{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure 3: calculate feedback ratios as time series for all available models\n",
    "\n",
    "(a)(b) is anomalous cLand and cOcean, (c)(d) is time series of feedback ratio, (e) is feedback ratio averaged 2040-2060. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import pandas as pd\n",
    "import sys\n",
    "import my_io\n",
    "from my_io import Figure1\n",
    "\n",
    "ppm2GtCO2 = 2.124/0.272912\n",
    "fontsize = 18\n",
    "def ens_values(df_list,var_name):\n",
    "    '''\n",
    "    df_list: a list of DataFrame\n",
    "    '''\n",
    "    yrstart = 2015\n",
    "    diff_realizaions = [] # (nreal,nyear)\n",
    "    for ireal in range(len(df_list)):\n",
    "        diff_realizaions.append([])\n",
    "    for iyear in range(yrstart,2100):\n",
    "        for ireal in range(len(df_list)):\n",
    "            diff_realizaions[ireal].append(df_list[ireal][var_name].loc[iyear])           \n",
    "    diff_realizaions = np.array(diff_realizaions)\n",
    "    ens_mean = np.average(diff_realizaions,axis=0)\n",
    "    ens_min = np.min(diff_realizaions,axis=0)\n",
    "    ens_max = np.max(diff_realizaions,axis=0)\n",
    "    ens_stddev = np.std(diff_realizaions,axis=0)\n",
    "    yrindex = np.array(range(yrstart,2100)) \n",
    "    df_ens = pd.DataFrame(data=ens_max - ens_min, index=yrindex, columns=['ens_range'])\n",
    "    df_ens['ens_mean'] = ens_mean\n",
    "    df_ens['ens_min'] = ens_min\n",
    "    df_ens['ens_max'] = ens_max\n",
    "    return df_ens\n",
    "def cOcean_from_fgco2(df,yrstart):\n",
    "    \"\"\"\n",
    "    calculate cOcean by integrating annual fgco2\n",
    "    df: a DataFrame containing fgco2\n",
    "    yrstart: year of starting integration\n",
    "    \"\"\"\n",
    "    cOcean = []\n",
    "    for year in range(yrstart,2100):\n",
    "        cOcean.append(df['fgco2'].loc[yrstart:year].sum())\n",
    "    cOcean = np.array(cOcean)\n",
    "    cOcean = pd.DataFrame(data=cOcean, index=range(yrstart,2100), columns=['cOcean'])\n",
    "    cOcean.index.name = 'year'\n",
    "    return cOcean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x900 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feedback_ratio_limit = [-30,20]\n",
    "try:\n",
    "    del fig\n",
    "except:\n",
    "    pass\n",
    "fig = plt.figure(figsize=(14,9),layout=\"constrained\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up model list and corresponding colors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5 C0\n",
      "CESM2 C1\n",
      "CanESM5 C2\n",
      "FOCI C3\n",
      "MIROC-ES2L C4\n",
      "MPI-ESM1-2-LR C5\n",
      "NorESM2-LM C6\n",
      "UKESM1-0-LL C7\n"
     ]
    }
   ],
   "source": [
    "model_list = ['ACCESS-ESM1-5', 'CESM2', 'CanESM5', \n",
    "              'FOCI', 'MIROC-ES2L', 'MPI-ESM1-2-LR',\n",
    "              'NorESM2-LM','UKESM1-0-LL']\n",
    "color_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    color_list.append('C'+str(imodel))\n",
    "    print(model_list[imodel],color_list[imodel])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (a) Difference in cLand and cOcean for A/R-REF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x146564737550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "ax = fig.add_subplot(321)\n",
    "handles = []\n",
    "for imodel in range(len(model_list)):\n",
    "    # for legend\n",
    "    patch = mpatches.Patch(color=color_list[imodel], label=model_list[imodel])\n",
    "    handles.append(patch) \n",
    "\n",
    "    if model_list[imodel] in ['NorESM2-LM']:\n",
    "        continue\n",
    "\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        \n",
    "        # read cLand and plot\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1 = Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        df_ens = ens_values([fig1.dflist[3][0]-fig1.dflist[2][0],\n",
    "                             fig1.dflist[3][1]-fig1.dflist[2][1],\n",
    "                             fig1.dflist[3][2]-fig1.dflist[2][2]],'cLand')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'-',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "        \n",
    "        # calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1 = Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        cOcean = []\n",
    "        for iexp in range(4):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                if iexp in [0,2,3]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "                if iexp in [1]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020)) \n",
    "                    cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                    cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "        df_ens = ens_values([cOcean[3][0]-cOcean[2][0],\n",
    "                             cOcean[3][1]-cOcean[2][1],\n",
    "                             cOcean[3][2]-cOcean[2][2],],'cOcean')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'--',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "\n",
    "        # read cLand and plot\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        df_ens = ens_values([fig1.dflist[2][0]-fig1.dflist[0][0],\n",
    "                             fig1.dflist[2][1]-fig1.dflist[0][1],\n",
    "                             fig1.dflist[2][2]-fig1.dflist[0][2]],'cLand')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'-',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "        # calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        df_ens = ens_values([cOcean[2][0]-cOcean[0][0],\n",
    "                             cOcean[2][1]-cOcean[0][1],\n",
    "                             cOcean[2][2]-cOcean[0][2],],'cOcean')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'--',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "    elif model_list[imodel]=='ACCESS-ESM1-5':\n",
    "        \n",
    "        # read cLand and plot\n",
    "        fig1=Figure1('cLand','foo')\n",
    "        fig1.read_ACCESS_ensemble(debug=True)\n",
    "        foo = []\n",
    "        for ireal in range(10):\n",
    "            foo.append(fig1.dflist[1][ireal]-fig1.dflist[0][ireal])\n",
    "        df_ens = ens_values(foo,'cLand')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'-',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "        # calculate cOcean and plot\n",
    "        fig1 = Figure1('fgco2','foo')\n",
    "        fig1.read_ACCESS_ensemble(debug=True)\n",
    "        cOcean = []\n",
    "        for iexp in [0,1]:\n",
    "            cOcean.append([])\n",
    "            for ireal in range(10):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        foo = []\n",
    "        for ireal in range(10):\n",
    "            foo.append(cOcean[1][ireal]-cOcean[0][ireal])\n",
    "        df_ens = ens_values(foo,'cOcean')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'--',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "    # rest of the models, which has only one realization\n",
    "    else:\n",
    "\n",
    "        # read cLand and plot\n",
    "        var_name, exp = 'cLand', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            ax.plot(fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'],'-',color=color_list[imodel],label=model_list[imodel])\n",
    "\n",
    "        # calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(1):\n",
    "                if iexp==1:\n",
    "                    pass\n",
    "                else:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        for ireal in range(1):\n",
    "            ax.plot(range(2015,2100),cOcean[2][ireal]['cOcean']-cOcean[0][ireal]['cOcean'],'--',color='C'+str(imodel),label=model_list[imodel])\n",
    "ax.grid()\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_title('(a) $\\Delta C_{land}$ and $\\Delta C_{ocean}$ (A/R-REF)',fontsize=18)\n",
    "ax.set_ylabel('PgC', fontsize = 18)\n",
    "ax.set_ylim([-25,120])\n",
    "######\n",
    "# subplot legend for dashed and solid lines\n",
    "from matplotlib.lines import Line2D\n",
    "line_solid = Line2D([], [], color='black', linestyle='--', linewidth=2, label='$C_{ocean}$')\n",
    "line_dashed = Line2D([], [], color='black', linestyle='-', linewidth=2, label='$C_{land}$')\n",
    "ax.legend(handles=[line_solid, line_dashed], loc='upper left',fontsize=fontsize)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (b) Difference in cLand and cOcean of OAE-REF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-25.0, 120.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "ax = fig.add_subplot(322)\n",
    "handles = []\n",
    "for imodel in range(len(model_list)):\n",
    "    # for legend\n",
    "    patch = mpatches.Patch(color=color_list[imodel], label=model_list[imodel])\n",
    "    handles.append(patch) \n",
    "\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5', 'CESM2', 'CanESM5', 'MIROC-ES2L']:\n",
    "        \n",
    "        # Skip models not having OAE runs\n",
    "        continue\n",
    "        \n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "\n",
    "        # read cLand and plot\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "\n",
    "        df_ens = ens_values([fig1.dflist[1][0]-fig1.dflist[0][0],\n",
    "                             fig1.dflist[1][1]-fig1.dflist[0][1],\n",
    "                             fig1.dflist[1][2]-fig1.dflist[0][2]],'cLand')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'-',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "        # calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        cOcean = []\n",
    "        for iexp in range(4):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                if iexp in [0,2,3]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "                if iexp in [1]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020)) \n",
    "                    cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                    cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "        df_ens = ens_values([cOcean[1][0]-cOcean[0][0],\n",
    "                             cOcean[1][1]-cOcean[0][1],\n",
    "                             cOcean[1][2]-cOcean[0][2],],'cOcean')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'--',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "\n",
    "        # read cLand and plot\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        df_ens = ens_values([fig1.dflist[1][0]-fig1.dflist[0][0],\n",
    "                             fig1.dflist[1][1]-fig1.dflist[0][1],\n",
    "                             fig1.dflist[1][2]-fig1.dflist[0][2],],'cLand')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'-',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "        \n",
    "        # calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        df_ens = ens_values([cOcean[1][0]-cOcean[0][0],\n",
    "                             cOcean[1][1]-cOcean[0][1],\n",
    "                             cOcean[1][2]-cOcean[0][2],],'cOcean')\n",
    "        ax.plot(range(2015,2100),df_ens['ens_mean'],'--',linewidth=1.5,color=color_list[imodel],label=model_list[imodel])\n",
    "        ax.fill_between(range(2015,2100), df_ens['ens_min'], df_ens['ens_max'] ,alpha=0.2, facecolor=color_list[imodel])\n",
    "\n",
    "    # rest of the models that have only one realization\n",
    "    else:\n",
    "        #### read cLand and plot\n",
    "        var_name, exp = 'cLand', 'OAE' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            ax.plot(fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'],'-',color=color_list[imodel],label='cLand (OAE)')\n",
    "            \n",
    "        ##### calculate cOcean and plot\n",
    "        var_name, exp = 'fgco2', 'OAE' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(1): \n",
    "                \n",
    "                # REF\n",
    "                if iexp in [0]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "\n",
    "                # OAE\n",
    "                if iexp in [1]:\n",
    "\n",
    "                    # OAE of UKESM starts from 2020 \n",
    "                    if model_list[imodel] in ['UKESM1-0-LL']:\n",
    "                        cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020)) \n",
    "                        cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                        cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "\n",
    "                    # Actually just NorESM, which has ocn-alk starting also from 2015\n",
    "                    else:\n",
    "                        cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        for ireal in range(1):\n",
    "            ax.plot(range(2015,2100),cOcean[1][ireal]['cOcean']-cOcean[0][ireal]['cOcean'],'--',color=color_list[imodel],label='cOcean (OAE)')\n",
    "ax.grid()\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_title('(b) $\\Delta C_{land}$ and $\\Delta C_{ocean}$ (OAE-REF)',fontsize=18)\n",
    "ax.set_ylabel('PgC', fontsize = 18)\n",
    "ax.set_ylim([-25,120])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (c) A/R feedback ratio of other models (CDRMIP)\n",
    "**Note: OAE runs of NorESM starts from 2015!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5 feedback ratio A/R (r0): -11.7\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r1): -12.6\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r2): -13.1\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r3): -20.2\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r4): -16.3\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r5): -15.2\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r6): -16.6\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r7): -10.6\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r8): -15.4\n",
      "ACCESS-ESM1-5 feedback ratio A/R (r9): -18.1\n",
      "ACCESS-ESM1-5 feedback ratio A/R (ensmean): -15.0\n",
      "CESM2 feedback ratio A/R: -14.1\n",
      "CanESM5 feedback ratio A/R: -25.3\n",
      "FOCI feedback ratio A/R (r0): -18.4\n",
      "FOCI feedback ratio A/R (r1): -14.2\n",
      "FOCI feedback ratio A/R (r2): -17.1\n",
      "FOCI feedback ratio A/R (ensmean): -16.6\n",
      "MIROC-ES2L feedback ratio A/R: -13.4\n",
      "MPI-ESM1-2-LR feedback ratio A/R (r0): -14.6\n",
      "MPI-ESM1-2-LR feedback ratio A/R (r1): -14.1\n",
      "MPI-ESM1-2-LR feedback ratio A/R (r2): -17.2\n",
      "MPI-ESM feedback ratio A/R (ensmean): -15.3\n",
      "UKESM1-0-LL feedback ratio A/R: -14.1\n",
      "--- lambda_AR\n",
      "len(lambda) 7\n",
      "mean=-15.9, std=4.3\n",
      "--- cLand_AR\n",
      "len(lambda) 7\n",
      "mean=30.9, std=8.3\n",
      "--- cOcean_AR\n",
      "len(lambda) 7\n",
      "mean=-4.8, std=1.5\n"
     ]
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "ax = fig.add_subplot(323)\n",
    "ax_ins = inset_axes(ax,width='30%',height='30%',loc='upper center',\n",
    "                  bbox_to_anchor=(0,-0.02,1,1),bbox_transform=ax.transAxes)\n",
    "\n",
    "# Not for plotting, for reporting the mean and stddev in Sec. 3.2\n",
    "lambda_AR_list = []\n",
    "d_cLand_AR_list = []\n",
    "d_cOcean_AR_list = []\n",
    "\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['NorESM2-LM']:\n",
    "        lambda_AR_list.append(0)\n",
    "        continue\n",
    "    if model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        feedback_ratio_OAE, feedback_ratio_AR = [], []\n",
    "        for ireal in range(3):\n",
    "            d_cLand_OAE = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_OAE = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            d_cLand_AR = fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[2][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "\n",
    "            # calculate feedback ratio from 2020 because d_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_OAE.append(d_cLand_OAE.loc[2020:] / d_cOcean_OAE.loc[2020:])\n",
    "            feedback_ratio_AR.append(d_cOcean_AR.loc[2020:] / d_cLand_AR.loc[2020:])\n",
    "            print(f'{model_list[imodel]} feedback ratio A/R (r{ireal}): {feedback_ratio_AR[-1][-60:-39].mean()*100:.1f}')\n",
    "            if ireal == 0:\n",
    "                d_cLand_AR_list.append(d_cLand_AR[-60:-39].mean())\n",
    "                d_cOcean_AR_list.append(d_cOcean_AR[-60:-39].mean())\n",
    "        feedback_ratio_OAE_FOCI, feedback_ratio_AR_FOCI = np.array(feedback_ratio_OAE) * 100., np.array(feedback_ratio_AR) * 100. # to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_AR_FOCI,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='A/R')    \n",
    "        ax.fill_between(range(2025,2100),np.min(feedback_ratio_AR_FOCI,axis=0)[-75:],np.max(feedback_ratio_AR_FOCI,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_AR_FOCI,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='A/R')    \n",
    "        ax_ins.fill_between(range(2025,2100),np.min(feedback_ratio_AR_FOCI,axis=0)[-75:],np.max(feedback_ratio_AR_FOCI,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        print(f'FOCI feedback ratio A/R (ensmean): {np.average(feedback_ratio_AR_FOCI,axis=0)[-60:-39].mean():.1f}')\n",
    "        \n",
    "        # add the first member to list, later for calculating multi-model mean \n",
    "        lambda_AR_list.append(feedback_ratio_AR_FOCI[0][-60:-39].mean()) \n",
    "\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        fig1=Figure1('fgco2','BOTH')\n",
    "        fig1.read_MPIESM()\n",
    "        cOcean = []\n",
    "        for iexp in range(4):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                if iexp in [0,2,3]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "                if iexp in [1]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020))\n",
    "                    cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                    cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "        feedback_ratio_OAE, feedback_ratio_AR = [], []\n",
    "        fig1=Figure1('cLand','BOTH')\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(3):\n",
    "            d_cLand_OAE = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_OAE = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            d_cLand_AR = fig1.dflist[3][ireal]['cLand']-fig1.dflist[2][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[3][ireal]['cOcean'] - cOcean[2][ireal]['cOcean']\n",
    "            \n",
    "            # calculate feedback ratio from 2020 because d_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_OAE.append(d_cLand_OAE.loc[2020:] / d_cOcean_OAE.loc[2020:])\n",
    "            feedback_ratio_AR.append(d_cOcean_AR.loc[2020:] / d_cLand_AR.loc[2020:])\n",
    "            print(f'{model_list[imodel]} feedback ratio A/R (r{ireal}): {feedback_ratio_AR[-1][-60:-39].mean()*100:.1f}')\n",
    "            if ireal == 0:\n",
    "                d_cLand_AR_list.append(d_cLand_AR[-60:-39].mean())\n",
    "                d_cOcean_AR_list.append(d_cOcean_AR[-60:-39].mean())\n",
    "        feedback_ratio_OAE_MPIESM, feedback_ratio_AR_MPIESM = np.array(feedback_ratio_OAE) * 100., np.array(feedback_ratio_AR) * 100. # to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_AR_MPIESM,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        ax.fill_between(range(2025,2100),np.min(feedback_ratio_AR_MPIESM,axis=0)[-75:],np.max(feedback_ratio_AR_MPIESM,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])        \n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_AR_MPIESM,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        ax_ins.fill_between(range(2025,2100),np.min(feedback_ratio_AR_MPIESM,axis=0)[-75:],np.max(feedback_ratio_AR_MPIESM,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])        \n",
    "        print(f'MPI-ESM feedback ratio A/R (ensmean): {np.average(feedback_ratio_AR_MPIESM,axis=0)[-60:-39].mean():.1f}')\n",
    "        \n",
    "        # add the first member to list, later for calculating multi-model mean \n",
    "        lambda_AR_list.append(feedback_ratio_AR_MPIESM[0][-60:-39].mean()) \n",
    "\n",
    "    elif model_list[imodel]=='ACCESS-ESM1-5':\n",
    "        fig1=Figure1('fgco2','foo')\n",
    "        fig1.read_ACCESS_ensemble(debug=True)\n",
    "        fig1 = Figure1('fgco2','foo')\n",
    "        fig1.read_ACCESS_ensemble(debug=True)\n",
    "        cOcean = []\n",
    "        for iexp in [0,1]:\n",
    "            cOcean.append([])\n",
    "            for ireal in range(10):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        feedback_ratio_OAE, feedback_ratio_AR = [], []\n",
    "        fig1=Figure1('cLand','foo')\n",
    "        fig1.read_ACCESS_ensemble(debug=True)\n",
    "        for ireal in range(10):\n",
    "            d_cLand_AR = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            feedback_ratio_AR.append(d_cOcean_AR.loc[2020:] / d_cLand_AR.loc[2020:])\n",
    "            print(f'{model_list[imodel]} feedback ratio A/R (r{ireal}): {feedback_ratio_AR[-1][-60:-39].mean()*100:.1f}')\n",
    "            if ireal == 0:\n",
    "                d_cLand_AR_list.append(d_cLand_AR[-60:-39].mean())\n",
    "                d_cOcean_AR_list.append(d_cOcean_AR[-60:-39].mean())\n",
    "        feedback_ratio_AR_ACCESS = np.array(feedback_ratio_AR) * 100. # multiply by 100 to convert to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_AR_ACCESS,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        ax.fill_between(range(2025,2100),np.min(feedback_ratio_AR_ACCESS,axis=0)[-75:],\n",
    "                        np.max(feedback_ratio_AR_ACCESS,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_AR_ACCESS,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        ax_ins.fill_between(range(2025,2100),np.min(feedback_ratio_AR_ACCESS,axis=0)[-75:],\n",
    "                        np.max(feedback_ratio_AR_ACCESS,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        print(f'{model_list[imodel]} feedback ratio A/R (ensmean): {np.average(feedback_ratio_AR_ACCESS,axis=0)[-60:-39].mean():.1f}')\n",
    "        \n",
    "        # add the first member to list, later for calculating multi-model mean \n",
    "        lambda_AR_list.append(feedback_ratio_AR_ACCESS[0][-60:-39].mean()) \n",
    "\n",
    "    else:\n",
    "        fig1=Figure1('fgco2','AFF')\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(1):\n",
    "                if iexp==1:\n",
    "                    cOcean[iexp].append([])\n",
    "                else:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "\n",
    "        feedback_ratio_OAE, feedback_ratio_AR = [], []\n",
    "        fig1=Figure1('cLand','AFF')\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            d_cLand_AR = fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[2][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            \n",
    "            # calculate feedback ratio from 2020 because d_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_AR.append(d_cOcean_AR.loc[2020:] / d_cLand_AR.loc[2020:])\n",
    "            if ireal == 0:\n",
    "                d_cLand_AR_list.append(d_cLand_AR[-60:-39].mean())\n",
    "                d_cOcean_AR_list.append(d_cOcean_AR[-60:-39].mean())\n",
    "        feedback_ratio_AR = np.array(feedback_ratio_AR) * 100. # multyply by 100 to convert from unitless to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_AR,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_AR,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel])\n",
    "        print(f'{model_list[imodel]} feedback ratio A/R: {np.average(feedback_ratio_AR,axis=0)[-60:-39].mean():.1f}')\n",
    "        lambda_AR_list.append(np.average(feedback_ratio_AR,axis=0)[-60:-39].mean())\n",
    "ax.grid()\n",
    "ax.set_ylabel('%', fontsize = 18)\n",
    "ax.set_xticks([2025, 2040, 2060, 2080, 2100])\n",
    "ax.set_xticklabels([2025, 2040, 2060, 2080, 2100])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_title('(c) Carbon cycle feedback ratio $\\lambda_{A/R}$',fontsize=18)\n",
    "ax.set_ylim(feedback_ratio_limit)\n",
    "\n",
    "# config for inset \n",
    "ax_ins.grid()\n",
    "ax_ins.set_ylabel('%')\n",
    "ax_ins.set_xticks([2025, 2050, 2075, 2100])\n",
    "ax_ins.set_xticklabels([2025, 2050, 2075, 2100])\n",
    "ax_ins.set_ylim(-100,100)\n",
    "\n",
    "# print out mean and stddev of lambda, cLand and cOcean for reporting in Sec. 3.2\n",
    "foo = []\n",
    "for bar in lambda_AR_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- lambda_AR')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "####\n",
    "foo = []\n",
    "for bar in d_cLand_AR_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- cLand_AR')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "####\n",
    "foo = []\n",
    "for bar in d_cOcean_AR_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- cOcean_AR')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (d) OAE feedback ratio of all models (CDRMIP)\n",
    "**OAE runs of UKESM starts from 2020!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FOCI feedback ratio OAE (r0): -21.0\n",
      "FOCI feedback ratio OAE (r1): -11.5\n",
      "FOCI feedback ratio OAE (r2): -15.1\n",
      "FOCI feedback ratio OAE (ensmean): -15.9\n",
      "MPI-ESM1-2-LR feedback ratio OAE (r0): -4.0\n",
      "MPI-ESM1-2-LR feedback ratio OAE (r1): -16.6\n",
      "MPI-ESM1-2-LR feedback ratio OAE (r2): -7.5\n",
      "MPI-ESM feedback ratio OAE (ensmean): -9.4\n",
      "NorESM2-LM feedback ratio OAE: -14.8\n",
      "UKESM1-0-LL feedback ratio OAE: -12.1\n",
      "--- lambda_OAE\n",
      "len(lambda) 4\n",
      "mean=-13.0, std=2.5\n",
      "--- d_cLand_OAE\n",
      "len(lambda) 4\n",
      "mean=-4.7, std=2.0\n",
      "--- d_cOcean_OAE\n",
      "len(lambda) 4\n",
      "mean=34.8, std=0.6\n"
     ]
    }
   ],
   "source": [
    "ax = fig.add_subplot(324)\n",
    "ax_ins = inset_axes(ax,width='30%',height='30%',loc='upper center',\n",
    "                  bbox_to_anchor=(0.1,-0.02,1,1),bbox_transform=ax.transAxes)\n",
    "\n",
    "# Not for plotting, for reporting the mean and stddev in Sec. 3.2\n",
    "lambda_OAE_list = []\n",
    "d_cLand_OAE_list = []\n",
    "d_cOcean_OAE_list = []\n",
    "\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5', 'CESM2', 'CanESM5', 'MIROC-ES2L']:\n",
    "        lambda_OAE_list.append(0)\n",
    "        continue\n",
    "        \n",
    "    if model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'fgco2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        feedback_ratio_OAE = []\n",
    "        for ireal in range(3):\n",
    "            d_cLand_OAE = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_OAE = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            d_cLand_AR = fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[2][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            \n",
    "            # calculate feedback ratio from 2020 because d_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_OAE.append(d_cLand_OAE.loc[2020:] / d_cOcean_OAE.loc[2020:])\n",
    "            print(f'{model_list[imodel]} feedback ratio OAE (r{ireal}): {feedback_ratio_OAE[-1][-60:-39].mean()*100:.1f}')\n",
    "            if ireal == 0:\n",
    "                d_cLand_OAE_list.append(d_cLand_OAE[-60:-39].mean())\n",
    "                d_cOcean_OAE_list.append(d_cOcean_OAE[-60:-39].mean())\n",
    "        feedback_ratio_OAE_FOCI = np.array(feedback_ratio_OAE) * 100. # to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_OAE_FOCI,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        ax.fill_between(range(2025,2100),np.min(feedback_ratio_OAE_FOCI,axis=0)[-75:],np.max(feedback_ratio_OAE_FOCI,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_OAE_FOCI,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        ax_ins.fill_between(range(2025,2100),np.min(feedback_ratio_OAE_FOCI,axis=0)[-75:],np.max(feedback_ratio_OAE_FOCI,axis=0)[-75:],alpha=0.2, facecolor=color_list[imodel])\n",
    "        print(f'FOCI feedback ratio OAE (ensmean): {np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].mean():.1f}')\n",
    "        lambda_OAE_list.append(np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].mean())\n",
    "        \n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        fig1=Figure1('fgco2','BOTH')\n",
    "        fig1.read_MPIESM()\n",
    "        cOcean = []\n",
    "        for iexp in range(4):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(3):\n",
    "                if iexp in [0,2,3]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "                if iexp in [1]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020))\n",
    "                    cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                    cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "        feedback_ratio_OAE = []\n",
    "        fig1=Figure1('cLand','BOTH')\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(3):\n",
    "            d_cLand_OAE = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_OAE = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            d_cLand_AR = fig1.dflist[3][ireal]['cLand']-fig1.dflist[2][ireal]['cLand']\n",
    "            d_cOcean_AR = cOcean[3][ireal]['cOcean'] - cOcean[2][ireal]['cOcean']\n",
    "            \n",
    "            # calculate feedback ratio from 2020 becaused_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_OAE.append(d_cLand_OAE.loc[2020:] / d_cOcean_OAE.loc[2020:])\n",
    "            print(f'{model_list[imodel]} feedback ratio OAE (r{ireal}): {feedback_ratio_OAE[-1][-60:-39].mean()*100:.1f}')\n",
    "            if ireal == 0:\n",
    "                d_cLand_OAE_list.append(d_cLand_OAE[-60:-39].mean())\n",
    "                d_cOcean_OAE_list.append(d_cOcean_OAE[-60:-39].mean())\n",
    "        feedback_ratio_OAE_MPIESM = np.array(feedback_ratio_OAE) * 100. # to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_OAE_MPIESM,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        ax.fill_between(range(2025,2100),np.min(feedback_ratio_OAE_MPIESM,axis=0)[-75:],np.max(feedback_ratio_OAE_MPIESM,axis=0)[-75:],\n",
    "                        alpha=0.2,facecolor=color_list[imodel])\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_OAE_MPIESM,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        ax_ins.fill_between(range(2025,2100),np.min(feedback_ratio_OAE_MPIESM,axis=0)[-75:],np.max(feedback_ratio_OAE_MPIESM,axis=0)[-75:],\n",
    "                        alpha=0.2,facecolor=color_list[imodel])\n",
    "        print(f'MPI-ESM feedback ratio OAE (ensmean): {np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].mean():.1f}')\n",
    "        lambda_OAE_list.append(np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].mean())\n",
    "        \n",
    "    else:\n",
    "        fig1=Figure1('fgco2','OAE')\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        cOcean = []\n",
    "        for iexp in range(3):\n",
    "            cOcean.append([])\n",
    "            for ireal in range(1):\n",
    "                if iexp in [0]:\n",
    "                    cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "                if iexp in [1]:\n",
    "                    if model_list[imodel]=='UKESM1-0-LL':\n",
    "                        cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2020))\n",
    "                        cOcean[iexp][ireal] = cOcean[iexp][ireal] + cOcean[0][ireal].loc[2019]\n",
    "                        cOcean[iexp][ireal] = pd.concat([cOcean[iexp][ireal], cOcean[0][ireal].loc[2015:2019]]).sort_values('year')\n",
    "                    elif model_list[imodel]=='NorESM2-LM':\n",
    "                        cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        feedback_ratio_OAE = []\n",
    "        fig1=Figure1('cLand','OAE')\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            d_cLand_OAE = fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand']\n",
    "            d_cOcean_OAE = cOcean[1][ireal]['cOcean'] - cOcean[0][ireal]['cOcean']\n",
    "            \n",
    "            # calculate feedback ratio from 2020 because d_cOcean is zero until 2020 for OAE runs\n",
    "            feedback_ratio_OAE.append(d_cLand_OAE.loc[2020:] / d_cOcean_OAE.loc[2020:])\n",
    "            ax.plot(feedback_ratio_OAE[-1][-75:]*100.,'--',linewidth=1,color=color_list[imodel])\n",
    "            if ireal == 0:\n",
    "                d_cLand_OAE_list.append(d_cLand_OAE[-60:-39].mean())\n",
    "                d_cOcean_OAE_list.append(d_cOcean_OAE[-60:-39].mean())\n",
    "        feedback_ratio_OAE = np.array(feedback_ratio_OAE) * 100. # to percentage\n",
    "        ax.plot(range(2025,2100),np.average(feedback_ratio_OAE,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        ax_ins.plot(range(2025,2100),np.average(feedback_ratio_OAE,axis=0)[-75:],'-',linewidth=1.5,color=color_list[imodel],label='OAE')\n",
    "        print(f'{model_list[imodel]} feedback ratio OAE: {np.average(feedback_ratio_OAE,axis=0)[-60:-39].mean():.1f}')\n",
    "        lambda_OAE_list.append(np.average(feedback_ratio_OAE,axis=0)[-60:-39].mean())\n",
    "ax.grid()\n",
    "ax.set_ylabel('%', fontsize = 18)\n",
    "ax.set_xticks([2025, 2040, 2060, 2080, 2100])\n",
    "ax.set_xticklabels([2025, 2040, 2060, 2080, 2100])\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_title('(d) Carbon cycle feedback ratio $\\lambda_{OAE}$',fontsize=18)\n",
    "ax.set_ylim(feedback_ratio_limit)\n",
    "\n",
    "# config for inset\n",
    "ax_ins.grid()\n",
    "ax_ins.set_ylabel('%')\n",
    "ax_ins.set_xticks([2025, 2050, 2075, 2100])\n",
    "ax_ins.set_xticklabels([2025, 2050, 2075, 2100])\n",
    "ax_ins.set_ylim(-100,100)\n",
    "\n",
    "# print out mean and stddev of lambda, cLand and cOcean for reporting in Sec. 3.2\n",
    "foo = []\n",
    "for bar in lambda_OAE_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- lambda_OAE')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "#######\n",
    "foo = []\n",
    "for bar in d_cLand_OAE_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- d_cLand_OAE')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "#######\n",
    "foo = []\n",
    "for bar in d_cOcean_OAE_list:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print('--- d_cOcean_OAE')\n",
    "print(f'len(lambda) {len(foo)}')\n",
    "print(f'mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (e) comparing feedback ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = fig.add_subplot(325)\n",
    "\n",
    "# incorporate Table 1\n",
    "lambda_AR_theo_4x = [-26.616185834154027,-23.18052744372935,-23.69298287390421,0,-22.960520775578384,-23.90033909512007,-24.774695226066793,-23.393045143429678]\n",
    "lambda_OAE_theo_4x = [-10.711631662793994,-26.41489574976597,-39.7253330168976,0,-27.77288378785428,-24.347405962452434,-26.458134978422375,-18.218499852339725]\n",
    "lambda_AR_theo_2x = [-31.398857138214954,-27.523245379577126,-28.019632577033065,0,-27.228266479860828,-27.54672380778056,-28.158522530527147,-27.73055504767143]\n",
    "lambda_OAE_theo_2x = [-23.70798183629439,-29.27959261492038,-39.05293882765925,0,-34.4091966240245,-33.602443435192164,-28.83460691820848,-27.049385610535193]\n",
    "# lambda_AR = [-11.7,-14.1,-25.3,-16.6,-13.4,-15.3,0,-14.1]\n",
    "# lambda_OAE = [0,0,0,-15.9,0,-9.4,-14.8,-12.1]\n",
    "\n",
    "# markersize \n",
    "msize = 8\n",
    "for imodel in range(8):\n",
    "    \n",
    "    # theoretical feedback ratios\n",
    "    if lambda_AR_theo_4x[imodel] != 0:\n",
    "        ax.plot(lambda_AR_theo_4x[imodel],1,'o',markersize=msize,color=color_list[imodel])\n",
    "    if lambda_OAE_theo_4x[imodel] != 0:\n",
    "        ax.plot(lambda_OAE_theo_4x[imodel],2,'o',markersize=msize,color=color_list[imodel])\n",
    "    if lambda_AR_theo_2x[imodel] != 0:\n",
    "        ax.plot(lambda_AR_theo_2x[imodel],1,'o',markersize=msize,color=color_list[imodel],fillstyle='top')\n",
    "    if lambda_OAE_theo_2x[imodel] != 0:\n",
    "        ax.plot(lambda_OAE_theo_2x[imodel],2,'o',markersize=msize,color=color_list[imodel],fillstyle='top')\n",
    "        \n",
    "    # simulated feedback ratios A/R\n",
    "    if lambda_AR_list[imodel] != 0:\n",
    "        if model_list[imodel] == 'MPI-ESM1-2-LR':\n",
    "            asymmetric_error = [[np.average(feedback_ratio_AR_MPIESM,axis=0)[-60:-39].mean()-np.average(feedback_ratio_AR_MPIESM,axis=0)[-60:-39].min()],\n",
    "                                [np.average(feedback_ratio_AR_MPIESM,axis=0)[-60:-39].max()-np.average(feedback_ratio_AR_MPIESM,axis=0)[-60:-39].mean()]]\n",
    "            ax.errorbar(lambda_AR_list[imodel], 3.1, xerr=asymmetric_error, fmt='o',\n",
    "                        ecolor=color_list[imodel],markerfacecolor=color_list[imodel],\n",
    "                        markeredgecolor=color_list[imodel],\n",
    "                        markersize=msize)\n",
    "        elif model_list[imodel]=='FOCI':\n",
    "            asymmetric_error = [[np.average(feedback_ratio_AR_FOCI,axis=0)[-60:-39].mean()-np.average(feedback_ratio_AR_FOCI,axis=0)[-60:-39].min()],\n",
    "                                [np.average(feedback_ratio_AR_FOCI,axis=0)[-60:-39].max()-np.average(feedback_ratio_AR_FOCI,axis=0)[-60:-39].mean()]]\n",
    "            ax.errorbar(lambda_AR_list[imodel], 2.9, xerr=asymmetric_error, fmt='o',\n",
    "                        ecolor=color_list[imodel],markerfacecolor=color_list[imodel],\n",
    "                        markeredgecolor=color_list[imodel],\n",
    "                        markersize=msize)\n",
    "        elif model_list[imodel]=='ACCESS-ESM1-5':\n",
    "            asymmetric_error = [[np.average(feedback_ratio_AR_ACCESS,axis=0)[-60:-39].mean()-np.average(feedback_ratio_AR_ACCESS,axis=0)[-60:-39].min()],\n",
    "                                [np.average(feedback_ratio_AR_ACCESS,axis=0)[-60:-39].max()-np.average(feedback_ratio_AR_ACCESS,axis=0)[-60:-39].mean()]]\n",
    "            ax.errorbar(lambda_AR_list[imodel], 3.2, xerr=asymmetric_error, fmt='o',\n",
    "                        ecolor=color_list[imodel],markerfacecolor=color_list[imodel],\n",
    "                        markeredgecolor=color_list[imodel],\n",
    "                        markersize=msize)\n",
    "        else:\n",
    "            ax.plot(lambda_AR_list[imodel],3,'o',markersize=msize,color=color_list[imodel])\n",
    "\n",
    "    # simulated feedback ratios OAE            \n",
    "    if lambda_OAE_list[imodel]!=0:\n",
    "        if model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "            asymmetric_error = [[np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].mean()-np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].min()],\n",
    "                                [np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].max()-np.average(feedback_ratio_OAE_MPIESM,axis=0)[-60:-39].mean()]]\n",
    "            ax.errorbar(lambda_OAE_list[imodel], 4.1, xerr=asymmetric_error, fmt='o',\n",
    "                        ecolor=color_list[imodel],markerfacecolor=color_list[imodel],\n",
    "                        markeredgecolor=color_list[imodel],\n",
    "                        markersize=msize)\n",
    "        elif model_list[imodel]=='FOCI':\n",
    "            asymmetric_error = [[np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].mean()-np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].min()],\n",
    "                                [np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].max()-np.average(feedback_ratio_OAE_FOCI,axis=0)[-60:-39].mean()]]\n",
    "            ax.errorbar(lambda_OAE_list[imodel], 3.9, xerr=asymmetric_error, fmt='o',\n",
    "                        ecolor=color_list[imodel],markerfacecolor=color_list[imodel],\n",
    "                        markeredgecolor=color_list[imodel],\n",
    "                        markersize=msize)\n",
    "        else:\n",
    "            ax.plot(lambda_OAE_list[imodel],4,'o',markersize=msize,color=color_list[imodel])\n",
    "ax.hlines(y=[1.5,2.5,3.5],xmin=-40,xmax=0,linewidth=1,linestyles='--',color='k')\n",
    "ax.set_title('(e) Comparison of carbon cycle feedback ratios',fontsize=18)\n",
    "ax.set_xlabel('%', fontsize = 18)\n",
    "ax.grid(visible=True,axis='x')\n",
    "ax.tick_params(labelsize=fontsize)\n",
    "ax.set_ylim([0.5,4.5])\n",
    "ax.set_xlim([-40,0])\n",
    "ax.set_yticks([1,2,3,4])\n",
    "ax.set_yticklabels(['','','',''])\n",
    "ann = ax.annotate('$\\lambda_{A/R}^{theo}$',xy=(-44,1),fontsize=18,ha='left',annotation_clip=False)\n",
    "ann2 = ax.annotate('$\\lambda_{OAE}^{theo}$',xy=(-44,2),fontsize=18,ha='left',annotation_clip=False)\n",
    "ann2 = ax.annotate('$\\lambda_{A/R}$',xy=(-44,3),fontsize=18,ha='left',annotation_clip=False)\n",
    "ann2 = ax.annotate('$\\lambda_{OAE}$',xy=(-44,4),fontsize=18,ha='left',annotation_clip=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### print out mean and stddev of theoretical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda_AR_theo_4x mean=-24.1, std=1.2\n",
      "lambda_OAE_theo_4x mean=-24.8, std=8.3\n",
      "lambda_AR_theo_2x mean=-28.2, std=1.3\n",
      "lambda_OAE_theo_2x mean=-30.8, std=4.8\n"
     ]
    }
   ],
   "source": [
    "foo = []\n",
    "for bar in lambda_AR_theo_4x:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print(f'lambda_AR_theo_4x mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "###############\n",
    "foo = []\n",
    "for bar in lambda_OAE_theo_4x:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print(f'lambda_OAE_theo_4x mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "foo = []\n",
    "for bar in lambda_AR_theo_2x:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print(f'lambda_AR_theo_2x mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "foo = []\n",
    "for bar in lambda_OAE_theo_2x:\n",
    "    if bar != 0:\n",
    "        foo.append(bar)\n",
    "print(f'lambda_OAE_theo_2x mean={np.array(foo).mean():.1f}, std={np.array(foo).std():.1f}')\n",
    "#####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x900 with 7 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig.legend(handles=handles, loc='outside lower center',fontsize=fontsize,ncol=4,bbox_to_anchor=(0.5, -0.12))\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig.savefig('Fig3_rev.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "co2sys",
   "language": "python",
   "name": "co2sys"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}