x-sun  :\", slabsun_mean[\"ts\"].isel(year=slice(-10,-1)).mean().values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "179d1984-872d-42bf-8722-7a53b9b87989",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(6,4))\n",
-    "ax=plt.subplot(1,1,1)\n",
-    "\n",
-    "plt.plot(slabctr_mean.year, slabctr_mean[\"ts\"] - 273.15, color=\"royalblue\")\n",
-    "plt.plot(slab4x_mean.year, slab4x_mean[\"ts\"] - 273.15, color=\"crimson\")\n",
-    "plt.plot(slabvap_mean.year, slabvap_mean[\"ts\"] - 273.15, color=\"coral\")\n",
-    "plt.plot(slabsun_mean.year, slabsun_mean[\"ts\"] - 273.15, color=\"seagreen\")\n",
-    "plt.ylim(287 - 273.15,295 - 273.15)\n",
-    "plt.xlim(1980,2038)\n",
-    "core.beautify_timeseries(ax, yaxis0=286.5 - 273.15)\n",
-    "\n",
-    "plt.xlabel(\"year\", loc=\"right\", size=12)\n",
-    "plt.ylabel(\"global-mean surface temperature / K\", loc=\"top\", size=12)\n",
-    "\n",
-    "plt.text(2038,288.2 - 273.15, \"present-day simulation: 14.8 deg C\", ha=\"right\", color=\"royalblue\", size=12)\n",
-    "plt.text(2038,294.7 - 273.15, \"4xCO2: +6.5 deg C\", ha=\"right\", color=\"crimson\", size=12)\n",
-    "plt.text(2038,293.2 - 273.15, \"4xCO2-vapor: +6.1 deg C\", ha=\"right\", color=\"coral\", size=12)\n",
-    "plt.text(2038,290.6 - 273.15, \"4xCO2-sun: +2.4 deg C\", ha=\"right\", color=\"seagreen\", size=12)\n",
-    "\n",
-    "plt.text(1980, 286.65 - 273.15, \"(c) Aiko Voigt, CC BY 4.0\", size=8)\n",
-    "\n",
-    "plt.savefig(\"globalmean_ts.pdf\", bbox_inches=\"tight\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4dbf37eb-835a-40ed-b2fc-71b91080f4ca",
-   "metadata": {},
-   "source": [
-    "Global mean sea ice cover"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e74f9f0c-b147-45b3-8075-be7efa5eefbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(6,4))\n",
-    "ax=plt.subplot(1,1,1)\n",
-    "\n",
-    "# surface area of earth in km2\n",
-    "aearth=4*np.pi*np.power(6371,2)\n",
-    "\n",
-    "plt.plot(slabctr_mean.year, 1e-6*aearth*slabctr_mean[\"sic\"], color=\"royalblue\")\n",
-    "plt.plot(slab4x_mean.year, 1e-6*aearth*slab4x_mean[\"sic\"], color=\"crimson\")\n",
-    "plt.plot(slabvap_mean.year, 1e-6*aearth*slabvap_mean[\"sic\"], color=\"coral\")\n",
-    "plt.plot(slabsun_mean.year, 1e-6*aearth*slabsun_mean[\"sic\"], color=\"seagreen\")\n",
-    "plt.ylim(7, 20)\n",
-    "plt.xlim(1980,2038)\n",
-    "core.beautify_timeseries(ax, yaxis0=6)\n",
-    "\n",
-    "plt.xlabel(\"year\", loc=\"right\", size=12)\n",
-    "plt.ylabel(r\"global sea ice area  / 10$^\\text{6}$ km$^\\text{2}$\", loc=\"top\", size=12)\n",
-    "\n",
-    "plt.text(1980, 6.2, \"(c) Aiko Voigt, CC BY 4.0\", size=8)\n",
-    "\n",
-    "plt.savefig(\"globalmean_sic.pdf\", bbox_inches=\"tight\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "NWP",
-   "language": "python",
-   "name": "nwp"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/analysis/globalmean_sic.pdf b/analysis/globalmean_sic.pdf
deleted file mode 100644
index 874ef70dfd74393922539f8a76a7afc1656a1069..0000000000000000000000000000000000000000
Binary files a/analysis/globalmean_sic.pdf and /dev/null differ
diff --git a/analysis/globalmean_ts.pdf b/analysis/globalmean_ts.pdf
deleted file mode 100644
index cbc0fdd05be055c4973e1c9244ea857bb7e6f043..0000000000000000000000000000000000000000
Binary files a/analysis/globalmean_ts.pdf and /dev/null differ
diff --git a/analysis/maps.ipynb b/analysis/maps.ipynb
deleted file mode 100644
index a1bfdcd90f8ab22f6144f685f13d9f1c98f8cbeb..0000000000000000000000000000000000000000
--- a/analysis/maps.ipynb
+++ /dev/null
@@ -1,207 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "1d1abe75-9ec9-46a7-b938-7eefaf5e37ad",
-   "metadata": {},
-   "source": [
-    "# Change in mean surface temperature and precipitation in response to 4xCO2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "80f55624-7ed4-4e11-ab49-4ce095a98d71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "import cartopy.crs as ccrs\n",
-    "import matplotlib.pyplot as plt\n",
-    "from cartopy.util import add_cyclic_point"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "dda27ec2-7f9f-44d5-9c31-cbf8188fe59e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import core as core"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "caf0443b-bb04-4952-b0c0-2432a95d991f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# path to model output\n",
-    "path=\"/home/voigta80/LEHRE/climmodlab_s2024/output4students/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "115178b5-2b01-4441-a373-eaf55555dc6f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slabctr = core.load_yearmean_dataset(file=path+\"/slabctr/slabctr_atm_2d_ml_197901-202901.r180x90.nc\", startdate=\"1979-01-01\") # control run\n",
-    "slab4x  = core.load_yearmean_dataset(file=path+\"/slab4x/slab4x_atm_2d_ml_199002-203901.r180x90.nc\", startdate=\"1999-02-01\")   # 4xCO2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8dfda055-ea17-4a09-854f-617107fb7c6d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# some data massaging to remove faulty years\n",
-    "# year 2029 of slabctr is not complete\n",
-    "slabctr = slabctr.where(slabctr.year < 2029, drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79dd7471-f1c5-4cb1-8061-eb5b4a9267c3",
-   "metadata": {},
-   "source": [
-    "Surface temperature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "ec5bbbe4-5608-4278-b901-fa7df7f63f4e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dts = slab4x[\"ts\"].isel(year=slice(-10,-1)).mean(\"year\") - slabctr[\"ts\"].isel(year=slice(-10,-1)).mean(\"year\")\n",
-    "\n",
-    "# add cyclic point at 0E\n",
-    "lon = slabctr.lon\n",
-    "lat = slabctr.lat\n",
-    "dts, lon = add_cyclic_point(dts, coord=lon)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "049d0c2b-6f59-4004-9f5b-7bfe2da568aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "ax = plt.axes(projection=ccrs.Robinson(central_longitude=0))\n",
-    "mesh = plt.contourf(lon, lat, dts, transform=ccrs.PlateCarree(), levels=np.linspace(0,12,13), extend=\"max\", cmap=\"Reds\")\n",
-    "ax.coastlines()\n",
-    "gl = ax.gridlines(draw_labels=False, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')\n",
-    "\n",
-    "# Add a colorbar\n",
-    "cbar = plt.colorbar(mesh, orientation='horizontal', aspect=50, fraction=0.03, pad=0.05)\n",
-    "cbar.set_label(\"surface temperature change / K\", size=12)\n",
-    "\n",
-    "plt.text(0.02,-0.02, \"(c) Aiko Voigt, CC BY 4.0\", size=8, transform = ax.transAxes)\n",
-    "\n",
-    "plt.savefig(\"dts_4xco2_map.pdf\", bbox_inches=\"tight\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6954c75c-eb9c-475c-81f5-13bfae611eb3",
-   "metadata": {},
-   "source": [
-    "Precipitation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a5f2ce94-a16f-4345-b0ee-07cc9be40d1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dpr = slab4x[\"pr\"].isel(year=slice(-10,-1)).mean(\"year\") - slabctr[\"pr\"].isel(year=slice(-10,-1)).mean(\"year\")\n",
-    "\n",
-    "dpr_percent= 100*dpr/slabctr[\"pr\"].isel(year=slice(-10,-1)).mean(\"year\")\n",
-    "\n",
-    "# add cyclic point at 0E\n",
-    "lon = slabctr.lon\n",
-    "lat = slabctr.lat\n",
-    "dpr_percent, lon = add_cyclic_point(dpr_percent, coord=lon)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "4bdde932-8b0c-48cd-b80f-ce627062b3ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(10, 5))\n",
-    "\n",
-    "ax = plt.axes(projection=ccrs.Robinson(central_longitude=0))\n",
-    "mesh = plt.contourf(lon, lat, dpr_percent, transform=ccrs.PlateCarree(), extend=\"both\", levels=[-100,-80,-60,-40,-20,20,40,60,80,100], cmap=\"BrBG\")\n",
-    "ax.coastlines()\n",
-    "gl = ax.gridlines(draw_labels=False, linewidth=0.5, color='gray', alpha=0.5, linestyle='--')\n",
-    "\n",
-    "# Add a colorbar\n",
-    "cbar = plt.colorbar(mesh, orientation='horizontal', aspect=50, fraction=0.03, pad=0.05)\n",
-    "cbar.set_label(\"precipitation change in per cent of present-day climate\", size=12)\n",
-    "\n",
-    "plt.text(0.02,-0.02, \"(c) Aiko Voigt, CC BY 4.0\", size=8, transform = ax.transAxes)\n",
-    "\n",
-    "plt.savefig(\"dpr_4xco2_map.pdf\", bbox_inches=\"tight\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "NWP",
-   "language": "python",
-   "name": "nwp"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/blogpost/blogpost_vsc_img_websites.docx b/blogpost/blogpost_vsc_img_websites.docx
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diff --git a/blogpost/blogpost_vsc_img_websites.pdf b/blogpost/blogpost_vsc_img_websites.pdf
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diff --git a/blogpost/dts_4xco2_map.pdf b/blogpost/dts_4xco2_map.pdf
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diff --git a/blogpost/figure1.pdf b/blogpost/figure1.pdf
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diff --git a/blogpost/globalmean_sic.pdf b/blogpost/globalmean_sic.pdf
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diff --git a/blogpost/globalmean_ts.pdf b/blogpost/globalmean_ts.pdf
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diff --git a/fix-mac-sp-aerosols.ipynb b/fix-mac-sp-aerosols.ipynb
deleted file mode 100644
index 3e2c862056ec04f21d847791a0fed3b5f49c07c8..0000000000000000000000000000000000000000
--- a/fix-mac-sp-aerosols.ipynb
+++ /dev/null
@@ -1,171 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ddd5af91-af0b-4379-88d4-6edc6ef337b2",
-   "metadata": {},
-   "source": [
-    "# Fix MAC-SP input data to use beyond year 2016.\n",
-    "\n",
-    "Reason: all runs crash on Jan 5, 2017. This indicates that the crash is caused by some issue in the input data, instead of a model instability.\n",
-    "\n",
-    "Indeed, it turns out to be caused by the fact that the MAC-SP aerosol data is nan beyond year 2016. This is fixed here by using the 2016 year values to extend the dataset to year 2100."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3b52083f-c1f5-406e-917c-97b385ccd6fd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "6ca6b837-288f-4d4f-8d21-521fe4b4ed57",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load MAC SP\n",
-    "path=\"/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie/data/\"\n",
-    "macsp = xr.open_dataset(path+\"MACv2.0-SP_v1.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82cc7e00-3d3c-42cf-bea7-e60c6faa7129",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "The problem is caused by macsp.years_weight=nan beyond year 2016."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "56b85c79-01c0-4feb-939a-2984e3b08fa5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.71, 0.71,  nan,  nan,  nan,  nan],\n",
-       "       [0.53, 0.53,  nan,  nan,  nan,  nan],\n",
-       "       [0.99, 0.99,  nan,  nan,  nan,  nan],\n",
-       "       [1.28, 1.28,  nan,  nan,  nan,  nan],\n",
-       "       [1.23, 1.23,  nan,  nan,  nan,  nan],\n",
-       "       [1.09, 1.09,  nan,  nan,  nan,  nan],\n",
-       "       [1.1 , 1.1 ,  nan,  nan,  nan,  nan],\n",
-       "       [1.29, 1.29,  nan,  nan,  nan,  nan],\n",
-       "       [0.94, 0.94,  nan,  nan,  nan,  nan]], dtype=float32)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "macsp.year_weight.sel(years=slice(2015,2020)).values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a1d9f144-1a3e-421f-9028-c6f318b8b043",
-   "metadata": {},
-   "source": [
-    "Create new dataset with year 2016 values used to fill the values from 2017 to 2100."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cc3b433e-835c-4d3b-8f29-df2f7149e684",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "macsp_beyond2016 = macsp.copy(deep=True)\n",
-    "for ind in np.arange(167,251):\n",
-    "    macsp_beyond2016.year_weight[:,ind] = macsp.year_weight[:,166]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "027f417c-9e45-44d4-b0e5-b4584326c54a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.71, 0.71, 0.71, 0.71, 0.71],\n",
-       "       [0.53, 0.53, 0.53, 0.53, 0.53],\n",
-       "       [0.99, 0.99, 0.99, 0.99, 0.99],\n",
-       "       [1.28, 1.28, 1.28, 1.28, 1.28],\n",
-       "       [1.23, 1.23, 1.23, 1.23, 1.23],\n",
-       "       [1.09, 1.09, 1.09, 1.09, 1.09],\n",
-       "       [1.1 , 1.1 , 1.1 , 1.1 , 1.1 ],\n",
-       "       [1.29, 1.29, 1.29, 1.29, 1.29],\n",
-       "       [0.94, 0.94, 0.94, 0.94, 0.94]], dtype=float32)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# check\n",
-    "macsp_beyond2016[\"year_weight\"].sel(years=slice(2016,2020)).values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "be3ea71b-1a0d-4176-8367-f502c8949dd0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# save to netcdf file\n",
-    "macsp_beyond2016.to_netcdf(path+\"MACv2.0-SP_v1.fixed-for-use-beyond2016.nc\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/postprocessing/pp_2d.sh b/postprocessing/pp_2d.sh
deleted file mode 100644
index cc71611d40f7f85382ec90c84da72283731605d4..0000000000000000000000000000000000000000
--- a/postprocessing/pp_2d.sh
+++ /dev/null
@@ -1,42 +0,0 @@
-#!/bin/bash
-#
-#SBATCH -J pp_2d
-#SBATCH -N 1
-#SBATCH --ntasks-per-node=1
-#SBATCH --ntasks-per-core=48
-#SBATCH --mail-type=BEGIN    # first have to state the type of event to occur 
-#SBATCH --mail-user=aiko.voigt@univie.ac.at   # and then your email address
-#SBATCH --time=08:00:00 
-
-module load cdo --auto
-
-
-echo "Working on slabctr:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slabctr
-cdo -P 48 mergetime slabctr_atm_2d_ml_????????T000000Z.nc slabctr_atm_2d_ml_197901-202901.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slabctr_atm_2d_ml_197901-202901.nc slabctr_atm_2d_ml_197901-202901.r180x90.nc
-cp slabctr_atm_2d_ml_197901-202901.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slabctr
-echo "slabctr finished!"
-
-echo "Working on slab4x:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x
-cdo -P 48 mergetime slab4x_atm_2d_ml_????????T000000Z.nc slab4x_atm_2d_ml_199002-203901.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x_atm_2d_ml_199002-203901.nc slab4x_atm_2d_ml_199002-203901.r180x90.nc
-cp slab4x_atm_2d_ml_199002-203901.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x
-echo "slab4x finished!"
-
-echo "Working on slab4x-sun:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-sun
-cdo -P 48 mergetime slab4x-sun_atm_2d_ml_????????T000000Z.nc slab4x-sun_atm_2d_ml_199002-203901.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x-sun_atm_2d_ml_199002-203901.nc slab4x-sun_atm_2d_ml_199002-203901.r180x90.nc
-cp slab4x-sun_atm_2d_ml_199002-203901.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x-sun
-echo "slab4x-sun finished!"
-
-echo "Working on slab4x-vap:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-vap
-cdo -P 48 mergetime slab4x-vap_atm_2d_ml_????????T000000Z.nc slab4x-vap_atm_2d_ml_199002-203901.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x-vap_atm_2d_ml_199002-203901.nc slab4x-vap_atm_2d_ml_199002-203901.r180x90.nc
-cp slab4x-vap_atm_2d_ml_199002-203901.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x-vap
-echo "slab4x-vap finished!"
-
-cd /home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/postprocessing
diff --git a/postprocessing/pp_2d_daily_precip.sh b/postprocessing/pp_2d_daily_precip.sh
deleted file mode 100644
index 448f0693523edb8ede1b1b7c24ba46a630002702..0000000000000000000000000000000000000000
--- a/postprocessing/pp_2d_daily_precip.sh
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/bin/bash
-#
-#SBATCH -J pp_2d_daily_precip
-#SBATCH -N 1
-#SBATCH --ntasks-per-node=1
-#SBATCH --ntasks-per-core=48
-#SBATCH --mail-type=BEGIN    # first have to state the type of event to occur 
-#SBATCH --mail-user=aiko.voigt@univie.ac.at   # and then your email address
-#SBATCH --time=08:00:00 
-
-module load cdo --auto
-
-echo "Working on slabctr:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slabctr
-for file in slabctr_atm_2d_daily_ml_????????T000000Z.nc; do 
-    cdo -s -O -P 48 -remapnn,r360x180 -setgrid,icon_grid_G.nc -selvar,pr $file ${file}.pr.remapnn-r360x180.nc
-done
-cdo -s -O -P 48 mergetime slabctr_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc ../output4students/slabctr/slabctr_atm_2d_daily_ml_197901-202901.pr.remapnn-r360x180.nc
-rm -f slabctr_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc
-echo "slabctr finished!"
-
-echo "Working on slab4x:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x
-for file in slab4x_atm_2d_daily_ml_????????T000000Z.nc; do 
-    cdo -s -O -P 48 -remapnn,r360x180 -setgrid,icon_grid_G.nc -selvar,pr $file ${file}.pr.remapnn-r360x180.nc
-done
-cdo -s -O -P 48 mergetime slab4x_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc ../output4students/slab4x/slab4x_atm_2d_daily_ml_199001-203901.pr.remapnn-r360x180.nc
-rm -f slab4x_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc
-echo "slab4x finished!"
-
-echo "Working on slab4x-vap:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-vap
-for file in slab4x-vap_atm_2d_daily_ml_????????T000000Z.nc; do 
-    cdo -s -O -P 48 -remapnn,r360x180 -setgrid,icon_grid_G.nc -selvar,pr $file ${file}.pr.remapnn-r360x180.nc
-done
-cdo -s -O -P 48 mergetime slab4x-vap_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc ../output4students/slab4x-vap/slab4x-vap_atm_2d_daily_ml_199001-203901.pr.remapnn-r360x180.nc
-rm -f slab4x-vap_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc
-echo "slab4x-vap finished!"
-
-echo "Working on slab4x-sun:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-sun
-for file in slab4x-sun_atm_2d_daily_ml_????????T000000Z.nc; do 
-    cdo -s -O -P 48 -remapnn,r360x180 -setgrid,icon_grid_G.nc -selvar,pr $file ${file}.pr.remapnn-r360x180.nc
-done
-cdo -s -O -P 48 mergetime slab4x-sun_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc ../output4students/slab4x-sun/slab4x-sun_atm_2d_daily_ml_199001-203901.pr.remapnn-r360x180.nc
-rm -f slab4x-sun_atm_2d_daily_ml_????????T000000Z.nc.pr.remapnn-r360x180.nc
-echo "slab4x-sun finished!"
-
-cd /home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/postprocessing
diff --git a/postprocessing/pp_3d.sh b/postprocessing/pp_3d.sh
deleted file mode 100644
index dd2a77a0b448eaf6285fb3e8322cb4d5de091c0b..0000000000000000000000000000000000000000
--- a/postprocessing/pp_3d.sh
+++ /dev/null
@@ -1,48 +0,0 @@
-#!/bin/bash
-#
-#SBATCH -J pp_3d
-#SBATCH -N 1
-#SBATCH --ntasks-per-node=1
-#SBATCH --ntasks-per-core=48
-#SBATCH --mail-type=BEGIN    # first have to state the type of event to occur 
-#SBATCH --mail-user=aiko.voigt@univie.ac.at   # and then your email address
-#SBATCH --time=08:00:00 
-
-module load cdo --auto
-
-# interpolate to these pressure levels
-plev="100000,95000,90000,85000,80000,75000,70000,65000,60000,55000,50000,45000,40000,35000,30000,25000,20000,15000,10000,5000,10"
-
-echo "Working on slabctr:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slabctr
-cdo -P 48 mergetime slabctr_atm_3d_ml_????????T000000Z.nc slabctr_atm_3d_ml_197901-202901.nc
-cdo -P 48 -ap2pl,$plev slabctr_atm_3d_ml_197901-202901.nc slabctr_atm_3d_ml_197901-202901.plev.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slabctr_atm_3d_ml_197901-202901.plev.nc slabctr_atm_3d_ml_197901-202901.plev.r180x90.nc
-cp slabctr_atm_3d_ml_197901-202901.plev.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slabctr
-echo "slabctr finished!"
-
-echo "Working on slab4x:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x
-cdo -P 48 mergetime slab4x_atm_3d_ml_????????T000000Z.nc slab4x_atm_3d_ml_199002-203901.nc
-cdo -P 48 -ap2pl,$plev slab4x_atm_3d_ml_199002-203901.nc slab4x_atm_3d_ml_199002-203901.plev.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x_atm_3d_ml_199002-203901.plev.nc slab4x_atm_3d_ml_199002-203901.plev.r180x90.nc
-cp slab4x_atm_3d_ml_199002-203901.plev.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x
-echo "slab4x finished!"
-
-echo "Working on slab4x-sun:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-sun
-cdo -P 48 mergetime slab4x-sun_atm_3d_ml_????????T000000Z.nc slab4x-sun_atm_3d_ml_199002-203901.nc
-cdo -P 48 -ap2pl,$plev slab4x-sun_atm_3d_ml_199002-203901.nc slab4x-sun_atm_3d_ml_199002-203901.plev.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x-sun_atm_3d_ml_199002-203901.plev.nc slab4x-sun_atm_3d_ml_199002-203901.plev.r180x90.nc
-cp slab4x-sun_atm_3d_ml_199002-203901.plev.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x-sun
-echo "slab4x-sun finished!"
-
-echo "Working on slab4x-vap:"
-cd /gpfs/data/fs72044/avoigt_teach/experiments/s2024/slab4x-vap
-cdo -P 48 mergetime slab4x-vap_atm_3d_ml_????????T000000Z.nc slab4x-vap_atm_3d_ml_199002-203901.nc
-cdo -P 48 -ap2pl,$plev slab4x-vap_atm_3d_ml_199002-203901.nc slab4x-vap_atm_3d_ml_199002-203901.plev.nc
-cdo -s -P 48 -remapcon,r180x90 -setgrid,icon_grid_G.nc slab4x-vap_atm_3d_ml_199002-203901.plev.nc slab4x-vap_atm_3d_ml_199002-203901.plev.r180x90.nc
-cp slab4x-vap_atm_3d_ml_199002-203901.plev.r180x90.nc /gpfs/data/fs72044/avoigt_teach/experiments/s2024/output4students/slab4x-vap
-echo "slab4x-vap finished!"
-
-cd /home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/postprocessing
diff --git a/runs/slab4x-sun/exp.slab4x-sun.run b/runs/slab4x-sun/exp.slab4x-sun.run
deleted file mode 100755
index 7abd69587fb461027207af3cc91da877be9961db..0000000000000000000000000000000000000000
--- a/runs/slab4x-sun/exp.slab4x-sun.run
+++ /dev/null
@@ -1,576 +0,0 @@
-#! /bin/ksh
-#=============================================================================
-#SBATCH --account=p72044
-#SBATCH --partition=skylake_0096
-#SBATCH --qos=skylake_0096
-#SBATCH --job-name=slab4x-sun
-#SBATCH --nodes=3
-#SBATCH --ntasks-per-node=48
-#SBATCH --ntasks-per-core=1
-#SBATCH --output=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-sun/logfiles/LOG.exp.slab4x-sun.run.%j.o
-#SBATCH --error=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-sun/logfiles/LOG.exp.slab4x-sun.run.%j.o
-#SBATCH --exclusive
-#SBATCH --time=03:00:00
-#SBATCH --mail-user=aiko.voigt@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=3
-mpi_procs_pernode=48
-((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
-echo $mpi_total_procs
-
-# manual fix for mpi pinning with intel mpirun on vsc
-# see https://wiki.vsc.ac.at/doku.php?id=doku:vsc5quickstart#intel_mpi
-export I_MPI_PIN_RESPECT_CPUSET=0
-
-#
-# blocking length
-# ---------------
-nproma=16
-
-#=============================================================================
-# Input variables:
-
-# SIMULATION NAME
-EXP=slab4x-sun
-
-ICONFOLDER=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie       # DIRECTORY OF ICON MODEL CODE
-RUNSCRIPTDIR=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-sun/
-basedir=$ICONFOLDER # icon base directory
-
-. ${ICONFOLDER}/run/add_run_routines
-
-# experiment directory, with plenty of space, create if new
-EXPDIR=/gpfs/data/fs72044/avoigt_teach/experiments/s2024/${EXP}
-if [ ! -d ${EXPDIR} ] ;  then
-  mkdir -p ${EXPDIR}
-fi
-#
-ls -ld ${EXPDIR}
-if [ ! -d ${EXPDIR} ] ;  then
-    mkdir ${EXPDIR}
-fi
-ls -ld ${EXPDIR}
-
-cd $EXPDIR
-
-
-
-
-#=================================================================================
-
-#-----------------------------------------------------------------------------
-# global timing
-initial_date="1979-01-01"
-final_date="2039-01-01"
-start_date=$initial_date
-end_date=$final_date
-y0=${start_date%%-*}
-yN=${end_date%%-*}
-
-
-# restart intervals
-restart_interval="P2Y"
-checkpoint_interval="P1Y"
-
-file_interval="P1M"
-
-############################################################
-#
-# NO FURTHER CHANGES TO THE DIRECTORIES AND SIMULATION NAME
-# SHOULD BE NEEDED BELOW THIS LINE
-#
-############################################################
-
-#-----------------------------------------------------------------------------
-# Provide input files
-# $Id: format.tmpl 9264 2021-06-21 21:24:57Z m221078 $
-#
-# [files]
-
-# [files.atmosphere]
-data_dir=/gpfs/data/fs72044/avoigt_teach/ICON-inputdata/amip-VSC4
-
-# [files.atmosphere.mapped]
-grid_dir=$data_dir/grid
-ln -sfv $grid_dir/icon_grid_0013_R02B04_G.nc icon_grid_G.nc
-
-# [files.atmosphere.mapped.initial]
-initial_dir=$data_dir/initial_condition
-ln -sfv $initial_dir/ifs2icon_1979010100_R02B04_G.nc ifs2icon.nc
-
-# [files.atmosphere.mapped.ozone]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    label=${yr}
-        ((yr >= 2015)) && label=2014
-    ozone_dir=$data_dir/ozone
-    ln -sfv $ozone_dir/bc_ozone_historical_1979-2008.ymonmean.${label}.nc bc_ozone_${yr}.nc
-done # offsets
-
-# [files.atmosphere.mapped.ocean_surface]
-ocean_surface_dir=$data_dir/sst_and_seaice
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_sst.nc
-
-# files for slab ocean
-# sst, sic, and seb
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_mlo_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_mlo_sst.nc
-# q-flux file is taken from Phaidra archive of S2023 course
-ln -sfv /gpfs/data/fs72044/avoigt_teach/ICON-inputdata/slabocean/sstclim_seb_atm_seb_2d_ml_1980-2008.ymonmean.seb_wtr.addc_3.1970-2069.nc bc_mlo_seb.nc
-
-# [files.atmosphere.mapped.aerosols]
-aerosols_dir=$data_dir/aerosol
-ln -sfv $aerosols_dir/bc_aeropt_kinne_lw_b16_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_fin_1850.nc bc_aeropt_kinne_sw_b14_fin.nc
-
-# [files.atmosphere.model]
-model_dir=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie
-
-# [files.atmosphere.model.data]
-ln -sfv $model_dir/data/lsdata.nc .
-ln -sfv $model_dir/data/ECHAM6_CldOptProps.nc .
-#ln -sfv $model_dir/data/MACv2.0-SP_v1.nc .
-rm -f MACv2.0-SP_v1.nc
-cp $model_dir/data/MACv2.0-SP_v1.fixed-for-use-beyond2016.nc MACv2.0-SP_v1.nc
-
-# [files.atmosphere.model.run]
-run_dir=$model_dir/run
-cp -fv $run_dir/dict.iconam.mpim dict.txt
-
-# [files.atmosphere.independent.volcano_aerosols]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    volcano_aerosols_dir=$data_dir/aerosol
-    ln -sfv $volcano_aerosols_dir/bc_aeropt_cmip6_volc_lw_b16_sw_b14_2000.nc \
-        bc_aeropt_cmip6_volc_lw_b16_sw_b14_${yr}.nc
-done # offsets
-
-
-# [files.land]
-land_dir=$data_dir/land
-
-# [files.land.mapped]
-ln -sfv $land_dir/ic_land_soil_1976.nc ic_land_soil.nc
-ln -sfv $land_dir/bc_land_frac_11pfts_1976.nc bc_land_frac.nc
-ln -sfv $land_dir/bc_land_phys_1976.nc bc_land_phys.nc
-ln -sfv $land_dir/bc_land_soil_1976.nc bc_land_soil.nc
-ln -sfv $land_dir/bc_land_sso_1976.nc bc_land_sso.nc
-
-# [files.land.hydro]
-hydro_dir=$land_dir
-# preliminary test version
-ln -sfv $hydro_dir/hdpara_r2b4_0013_0035_v3.nc bc_land_hd.nc
-
-# [files.land.model]
-model_dir=$basedir/externals/jsbach/data
-ln -sfv $model_dir/lctlib_nlct21.def .
-
-
-#-----------------------------------------------------------------------------
-# automatic restart setup
-# set some default values and derive some run parameteres
-restart=${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, coupling and model namelists
-# ------------------------------------------------
-# For a complete list see Namelist_overview and Namelist_overview.pdf
-#
-
-cat > icon_master.namelist << EOF
-&master_nml
-    lrestart =  ${restart}
-/
-&master_time_control_nml
-    calendar = 'proleptic gregorian'
-    checkpointtimeintval = '$checkpoint_interval'
-    restarttimeintval = '$restart_interval'
-    experimentstartdate = '1979-01-01' ! TODO: hack to reproduce result
-    experimentstopdate = '$final_date'
-/
-&master_model_nml ! 'atmo'
-    model_name = 'atmo'
-    model_namelist_filename = 'NAMELIST_atm'
-    model_type = 1
-/
-&jsb_control_nml
-    is_standalone = .false.
-/
-&jsb_model_nml
-    model_id = 1
-    model_name = 'JSBACH'
-    model_shortname = 'jsb'
-    model_description = 'JSBACH land surface model'
-    model_namelist_filename = 'NAMELIST_lnd'
-/
-
-EOF
-
-#-----------------------------------------------------------------------------
-# II. ATMOSPHERE and LAND
-#-----------------------------------------------------------------------------
-#
-# atmosphere namelist
-# -------------------
-cat > NAMELIST_atm << EOF
-&parallel_nml
-    nproma = $nproma
-    num_io_procs = 0
-    num_prefetch_proc = 0
-    pio_type = 1 !1=default, assync I/O, 2=experimental CDI, 0=nothing?
-/
-&grid_nml
-    dynamics_grid_filename = 'icon_grid_G.nc'
-/
-&run_nml
-    num_lev = 47 ! number of full levels
-    modeltimestep = 'PT15M'
-    ltestcase = .false. ! run testcase
-    ldynamics = .true. ! dynamics
-    ltransport = .true. ! transport
-    iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
-    output = 'nml'
-    msg_level = 8 ! level of details report during integration
-    restart_filename = '${EXP}_restart_atm_<rsttime>.nc'
-    activate_sync_timers = .true.
-/
-&extpar_nml
-    itopo = 1 ! 1: read topography from the grid file
-    itype_lwemiss = 0
-/
-&initicon_nml
-    init_mode = 2 ! 2: initialize from IFS analysis
-    ifs2icon_filename = 'ifs2icon.nc'
-/
-&nonhydrostatic_nml
-    ndyn_substeps = 8 ! dtime/dt_dyn
-    damp_height = 50000. ! [m]
-    rayleigh_coeff = 0.1000 ! set to 0.1001 for rerun with little change
-    vwind_offctr = 0.2
-    divdamp_fac = 0.004
-/
-&interpol_nml
-    rbf_scale_mode_ll = 1
-/
-&sleve_nml
-    min_lay_thckn = 40. ! [m]
-    top_height = 83000. ! [m]
-    stretch_fac = 0.9
-    decay_scale_1 = 4000. ! [m]
-    decay_scale_2 = 2500. ! [m]
-    decay_exp = 1.2
-    flat_height = 16000. ! [m]
-/
-&diffusion_nml
-!    hdiff_smag_fac = 0.015000001
-/
-&transport_nml
-    ihadv_tracer = 52, 2, 2
-    itype_hlimit = 3, 4, 4
-    ivadv_tracer = 3, 3, 3
-    tracer_names = 'hus', 'clw', 'cli'
-/
-&echam_phy_nml
-    ! domain 1
-    ! atmospheric physics ("" = never)
-    echam_phy_config(1)%dt_rad = 'PT90M'
-    echam_phy_config(1)%dt_vdf = 'PT15M'
-    echam_phy_config(1)%dt_cnv = 'PT15M'
-    echam_phy_config(1)%dt_cld = 'PT15M'
-    echam_phy_config(1)%dt_gwd = 'PT15M'
-    echam_phy_config(1)%dt_sso = 'PT15M'
-    ! atmospheric chemistry ("" = never)
-    echam_phy_config(1)%dt_mox = 'PT15M'
-    ! sea ice on mixed-layer ocean (""=never)
-    echam_phy_config(1)%dt_ice = 'PT15M'
-    ! surface (true or false)
-    echam_phy_config(1)%ljsb = .true.
-    echam_phy_config(1)%lamip = .false. 
-    echam_phy_config(1)%lice = .true.
-    echam_phy_config(1)%lmlo = .true.
-    echam_phy_config(1)%llake = .true.
-/
-&echam_rad_nml
-    ! domain 1
-    echam_rad_config(1)%isolrad = 3  ! Use insolation for AMIP type CMIP5 simulation (average from 1979-1988
-    echam_rad_config(1)%irad_h2o = 1
-    echam_rad_config(1)%irad_co2 = 2 ! constant concentration given by vmr_co2 etc.
-    echam_rad_config(1)%irad_ch4 = 2
-    echam_rad_config(1)%irad_n2o = 2
-    echam_rad_config(1)%irad_o3 = 8 ! constant annual cycle climatology
-    echam_rad_config(1)%irad_o2 = 2
-    echam_rad_config(1)%irad_cfc11 = 2
-    echam_rad_config(1)%irad_cfc12 = 2
-    echam_rad_config(1)%irad_aero = 18 ! as in AMIP
-    echam_rad_config(1)%vmr_co2 = 1436.0e-6 !359.0e-6 --> 4xCO2
-    echam_rad_config(1)%vmr_ch4 = 1693.0e-9
-    echam_rad_config(1)%vmr_n2o = 311.0e-9
-    echam_rad_config(1)%vmr_o2  = 0.20946
-    echam_rad_config(1)%vmr_cfc11 = 237.0e-12
-    echam_rad_config(1)%vmr_cfc12 = 462.0e-12
-    echam_rad_config(1)%fsolrad = 0.97  ! dim the sun by 3% to compensate for 4xco2
-/
-&echam_gwd_nml
-/
-&echam_sso_nml
-/
-&echam_vdf_nml
-/
-&echam_cnv_nml
-/
-&echam_cld_nml
-/
-&echam_cov_nml
-/
-&ccycle_nml
-/
-&sea_ice_nml
- i_ice_therm  = 1    ! 1=0L-Semtner
-/
-&echam_seaice_mlo_nml
- lqflux               = .true.  ! default .TRUE.
- max_seaice_thickness = 99999.  ! default 5
- qbot_mlo_nh          = 0.      ! default 10
- qbot_mlo_sh          = 0.      ! default 10
-/
-! Parameters for all output files
-! -------------------------------
-&io_nml
-    output_nml_dict = 'dict.txt'
-    netcdf_dict = 'dict.txt'
-    itype_pres_msl = 4
-    ! restart_file_type = 5
-    ! restart_write_mode = 'joint procs multifile' ! not useful in r2b4 setup
-    ! lnetcdf_flt64_output = .true. ! 64 bit output in all files
-    ! lkeep_in_sync = .true. ! sync after each timestep
-    write_initial_state = .false.
-/
-&dbg_index_nml
-    idbg_mxmn = 0 ! initialize MIN/MAX debug output
-    idbg_val = 0 ! initialize one cell debug output
-    idbg_slev = 1 ! initialize start level for debug output
-    idbg_elev = 2 ! initialize end level for debug output
-    dbg_lat_in = 30.0 ! latitude location of one cell debug output
-    dbg_lon_in = -30.0 ! longitude location of one cell debug output
-    str_mod_tst = 'InterFaceOce' ! define modules to print out in debug mode
-/
-
-! Define output files
-! -------------------
-!
-! 3-dimensional files include 'ps' and 'pfull' to allow the vertical
-! interpolation to pressure levels by cdo ap2pl.
-
-/
-! Standard AMIP output...
-&output_nml ! 'atm_3d'
-    output_filename = '${EXP}_atm_3d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'zg', 'ps', 'pfull', 'rho', 'ta', 'ua', 'va', 'wap', 'hus',
-                 'clw', 'cli', 'hur', 'cl'
-/
-! Standard AMIP output...
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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'
-/
-! Standard AMIP output...for daily means
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d_daily'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1D'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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
-
-
-# jsbach namelist
-# ---------------
-
-cat > NAMELIST_lnd << EOF
-
-&jsb_model_nml
-    usecase = 'jsbach_pfts'
-    use_lakes = .true.
-/
-&jsb_seb_nml
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_rad_nml
-    use_alb_veg_simple = .false. ! if jsbach_lite
-    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 = .false.
-    l_heat_cond_dyn = .false.
-    l_snow = .true.
-    l_dynsnow = .true.
-    l_freeze = .true.
-    l_supercool = .true.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_hydro_nml
-    l_organic = .false.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-    bc_sso_filename = 'bc_land_sso.nc'
-/
-&jsb_assimi_nml
-    active = .true. ! if jsbach_pfts
-/
-&jsb_pheno_nml
-    scheme = 'logrop' ! 'climatology' if jsbach_lite
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_carbon_nml
-    active = .true.
-    bc_filename = 'bc_land_carbon.nc'
-    ic_filename = 'ic_land_carbon.nc'
-    read_cpools = .false.
-/
-&jsb_fuel_nml
-    active = .true.
-    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
-
-
-## 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
-
-ldd icon.exe
-
-START="/gpfs/opt/sw/skylake/spack-0.19.0/opt/spack/linux-almalinux8-skylake_avx512/intel-2021.7.1/intel-oneapi-mpi-2021.7.1-fzg6q4xcj7efjmce3cuqa2b7cum5d3po/mpi/2021.7.1/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/runs/slab4x-vap/exp.slab4x-vap.run b/runs/slab4x-vap/exp.slab4x-vap.run
deleted file mode 100755
index b2572cad5d4bdc90e866f1bea5270f73431c3ca9..0000000000000000000000000000000000000000
--- a/runs/slab4x-vap/exp.slab4x-vap.run
+++ /dev/null
@@ -1,577 +0,0 @@
-#! /bin/ksh
-#=============================================================================
-#SBATCH --account=p72044
-#SBATCH --partition=skylake_0096
-#SBATCH --qos=skylake_0096
-#SBATCH --job-name=slab4x-vap
-#SBATCH --nodes=3
-#SBATCH --ntasks-per-node=48
-#SBATCH --ntasks-per-core=1
-#SBATCH --output=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-vap/logfiles/LOG.exp.slab4x-vap.run.%j.o
-#SBATCH --error=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-vap/logfiles/LOG.exp.slab4x-vap.run.%j.o
-#SBATCH --exclusive
-#SBATCH --time=03:00:00
-#SBATCH --mail-user=aiko.voigt@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=3
-mpi_procs_pernode=48
-((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
-echo $mpi_total_procs
-
-# manual fix for mpi pinning with intel mpirun on vsc
-# see https://wiki.vsc.ac.at/doku.php?id=doku:vsc5quickstart#intel_mpi
-export I_MPI_PIN_RESPECT_CPUSET=0
-
-#
-# blocking length
-# ---------------
-nproma=16
-
-#=============================================================================
-# Input variables:
-
-# SIMULATION NAME
-EXP=slab4x-vap
-
-ICONFOLDER=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie       # DIRECTORY OF ICON MODEL CODE
-RUNSCRIPTDIR=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x-vap/
-basedir=$ICONFOLDER # icon base directory
-
-. ${ICONFOLDER}/run/add_run_routines
-
-# experiment directory, with plenty of space, create if new
-EXPDIR=/gpfs/data/fs72044/avoigt_teach/experiments/s2024/${EXP}
-if [ ! -d ${EXPDIR} ] ;  then
-  mkdir -p ${EXPDIR}
-fi
-#
-ls -ld ${EXPDIR}
-if [ ! -d ${EXPDIR} ] ;  then
-    mkdir ${EXPDIR}
-fi
-ls -ld ${EXPDIR}
-
-cd $EXPDIR
-
-
-
-
-#=================================================================================
-
-#-----------------------------------------------------------------------------
-# global timing
-initial_date="1979-01-01"
-final_date="2039-01-01"
-start_date=$initial_date
-end_date=$final_date
-y0=${start_date%%-*}
-yN=${end_date%%-*}
-
-
-# restart intervals
-restart_interval="P2Y"
-checkpoint_interval="P1Y"
-
-file_interval="P1M"
-
-############################################################
-#
-# NO FURTHER CHANGES TO THE DIRECTORIES AND SIMULATION NAME
-# SHOULD BE NEEDED BELOW THIS LINE
-#
-############################################################
-
-#-----------------------------------------------------------------------------
-# Provide input files
-# $Id: format.tmpl 9264 2021-06-21 21:24:57Z m221078 $
-#
-# [files]
-
-# [files.atmosphere]
-data_dir=/gpfs/data/fs72044/avoigt_teach/ICON-inputdata/amip-VSC4
-
-# [files.atmosphere.mapped]
-grid_dir=$data_dir/grid
-ln -sfv $grid_dir/icon_grid_0013_R02B04_G.nc icon_grid_G.nc
-
-# [files.atmosphere.mapped.initial]
-initial_dir=$data_dir/initial_condition
-ln -sfv $initial_dir/ifs2icon_1979010100_R02B04_G.nc ifs2icon.nc
-
-# [files.atmosphere.mapped.ozone]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    label=${yr}
-        ((yr >= 2015)) && label=2014
-    ozone_dir=$data_dir/ozone
-    ln -sfv $ozone_dir/bc_ozone_historical_1979-2008.ymonmean.${label}.nc bc_ozone_${yr}.nc
-done # offsets
-
-# [files.atmosphere.mapped.ocean_surface]
-ocean_surface_dir=$data_dir/sst_and_seaice
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_sst.nc
-
-# files for slab ocean
-# sst, sic, and seb
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_mlo_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_mlo_sst.nc
-# q-flux file is taken from Phaidra archive of S2023 course
-ln -sfv /gpfs/data/fs72044/avoigt_teach/ICON-inputdata/slabocean/sstclim_seb_atm_seb_2d_ml_1980-2008.ymonmean.seb_wtr.addc_3.1970-2069.nc bc_mlo_seb.nc
-
-# [files.atmosphere.mapped.aerosols]
-aerosols_dir=$data_dir/aerosol
-ln -sfv $aerosols_dir/bc_aeropt_kinne_lw_b16_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_fin_1850.nc bc_aeropt_kinne_sw_b14_fin.nc
-
-# [files.atmosphere.model]
-model_dir=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie
-
-# [files.atmosphere.model.data]
-ln -sfv $model_dir/data/lsdata.nc .
-ln -sfv $model_dir/data/ECHAM6_CldOptProps.nc .
-#ln -sfv $model_dir/data/MACv2.0-SP_v1.nc .
-rm -f MACv2.0-SP_v1.nc
-cp $model_dir/data/MACv2.0-SP_v1.fixed-for-use-beyond2016.nc MACv2.0-SP_v1.nc
-
-# [files.atmosphere.model.run]
-run_dir=$model_dir/run
-cp -fv $run_dir/dict.iconam.mpim dict.txt
-
-# [files.atmosphere.independent.volcano_aerosols]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    volcano_aerosols_dir=$data_dir/aerosol
-    ln -sfv $volcano_aerosols_dir/bc_aeropt_cmip6_volc_lw_b16_sw_b14_2000.nc \
-        bc_aeropt_cmip6_volc_lw_b16_sw_b14_${yr}.nc
-done # offsets
-
-
-# [files.land]
-land_dir=$data_dir/land
-
-# [files.land.mapped]
-ln -sfv $land_dir/ic_land_soil_1976.nc ic_land_soil.nc
-ln -sfv $land_dir/bc_land_frac_11pfts_1976.nc bc_land_frac.nc
-ln -sfv $land_dir/bc_land_phys_1976.nc bc_land_phys.nc
-ln -sfv $land_dir/bc_land_soil_1976.nc bc_land_soil.nc
-ln -sfv $land_dir/bc_land_sso_1976.nc bc_land_sso.nc
-
-# [files.land.hydro]
-hydro_dir=$land_dir
-# preliminary test version
-ln -sfv $hydro_dir/hdpara_r2b4_0013_0035_v3.nc bc_land_hd.nc
-
-# [files.land.model]
-model_dir=$basedir/externals/jsbach/data
-ln -sfv $model_dir/lctlib_nlct21.def .
-
-
-#-----------------------------------------------------------------------------
-# automatic restart setup
-# set some default values and derive some run parameteres
-restart=${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, coupling and model namelists
-# ------------------------------------------------
-# For a complete list see Namelist_overview and Namelist_overview.pdf
-#
-
-cat > icon_master.namelist << EOF
-&master_nml
-    lrestart =  ${restart}
-/
-&master_time_control_nml
-    calendar = 'proleptic gregorian'
-    checkpointtimeintval = '$checkpoint_interval'
-    restarttimeintval = '$restart_interval'
-    experimentstartdate = '1979-01-01' ! TODO: hack to reproduce result
-    experimentstopdate = '$final_date'
-/
-&master_model_nml ! 'atmo'
-    model_name = 'atmo'
-    model_namelist_filename = 'NAMELIST_atm'
-    model_type = 1
-/
-&jsb_control_nml
-    is_standalone = .false.
-/
-&jsb_model_nml
-    model_id = 1
-    model_name = 'JSBACH'
-    model_shortname = 'jsb'
-    model_description = 'JSBACH land surface model'
-    model_namelist_filename = 'NAMELIST_lnd'
-/
-
-EOF
-
-#-----------------------------------------------------------------------------
-# II. ATMOSPHERE and LAND
-#-----------------------------------------------------------------------------
-#
-# atmosphere namelist
-# -------------------
-cat > NAMELIST_atm << EOF
-&parallel_nml
-    nproma = $nproma
-    num_io_procs = 0
-    num_prefetch_proc = 0
-    pio_type = 1 !1=default, assync I/O, 2=experimental CDI, 0=nothing?
-/
-&grid_nml
-    dynamics_grid_filename = 'icon_grid_G.nc'
-/
-&run_nml
-    num_lev = 47 ! number of full levels
-    modeltimestep = 'PT15M'
-    ltestcase = .false. ! run testcase
-    ldynamics = .true. ! dynamics
-    ltransport = .true. ! transport
-    iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
-    output = 'nml'
-    msg_level = 8 ! level of details report during integration
-    restart_filename = '${EXP}_restart_atm_<rsttime>.nc'
-    activate_sync_timers = .true.
-/
-&extpar_nml
-    itopo = 1 ! 1: read topography from the grid file
-    itype_lwemiss = 0
-/
-&initicon_nml
-    init_mode = 2 ! 2: initialize from IFS analysis
-    ifs2icon_filename = 'ifs2icon.nc'
-/
-&nonhydrostatic_nml
-    ndyn_substeps = 8 ! dtime/dt_dyn
-    damp_height = 50000. ! [m]
-    rayleigh_coeff = 0.1000 ! set to 0.1001 for rerun with little change
-    vwind_offctr = 0.2
-    divdamp_fac = 0.004
-/
-&interpol_nml
-    rbf_scale_mode_ll = 1
-/
-&sleve_nml
-    min_lay_thckn = 40. ! [m]
-    top_height = 83000. ! [m]
-    stretch_fac = 0.9
-    decay_scale_1 = 4000. ! [m]
-    decay_scale_2 = 2500. ! [m]
-    decay_exp = 1.2
-    flat_height = 16000. ! [m]
-/
-&diffusion_nml
-!    hdiff_smag_fac = 0.015000001
-/
-&transport_nml
-    ihadv_tracer = 52, 2, 2
-    itype_hlimit = 3, 4, 4
-    ivadv_tracer = 3, 3, 3
-    tracer_names = 'hus', 'clw', 'cli'
-/
-&echam_phy_nml
-    ! domain 1
-    ! atmospheric physics ("" = never)
-    echam_phy_config(1)%dt_rad = 'PT90M'
-    echam_phy_config(1)%dt_vdf = 'PT15M'
-    echam_phy_config(1)%dt_cnv = 'PT15M'
-    echam_phy_config(1)%dt_cld = 'PT15M'
-    echam_phy_config(1)%dt_gwd = 'PT15M'
-    echam_phy_config(1)%dt_sso = 'PT15M'
-    ! atmospheric chemistry ("" = never)
-    echam_phy_config(1)%dt_mox = 'PT15M'
-    ! sea ice on mixed-layer ocean (""=never)
-    echam_phy_config(1)%dt_ice = 'PT15M'
-    ! surface (true or false)
-    echam_phy_config(1)%ljsb = .true.
-    echam_phy_config(1)%lamip = .false. 
-    echam_phy_config(1)%lice = .true.
-    echam_phy_config(1)%lmlo = .true.
-    echam_phy_config(1)%llake = .true.
-/
-&echam_rad_nml
-    ! domain 1
-    echam_rad_config(1)%isolrad = 3  ! Use insolation for AMIP type CMIP5 simulation (average from 1979-1988
-    echam_rad_config(1)%irad_h2o = 1
-    echam_rad_config(1)%irad_co2 = 2 ! constant concentration given by vmr_co2 etc.
-    echam_rad_config(1)%irad_ch4 = 2
-    echam_rad_config(1)%irad_n2o = 2
-    echam_rad_config(1)%irad_o3 = 8 ! constant annual cycle climatology
-    echam_rad_config(1)%irad_o2 = 2
-    echam_rad_config(1)%irad_cfc11 = 2
-    echam_rad_config(1)%irad_cfc12 = 2
-    echam_rad_config(1)%irad_aero = 18 ! as in AMIP
-    echam_rad_config(1)%vmr_co2 = 1436.0e-6 !359.0e-6 --> 4xCO2
-    echam_rad_config(1)%vmr_ch4 = 1693.0e-9
-    echam_rad_config(1)%vmr_n2o = 311.0e-9
-    echam_rad_config(1)%vmr_o2  = 0.20946
-    echam_rad_config(1)%vmr_cfc11 = 237.0e-12
-    echam_rad_config(1)%vmr_cfc12 = 462.0e-12
-    echam_rad_config(1)%l_stratvapor_zero = .true. ! transparent stratospheric water vapor
-    echam_rad_config(1)%klev_stratvapor_zero = 22  ! transparent stratospheric water vapor
-/
-&echam_gwd_nml
-/
-&echam_sso_nml
-/
-&echam_vdf_nml
-/
-&echam_cnv_nml
-/
-&echam_cld_nml
-/
-&echam_cov_nml
-/
-&ccycle_nml
-/
-&sea_ice_nml
- i_ice_therm  = 1    ! 1=0L-Semtner
-/
-&echam_seaice_mlo_nml
- lqflux               = .true.  ! default .TRUE.
- max_seaice_thickness = 99999.  ! default 5
- qbot_mlo_nh          = 0.      ! default 10
- qbot_mlo_sh          = 0.      ! default 10
-/
-! Parameters for all output files
-! -------------------------------
-&io_nml
-    output_nml_dict = 'dict.txt'
-    netcdf_dict = 'dict.txt'
-    itype_pres_msl = 4
-    ! restart_file_type = 5
-    ! restart_write_mode = 'joint procs multifile' ! not useful in r2b4 setup
-    ! lnetcdf_flt64_output = .true. ! 64 bit output in all files
-    ! lkeep_in_sync = .true. ! sync after each timestep
-    write_initial_state = .false.
-/
-&dbg_index_nml
-    idbg_mxmn = 0 ! initialize MIN/MAX debug output
-    idbg_val = 0 ! initialize one cell debug output
-    idbg_slev = 1 ! initialize start level for debug output
-    idbg_elev = 2 ! initialize end level for debug output
-    dbg_lat_in = 30.0 ! latitude location of one cell debug output
-    dbg_lon_in = -30.0 ! longitude location of one cell debug output
-    str_mod_tst = 'InterFaceOce' ! define modules to print out in debug mode
-/
-
-! Define output files
-! -------------------
-!
-! 3-dimensional files include 'ps' and 'pfull' to allow the vertical
-! interpolation to pressure levels by cdo ap2pl.
-
-/
-! Standard AMIP output...
-&output_nml ! 'atm_3d'
-    output_filename = '${EXP}_atm_3d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'zg', 'ps', 'pfull', 'rho', 'ta', 'ua', 'va', 'wap', 'hus',
-                 'clw', 'cli', 'hur', 'cl'
-/
-! Standard AMIP output...
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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'
-/
-! Standard AMIP output...for daily means
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d_daily'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1D'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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
-
-
-# jsbach namelist
-# ---------------
-
-cat > NAMELIST_lnd << EOF
-
-&jsb_model_nml
-    usecase = 'jsbach_pfts'
-    use_lakes = .true.
-/
-&jsb_seb_nml
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_rad_nml
-    use_alb_veg_simple = .false. ! if jsbach_lite
-    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 = .false.
-    l_heat_cond_dyn = .false.
-    l_snow = .true.
-    l_dynsnow = .true.
-    l_freeze = .true.
-    l_supercool = .true.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_hydro_nml
-    l_organic = .false.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-    bc_sso_filename = 'bc_land_sso.nc'
-/
-&jsb_assimi_nml
-    active = .true. ! if jsbach_pfts
-/
-&jsb_pheno_nml
-    scheme = 'logrop' ! 'climatology' if jsbach_lite
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_carbon_nml
-    active = .true.
-    bc_filename = 'bc_land_carbon.nc'
-    ic_filename = 'ic_land_carbon.nc'
-    read_cpools = .false.
-/
-&jsb_fuel_nml
-    active = .true.
-    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
-
-
-## 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
-
-ldd icon.exe
-
-START="/gpfs/opt/sw/skylake/spack-0.19.0/opt/spack/linux-almalinux8-skylake_avx512/intel-2021.7.1/intel-oneapi-mpi-2021.7.1-fzg6q4xcj7efjmce3cuqa2b7cum5d3po/mpi/2021.7.1/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/runs/slab4x/exp.slab4x.run b/runs/slab4x/exp.slab4x.run
deleted file mode 100755
index f965f49a3a0abbd8366d4d414a70725a351cd0c5..0000000000000000000000000000000000000000
--- a/runs/slab4x/exp.slab4x.run
+++ /dev/null
@@ -1,575 +0,0 @@
-#! /bin/ksh
-#=============================================================================
-#SBATCH --account=p72044
-#SBATCH --partition=skylake_0096
-#SBATCH --qos=skylake_0096
-#SBATCH --job-name=slab4x
-#SBATCH --nodes=3
-#SBATCH --ntasks-per-node=48
-#SBATCH --ntasks-per-core=1
-#SBATCH --output=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x/logfiles/LOG.exp.slab4x.run.%j.o
-#SBATCH --error=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x/logfiles/LOG.exp.slab4x.run.%j.o
-#SBATCH --exclusive
-#SBATCH --time=03:00:00
-#SBATCH --mail-user=aiko.voigt@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=3
-mpi_procs_pernode=48
-((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
-echo $mpi_total_procs
-
-# manual fix for mpi pinning with intel mpirun on vsc
-# see https://wiki.vsc.ac.at/doku.php?id=doku:vsc5quickstart#intel_mpi
-export I_MPI_PIN_RESPECT_CPUSET=0
-
-#
-# blocking length
-# ---------------
-nproma=16
-
-#=============================================================================
-# Input variables:
-
-# SIMULATION NAME
-EXP=slab4x
-
-ICONFOLDER=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie       # DIRECTORY OF ICON MODEL CODE
-RUNSCRIPTDIR=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slab4x/
-basedir=$ICONFOLDER # icon base directory
-
-. ${ICONFOLDER}/run/add_run_routines
-
-# experiment directory, with plenty of space, create if new
-EXPDIR=/gpfs/data/fs72044/avoigt_teach/experiments/s2024/${EXP}
-if [ ! -d ${EXPDIR} ] ;  then
-  mkdir -p ${EXPDIR}
-fi
-#
-ls -ld ${EXPDIR}
-if [ ! -d ${EXPDIR} ] ;  then
-    mkdir ${EXPDIR}
-fi
-ls -ld ${EXPDIR}
-
-cd $EXPDIR
-
-
-
-
-#=================================================================================
-
-#-----------------------------------------------------------------------------
-# global timing
-initial_date="1979-01-01"
-final_date="2039-01-01"
-start_date=$initial_date
-end_date=$final_date
-y0=${start_date%%-*}
-yN=${end_date%%-*}
-
-
-# restart intervals
-restart_interval="P2Y"
-checkpoint_interval="P1Y"
-
-file_interval="P1M"
-
-############################################################
-#
-# NO FURTHER CHANGES TO THE DIRECTORIES AND SIMULATION NAME
-# SHOULD BE NEEDED BELOW THIS LINE
-#
-############################################################
-
-#-----------------------------------------------------------------------------
-# Provide input files
-# $Id: format.tmpl 9264 2021-06-21 21:24:57Z m221078 $
-#
-# [files]
-
-# [files.atmosphere]
-data_dir=/gpfs/data/fs72044/avoigt_teach/ICON-inputdata/amip-VSC4
-
-# [files.atmosphere.mapped]
-grid_dir=$data_dir/grid
-ln -sfv $grid_dir/icon_grid_0013_R02B04_G.nc icon_grid_G.nc
-
-# [files.atmosphere.mapped.initial]
-initial_dir=$data_dir/initial_condition
-ln -sfv $initial_dir/ifs2icon_1979010100_R02B04_G.nc ifs2icon.nc
-
-# [files.atmosphere.mapped.ozone]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    label=${yr}
-        ((yr >= 2015)) && label=2014
-    ozone_dir=$data_dir/ozone
-    ln -sfv $ozone_dir/bc_ozone_historical_1979-2008.ymonmean.${label}.nc bc_ozone_${yr}.nc
-done # offsets
-
-# [files.atmosphere.mapped.ocean_surface]
-ocean_surface_dir=$data_dir/sst_and_seaice
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_sst.nc
-
-# files for slab ocean
-# sst, sic, and seb
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_mlo_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_mlo_sst.nc
-# q-flux file is taken from Phaidra archive of S2023 course
-ln -sfv /gpfs/data/fs72044/avoigt_teach/ICON-inputdata/slabocean/sstclim_seb_atm_seb_2d_ml_1980-2008.ymonmean.seb_wtr.addc_3.1970-2069.nc bc_mlo_seb.nc
-
-# [files.atmosphere.mapped.aerosols]
-aerosols_dir=$data_dir/aerosol
-ln -sfv $aerosols_dir/bc_aeropt_kinne_lw_b16_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_fin_1850.nc bc_aeropt_kinne_sw_b14_fin.nc
-
-# [files.atmosphere.model]
-model_dir=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie
-
-# [files.atmosphere.model.data]
-ln -sfv $model_dir/data/lsdata.nc .
-ln -sfv $model_dir/data/ECHAM6_CldOptProps.nc .
-#ln -sfv $model_dir/data/MACv2.0-SP_v1.nc .
-rm -f MACv2.0-SP_v1.nc
-cp $model_dir/data/MACv2.0-SP_v1.fixed-for-use-beyond2016.nc MACv2.0-SP_v1.nc
-
-# [files.atmosphere.model.run]
-run_dir=$model_dir/run
-cp -fv $run_dir/dict.iconam.mpim dict.txt
-
-# [files.atmosphere.independent.volcano_aerosols]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    volcano_aerosols_dir=$data_dir/aerosol
-    ln -sfv $volcano_aerosols_dir/bc_aeropt_cmip6_volc_lw_b16_sw_b14_2000.nc \
-        bc_aeropt_cmip6_volc_lw_b16_sw_b14_${yr}.nc
-done # offsets
-
-
-# [files.land]
-land_dir=$data_dir/land
-
-# [files.land.mapped]
-ln -sfv $land_dir/ic_land_soil_1976.nc ic_land_soil.nc
-ln -sfv $land_dir/bc_land_frac_11pfts_1976.nc bc_land_frac.nc
-ln -sfv $land_dir/bc_land_phys_1976.nc bc_land_phys.nc
-ln -sfv $land_dir/bc_land_soil_1976.nc bc_land_soil.nc
-ln -sfv $land_dir/bc_land_sso_1976.nc bc_land_sso.nc
-
-# [files.land.hydro]
-hydro_dir=$land_dir
-# preliminary test version
-ln -sfv $hydro_dir/hdpara_r2b4_0013_0035_v3.nc bc_land_hd.nc
-
-# [files.land.model]
-model_dir=$basedir/externals/jsbach/data
-ln -sfv $model_dir/lctlib_nlct21.def .
-
-
-#-----------------------------------------------------------------------------
-# automatic restart setup
-# set some default values and derive some run parameteres
-restart=${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, coupling and model namelists
-# ------------------------------------------------
-# For a complete list see Namelist_overview and Namelist_overview.pdf
-#
-
-cat > icon_master.namelist << EOF
-&master_nml
-    lrestart =  ${restart}
-/
-&master_time_control_nml
-    calendar = 'proleptic gregorian'
-    checkpointtimeintval = '$checkpoint_interval'
-    restarttimeintval = '$restart_interval'
-    experimentstartdate = '1979-01-01' ! TODO: hack to reproduce result
-    experimentstopdate = '$final_date'
-/
-&master_model_nml ! 'atmo'
-    model_name = 'atmo'
-    model_namelist_filename = 'NAMELIST_atm'
-    model_type = 1
-/
-&jsb_control_nml
-    is_standalone = .false.
-/
-&jsb_model_nml
-    model_id = 1
-    model_name = 'JSBACH'
-    model_shortname = 'jsb'
-    model_description = 'JSBACH land surface model'
-    model_namelist_filename = 'NAMELIST_lnd'
-/
-
-EOF
-
-#-----------------------------------------------------------------------------
-# II. ATMOSPHERE and LAND
-#-----------------------------------------------------------------------------
-#
-# atmosphere namelist
-# -------------------
-cat > NAMELIST_atm << EOF
-&parallel_nml
-    nproma = $nproma
-    num_io_procs = 0
-    num_prefetch_proc = 0
-    pio_type = 1 !1=default, assync I/O, 2=experimental CDI, 0=nothing?
-/
-&grid_nml
-    dynamics_grid_filename = 'icon_grid_G.nc'
-/
-&run_nml
-    num_lev = 47 ! number of full levels
-    modeltimestep = 'PT15M'
-    ltestcase = .false. ! run testcase
-    ldynamics = .true. ! dynamics
-    ltransport = .true. ! transport
-    iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
-    output = 'nml'
-    msg_level = 8 ! level of details report during integration
-    restart_filename = '${EXP}_restart_atm_<rsttime>.nc'
-    activate_sync_timers = .true.
-/
-&extpar_nml
-    itopo = 1 ! 1: read topography from the grid file
-    itype_lwemiss = 0
-/
-&initicon_nml
-    init_mode = 2 ! 2: initialize from IFS analysis
-    ifs2icon_filename = 'ifs2icon.nc'
-/
-&nonhydrostatic_nml
-    ndyn_substeps = 8 ! dtime/dt_dyn
-    damp_height = 50000. ! [m]
-    rayleigh_coeff = 0.1000 ! set to 0.1001 for rerun with little change
-    vwind_offctr = 0.2
-    divdamp_fac = 0.004
-/
-&interpol_nml
-    rbf_scale_mode_ll = 1
-/
-&sleve_nml
-    min_lay_thckn = 40. ! [m]
-    top_height = 83000. ! [m]
-    stretch_fac = 0.9
-    decay_scale_1 = 4000. ! [m]
-    decay_scale_2 = 2500. ! [m]
-    decay_exp = 1.2
-    flat_height = 16000. ! [m]
-/
-&diffusion_nml
-!    hdiff_smag_fac = 0.015000001
-/
-&transport_nml
-    ihadv_tracer = 52, 2, 2
-    itype_hlimit = 3, 4, 4
-    ivadv_tracer = 3, 3, 3
-    tracer_names = 'hus', 'clw', 'cli'
-/
-&echam_phy_nml
-    ! domain 1
-    ! atmospheric physics ("" = never)
-    echam_phy_config(1)%dt_rad = 'PT90M'
-    echam_phy_config(1)%dt_vdf = 'PT15M'
-    echam_phy_config(1)%dt_cnv = 'PT15M'
-    echam_phy_config(1)%dt_cld = 'PT15M'
-    echam_phy_config(1)%dt_gwd = 'PT15M'
-    echam_phy_config(1)%dt_sso = 'PT15M'
-    ! atmospheric chemistry ("" = never)
-    echam_phy_config(1)%dt_mox = 'PT15M'
-    ! sea ice on mixed-layer ocean (""=never)
-    echam_phy_config(1)%dt_ice = 'PT15M'
-    ! surface (true or false)
-    echam_phy_config(1)%ljsb = .true.
-    echam_phy_config(1)%lamip = .false. 
-    echam_phy_config(1)%lice = .true.
-    echam_phy_config(1)%lmlo = .true.
-    echam_phy_config(1)%llake = .true.
-/
-&echam_rad_nml
-    ! domain 1
-    echam_rad_config(1)%isolrad = 3  ! Use insolation for AMIP type CMIP5 simulation (average from 1979-1988
-    echam_rad_config(1)%irad_h2o = 1
-    echam_rad_config(1)%irad_co2 = 2 ! constant concentration given by vmr_co2 etc.
-    echam_rad_config(1)%irad_ch4 = 2
-    echam_rad_config(1)%irad_n2o = 2
-    echam_rad_config(1)%irad_o3 = 8 ! constant annual cycle climatology
-    echam_rad_config(1)%irad_o2 = 2
-    echam_rad_config(1)%irad_cfc11 = 2
-    echam_rad_config(1)%irad_cfc12 = 2
-    echam_rad_config(1)%irad_aero = 18 ! as in AMIP
-    echam_rad_config(1)%vmr_co2 = 1436.0e-6 !359.0e-6 --> 4xCO2
-    echam_rad_config(1)%vmr_ch4 = 1693.0e-9
-    echam_rad_config(1)%vmr_n2o = 311.0e-9
-    echam_rad_config(1)%vmr_o2  = 0.20946
-    echam_rad_config(1)%vmr_cfc11 = 237.0e-12
-    echam_rad_config(1)%vmr_cfc12 = 462.0e-12
-/
-&echam_gwd_nml
-/
-&echam_sso_nml
-/
-&echam_vdf_nml
-/
-&echam_cnv_nml
-/
-&echam_cld_nml
-/
-&echam_cov_nml
-/
-&ccycle_nml
-/
-&sea_ice_nml
- i_ice_therm  = 1    ! 1=0L-Semtner
-/
-&echam_seaice_mlo_nml
- lqflux               = .true.  ! default .TRUE.
- max_seaice_thickness = 99999.  ! default 5
- qbot_mlo_nh          = 0.      ! default 10
- qbot_mlo_sh          = 0.      ! default 10
-/
-! Parameters for all output files
-! -------------------------------
-&io_nml
-    output_nml_dict = 'dict.txt'
-    netcdf_dict = 'dict.txt'
-    itype_pres_msl = 4
-    ! restart_file_type = 5
-    ! restart_write_mode = 'joint procs multifile' ! not useful in r2b4 setup
-    ! lnetcdf_flt64_output = .true. ! 64 bit output in all files
-    ! lkeep_in_sync = .true. ! sync after each timestep
-    write_initial_state = .false.
-/
-&dbg_index_nml
-    idbg_mxmn = 0 ! initialize MIN/MAX debug output
-    idbg_val = 0 ! initialize one cell debug output
-    idbg_slev = 1 ! initialize start level for debug output
-    idbg_elev = 2 ! initialize end level for debug output
-    dbg_lat_in = 30.0 ! latitude location of one cell debug output
-    dbg_lon_in = -30.0 ! longitude location of one cell debug output
-    str_mod_tst = 'InterFaceOce' ! define modules to print out in debug mode
-/
-
-! Define output files
-! -------------------
-!
-! 3-dimensional files include 'ps' and 'pfull' to allow the vertical
-! interpolation to pressure levels by cdo ap2pl.
-
-/
-! Standard AMIP output...
-&output_nml ! 'atm_3d'
-    output_filename = '${EXP}_atm_3d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'zg', 'ps', 'pfull', 'rho', 'ta', 'ua', 'va', 'wap', 'hus',
-                 'clw', 'cli', 'hur', 'cl'
-/
-! Standard AMIP output...
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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'
-/
-! Standard AMIP output...for daily means
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d_daily'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1D'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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
-
-
-# jsbach namelist
-# ---------------
-
-cat > NAMELIST_lnd << EOF
-
-&jsb_model_nml
-    usecase = 'jsbach_pfts'
-    use_lakes = .true.
-/
-&jsb_seb_nml
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_rad_nml
-    use_alb_veg_simple = .false. ! if jsbach_lite
-    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 = .false.
-    l_heat_cond_dyn = .false.
-    l_snow = .true.
-    l_dynsnow = .true.
-    l_freeze = .true.
-    l_supercool = .true.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_hydro_nml
-    l_organic = .false.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-    bc_sso_filename = 'bc_land_sso.nc'
-/
-&jsb_assimi_nml
-    active = .true. ! if jsbach_pfts
-/
-&jsb_pheno_nml
-    scheme = 'logrop' ! 'climatology' if jsbach_lite
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_carbon_nml
-    active = .true.
-    bc_filename = 'bc_land_carbon.nc'
-    ic_filename = 'ic_land_carbon.nc'
-    read_cpools = .false.
-/
-&jsb_fuel_nml
-    active = .true.
-    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
-
-
-## 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
-
-ldd icon.exe
-
-START="/gpfs/opt/sw/skylake/spack-0.19.0/opt/spack/linux-almalinux8-skylake_avx512/intel-2021.7.1/intel-oneapi-mpi-2021.7.1-fzg6q4xcj7efjmce3cuqa2b7cum5d3po/mpi/2021.7.1/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/runs/slabctr/exp.slabctr.run b/runs/slabctr/exp.slabctr.run
deleted file mode 100755
index 149805e9e8517bd745041fd4a5b432e60979019f..0000000000000000000000000000000000000000
--- a/runs/slabctr/exp.slabctr.run
+++ /dev/null
@@ -1,575 +0,0 @@
-#! /bin/ksh
-#=============================================================================
-#SBATCH --account=p72044
-#SBATCH --partition=skylake_0096
-#SBATCH --qos=skylake_0096
-#SBATCH --job-name=slabctr
-#SBATCH --nodes=3
-#SBATCH --ntasks-per-node=48
-#SBATCH --ntasks-per-core=1
-#SBATCH --output=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slabctr/logfiles/LOG.exp.slabctr.run.%j.o
-#SBATCH --error=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slabctr/logfiles/LOG.exp.slabctr.run.%j.o
-#SBATCH --exclusive
-#SBATCH --time=03:00:00
-#SBATCH --mail-user=aiko.voigt@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=3
-mpi_procs_pernode=48
-((mpi_total_procs=no_of_nodes * mpi_procs_pernode))
-echo $mpi_total_procs
-
-# manual fix for mpi pinning with intel mpirun on vsc
-# see https://wiki.vsc.ac.at/doku.php?id=doku:vsc5quickstart#intel_mpi
-export I_MPI_PIN_RESPECT_CPUSET=0
-
-#
-# blocking length
-# ---------------
-nproma=16
-
-#=============================================================================
-# Input variables:
-
-# SIMULATION NAME
-EXP=slabctr
-
-ICONFOLDER=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie       # DIRECTORY OF ICON MODEL CODE
-RUNSCRIPTDIR=/home/fs72044/avoigt_teach/climlab_s2024/msc-climmodlab-s2024/runs/slabctr/
-basedir=$ICONFOLDER # icon base directory
-
-. ${ICONFOLDER}/run/add_run_routines
-
-# experiment directory, with plenty of space, create if new
-EXPDIR=/gpfs/data/fs72044/avoigt_teach/experiments/s2024/${EXP}
-if [ ! -d ${EXPDIR} ] ;  then
-  mkdir -p ${EXPDIR}
-fi
-#
-ls -ld ${EXPDIR}
-if [ ! -d ${EXPDIR} ] ;  then
-    mkdir ${EXPDIR}
-fi
-ls -ld ${EXPDIR}
-
-cd $EXPDIR
-
-
-
-
-#=================================================================================
-
-#-----------------------------------------------------------------------------
-# global timing
-initial_date="1979-01-01"
-final_date="2029-01-01"
-start_date=$initial_date
-end_date=$final_date
-y0=${start_date%%-*}
-yN=${end_date%%-*}
-
-
-# restart intervals
-restart_interval="P2Y"
-checkpoint_interval="P1Y"
-
-file_interval="P1M"
-
-############################################################
-#
-# NO FURTHER CHANGES TO THE DIRECTORIES AND SIMULATION NAME
-# SHOULD BE NEEDED BELOW THIS LINE
-#
-############################################################
-
-#-----------------------------------------------------------------------------
-# Provide input files
-# $Id: format.tmpl 9264 2021-06-21 21:24:57Z m221078 $
-#
-# [files]
-
-# [files.atmosphere]
-data_dir=/gpfs/data/fs72044/avoigt_teach/ICON-inputdata/amip-VSC4
-
-# [files.atmosphere.mapped]
-grid_dir=$data_dir/grid
-ln -sfv $grid_dir/icon_grid_0013_R02B04_G.nc icon_grid_G.nc
-
-# [files.atmosphere.mapped.initial]
-initial_dir=$data_dir/initial_condition
-ln -sfv $initial_dir/ifs2icon_1979010100_R02B04_G.nc ifs2icon.nc
-
-# [files.atmosphere.mapped.ozone]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    label=${yr}
-        ((yr >= 2015)) && label=2014
-    ozone_dir=$data_dir/ozone
-    ln -sfv $ozone_dir/bc_ozone_historical_1979-2008.ymonmean.${label}.nc bc_ozone_${yr}.nc
-done # offsets
-
-# [files.atmosphere.mapped.ocean_surface]
-ocean_surface_dir=$data_dir/sst_and_seaice
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_sst.nc
-
-# files for slab ocean
-# sst, sic, and seb
-ln -sfv $ocean_surface_dir/bc_sic_1979_2008.ymonmean.1978-2079.nc bc_mlo_sic.nc
-ln -sfv $ocean_surface_dir/bc_sst_1979_2008.ymonmean.1978-2079.nc bc_mlo_sst.nc
-# q-flux file is taken from Phaidra archive of S2023 course
-ln -sfv /gpfs/data/fs72044/avoigt_teach/ICON-inputdata/slabocean/sstclim_seb_atm_seb_2d_ml_1980-2008.ymonmean.seb_wtr.addc_3.1970-2069.nc bc_mlo_seb.nc
-
-# [files.atmosphere.mapped.aerosols]
-aerosols_dir=$data_dir/aerosol
-ln -sfv $aerosols_dir/bc_aeropt_kinne_lw_b16_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_coa.nc .
-ln -sfv $aerosols_dir/bc_aeropt_kinne_sw_b14_fin_1850.nc bc_aeropt_kinne_sw_b14_fin.nc
-
-# [files.atmosphere.model]
-model_dir=/home/fs72044/avoigt_teach/climlab_s2024/icon-esm-univie
-
-# [files.atmosphere.model.data]
-ln -sfv $model_dir/data/lsdata.nc .
-ln -sfv $model_dir/data/ECHAM6_CldOptProps.nc .
-#ln -sfv $model_dir/data/MACv2.0-SP_v1.nc .
-rm -f MACv2.0-SP_v1.nc
-cp $model_dir/data/MACv2.0-SP_v1.fixed-for-use-beyond2016.nc MACv2.0-SP_v1.nc
-
-# [files.atmosphere.model.run]
-run_dir=$model_dir/run
-cp -fv $run_dir/dict.iconam.mpim dict.txt
-
-# [files.atmosphere.independent.volcano_aerosols]
-for((yr = y0 + -1; yr <= yN + 1; ++yr)) 
-do
-    volcano_aerosols_dir=$data_dir/aerosol
-    ln -sfv $volcano_aerosols_dir/bc_aeropt_cmip6_volc_lw_b16_sw_b14_2000.nc \
-        bc_aeropt_cmip6_volc_lw_b16_sw_b14_${yr}.nc
-done # offsets
-
-
-# [files.land]
-land_dir=$data_dir/land
-
-# [files.land.mapped]
-ln -sfv $land_dir/ic_land_soil_1976.nc ic_land_soil.nc
-ln -sfv $land_dir/bc_land_frac_11pfts_1976.nc bc_land_frac.nc
-ln -sfv $land_dir/bc_land_phys_1976.nc bc_land_phys.nc
-ln -sfv $land_dir/bc_land_soil_1976.nc bc_land_soil.nc
-ln -sfv $land_dir/bc_land_sso_1976.nc bc_land_sso.nc
-
-# [files.land.hydro]
-hydro_dir=$land_dir
-# preliminary test version
-ln -sfv $hydro_dir/hdpara_r2b4_0013_0035_v3.nc bc_land_hd.nc
-
-# [files.land.model]
-model_dir=$basedir/externals/jsbach/data
-ln -sfv $model_dir/lctlib_nlct21.def .
-
-
-#-----------------------------------------------------------------------------
-# automatic restart setup
-# set some default values and derive some run parameteres
-restart=${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, coupling and model namelists
-# ------------------------------------------------
-# For a complete list see Namelist_overview and Namelist_overview.pdf
-#
-
-cat > icon_master.namelist << EOF
-&master_nml
-    lrestart =  ${restart}
-/
-&master_time_control_nml
-    calendar = 'proleptic gregorian'
-    checkpointtimeintval = '$checkpoint_interval'
-    restarttimeintval = '$restart_interval'
-    experimentstartdate = '1979-01-01' ! TODO: hack to reproduce result
-    experimentstopdate = '$final_date'
-/
-&master_model_nml ! 'atmo'
-    model_name = 'atmo'
-    model_namelist_filename = 'NAMELIST_atm'
-    model_type = 1
-/
-&jsb_control_nml
-    is_standalone = .false.
-/
-&jsb_model_nml
-    model_id = 1
-    model_name = 'JSBACH'
-    model_shortname = 'jsb'
-    model_description = 'JSBACH land surface model'
-    model_namelist_filename = 'NAMELIST_lnd'
-/
-
-EOF
-
-#-----------------------------------------------------------------------------
-# II. ATMOSPHERE and LAND
-#-----------------------------------------------------------------------------
-#
-# atmosphere namelist
-# -------------------
-cat > NAMELIST_atm << EOF
-&parallel_nml
-    nproma = $nproma
-    num_io_procs = 0
-    num_prefetch_proc = 0
-    pio_type = 1 !1=default, assync I/O, 2=experimental CDI, 0=nothing?
-/
-&grid_nml
-    dynamics_grid_filename = 'icon_grid_G.nc'
-/
-&run_nml
-    num_lev = 47 ! number of full levels
-    modeltimestep = 'PT15M'
-    ltestcase = .false. ! run testcase
-    ldynamics = .true. ! dynamics
-    ltransport = .true. ! transport
-    iforcing = 2 ! 0: none, 1: HS, 2: ECHAM, 3: NWP
-    output = 'nml'
-    msg_level = 8 ! level of details report during integration
-    restart_filename = '${EXP}_restart_atm_<rsttime>.nc'
-    activate_sync_timers = .true.
-/
-&extpar_nml
-    itopo = 1 ! 1: read topography from the grid file
-    itype_lwemiss = 0
-/
-&initicon_nml
-    init_mode = 2 ! 2: initialize from IFS analysis
-    ifs2icon_filename = 'ifs2icon.nc'
-/
-&nonhydrostatic_nml
-    ndyn_substeps = 8 ! dtime/dt_dyn
-    damp_height = 50000. ! [m]
-    rayleigh_coeff = 0.1000 ! set to 0.1001 for rerun with little change
-    vwind_offctr = 0.2
-    divdamp_fac = 0.004
-/
-&interpol_nml
-    rbf_scale_mode_ll = 1
-/
-&sleve_nml
-    min_lay_thckn = 40. ! [m]
-    top_height = 83000. ! [m]
-    stretch_fac = 0.9
-    decay_scale_1 = 4000. ! [m]
-    decay_scale_2 = 2500. ! [m]
-    decay_exp = 1.2
-    flat_height = 16000. ! [m]
-/
-&diffusion_nml
-!    hdiff_smag_fac = 0.015000001
-/
-&transport_nml
-    ihadv_tracer = 52, 2, 2
-    itype_hlimit = 3, 4, 4
-    ivadv_tracer = 3, 3, 3
-    tracer_names = 'hus', 'clw', 'cli'
-/
-&echam_phy_nml
-    ! domain 1
-    ! atmospheric physics ("" = never)
-    echam_phy_config(1)%dt_rad = 'PT90M'
-    echam_phy_config(1)%dt_vdf = 'PT15M'
-    echam_phy_config(1)%dt_cnv = 'PT15M'
-    echam_phy_config(1)%dt_cld = 'PT15M'
-    echam_phy_config(1)%dt_gwd = 'PT15M'
-    echam_phy_config(1)%dt_sso = 'PT15M'
-    ! atmospheric chemistry ("" = never)
-    echam_phy_config(1)%dt_mox = 'PT15M'
-    ! sea ice on mixed-layer ocean (""=never)
-    echam_phy_config(1)%dt_ice = 'PT15M'
-    ! surface (true or false)
-    echam_phy_config(1)%ljsb = .true.
-    echam_phy_config(1)%lamip = .false. 
-    echam_phy_config(1)%lice = .true.
-    echam_phy_config(1)%lmlo = .true.
-    echam_phy_config(1)%llake = .true.
-/
-&echam_rad_nml
-    ! domain 1
-    echam_rad_config(1)%isolrad = 3  ! Use insolation for AMIP type CMIP5 simulation (average from 1979-1988
-    echam_rad_config(1)%irad_h2o = 1
-    echam_rad_config(1)%irad_co2 = 2 ! constant concentration given by vmr_co2 etc.
-    echam_rad_config(1)%irad_ch4 = 2
-    echam_rad_config(1)%irad_n2o = 2
-    echam_rad_config(1)%irad_o3 = 8 ! constant annual cycle climatology
-    echam_rad_config(1)%irad_o2 = 2
-    echam_rad_config(1)%irad_cfc11 = 2
-    echam_rad_config(1)%irad_cfc12 = 2
-    echam_rad_config(1)%irad_aero = 18 ! as in AMIP
-    echam_rad_config(1)%vmr_co2 = 359.0e-6
-    echam_rad_config(1)%vmr_ch4 = 1693.0e-9
-    echam_rad_config(1)%vmr_n2o = 311.0e-9
-    echam_rad_config(1)%vmr_o2  = 0.20946
-    echam_rad_config(1)%vmr_cfc11 = 237.0e-12
-    echam_rad_config(1)%vmr_cfc12 = 462.0e-12
-/
-&echam_gwd_nml
-/
-&echam_sso_nml
-/
-&echam_vdf_nml
-/
-&echam_cnv_nml
-/
-&echam_cld_nml
-/
-&echam_cov_nml
-/
-&ccycle_nml
-/
-&sea_ice_nml
- i_ice_therm  = 1    ! 1=0L-Semtner
-/
-&echam_seaice_mlo_nml
- lqflux               = .true.  ! default .TRUE.
- max_seaice_thickness = 99999.  ! default 5
- qbot_mlo_nh          = 0.      ! default 10
- qbot_mlo_sh          = 0.      ! default 10
-/
-! Parameters for all output files
-! -------------------------------
-&io_nml
-    output_nml_dict = 'dict.txt'
-    netcdf_dict = 'dict.txt'
-    itype_pres_msl = 4
-    ! restart_file_type = 5
-    ! restart_write_mode = 'joint procs multifile' ! not useful in r2b4 setup
-    ! lnetcdf_flt64_output = .true. ! 64 bit output in all files
-    ! lkeep_in_sync = .true. ! sync after each timestep
-    write_initial_state = .false.
-/
-&dbg_index_nml
-    idbg_mxmn = 0 ! initialize MIN/MAX debug output
-    idbg_val = 0 ! initialize one cell debug output
-    idbg_slev = 1 ! initialize start level for debug output
-    idbg_elev = 2 ! initialize end level for debug output
-    dbg_lat_in = 30.0 ! latitude location of one cell debug output
-    dbg_lon_in = -30.0 ! longitude location of one cell debug output
-    str_mod_tst = 'InterFaceOce' ! define modules to print out in debug mode
-/
-
-! Define output files
-! -------------------
-!
-! 3-dimensional files include 'ps' and 'pfull' to allow the vertical
-! interpolation to pressure levels by cdo ap2pl.
-
-/
-! Standard AMIP output...
-&output_nml ! 'atm_3d'
-    output_filename = '${EXP}_atm_3d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'zg', 'ps', 'pfull', 'rho', 'ta', 'ua', 'va', 'wap', 'hus',
-                 'clw', 'cli', 'hur', 'cl'
-/
-! Standard AMIP output...
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1M'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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'
-/
-! Standard AMIP output...for daily means
-&output_nml ! 'atm_2d'
-    output_filename = '${EXP}_atm_2d_daily'
-    filename_format = '<output_filename>_<levtype_l>_<datetime2>'
-    filetype = 5
-    remap = 0
-    operation = 'mean'
-    output_grid = .TRUE.
-    output_start = '${initial_date}'
-    output_end = '${final_date}'
-    output_interval = 'P1D'
-    file_interval = '$file_interval' !'P10YT1S'
-    include_last = .false.
-    ml_varlist = 'orog', 'ps', 'psl', 'cosmu0', 'rsdt', 'rsut', 'rsutcs',
-                 'rlut', 'rlutcs', 'rsds', 'rsdscs', 'rlds', 'rldscs', 'rsus',
-                 'rsuscs', 'rlus', 'ts', 'sic', 'sit', '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
-
-
-# jsbach namelist
-# ---------------
-
-cat > NAMELIST_lnd << EOF
-
-&jsb_model_nml
-    usecase = 'jsbach_pfts'
-    use_lakes = .true.
-/
-&jsb_seb_nml
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_rad_nml
-    use_alb_veg_simple = .false. ! if jsbach_lite
-    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 = .false.
-    l_heat_cond_dyn = .false.
-    l_snow = .true.
-    l_dynsnow = .true.
-    l_freeze = .true.
-    l_supercool = .true.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_hydro_nml
-    l_organic = .false.
-    bc_filename = 'bc_land_soil.nc'
-    ic_filename = 'ic_land_soil.nc'
-    bc_sso_filename = 'bc_land_sso.nc'
-/
-&jsb_assimi_nml
-    active = .true. ! if jsbach_pfts
-/
-&jsb_pheno_nml
-    scheme = 'logrop' ! 'climatology' if jsbach_lite
-    bc_filename = 'bc_land_phys.nc'
-    ic_filename = 'ic_land_soil.nc'
-/
-&jsb_carbon_nml
-    active = .true.
-    bc_filename = 'bc_land_carbon.nc'
-    ic_filename = 'ic_land_carbon.nc'
-    read_cpools = .false.
-/
-&jsb_fuel_nml
-    active = .true.
-    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
-
-
-## 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
-
-ldd icon.exe
-
-START="/gpfs/opt/sw/skylake/spack-0.19.0/opt/spack/linux-almalinux8-skylake_avx512/intel-2021.7.1/intel-oneapi-mpi-2021.7.1-fzg6q4xcj7efjmce3cuqa2b7cum5d3po/mpi/2021.7.1/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 "============================"
-#-----------------------------------------------------------------------------