notebooks/350_chapter7_box7.2_fig1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot fig 1 box 7.2\n",
    "\n",
    "Theme Song: Fix The Sky A Little<br>\n",
    "Artist: 65daysofstatic<br>\n",
    "Album: The Fall of Math<br>\n",
    "Released: 2004\n",
    "\n",
    "Original code and data provided by Matt Palmer, post-edit by Chris Smith"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import string\n",
    "from ar6.constants import NINETY_TO_ONESIGMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_energy(yrs, series, refyr=1971.):\n",
    "    \"\"\"\n",
    "    This function converts radiative forcing timeseries in W m-2 to an equivalent energy\n",
    "    change in Earth system in Zeta Joules (1e21 J).\n",
    "\n",
    "    Inputs:\n",
    "    yrs = input array of years\n",
    "    series = input timeseries of (effective) radiative forcing\n",
    "    refyr  = the reference year that energy changes are computed relative to\n",
    "\n",
    "    Outputs:\n",
    "    Jyrs = subset of years from refyr onwards\n",
    "    Jseries = subset of series expressed in Zeta Joules\n",
    "    \"\"\"\n",
    "    # surface area x seconds / zeta factor\n",
    "    WtoZJ = (5.1e14 * 31557600) / 1e21  # Conversion factor Watts per square metre to Zeta Joules\n",
    "    refi = np.where(yrs >= refyr)\n",
    "    Jyrs = yrs[refi]\n",
    "    Jseries = np.cumsum(series[refi]) * WtoZJ\n",
    "    Jseries -= Jseries[0]  # Make sure that series is zero in 1971.\n",
    "    return Jyrs, Jseries\n",
    "\n",
    "\n",
    "def read_ERF_2_Joules(datadir='../data_output/',\n",
    "                  filename='AR6_ERF_1750-2019.csv', skip=1, refyr1=1850., refyr2=1900.):\n",
    "    \"\"\"\n",
    "    This function reads in the ERF component timeseries and integrates into energy change in ZJ\n",
    "    returning a dictionary. \n",
    "    \"\"\"\n",
    "    f = open(datadir + filename, \"r\")\n",
    "    lines = f.readlines()\n",
    "    header = lines[skip-1].strip('\\n')\n",
    "    keys = header.split(',')\n",
    "    f.close()\n",
    "    ncols = len(lines[-1].split(','))  # Get number of columns from the last line..\n",
    "    nrows = len(lines) - skip  # Get number of data lines in text file\n",
    "    data = np.zeros([nrows, ncols])  # Convention of rows then columns for Numpy arrays (?)\n",
    "    for jj in range(nrows):\n",
    "        for ii in range(ncols):\n",
    "            data[jj, ii] = float(lines[jj + skip].split(',')[ii])\n",
    "    data_dict = dict.fromkeys(keys)\n",
    "    for kk, key in enumerate(keys):\n",
    "        data_dict[key] = data[:, kk]\n",
    "    \n",
    "    # Reference period for anomalising ERF\n",
    "    yrs = data_dict['year'] # Extract array of years from data\n",
    "    index = np.where((yrs >= refyr1) & (yrs <= refyr2))[0]\n",
    "    \n",
    "    # Delete the years from the dictionary for now, to save any confusion:\n",
    "    del data_dict['year']\n",
    "        \n",
    "    data_dictJ = data_dict.copy() # Copy the dictionary for version in Joules\n",
    "    \n",
    "    # Loop over each key, apply 1850-1900 reference period and convert to joules\n",
    "    for key in data_dict.keys():\n",
    "        series = data_dict[key]\n",
    "        series -= series[index].mean()\n",
    "        Jyrs, Jseries = compute_energy(yrs, series)\n",
    "        data_dictJ[key] = Jseries\n",
    "    \n",
    "    data_dictJ['yrs'] = Jyrs\n",
    "    return data_dictJ\n",
    "\n",
    "\n",
    "def plot_heating_rates(rates=[0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0],\n",
    "                      fyr=1971., lyr=2018., ymax=1400., invert=False,\n",
    "                       color='grey', linewidth=0.5, linestyle=':'):\n",
    "    '''\n",
    "    Function to plot heating rates expressed in W m-2 as lines in Zetta Joules\n",
    "    Inputs:\n",
    "     - rates = list of heating rates in W m-2\n",
    "     - fyr, lyr = the first and last year to plot\n",
    "     - ymax = the maximum value on the y-axis\n",
    "     - invert = keyword for inverted values\n",
    "    '''\n",
    "    # To express W m-2 in Zetta Joules simply integrate over time and area then divide by 1e21 (Zetta)\n",
    "    period = lyr-fyr\n",
    "    yrsecs = 365.25*60*60*24\n",
    "    area = 5.1e14\n",
    "    zetta = 1e21\n",
    "    \n",
    "    if invert == True:\n",
    "        rates = -np.array(rates)\n",
    "        ymax  = -ymax\n",
    "    \n",
    "    for rr, rate in enumerate(rates):\n",
    "        zj1 = 0.0\n",
    "        zj2 = (period * area * yrsecs * rate) / zetta\n",
    "        plt.plot([fyr, lyr], [zj1, zj2], color=color, linewidth=linewidth, linestyle=linestyle)\n",
    "        if rate in [2.0, -2.0]:\n",
    "            tx = 2009.\n",
    "            ty = ymax * 0.95\n",
    "        elif rate in [3.0, -3.0]:\n",
    "            tx = 1995.\n",
    "            ty = ymax * 0.95\n",
    "        else:\n",
    "            tx = lyr - 0.1 * period\n",
    "            scale = 0.05 * ymax\n",
    "            ty = zj2 + ((5 - rr) * 0.005 * ymax)\n",
    "        if invert == True:\n",
    "            tx = tx-1\n",
    "            ty = ty-30\n",
    "        plt.text(tx,ty,str(rate), color=color, fontsize=4.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Energy inventory change (ZJ) = 434.89 [324.53 to 545.25]\n",
      "Pre-industrial energy imbalance (ZJ) = 151.29 [ 0.00 to 302.57]\n",
      "Energy change relative to pre-industrial (ZJ) = 283.60 [96.34 to 470.86]\n",
      "Effective Radiative Forcing (ZJ) = 936.54 [643.56 to 1258.92]\n",
      "Radiative Response (ZJ) = -596.82 [-789.83 to -409.05]\n",
      "Response uncertainty + Central ERF (ZJ) = 339.71 [146.71 to 527.49]\n",
      "ERF uncertainty + Central Response (ZJ) = 339.71 [46.73 to 662.10]\n",
      "Total uncertainty (correlated) (ZJ) = 339.71 [234.50 to 469.08]\n",
      "Total uncertainty (uncorrelated) (ZJ) = 339.71 [-9.09 to 713.39]\n"
     ]
    }
   ],
   "source": [
    "datadir = '../data_input/fig7.1_box7.2/'\n",
    "pfile1   = 'AR6_energy_GMSL_timeseries_FGD_1971to2018_corrigendum.pickle'\n",
    "pfile2   = 'Box7.2_ERF_ZJ_percentiles_FGD_1971to2018.pickle'\n",
    "pfile3   = 'Box7.2_Response_ZJ_percentiles_FGD_1971to2018.pickle'\n",
    "pfile4   = 'Box7.2_ERFResp_uncorrelated_ZJ_percentiles_FGD_1971to2018.pickle'\n",
    "pfile5   = 'Box7.2_ERFResp_correlated_ZJ_percentiles_FGD_1971to2018.pickle'\n",
    "\n",
    "data = pickle.load( open( datadir + pfile1, 'rb' ) )\n",
    "energy_dict = data['energy_dict']\n",
    "\n",
    "ERF_dict = pickle.load( open( datadir + pfile2, 'rb' ) )\n",
    "Resp_dict = pickle.load( open( datadir + pfile3, 'rb' ) )\n",
    "ERFResp_uncorr_dict = pickle.load( open( datadir + pfile4, 'rb' ) )\n",
    "ERFResp_corr_dict = pickle.load( open( datadir + pfile5, 'rb' ) )\n",
    "\n",
    "# Extract total energy change associated with global energy inventory\n",
    "TotalE = energy_dict['Total'] # Total energy change\n",
    "TotalE_err = energy_dict['Total_error']\n",
    "delE = TotalE[-1]\n",
    "errE = np.sqrt(TotalE_err[0]**2 + TotalE_err[-1]**2) * NINETY_TO_ONESIGMA # Expressed as very likely range\n",
    "\n",
    "# Energy change based on pre-industrial imbalance of 0.2 +/- 0.2 W m^-2 (very likely range) \n",
    "WtoZJ = (5.1e14 * 31557600) / 1e21\n",
    "delPI = (2018.-1971.) * 0.2 * WtoZJ\n",
    "errPI = delPI # Error is also 0.2 W m^-2. \n",
    "\n",
    "# Express total energy change relative to the preindustrial \n",
    "delE_pi = delE - delPI\n",
    "errE_pi = np.sqrt(errE**2 + errPI**2)\n",
    "\n",
    "# Load the ERF and Response percentiles\n",
    "R17 = Resp_dict['p17'][-1]\n",
    "R50 = Resp_dict['p50'][-1]\n",
    "R83 = Resp_dict['p83'][-1]\n",
    "ERF17 =   ERF_dict['p17'][-1]\n",
    "ERFbest = ERF_dict['best'][-1]\n",
    "ERF83 =   ERF_dict['p83'][-1]\n",
    "\n",
    "\n",
    "# Energy budget dictionary based on pre-computed distributions.. \n",
    "budget_dict = {'Energy inventory change':[delE, delE-errE, delE+errE],   # See Chapter 7 Table 7.1 \n",
    "               'Pre-industrial energy imbalance':[delPI, delPI-errPI, delPI+errPI], \n",
    "               'Energy change relative to pre-industrial':[delE_pi, delE_pi-errE_pi, delE_pi+errE_pi],\n",
    "               'Effective Radiative Forcing':[ERFbest, ERF17, ERF83],\n",
    "               'Radiative Response':[R50, R17, R83], \n",
    "               'Response uncertainty + Central ERF':[ERFbest+R50, ERFbest+R17, ERFbest+R83],\n",
    "               'ERF uncertainty + Central Response':[ERFbest+R50, ERF17+R50, ERF83+R50],\n",
    "               'Total uncertainty (correlated)':[ERFbest+R50, ERFResp_corr_dict['p17'][-1], ERFResp_corr_dict['p83'][-1]],\n",
    "               'Total uncertainty (uncorrelated)':[ERFbest+R50, ERFResp_uncorr_dict['p17'][-1], ERFResp_uncorr_dict['p83'][-1]]}\n",
    "\n",
    "for key in budget_dict.keys():\n",
    "    v1, v2, v3 = budget_dict[key]\n",
    "    print(key + ' (ZJ) = {0:5.2f} [{1:5.2f} to {2:5.2f}]'.format(v1, v2, v3))\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up the figure plotting\n",
    "plotdir = '../figures/'\n",
    "plotfile1 = 'box7.2_fig1.png'\n",
    "plotfile2 = 'box7.2_fig1.pdf'\n",
    "\n",
    "xmin, xmax = 1971., 2018. \n",
    "ymin, ymax = -100., 1400.\n",
    "hcmin, hcmax = -25., 500.\n",
    "\n",
    "LW = 1.5 # Set linewidth\n",
    "alpha=0.2 # Figures guidance specifies 20% opacity for uncertainty/ensemble shading\n",
    "\n",
    "\n",
    "# Code from Chris Smith / Johannes Quaas\n",
    "# CS: defo put this in a module!\n",
    "colors = {\n",
    "    'co2': '#7f0089',\n",
    "    'ch4': '#ad5e00',\n",
    "    'n2o': '#008e83',\n",
    "    'ghg': '#0070c0',\n",
    "    'o3': '#66665f',\n",
    "    'aerosols': '#e5da47',\n",
    "    'other_anth': '#c0f080',\n",
    "    'anthro' : '#000000',\n",
    "    'volcanic': '#006000',\n",
    "    'solar': '#d0a070',  \n",
    "}\n",
    "\n",
    "color_dict = {'atmosphere':'darkmagenta',\n",
    "              'seaice':'silver',\n",
    "              'cryosphere':'darkgrey',\n",
    "              'landwater':'peru',\n",
    "              'land':'peru',\n",
    "              'antarctica':'slategrey',\n",
    "              'greenland':'darkgrey',\n",
    "              'glaciers':'lightgray', \n",
    "              'ocean_0-700m':'steelblue', \n",
    "              'ocean_700-2000m':'mediumblue',\n",
    "              'ocean_700-2000m_ishii':'mediumblue',\n",
    "              'ocean_0-2000m':'mediumblue',\n",
    "              'ocean_full-depth':'mediumblue', \n",
    "              'ocean_>2000m':'navy',\n",
    "              'tidegauge':'k', \n",
    "              'Total':'r',\n",
    "              'energy_error':'r',\n",
    "              'Energy change relative to pre-industrial':'orangered',\n",
    "              'Energy inventory change':'orangered',\n",
    "              'Response uncertainty + Central ERF':'royalblue',\n",
    "              'ERF uncertainty + Central Response':'firebrick', \n",
    "              'volcanic':'#006000', \n",
    "              'aerosol-radiation_interactions':'silver', \n",
    "              'aerosol-cloud_interactions':'#66665f',\n",
    "              'solar':'#d0a070', \n",
    "              'otherGHG':'#0070c0',\n",
    "              'otherAnth':'#c0f080',\n",
    "              'ozone':'#e5da47',\n",
    "              'co2':'#7f0089',\n",
    "              'ch4': '#ad5e00',\n",
    "              'land_use':'tab:brown'}\n",
    "\n",
    "label_dict = {'atmosphere':'Atmosphere',\n",
    "              'seaice':'Sea ice',\n",
    "              'cryosphere':'Ice',\n",
    "              'landwater':'Land Water Storage',\n",
    "              'land':'Land',\n",
    "              'antarctica':'Antarctica',\n",
    "              'greenland':'Greenland',\n",
    "              'glaciers':'Glaciers', \n",
    "              'ocean_0-700m':'Ocean (0-700m)', \n",
    "              'ocean_700-2000m':'Ocean (700-2000m)',\n",
    "              'ocean_700-2000m_ishii':'Ocean (700-2000m)',\n",
    "              'ocean_0-2000m':'Ocean (0-2000m)',\n",
    "              'ocean_full-depth':'Ocean', \n",
    "              'ocean_>2000m':'Ocean (>2000m)',\n",
    "              'gmsl':'GMSL total', \n",
    "              'contrails':'Contrails', \n",
    "              'solar':'Solar', \n",
    "              'h2o_stratospheric':'Strat H$_{2}$O',\n",
    "              'ozone':'Ozone (O$_{3}$)',\n",
    "              'ch4':'Methane (CH$_{4}$)',\n",
    "              'otherGHG':'Other GHGs', \n",
    "              'otherAnth':'Other Anthropogenic',\n",
    "              'co2':'Carbon dioxide (CO$_{2}$)', \n",
    "              'volcanic':'Volcanic',  \n",
    "              'albedo':'Albedo',  \n",
    "              'aerosol-radiation_interactions': 'Aerosol-rad.', \n",
    "              'aerosol-cloud_interactions': 'Aerosol-\\ncloud',\n",
    "              'land_use':'Land use',\n",
    "              'Total':'Component total'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving file:  ../figures/box7.2_fig1.png\n",
      "Saving file:  ../figures/box7.2_fig1.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 510.192x396.864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "f = plt.gcf()\n",
    "# Set dimensions to 18 x 14 cm.. = 7.086 x 4.724 inces\n",
    "f.set_size_inches(7.086, 5.512)\n",
    "matplotlib.rcParams['font.size']=6.5\n",
    "matplotlib.rcParams['axes.linewidth']=0.5 # set the value globally\n",
    "\n",
    "#=========================================================\n",
    "# Panel A = total Earth system warming with uncertainties\n",
    "#=========================================================\n",
    "WtoZJ = (5.1e14 * 31557600) / 1e21\n",
    "\n",
    "e_yrs = energy_dict['yrs']\n",
    "e_total = energy_dict['Total']\n",
    "e_error = energy_dict['Total_error'] \n",
    "\n",
    "pi_total = (e_yrs - e_yrs[0]) * 0.2 * WtoZJ # convert 0.2 W m^-2 energy imbalance to Zetta Joules timeseries \n",
    "\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "plot_heating_rates()\n",
    "\n",
    "ax1.fill_between(e_yrs, e_total-e_error*NINETY_TO_ONESIGMA, e_total+e_error*NINETY_TO_ONESIGMA, \n",
    "                 alpha=alpha, facecolor='orangered', label='$\\it{very}$ $\\it{likely}$ range')\n",
    "ax1.plot(e_yrs, e_total, 'k', linewidth=LW, linestyle=':', label='Central estimate')\n",
    "ax1.plot(e_yrs, pi_total, color='grey', linewidth=LW, linestyle=':', label='Pre-industrial energy imbalance')\n",
    "ax1.text(1988, -60, 'Equivalent heating rates in W m$^{-2}$', fontsize=4.5, color='grey')\n",
    "ax1.legend(loc='upper left', frameon=False, fontsize=5)\n",
    "plt.xlim(xmin, xmax)\n",
    "plt.ylim(ymin, ymax)\n",
    "ax1.yaxis.set_ticks_position('both')\n",
    "ax1.tick_params(width=0.5)\n",
    "plt.ylabel('Energy Change (ZJ)')\n",
    "plt.title('a) Global Energy Inventory')\n",
    "\n",
    "\n",
    "#===========================================\n",
    "# Panel B = Effective Radiative Forcing.. \n",
    "#===========================================\n",
    "\n",
    "ax2 = plt.subplot(2, 3, 2)\n",
    "plot_heating_rates()\n",
    "plt.fill_between(ERF_dict['yrs'], ERF_dict['p17'], ERF_dict['p83'], \n",
    "                 alpha=alpha, facecolor='firebrick', label='$\\it{likely}$ range')\n",
    "plt.plot(ERF_dict['yrs'], ERF_dict['best'], color='k', linewidth=LW, linestyle=':', \n",
    "         label='Central estimate')\n",
    "#plt.text(1984, -60, 'Equivalent heating rates in Wm$^{-2}$', fontsize=7.5, color='grey')\n",
    "plt.xlim(xmin, xmax)\n",
    "plt.ylim(ymin, ymax)\n",
    "ax2.yaxis.set_ticks_position('both')\n",
    "ax2.tick_params(width=0.5)\n",
    "plt.title('b) Integrated Radiative Forcing')\n",
    "plt.legend(loc='upper left', frameon=False, fontsize=5)\n",
    "\n",
    "#=======================================\n",
    "# Panel C = Radiative Response \n",
    "#=======================================\n",
    "\n",
    "ax3 = plt.subplot(2, 3, 3)\n",
    "plot_heating_rates(invert=True)\n",
    "plt.fill_between(Resp_dict['yrs'], Resp_dict['p17'], Resp_dict['p83'], \n",
    "                 alpha=alpha, facecolor='royalblue', label='$\\it{likely}$ range')\n",
    "plt.plot(Resp_dict['yrs'], Resp_dict['p50'], color='k', linewidth=LW, linestyle=':', \n",
    "         label='Central estimate (ECS)')\n",
    "plt.plot(Resp_dict['yrs'], Resp_dict['p50_0.5_pattern'], color='grey', linewidth=LW, linestyle=':', \n",
    "         label='Pattern effect -0.5 W m$^{-2}$ C$^{-1}$')\n",
    "plt.xlim(xmin, xmax)\n",
    "plt.ylim(-ymax, -ymin)\n",
    "ax3.yaxis.set_ticks_position('both')\n",
    "ax3.tick_params(width=0.5)\n",
    "plt.title('c) Integrated Radiative Response')\n",
    "plt.legend(loc='lower left', frameon=False, fontsize=5)\n",
    "\n",
    "\n",
    "#============================================\n",
    "# Panel D = Observed Energy Storage Change..\n",
    "#============================================\n",
    "e_comps = ['atmosphere', 'land', 'cryosphere', \n",
    "           'ocean_>2000m', 'ocean_700-2000m', 'ocean_0-700m']\n",
    "\n",
    "ax4 = plt.subplot(2, 3, 4)\n",
    "\n",
    "# Plot from total \"downwards\" for better legend/plot consistency\n",
    "series = energy_dict['Total']\n",
    "e_comps.reverse()\n",
    "\n",
    "for cc, comp in enumerate(e_comps):\n",
    "    label = label_dict[comp]\n",
    "    newseries = series - energy_dict[comp]\n",
    "    plt.fill_between(e_yrs, series, newseries, color=color_dict[comp], label=label, linewidth=0.0, alpha=0.8)\n",
    "    series = newseries\n",
    "\n",
    "plt.plot(e_yrs, e_total, 'k', linewidth=LW, linestyle=':', label='TOTAL')\n",
    "ax4.legend(loc='upper left', frameon=False, fontsize=5)\n",
    "plt.xlim(xmin, xmax)\n",
    "plt.ylim(0, hcmax)\n",
    "ax4.yaxis.set_ticks_position('both')\n",
    "ax4.tick_params(width=0.5)\n",
    "plt.ylabel('Energy Change (ZJ)')\n",
    "plt.title('d) Energy Inventory Components')\n",
    "\n",
    "#=============================================\n",
    "# Panel E = Components of Radiative Forcing\n",
    "#=============================================\n",
    "\n",
    "# Color specification from Chris Smith / Johannes Quaas\n",
    "colors = {'co2': '#7f0089',\n",
    "          'ch4': '#ad5e00',\n",
    "          'n2o': '#008e83',\n",
    "          'ghg': '#0070c0',\n",
    "          'o3': '#66665f',\n",
    "          'aerosols': '#e5da47',\n",
    "          'other_anth': '#c0f080',\n",
    "          'anthro' : '#000000',\n",
    "          'volcanic': '#006000',\n",
    "          'solar': '#d0a070'}\n",
    "\n",
    "\n",
    "ax5 = plt.subplot(2, 3, 5)\n",
    "ERF_dictJ = read_ERF_2_Joules()\n",
    "Jyrs = ERF_dictJ['yrs']\n",
    "ERF_dictJ['otherAnth'] = ERF_dictJ['h2o_stratospheric'] + ERF_dictJ['contrails'] + ERF_dictJ['bc_on_snow']\n",
    "ERF_dictJ['otherGHG'] =  ERF_dictJ['n2o'] + ERF_dictJ['other_wmghg']\n",
    "ERF_dictJ['ozone'] = ERF_dictJ['o3']\n",
    "ERF_pos = ['solar', 'otherAnth', 'ozone', 'otherGHG', 'ch4', 'co2']\n",
    "ERF_neg = ['volcanic', 'land_use', 'aerosol-radiation_interactions', \n",
    "           'aerosol-cloud_interactions']\n",
    "ERF_pos.reverse()\n",
    "\n",
    "# Compute total positive ERFs\n",
    "Tpos = + ERF_dictJ['solar'] + ERF_dictJ['ozone'] + ERF_dictJ['otherAnth'] + ERF_dictJ['otherGHG'] + ERF_dictJ['ch4'] + ERF_dictJ['co2']\n",
    "\n",
    "\n",
    "series = Tpos\n",
    "for cc, comp in enumerate(ERF_pos):\n",
    "    label = label_dict[comp]\n",
    "    color = color_dict[comp]\n",
    "    newseries = series - ERF_dictJ[comp]\n",
    "    plt.fill_between(Jyrs, series, newseries, label=label, linewidth=0.0, color=color, alpha=0.8)\n",
    "    series = newseries\n",
    "\n",
    "series = np.zeros(len(Jyrs)) # Initialise empty time series for plotting\n",
    "for cc, comp in enumerate(ERF_neg):\n",
    "    label = label_dict[comp]\n",
    "    color = color_dict[comp]\n",
    "    newseries = series + ERF_dictJ[comp]\n",
    "    plt.fill_between(Jyrs, series, newseries, label=label, linewidth=0.0, color=color, alpha=0.8)\n",
    "    series = newseries\n",
    "\n",
    "    \n",
    "# plt.plot(Jyrs, ERF_dictJ['total'], 'k', linewidth=LW, linestyle=':', label='TOTAL') # Cross-check: gives same result\n",
    "plt.plot(ERF_dict['yrs'], ERF_dict['best'], 'k', linewidth=LW, linestyle=':', label='TOTAL')\n",
    "plt.legend(loc='upper left', ncol=1, frameon=False, fontsize=5)\n",
    "plt.xlim(xmin, xmax)\n",
    "plt.ylim(-1100, 2150)\n",
    "ax5.axhline(0, ls=':', lw=0.5, color='k')\n",
    "ax5.yaxis.set_ticks_position('both')\n",
    "ax5.tick_params(width=0.5)\n",
    "plt.title('e) Radiative Forcing Components')\n",
    "\n",
    "#=======================================================================\n",
    "# Panel F = Energy Budget for period 1971-2015 (1971-2018 placeholder)\n",
    "#=======================================================================\n",
    "ax6 = plt.subplot(2, 3, 6)\n",
    "\n",
    "names = ['Energy change relative to pre-industrial',\n",
    "         'ERF uncertainty + Central Response',\n",
    "         'Response uncertainty + Central ERF'] \n",
    "\n",
    "mid, low_c, upp_c = budget_dict['Total uncertainty (correlated)']\n",
    "mid, low_uc, upp_uc = budget_dict['Total uncertainty (uncorrelated)']\n",
    "\n",
    "for nn, name in enumerate(names):\n",
    "    xpts = [nn-0.3, nn+0.3]\n",
    "    mid, low, upp = budget_dict[name]\n",
    "    color = color_dict[name]\n",
    "    plt.fill_between(xpts, low, upp, facecolor=color, alpha=alpha, \n",
    "                 label=name)\n",
    "    plt.plot(xpts, [mid, mid], linewidth=2.0, color=color)\n",
    "\n",
    "nn += 1\n",
    "xpts = [nn-0.3, nn+0.3]\n",
    "plt.fill_between(xpts, low_c, upp_c, facecolor='rebeccapurple', alpha=0.8, \n",
    "                 label='Total uncertainty (correlated)')\n",
    "plt.fill_between(xpts, upp_c, upp_uc, facecolor='mediumpurple', alpha=0.8, \n",
    "                 label='Total uncertainty (uncorrelated)')\n",
    "plt.fill_between(xpts, low_c, low_uc, facecolor='mediumpurple', alpha=0.8)\n",
    " \n",
    "plt.legend(loc='upper left', frameon=False, fontsize=5)\n",
    "plt.xlim(-0.75, 4.0)\n",
    "plt.title('f) Energy Budget 1971-2018')\n",
    "plt.ylim(hcmin*2, hcmax*2)\n",
    "ax6.axhline(0, ls=':', lw=0.5, color='k')\n",
    "ax6.yaxis.set_ticks_position('both')\n",
    "ax6.tick_params(width=0.5)\n",
    "ax6.set_xticks([-100.])\n",
    "\n",
    "\n",
    "plt.tight_layout(w_pad=0.1)\n",
    "print(\"Saving file: \", plotdir+plotfile1)\n",
    "print(\"Saving file: \", plotdir+plotfile2)\n",
    "plt.savefig(plotdir+plotfile1,dpi=300,format='png')\n",
    "plt.savefig(plotdir+plotfile2,dpi=300,format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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