{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### calculate the values used in the manuscript"
   ]
  },
  {
   "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",
    "from my_io import Figure1\n",
    "def cOcean_from_fgco2(df,yrstart):\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": [
    {
     "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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5: 0.04862 deg\n",
      "CESM2: -0.09394 deg\n",
      "CanESM5: -0.15678 deg\n",
      "FOCI: -0.07627 deg\n",
      "MIROC-ES2L: -0.09102 deg\n",
      "MPI-ESM1-2-LR: -0.09050 deg\n",
      "NorESM2-LM!!!\n",
      "UKESM1-0-LL: -0.08796 deg\n",
      "2-m temperature mean: -0.08, stddev: 0.06\n"
     ]
    }
   ],
   "source": [
    "####### Tmiti A/R\n",
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['NorESM2-LM']:\n",
    "        df_list.append([])\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'temp2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "            df_list.append((fig1.dflist[3][0]['temp2']-fig1.dflist[2][0]['temp2']\n",
    "                          + fig1.dflist[3][1]['temp2']-fig1.dflist[2][1]['temp2']\n",
    "                          + fig1.dflist[3][2]['temp2']-fig1.dflist[2][2]['temp2'])/3)\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'temp2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list.append((fig1.dflist[2][0]['temp2']-fig1.dflist[0][ireal]['temp2']\n",
    "                          + fig1.dflist[2][1]['temp2']-fig1.dflist[0][1]['temp2']\n",
    "                          + fig1.dflist[2][2]['temp2']-fig1.dflist[0][2]['temp2'])/3)\n",
    "    else:\n",
    "        var_name, exp = 'tas', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[2][ireal]['tas']-fig1.dflist[0][ireal]['tas'])\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list[idf].loc[2040:2060].mean():.5f} deg')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'2-m temperature mean: {np.array(foo).mean():.2f}, stddev: {np.array(foo).std():.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5: -5.58646 ppm\n",
      "CESM2: -18.45009 ppm\n",
      "CanESM5!!!\n",
      "FOCI: -18.42547 ppm\n",
      "MIROC-ES2L: -11.36106 ppm\n",
      "MPI-ESM1-2-LR: -18.92149 ppm\n",
      "NorESM2-LM!!!\n",
      "UKESM1-0-LL: -8.83160 ppm\n",
      "+++ co2 concentration mean 2060: -13.6, stddev: 5.28\n",
      "ACCESS-ESM1-5: -5.58646 ppm\n",
      "CESM2: -18.45009 ppm\n",
      "CanESM5!!!\n",
      "FOCI: -18.42547 ppm\n",
      "MIROC-ES2L: -11.36106 ppm\n",
      "MPI-ESM1-2-LR: -18.92149 ppm\n",
      "NorESM2-LM!!!\n",
      "UKESM1-0-LL: -8.83160 ppm\n",
      "+++ co2 concentration mean 2075: -15.4, stddev: 6.65\n"
     ]
    }
   ],
   "source": [
    "####### co2 concentration A/R\n",
    "df_list_co2 = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['NorESM2-LM','CanESM5']:\n",
    "        df_list_co2.append([])\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'co2_concentration', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "            df_list_co2.append((fig1.dflist[3][0]['co2_concentration']-fig1.dflist[2][0]['co2_concentration']\n",
    "                              + fig1.dflist[3][1]['co2_concentration']-fig1.dflist[2][1]['co2_concentration']\n",
    "                              + fig1.dflist[3][2]['co2_concentration']-fig1.dflist[2][2]['co2_concentration'])/3)\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'co2_concentration', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list_co2.append((fig1.dflist[2][0]['co2_concentration']-fig1.dflist[0][0]['co2_concentration']\n",
    "                              + fig1.dflist[2][1]['co2_concentration']-fig1.dflist[0][1]['co2_concentration']\n",
    "                              + fig1.dflist[2][2]['co2_concentration']-fig1.dflist[0][2]['co2_concentration'])/3)\n",
    "    else:\n",
    "        var_name, exp = 'co2_concentration', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            df_list_co2.append(fig1.dflist[2][ireal]['co2_concentration']-fig1.dflist[0][ireal]['co2_concentration'])\n",
    "foo = []\n",
    "for idf in range(len(df_list_co2)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list_co2[idf].loc[2060].mean():.5f} ppm')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "#     foo.append(df_list_co2[idf].loc[2040:2060].mean())\n",
    "    foo.append(df_list_co2[idf].loc[2060].mean())\n",
    "print(f'+++ co2 concentration mean 2060: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.2f}')\n",
    "\n",
    "foo = []\n",
    "for idf in range(len(df_list_co2)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list_co2[idf].loc[2060].mean():.5f} ppm')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "#     foo.append(df_list_co2[idf].loc[2040:2060].mean())\n",
    "    foo.append(df_list_co2[idf].loc[2075].mean())\n",
    "print(f'+++ co2 concentration mean 2075: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.2f}')\n",
    "# #######\n",
    "# for idf in range(len(df_list_co2)):\n",
    "#     try:\n",
    "#         print(f'{model_list[idf]}: {df_list[idf].loc[2040:2059].mean()/df_list_co2[idf].loc[2040:2059].mean()*1000:.2f}')\n",
    "#     except:\n",
    "#         print(f'{model_list[idf]}!!!')\n",
    "#         continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### warming mitigation OAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5!!!\n",
      "CESM2!!!\n",
      "CanESM5!!!\n",
      "FOCI: -0.05442 deg\n",
      "MIROC-ES2L!!!\n",
      "MPI-ESM1-2-LR: -0.04829 deg\n",
      "NorESM2-LM: -0.09223 deg\n",
      "UKESM1-0-LL: -0.09758 deg\n",
      "2-m temperature mean: -0.07, stddev: 0.02\n"
     ]
    }
   ],
   "source": [
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5','CESM2','CanESM5','MIROC-ES2L']:\n",
    "        df_list.append([])\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'temp2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[1][0]['temp2']-fig1.dflist[0][0]['temp2'])\n",
    "#             df_list.append((fig1.dflist[1][0]['temp2']-fig1.dflist[0][0]['temp2']\n",
    "#                           + fig1.dflist[1][1]['temp2']-fig1.dflist[0][1]['temp2']\n",
    "#                           + fig1.dflist[1][2]['temp2']-fig1.dflist[0][2]['temp2'])/3)\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'temp2', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[1][0]['temp2']-fig1.dflist[0][0]['temp2'])\n",
    "#             df_list.append((fig1.dflist[1][0]['temp2']-fig1.dflist[0][ireal]['temp2']\n",
    "#                           + fig1.dflist[1][1]['temp2']-fig1.dflist[0][1]['temp2']\n",
    "#                           + fig1.dflist[1][2]['temp2']-fig1.dflist[0][2]['temp2'])/3)\n",
    "    else:\n",
    "        var_name, exp = 'tas', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[1][ireal]['tas']-fig1.dflist[0][ireal]['tas'])\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list[idf].loc[2040:2060].mean():.5f} deg')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'2-m temperature mean: {np.array(foo).mean():.2f}, stddev: {np.array(foo).std():.2f}')\n",
    "#######\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CO2 concentration OAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS-ESM1-5!!!\n",
      "CESM2!!!\n",
      "CanESM5!!!\n",
      "FOCI: -13.45319 ppm\n",
      "MIROC-ES2L!!!\n",
      "MPI-ESM1-2-LR: -15.43555 ppm\n",
      "NorESM2-LM: -14.13298 ppm\n",
      "UKESM1-0-LL: -14.22755 ppm\n",
      "+++ 2040 to 2060 mean: -18.5, stddev: 0.86\n",
      "ACCESS-ESM1-5!!!\n",
      "CESM2!!!\n",
      "CanESM5!!!\n",
      "FOCI: -13.45319 ppm\n",
      "MIROC-ES2L!!!\n",
      "MPI-ESM1-2-LR: -15.43555 ppm\n",
      "NorESM2-LM: -14.13298 ppm\n",
      "UKESM1-0-LL: -14.22755 ppm\n",
      "+++ 2099 mean: -44.4, stddev: 2.22\n"
     ]
    }
   ],
   "source": [
    "df_list_co2 = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5','CESM2','CanESM5','MIROC-ES2L']:\n",
    "        df_list_co2.append([])\n",
    "        continue\n",
    "\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'co2_concentration', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "                df_list_co2.append(fig1.dflist[1][0]['co2_concentration']-fig1.dflist[0][0]['co2_concentration'])\n",
    "#             df_list_co2.append((fig1.dflist[1][0]['co2_concentration']-fig1.dflist[0][0]['co2_concentration']\n",
    "#                               + fig1.dflist[1][1]['co2_concentration']-fig1.dflist[0][1]['co2_concentration']\n",
    "#                               + fig1.dflist[1][2]['co2_concentration']-fig1.dflist[0][2]['co2_concentration'])/3)\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'co2_concentration', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list_co2.append(fig1.dflist[1][0]['co2_concentration']-fig1.dflist[0][0]['co2_concentration'])\n",
    "#             df_list_co2.append((fig1.dflist[1][0]['co2_concentration']-fig1.dflist[0][0]['co2_concentration']\n",
    "#                               + fig1.dflist[1][1]['co2_concentration']-fig1.dflist[0][1]['co2_concentration']\n",
    "#                               + fig1.dflist[1][2]['co2_concentration']-fig1.dflist[0][2]['co2_concentration'])/3)\n",
    "    else:\n",
    "        var_name, exp = 'co2_concentration', 'AFF' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_model(model_list[imodel])\n",
    "        for ireal in range(1):\n",
    "            df_list_co2.append(fig1.dflist[1][ireal]['co2_concentration']-fig1.dflist[0][ireal]['co2_concentration'])\n",
    "foo = []\n",
    "for idf in range(len(df_list_co2)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list_co2[idf].loc[2040:2060].mean():.5f} ppm')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "    foo.append(df_list_co2[idf].loc[2060].mean())\n",
    "print(f'+++ 2040 to 2060 mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.2f}')\n",
    "#######\n",
    "# for idf in range(len(df_list_co2)):\n",
    "#     try:\n",
    "#         print(f'{model_list[idf]}: {df_list[idf].loc[2040:2059].mean()/df_list_co2[idf].loc[2040:2059].mean()*1000:.2f}')\n",
    "#     except:\n",
    "#         print(f'{model_list[idf]}!!!')\n",
    "#         continue\n",
    "foo = []\n",
    "for idf in range(len(df_list_co2)):\n",
    "    try:\n",
    "        print(f'{model_list[idf]}: {df_list_co2[idf].loc[2040:2060].mean():.5f} ppm')\n",
    "    except:\n",
    "        print(f'{model_list[idf]}!!!')\n",
    "        continue\n",
    "    foo.append(df_list_co2[idf].loc[2099].mean())\n",
    "print(f'+++ 2099 mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: 30.9, stddev: 8.3\n"
     ]
    }
   ],
   "source": [
    "####### d_cLand A/R\n",
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['NorESM2-LM']:\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[3][ireal]['cLand']-fig1.dflist[2][ireal]['cLand'])\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'])\n",
    "    else:\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",
    "            df_list.append(fig1.dflist[2][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'])\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean: -4.8, stddev: 1.5\n"
     ]
    }
   ],
   "source": [
    "######### d_cOcean A/R\n",
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['NorESM2-LM']:\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\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",
    "        for ireal in range(1):\n",
    "            df_list.append(cOcean[3][ireal]['cOcean']-cOcean[2][ireal]['cOcean'])\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'cLand', '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",
    "        for ireal in range(1):\n",
    "            df_list.append(cOcean[2][ireal]['cOcean']-cOcean[0][ireal]['cOcean'])\n",
    "    else:\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",
    "            df_list.append(cOcean[2][ireal]['cOcean']-cOcean[0][ireal]['cOcean'])\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "mean: 34.8, stddev: 0.6\n"
     ]
    }
   ],
   "source": [
    "######### d_cOcean OAE\n",
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5', 'CESM2', 'CanESM5', 'MIROC-ES2L']:\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\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",
    "        for ireal in range(1):\n",
    "            df_list.append(cOcean[1][ireal]['cOcean']-cOcean[0][ireal]['cOcean'])\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'cLand', '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",
    "        for ireal in range(1):\n",
    "            df_list.append(cOcean[1][ireal]['cOcean']-cOcean[0][ireal]['cOcean'])\n",
    "    else:\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",
    "                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]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",
    "                    else:\n",
    "                        # NorESM ocn-alk starts also from 2015\n",
    "                        cOcean[iexp].append(cOcean_from_fgco2(fig1.dflist[iexp][ireal],2015))\n",
    "        for ireal in range(1):\n",
    "            df_list.append(cOcean[1][ireal]['cOcean']-cOcean[0][ireal]['cOcean'])\n",
    "print(len(df_list))\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "mean: -4.7, stddev: 2.0\n"
     ]
    }
   ],
   "source": [
    "####### d_cLand OAE\n",
    "df_list = []\n",
    "for imodel in range(len(model_list)):\n",
    "    if model_list[imodel] in ['ACCESS-ESM1-5', 'CESM2', 'CanESM5', 'MIROC-ES2L']:\n",
    "        continue\n",
    "    elif model_list[imodel]=='MPI-ESM1-2-LR':\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_MPIESM()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'])\n",
    "        pass\n",
    "    elif model_list[imodel]=='FOCI':\n",
    "        var_name, exp = 'cLand', 'BOTH' \n",
    "        fig1=Figure1(var_name,exp)\n",
    "        fig1.read_FOCI()\n",
    "        for ireal in range(1):\n",
    "            df_list.append(fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'])\n",
    "    else:\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",
    "            df_list.append(fig1.dflist[1][ireal]['cLand']-fig1.dflist[0][ireal]['cLand'])\n",
    "print(len(df_list))\n",
    "foo = []\n",
    "for idf in range(len(df_list)):\n",
    "    foo.append(df_list[idf].loc[2040:2060].mean())\n",
    "print(f'mean: {np.array(foo).mean():.1f}, stddev: {np.array(foo).std():.1f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambda_AR_theo = np.array([-26.6,-23.2,-23.7,np.nan,-23,-23.9,-24.8,-23.4])\n",
    "lambda_OAE_theo = np.array([-10.7,-26.4,-39.7,np.nan,-27.8,-24.3,-26.5,-18.2])\n",
    "lambda_AR = np.array([-11.7,-14.1,-25.3,-16.6,-13.4,-15.3,np.nan,-14.1])\n",
    "lambda_OAE = np.array([np.nan,np.nan,np.nan,-15.9,np.nan,-9.4,-14.8,-12.1])\n",
    "lambda_AR_theo = lambda_AR_theo[~np.isnan(lambda_AR_theo)]\n",
    "lambda_OAE_theo = lambda_OAE_theo[~np.isnan(lambda_OAE_theo)]\n",
    "lambda_AR = lambda_AR[~np.isnan(lambda_AR)]\n",
    "lambda_OAE = lambda_OAE[~np.isnan(lambda_OAE)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean lambda_AR_theo: -24.1\n",
      "mean lambda_OAE_theo: -24.8\n",
      "mean lambda_AR: -15.8\n",
      "mean lambda_OAE: -13.1\n",
      "stddev lambda_AR_theo: 1.2\n",
      "stddev lambda_OAE_theo: 8.3\n",
      "stddev lambda_AR: 4.1\n",
      "stddev lambda_OAE: 2.5\n"
     ]
    }
   ],
   "source": [
    "print(f'mean lambda_AR_theo: {np.mean(lambda_AR_theo):.1f}')\n",
    "print(f'mean lambda_OAE_theo: {np.mean(lambda_OAE_theo):.1f}')\n",
    "print(f'mean lambda_AR: {np.mean(lambda_AR):.1f}')\n",
    "print(f'mean lambda_OAE: {np.mean(lambda_OAE):.1f}')\n",
    "print(f'stddev lambda_AR_theo: {np.std(lambda_AR_theo):.1f}')\n",
    "print(f'stddev lambda_OAE_theo: {np.std(lambda_OAE_theo):.1f}')\n",
    "print(f'stddev lambda_AR: {np.std(lambda_AR):.1f}')\n",
    "print(f'stddev lambda_OAE: {np.std(lambda_OAE):.1f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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