pp_FHIST_STDC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Surface Temperature Change decomposition routine\n",
    "Written by Inne Vanderkelen (Aug 2020), based on script from Thiery et al., 2017. https://github.com/VUB-HYDR/2017_Thiery_etal_JGR/blob/master/mf_STCdecomp.m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Import the necessary python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import sys\n",
    "import os\n",
    "from getpass import getuser\n",
    "import string\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as netcdf4\n",
    "import xarray as xr\n",
    "import pandas\n",
    "import regionmask\n",
    "import cartopy.crs as ccrs\n",
    "from IPython.display import display, Math, Latex\n",
    "import warnings\n",
    "\n",
    "# import own utils module\n",
    "from iv_utils import *\n",
    "\n",
    "# do plotting settings\n",
    "set_plot_param()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 General Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set directories\n",
    "outdir = '/glade/scratch/ivanderk/'\n",
    "\n",
    "# Define directory where processing is done -- subject to change\n",
    "procdir =  '/glade/work/ivanderk/' + 'postprocessing/' \n",
    "\n",
    "# go to processing directory \n",
    "os.chdir(procdir)\n",
    "\n",
    "# ignore all runtime warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 User settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set case name\n",
    "case_res   = 'f.FHIST.f09_f09_mg17.CTL'\n",
    "case_nores = 'f.FHIST.f09_f09_mg17.NORES'\n",
    "\n",
    "# set number of ensemble members\n",
    "n_ens = 5 \n",
    "\n",
    "# set individual case names for reference\n",
    "case_res_ind   = 'f.FHIST.f09_f09_mg17.CTL.001'\n",
    "case_nores_ind   = 'f.FHIST.f09_f09_mg17.NORES.001'\n",
    "case   = 'f.FHIST.f09_f09_mg17.CTL.001'\n",
    "\n",
    "# run settings -- change this to terms directly? \n",
    "block = 'atm'  # lnd data\n",
    "               # atm data\n",
    "               # rof data\n",
    "        \n",
    "stream = 'h0'  # h0 output block\n",
    "               # h1 output block\n",
    "               # h2 output block\n",
    "               # xtrm calculated (annual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overview of necessary functions\n",
    "\n",
    "# plot SEB\n",
    "def plot_SEB(dSWnet,dLWnet,dSHF,dLHF,dR, threshold):\n",
    "    ax = plt.subplot(111)\n",
    "\n",
    "    ind = np.arange(5)  \n",
    "    ax.bar(ind,[dSWnet,dLWnet,dSHF,dLHF,dR])\n",
    "    ax.set_xticks(ind)\n",
    "    ax.set_xticklabels(('$\\Delta$SWnet','$\\Delta$LWnet','$\\Delta$SHF','$\\Delta$LHF','$\\Delta$R'))\n",
    "    ax.set_ylabel('Reservoir impact [W m$^{-2}$]')\n",
    "    ax.set_title('Surface Energy Balance for reservoir cells > '+str(threshold)+'%', loc='right', fontsize=13)\n",
    "    ax.axhline(0, color='gray', linewidth=1)\n",
    "\n",
    "\n",
    "def do_STDC(LWout,LWout_ctl, SWin,SWout,SWin_ctl,SWout_ctl,dSWin,dLWin,dLHF,dSHF,dR):\n",
    "    \"\"\"Do Surface Temperature Decomposition\"\"\"\n",
    "    \n",
    "    \n",
    "    # Stefan Boltzman constant\n",
    "    sigma = 5.67e-8 # W m² K^-4\n",
    "\n",
    "    # assumption: emissivity is constant (1)\n",
    "\n",
    "    # get radiative temperature from LWout (nores) - see equation 4.11 in (Oleson et al., 2010)\n",
    "    Ts = (LWout/sigma)**(1/4) # LWin for res case -> double check!!\n",
    "    Ts_ctl = (LWout_ctl/sigma)**(1/4) # LWin for nores case -> double check!!\n",
    "\n",
    "    dTs = Ts - Ts_ctl\n",
    "    \n",
    "    term = 1 / (4 * sigma * Ts**3)\n",
    "\n",
    "    dT_albedo = term * (-1) * SWin * (SWout/SWin - SWout_ctl/SWin_ctl) # why SWin_res here? \n",
    "    dT_SWin   = term * (1 - SWout/SWin) * dSWin\n",
    "    dT_LWin   = term * dLWin \n",
    "    dT_LHF    = term * (-1) * dLHF\n",
    "    dT_SHF    = term * (-1) * dSHF\n",
    "    dT_RES    = term * (-1) * dR\n",
    "\n",
    "\n",
    "    # Wim didn't calculate emissivity -> check why? \n",
    "    # also don't include here (suppose is 1, black body)\n",
    "\n",
    "    dT       = [ dT_LHF, dT_SHF,dT_RES, dT_albedo, dT_SWin, dT_LWin,  dTs]\n",
    "    \n",
    "    # Again check closure for dTs (calculated and model diagnostic)\n",
    "\n",
    "    closure_dT    = dTs - sum(dT[:6]);\n",
    "    closure_dT_rel = closure_dT / dTs * 100\n",
    "    \n",
    "    #print('closure dTs')\n",
    "    #print(closure_dT)\n",
    "    #print('closure dT relative')\n",
    "    #print(closure_dT_rel)\n",
    "    \n",
    "    return dT\n",
    "\n",
    "\n",
    "def plot_STDC(dT_array, threshold, ax = False, ylims = False, panel_label=False):\n",
    "    \"\"\"Plot Surface Temperature Decomposition for annual data\"\"\"\n",
    "\n",
    "    if ax == False:\n",
    "        ax = plt.subplot(111)\n",
    "        \n",
    "    cmap = mpl.cm.get_cmap('Paired')\n",
    "    colors_terms = [cmap(0.2), cmap(0.3), cmap(0.5),cmap(0.4), cmap(0.05), cmap(0.1), 'maroon', 'maroon']\n",
    "\n",
    "    # plot STDC\n",
    "    ind = np.arange(8)  \n",
    "    ax.bar(ind,dT_array, color=colors_terms)\n",
    "\n",
    "    ax.set_xticks(ind)\n",
    "    ax.set_xticklabels(('LHF','SHF','R',r'$\\alpha$','SWin','LWin','$\\Delta$Ts', '$\\Delta$T2M'))\n",
    "    ax.set_ylabel('$\\Delta$ Ts forcing [K]')\n",
    "    ax.set_title('Reservoir grid cells > '+str(threshold)+'%', loc='right');\n",
    "    ax.set_xlim([ind.min()-1,ind.max()+1])\n",
    "    ax.axhline(0, color='gray', linewidth=1)\n",
    "    ax.axvline(5.5, color='lightgray',  linestyle='dashed',linewidth=1)\n",
    "    if panel_label != False:\n",
    "        ax.text(0, 1.02, panel_label, color='dimgrey', fontsize=12, transform=ax.transAxes, weight = 'bold') \n",
    "    if ylims != False:\n",
    "        ax.set_ylim(ylims)\n",
    "    \n",
    "    \n",
    "def plot_STDC_seas(dT_month,dT_year, threshold, ax = False, legend = True, ylims = False, panel_label=False):\n",
    "    \"\"\"Plot Surface Temperature Decomposition for seasonal cycle and year\"\"\"\n",
    "\n",
    "    if ax == False:\n",
    "        ax = plt.subplot(111)\n",
    "\n",
    "    cmap = mpl.cm.get_cmap('Paired')\n",
    "    colors_terms = [ cmap(0.2), cmap(0.3),cmap(0.5), cmap(0.4), cmap(0.05), cmap(0.1)]\n",
    "    months = ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']\n",
    "\n",
    "\n",
    "    # plot monthly STCD\n",
    "    ind_months = np.arange(12)  \n",
    "    dT_month = np.asarray(dT_month)\n",
    "    dT_month_stack = get_bottom(dT_month)\n",
    "\n",
    "    for i in np.arange(0, dT_month.shape[0]-1):\n",
    "        ax.bar(ind_months,dT_month[i], bottom=dT_month_stack[i], color=colors_terms[i])\n",
    "\n",
    "    # plot annual STCD\n",
    "    ind_year = 12.5\n",
    "    dT_year = np.asarray(dT_year)\n",
    "    dT_year_stack = get_bottom(dT_year)\n",
    "\n",
    "    for j, term in enumerate(dT_year[:-1]):\n",
    "        ax.bar(ind_year,dT_year[j], bottom = dT_year_stack[j], color=colors_terms[j])\n",
    "\n",
    "    # plot mean temperature as line\n",
    "    ax.plot(ind_months,dT_month[-1,:],  '-o', markersize=4, linewidth=1.5, color='maroon', alpha=0.7)\n",
    "    ax.plot(ind_year,dT_year[-1],  'o', markersize=4, linewidth=1.5, color='maroon', alpha=0.7, label='_nolegend_')\n",
    "\n",
    "    ind = np.append(ind_months,ind_year)\n",
    "    # plot settings\n",
    "    xticklabels = months+ ['year']\n",
    "    ax.set_xticks(ind)\n",
    "    ax.set_xticklabels(xticklabels)\n",
    "    ax.set_ylabel('$\\Delta$ Ts forcing [K]')\n",
    "    ax.set_title('Reservoir grid cells >'+str(threshold)+'%', loc='right');\n",
    "    ax.set_xlim([ind.min()-1,ind.max()+1]) \n",
    "    ax.axhline(0, color='gray', linewidth=1, label='_nolegend_');\n",
    "    ax.axvline(11.75, color='lightgray',  linestyle='dashed',linewidth=1, label='_nolegend_') ;\n",
    "    \n",
    "    # optional arguments\n",
    "    if legend:\n",
    "        ax.legend([r'$\\Delta$Ts','LHF','SHF','R',r'$\\alpha$','SWin','LWin'], loc='center left',  bbox_to_anchor=(1, 0.5), frameon=False)\n",
    "    if panel_label != False:\n",
    "        ax.text(0, 1.02, panel_label, color='dimgrey', fontsize=12, transform=ax.transAxes, weight = 'bold') \n",
    "    if ylims != False:\n",
    "        ax.set_ylim(ylims)\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Check SEB closure for reservoir simulation\n",
    "This implies to load individual components of SEB (time consuming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-9b55d7c540e1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mG\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_da_ens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FGR'\u001b[0m        \u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'lnd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcase_res\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# heat flux into soil/snow including snow melt and lake / snow light transmission\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mFSM\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_da_ens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FSM'\u001b[0m        \u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'lnd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcase_res\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# snow melt heat flux\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mWASTEHEAT\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mopen_da_ens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'WASTEHEAT'\u001b[0m        \u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'lnd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcase_res\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/postprocessing_scripts/notebooks/iv_utils.py\u001b[0m in \u001b[0;36mopen_da_ens\u001b[0;34m(var, case, n_ens, stream, block, lunit, mode)\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0mcase_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcase\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'.00'\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         \u001b[0mda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_da\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcase_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlunit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlunit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/postprocessing_scripts/notebooks/iv_utils.py\u001b[0m in \u001b[0;36mopen_da\u001b[0;34m(var, case, stream, block, lunit)\u001b[0m\n\u001b[1;32m    134\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mopen_da\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlunit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 136\u001b[0;31m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlunit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlunit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    137\u001b[0m     \u001b[0mda\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/postprocessing_scripts/notebooks/iv_utils.py\u001b[0m in \u001b[0;36mopen_ds\u001b[0;34m(var, case, stream, block, lunit)\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mblock\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'lnd'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m             \u001b[0mds_atm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_ds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'TREFHT'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcase\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'atm'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'h0'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlunit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m             \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds_atm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     97\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36minterp_like\u001b[0;34m(self, other, method, assume_sorted, kwargs)\u001b[0m\n\u001b[1;32m   2696\u001b[0m             \u001b[0;31m# reindex instead.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2697\u001b[0m             \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_coords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0massume_sorted\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2700\u001b[0m     \u001b[0;31m# Helper methods for rename()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/xarray/core/dataset.py\u001b[0m in \u001b[0;36minterp\u001b[0;34m(self, coords, method, assume_sorted, kwargs, **coords_kwargs)\u001b[0m\n\u001b[1;32m   2608\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2609\u001b[0m                 }\n\u001b[0;32m-> 2610\u001b[0;31m                 \u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmissing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_indexers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2611\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindexers\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2612\u001b[0m                 \u001b[0;31m# keep unrelated object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/xarray/core/missing.py\u001b[0m in \u001b[0;36minterp\u001b[0;34m(var, indexes_coords, method, **kwargs)\u001b[0m\n\u001b[1;32m    624\u001b[0m     \u001b[0mnew_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbroadcast_dims\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestination\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    625\u001b[0m     interped = interp_func(\n\u001b[0;32m--> 626\u001b[0;31m         \u001b[0mvar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0moriginal_dims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestination\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    627\u001b[0m     )\n\u001b[1;32m    628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/xarray/core/missing.py\u001b[0m in \u001b[0;36minterp_func\u001b[0;34m(var, x, new_x, method, kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m         )\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_interpnd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/xarray/core/missing.py\u001b[0m in \u001b[0;36m_interpnd\u001b[0;34m(var, x, new_x, func, kwargs)\u001b[0m\n\u001b[1;32m    724\u001b[0m     \u001b[0;31m# stack new_x to 1 vector, with reshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    725\u001b[0m     \u001b[0mxi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnew_x\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 726\u001b[0;31m     \u001b[0mrslt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    727\u001b[0m     \u001b[0;31m# move back the interpolation axes to the last position\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m     \u001b[0mrslt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrslt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mrslt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36minterpn\u001b[0;34m(points, values, xi, method, bounds_error, fill_value)\u001b[0m\n\u001b[1;32m   2653\u001b[0m                                          \u001b[0mbounds_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbounds_error\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2654\u001b[0m                                          fill_value=fill_value)\n\u001b[0;32m-> 2655\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0minterp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2656\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nearest\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2657\u001b[0m         interp = RegularGridInterpolator(points, values, method=\"nearest\",\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, xi, method)\u001b[0m\n\u001b[1;32m   2491\u001b[0m             result = self._evaluate_linear(indices,\n\u001b[1;32m   2492\u001b[0m                                            \u001b[0mnorm_distances\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2493\u001b[0;31m                                            out_of_bounds)\n\u001b[0m\u001b[1;32m   2494\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nearest\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2495\u001b[0m             result = self._evaluate_nearest(indices,\n",
      "\u001b[0;32m~/miniconda/envs/ctsm/lib/python3.7/site-packages/scipy/interpolate/interpolate.py\u001b[0m in \u001b[0;36m_evaluate_linear\u001b[0;34m(self, indices, norm_distances, out_of_bounds)\u001b[0m\n\u001b[1;32m   2513\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mei\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0medge_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_distances\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2514\u001b[0m                 \u001b[0mweight\u001b[0m \u001b[0;34m*=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mei\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0myi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2515\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0medge_indices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvslice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2516\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# control with handle\n",
    "\n",
    "check_closure = True\n",
    "\n",
    "if check_closure:\n",
    "    # Short wave radiation\n",
    "    SWin = open_da_ens('FSDS', block = 'lnd', case = case_res)\n",
    "    SWout= open_da_ens('FSR' , block = 'lnd', case = case_res)\n",
    "\n",
    "\n",
    "    # !!!! TRY HERE WITH FIRA - net LONGWAVE\n",
    "    # Long wave radiation\n",
    "    LWin    = open_da_ens('FLDS', block = 'lnd', case = case_res)\n",
    "    LWout   = open_da_ens('FIRE', block = 'lnd', case = case_res) # emitted longwave radiation\n",
    "    # FIRA is net LW radiation\n",
    "\n",
    "    # surface fluxes\n",
    "    SHF = open_da_ens('FSH'        , block = 'lnd', case = case_res)\n",
    "    LHF = open_da_ens('EFLX_LH_TOT', block = 'lnd', case = case_res)\n",
    "\n",
    "    # rest term\n",
    "    G = open_da_ens('FGR'        , block = 'lnd', case = case_res) # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    "\n",
    "    FSM = open_da_ens('FSM'        , block = 'lnd', case = case_res) # snow melt heat flux\n",
    "\n",
    "    WASTEHEAT =  open_da_ens('WASTEHEAT'        , block = 'lnd', case = case_res) \n",
    "    HEAT_FROM_AC =  open_da_ens('HEAT_FROM_AC'        , block = 'lnd', case = case_res) # Heat from AC\n",
    "\n",
    "    closure_SEB = SWin - SWout + LWin - LWout - LHF - SHF - G + WASTEHEAT + HEAT_FROM_AC #- FSM #- \n",
    "\n",
    "    closure_SEB.mean(dim='time').where(get_resmask()).plot()\n",
    "\n",
    "    errmax  = np.abs(closure_SEB.mean(dim='time')).max(dim=('lat','lon')).values\n",
    "    print('maximum SEB error for res is ' + str(errmax) + ' W m^-2')\n",
    "\n",
    "    # error has something to do with n-snow melt heat flux\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum SEB error for res is 0.04920314571400918 W m^-2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "closure_SEB.mean(dim='time').where(get_resmask()).plot()\n",
    "\n",
    "errmax  = np.abs(closure_SEB.mean(dim='time')).where(get_resmask()).max(dim=('lat','lon')).values\n",
    "print('maximum SEB error for res is ' + str(errmax) + ' W m^-2')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load delta's of SEB components\n",
    "Takes about 5.5 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 8s, sys: 1min 51s, total: 5min\n",
      "Wall time: 5min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# load delta of SEB terms\n",
    "\n",
    "# Short wave radiation\n",
    "da_dSWin = open_da_delta('FSDS',  case_res, case_nores, block = 'lnd')\n",
    "da_dSWout= open_da_delta('FSR' ,  case_res, case_nores, block = 'lnd')\n",
    "\n",
    "# Long wave radiation\n",
    "da_dLWin = open_da_delta('FLDS', case_res, case_nores, block = 'lnd')\n",
    "da_dLWout   = open_da_delta('FIRE', case_res, case_nores, block = 'lnd')\n",
    "\n",
    "# surface fluxes\n",
    "da_dSHF = open_da_delta('FSH'        ,  case_res, case_nores, block = 'lnd')\n",
    "da_dLHF = open_da_delta('EFLX_LH_TOT',  case_res, case_nores, block = 'lnd')\n",
    "\n",
    "# rest term\n",
    "da_dG = open_da_delta('FGR'        ,  case_res, case_nores, block = 'lnd') # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    " \n",
    "da_dWASTEHEAT    =  open_da_delta('WASTEHEAT'        , case_res, case_nores, block = 'lnd')\n",
    "da_dHEAT_FROM_AC =  open_da_delta('HEAT_FROM_AC'  , case_res, case_nores, block = 'lnd')\n",
    "\n",
    "# surface temperature\n",
    "da_dTSA    = open_da_delta('TS'        ,  case_res, case_nores, block = 'atm')\n",
    "da_dT2M    = open_da_delta('TREFHT'        ,  case_res, case_nores, block = 'atm')\n",
    "\n",
    "# albedo \n",
    "da_SWin_res   = open_da_ens('FSDS', block = 'lnd', case = case_res)\n",
    "da_SWout_res  = open_da_ens('FSR' , block = 'lnd', case = case_res)\n",
    "\n",
    "\n",
    "da_SWin_nores = open_da_ens('FSDS', block = 'lnd', case = case_nores)\n",
    "da_SWout_nores = open_da_ens('FSR' , block = 'lnd', case = case_nores)\n",
    "\n",
    "\n",
    "# check if balance closes for averaged values - works\n",
    "cloure_SEB = da_dSWin.mean() - da_dSWout.mean() + da_dLWin.mean() - da_dLWout.mean() - da_dLHF.mean() - da_dSHF.mean() - da_dG.mean() + da_dWASTEHEAT.mean() + da_dHEAT_FROM_AC.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Calculate global averages\n",
    "### 3.1 Annual >0% reservoirmask\n",
    "1.5 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 51.1 s, sys: 31.2 s, total: 1min 22s\n",
      "Wall time: 1min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# apply reservoir mask and calculate global averages\n",
    "threshold = 0\n",
    "\n",
    "# calculate resmask \n",
    "resmask = get_resmask(threshold=threshold)\n",
    "\n",
    "# calculate annual global averaged of terms and calculate resmask\n",
    "\n",
    "# Short wave radiation\n",
    "dSWin  = da_dSWin.where(resmask).mean().values\n",
    "dSWout = da_dSWout.where(resmask).mean().values\n",
    "\n",
    "dSWnet  = (dSWin - dSWout)\n",
    "\n",
    "# Long wave radiation\n",
    "dLWin = da_dLWin.where(resmask).mean().values\n",
    "dLWout   = da_dLWout.where(resmask).mean().values\n",
    "\n",
    "dLWnet = (dLWin - dLWout)\n",
    "\n",
    "# surface fluxes\n",
    "dSHF = da_dSHF.where(resmask).mean().values\n",
    "dLHF = da_dLHF.where(resmask).mean().values\n",
    "\n",
    "# rest term\n",
    "dG = da_dG.where(resmask).mean().values # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    " \n",
    "dWASTEHEAT    =  da_dWASTEHEAT.where(resmask).mean().values\n",
    "dHEAT_FROM_AC =  da_dHEAT_FROM_AC.where(resmask).mean().values # Heat from AC\n",
    "\n",
    "dR = dG - dWASTEHEAT - dHEAT_FROM_AC\n",
    "\n",
    "# surface temperature\n",
    "dTSA    = da_dTSA.where(resmask).mean().values \n",
    "dT2M    = da_dT2M.where(resmask).mean().values \n",
    "\n",
    "# terms necessary for STDC\n",
    "SWin  = open_da_ens('FSDS', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "SWout = open_da_ens('FSR', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "\n",
    "SWin_ctl  = open_da_ens('FSDS', block = 'lnd', case = case_nores).where(resmask).mean().values\n",
    "SWout_ctl = open_da_ens('FSR', block = 'lnd', case = case_nores).where(resmask).mean().values\n",
    "\n",
    "LWout     = open_da_ens('FIRE', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "LWout_ctl = open_da_ens('FIRE', block = 'lnd', case = case_nores).where(resmask).mean().values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Seasonal > 0% reservoir mask\n",
    "1.5 min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56 s, sys: 29.2 s, total: 1min 25s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# load in monthly means of terms\n",
    "\n",
    "# Short wave radiation\n",
    "dSWin_seas = da_dSWin.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dSWout_seas   = da_dSWout.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dSWnet_seas  = (dSWin_seas - dSWout_seas)\n",
    "\n",
    "\n",
    "# Long wave radiation\n",
    "dLWin_seas = da_dLWin.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLWout_seas   = da_dLWout.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLWnet_seas = (dLWin_seas - dLWout_seas)\n",
    "\n",
    "\n",
    "# surface fluxes\n",
    "dSHF_seas = da_dSHF.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLHF_seas = da_dLHF.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "\n",
    "# rest term\n",
    "dG_seas = da_dG.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    " \n",
    "dWASTEHEAT_seas    =  da_dWASTEHEAT.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dHEAT_FROM_AC_seas =  da_dHEAT_FROM_AC.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values # Heat from AC\n",
    "\n",
    "dR_seas = dG_seas - dWASTEHEAT_seas - dHEAT_FROM_AC_seas\n",
    "\n",
    "\n",
    "# surface temperature\n",
    "dTSA_seas    = da_dTSA.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dT2M_seas   = da_dT2M.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values \n",
    "\n",
    "\n",
    "# terms necessary for STDC\n",
    "SWin_seas = open_da_ens('FSDS', block = 'lnd', case = case_res).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "SWout_seas = open_da_ens('FSR', block = 'lnd', case = case_res).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "SWin_ctl_seas = open_da_ens('FSDS', block = 'lnd', case = case_nores).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "SWout_ctl_seas = open_da_ens('FSR', block = 'lnd', case = case_nores).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "LWout_seas    = open_da_ens('FIRE', block = 'lnd', case = case_res).groupby('time.month').mean().where(resmask).mean(dim=('lat','lon')).values\n",
    "LWout_ctl_seas    = open_da_ens('FIRE', block = 'lnd', case = case_nores).groupby('time.month').mean().where(resmask).mean(dim=('lat','lon')).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Annual res > 15%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 50.9 s, sys: 30.1 s, total: 1min 20s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# apply reservoir mask and calculate global averages\n",
    "threshold = 15\n",
    "\n",
    "# calculate resmask \n",
    "resmask = get_resmask(threshold=threshold)\n",
    "\n",
    "# calculate annual global averaged of terms and calculate resmask\n",
    "\n",
    "# Short wave radiation\n",
    "dSWin_15pct = da_dSWin.where(resmask).mean().values\n",
    "dSWout_15pct   = da_dSWout.where(resmask).mean().values\n",
    "\n",
    "dSWnet_15pct  = (dSWin_15pct - dSWout_15pct)\n",
    "\n",
    "# Long wave radiation\n",
    "dLWin_15pct = da_dLWin.where(resmask).mean().values\n",
    "dLWout_15pct   = da_dLWout.where(resmask).mean().values\n",
    "\n",
    "dLWnet_15pct = (dLWin_15pct - dLWout_15pct)\n",
    "\n",
    "# surface fluxes\n",
    "dSHF_15pct = da_dSHF.where(resmask).mean().values\n",
    "dLHF_15pct = da_dLHF.where(resmask).mean().values\n",
    "\n",
    "# rest term\n",
    "dG_15pct = da_dG.where(resmask).mean().values # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    " \n",
    "dWASTEHEAT_15pct    =  da_dWASTEHEAT.where(resmask).mean().values\n",
    "dHEAT_FROM_AC_15pct =  da_dHEAT_FROM_AC.where(resmask).mean().values # Heat from AC\n",
    "\n",
    "dR_15pct = dG_15pct - dWASTEHEAT_15pct - dHEAT_FROM_AC_15pct\n",
    "\n",
    "# surface temperature\n",
    "dTSA_15pct    = da_dTSA.where(resmask).mean().values \n",
    "dT2M_15pct    = da_dT2M.where(resmask).mean().values \n",
    "\n",
    "# terms necessary for STDC\n",
    "SWin_15pct  = open_da_ens('FSDS', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "SWout_15pct = open_da_ens('FSR', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "\n",
    "SWin_ctl_15pct  = open_da_ens('FSDS', block = 'lnd', case = case_nores).where(resmask).mean().values\n",
    "SWout_ctl_15pct = open_da_ens('FSR', block = 'lnd', case = case_nores).where(resmask).mean().values\n",
    "\n",
    "LWout_15pct     = open_da_ens('FIRE', block = 'lnd', case = case_res).where(resmask).mean().values\n",
    "LWout_ctl_15pct = open_da_ens('FIRE', block = 'lnd', case = case_nores).where(resmask).mean().values\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Seasonal > 15%\n",
    "1.5 min "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56.2 s, sys: 28.9 s, total: 1min 25s\n",
      "Wall time: 1min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# load in monthly means of terms\n",
    "\n",
    "# Short wave radiation\n",
    "dSWin_seas_15pct = da_dSWin.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dSWout_seas_15pct   = da_dSWout.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dSWnet_seas_15pct  = (dSWin_seas_15pct - dSWout_seas_15pct)\n",
    "\n",
    "\n",
    "# Long wave radiation\n",
    "dLWin_seas_15pct = da_dLWin.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLWout_seas_15pct   = da_dLWout.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLWnet_seas_15pct = (dLWin_seas_15pct - dLWout_seas_15pct)\n",
    "\n",
    "\n",
    "# surface fluxes\n",
    "dSHF_seas_15pct = da_dSHF.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dLHF_seas_15pct = da_dLHF.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "\n",
    "# rest term\n",
    "dG_seas_15pct = da_dG.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values # heat flux into soil/snow including snow melt and lake / snow light transmission \n",
    " \n",
    "dWASTEHEAT_seas_15pct    =  da_dWASTEHEAT.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dHEAT_FROM_AC_seas_15pct =  da_dHEAT_FROM_AC.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values # Heat from AC\n",
    "\n",
    "dR_seas_15pct = dG_seas_15pct - dWASTEHEAT_seas_15pct - dHEAT_FROM_AC_seas_15pct\n",
    "\n",
    "\n",
    "# surface temperature\n",
    "dTSA_seas_15pct    = da_dTSA.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "dT2M_seas_15pct   = da_dT2M.where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values \n",
    "\n",
    "\n",
    "# terms necessary for STDC\n",
    "SWin_seas_15pct = open_da_ens('FSDS', block = 'lnd', case = case_res).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "SWout_seas_15pct = open_da_ens('FSR', block = 'lnd', case = case_res).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "SWin_ctl_seas_15pct = open_da_ens('FSDS', block = 'lnd', case = case_nores).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "SWout_ctl_seas_15pct = open_da_ens('FSR', block = 'lnd', case = case_nores).where(resmask).groupby('time.month').mean().mean(dim=('lat','lon')).values\n",
    "\n",
    "LWout_seas_15pct    = open_da_ens('FIRE', block = 'lnd', case = case_res).groupby('time.month').mean().where(resmask).mean(dim=('lat','lon')).values\n",
    "LWout_ctl_seas_15pct    = open_da_ens('FIRE', block = 'lnd', case = case_nores).groupby('time.month').mean().where(resmask).mean(dim=('lat','lon')).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 surface energy balance decomposition\n",
    "\n",
    "plot_SEB(dSWnet,dLWnet,dSHF,dLHF,dR, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Calculate and plot Surface Temperature Decomposition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# figure to include in paper\n",
    "\n",
    "gs_kw = {'width_ratios' : [1,1.5], 'height_ratios' : [1,1]}\n",
    "fig, axes = plt.subplots(nrows=2,ncols=2, figsize=(16,8), gridspec_kw=gs_kw)\n",
    "axes=axes.flatten()\n",
    "\n",
    "# plot annual STDC >0%\n",
    "dT = do_STDC(LWout,LWout_ctl, SWin,SWout,SWin_ctl,SWout_ctl,dSWin,dLWin,dLHF,dSHF,dR)\n",
    "dT.append(dT2M)\n",
    "plot_STDC(dT, 0, ax = axes[0], panel_label='a', ylims = [-0.12,0.12])\n",
    "\n",
    "# plot seasonal STDC > 0% \n",
    "dT_month = do_STDC(LWout_seas, LWout_ctl_seas, SWin_seas,SWout_seas,SWin_ctl_seas,SWout_ctl_seas, dSWin_seas, dLWin_seas, dLHF_seas, dSHF_seas, dR_seas)\n",
    "dT_year = do_STDC(LWout, LWout_ctl, SWin, SWout, SWin_ctl, SWout_ctl, dSWin, dLWin, dLHF, dSHF, dR)\n",
    "\n",
    "plot_STDC_seas(dT_month,dT_year, 0, ax = axes[1], ylims = [-0.3,0.3], panel_label='b')\n",
    "\n",
    "\n",
    "# plot annual STDC >15%\n",
    "dT_15pct = do_STDC(LWout_15pct, LWout_ctl_15pct, SWin_15pct, SWout_15pct, SWin_ctl_15pct, SWout_ctl_15pct, dSWin_15pct, dLWin_15pct, dLHF_15pct, dSHF_15pct, dR_15pct)\n",
    "dT_15pct.append(dT2M_15pct)\n",
    "plot_STDC(dT_15pct, 15, ax = axes[2], panel_label='c', ylims = [-1.2,1.2])\n",
    "\n",
    "\n",
    "# plot seasonal STDC >15%\n",
    "dT_month_15pct = do_STDC(LWout_seas_15pct, LWout_ctl_seas_15pct, SWin_seas_15pct,SWout_seas_15pct,SWin_ctl_seas_15pct,SWout_ctl_seas_15pct, dSWin_seas_15pct, dLWin_seas_15pct, dLHF_seas_15pct, dSHF_seas_15pct, dR_seas_15pct)\n",
    "dT_year_15pct = do_STDC(LWout_15pct, LWout_ctl_15pct, SWin_15pct, SWout_15pct, SWin_ctl_15pct, SWout_ctl_15pct, dSWin_15pct, dLWin_15pct, dLHF_15pct, dSHF_15pct, dR_15pct)\n",
    "\n",
    "plot_STDC_seas(dT_month_15pct,dT_year_15pct, 15, ax = axes[3], legend = False, ylims = [-3.5,3.5], panel_label='d')\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('./plots/STDC.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Plot annual STDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 annual STDC >0%\n",
    "dT = do_STDC(LWout,LWout_ctl, SWin,SWout,SWin_ctl,SWout_ctl,dSWin,dLWin,dLHF,dSHF,dR)\n",
    "dT.append(dT2M)\n",
    "plot_STDC(dT, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('LHF', 'SHF', 'R', '$\\\\alpha$', 'SWin', 'LWin', '$\\\\Delta$Ts', '$\\\\Delta$T2M')\n",
      "[-0.08986225442954837, 0.05437934181217395, 0.004485333838625675, 0.038098065863984155, 0.0167215537910979, 0.022270186825018794, 0.04606254791281117, array(0.05562609, dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "# print values\n",
    "print(('LHF','SHF','R',r'$\\alpha$','SWin','LWin','$\\Delta$Ts', '$\\Delta$T2M'))\n",
    "print(dT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Plot seasonal STDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "dT_month = do_STDC(LWout_seas, LWout_ctl_seas, SWin_seas,SWout_seas,SWin_ctl_seas,SWout_ctl_seas, dSWin_seas, dLWin_seas, dLHF_seas, dSHF_seas, dR_seas)\n",
    "dT_year = do_STDC(LWout, LWout_ctl, SWin, SWout, SWin_ctl, SWout_ctl, dSWin, dLWin, dLHF, dSHF, dR)\n",
    "\n",
    "plot_STDC_seas(dT_month,dT_year, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Calculate and plot Surface Temperature Decomposition > 15%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot annual STDC\n",
    "\n",
    "dT_15pct = do_STDC(LWout_15pct,LWout_ctl_15pct, SWin_15pct,SWout_15pct,SWin_ctl_15pct,SWout_ctl_15pct,dSWin_15pct,dLWin_15pct,dLHF_15pct,dSHF_15pct,dR_15pct)\n",
    "dT.append(dT2M_15pct)\n",
    "#plot_STDC(dT_15pct, threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('LHF', 'SHF', 'R', '$\\\\alpha$', 'SWin', 'LWin', '$\\\\Delta$Ts', '$\\\\Delta$T2M')\n",
      "[-1.1033584522845914, 0.7773461798675817, 0.015794280689242154, 0.1335532943576614, 0.09772640918197, 0.08197175537081226, 0.002331377438395066]\n"
     ]
    }
   ],
   "source": [
    "print(('LHF','SHF','R',r'$\\alpha$','SWin','LWin','$\\Delta$Ts', '$\\Delta$T2M'))\n",
    "print(dT_15pct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Plot seasonal STDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dT_month = do_STDC(LWout_seas_15pct, LWout_ctl_seas_15pct, SWin_seas_15pct,SWout_seas_15pct,SWin_ctl_seas_15pct,SWout_ctl_seas_15pct, dSWin_seas_15pct, dLWin_seas_15pct, dLHF_seas_15pct, dSHF_seas_15pct, dR_seas_15pct)\n",
    "dT_year = do_STDC(LWout_15pct, LWout_ctl_15pct, SWin_15pct, SWout_15pct, SWin_ctl_15pct, SWout_ctl_15pct, dSWin_15pct, dLWin_15pct, dLHF_15pct, dSHF_15pct, dR_15pct)\n",
    "\n",
    "plot_STDC_seas(dT_month_15pct,dT_year_15pct, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Difference of radiative Ts with model TSA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$\\\\Delta TSA (as modeled by CAM)')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzAb+aIyprE87/jSqezt6d4rj1WW5XDYiNZBdKaXUz3y+fg93vbmGkvJK/nbRIK4O8lxDTfw9gngGKAeSgEnAsyIy0Md+rYDbgQ5ABjAO+H0D2vEbh8PBpIw0VucdZG3+oUB2pZRSAJRWOPnru2u58ZUVJMW34P1bxjIpIz0kkgP4cQQhIq2BS4BBxphi4BsRmQ9MBv7gva8x5lmvb/NFZBZwWn3b8beLjkvh0Y82MmtZDg9fPCSQXSmlmrj1uw5z22vfsWlPMTeN7c5dZ/eleUy03WH9jD9HEH0ApzFmk9e21UBdzvxPBrL80E6jJLSM5fyhXXj3+50cLq0IdHdKqSbI7Xbz4jfbuOCZxRwoqWDGDaP488QBIZccwL9zEHFA9drMIaDNsR4kItcDI4CbGtKOiEwFpgL06tWrfhH7MGl0Gq+vyOOd7/KZMqZbo9tTSqkq+4rK+P0bq/ly0z7G9evEY5cOoX1cc7vDqpE/E0QxEF9tWzxQ4z1oReRC4BHgDGNMQUPaMcZMB6YDTJ8+3V3/sH9uSEoiQ1ISmLU0l8mjQ6cWqJQKbws37OWuN1dTVFrJgxcM5JowOL74s8S0CYgRkd5e24byU+noZ0TkbOB54DxjzA8NbScQJmWksXFPEStyDgSrS6VUhCqtcHL//Cyuf3k5HeKa894tY5k8plvIJwfwY4IwxhwB5gEPiEhrETkRuACYWX1fETkdmAVcYozJbGg7gXLe0C60aRHDrKU5wepSKRWBNu4u4sJnFvPyt9u5/sRuvPObE+mTdMyqe0jx9836BHgR2AsUAjcbY7JEJA1YBwwwxuQCfwESgA9EpOqxXxtjzjlWO36OtUatmsVwyXEpzF6Wy18mloV0jVApFXrcbjczluTwtw/WE98ilpevH8mpfTvZHVa9OdzuRpftQ8b06dPdU6dO9Utbm/YUcdYTX3HvOf341Sk9/dKmUiryuVxubp61ko+z9nBa347847KhdAjxk8xp06atvO+++0ZU36632qhBn6Q2jOrejtmZubhckZNElVKB9eHa3XyctYc7z+zDi9eNDPnkcCyaII5hUkYaOYUlfJNdUPvOSqkmz+Vy85/PN9GrUxxyWq+wmIg+Fk0Qx3D2oM60b92MV3WyWilVBx+s3cWmPcXcOq430VHhnRxAE8QxNY+J5rIRqXy+YS+7Dh21OxylVAhzutz857PN9OoUx7mDk+0Oxy80QdTi6lFpuNxuXsvMszsUpVQI++CHXWzeW8xtETJ6AE0QtUpr34qTe3fkteW5VDpddoejlApBTpeb/3y+md4RNHoATRB1cs3odPYcLuOz9XvtDkUpFYIW/LCL7L3F3HZGb6IiZPQAmiDq5LS+HUlOaMGsZTpZrZT6OWvuYRN9kuKYMChyRg+gCaJOYqKjuGpUGl9vLmB7wRG7w1FKhZD31+xky74j3DauT0SNHkATRJ1dMTKV6CgHczJz7Q5FKRUiquYe+nVuwzmDOtsdjt9pgqijpPgWnDUgibkr8iitcNodjlIqBLy3eidb9x3htnGRNfdQRRNEPUzKSOdASQUfrd1tdyhKKZs5XW6e9Iwexg+MvNEDaIKolxN6tqd7h9Z6ZbVSivmr89lacITbI2zlkjdNEPUQFeXg6lFprMg5wIbdh+0ORyllk0qniyc/z6Zf5zacNSAyRw+gCaLeLj0+hWYxUcxeppPVSjVV81fvZFvBEW4/I/JWLnnTBFFPbVs3Y+LgZOatyudIWaXd4SilgswaPWxmQHI84wcm2R1OQGmCaIBJo9MoLqtk/uqddoeilAqyd7/fyfbCEm47o3fY3867NpogGuC4tLb069yGV5fmEEmfyKeUOrZKp4unvrBGD2cNiOzRA2iCaBCHw8Gk0elk7TzM6h2H7A5HKRUkb3+Xz/bCEm5vAqMH0ATRYBcN70rrZtG65FWpJqLS6eLphdkM6hrPmU1g9ACaIBosrnkMFwzvynurd3KopMLucJRSATbvu3xyCku4fVyfJjF6AE0QjXJNRjpllS7eXLXD7lCUUgFU4XTx9BfZDO6awLj+newOJ2g0QTTCgC7xDE9LZNYynaxWKpK9vSqf3P1NZ+6hiiaIRromI52t+46wZGuh3aEopQKgwuniqYWbGZKSwOn9ms7oATRBNNq5Q5JJaBnLLL2yWqmING/VDvL2H21yoweAGH82JiLtgBeAs4AC4F5jzGwf+w0C/gUcD7Q3xjiq/XwRMBqoulQ53xjT15+x+kuL2GguOz6Fl7/dzt6iUjq1aWF3SEopPymvdPHUF9kMTUngtL5Na/QA/h9BPAOUA0nAJOBZERnoY78KYC5w4zHa+q0xJs7zFZLJocrVGWlUuty8sUInq5WKJPNW7WDHgaPcfkbTWbnkzW8JQkRaA5cAfzHGFBtjvgHmA5Or72uM2WiMeQHI8lf/durRMY4Te7Vn9rJcnC6drFYqEvw4ekhN5NS+He0Oxxb+LDH1AZzGmE1e21YDpzSwvYdF5BFgI/AnY8wiXzuJyFRgKkCvXr0a2FXjTcpIR2at4stNezm9X9O4iEapSPbmyh3kHzzKQxcNapKjB/BviSkOqH7fiUNAmwa0dQ/QA+gKTAfeE5GevnY0xkw3xowwxoyIi4trQFf+ceaAJDq2ac6rS3WyWqlwV17p4pmF2QxLTeTUPk1z9AD+HUEUA/HVtsUDRfVtyBizzOvbV0TkKmAC8FTDwwus2OgorhyZytMLs9lxoISUtq3sDkkp1UBvrMwj/+BR/n7x4CY7egD/jiA2ATEi0ttr21D8M8/gBkL+VbpyVBoOYE6mjiKUClfllS6e+SKb4WmJnNy7g93h2MpvIwhjzBERmQc8ICI3AcOAC4ATqu8rIg6gOdDM830LwG2MKRORRCAD+BJrmesVwMnA7f6KNVC6Jrbk9H6deH35Dm4b14dmMXqZiVLhZu6KPHYeKuWRS4Y06dED+Pk6CECAF4G9QCFwszEmS0TSgHXAAGNMLpAObPN63FEgB+gGxAIPAf0AJ7ABuNAYs9HPsQbEpIx0Plu/nE/W7WbikC52h6OUqoeySifPLMzmuLRETmriowfwc4IwxuwHLvSxPRdrErvq++3UUDIyxuwDRvozrmA6uU9HUtq2ZNbSXE0QSoWZuSt2sOtQKY9dqqMH0Ftt+F10lIOrRqWxZGsh2XuL7Q5HKVVHZZVOzMJsRqS3ZWwvHT2AJoiAuHxEKrHRDmbr/ZmUChtzl+ex61Bpk71q2hdNEAHQsU1zxg/szJsr8zha7rQ7HKVULUornDyzcAsju7XlxF7t7Q4nZGiCCJBrRqdzuLSS99fstDsUpVQtXl+ex+7Dpdyho4ef0QQRIBnd29GrUxyvaplJqZBWWuHELMpmVLd2jOmpowdvmiACxOFwMCkjjdV5B1mbX/0OJEqpUPFaZi57Dpdx+5lN7/MeaqMJIoAuPi6FFrFRzFqWY3coSikfrNHDFkZ1b8eYHjp6qE4TRAAltIzl/KFdePf7nRwurbA7HKVUNXMyc9lbVKZzDzXQBBFgkzLSKSl38s53+XaHopTyUjV6GN1D5x5qogkiwIamJjK4awKzlubiduuHCSkVKmYvy2VfURm3n9HH7lBCliaIIJiUkcbGPUWsyDlgdyhKKazRw7NfbmFMj/aM1rmHGmmCCILzh3WhTfMYZi3VyWqlQsGsH0cPvWvfuQnTBBEErZrFcPFxXfngh90UFpfZHY5STdrRcifPLtrCCT3bk6Gjh2PSBBEkk0anU+508ebKHXaHolSTNmtZDgXFOvdQF5oggqRPUhtGdWvH7MxcXC6drFbKDkfLnfz3yy2c2Ks9o7q3szuckKcJIogmjU4jp7CEb7IL7A5FqSbp1aU5FBSX6+ihjjRBBNHZgzrTrnUzvbJaKRuUlFfy3FdbGNurAyO76eihLjRBBFHzmGguG5HCZ+v3svtQqd3hKNWkVI0e7jhTVy7VlSaIIJs0Kh2X283sTL3Lq1LBUlJeyXNfbuWk3h04Pl1HD3WlCSLI0tq34pQ+HXktM5cKp8vucJRqEl5avJ3CIzr3UF+aIGwwZUw6e4vK+Dhrt92hKBXxPl23h399spHxA5M4Pr2t3eGEFU0QNjilTydS27Vkxrc6Wa1UIC3bWshvZq9iSEoiT1wxzO5wwo4mCBtERzm4JiOdzO372bD7sN3hKBWR1u08zE2vrCCtXSteum4krZrF2B1S2NEEYZPLR6TSPCaKGUt0FKGUv+UWljDlxUziWsQw44ZRtG3dzO6QwpJfU6qItANeAM4CCoB7jTGzfew3CPgXcDzQ3hjjaEg74axt62acP7QL73yXzx/O6Ud8i1i7Q1IqIuwtKmXyi8twuly8NnUMXRJb2h1S2PL3COIZoBxIAiYBz4rIQB/7VQBzgRsb2U5YmzKmGyXlTt7S+zMp5ReHSyu49sXl7Csq46XrR9GrUxu7QwprfksQItIauAT4izGm2BjzDTAfmFx9X2PMRmPMC0BWY9oJd4NTEhiWmsjMpTn6YUJKNVJphZObXllB9t4i/nvN8QxLTbQ7pLDnzxJTH8BpjNnktW01cEog2xGRqcBUgF69etWzK/tNGZPO7+auZnF2IWN7d7A7HKXCUqXTxa1zvmP59v38+4phnNyno90hRQR/Jog44FC1bYeA+o7x6tWOMWY6MB1g+vTpYXcaPmFwMg8tWM+MJds1QSjVAG63mz+9vZZP1u1h2vkDuWBYV7tDihj+nIMoBuKrbYsHimxqJyy0iI3mipGpfLZ+D/kHj9odjlJh57GPN/L6ijxuHdeba0/oZnc4EcWfCWITECMi3nfCGoqPeYYgtRM2JmWkAehHkipVT//7eivPLtrCpIw07tCPD/U7v5WYjDFHRGQe8ICI3AQMAy4ATqi+r4g4gOZAM8/3LQC3MaasPu1EipS2rRjXP4nXl+dx2xm9aR4TbXdISoW8eat28NCC9UwY3JkHLhiEw+Go/UGqXvy9zFWAlsBeYA5wszEmS0TSRKRYRNI8+6UDR/lpVHAU2FhbO36ONaRMGZNO4ZFyPvhhl92hKBXyvtiwh7veXMOJvdrzxBXDiI7S5BAIjkhaXjl9+nT31KlT7Q6jQVwuN2c8/iUJrWJ5W060OxylQtaK7fu55oVl9O7UhjlTRxPXXG+h0VjTpk1bed99942ovl1vtREioqIcXDM6ne9yD7I2v/oiLqUUwMbdRdzw8nK6JLTk5etHanIIME0QIeSS41NoGRvNjCXb7Q5FqZCTt7+EKS8uo2WzaGbcOIr2cc3tDiniaYIIIQktY7lweFfe/X4nB0vK7Q5H2ezhD9dz95ur7Q4jJBQUlzHlxUxKK1zMvDGDlLat7A6pSdAEEWKmjEmnrNLF3BV5doeibJRTeIT/fb2NN1fuYF9Rmd3h2KqotILrXspk16GjvHjdCPok6f2VgkUTRIjpnxzPyG5teXVpLi5X5CwgUPXzzMJsHIDLDR814U8eLKt08quZK1m/q4hnJx2vnycdZJogQtCUMd3I3V/Cl5v22R2KskHe/hLmrcpnUkYaPTu25oM1TXPps9Pl5vbXvufbLYX887IhnNavk90hNTmaIELQ+IGd6dimuU5WN1Fm0RaiHA5+fWpPzh2czLJthRQUN60yk9vt5i/vruXDtbv5y8QBXDQ8xe6QmiRNECGoWUwUV41KY9GmfeQWltgdjgqi/INHeXNlHleMTCU5oSXnDulilZnWNq0y0xOfbmL2slzk1J7cOLa73eE0WZogQtTVo9KIcjh4dZnen6kpeXZRNgC/PrUnAH2S4qwyUxO6wv7lxdt48otsrhiRyl3j+9odTpOmCSJEdU5owfiBScxdkUdphdPucFQQ7Dp0lLnLd3Dp8al09XxMpsPh4NzBySzd2jTKTO9+n8/9763jrAFJ/O0ivb+S3TRBhLDJo7txsKSC+at32h2KCoL/LtqCy+1GPKOHKhOGJDeJMtOijXu5c+5qMrq348mrhhMTrYcnu+krEMJG92hHn6Q4ZizZrh9JGuH2HC5lzvI8LjkuhdR2P78IrG9SG3pEeJnpu9wD3PzqKvokteH5a0fQIlbvaBwKNEGEMIfDweTR6azNP8x3eQftDkcF0HNfbsXpcvOb0375sebAANgAAB5xSURBVLkOh4OJEVxm2ryniOtfXk6n+Oa8csMo4lvE2h2S8tAEEeIuOi6FuOYxzFyik9WRam9RKbOW5XDR8K6ktfd9C4mqMtPHEXbRXP7Bo0x5MZPY6Chm3pBBxzZ6f6VQogkixMU1j+GS47qyYM2uiDx7VPD8V1upcLp8jh6qVJWZFkTQRXP7j5Qz5YVlFJdVMuOGUTUmR2UfTRBhYPKYdMqdLl5frvdnijQFxWW8ujSXC4d1pXuH1jXuF2mrmUrKK7n+5eXsOHCUF64dSf/k6h9Dr0KBJogw0KtTG07o2Z7Zy3Jx6v2ZIsrzX2+ltNLJb06vefRQZcLgyCkzzViSw+q8gzx11XBGddf7K4UqTRBhYsqYdPIPHuXz9XvsDkX5yf4j5cxcksN5Q7rQs2Ncrfv369yGHh3CfzWTy+XmtcxcMrq346yBne0ORx2DJogwcUb/JJITWjBzqU5WR4oXvtnK0Qont9Rh9ACeMtOQZJZsKaQwjMtMS7cWsr2whKtGpdW+s7KVJogwERMdxdWj0vh6cwFb9hXbHY5qpIMl5bzybQ4TBifTux6fb1BVZgrnW4DPzswloWUsZw/S0UOo0wQRRq4clUZstEOXvEaAF7/ZRnFZZZ1HD1XCvcxUWFzGJ1l7uOS4FL0YLgxogggjHds0Z8LgZN5auYMjZZV2h6Ma6NDRCl5avJ1zBnWmX+f6rd5xOBxMGBy+ZaZ5q/Ipd7q4alSq3aGoOtAEEWamjEmnqKySd77PtzsU1UAvLd5GUVklv63n6KHKT6uZwmvBgtvtZk5mLiO7ta1XWU3ZRxNEmDkurS0DkuOZuSRH788Uhg6XVvDiN9s4c0ASA7skNKiN/snhWWZatm0/WwuOcOVInZwOFzH+bExE2gEvAGcBBcC9xpjZNex7B3AP0BJ4C7jZGFPm+dkiYDRQVUfJN8bojeGxSgxTxqTzh3k/sHz7AV1DHmZmfLudw6WV3Daud4PbqCozmUXZFBaX0T4uPG5PMSczl/gWMZw7JNnuUFQd+XsE8QxQDiQBk4BnRWRg9Z1EZDzwB2Ac0A3oAUyrtttvjTFxni9NDl4uGNaV+BYx+pGkYaa4rJL/fbONcf06Mahrw0YPVcKtzHTgSDkfrt3NxTo5HVb8liBEpDVwCfAXY0yxMeYbYD4w2cfu1wIvGGOyjDEHgAeB6/wVS6Rr2Syay0ak8tHa3ew9XGp3OKqOZizZzsGSCm5txOihSv/kNnQPozLTvO/yKa90caVOTocVf44g+gBOY8wmr22rgV+MIDzbVlfbL0lE2ntte1hECkRksYicWlOnIjJVRFaIyIri4qZzfcA1o9OpdLmZk6n3ZwoHR8oq+d/X2zi1b0eGpiY2uj2rzNSZJVsL2X+k3A8RBk7V5PTwtMR6r9pS9vJngogDDlXbdgjwtVyh+r5V/6/a9x6sslNXYDrwnoj8/GO2PIwx040xI4wxI+Liar9dQaTo3qE1p/TpyKxlOVQ4XXaHo2rx6tIc9h8p55bTGz96qDJhcDJOlzvk7820IucA2XuL9crpMOTPBFEMVD89iAeK6rBv1f+LAIwxy4wxRcaYMmPMK8BiYIIfY40IU8aks7fIuvBIha6j5U6mf7WVk3p34Pj0tn5rd0ByPN07hP4twOdk5tKmeQwTdXI67PgzQWwCYkTE+xRpKJDlY98sz8+899tjjCmsoW03oJ9eXs2pfTuR0ralTlaHuFnLcig8Ut6olUu+hEOZ6VBJBQvW7OLC4V1p1cyviyZVEPjtFTPGHBGRecADInITMAy4ADjBx+4zgJdFZBawC/gz8DKAiCQCGcCXWMtcrwBOBm73V6yRIjrKwTWj03nkww1s3F1E38568VGoKa1w8txXWzmhZ3tGdPP/kuQJg5N5ZuEWPs7aHZIlnLe/20GZTk6HLX8vcxWs6xr2AnOwrm3IEpE0ESkWkTQAY8xHwGPAQiDH83Wfp41Y4CFgH9a1FLcAFxpjNvo51ohw+YhUmsVEMXPpdrtDUT7MycxlX1GZX1Yu+TIgOZ5u7VuF5Goma3I6j6EpCQ2+KFDZy69jPmPMfuBCH9tzsSamvbc9DjzuY999wEh/xhXJ2rVuxnlDuvD2qnzuObsfbfQD30NGaYWT/365hYzu7Rjdo33tD2iAqovmnvtqK/uPlNOudbOA9NMQq3IPsnFPEY9cPNjuUFQD6a02IsCUMekcKXcyb5XenymUvLEijz2Hy/w+91BdqK5mei0zl9bNojlvaBe7Q1ENpAkiAgxNTWRoSgIzlmzX+zOFiLJKJ2bRFkakt2VMz8CMHqoM7BJ6ZabDpRW8t2YnFwzvSuvmOjkdrjRBRIgpY7qxZd8Rvt1S00IwFUxvrtzBrkOl3HZGbxyOwC7AqyozfbsldFYzvftdPqUVLq7SG/OFNU0QEeLcIcm0a91Ml7yGgPJKF2bhFoanJTK2V4eg9FlVZvokBMpMbreb2Zl5DOoaz+AUnZwOZ5ogIkSL2GguH5HKp+v2sPPgUbvDadLmrdpB/sGj3Dou8KOHKgO7xJPevhULQqDMtGbHIdbvOhySy25V/WiCiCCTMtJwA7OX5dodSpNV4XTxzKJshqYkcGqfjkHrN5TKTHMyc2nVLJrzdXI67GmCiCCp7Voxrl8nXlueS1ml0+5wmqR3vssnb39wRw9Vzg2BMlNRaQXzV+/kvCFddMl1BNAEEWEmj+lGQXE5H621vxbd1FQ6XTy9MJtBXeM5vV+noPcfCmWm+at3UlLu5KoMLS9FAk0QEeakXh3o1r4VM5bk2B1KkzN/9U5yCku49fTgjx7g52WmAzaVmeZk5tI/OZ6hOjkdETRBRJioKAeTx3RjZc4BsnZWv/u6ChSny83TX2TTPzmeMwck2RbHj2WmdcEfQf6w4xBr8w9z9ahUWxKk8j9NEBHo0uNTaBkbzUwdRQTN+2t2srXgCLee3svWg+PALvGktWvF+zbcAnzO8lxaxEZxwfCuQe9bBYYmiAiU0DKWC4d34Z3v8zlUUmF3OBHP6XLz1BfZ9E1qw/iBnW2Nxa4y05GySt79Lp+JQ7oQr5PTEUMTRISaPLobpRUu3lipH0kaaB+u3UX23mJuGdeLqCj7SysThwS/zPTe6p0cKXfqtQ8RRhNEhBrQJZ4R6W2ZuTQHl0vvzxQoLpebJz/fTK9OcZwzKDQ+Ma2qzLTgh+AliDnL8+ib1Ibj0hr/edsqdGiCiGCTx6STU1jCV5v32R1KxPo4azeb9hRzy+m9iA6B0QN4lZmyC4JSZsraeYjVeQe5UienI44miAh2zqBkOsQ118nqAHG53Pzn88306NCaiUNC66rhcwcnUxmkMtNrmXk0j4niIp2cjjiaICJYs5gorh6Vyucb9nLzqytZnF2gtwP3o0/X72HD7iJ+G0KjhyqDusaT2q5lwMtMJeWVvPNdPucOTiaxVeh8WJHyD71Re4S7+dRelFW6eH1FHh+u3U2Pjq2ZlJHOpcelkNBKV5s0lNttzT10a98qJO85VFVmeuHrbRwsKQ/Ywfv9NbsoKqvUK6cjlI4gIlzLZtHcO6E/S+8dx+OXDyWhZSwPvr+OjIc/4+43V7Nmx0G7QwxLX2zYS9bOw/zmtF7ERIfmn9HEwV2sMlPWnoD18VpmLr06xTEivW3A+lD20RFEE9EiNpqLj0vh4uNSyNp5iFeX5vLOd/nMXbGDoSkJTBqdznlDutCyWbTdoYY8t9uae0ht15ILQ7ju/lOZaReXj0z1e/sbdh9mVe5B/nxuf52cjlCheeqjAmpglwQevngwy/40jmnnD6Sk3Mndb64h4++f8eD769i6r9juEEPaok37WLPjEL89rRexITp6gJ/KTIuzCzhY4v/VTK9l5tEsOopLjkvxe9sqNITuu1sFXHyLWK49oRuf3HEyr00dzcl9OvLKt9s5/V9fMul/S/lo7S4qnS67wwwpbreb/3y2ma6JLbloeOgfGH9czeTnMlNphZN5q3ZwzuDOtG2tk9ORSktMCofDwege7Rndoz17i0qZuzyP2cty+fWrq0iKb86VI9O4alQanRNa2B2q7b7eXMD3eQf520WDaBYT+udXg7smkNLW/2WmD37YxeHSSr1yOsKF/jtcBVWnNi347em9+fqe03l+ygj6dY7nyS82c+KjX/DrmU17qWzV3EOXhBZcenzojx7ASv7nDvF/mWlOZi49OrQmo3s7v7WpQo9fRxAi0g54ATgLKADuNcbMrmHfO4B7gJbAW8DNxpiy+rajAiM6ysGZA5I4c0ASOYVHmL0sl7kr8vgoazc9OrTm6ow0Ljs+tUktlV2cXcjKnAM8eMFAmseEz2T+uYOTee7LrXyybg+Xj2j8KGLzniKWbz/AHyf008npCOfvEcQzQDmQBEwCnhWRgdV3EpHxwB+AcUA3oAcwrb7tqOBIb9+aeyf0Z8m943jiiqEktorloQXryXj4M+56YzWr8yJ/qazL5ebhD9fTNbEll/nhIBtMP5aZ/HQL8DmZecRGO3RyugnwW4IQkdbAJcBfjDHFxphvgPnAZB+7Xwu8YIzJMsYcAB4ErmtAOyqIWsRGc9HwFObJiSy4dSwXDU9hwQ+7uOCZxZz/9DfMXZ7H0fLI/Czsd77PJ2vnYe4+uy8tYsNn9ACeMpOfVjOVVjiZ990Oxg/sTPu45n6KUIUqf44g+gBOY8wmr22rAV9n/gM9P/PeL0lE2tezHURkqoisEJEVxcW6PDNYqpbKLv3jOB64YCBHy53c/Za1VPbFb7bZHZ5flVY4+efHGxmSksB5IXbPpbqa8OO9mRq3munjrN0cLKnQyekmwp9zEHFA9c+4PAS0qcO+Vf9vU892MMZMB6YDTJ8+vWnOntoovkUsU8Z0Y/LodDK37eepL7J5aME6Mnq0Y2CXyPhc4hcXb2PnoVIev2JYSHzeQ0MMSbHKTB/8sKtR8xCzl+WS3r4VY3q092N0KlT5cwRRDMRX2xYPFNVh36r/F9WzHRUiHA4HGT3a88yk42jbqhn3z8+KiNVOhcVlmIVbOKN/EqPD+KBYVWb6ZnNBgz9lcMu+YpZt28+VI9PCNlGq+vFngtgExIhIb69tQ4EsH/tmeX7mvd8eY0xhPdtRISahZSx3n92X5dsP8O73O+0Op9Ge/HwzRyuc/OGcfnaH0mhVZaaPG3gL8Ncyc4mJcoTNEl/VeH5LEMaYI8A84AERaS0iJwIXADN97D4DuFFEBohIW+DPwMsNaEeFoMuOT2VoSgJ//2A9xWWVdofTYFv3FTNrWS5XjUqlV6c4u8NpNO8yU32VVTp5a1U+Zw1MomMbnZxuKvy9zFWwrmvYC8zBurYhS0TSRKRYRNIAjDEfAY8BC4Ecz9d9tbXj51hVgERFObj//IHsLSrjqc832x1Ogz360Qaax0Rx27g+dofiF973ZqpvmemTrD3sP1LOlSN1crop8euFcsaY/cCFPrbnYk0+e297HHi8Pu2o8DE8rS2Xj0jhxcXbuHxkKj07htcZeOa2/XyctYffn9Unos6YJwxOZvpXW/lk3e56Xc8xJzOXlLYtGdurQwCjU6FGb7WhAubus/vRIjaaae+tC6sJa7fbzd8+WE/n+BbcOLaH3eH41dCUBLom1q/MtK3gCN9uKeSqUTo53dRoglAB0yGuOXec0YevNu3j00auvw+m99fsYnXeQe48q0/EfT5G1b2ZvqlHmem15blERzm4TCenmxxNECqgJo9Jp09SHA8uWEdpRehfZV1W6eTRjzbQPzmeiyP0VhITBidT4XTzSR1WM5VXunhr5Q7G9etEp3i9m29TowlCBVRsdBT3nz+QvP1Hee7LrXaHU6uZS3LYceAof5zQj+gILafUp8z02fo9FBSX62dON1GaIFTAndCzA+cOScYsymbHgRK7w6nRwZJynvx8M6f06chJvTvaHU7AWKuZOltlpqPHLjPNycyla2JLTo7g34eqmSYIFRR/mtCfKIeDvy1Yb3coNXr6i2yKyyq5d0L4XxRXm6oy07HmhnILS/h6cwFXjEyN2NGUOjZNECoouiS25Den9eTDtbtZnF1gdzi/kFtYwitLtnPZ8an061z9Ti+RZ1hqYq1lptdX5BLlgMtGROZcjKqdJggVNDed1IO0dq24b34WFSH2WdePfryBmKgofndWZFwUV5uqMtPXm/f5LDNVOF3MXbGD0/t1IjmhpQ0RqlCgCUIFTYvYaP46cQDZe4t55dvtdofzo1W5B1iwZhf/d3IPkprQSp1jlZk+X7+XfUVlelvvJk4ThAqqcf07cVrfjvz7s83sLSq1Oxzcbjd/X7CeDnHN+dXJkXVRXG2OVWaak5lLckILTumjk9NNmSYIFVQOh4O/njeQ8koXj3640e5w+DhrDytyDvC7M/vQurlf7zwT8hwOB+cM+mWZKW9/CV9t3sdlI1KJidZDRFOmr74Kuu4dWnPjSd15a9UOVuYcsC2OCqeLRz/aQO9OcVzeRCdiJwyxykyfeZWZ3liRB8AVI8Prs7eV/2mCULb47Wm96BzfgvvnZ+F02XOfptnLctlWcIR7J/RrsmfKwz1lpgWeMlOl08XrK/I4tU9Huibq5HRT1zT/KpTtWjeP4d4J/fgh/xCvL88Lev+HSyv492ebOKFne07r2yno/YeK6mWmhRv3seewTk4riyYIZZvzh3ZhVPd2/OPjDRwsKQ9q32bhFg6UVPDHCf1xOJr2RWDeZaY5mbl0atOc0/s13aSpfqIJQtnG4XAw7fyBHDpaweOfbgpav/kHj/Li4m1cPLwrg7omBK3fUDU8NZEuCS14Zcl2Fm3cy+U6Oa089F2gbNU/OZ7Jo9N5dWkO63YeDkqf//zYWj115/i+Qekv1DkcDs4ZnMyaHYdwo5PT6ieaIJTtfndmXxJbNeP++VkB/2ChtfmHePu7fG4c210nYb2cOyQZgJN6dyS1XSubo1GhQhOEsl1Cq1juGt+XzO37mb96Z8D6cbvd/G3Betq1bsbNp/YMWD/haHhqIjeO7c6dZzaNW42outEEoULC5SNSGZKSwN8/WM+RssqA9LFw416WbC3ktnG9iW8RG5A+wpXD4eAvEwcwNDXR7lBUCNEEoUJCdJQ1Yb3ncBlPfZHt9/YrnS7+/sEGundozdX64TdK1YkmCBUyhqe15dLjU3jhm61s3Vfs17ZfX5FH9t5i7jm7H7G6QkepOtG/FBVS7jm7Hy1iopn23jq/TVgXl1XyxKebGdmtLeMHJvmlTaWaAk0QKqR0bNOc287ozZeb9vHZ+r1+aXP6l1soKC7Ti+KUqie/3L5SRNoBLwBnAQXAvcaY2cfY/w7gHqAl8BZwszGmzPOzRcBooGqmMt8YowvWm5BrT+jG68vzePD9dZzUuwMtYqMb3NbuQ6VM/3orE4ckMzytrR+jVCry+WsE8QxQDiQBk4BnRWSgrx1FZDzwB2Ac0A3oAUyrtttvjTFxni9NDk1MbHQU084fSO7+Ep7/amuj2nr80404XW7uHh/5nzOtlL81OkGISGvgEuAvxphiY8w3wHxgcg0PuRZ4wRiTZYw5ADwIXNfYOFRkOaFXByYM7swzi7LJP3i0QW2s33WYN1bu4Nox3Uhrrxd/KVVf/hhB9AGcxhjvm+msBnyOIDzbV1fbN0lE2ntte1hECkRksYiceqzORWSqiKwQkRXFxf5d+aLs9adzBwDwtwXrGvT4hz/cQJvmMfz29F7+DEupJsMfCSIOOFRt2yGgTR33r/p/1f73YJWdugLTgfdEpMbLXo0x040xI4wxI+Li4uobuwphXRNbIqf24oMfdrM4u6Bej/1q0z6+2rSPW8f1JrFVswBFqFRkq3WS2jNpfEoNP14M3ALEV9seDxTV8JjiavtX/b8IwBizzOtnr4jIVcAE4KnaYlWRZ+rJPXhjZR73z8/ig9tOqtM1DE6Xm79/sJ7Udi2ZPCY9CFEqFZlqTRDGmFOP9XPPHESMiPQ2xmz2bB4KZNXwkCzPz+d67bvHGFNYw/5uQNcmNlEtYqP568SB/N+MFcxYksONY7vX+pi3Vu1gw+4inrpqOM1jGr4CSqmmrtHLXI0xR0RkHvCAiNwEDAMuAE6o4SEzgJdFZBawC/gz8DKAiCQCGcCXWMtcrwBOBm5vbJwqfJ3RvxOn9OnIvz/dxPlDu9CxTfMa9z1a7uRfn2xkWGoiEz13KFVKNYy/lrkK1jUNe4E5WNc1ZAGISJqIFItIGoAx5iPgMWAhkOP5us/TTizwELAP63qKW4ALjTEb/RSnCkMOh4P7zhtAaaWTRz/acMx9//f1VvYcLuNP5+pFcUo1liPQ998PpunTp7unTp1qdxgqQB7+cD3PfbmVeXICx/m46G1fURmn/mMhY3t34LnJI2yIUKnwNG3atJX33XffL/5o9FYbKmzccnpvOrVpzv3zs3C5fnli8+/PNlFW6eKes/WiOKX8QROEChtxzWP407n9WbPjEHNX5P3sZ9l7i3hteR6TMtLo0VGXOyvlD5ogVFg5f2gXRnVrx2Mfb+RQScWP2x/5cAOtYqO5dVxvG6NTKrJoglBhxeFwcP/5AzlYUs7jn1prF5ZsKeSz9Xu5+bSetI+reYWTUqp+NEGosDOgSzyTMtKZuTSHdTsP8/cP1tMloQU3nFj7NRJKqbrTBKHC0p1n9SGhZSxTXszkh/xD/H5830bdFlwp9UuaIFRYSmzVjLvG96OguIyBXeK5cFhXu0NSKuL45QODlLLDFSNT2VdUxtmDOhMVpRfFKeVvmiBU2IqOcnDbGbpqSalA0RKTUkopnzRBKKWU8kkThFJKKZ80QSillPJJE4RSSimfNEEopZTySROEUkopnzRBKKWU8imiLpTbtWtXwbRp03Ia8lin09khOjq6wN8xhUJ/kfzcgt1fJD+3YPcXyc8t2P35oa90n1vdbrd+ud3cfPPNKyK1v0h+bvq7DN/+Ivm5RcrvUktMSimlfNIEoZRSyidNED+ZHsH9RfJzC3Z/kfzcgt1fJD+3YPcXkL4cbrc7EO0qpZQKczqCUEop5ZMmCKWUUj5pglBKKeVTRF0o1xgisgh41RjzvwD2sR1IApxem/sYY3YGoJ8uQBdjTIHX9u+BoUB3Y8x2f/bp1cciTx+djTFlAepjO/Y+v4C+T+zqLxivnaefscBjwECsv4X1wO3GmOUB6u864E6gJ3AYeBu41xhz0M/9bAdaAj2MMUc8224CrjHGnOrPvrz6SwIqsX6P64AZwHRjjMsffegIIvjOM8bEeX35NTl42QZcVfWNiAzGevMGjIh0A04C3MD5gewLG55fJAvWayci8cD7wFNAO6ArMA0I1MnEncCjwF1AAjAa66rhT0WkWQC6jAFuC0C7NTnPGNMG6zk9AtwDvOCvxnUEEblmAlOw/hABrsU6u3gogH1OAZYCyzz9vRHAvux4fpEsWK9dHwBjzBzP90eBTwLRkScZTQNuMMZ85Nm8XUQuB7YC1wAv+rnbfwB3i4jx9wjlWIwxh4D5IrIbWCoi/zLGrG1suzqCiFxLgXgR6S8i0cAVwKsB7nMKMMvzNV5EkgLYlx3PL5IF67XbBDhF5BUROUdE2gaoH4ATgBbAPO+Nxphi4EPgzAD0uQJYBPw+AG3XyhiTCezAGg02miaI4HtHRA56vt4JcF9VZ9lnAhuA/EB15KkrpwNzjTErgS3A1YHqzyNozy+SBfO1M8YcBsZilbKeB/aJyPwAJaQOQIExptLHz3Z5fh4IfwVuEZGOAWq/NjuxyneNpiWm4LvQGPNZkPqaCXwFdMcqvwTStcAnXpPGsz3bnghgn8F8fpEsqK+dMWY9cB2AiPTDGvn9G685JT8pADqISIyPJJHs+bnfGWPWisj7wB+wJuCDrSuw3x8N6QgighljcrAmcydQbZjtTyLSErgcOEVEdnvqoHcAQ0VkaKD6Ddbzi2R2vXZVjDEbgJeBQQFofgnW5PfF3htFpDVwDvB5APqsch/wf1gH66ARkZGePr/xR3uaICLfjcDpVcvuAuRCrGV2A4Bhnq/+wNdYJaBACsbzi2RBfe1EpJ+I3CkiKZ7vU7FGDkv93Zdn4nYa8JSInC0isZ7VWm9g1eln+rtPr76zgdeBWwPVhzcRiReRicBrWMuif/BHu1pi+rmIuzGVMWZLELq5FnjJGJPrvVFEngaeFJF7aqgDN1qQnl91wX6fBLK/YL92RUAG8DsRSQQOYi17vcuPffzIGPOYiBQC/+Sn6yDeASYF8loPjweAyQHu4z0RqQRcWNdBPA7811+N6836PERkFfCAMSbQE8cqjAX7faLvS2UnLTEBIjIQa1j9nd2xqNAV7PeJvi+V3Zp8ghCRR7Eu1LnHM+mp1C8E+32i70sVCrTEpJRSyqcmP4JQSinlmyYIpZRSPmmCUEop5ZMmCKWUUj5pglBKKeXT/wMsnsyfPq6rmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(111)\n",
    "ax.plot(dTSA_seas)\n",
    "ax.set_xticks(np.arange(0,12)); \n",
    "ax.set_xticklabels(['J','F','M','A','M','J','J','A','S','O','N','D']); \n",
    "ax.set_title('$\\Delta TSA (as modeled by CAM)')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Radiative Ts, based on LWout')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = plt.subplot(111)\n",
    "ax.plot(np.asarray(dT_month)[-1,:])\n",
    "ax.set_xticks(np.arange(0,12)); \n",
    "ax.set_xticklabels(['J','F','M','A','M','J','J','A','S','O','N','D']); \n",
    "ax.set_title('Radiative Ts, based on LWout')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Relative diffence between modeled TSA and calculated radiative Ts')"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = plt.subplot(111)\n",
    "ax.plot((dTSA_seas-np.asarray(dT_month)[-1,:])/np.asarray(dT_month)[-1,:]*100)\n",
    "ax.set_xticks(np.arange(0,12)); \n",
    "ax.set_xticklabels(['J','F','M','A','M','J','J','A','S','O','N','D']); \n",
    "ax.set_title('Relative diffence between modeled TSA and calculated radiative Ts')\n",
    "    "
   ]
  }
 ],
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