Conc vs emissions cmip5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/statsmodels/compat/pandas.py:49: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  data_klasses = (pandas.Series, pandas.DataFrame, pandas.Panel)\n"
     ]
    }
   ],
   "source": [
    "#!/usr/local/bin/python3.7\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import numpy_indexed as npi\n",
    "import os, glob\n",
    "\n",
    "os.chdir('/Users/hausfath/Desktop/Climate Science/Carbon Brief/Carbon Cycle Feedbacks/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method to merge C4MIP model runs into a single file with the desired variable.\n",
    "\n",
    "def merge_models(folder, variable = 'temp'):\n",
    "    all_files = glob.glob(folder + \"/*.txt\")\n",
    "    li = []\n",
    "    \n",
    "    for filename in all_files:\n",
    "        df = pd.read_csv(filename, index_col=0, header=None, delim_whitespace=True)\n",
    "        if df.shape[1] > 1:\n",
    "            model_name = filename.partition(\"_Amon_\")[2].partition(\"_esmrcp85\")[0]\n",
    "            df.columns = ['co2', 'temp', 'land', 'ocean']\n",
    "            df = df[[variable]]\n",
    "        else:\n",
    "            model_name = filename.partition(\"tas_Amon_\")[2].partition(\"_rcp85\")[0]\n",
    "        df.columns = [model_name]\n",
    "        li.append(df)\n",
    "    \n",
    "    frame = pd.concat(li, axis=1, join='outer')\n",
    "    return frame\n",
    "\n",
    "#Import datafiles \n",
    "\n",
    "folder = 'co2tasnbpfgco2_emissiondriven' #'tas_concdriven'\n",
    "c4mip = merge_models(folder, variable = 'co2')\n",
    "rcp85_full = pd.read_excel('RCP85 concentrations.xlsx')\n",
    "rcp26_full = pd.read_excel('RCP26 concentrations.xlsx')\n",
    "a1b_full = pd.read_excel('A1B concentrations.xlsx')\n",
    "\n",
    "rcp85 = rcp85_full['co2_ppm'].fillna(0)\n",
    "rcp26 = rcp26_full['co2_ppm'].fillna(0)\n",
    "a1b = a1b_full['co2_ppm'].fillna(0)\n",
    "\n",
    "#Dict of 2100 RCP concentrations\n",
    "\n",
    "rcp_concs = {'rcp85': 935.874,\n",
    "             'rcp60': 669.723,\n",
    "             'rcp45': 538.358,\n",
    "             'rcp26': 420.895}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot C4MIP results vs RCP8.5 fixed concentrations\n",
    "\n",
    "def plot_c4mip_vs_conc(c4mip, rcp85):\n",
    "    cc_val = c4mip.copy().reset_index()\n",
    "    for var in list(c4mip.columns):\n",
    "        cc_val[var] = cc_val[var] - rcp85\n",
    "        plt.plot(rcp85, cc_val[var])\n",
    "    \n",
    "    plt.ylabel('Carbon cycle feedback magnitude')\n",
    "    plt.xlabel('Fixed RCP8.5 concentration pathway')\n",
    "    plt.title('Carbon cycle feedback uncertainty increases with more emissions')\n",
    "    plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n",
    "    \n",
    "    # Decoration\n",
    "    plt.yticks(fontsize=12, alpha=.7)\n",
    "    plt.grid(axis='y', alpha=.3)\n",
    "    \n",
    "    # Remove borders\n",
    "    plt.gca().spines[\"top\"].set_alpha(0.0)    \n",
    "    plt.gca().spines[\"bottom\"].set_alpha(0.3)\n",
    "    plt.gca().spines[\"right\"].set_alpha(0.0)    \n",
    "    plt.gca().spines[\"left\"].set_alpha(0.3) \n",
    "    \n",
    "    #Save graphed data as a csv file\n",
    "    cc_val['RCP8.5_concs'] = rcp85\n",
    "    cc_val.to_csv('ccf_diffs_from_rcp8.5_concs.csv')\n",
    "\n",
    "cc_val = plot_c4mip_vs_conc(c4mip, rcp85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate difference between PPE runs and fixed concentration scenarios\n",
    "\n",
    "def calc_ppe_co2_diffs(rcp85_full, rcp26_full, a1b_full):\n",
    "    for scen in ['85', '26', 'a1b']:\n",
    "        ppe_co2 = pd.read_csv('rcp'+scen+'_co2_mk2.csv', index_col=0)\n",
    "        ppe_co2_diffs = ppe_co2.copy()\n",
    "        \n",
    "        for row in enumerate(ppe_co2.iteritems()):\n",
    "            if scen == '85':\n",
    "                rcp_co2 = rcp85_full[10:250]['co2_ppm'].values\n",
    "            elif scen == '26':\n",
    "                rcp_co2 = rcp26_full[10:249]['co2_ppm'].values\n",
    "            elif scen == 'a1b':\n",
    "                rcp_co2 = a1b_full[10:249]['co2_ppm'].values\n",
    "            if row[1][0] != 'dates':\n",
    "                ppe_co2_diffs[row[1][0]] = ppe_co2_diffs[row[1][0]] - rcp_co2\n",
    "        \n",
    "        ppe_co2.to_csv('ppe_'+scen+'_co2.csv')\n",
    "        ppe_co2_diffs.to_csv('ppe_'+scen+'_co2_diffs.csv')\n",
    "\n",
    "calc_ppe_co2_diffs(rcp85_full, rcp26_full, a1b_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate values for RCP6.0, 4.5, and 2.6 by sampling RCP8.5 and A1B runs\n",
    "#in years where those runs reach 2100 CO2 concentrations in each scenario.\n",
    "\n",
    "def carbon_cycle_feedback_scenarios(c4mip, rcp_concs):\n",
    "    \n",
    "    df = c4mip.fillna(method = 'ffill')\n",
    "    c4mip_2100_conc = pd.DataFrame()\n",
    "    c4mip_2100_conc['models'] = df.columns.values\n",
    "    c4mip_2100_conc['rcp85'] = df.loc[2100].values\n",
    "    c4mip_2100_conc['rcp60'] = df.loc[2069].values\n",
    "    c4mip_2100_conc['rcp45'] = df.loc[2050].values\n",
    "    c4mip_2100_conc['rcp26'] = df.loc[2022].values\n",
    "    \n",
    "    df = pd.read_csv('rcp85_co2_mk2.csv', index_col=0)\n",
    "    ppe_2100_conc = pd.DataFrame()\n",
    "    ppe_2100_conc['models'] = df.columns[1:].values\n",
    "    ppe_2100_conc['rcp85'] = df.tail(1).values[0][1:]\n",
    "    ppe_2100_conc['rcp60'] = df[df['dates']==2069].values[0][1:]\n",
    "    ppe_2100_conc['rcp45'] = df[df['dates']==2050].values[0][1:]\n",
    "    ppe_2100_conc['rcp26'] = pd.read_csv('rcp26_co2_mk2.csv', index_col=0).tail(1).values[0][1:]\n",
    "    ppe_2100_conc['rcp26_est'] = df[df['dates']==2022].values[0][1:]\n",
    "    \n",
    "    df = pd.read_csv('rcpa1b_co2_mk2.csv', index_col=0)\n",
    "    ppe_2100_conc_a1b = pd.DataFrame()\n",
    "    ppe_2100_conc_a1b['models'] = df.columns[1:].values\n",
    "    ppe_2100_conc_a1b['rcp85_actual'] = ppe_2100_conc['rcp85']\n",
    "    ppe_2100_conc_a1b['rcp60'] = df[df['dates']==2086].values[0][1:]\n",
    "    ppe_2100_conc_a1b['rcp45'] = df[df['dates']==2052].values[0][1:]\n",
    "    ppe_2100_conc_a1b['rcp26_est'] = df[df['dates']==2020].values[0][1:]\n",
    "    ppe_2100_conc_a1b['rcp26_actual'] = ppe_2100_conc['rcp26']\n",
    "    \n",
    "    c4mip_2100_conc.to_csv('c4mip_2100_conc.csv')\n",
    "    ppe_2100_conc.to_csv('ppe_2100_conc.csv')\n",
    "    ppe_2100_conc_a1b.to_csv('ppe_2100_conc_a1b.csv')\n",
    "\n",
    "    c4mip_2100_conc_diffs = c4mip_2100_conc.copy()\n",
    "    ppe_2100_conc_diffs = ppe_2100_conc.copy()\n",
    "    for rcp in ['rcp85', 'rcp60', 'rcp45', 'rcp26']:\n",
    "        c4mip_2100_conc_diffs[rcp] = c4mip_2100_conc[rcp] - rcp_concs[rcp]\n",
    "        ppe_2100_conc_diffs[rcp] = ppe_2100_conc[rcp] - rcp_concs[rcp]\n",
    "    \n",
    "    c4mip_2100_conc_diffs.to_csv('c4mip_2100_conc_diffs.csv')\n",
    "    ppe_2100_conc_diffs.to_csv('ppe_2100_conc_diffs.csv')\n",
    "    \n",
    "    return {\n",
    "            'c4mip_2100_conc_diffs' : c4mip_2100_conc_diffs,\n",
    "            'ppe_2100_conc_diffs' : ppe_2100_conc_diffs\n",
    "    }\n",
    "    \n",
    "\n",
    "ccf_scens = carbon_cycle_feedback_scenarios(c4mip, rcp_concs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate estimated warming in each CMIP5 model based on carbon cycle feedback\n",
    "#uncertainties from C4MIP and PPE runs and model TCR.\n",
    "\n",
    "def calc_warming(base_co2, add_co2, tcr, f_2xco2 = 3.7):\n",
    "    forcing = 5.35*np.log((base_co2 + add_co2) / base_co2)\n",
    "    warming = tcr * forcing / f_2xco2\n",
    "    return warming\n",
    "\n",
    "\n",
    "def ccf_temp(rcp_concs, ppm_diffs):\n",
    "    cmip5 = pd.read_excel('cmip5 warming and sensitivity.xlsx')\n",
    "    cmip5.dropna(subset=['tcr'], inplace=True)\n",
    "    \n",
    "    results = pd.DataFrame()\n",
    "\n",
    "    for row in enumerate(cmip5.iterrows()):\n",
    "        model = row[1][1][0]\n",
    "        rcp = row[1][1][1]\n",
    "        base_warming = row[1][1][2]\n",
    "        ecs = row[1][1][3]\n",
    "        tcr = row[1][1][4]\n",
    "        \n",
    "        ccf_warming = calc_warming(rcp_concs[rcp], ppm_diffs[rcp], tcr)\n",
    "        df = pd.DataFrame({'ccf_warming' : ccf_warming})\n",
    "        df['model'] = model\n",
    "        df['ccf_variant'] = ppm_diffs['models']\n",
    "        df['rcp'] = rcp\n",
    "        df['base_warming'] = base_warming\n",
    "        df['total_warming'] = df['base_warming'] + df['ccf_warming']\n",
    "        df['ccf_ppm'] = ppm_diffs[rcp]\n",
    "        df['ecs'] = ecs\n",
    "        df['tcr'] = tcr\n",
    "        results = results.append(df)\n",
    "    \n",
    "    return results\n",
    "\n",
    "cmip5_c4mip_warming_2100 = ccf_temp(rcp_concs, ccf_scens['c4mip_2100_conc_diffs'])\n",
    "cmip5_ppe_warming_2100 = ccf_temp(rcp_concs, ccf_scens['ppe_2100_conc_diffs'])\n",
    "\n",
    "cmip5_c4mip_warming_2100.to_csv('cmip5_c4mip_warming_2100.csv')\n",
    "cmip5_ppe_warming_2100.to_csv('cmip5_ppe_warming_2100.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CMIP5\n",
      "rcp85 Min: 3.2655478 Mean: 4.583975085714286 Max: 5.9983669\n",
      "rcp60 Min: 2.2833327 Mean: 3.02516515 Max: 3.8763026\n",
      "rcp45 Min: 1.6335617 Mean: 2.5335305657894738 Max: 3.2959251\n",
      "rcp26 Min: 0.89347912 Mean: 1.711066773 Max: 2.452896\n",
      "C4MIP\n",
      "rcp85 Min: 2.909802524905616 Mean: 4.70897215903032 Max: 6.650464174461892\n",
      "rcp60 Min: 1.9877375551861025 Mean: 3.120141980614358 Max: 4.438549243678993\n",
      "rcp45 Min: 1.4586699350634065 Mean: 2.633325424413039 Max: 3.810275287531546\n",
      "rcp26 Min: 0.7689556277764602 Mean: 1.7849499624006753 Max: 2.7146989554594922\n",
      "PPE\n",
      "rcp85 Min: 3.0628887774053313 Mean: 4.918350234950435 Max: 7.695799648835254\n",
      "rcp60 Min: 2.0859427056962714 Mean: 3.205628319314111 Max: 4.973671446535853\n",
      "rcp45 Min: 1.5005621401531788 Mean: 2.6437141391856342 Max: 3.99539020135304\n",
      "rcp26 Min: 0.7708818694343643 Mean: 1.7781978734924018 Max: 2.9709349074049536\n"
     ]
    }
   ],
   "source": [
    "#Analyse standard CMIP5 model warming for models with TCR values for each RCP \n",
    "#Compare the results to simulated CMIP5 with C4MIP and PPE carbon cycle feedbacks\n",
    "\n",
    "cmip5 = pd.read_excel('cmip5 warming and sensitivity.xlsx')\n",
    "cmip5.dropna(subset=['tcr'], inplace=True)\n",
    "\n",
    "rcp = 'rcp45'\n",
    "\n",
    "print('CMIP5')\n",
    "for rcp in ['rcp85', 'rcp60', 'rcp45', 'rcp26']:\n",
    "    print(rcp, 'Min:' , cmip5[cmip5['scenario']==rcp]['warming_2090_2100'].min(),\n",
    "          'Mean:', cmip5[cmip5['scenario']==rcp]['warming_2090_2100'].mean(),\n",
    "          'Max:' , cmip5[cmip5['scenario']==rcp]['warming_2090_2100'].max())\n",
    "\n",
    "print('C4MIP')\n",
    "for rcp in ['rcp85', 'rcp60', 'rcp45', 'rcp26']:\n",
    "    print(rcp, 'Min:' , cmip5_c4mip_warming_2100[cmip5_c4mip_warming_2100['rcp']==rcp]['total_warming'].min(),\n",
    "          'Mean:', cmip5_c4mip_warming_2100[cmip5_c4mip_warming_2100['rcp']==rcp]['total_warming'].mean(),\n",
    "          'Max:' , cmip5_c4mip_warming_2100[cmip5_c4mip_warming_2100['rcp']==rcp]['total_warming'].max())\n",
    "\n",
    "print('PPE')\n",
    "for rcp in ['rcp85', 'rcp60', 'rcp45', 'rcp26']:\n",
    "    print(rcp, 'Min:' , cmip5_ppe_warming_2100[cmip5_ppe_warming_2100['rcp']==rcp]['total_warming'].min(),\n",
    "          'Mean:', cmip5_ppe_warming_2100[cmip5_ppe_warming_2100['rcp']==rcp]['total_warming'].mean(),\n",
    "          'Max:' , cmip5_ppe_warming_2100[cmip5_ppe_warming_2100['rcp']==rcp]['total_warming'].max())\n",
    "\n",
    "cmip5.to_csv('cmip5_with_tcr.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}