abrupt0p5xCO2-ex.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# abrupt-0p5xCO₂ in Pymagicc\n",
    "##### CC-BY 4.0 2020 @safiume, https://github.com/hsbay/cdrmex¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Abrupt .5% decrease in CO₂ concentration\n",
    "##### Pymagicc https://pymagicc.readthedocs.io/ AGPL-3.0, \n",
    "##### and MAGICC6, http://www.magicc.org/download6, CC-BY-NC-SA-4.0,\n",
    "##### CDR calibration file and experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python verison: 3.7.4 (v3.7.4:e09359112e, Jul  8 2019, 14:54:52) \n",
      "[Clang 6.0 (clang-600.0.57)]\n"
     ]
    }
   ],
   "source": [
    "# NBVAL_IGNORE_OUTPUT\n",
    "from os.path import join, abspath, split\n",
    "from datetime import datetime\n",
    "import sys, dateutil\n",
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "print(\"Python verison:\", sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "pyam - INFO: Running in a notebook, setting `pyam` logging level to `logging.INFO` and adding stderr handler\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pymagicc version: 2.0.0rc8+19.g7aade1e.dirty\n",
      "Scenario API (<class 'scmdata.run.ScmRun'>,)\n"
     ]
    }
   ],
   "source": [
    "import pymagicc\n",
    "from pymagicc import MAGICC6, rcp26\n",
    "from pymagicc.io import MAGICCData\n",
    "print(\"Pymagicc version:\", pymagicc.__version__)\n",
    "print(\"Scenario API\", rcp26.__class__.__bases__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphstart = 1848\n",
    "graphend = 2000\n",
    "SCEN_DIR = \"SCEN\"\n",
    "co2_conc_filename = \"ABRUPT0P5XCO2_CO2_CONC.IN\"\n",
    "pymgc_hm = \"pymagicc_1.3.2\"\n",
    "\n",
    "# Turn off emissions caculations by forcing emissions\n",
    "# to switch on post experiment. Climate sensitivity \n",
    "# can be tuned to emulate ESMs.\n",
    "\n",
    "# See 'Reduced complexity model intercomparison project phase \n",
    "# 1: Protocol, results and initial observations' \n",
    "# https://doi.org/10.5194/gmd-2019-375 for more details on \n",
    "# calibrating.\n",
    "\n",
    "magcfg = {\n",
    "    'startyear' : graphstart,\n",
    "    'endyear': graphend,\n",
    "    'out_inverseemis': 1,\n",
    "    'core_climatesensitivity' : 3.257,            # [2.4-3.5] best linefits for 2015-2020 Current Temps\n",
    "    'core_feedback_qsensitivity': 0,\n",
    "    'core_verticaldiffusivity' : 2,\n",
    "    'core_verticaldiffusivity_min' : 0.1,\n",
    "    'core_verticaldiff_top_dkdt' : 0,\n",
    "    'core_adjust_sst2ocnatm' : 1.25,\n",
    "    'core_rlo' : 1.45,\n",
    "    'core_heatxchange_landocean' : 2.35,\n",
    "    'core_amplify_ocn2land_heatxchng' : .9,\n",
    "    'core_heatxchange_northsouth' : 3.45,\n",
    "    'co2_preindco2conc' : 276.744,\n",
    "    # f_terr initial bin sizes\n",
    "    'co2_gpp_initial' : 40,\n",
    "    'co2_plantpool_initial' : 450,\n",
    "    'co2_detrituspool_initial' : 85,\n",
    "    'co2_soilpool_initial' : 1100,\n",
    "    'co2_respiration_initial' : 12,\n",
    "    # start of fluxes ∆\n",
    "    'co2_fraction_gpp_2_plant' : 0.305,\n",
    "    'co2_fraction_gpp_2_detritus' : 0.3,\n",
    "    'co2_fraction_plant_2_detritus' : 0.35,\n",
    "    'co2_fraction_detritus_2_soil' : 0.3,\n",
    "    'co2_fraction_deforest_plant' : 0.29,\n",
    "    'co2_fraction_deforest_detritus' : .4,\n",
    "    'co2_tempfeedback_yrstart' : 1750,\n",
    "    'co2_tempfeedback_switch' : 1,\n",
    "    'co2_feedbackfactor_gpp' : 0,\n",
    "    'co2_feedbackfactor_detritus' : 0,\n",
    "    'co2_feedbackfactor_soil' : 0.046,\n",
    "    'co2_feedbackfactor_respiration' : 0,\n",
    "    'co2_fertilization_yrstart' : 1750,\n",
    "    'co2_fertilization_method' : 1.03,\n",
    "    'co2_fertilization_factor' : .63,\n",
    "    'co2_gifford_conc_for_zeronpp' : 31,\n",
    "    'co2_norgrwth_frac_defo' : .5,\n",
    "    'co2_switchfromconc2emis_year': 5000,\n",
    "    'co2_tempfeedback_switch': 1,\n",
    "    'rf_total_runmodus':\"CO2\",\n",
    "    'rf_total_constantafteryr': 5000,    \n",
    "}\n",
    "\n",
    "vars = [\"Surface Temperature\",\n",
    "        \"Radiative Forcing\",\n",
    "        \"Atmospheric Concentrations|CO2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy abrupt-0p5xCO₂ idealized file to pymagicc/MAGICC6/run\n",
    "# Workaround a pathing issue with magicc.run and putting files in pymagicc/MAGICC6/run\n",
    "# glue on fullworking dir to provide full path to be copied by pymagicc.run\n",
    "cwd = split(abspath('__file__'))[0]\n",
    "SCEN_DIR = join(cwd, SCEN_DIR)\n",
    "co2testfh = join(SCEN_DIR,co2_conc_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Date range for graphs\n",
    "x = (datetime(graphstart, 1, 1), datetime(graphend, 1, 1))\n",
    "\n",
    "# Adjust baseline temp to the mean of 1720 to 1800 \n",
    "# per https://doi.org/10.1175/BAMS-D-16-0007.1\n",
    "#\n",
    "# Reassign startyear to include the year 1720.\n",
    "if magcfg['startyear'] > 1720:\n",
    "    magcfg['startyear'] = 1720\n",
    "\n",
    "styr = 1720\n",
    "eyr = 1800\n",
    "\n",
    "selectftr = {\n",
    "    \"region\": \"World\",\n",
    "    \"variable\": \"Surface Temperature\",\n",
    "    \"year\": range(magcfg[\"startyear\"], magcfg[\"endyear\"]),\n",
    "}\n",
    "\n",
    "def meanbln(scen,mgdata,**mcfg):\n",
    "    origts = mgdata.timeseries()\n",
    "    default = magicc.run(file_co2_conc=scen, **mcfg)\n",
    "    pmean = default.filter(**selectftr).timeseries().loc[:,datetime(styr, 1, 1): datetime(eyr, 1, 1)].T.mean()\n",
    "    defaultts = default.timeseries()\n",
    "    updated = defaultts.xs((selectftr['variable']),level = ('variable'), drop_level=False).sub(pmean[0])\n",
    "    origts.loc[updated.index.values] = updated\n",
    "    return(MAGICCData(origts.copy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>1600-01-01 00:00:00</th>\n",
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       "      <th>unspecified</th>\n",
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       "      <th>abrupt0p5xCO2</th>\n",
       "      <th>SET</th>\n",
       "      <th>ppm</th>\n",
       "      <th>Atmospheric Concentrations|CO2</th>\n",
       "      <td>276.744</td>\n",
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       "time                                                                                     1600-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1601-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1602-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1603-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1604-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1605-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1606-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1607-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1608-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     1609-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              276.744   \n",
       "\n",
       "time                                                                                     ...  \\\n",
       "climate_model model       region scenario      todo unit variable                        ...   \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2  ...   \n",
       "\n",
       "time                                                                                     4991-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4992-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4993-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4994-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4995-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4996-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4997-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4998-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     4999-01-01 00:00:00  \\\n",
       "climate_model model       region scenario      todo unit variable                                              \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372   \n",
       "\n",
       "time                                                                                     5000-01-01 00:00:00  \n",
       "climate_model model       region scenario      todo unit variable                                             \n",
       "unspecified   unspecified World  abrupt0p5xCO2 SET  ppm  Atmospheric Concentrations|CO2              138.372  \n",
       "\n",
       "[1 rows x 3401 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "abtests = MAGICCData(join(SCEN_DIR, co2_conc_filename), columns={\"scenario\": ['abrupt0p5xCO2']})\n",
    "abtests.metadata = {\"header\": \n",
    "                    \"abrupt-0p5xCO2 experiment file, CO2 concentrations halve in 1850 pymagicc, doi.org/10.21105/joss.00516, AGPL-3.0 License\"}\n",
    "abtests.line_plot()\n",
    "abtests.timeseries()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with MAGICC6() as magicc:\n",
    "    tests = magicc.run(file_co2_conc = co2testfh, **magcfg)\n",
    "    res = meanbln(co2testfh, tests, **magcfg)\n",
    "\n",
    "fig, axes = plt.subplots((len(vars)), 1, figsize=(8, 16))\n",
    "for n, var in enumerate(vars):\n",
    "    res.filter(variable=var,region='World').lineplot(hue=\"scenario\", ax=axes[n])\n",
    "    ylab = res.filter(variable=var)[\"unit\"].iloc[[0]].all()\n",
    "    axes[n].set_ylabel(ylab)\n",
    "    axes[n].set_title(var)                        \n",
    "    axes[n].set_xlim(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>1848-01-01</th>\n",
       "      <th>1849-01-01</th>\n",
       "      <th>1850-01-01</th>\n",
       "      <th>1851-01-01</th>\n",
       "      <th>1852-01-01</th>\n",
       "      <th>1853-01-01</th>\n",
       "      <th>1854-01-01</th>\n",
       "      <th>1855-01-01</th>\n",
       "      <th>1856-01-01</th>\n",
       "      <th>1857-01-01</th>\n",
       "      <th>...</th>\n",
       "      <th>1991-01-01</th>\n",
       "      <th>1992-01-01</th>\n",
       "      <th>1993-01-01</th>\n",
       "      <th>1994-01-01</th>\n",
       "      <th>1995-01-01</th>\n",
       "      <th>1996-01-01</th>\n",
       "      <th>1997-01-01</th>\n",
       "      <th>1998-01-01</th>\n",
       "      <th>1999-01-01</th>\n",
       "      <th>2000-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>climate_model</th>\n",
       "      <th>model</th>\n",
       "      <th>region</th>\n",
       "      <th>scenario</th>\n",
       "      <th>todo</th>\n",
       "      <th>unit</th>\n",
       "      <th>variable</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MAGICC6</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">unspecified</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">World</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">unspecified</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">not_relevant</th>\n",
       "      <th>ppm</th>\n",
       "      <th>Atmospheric Concentrations|CO2</th>\n",
       "      <td>276.744</td>\n",
       "      <td>276.744</td>\n",
       "      <td>276.744</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.37200</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.37200</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>...</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.372000</td>\n",
       "      <td>138.3720</td>\n",
       "      <td>138.37200</td>\n",
       "      <td>138.372000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K</th>\n",
       "      <th>Surface Temperature</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.529974</td>\n",
       "      <td>-0.85517</td>\n",
       "      <td>-1.041921</td>\n",
       "      <td>-1.177396</td>\n",
       "      <td>-1.282989</td>\n",
       "      <td>-1.36886</td>\n",
       "      <td>-1.440782</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.568951</td>\n",
       "      <td>-2.571175</td>\n",
       "      <td>-2.573384</td>\n",
       "      <td>-2.575577</td>\n",
       "      <td>-2.577755</td>\n",
       "      <td>-2.579918</td>\n",
       "      <td>-2.582066</td>\n",
       "      <td>-2.5842</td>\n",
       "      <td>-2.58632</td>\n",
       "      <td>-2.588426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 153 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "time                                                                                           1848-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2     276.744   \n",
       "                                                          K    Surface Temperature                  0.000   \n",
       "\n",
       "time                                                                                           1849-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2     276.744   \n",
       "                                                          K    Surface Temperature                  0.000   \n",
       "\n",
       "time                                                                                           1850-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2     276.744   \n",
       "                                                          K    Surface Temperature                  0.000   \n",
       "\n",
       "time                                                                                           1851-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -0.529974   \n",
       "\n",
       "time                                                                                           1852-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2   138.37200   \n",
       "                                                          K    Surface Temperature               -0.85517   \n",
       "\n",
       "time                                                                                           1853-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -1.041921   \n",
       "\n",
       "time                                                                                           1854-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -1.177396   \n",
       "\n",
       "time                                                                                           1855-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -1.282989   \n",
       "\n",
       "time                                                                                           1856-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2   138.37200   \n",
       "                                                          K    Surface Temperature               -1.36886   \n",
       "\n",
       "time                                                                                           1857-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -1.440782   \n",
       "\n",
       "time                                                                                           ...  \\\n",
       "climate_model model       region scenario    todo         unit variable                        ...   \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  ...   \n",
       "                                                          K    Surface Temperature             ...   \n",
       "\n",
       "time                                                                                           1991-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.568951   \n",
       "\n",
       "time                                                                                           1992-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.571175   \n",
       "\n",
       "time                                                                                           1993-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.573384   \n",
       "\n",
       "time                                                                                           1994-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.575577   \n",
       "\n",
       "time                                                                                           1995-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.577755   \n",
       "\n",
       "time                                                                                           1996-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.579918   \n",
       "\n",
       "time                                                                                           1997-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000   \n",
       "                                                          K    Surface Temperature              -2.582066   \n",
       "\n",
       "time                                                                                           1998-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2    138.3720   \n",
       "                                                          K    Surface Temperature                -2.5842   \n",
       "\n",
       "time                                                                                           1999-01-01  \\\n",
       "climate_model model       region scenario    todo         unit variable                                     \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2   138.37200   \n",
       "                                                          K    Surface Temperature               -2.58632   \n",
       "\n",
       "time                                                                                           2000-01-01  \n",
       "climate_model model       region scenario    todo         unit variable                                    \n",
       "MAGICC6       unspecified World  unspecified not_relevant ppm  Atmospheric Concentrations|CO2  138.372000  \n",
       "                                                          K    Surface Temperature              -2.588426  \n",
       "\n",
       "[2 rows x 153 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.filter(\n",
    "    variable=[\"Surface Temperature\", \"Atmospheric Concentrations|CO2\"], region=\"World\"\n",
    ").timeseries().loc[:, (datetime(1848, 1, 1, 0)) : (datetime(2000, 1, 1, 0))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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