diff --git a/.ipynb_checkpoints/Fig1-overview_bifurcation-checkpoint.ipynb b/.ipynb_checkpoints/Fig1-overview_bifurcation-checkpoint.ipynb
deleted file mode 100644
index b936f92b584a7745777c3bd73833e543260b4b9a..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/Fig1-overview_bifurcation-checkpoint.ipynb
+++ /dev/null
@@ -1,216 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6e13661d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'ICON_tools' from '../../python_packages/ICON_tools.py'>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "from os import path\n",
-    "import sys, importlib\n",
-    "\n",
-    "sys.path.append(\"../../python_packages\")\n",
-    "import ICON_tools\n",
-    "\n",
-    "importlib.reload(ICON_tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "31e3b222",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ape_ia_7000_56_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_7000_56_3W\n",
-      "ape_ia_8000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_90_3W\n",
-      "ape_ia_8500_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8500_90_3W\n",
-      "ape_ia_9000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_90_3W\n",
-      "ape_ia_15000_17_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_15000_17_3W\n",
-      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
-      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
-      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
-      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
-      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
-      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_path = \"/jetfs/scratch/jhoerner/postprocessing\"\n",
-    "Wexplist, Wnexp = ICON_tools.get_explist(data_path, [\"ape_ia_7000_56_3W\", \"ape_ia_8000_90_3W\", \"ape_ia_8500_90_3W\", \"ape_ia_9000_90_3W\", \"ape_ia_15000_17_3W\"])\n",
-    "WDSlistgm, _ = ICON_tools.load_ds_2d(data_path, Wexplist, True)\n",
-    "\n",
-    "\n",
-    "Sexplist, Snexp = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
-    "SDSlistgm, SDSlistzm = ICON_tools.load_ds_2d(data_path, Sexplist, True)\n",
-    "\n",
-    "SDSlistgmym = np.empty([Snexp], dtype=\"object\")\n",
-    "SDSlistzmym = np.empty([Snexp], dtype=\"object\")\n",
-    "WDSlistgmym = np.empty([Wnexp], dtype=\"object\")\n",
-    "\n",
-    "for i in range(Snexp):\n",
-    "    # fillna\n",
-    "    SDSlistgm[i] = SDSlistgm[i].where(SDSlistgm[i]['sic'] < 1e36)  \n",
-    "    SDSlistgmym[i] = xr.decode_cf(SDSlistgm[i]).groupby('time.year').mean(dim='time')\n",
-    "    SDSlistzm[i] = SDSlistzm[i].where(SDSlistzm[i]['sic'] < 1e36)  \n",
-    "    SDSlistzmym[i] = xr.decode_cf(SDSlistzm[i]).groupby('time.year').mean(dim='time')\n",
-    "\n",
-    "for i in range(Wnexp):\n",
-    "    # fillna\n",
-    "    WDSlistgm[i] = WDSlistgm[i].where(WDSlistgm[i]['sic'] < 1e36)\n",
-    "    WDSlistgmym[i] = xr.decode_cf(WDSlistgm[i]).groupby('time.year').mean(dim='time')\n",
-    "\n",
-    "\n",
-    "colorlist = [\"C1\",\"C0\",\"C2\",\"C3\",\"C5\",\"C6\",\"C7\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "d3b41d22",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(1,2,figsize=(12,4))\n",
-    "\n",
-    "# first plot: simoverview\n",
-    "for j in [0,1]:\n",
-    "    if j==0:\n",
-    "        DSlistgm=WDSlistgmym\n",
-    "        explist=Wexplist\n",
-    "        legend_label=\"Winton\"\n",
-    "    elif j==1:\n",
-    "        DSlistgm=SDSlistgmym\n",
-    "        explist=Sexplist\n",
-    "        legend_label=\"Semtner\"\n",
-    "    color=colorlist[j]\n",
-    "    for i, exp in enumerate(explist): # simulations\n",
-    "        ax[0].plot(DSlistgm[i].year,(DSlistgm[i][\"sic\"].squeeze()), color=color, \n",
-    "                            label= ICON_tools.find_co2_expname_vscicona(exp) +\"ppm\",  ls=\"-\", clip_on=False) \n",
-    "\n",
-    "#ax[0].legend(ncol=2)\n",
-    "ax[0].set_xlabel(\"time [years]\")\n",
-    "\n",
-    "\n",
-    "yticks=[90,60,45,30,20,10,0]\n",
-    "ax[0].set_yticks((ICON_tools.icelatosic(yticks)))\n",
-    "ax[0].set_yticklabels(yticks)\n",
-    "ax[0].invert_yaxis()\n",
-    "ax[0].set_xlim(0,400)\n",
-    "ax[0].set_ylim(1,0)\n",
-    "\n",
-    "ax[0].set_ylabel(\"ice-margin latitude [°]\")\n",
-    "ax[0].spines['left'].set_position(('outward',5))\n",
-    "ax[0].spines['bottom'].set_position(('outward',5))\n",
-    "\n",
-    "\n",
-    "ax[0].spines['top'].set_visible(False)\n",
-    "ax[0].spines['right'].set_visible(False)\n",
-    "\n",
-    "\n",
-    "\n",
-    "#second plot: bifurcation\n",
-    "color=colorlist[0]\n",
-    "for i, exp in enumerate(Wexplist):\n",
-    "    ax[1].scatter(float(ICON_tools.find_co2_expname_vscicona(exp)),(1-WDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False)\n",
-    "\n",
-    "color=colorlist[1]\n",
-    "for i, exp in enumerate(Sexplist):\n",
-    "    #ax[1].vlines(float(ICON_tools.find_co2_expname_vscicona(exp))*1.006,(1-SDSlistgmym[i][\"sic\"][0]),(1-SDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, lw=1)\n",
-    "    if exp==\"ape_ia_9000_13_0S\" or exp==\"ape_ia_10000_13_0S\":\n",
-    "        ax[1].scatter(float(ICON_tools.find_co2_expname_vscicona(exp)),(1-SDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=\"^\")\n",
-    "    else:\n",
-    "        ax[1].scatter(float(ICON_tools.find_co2_expname_vscicona(exp)),(1-SDSlistgmym[i][\"sic\"][-1]), color=color, clip_on=False, marker=\"o\")\n",
-    "\n",
-    "    \n",
-    "yticks_deg=[0,10,20,30,45,60,90]\n",
-    "ax[1].set_yticks(1-ICON_tools.icelatosic(yticks_deg))\n",
-    "ax[1].set_ylim(0,1)\n",
-    "ax[1].set_yticklabels(yticks_deg)\n",
-    "ax[1].set_xscale('log')\n",
-    "ax[1].set_xlim(4500,15000)\n",
-    "ax[1].set_xticks([5000,6000,7000,8000,9000,10000,15000])\n",
-    "ax[1].set_xticklabels([\"5e3\",\"6e3\",\"7e3\",\"8e3\",\"9e3\",\"1e4\",\"1.5e4\"])\n",
-    "ax[1].set_xlabel(\"CO$_2$ [ppmv]\")\n",
-    "#ax[1].set_ylabel(\"ice-margin latitude [°]\")\n",
-    "ax[1].spines['left'].set_position(('outward', 5))\n",
-    "ax[1].spines['bottom'].set_position(('outward', 5))\n",
-    "ax[1].spines['top'].set_visible(False)\n",
-    "ax[1].spines['right'].set_visible(False)\n",
-    "\n",
-    "ax[0].annotate(\"a)\", xycoords=\"axes fraction\", xy=(0.01,0.98), fontweight=\"bold\")\n",
-    "ax[1].annotate(\"b)\", xycoords=\"axes fraction\", xy=(0.01,0.98), fontweight=\"bold\")\n",
-    "\n",
-    "#ax[1].axhspan(1-0,1-ICON_tools.icelatosic(49), color='lightgray', alpha=0.4, lw=0)\n",
-    "#ax[1].axhspan(1-ICON_tools.icelatosic(16), 1-ICON_tools.icelatosic(11.5), color='lightgray', alpha=0.4, lw=0)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"plots/Fig1-overview_bifurcation.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7155417f-5a4d-4c2a-95ad-b6440b793082",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "baseenv - Python 3.7",
-   "language": "python",
-   "name": "baseenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Fig2-bareicefrac-checkpoint.ipynb b/.ipynb_checkpoints/Fig2-bareicefrac-checkpoint.ipynb
deleted file mode 100644
index f0d0fd62e3ab6294669e2b09e68482c2d8df5859..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/Fig2-bareicefrac-checkpoint.ipynb
+++ /dev/null
@@ -1,284 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cd6360e6",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## global mean plots for 0-Semtner and Winton runs on VSC4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f5eedbf3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.core.options.set_options at 0x14748fab1410>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "from os import path\n",
-    "import sys, importlib\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
-    "\n",
-    "sys.path.append(\"../../python_packages\")\n",
-    "import ICON_tools\n",
-    "importlib.reload(ICON_tools)\n",
-    "\n",
-    "xr.set_options(display_style=\"text\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bb48fd25",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ape_ia_7000_56_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_7000_56_3W\n",
-      "ape_ia_8000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_90_3W\n",
-      "ape_ia_8500_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8500_90_3W\n",
-      "ape_ia_9000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_90_3W\n",
-      "ape_ia_15000_17_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_15000_17_3W\n",
-      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
-      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
-      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
-      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
-      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
-      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n",
-      "ape_ia_7000_56_3W yearly mean\n",
-      "ape_ia_8000_90_3W yearly mean\n",
-      "ape_ia_8500_90_3W yearly mean\n",
-      "ape_ia_9000_90_3W yearly mean\n",
-      "ape_ia_15000_17_3W yearly mean\n",
-      "ape_ia_5000_13_0S yearly mean\n",
-      "ape_ia_5500_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_8000_13_0S yearly mean\n",
-      "ape_ia_9000_13_0S yearly mean\n",
-      "ape_ia_10000_13_0S yearly mean\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_path=\"/jetfs/scratch/jhoerner/postprocessing\"\n",
-    "explist_W, nexp_W = ICON_tools.get_explist(data_path, [\"ape_ia_7000_56_3W\", \"ape_ia_8000_90_3W\", \"ape_ia_8500_90_3W\", \"ape_ia_9000_90_3W\", \"ape_ia_15000_17_3W\"])\n",
-    "explist_S, nexp_S = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
-    "\n",
-    "\n",
-    "DSlistgm_W_decode=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgm_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgmzm_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgmym_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_W, DSlistgmzm_W =ICON_tools.load_ds_2d(data_path,explist_W)\n",
-    "\n",
-    "DSlistgm_S_decode=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgm_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgm_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgmym_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_S,DSlistgmzm_S =ICON_tools.load_ds_2d(data_path,explist_S)\n",
-    "\n",
-    "# load the data again with decoded times, as decoding afterwards doesn't seem to work...\n",
-    "DSlistgm_W_decode, _=ICON_tools.load_ds_2d(data_path,explist_W, True)\n",
-    "DSlistgm_S_decode, _=ICON_tools.load_ds_2d(data_path,explist_S, True)\n",
-    "\n",
-    "\n",
-    "for i in range(nexp_W):\n",
-    "    #fillna \n",
-    "    DSlistgmzm_W[i] = DSlistgmzm_W[i].where(DSlistgm_W[i]['sic'] < 1e36)\n",
-    "    DSlistgm_W[i] = DSlistgm_W[i].where(DSlistgm_W[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_W_decode[i] = DSlistgm_W_decode[i].where(DSlistgm_W_decode[i]['sic'] < 1e36)\n",
-    "    \n",
-    "    \n",
-    "    print(explist_W[i] +\" yearly mean\")\n",
-    "    DSlistgmym_W[i]=xr.decode_cf(DSlistgm_W_decode[i]).groupby('time.year').mean(dim='time')\n",
-    "    \n",
-    "for i in range(nexp_S):\n",
-    "    #fillna \n",
-    "    DSlistgmzm_S[i] = DSlistgmzm_S[i].where(DSlistgm_S[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S[i] = DSlistgm_S[i].where(DSlistgm_S[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S_decode[i] = DSlistgm_S_decode[i].where(DSlistgm_S_decode[i]['sic'] < 1e36)  \n",
-    "    \n",
-    "    print(explist_S[i] +\" yearly mean\")\n",
-    "    DSlistgmym_S[i]=xr.decode_cf(DSlistgm_S_decode[i]).groupby('time.year').mean(dim='time')\n",
-    "    \n",
-    "\n",
-    "\n",
-    "\n",
-    "colorlist=[\"C1\",\"C0\",\"C2\",\"C3\",\"C5\",\"C6\",\"C7\"]\n",
-    "linestylelist=[\"-\",\"--\",\":\"]\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "7a425e64",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1. 1. 1.]\n",
-      "[0.91403604 1.         1.        ]\n",
-      "[0. 0. 0.]\n",
-      "[0. 0. 0.]\n",
-      "[1. 1. 1.]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:45: RuntimeWarning: Mean of empty slice\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(4,4))\n",
-    "\n",
-    "alpha=0.8\n",
-    "xfac= 1\n",
-    "omitlast=True\n",
-    "\n",
-    "\n",
-    "# init arrays for spline interpolation\n",
-    "xnew=np.arange(0,1,step=0.01)\n",
-    "yarray_S = np.empty([nexp_S,np.size(xnew)])\n",
-    "yarray_W = np.empty([nexp_W,np.size(xnew)])\n",
-    "\n",
-    "\n",
-    "\n",
-    "if omitlast:\n",
-    "    lastind=-1\n",
-    "j=0\n",
-    "explist=explist_W\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x = xfac*(DSlistgmym_W[i][\"sic\"].squeeze())\n",
-    "    y = ICON_tools.sictoicelat(DSlistgmym_W[i][\"snowfrac\"].squeeze()) - ICON_tools.sictoicelat(DSlistgmym_W[i][\"sic\"].squeeze())\n",
-    "    print(x[-3:].values)\n",
-    "    l2, = ax.plot(x, y, color=color, ls=linestylelist[0],  lw=1, label=exp,alpha=alpha)\n",
-    "    yarray_W[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "    ind = ((DSlistgmym_W[i].year-DSlistgmym_W[i].year[0]+1).values % 50 ==0)    \n",
-    "    ax.scatter(x[ind], y[ind],marker= \".\", color=\"black\",zorder=100, lw=1, s=8)\n",
-    "\n",
-    "j=1\n",
-    "explist=explist_S\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x = xfac*(DSlistgmym_S[i][\"sic\"].squeeze()[:lastind])\n",
-    "    y = ICON_tools.sictoicelat(DSlistgmym_S[i][\"snowfrac\"].squeeze()[:lastind]) - ICON_tools.sictoicelat(DSlistgmym_S[i][\"sic\"].squeeze()[:lastind])\n",
-    "    l2, = ax.plot(x, y, color=color, ls=linestylelist[0],  lw=1, label=exp,alpha=alpha)\n",
-    "    yarray_S[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "\n",
-    "    ind = ((DSlistgmym_S[i].year-DSlistgmym_S[i].year[0]+1)[:lastind].values % 50 ==0)    \n",
-    "    ax.scatter(x[ind], y[ind],marker= \".\", color=\"black\",zorder=100, lw=1, s=8)\n",
-    "    #ax[0].scatter(tas.year[ind], DSlistgm[i].sic[ind],marker= \"x\", color=\"black\",zorder=100)\n",
-    "\n",
-    "\n",
-    "\n",
-    "# spline interpolation\n",
-    "ymean_S = np.nanmean(yarray_S,axis=0)\n",
-    "ymean_W = np.nanmean(yarray_W,axis=0)\n",
-    "\n",
-    "#lSmean, =ax.plot(xnew,ymean_S,color='black', lw=1)\n",
-    "#lWmean, =ax.plot(xnew,ymean_W,color='black', lw=1, ls=\"--\")\n",
-    "\n",
-    "ax.axvspan(0,ICON_tools.icelatosic(49), color='lightgray', alpha=0.4, lw=0)\n",
-    "ax.axvspan(ICON_tools.icelatosic(16), ICON_tools.icelatosic(11.5), color='lightgray', alpha=0.4, lw=0)\n",
-    "ax.axvspan(0.99, 1, color='lightgray', alpha=0.4, lw=0)\n",
-    "\n",
-    "\n",
-    "#ax.set_xlabel(\"ice covered area [km$^2$]\")\n",
-    "ax.set_ylabel(\"BASIR width [$\\Delta$°]\")\n",
-    "#ax.set_xlim(xfac*-0.01,xfac*1.05)\n",
-    "ax.set_ylim(-0.1,15)\n",
-    "\n",
-    "ax.set_xlim(xfac*-0.01,xfac*1.05)\n",
-    "ax.set_xlabel(\"ice-margin latitude [°]\")\n",
-    "\n",
-    "xticks=[90,60,45,30,20,10,0]\n",
-    "ax.set_xticks(xfac*(ICON_tools.icelatosic(xticks)))\n",
-    "ax.set_xticklabels(xticks)\n",
-    "\n",
-    "\n",
-    "\n",
-    "ax.spines['top'].set_visible(False)\n",
-    "ax.spines['right'].set_visible(False)\n",
-    "#ax2.spines['right'].set_visible(False)\n",
-    "\n",
-    "\n",
-    "ax.spines['left'].set_position(('outward',5))\n",
-    "#ax.spines['bottom'].set_position(('outward',5))\n",
-    "\n",
-    "#ax.hlines(5,0,1)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"plots/paper_bareicelat.pdf\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "baseenv - Python 3.7",
-   "language": "python",
-   "name": "baseenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Fig3-toaalb_phase-checkpoint.ipynb b/.ipynb_checkpoints/Fig3-toaalb_phase-checkpoint.ipynb
deleted file mode 100644
index 26614ff2540961f66fcb49f3812e8f44abafa20b..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/Fig3-toaalb_phase-checkpoint.ipynb
+++ /dev/null
@@ -1,428 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f6b388f0",
-   "metadata": {},
-   "source": [
-    "Compare climate feedback processes between Winton & Semtner 0L, ICON-A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9ac577df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'EBMs' from '../../python_packages/EBMs.py'>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "from os import path\n",
-    "import sys, importlib\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
-    "\n",
-    "from scipy.interpolate import interp1d\n",
-    "from scipy.interpolate import UnivariateSpline\n",
-    "from scipy import integrate\n",
-    "\n",
-    "sys.path.append(\"../../python_packages\")\n",
-    "import ICON_tools\n",
-    "import EBMs\n",
-    "importlib.reload(ICON_tools)\n",
-    "importlib.reload(EBMs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d6aab1ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ape_ia_7000_56_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_7000_56_3W\n",
-      "ape_ia_8000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_90_3W\n",
-      "ape_ia_8500_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8500_90_3W\n",
-      "ape_ia_9000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_90_3W\n",
-      "ape_ia_15000_17_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_15000_17_3W\n",
-      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
-      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
-      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
-      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
-      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
-      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n",
-      "ape_ia_7000_56_3W yearly mean\n",
-      "ape_ia_8000_90_3W yearly mean\n",
-      "ape_ia_8500_90_3W yearly mean\n",
-      "ape_ia_9000_90_3W yearly mean\n",
-      "ape_ia_15000_17_3W yearly mean\n",
-      "ape_ia_5000_13_0S yearly mean\n",
-      "ape_ia_5500_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_8000_13_0S yearly mean\n",
-      "ape_ia_9000_13_0S yearly mean\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_path=\"/jetfs/scratch/jhoerner/postprocessing\"\n",
-    "explist_W, nexp_W = ICON_tools.get_explist(data_path, [\"ape_ia_7000_56_3W\", \"ape_ia_8000_90_3W\", \"ape_ia_8500_90_3W\", \"ape_ia_9000_90_3W\", \"ape_ia_15000_17_3W\"])\n",
-    "explist_S, nexp_S = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
-    "\n",
-    "\n",
-    "DSlistgm_W_decode=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgm_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistzm_W_decode=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgmym_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistzmym_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_W, _ =ICON_tools.load_ds_2d(data_path,explist_W)\n",
-    "\n",
-    "DSlistgm_S_decode=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgm_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistzm_S_decode=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgmym_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistzmym_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_S, _ =ICON_tools.load_ds_2d(data_path,explist_S)\n",
-    "\n",
-    "# load the data again with decoded times, as decoding afterwards doesn't seem to work...\n",
-    "DSlistgm_W_decode, DSlistzm_W_decode=ICON_tools.load_ds_2d(data_path,explist_W, True)\n",
-    "DSlistgm_S_decode, DSlistzm_S_decode=ICON_tools.load_ds_2d(data_path,explist_S, True)\n",
-    "\n",
-    "\n",
-    "for i in range(nexp_W):\n",
-    "    #fillna \n",
-    "    DSlistzm_W_decode[i] = DSlistzm_W_decode[i].where(DSlistgm_W_decode[i]['sic'] < 1e36)\n",
-    "    DSlistgm_W[i] = DSlistgm_W[i].where(DSlistgm_W[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_W_decode[i] = DSlistgm_W_decode[i].where(DSlistgm_W_decode[i]['sic'] < 1e36)\n",
-    "    \n",
-    "    print(explist_W[i] +\" yearly mean\")\n",
-    "    DSlistgmym_W[i]=xr.decode_cf(DSlistgm_W_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    DSlistzmym_W[i]=xr.decode_cf(DSlistzm_W_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    \n",
-    "for i in range(nexp_S):\n",
-    "    #fillna \n",
-    "    DSlistzm_S_decode[i] = DSlistzm_S_decode[i].where(DSlistgm_S_decode[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S[i] = DSlistgm_S[i].where(DSlistgm_S[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S_decode[i] = DSlistgm_S_decode[i].where(DSlistgm_S_decode[i]['sic'] < 1e36)  \n",
-    "    \n",
-    "    print(explist_S[i] +\" yearly mean\")\n",
-    "    DSlistgmym_S[i]=xr.decode_cf(DSlistgm_S_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    DSlistzmym_S[i]=xr.decode_cf(DSlistzm_S_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "\n",
-    "colorlist=[\"C1\",\"C0\",\"C2\",\"C3\",\"C5\",\"C6\",\"C7\"]\n",
-    "linestylelist=[\"-\",\"--\",\":\"]\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd814c57-6eb0-4382-b47b-7c58f15fb72a",
-   "metadata": {},
-   "source": [
-    "## albedo binning, plotting in dependece of ice-edge latitude "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "510a10bb-8714-4232-8e3f-5cd2769812ff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simind1 = explist_S.index('ape_ia_5500_90_0S')\n",
-    "simind2 = explist_S.index('ape_ia_5000_13_0S')\n",
-    "\n",
-    "simind1_W = explist_W.index('ape_ia_8000_90_3W')\n",
-    "simind2_W = explist_W.index('ape_ia_8500_90_3W')\n",
-    "\n",
-    "\n",
-    "DS1 = DSlistzm_S_decode[simind1].squeeze().isel(time=slice(12, -12)).copy()\n",
-    "DS2 = DSlistzm_S_decode[simind2].squeeze().isel(time=slice(12, -12)).copy()\n",
-    "\n",
-    "DSW1 = DSlistzm_W_decode[simind1_W].squeeze().isel(time=slice(12, -12)).copy()\n",
-    "DSW2 = DSlistzm_W_decode[simind2_W].squeeze().isel(time=slice(12, -12)).copy()\n",
-    "\n",
-    "lat = DS1.lat.squeeze().values\n",
-    "icelat_combined = np.append(ICON_tools.sictoicelat(DSlistgm_S_decode[simind1].sic.isel(time=slice(12, -12)).squeeze().values), ICON_tools.sictoicelat(DSlistgm_S_decode[simind2].sic.isel(time=slice(12, -12)).squeeze().values))\n",
-    "icelat = ICON_tools.sictoicelat(DSlistgm_S_decode[simind1].sic.squeeze().isel(time=slice(12, -12)).values)\n",
-    "\n",
-    "\n",
-    "icelatW_combined = np.append(ICON_tools.sictoicelat(DSlistgm_W_decode[simind1_W].sic.isel(time=slice(12, -12)).squeeze().values), ICON_tools.sictoicelat(DSlistgm_W_decode[simind2_W].sic.isel(time=slice(12, -12)).squeeze().values))\n",
-    "icelatW = ICON_tools.sictoicelat(DSlistgm_W_decode[simind1_W].sic.squeeze().isel(time=slice(12, -12)).values)\n",
-    "\n",
-    "\n",
-    "#icelatW = ICON_tools.sictoicelat(DSlistgm_W_decode[simind1_W].sic.isel(time=slice(12, -12)).squeeze().values)\n",
-    "#icelatW_combined = np.append(ICON_tools.sictoicelat(DSlistgm_W_decode[simind2_W].sic.isel(time=slice(12, -12)).squeeze().values), ICON_tools.sictoicelat(DSlistgm_W_decode[simind1_W].sic.isel(time=slice(12, -12)).squeeze().values))\n",
-    "#time_combined = np.append(DSlistgm_S_decode[simind1].time.squeeze().values, DSlistgm_S_decode[simind2].time.squeeze().values)\n",
-    "\n",
-    "#plt.plot(time_combined,icelat_combined)\n",
-    "#plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30e87791-ec37-44ce-bbe3-f707885689cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "hist, bin_edges = np.histogram(icelat, bins=int(np.size(lat)/2), range=(0, 90))\n",
-    "bins = (bin_edges[1:]+bin_edges[:-1])/2\n",
-    "ind = np.digitize(icelat, bins=bin_edges[:-1], right=False) # right=False means \n",
-    "\n",
-    "hist_combined, bin_edges_combined = np.histogram(icelat_combined, bins=int(np.size(lat)/2), range=(0, 90))\n",
-    "bins_combined = (bin_edges_combined[1:]+bin_edges_combined[:-1])/2\n",
-    "ind_combined = np.digitize(icelat_combined, bins=bin_edges_combined[:-1], right=False) # right=False means \n",
-    "\n",
-    "histW, bin_edgesW = np.histogram(icelatW, bins=int(np.size(lat)/2), range=(0, 90))\n",
-    "binsW = (bin_edgesW[1:]+bin_edgesW[:-1])/2\n",
-    "indW = np.digitize(icelatW, bins=bin_edgesW[:-1], right=False) # right=False means\n",
-    "\n",
-    "histW_combined, bin_edgesW_combined = np.histogram(icelatW_combined, bins=int(np.size(lat)/2), range=(0, 90))\n",
-    "binsW_combined = (bin_edgesW_combined[1:]+bin_edgesW_combined[:-1])/2\n",
-    "indW_combined = np.digitize(icelatW_combined, bins=bin_edgesW_combined[:-1], right=False) # right=False means"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "97e84dfb-dff6-4ee2-9054-f0ce74b6b026",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "bins[1:]-bins[:-1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f8eec9af-71d5-4bc4-bfce-72044e0b78e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.bar(bins_combined,hist_combined,width=1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7ea07821-e893-490b-a6aa-9175ad3a53ba",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.bar(binsW_combined,histW_combined,width=1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79893157-156e-49d2-a988-7199935d3f67",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#sanity check\n",
-    "for n in range(np.size(icelat_combined)):\n",
-    "    print(bin_edges[ind_combined[n]-1], \"<=\", icelat_combined[n], \"<\", bin_edges[ind_combined[n]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce51e6c3-91e6-4def-abd6-f3f886ee21ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_bins(array, bins, inds):\n",
-    "    array_binned=np.empty((np.shape(bins)[0], np.shape(array)[1]))\n",
-    "\n",
-    "    for i in range(np.size(bins)):\n",
-    "        array_binned[i,:] = np.nanmean(array[inds==i+1], axis=0) # average ice lat of all points in each icelat bin, is basically a straight line\n",
-    "\n",
-    "    a, b = np.hsplit(array_binned,2)\n",
-    "    return (np.fliplr(b)+a)/2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "65902041-5b9f-443a-aa29-85f88718e8e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "icelat_binned=np.empty(np.shape(bins))\n",
-    "icelat_binned[:]=np.nan\n",
-    "a, b = np.hsplit(lat,2)\n",
-    "\n",
-    "lat_split = (abs(a)+abs(np.flip(b)))/2\n",
-    "\n",
-    "albedo_binned=calc_bins(ICON_tools.get_albedo(DS1,\"toa\").squeeze().values, bins, ind)\n",
-    "salbedo_binned=calc_bins(ICON_tools.get_albedo(DS1,\"surf\").squeeze().values, bins, ind)\n",
-    "albedocs_binned=calc_bins(ICON_tools.get_albedo(DS1,\"toacs\").squeeze().values, bins, ind)\n",
-    "hs_binned=calc_bins(DS1.hs_icecl.squeeze().values, bins, ind)\n",
-    "snowfrac_binned=calc_bins(DS1.snowfrac.squeeze().values, bins, ind)\n",
-    "sic_binned=calc_bins(DS1.sic.squeeze().values, bins, ind)\n",
-    "CRE_binned=calc_bins(ICON_tools.get_cre(DS1,\"toa\",\"net\").squeeze().values, bins, ind)\n",
-    "\n",
-    "albedo_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DS1,\"toa\").squeeze().values, ICON_tools.get_albedo(DS2,\"toa\").squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "albedocs_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DS1,\"toacs\").squeeze().values, ICON_tools.get_albedo(DS2,\"toacs\").squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "salbedo_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DS1,\"surf\").squeeze().values, ICON_tools.get_albedo(DS2,\"surf\").squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "hs_combined_binned=calc_bins(np.append(DS1.hs_icecl.squeeze().values, DS2.hs_icecl.squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "snowfrac_combined_binned=calc_bins(np.append(DS1.snowfrac.squeeze().values, DS2.snowfrac.squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "sic_combined_binned=calc_bins(np.append(DS1.sic.squeeze().values, DS2.sic.squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "CRE_combined_binned=calc_bins(np.append(ICON_tools.get_cre(DS1,\"toa\",\"net\").squeeze().values, ICON_tools.get_cre(DS2,\"toa\",\"net\").squeeze().values, axis=0) , bins_combined, ind_combined)\n",
-    "\n",
-    "albedoW_binned=calc_bins(ICON_tools.get_albedo(DSW1,\"toa\").squeeze().values, binsW, indW)\n",
-    "albedocsW_binned=calc_bins(ICON_tools.get_albedo(DSW1,\"toacs\").squeeze().values, binsW, indW)\n",
-    "salbedoW_binned=calc_bins(ICON_tools.get_albedo(DSW1,\"surf\").squeeze().values, binsW, indW)\n",
-    "hsW_binned=calc_bins(DSW1.hs_icecl.squeeze().values, binsW, indW)\n",
-    "snowfracW_binned=calc_bins(DSW1.snowfrac.squeeze().values, binsW, indW)\n",
-    "sicW_binned=calc_bins(DSW1.sic.squeeze().values, binsW, indW)\n",
-    "CREW_binned=calc_bins(ICON_tools.get_cre(DSW1,\"toa\",\"net\").squeeze().values, binsW, indW)\n",
-    "\n",
-    "albedoW_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DSW1,\"toa\").squeeze().values, ICON_tools.get_albedo(DSW2,\"toa\").squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "albedocsW_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DSW1,\"toacs\").squeeze().values, ICON_tools.get_albedo(DSW2,\"toacs\").squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "salbedoW_combined_binned=calc_bins(np.append(ICON_tools.get_albedo(DSW1,\"surf\").squeeze().values, ICON_tools.get_albedo(DSW2,\"surf\").squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "hsW_combined_binned=calc_bins(np.append(DSW1.hs_icecl.squeeze().values, DSW2.hs_icecl.squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "snowfracW_combined_binned=calc_bins(np.append(DSW1.snowfrac.squeeze().values, DSW2.snowfrac.squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "sicW_combined_binned=calc_bins(np.append(DSW1.sic.squeeze().values, DSW2.sic.squeeze().values, axis=0) , binsW_combined, indW_combined)\n",
-    "CREW_combined_binned=calc_bins(np.append(ICON_tools.get_cre(DSW1,\"toa\",\"net\").squeeze().values, ICON_tools.get_cre(DSW2,\"toa\",\"net\").squeeze().values, axis=0) , binsW_combined, indW_combined)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5656b944-c975-4e3e-9406-1660382d7c26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots(1,3, figsize=(12,4), sharex=True, sharey=True)\n",
-    "levels=np.linspace(0.1,0.8,15)\n",
-    "cmap = plt.colormaps['inferno']\n",
-    "norm = mpl.colors.BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n",
-    "\n",
-    "levels_diff=np.linspace(-0.2,0.2,22)\n",
-    "cmap_diff = plt.colormaps['seismic']\n",
-    "norm_diff = mpl.colors.BoundaryNorm(levels_diff, ncolors=cmap_diff.N, clip=True)\n",
-    "\n",
-    "im0 = ax[0].pcolormesh(ICON_tools.icelatosic(bins_combined), 1-ICON_tools.icelatosic(lat_split), albedo_combined_binned.T, norm=norm, cmap=cmap, linewidth=0,rasterized=True)\n",
-    "im1 = ax[1].pcolormesh(ICON_tools.icelatosic(bins_combined), 1-ICON_tools.icelatosic(lat_split), albedoW_combined_binned.T, norm=norm, cmap=cmap, linewidth=0,rasterized=True)\n",
-    "im2 = ax[2].pcolormesh(ICON_tools.icelatosic(bins_combined), 1-ICON_tools.icelatosic(lat_split), albedoW_combined_binned.T-albedo_combined_binned.T, norm=norm_diff, cmap=cmap_diff, linewidth=0,rasterized=True)\n",
-    "im0.set_edgecolor('face')\n",
-    "im1.set_edgecolor('face')\n",
-    "im2.set_edgecolor('face')\n",
-    "\n",
-    "plt.colorbar(im0,ax=ax[0], label=\"TOA albedo []\", pad=0.03)\n",
-    "plt.colorbar(im1,ax=ax[1], label=\"TOA albedo []\", pad=0.03)\n",
-    "plt.colorbar(im2,ax=ax[2], label=\"$\\Delta$ TOA albedo []\", extend=\"both\", ticks=np.linspace(-0.2,0.2,5), pad=0.03)\n",
-    "\n",
-    "\n",
-    "widtharray = (ICON_tools.icelatosic(bin_edges_combined)[:-1]-ICON_tools.icelatosic(bin_edges_combined)[1:])#+0.002\n",
-    "ax_hist = np.empty(2, dtype=\"object\")\n",
-    "ax_hist[0]=ax[0].inset_axes([0, 1.0, 1, 0.25])\n",
-    "ax_hist[0].bar(ICON_tools.icelatosic(bins_combined), hist_combined, width=widtharray, color=\"black\", edgecolor=\"black\", linewidth=0.1)\n",
-    "ax_hist[1]=ax[1].inset_axes([0, 1.0, 1, 0.25], sharex=ax_hist[0])\n",
-    "ax_hist[1].bar(ICON_tools.icelatosic(binsW_combined), histW_combined, width=widtharray, color=\"black\", edgecolor=\"black\", linewidth=0.1)\n",
-    "ax_hist[0].spines['bottom'].set_visible(False)\n",
-    "ax_hist[1].spines['bottom'].set_visible(False)\n",
-    "ax_hist[0].set_xticks([])\n",
-    "ax_hist[1].set_xticks([])\n",
-    "\n",
-    "ax_hist[0].set_yscale(\"log\")\n",
-    "ax_hist[1].set_yscale(\"log\")\n",
-    "ax_hist[1].set_ylim(0,4000)\n",
-    "ax_hist[0].set_ylim(0,4000)\n",
-    "#ax_hist[0].set_yticks([0,200,400],labels=[\"\", \"200\", \"400\"])\n",
-    "#ax_hist[1].set_yticks([0,200,400],labels=[\"\", \"200\", \"400\"])\n",
-    "ax_hist[0].yaxis.tick_right()\n",
-    "ax_hist[1].yaxis.tick_right()\n",
-    "ax[0].set_ylabel(\"latitude [°]\")\n",
-    "ax_hist[0].set_yticks([10,100,1000])\n",
-    "ax_hist[1].set_yticks([10,100,1000])\n",
-    "\n",
-    "xticks=[90,60,45,30,20,10,0]\n",
-    "\n",
-    "#ax[0].set_title(\"Semter-0L\")\n",
-    "#ax[1].set_title(\"Winton-3L\")\n",
-    "#ax[2].set_title(\"Winton-3L-Semter-0L\")\n",
-    "\n",
-    "ax_hist[0].annotate(\"a) Semter-0L\", xycoords=\"axes fraction\", xy=(0.05,0.75), fontweight=\"bold\")\n",
-    "ax_hist[1].annotate(\"b) Winton-3L\", xycoords=\"axes fraction\", xy=(0.05,0.75), fontweight=\"bold\")\n",
-    "ax[2].annotate(\"b) Winton-3L - Semter-0L\", xycoords=\"axes fraction\", xy=(0.05,1.02), fontweight=\"bold\")\n",
-    "\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[axind].set_xticks(ICON_tools.icelatosic(xticks))\n",
-    "    ax[axind].set_xticklabels(xticks)\n",
-    "    ax[axind].set_yticks(1-ICON_tools.icelatosic(xticks))\n",
-    "    ax[axind].set_yticklabels(xticks)\n",
-    "    ax[axind].set_xlabel(\"ice-margin latitude [°]\")\n",
-    "\n",
-    "    ax[axind].plot([0,1],[1,0],color=\"black\")\n",
-    "    #ax[axind].axvspan(0,ICON_tools.icelatosic(48), color='lightgray', alpha=0.4, lw=0)\n",
-    "    #ax[axind].axvspan(ICON_tools.icelatosic(18), ICON_tools.icelatosic(12), color='lightgray', alpha=0.4, lw=0)\n",
-    "    #ax[axind].axvspan(0.99, 1, color='lightgray', alpha=0.4, lw=0)\n",
-    "\n",
-    "\n",
-    "ax[0].set_xlim(ICON_tools.icelatosic(90),1)\n",
-    "ax_hist[0].set_xlim(ICON_tools.icelatosic(90),1)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"plots/toaalb_phase.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bf886993-fb91-449b-9112-1aacba168d1c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "baseenv - Python 3.7",
-   "language": "python",
-   "name": "baseenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Fig4-climatefeedback-checkpoint.ipynb b/.ipynb_checkpoints/Fig4-climatefeedback-checkpoint.ipynb
deleted file mode 100644
index b23200c4ec8346e6d224b34a3c7e50f45a21e03b..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/Fig4-climatefeedback-checkpoint.ipynb
+++ /dev/null
@@ -1,417 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f6b388f0",
-   "metadata": {},
-   "source": [
-    "Compare climate feedback processes between Winton & Semtner 0L, ICON-A"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9ac577df",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'EBMs' from '../../python_packages/EBMs.py'>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import xarray as xr\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "from os import path\n",
-    "import sys, importlib\n",
-    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
-    "\n",
-    "from scipy.interpolate import interp1d\n",
-    "from scipy.interpolate import UnivariateSpline\n",
-    "from scipy import integrate\n",
-    "\n",
-    "sys.path.append(\"../../python_packages\")\n",
-    "import ICON_tools\n",
-    "import EBMs\n",
-    "importlib.reload(ICON_tools)\n",
-    "importlib.reload(EBMs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d6aab1ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ape_ia_7000_56_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_7000_56_3W\n",
-      "ape_ia_8000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_90_3W\n",
-      "ape_ia_8500_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8500_90_3W\n",
-      "ape_ia_9000_90_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_90_3W\n",
-      "ape_ia_15000_17_3W: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_15000_17_3W\n",
-      "ape_ia_5000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5000_13_0S\n",
-      "ape_ia_5500_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_5500_90_0S\n",
-      "ape_ia_6000_90_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6000_90_0S_cltlim_dtime10\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_6500_90_0S_cltlim_dtime10\n",
-      "ape_ia_8000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0S\n",
-      "ape_ia_9000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_9000_13_0S\n",
-      "ape_ia_10000_13_0S: directory is /jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0S\n",
-      "ape_ia_7000_56_3W yearly mean\n",
-      "ape_ia_8000_90_3W yearly mean\n",
-      "ape_ia_8500_90_3W yearly mean\n",
-      "ape_ia_9000_90_3W yearly mean\n",
-      "ape_ia_15000_17_3W yearly mean\n",
-      "ape_ia_5000_13_0S yearly mean\n",
-      "ape_ia_5500_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S yearly mean\n",
-      "ape_ia_6000_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_6500_90_0S_cltlim_dtime10 yearly mean\n",
-      "ape_ia_8000_13_0S yearly mean\n",
-      "ape_ia_9000_13_0S yearly mean\n",
-      "ape_ia_10000_13_0S yearly mean\n"
-     ]
-    }
-   ],
-   "source": [
-    "data_path=\"/jetfs/scratch/jhoerner/postprocessing\"\n",
-    "explist_W, nexp_W = ICON_tools.get_explist(data_path, [\"ape_ia_7000_56_3W\", \"ape_ia_8000_90_3W\", \"ape_ia_8500_90_3W\", \"ape_ia_9000_90_3W\", \"ape_ia_15000_17_3W\"])\n",
-    "explist_S, nexp_S = ICON_tools.get_explist(data_path, [\"ape_ia_5000_13_0S\", \"ape_ia_5500_90_0S\", \"ape_ia_6000_90_0S\", \"ape_ia_6000_90_0S_cltlim_dtime10\", \"ape_ia_6500_90_0S_cltlim_dtime10\", \"ape_ia_8000_13_0S\", \"ape_ia_9000_13_0S\", \"ape_ia_10000_13_0S\" ]) # , \"ape_ia_6500_90_0S\" , \"ape_ia_7000_62_0S\"\n",
-    "\n",
-    "\n",
-    "DSlistgm_W_decode=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgm_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistzm_W_decode=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistgmym_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "DSlistzmym_W=np.empty([nexp_W],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_W, _ =ICON_tools.load_ds_2d(data_path,explist_W)\n",
-    "\n",
-    "DSlistgm_S_decode=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgm_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistzm_S_decode=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistgmym_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "DSlistzmym_S=np.empty([nexp_S],dtype=\"object\")\n",
-    "\n",
-    "DSlistgm_S, _ =ICON_tools.load_ds_2d(data_path,explist_S)\n",
-    "\n",
-    "# load the data again with decoded times, as decoding afterwards doesn't seem to work...\n",
-    "DSlistgm_W_decode, DSlistzm_W_decode=ICON_tools.load_ds_2d(data_path,explist_W, True)\n",
-    "DSlistgm_S_decode, DSlistzm_S_decode=ICON_tools.load_ds_2d(data_path,explist_S, True)\n",
-    "\n",
-    "\n",
-    "for i in range(nexp_W):\n",
-    "    #fillna \n",
-    "    DSlistzm_W_decode[i] = DSlistzm_W_decode[i].where(DSlistgm_W_decode[i]['sic'] < 1e36)\n",
-    "    DSlistgm_W[i] = DSlistgm_W[i].where(DSlistgm_W[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_W_decode[i] = DSlistgm_W_decode[i].where(DSlistgm_W_decode[i]['sic'] < 1e36)\n",
-    "    \n",
-    "    print(explist_W[i] +\" yearly mean\")\n",
-    "    DSlistgmym_W[i]=xr.decode_cf(DSlistgm_W_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    DSlistzmym_W[i]=xr.decode_cf(DSlistzm_W_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    \n",
-    "for i in range(nexp_S):\n",
-    "    #fillna \n",
-    "    DSlistzm_S_decode[i] = DSlistzm_S_decode[i].where(DSlistgm_S_decode[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S[i] = DSlistgm_S[i].where(DSlistgm_S[i]['sic'] < 1e36)  \n",
-    "    DSlistgm_S_decode[i] = DSlistgm_S_decode[i].where(DSlistgm_S_decode[i]['sic'] < 1e36)  \n",
-    "    \n",
-    "    print(explist_S[i] +\" yearly mean\")\n",
-    "    DSlistgmym_S[i]=xr.decode_cf(DSlistgm_S_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    DSlistzmym_S[i]=xr.decode_cf(DSlistzm_S_decode[i]).groupby('time.year').mean(dim='time', skipna=False)\n",
-    "    \n",
-    "\n",
-    "\n",
-    "\n",
-    "colorlist=[\"C1\",\"C0\",\"C2\",\"C3\",\"C5\",\"C6\",\"C7\"]\n",
-    "linestylelist=[\"-\",\"--\",\":\"]\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23064475",
-   "metadata": {},
-   "source": [
-    "## Simulations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "316ee205",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[None None None]\n",
-      "[        nan         nan         nan         nan         nan         nan\n",
-      "         nan  0.99226864  0.76750451  1.29668057  1.35744125  1.24747315\n",
-      "  1.01791834  0.79257413  0.51655573  0.22807818  0.01851994  0.92882673\n",
-      "  1.00170774 -0.06873432 -0.15706968 -0.08517393  0.15280628 -0.15680276\n",
-      "  0.44318422  0.08984579  0.46498034 -0.01757108  0.1519667   0.63398108\n",
-      "  0.11904673 -0.09070756  0.1945145   0.48254731 -0.5701713   0.22694437\n",
-      "  0.02241685 -0.28859064 -0.52263762  0.08904189  0.02344157 -0.15325449\n",
-      " -0.22788324 -0.70382975 -0.77064562 -1.07800065 -1.17678062 -1.06977691\n",
-      " -1.01685808 -1.09100074 -1.072613   -1.60312368 -0.8793015  -1.10903128\n",
-      " -1.36329294 -0.29100678 -0.32348977 -0.43834219 -0.46626386 -0.51268235\n",
-      " -1.30363801 -1.50305227 -1.23325544 -1.17167614 -1.89254798 -1.74662792\n",
-      " -1.63342415 -2.22378199 -2.60317541 -2.66743415 -3.40445873 -3.6907361\n",
-      " -3.89568864 -3.25496069 -3.69163601 -3.23259027 -3.24847735 -3.45173153\n",
-      " -3.20738836 -3.10442623 -2.87256823 -2.91583363 -3.2450209  -3.83211666\n",
-      " -3.90130296 -4.05874292 -4.21006192 -4.54443648 -5.01939987 -5.49436325\n",
-      " -5.74358877 -5.78503655 -5.79288172 -5.77798676 -5.60068067 -4.90407961\n",
-      " -4.20747856 -4.29502578 -4.51667539 -4.738325  ]\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:47: RuntimeWarning: Mean of empty slice\n",
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  keepdims=keepdims)\n",
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:84: RuntimeWarning: Mean of empty slice\n",
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  keepdims=keepdims)\n",
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/ipykernel_launcher.py:116: RuntimeWarning: Mean of empty slice\n",
-      "/jetfs/home/jhoerner/.conda/envs/baseenv/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice.\n",
-      "  keepdims=keepdims)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x288 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "omitlast = True\n",
-    "fig, ax = plt.subplots(1, 3, figsize=(12, 4))\n",
-    "ax2 = np.empty(np.shape(ax), dtype=\"object\")\n",
-    "print(ax2)\n",
-    "\n",
-    "alpha = 0.8\n",
-    "xfac = 1\n",
-    "firstind = 2\n",
-    "if omitlast:\n",
-    "    lastind = -1\n",
-    "\n",
-    "\n",
-    "# init arrays for spline interpolation\n",
-    "xnew = np.arange(0, 1, step=0.01)\n",
-    "yarray_S = np.empty([nexp_S, np.size(xnew)])\n",
-    "yarray_W = np.empty([nexp_W, np.size(xnew)])\n",
-    "\n",
-    "\n",
-    "axind = 0\n",
-    "j = 0\n",
-    "ax[axind].hlines(0,xfac*-1,xfac*2,color=\"black\", lw=1)\n",
-    "explist=explist_W\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x=xfac*(DSlistgmym_W[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y=((DSlistgmym_W[i][\"rsdt\"].squeeze()-DSlistgmym_W[i][\"rsut\"].squeeze()-DSlistgmym_W[i][\"rlut\"].squeeze())[firstind:lastind])\n",
-    "    ax[axind].plot(x,y,color=color, ls=linestylelist[0],  lw=2, label=exp, alpha=alpha, clip_on=False)\n",
-    "    yarray_W[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "j=1\n",
-    "explist=explist_S\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x=xfac*(DSlistgmym_S[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y=((DSlistgmym_S[i][\"rsdt\"].squeeze()-DSlistgmym_S[i][\"rsut\"].squeeze()-DSlistgmym_S[i][\"rlut\"].squeeze())[firstind:lastind])\n",
-    "    ax[axind].plot(x,y,color=color, ls=linestylelist[0],  lw=2, label=exp, alpha=alpha, clip_on=False)\n",
-    "    yarray_S[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "\n",
-    "    # indixes of every 100th year\n",
-    "    ind = ((DSlistgmym_S[i].year[firstind:lastind]-DSlistgmym_S[i].year[firstind]+1).values % 50 ==0)\n",
-    "    #ax[axind].scatter(x[ind], y[ind],marker= \".\", color=\"black\",zorder=100)\n",
-    "    #ax[0].scatter(tas.year[ind], DSlistgm[i].sic[ind],marker= \"x\", color=\"black\",zorder=100)\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "# spline interpolation\n",
-    "ymean_S = np.nanmean(yarray_S,axis=0)\n",
-    "ymean_W = np.nanmean(yarray_W,axis=0)\n",
-    "ymean_diff = ymean_W-ymean_S\n",
-    "ystd_diff = get_std_diff(yarray_W,yarray_S)\n",
-    "print(ymean_diff)\n",
-    "# difference\n",
-    "#ax2[axind]=ax[axind].twinx()\n",
-    "#ax2[axind].hlines(0,-1,1,color=\"black\",lw=1)\n",
-    "#ax2[axind].plot(xnew, ymean_diff, color=\"black\", lw=1)\n",
-    "\n",
-    "# Smean, =ax[axind].plot(xnew,ymean_S,color='black', lw=1)\n",
-    "# Wmean, =ax[axind].plot(xnew,ymean_W,color='black', lw=1, ls=\"--\")\n",
-    "\n",
-    "\n",
-    "axind=1\n",
-    "j=0\n",
-    "\n",
-    "explist = explist_W\n",
-    "color = colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x = xfac*(DSlistgmym_W[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y = ((DSlistgmym_W[i][\"rsdt\"].squeeze()-DSlistgmym_W[i][\"rsut\"].squeeze())[firstind:lastind])\n",
-    "\n",
-    "    lW, = ax[axind].plot(x, y, color=color, ls=linestylelist[0], lw=2, label=exp, alpha=alpha, clip_on=False)\n",
-    "    yarray_W[i, :] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing=1)\n",
-    "\n",
-    "j=1\n",
-    "explist=explist_S\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x=xfac*(DSlistgmym_S[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y=((DSlistgmym_S[i][\"rsdt\"].squeeze()-DSlistgmym_S[i][\"rsut\"].squeeze())[firstind:lastind])\n",
-    "    lS, =ax[axind].plot(x,y,color=color, ls=linestylelist[0],  lw=2, label=exp,alpha=alpha, clip_on=False)\n",
-    "    yarray_S[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "\n",
-    "\n",
-    "# spline interpolation\n",
-    "ymean_S = np.nanmean(yarray_S,axis=0)\n",
-    "ymean_W = np.nanmean(yarray_W,axis=0)\n",
-    "ymean_diff = ymean_W-ymean_S\n",
-    "ystd_diff = get_std_diff(yarray_W,yarray_S)\n",
-    "\n",
-    "# difference\n",
-    "#ax2[axind]=ax[axind].twinx()\n",
-    "#ax2[axind].hlines(0,-1,1,color=\"black\",lw=1)\n",
-    "#ax2[axind].plot(xnew, ymean_diff, color=\"black\", lw=1)\n",
-    "\n",
-    "# ax[axind].plot(xnew,ymean_S,color='black', lw=1)\n",
-    "# ax[axind].plot(xnew,ymean_W,color='black', lw=1, ls=\"--\")\n",
-    "\n",
-    "axind=2\n",
-    "j=0\n",
-    "explist=explist_W\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x=xfac*(DSlistgmym_W[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y=((-DSlistgmym_W[i][\"rlut\"].squeeze())[firstind:lastind])\n",
-    "    ax[axind].plot(x,y,color=color, ls=linestylelist[0],  lw=2, label=exp,alpha=alpha, clip_on=False)\n",
-    "    yarray_W[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "j=1\n",
-    "explist=explist_S\n",
-    "color=colorlist[j]\n",
-    "for i, exp in enumerate(explist): # simulations\n",
-    "    x=xfac*(DSlistgmym_S[i][\"sic\"].squeeze()[firstind:lastind])\n",
-    "    y=((-DSlistgmym_S[i][\"rlut\"].squeeze())[firstind:lastind])\n",
-    "    ax[axind].plot(x,y,color=color, ls=linestylelist[0],  lw=2, label=exp,alpha=alpha, clip_on=False)\n",
-    "    yarray_S[i,:] = ICON_tools.calc_spline(x, y, xnew=xnew, smoothing = 1)\n",
-    "\n",
-    "# spline interpolation\n",
-    "ymean_S = np.nanmean(yarray_S,axis=0)\n",
-    "ymean_W = np.nanmean(yarray_W,axis=0)\n",
-    "ymean_diff = ymean_W-ymean_S\n",
-    "ystd_diff = get_std_diff(yarray_W,yarray_S)\n",
-    "\n",
-    "# difference\n",
-    "#ax2[axind]=ax[axind].twinx()\n",
-    "#ax2[axind].hlines(0,-1,1,color=\"black\",lw=1)\n",
-    "#ax2[axind].plot(xnew, ymean_diff, color=\"black\", lw=1, zorder=-999)\n",
-    "\n",
-    "\n",
-    "\n",
-    "xticks=[90,60,45,30,20,10,0]\n",
-    "\n",
-    "ax[0].set_ylabel(\"F [W/m$^2$]\")\n",
-    "ax[0].annotate(\"a) TOA net\", xycoords=\"axes fraction\", xy=(0.05,0.96), fontweight=\"bold\")\n",
-    "ax[1].annotate(\"b) TOA shortwave\", xycoords=\"axes fraction\", xy=(0.09,0.96), fontweight=\"bold\")\n",
-    "ax[2].annotate(\"c) TOA longwave\", xycoords=\"axes fraction\", xy=(0.05,0.96), fontweight=\"bold\")\n",
-    "\n",
-    "ax[0].hlines(0,xfac*-1,xfac*2,color=\"black\", lw=1)\n",
-    "#ax[1].set_ylim(80,235)\n",
-    "\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[axind].set_xlim(0,xfac*1)\n",
-    "    ax[axind].set_xlabel(\"ice-edge latitude [°]\")\n",
-    "    ax[axind].set_xticks(xfac*(ICON_tools.icelatosic(xticks)))\n",
-    "    ax[axind].set_xticklabels(xticks)\n",
-    "    ax[axind].spines['top'].set_visible(False)\n",
-    "    ax[axind].spines['right'].set_visible(False)\n",
-    "    ax[axind].spines['left'].set_position(('outward',5))\n",
-    "    #ax[axind].spines['bottom'].set_position(('outward',5))\n",
-    "    ax[axind].set_zorder(1)\n",
-    "    ax[axind].axvspan(0,ICON_tools.icelatosic(49), color='lightgray', alpha=0.4, lw=0)\n",
-    "    ax[axind].axvspan(ICON_tools.icelatosic(16), ICON_tools.icelatosic(11.5), color='lightgray', alpha=0.4, lw=0)\n",
-    "    ax[axind].axvspan(0.99, 1, color='lightgray', alpha=0.4, lw=0)\n",
-    "\n",
-    "    if axind>2:\n",
-    "        ax2[axind].set_zorder(0)\n",
-    "        ax[axind].patch.set_visible(False)\n",
-    "        ax2[axind].patch.set_visible(True)\n",
-    "        ax2[axind].spines['top'].set_visible(False)\n",
-    "        ax2[axind].spines['left'].set_visible(False)\n",
-    "        ax2[axind].spines['bottom'].set_visible(False)\n",
-    "        ax2[axind].spines['right'].set_position(('outward',5))\n",
-    "\n",
-    "\n",
-    "#ax[2].annotate(\"temperate state\", xy=(0.001,-14))\n",
-    "#ax[2].annotate(\"Waterbelt state\", xy=(ICON_tools.icelatosic(25),-15))\n",
-    "\n",
-    "# fit budyko sellers model to the output\n",
-    "albedo_snow=0.65\n",
-    "albedo_ocean=0.25\n",
-    "\n",
-    "#x_BS, albedo_BS, SW_BS = EBMs.budyko_sellers_radiation(alpha_1=albedo_ocean, alpha_2=albedo_snow, x=np.linspace(0,1,101), S_exact=True)\n",
-    "#x_BS_Jor, albedo_BS_Jor, SW_BS_Jor = EBMs.budyko_sellers_radiation_Jor(alpha_1=albedo_ocean, alpha_2s=albedo_snow, alpha_2i=0.55,  x=np.linspace(0,1,101), S_exact=True)\n",
-    "\n",
-    "yoffset=-0\n",
-    "#lEBM,=ax[0].plot(1-x_BS,SW_BS+yoffset,color=\"gray\", lw=1, ls=\"--\")\n",
-    "#lEBM_Jor,=ax[0].plot(1-x_BS_Jor,SW_BS_Jor+yoffset,color=\"gray\", lw=1)\n",
-    "#ax[0].legend([lS,lEBM_Jor,lW,lEBM], [\"Semtner-0L\", r\"EBM ($\\Delta \\alpha=0.1$)\", \"Winton-3L\", r\"EBM ($\\Delta \\alpha=0$)\"], ncol=1 )  \n",
-    "\n",
-    "#ax[1,2].hlines(-3.7,xfac*-1,xfac*2,color=\"green\")\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.savefig(\"plots/paper_climatefeedback.pdf\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "baseenv - Python 3.7",
-   "language": "python",
-   "name": "baseenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/.ipynb_checkpoints/Fig5-0Sto3W-checkpoint.ipynb b/.ipynb_checkpoints/Fig5-0Sto3W-checkpoint.ipynb
deleted file mode 100644
index 0b47574dd3011e5b05b15cdefa6bb930431a5928..0000000000000000000000000000000000000000
--- a/.ipynb_checkpoints/Fig5-0Sto3W-checkpoint.ipynb
+++ /dev/null
@@ -1,394 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "51502909-de71-401c-851a-acfe2d6a49e8",
-   "metadata": {},
-   "source": [
-    "Compare simulations ape_ia_8000_13_0Sto3W and ape_ia_8000_13_0Sto0S. Both are started from a stable Waterbelt in ape_ia_8000_13_0S, then the ice model is changed (and output increased to daily). Winton simulations falls into a Snowball, Semnter stays stable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "84e12b77-4d3d-4b41-be4a-78cf18762eaf",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'ICON_tools' from '../../python_packages/ICON_tools.py'>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import xarray as xr\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib as mpl\n",
-    "import holoviews as hv\n",
-    "from holoviews import opts\n",
-    "from datetime import datetime\n",
-    "import pandas as pd\n",
-    "import sys, importlib\n",
-    "\n",
-    "sys.path.append(\"../../python_packages\")\n",
-    "import ICON_tools \n",
-    "importlib.reload(ICON_tools)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b2b5097f-f2ee-4abe-91e1-49c5a56fcbff",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path_3W = \"/jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0Sto3W/\"\n",
-    "path_0S = \"/jetfs/scratch/jhoerner/postprocessing/ape_ia_8000_13_0Sto0S/\"\n",
-    "\n",
-    "DS_3W = xr.open_dataset(path_3W + \"ape_ia_8000_13_0Sto3W_atm_2d_ml_y400-405.nc\")\n",
-    "DS_0S = xr.open_dataset(path_0S + \"ape_ia_8000_13_0Sto0S_atm_2d_ml_y400-405.nc\")\n",
-    "\n",
-    "DS_3Wzm = xr.open_dataset(path_3W + \"ape_ia_8000_13_0Sto3W_atm_2d_ml_y400-405.zm.nc\")\n",
-    "DS_0Szm = xr.open_dataset(path_0S + \"ape_ia_8000_13_0Sto0S_atm_2d_ml_y400-405.zm.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "d7864271-f246-42ed-b205-d1559d79028c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "startyear = DS_3W.time.values[0].year\n",
-    "time_3W = [DS_3W.time.values[i].day + (DS_3W.time.values[i].month-1)*30 + (DS_3W.time.values[i].year - startyear)*360 for i in range(len(DS_3W.time))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ae1be09d-e1d7-4981-9841-747901db7b22",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "startyear = DS_0S.time.values[0].year\n",
-    "time_0S = [DS_0S.time.values[i].day + (DS_0S.time.values[i].month-1)*30 + (DS_0S.time.values[i].year - startyear)*360 for i in range(len(DS_0S.time))]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d0a09a8f-255f-4187-9294-b2c2bef4925b",
-   "metadata": {},
-   "source": [
-    "## Surface albedo zonal mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b776f974-90b8-46c0-810e-a85956b708f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2,3, figsize=(12,6),sharex=True, gridspec_kw={'height_ratios': [1, 2]})\n",
-    "\n",
-    "nlevs = 9\n",
-    "levs=np.linspace(0,0.8,nlevs)\n",
-    "\n",
-    "colors1 = np.linspace(0,1,nlevs-1)\n",
-    "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
-    "\n",
-    "cmap= mpl.colors.ListedColormap(colors)\n",
-    "norm = mpl.colors.BoundaryNorm(levs, cmap.N)\n",
-    "\n",
-    "nlevs_diff = 30\n",
-    "\n",
-    "time_test = np.arange(0,len(DS_3W.time),1)\n",
-    "for yaxind in [0,1]:\n",
-    "    im0 = ax[yaxind, 0].contourf(DS_0Szm.lat, time_0S, ICON_tools.get_albedo(DS_0Szm, \"surf\").squeeze(), levels=levs, cmap=cmap, norm=norm)\n",
-    "    ax[yaxind, 0].contour(DS_0Szm.lat, time_0S, DS_0Szm.sic.squeeze(), levels=[0.5], colors=\"C0\")\n",
-    "    ax[yaxind, 0].contour(DS_0Szm.lat, time_0S, DS_0Szm.snowfrac.squeeze(), levels=[0.5], colors=\"C0\", linestyles=\"--\")\n",
-    "\n",
-    "    im1 = ax[yaxind, 1].contourf(DS_3Wzm.lat, time_3W, ICON_tools.get_albedo(DS_3Wzm, \"surf\").squeeze(), levels=levs, cmap=cmap, norm=norm)\n",
-    "    ax[yaxind, 1].contour(DS_3Wzm.lat, time_3W, DS_3Wzm.sic.squeeze(), levels=[0.5], colors=\"C1\")\n",
-    "    ax[yaxind, 1].contour(DS_3Wzm.lat, time_3W, DS_3Wzm.snowfrac.squeeze(), levels=[0.5], colors=\"C1\", linestyles=\"--\")\n",
-    "\n",
-    "    im2 = ax[yaxind, 2].contourf(DS_0Szm.lat, time_0S, ICON_tools.get_albedo(DS_3Wzm, \"surf\").squeeze() - ICON_tools.get_albedo(DS_0Szm, \"surf\").squeeze(), cmap=\"PRGn_r\", levels=np.linspace(-0.3,0.3,nlevs_diff), extend=\"both\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_0S, DS_0Szm.sic.squeeze(), levels=[0.5], colors=\"C0\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_3W, DS_3Wzm.sic.squeeze(), levels=[0.5], colors=\"C1\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_0S, DS_0Szm.snowfrac.squeeze(), levels=[0.5], colors=\"C0\", linestyles=\"--\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_3W, DS_3Wzm.snowfrac.squeeze(), levels=[0.5], colors=\"C1\", linestyles=\"--\")\n",
-    "\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[1, axind].set_xlabel(\"Latitude [°]\")\n",
-    "    ax[1, axind].set_yticks([1], labels=\"\")\n",
-    "    ax[1, axind].set_ylim(1,360)\n",
-    "    ax[0, axind].set_ylim(361,6*361)\n",
-    "    ax[0, axind].set_yticks(np.arange(2*361,6*361,360),labels=\"\")\n",
-    "    ax[1, axind].set_yticks(np.arange(1,361,30),labels=\"\")\n",
-    "\n",
-    "ax[1, 0].set_ylim(1,360)\n",
-    "ax[1, 0].set_xlim(-25,25)\n",
-    "ax[1, 0].set_yticks(np.arange(1,361,30), labels=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
-    "#plt.colorbar(im0, ax=ax[1, 0], label=\"surface albedo []\")\n",
-    "#plt.colorbar(im1, ax=ax[1, 1], label=\"surface albedo []\")\n",
-    "#plt.colorbar(im2, ax=ax[1, 2], label=\"diff surface albedo []\", ticks=np.linspace(-0.3,0.3,11))\n",
-    "#ax[0, 0].set_title(\"Semter-0L\")\n",
-    "#ax[0, 1].set_title(\"Winton-3L\")\n",
-    "#ax[0, 2].set_title(\"Winton-3L - Semter-0L\")\n",
-    "\n",
-    "ax[0, 0].annotate(\"a) Semter-0L\", xycoords=\"axes fraction\", xy=(0.05,1.03), fontweight=\"bold\")\n",
-    "ax[0, 1].annotate(\"b) Winton-3L\", xycoords=\"axes fraction\", xy=(0.05,1.03), fontweight=\"bold\")\n",
-    "ax[0, 2].annotate(\"b) Winton-3L - Semter-0L\", xycoords=\"axes fraction\", xy=(0.05,1.03), fontweight=\"bold\")\n",
-    "\n",
-    "ax[1, 0].set_ylabel(\"time [month]\")\n",
-    "ax[0, 0].set_ylabel(\"time [year]\")\n",
-    "ax[0, 0].set_yticks(np.arange(1*361,6*361,360),labels=[\"2\",\"3\",\"4\",\"5\",\"6\",\"\"])\n",
-    "\n",
-    "\n",
-    "\n",
-    "# split axis\"\n",
-    "# hide the spines between ax and ax2\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[0, axind].spines['bottom'].set_visible(False)\n",
-    "    ax[1, axind].spines['top'].set_visible(False)\n",
-    "    ax[0, axind].xaxis.tick_top()\n",
-    "    ax[0, axind].tick_params(labeltop=False)  # don't put tick labels at the top\n",
-    "    ax[1, axind].xaxis.tick_bottom()\n",
-    "\n",
-    "\n",
-    "\n",
-    "d = 0.5  # proportion of vertical to horizontal extent of the slanted line\n",
-    "kwargs = dict(marker=[(-1, -d), (1, d)], markersize=8,\n",
-    "              linestyle=\"none\", color='k', mec='k', mew=1, clip_on=False)\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[0,axind].plot([0], [0], transform=ax[0,axind].transAxes, **kwargs)\n",
-    "    ax[1,axind].plot([0], [1], transform=ax[1,axind].transAxes, **kwargs)\n",
-    "\n",
-    "    \n",
-    "# colorbars\n",
-    "ax_cb = np.empty(3,dtype=\"object\")\n",
-    "plt.subplots_adjust(left=0.05, bottom=0.1, top=0.95, hspace=0.02, wspace=0.3, right=0.87)\n",
-    "\n",
-    "x1 = ax[0,0].get_position().x1+0.005\n",
-    "x2 = ax[0,1].get_position().x1+0.005\n",
-    "x3 = ax[0,2].get_position().x1+0.005\n",
-    "y1 = ax[1,0].get_position().y0\n",
-    "y2 = ax[0,0].get_position().y1\n",
-    "\n",
-    "ax_cb[2] = fig.add_axes([x3, y1, 0.01, y2-y1])\n",
-    "cbar_diff = fig.colorbar(im2, cax=ax_cb[2], ticks=np.linspace(-0.3,0.3,11), label=\"diff surface albedo []\")\n",
-    "\n",
-    "ax_cb[1] = fig.add_axes([x2, y1, 0.01, y2-y1])\n",
-    "cbar2 = fig.colorbar(im1, cax=ax_cb[1], label=\"surface albedo []\")\n",
-    "\n",
-    "ax_cb[0] = fig.add_axes([x1, y1, 0.01, y2-y1])\n",
-    "cbar1 = fig.colorbar(im0, cax=ax_cb[0], label=\"surface albedo []\")\n",
-    "\n",
-    "#plt.tight_layout()\n",
-    "plt.savefig(\"plots/0Sto3W_surfalb_extend.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c7057312-2250-44f3-9c9f-748a854c9718",
-   "metadata": {},
-   "source": [
-    "### other simulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "70eeec78-11fa-4c48-89ba-01d89ee5ddb1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path_3W = \"/jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0Sto3W/\"\n",
-    "path_0S = \"/jetfs/scratch/jhoerner/postprocessing/ape_ia_10000_13_0Sto0S/\"\n",
-    "\n",
-    "DS_3W = xr.open_dataset(path_3W + \"ape_ia_10000_13_0Sto3W_atm_2d_ml_merged.nc\")\n",
-    "DS_0S = xr.open_dataset(path_0S + \"ape_ia_10000_13_0Sto0S_atm_2d_ml_merged.nc\")\n",
-    "\n",
-    "DS_3Wzm = xr.open_dataset(path_3W + \"ape_ia_10000_13_0Sto3W_atm_2d_ml.zm.nc\")\n",
-    "DS_0Szm = xr.open_dataset(path_0S + \"ape_ia_10000_13_0Sto0S_atm_2d_ml.zm.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "53570330-3574-4361-b7f6-18153c14e94f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "startyear = DS_3W.time.values[0].year\n",
-    "time_3W = [DS_3W.time.values[i].day + (DS_3W.time.values[i].month-1)*30 + (DS_3W.time.values[i].year - startyear)*360 for i in range(len(DS_3W.time))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "90375f07-a139-44cd-a5db-da5f1be67d93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "startyear = DS_0S.time.values[0].year\n",
-    "time_0S = [DS_0S.time.values[i].day + (DS_0S.time.values[i].month-1)*30 + (DS_0S.time.values[i].year - startyear)*360 for i in range(len(DS_0S.time))]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "0cb775b5-9959-4a3b-b214-dd642f3f5769",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 9 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(2,3, figsize=(12,6),sharex=True, gridspec_kw={'height_ratios': [1, 2]})\n",
-    "\n",
-    "nlevs = 9\n",
-    "levs=np.linspace(0,0.8,nlevs)\n",
-    "\n",
-    "colors1 = np.linspace(0,1,nlevs-1)\n",
-    "colors = np.append(\"darkblue\",colors1.astype(\"str\"))\n",
-    "\n",
-    "cmap= mpl.colors.ListedColormap(colors)\n",
-    "norm = mpl.colors.BoundaryNorm(levs, cmap.N)\n",
-    "\n",
-    "nlevs_diff = 30\n",
-    "\n",
-    "time_test = np.arange(0,len(DS_3W.time),1)\n",
-    "for yaxind in [0,1]:\n",
-    "    im0 = ax[yaxind, 0].contourf(DS_0Szm.lat, time_0S, ICON_tools.get_albedo(DS_0Szm, \"surf\").squeeze(), levels=levs, cmap=cmap, norm=norm)\n",
-    "    ax[yaxind, 0].contour(DS_0Szm.lat, time_0S, DS_0Szm.sic.squeeze(), levels=[0.5], colors=\"C0\")\n",
-    "    ax[yaxind, 0].contour(DS_0Szm.lat, time_0S, DS_0Szm.snowfrac.squeeze(), levels=[0.5], colors=\"C0\", linestyles=\"--\")\n",
-    "\n",
-    "    im1 = ax[yaxind, 1].contourf(DS_3Wzm.lat, time_3W, ICON_tools.get_albedo(DS_3Wzm, \"surf\").squeeze(), levels=levs, cmap=cmap, norm=norm)\n",
-    "    ax[yaxind, 1].contour(DS_3Wzm.lat, time_3W, DS_3Wzm.sic.squeeze(), levels=[0.5], colors=\"C1\")\n",
-    "    ax[yaxind, 1].contour(DS_3Wzm.lat, time_3W, DS_3Wzm.snowfrac.squeeze(), levels=[0.5], colors=\"C1\", linestyles=\"--\")\n",
-    "\n",
-    "    im2 = ax[yaxind, 2].contourf(DS_0Szm.lat, time_0S, ICON_tools.get_albedo(DS_3Wzm, \"surf\").squeeze() - ICON_tools.get_albedo(DS_0Szm, \"surf\").squeeze(), cmap=\"PRGn_r\", levels=np.linspace(-0.3,0.3,nlevs_diff), extend=\"both\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_0S, DS_0Szm.sic.squeeze(), levels=[0.5], colors=\"C0\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_3W, DS_3Wzm.sic.squeeze(), levels=[0.5], colors=\"C1\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_0S, DS_0Szm.snowfrac.squeeze(), levels=[0.5], colors=\"C0\", linestyles=\"--\")\n",
-    "    ax[yaxind, 2].contour(DS_0Szm.lat, time_3W, DS_3Wzm.snowfrac.squeeze(), levels=[0.5], colors=\"C1\", linestyles=\"--\")\n",
-    "\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[1, axind].set_xlabel(\"Latitude [°]\")\n",
-    "    ax[1, axind].set_yticks([1], labels=\"\")\n",
-    "    ax[1, axind].set_ylim(1,360)\n",
-    "    ax[0, axind].set_ylim(361,6*361)\n",
-    "    ax[0, axind].set_yticks(np.arange(2*361,10*361,360),labels=\"\")\n",
-    "    ax[1, axind].set_yticks(np.arange(1,361,30),labels=\"\")\n",
-    "\n",
-    "ax[1, 0].set_ylim(1,360)\n",
-    "ax[1, 0].set_xlim(-25,25)\n",
-    "ax[1, 0].set_yticks(np.arange(1,361,30), labels=[\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"])\n",
-    "#plt.colorbar(im0, ax=ax[1, 0], label=\"surface albedo []\")\n",
-    "#plt.colorbar(im1, ax=ax[1, 1], label=\"surface albedo []\")\n",
-    "#plt.colorbar(im2, ax=ax[1, 2], label=\"diff surface albedo []\", ticks=np.linspace(-0.3,0.3,11))\n",
-    "ax[0, 0].set_title(\"Semter-0L\")\n",
-    "ax[0, 1].set_title(\"Winton-3L\")\n",
-    "ax[0, 2].set_title(\"Winton-3L - Semter-0L\")\n",
-    "\n",
-    "ax[1, 0].set_ylabel(\"time [month]\")\n",
-    "ax[0, 0].set_ylabel(\"time [year]\")\n",
-    "ax[0, 0].set_yticks(np.arange(1*361,10*361,360),labels=[\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\"])\n",
-    "\n",
-    "\n",
-    "\n",
-    "# split axis\"\n",
-    "# hide the spines between ax and ax2\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[0, axind].spines['bottom'].set_visible(False)\n",
-    "    ax[1, axind].spines['top'].set_visible(False)\n",
-    "    ax[0, axind].xaxis.tick_top()\n",
-    "    ax[0, axind].tick_params(labeltop=False)  # don't put tick labels at the top\n",
-    "    ax[1, axind].xaxis.tick_bottom()\n",
-    "\n",
-    "\n",
-    "\n",
-    "d = 0.5  # proportion of vertical to horizontal extent of the slanted line\n",
-    "kwargs = dict(marker=[(-1, -d), (1, d)], markersize=8,\n",
-    "              linestyle=\"none\", color='k', mec='k', mew=1, clip_on=False)\n",
-    "for axind in [0,1,2]:\n",
-    "    ax[0,axind].plot([0], [0], transform=ax[0,axind].transAxes, **kwargs)\n",
-    "    ax[1,axind].plot([0], [1], transform=ax[1,axind].transAxes, **kwargs)\n",
-    "\n",
-    "    \n",
-    "# colorbars\n",
-    "ax_cb = np.empty(3,dtype=\"object\")\n",
-    "plt.subplots_adjust(left=0.05, bottom=0.05, top=0.95, hspace=0.02, wspace=0.3, right=0.87)\n",
-    "\n",
-    "x1 = ax[0,0].get_position().x1+0.005\n",
-    "x2 = ax[0,1].get_position().x1+0.005\n",
-    "x3 = ax[0,2].get_position().x1+0.005\n",
-    "y1 = ax[1,0].get_position().y0\n",
-    "y2 = ax[0,0].get_position().y1\n",
-    "\n",
-    "ax_cb[2] = fig.add_axes([x3, y1, 0.01, y2-y1])\n",
-    "cbar_diff = fig.colorbar(im2, cax=ax_cb[2], ticks=np.linspace(-0.3,0.3,11), label=\"diff surface albedo []\")\n",
-    "\n",
-    "ax_cb[1] = fig.add_axes([x2, y1, 0.01, y2-y1])\n",
-    "cbar2 = fig.colorbar(im1, cax=ax_cb[1], label=\"surface albedo []\")\n",
-    "\n",
-    "ax_cb[0] = fig.add_axes([x1, y1, 0.01, y2-y1])\n",
-    "cbar1 = fig.colorbar(im0, cax=ax_cb[0], label=\"surface albedo []\")\n",
-    "\n",
-    "#plt.tight_layout()\n",
-    "plt.savefig(\"plots/0Sto3W_surfalb_extend_10000.pdf\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "baseenv - Python 3.7",
-   "language": "python",
-   "name": "baseenv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}