{
 "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\")"
   ]
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