{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# CMIP6 Emulation\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from esem import gp_model\n",
    "from esem.utils import validation_plot\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('CMIP6_scenarios.csv', index_col=0).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CanESM5', 'ACCESS-ESM1-5', 'ACCESS-CM2', 'MPI-ESM1-2-HR',\n",
       "       'MIROC-ES2L', 'HadGEM3-GC31-LL', 'UKESM1-0-LL', 'MPI-ESM1-2-LR',\n",
       "       'CESM2', 'CESM2-WACCM', 'NorESM2-LM'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are the models included\n",
    "df.model.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ssp126', 'ssp119', 'ssp245', 'ssp370', 'ssp585', 'ssp434'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And these scenarios\n",
    "df.scenario.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mlD179qQuzJCoGs1nd90Tken/aL47H9hcYZkN7e/sAxCRqcCdJ/LiLgfuXEqiZ8MCEl3k7gTO92LQT6lSVVpbWweNXu7o6KCmpoby8nIPI8ufaDTKG2+8wSmnnJLaduTIEY4cOUJ9fb2HkeVeppJZPqZDyYb6+nrq6+vZs2dPqqt8Q0MDJ510Ut5iKLUfYOCuu/HTI2w/SOLCfiJSA3cARCQ5cCc9OVwO/MipK3xORCaJSBMwx8W5JguSg8vSiQiRSKRkEsvRo0eZNWvWoF+W9fX1HDp0qOgTy+mnn05LSwvRaBRItLmdeeaZHkflTjQa5fDhw6lp/lWV9vZ2IpFIXqoyC2nMTza56RUWEJGPicgapypqo3P/ehE50aqwTAN3Zrg8xs25Jgt8Pt+whk5VLZhfrdkQDAaHJddCGs9xImpqali+fDkNDQ1MnjyZiy66qGDGMLW3t6cSYlI8HufQoUN5iyFfvcImEjdVYT8GjgH/QuLiDYl+0NcA/z/w/hN4fTcDd0Y6xvWgHxG5DrgOoLm5eTzxGRJfjOnTp7N///7UxbWxsbFkGq8h8Ss9Go3i8/lSF4HkYLti19XVxU033UR3dzcA5eXlfPWrX6WhocHjyMZWUVExqH0IEm1E+fzcii1puOHm59Y5qnqDM0Byn3N7TlVvAM4+wdd3M3BnpGNcD/pR1XuSg46mTJlygiGXpvLycubOnUtzczNz585l0qRJXoeUV1VVVbz++uuD5pzauXNn0VeDAXz961+nq6sr9bivr4/bbiuMuWfr6upYsmQJoVAIn89HKBRi8eLFNDY25i0Ga7zP7KiIvA/4parGAUTEB7wPOHqCr58auAPsJzFw5wNDjnkYuNFpQzkf6FDVVhE57OJck0XJL2YpEhHOP/98KioqUNVUI3AplNq2bds26Fe3iLB//34PIxqfd77znZx88skcOnSIxsZGTj311Ly9diFWc4lIHfA54Aog+Uv8EIkp+W9T1WNjPYebxLIa+DLwHRFJJpJJwO+dfcdNVTMO3BGR6539d5Hoe70K2AH0Ah8Z7dwTiceMLjkzbCgUyvvoZa+Fw2EqKioGXSiSc2gV+79Fclni9LnCCmnNexFh0aJFno27KbTEAjwA/A5YpqoHIDGqn0Tzx89J9MQdlZteYbtw2lFEpAEQVW07/piHPf+wgTtOQkneV+Dv3J5rcqOrq4sNGzYAiQvL3LlzmTVr1hhnFY9kG8tQxVaFkUldXR1Hj75ZOaGqBVVS6+rq4tlnn6W7u5uqqire8pa35HWhsgJMLHNU9cvpG5wE82UR+V9unsDVt0JEakVkvqq2pycVEVk8rnBNQVJVNm7ciN/vx+/3EwgE2LNnz6B692I3UgIphWltmpubicVig25NTU1eh+VKLBbjd7/7HZ2dnalR97///e8z/kjIlQLsFbZbRG5yxioCiXGLklhNeO8o56W46W58FfAq8EsR2SIi56Xt/sE4AzYFKBwOp3pDpd8OHz7sdWh5IyKsX78+NcOvqvLqq6/m9QLllVgsNqyNpVCqwjo6OhgYGBi0bWBgIG8rYCandCmwxvv3Aw3A0yJyRESOAH8A6gFXs4+6qRy+BTjXaTBfCvxYRG5R1QfJ3OXXFJmR/tMXe9tCuq6uLnbu3MmuXbuoqqqir6+PWCxGTU0N8+fP9zq8nJoyZcqwNpZ89qo6Eb29vcO2iQjd3d15G30/wZLGmFT1KPAPzu24uLky+FW11XnBtSKyHPgvEZlJjheLMRNDMBikrKyMgYGBQReXffv2sW3bNmbPns2CBQs8jjK3RARVJRaL0dnZmdo2waowcqKtrY1oNEogEEg13OdzgOGJCAaDdHV1sWPHDvbt20dTUxOLFi3K28zGUJBtLIOIyIUkZknZrKqPuznHTWLpctpXXgdwSi7LSKxOdvrxBmsKSygUIhwOpy4szz33HN3d3UQiEYLBIMuXL+fiiy/2OsycqaqqoqGhgSNHjqR+vVdUVDBjRvFP9tDR0cGxY8dSF+NYLJYaLDnR9ff3c++99/Lyyy+nkuOpp57KX/zFX+QthkJLLCKyVlWXOvf/lkTnqYeAfxaRc1R1zEFMbspoNzCkyktVu4CVgKseAqawRaNRent7U/XF7e3tdHZ2EolEgMRU8k8++eSwEc7FxOfzcfHFFzNz5kzq6+uZNm0ay5cvL6jeUcdr2bJllJeXpxruy8vLueiii7wOy5Xe3l5eeuklwuEw8XiccDjM5s2bOXZszKEYWeG24X6CJZ/0HinXAStU9V+AvwQ+6OYJxkwsqrpRVXdk2B5R1Z8kH4vI/7h5QVN4fD7foHmyRmqwLvaG7Gg0SmdnJ52dnRw7dizjqprFaNWqVbz3ve+lrKyMUCjEO97xDq6+etSlQSaMaDQ6LPmXlZXltfNBASYWn4hMThtechhAVXsAV1/ybLYqlcY0tyXI5/PR1NSUaoRsaGgY9EVI7i/mX+/xeJynnnqK7u7u1LrvzzzzDH19fV6HlnNHjx7lV7/6FeFwmEgkwpo1azhw4IDXYbmyYMECqqurB20rKyvjtNNOy1sMBdgrrA54EVgH1DuDIxGRalx22Mrmu7GG/CLW3NzMvHnzOOmkk1iwYAHXXnttakqTuXPncs0113gdYk719fWlqv6SRCRv3Va9dPfdd3P06FF6e3vp7e2lq6uLb3zjG16H5UooFOLaa6+lqqoKEaGyspIPfehDVFZWjn1ylhRaiUVV56jqPFWd6/xN/oqIA+9x8xyl01/UnBARobGxcVA3009/+tMeRpRfoVBo2LT58Xi8JGY3bm1tHVTNGY/HC6bE0t7ezs9+9jMaGhpoamoiHA7z85//nE984hPMnDkz56+fbJcsRM4AyRkkCg0tzhpcb7g5N5uJZeKkXJNz+/fv56c//SkdHR3Mnz+f1atXF/VFNhgMctZZZ7Fx40YgccFobm4uidmNzz//fDZu3Eh/fz+QqEpaunSpx1G5s379egKBAH6/H5/PR3l5OcFgkKeffpoPftBVO/QJm0ilETdE5GwSK/XWkZjgFxLL0h8D/reqrh/rOVwlFkksA/yYql4yymEfcvNcpvB1dnZy++2309nZiarS1tbGsWPH+NSnPuV1aDm1aNEiGhsbOXbsGFVVVUybNs3rkPJi9erV7N69m9/85jcAXHjhhXzsYx/zOCp31q1bl0oq8Ga11EsvvWSJZWTfBz6mqs+nbxSRC5x9Yy4f6iqxqGpMRHpFpE5VO0Y4ZrOb5zKFKxKJpLpvHjt2LFU1NDAwwNatWxkYGCjqBnxgWHVgKfD7/dxyyy189rOfLbiVQ5csWcL9998/aJuIcM455+QthmwlFhG5F3gXcEhVz8iw/4O8OVq+G7hBVTc6+3YBXUAMiKrqklFeqmpoUgFQ1edEpMpNrOOpCusHXhaRJ4CetBf7xDiewxSozs5O/H4/oVCIgwcPDmtviEajBTN/1PFK9ojauXMnU6dO5bLLLqOqytX3rOBt2LCB73//+8Tjca6++mr+/M//3OuQXKmsrKS/v5/y8nJ8Ph/xeJyBgYG8La2c5TaWHwDfAn40wv43gItV9aiIXArcQ2INq6TlLmemXyMiv3VeJznp5Czgw8CjbgIdT2L5rXMzJSYSiaSqE0SEmTNnpqY4SRKRop47TFW566672Lt3L/F4nP3797Nt2zZuueWWop/heO3atXz84x9PDYB9/vnn+eIXv8iKFWMuy+G5zs5OwuEwsVgslVhisVhee/Nlq8Siqs+IyJxR9j+b9vA5EqvqHs/rfMJJTJeTaLwXEiv2fttZqmRMrq8EqvpDEakAmlX1teMJ2BSm5Jcy+QU5/fTTqauro6OjA1UlEAiwdOnSor7AdnR0sGfPnlQyVVW6u7vZvHkzZ599oit0T2y33357ajoUSJROv/GNbxREYjnrrLMABnUV9/l8nHfeeSOdknUetbF8FFiT9liBx0VEgbtV9Z7RTlbVNUPOHxfXZTQReTewAacoJCJnicjDx/vCpnAMnbCvvLw8NRLb7/dTV1fHlVde6VF0+dHW1sbAwACbNm3ij3/8Iy+++CLd3d3s2rXL69Byrqenh8bGRqZMmZK6FcrA0BkzZvCBD3wg9aMnGAxy2WWXsXDhwrzFMI5xLI0isi7tdt1xvt5yEoklfXbit6rqOcClwN+JyIhz8oiIX0Q+JiL/JiJ/PmTfP7qJYTx1F58nMcPlHwBUdYMk1ps3RS4YDNLf309FRQU+n48DBw7wk5/8JFWS6e7u5utf/zpf+MIXvA41Z6LRKOvXr6e3txdVZWBggBdffLFg5sw6EbNnz+aNN95IdTcOhUJ5GQOSLV/60pe4+OKL2bZtG/Pnz2fVqlV5e+1xDn5sG6NR3c3rLQa+C1yqqu3J7ara4vw9JCIPkbiWPzPC09wNVAJrgW+KyNOqmhy0diUw5hd9PIklqqodQ/6RbLR9iaipqUnN6rtlyxaAVPVYPB7n8OHD9PT0FG1jdjQapa+vj6amJqqrq+nv76e1tZW2tqyt0j1hhUIhIpFIqhE6/X4hEBFWrlzJypUrPXn9fP1biUgz8CDwIVXdlra9CvCpapdz/y+Bfx3lqZaq6mLn3G8B3xGRB4GrcTlecTyJZbOIfADwi8gC4BPAs2OcY4pE+kJP5eXlBAKBQV+YcDhcUN1Qx6uyspJ58+ZRXV2Nz+ejsrKSmpqakuh6/Oqrrw775b1///5RzjDpspVYROQ+YBmJKrN9wD/jzESsqncB/0Ri5cfvOJ9VslvxVOAhZ1sA+Kmqjta7K/VFVtUocJ2I/BPwO6B6xLPSjCexfBy4FRgA7gMeA/5tHOebApWs8kqaMmVKxnaXfC6elG/V1dXU1NSkLq4+n49AIFASgySj0eigz39oj0AzsmzOA6aqo04prap/A/xNhu07cTGoMc06EVmZnnxU9V9FpIXEiPwxjadXWC+JxHLrOAI0RSAWixGJRCgrK0st65pc+CtJRAiHw5SXF+ck18mLa/oFNRgMFnUyTQoEAoPWvVdVSyzjUEjVhgCq+tcjbP8uifYbAERkhao+kenY8fQKmyIi/yEij4jI75K3cUdtCk4gEKC7u5u9e/dy8OBBgsHgsMGQdXV1RT3qfsqUKdTV1Q2aGiQUCuW1d5FXTjnlFAYGBojH46klAwqp8d5rhTa78Th8eaQd46kK+wnwMxJTClwPXAMcPrG4Jq6urq7U2uY1NTWDqkFKjc/nY8eOHfT396d+qZ555pls3LgRVaWmpobrr7++qP99+vr6mDt3Li0tLXR3d1NRUcGsWbNSPaWKWXNzM9u3b0/1iKuoqGD27Nleh+VaT08PX/rSl9i0aROnnXYat956K7W1tXl7/SL+Xoz4xsaTWBpU9Xsi8klVfRp4WkSePvHYJp6enp5Bc2F1dHQgItTU1HgcmTfC4XBqaeKk6dOnc/755zNjxgwqKiqK+csDvDlt/rRp01JtDKpa1INCk1avXs3atWtTnTOCwSDXXnutt0G5FI/Hed/73peay27Tpk2sW7eOxx57LC8zRRTytPkujFgfOp5/2eTQ1VYReSfQwnFOGTDR9fT0DKpDVlV6enpKNrGMNAeYquZ1wSQvpbcxwJvVG8lpTorZ9OnT+eY3v8kzzzxDPB7nrW99K9OnT/c6LFdef/11Xn31VQYGBoDEj6Tdu3ezdetWFi9enJcYijixjGg8ieULIlIHfAb4JlALFOU86Zl+fRf7L/LRJBvt04lIwVxcsiHT55/eBbvYTZkyhb/6q7/yOoxxG+lzy+frF/H/kV0j7XCdSlX1v1S1Q1U3q+pyVT1XVYtySpe6urphv04nTZrkYUTeq6mpIRQK4ff7CQQCVFVVldwvsXA4PGiusFgsRm9vr8dRmdHMmzePU089NVWNFwqFmDNnTl7XvC/UxnsReZ+I1Dj3/1FEHhSR1HoDqjriPE6urgwi8g4RuVNEHhaRXzv333HioU9MoVCIadOmpRrtp06dWtQ9nsYSi8VS7Qnl5eWUlZXh8/kKZr6obDh69Ch9fX0MDAwQjUZT7U7t7e1jn2w84/P5WL16deq7XFNTw1VXXZXXmbgLNbEA/58zWv9C4B3AD8nWOBYR+QawkMTc/PuczTOBT4rIKlXGkt2KAAAgAElEQVT95PHFPLEFg8G8rdkw0fn9/oyD4kop2TY0NODz+RgYGEjV1/t8PqZOnepxZGY07e3tPPTQQyxevBi/3088HueJJ57gXe96FzNmzMhLDAVcsk82IL4TuFNVfy0in3dzopu0vUpVh3XWF5GfAduAokws5k0iwsknn8zOnTtTX876+nqqq13N7lAUVJXTTz+dLVu2pBLsggULqKur8zgyM5qXX3550KwQPp+PYDDI+vXr85JYCrxX2H4RuRu4BPiyiJThspbLTWLpF5Glqrp2yPbzSKwqaUpAsrt1ssE6GAyWVON1KBSitraWpUuXEg6HCQaDBAIBKioqvA7NjCJTBxMRYdasWXmLoYC/I1cBK4GvquoxEWkCPuvmRDeJ5VrgTqcRJ1kVNgvodPaZIqeqHDp0aFDDdU9PD319fSXT3djv93PGGWewefNmKisrUVXmzZtXtLM5F4sFCxYwffp0Wlpa8Pv9xGIxpkyZkreuxlC4icWZxutBETnJmTkZ4FU3546ZWFR1PXC+iEwjbZlKVT1wvAGbwpKcyiOdqhKNRj2KyBuTJ0+mtraWtra2VKcOM7GJCJ/5zGe47LLLOHbsGLW1tXzpS1/K51T2BVsVJiKXAbcD04FDQDOJxHL6WOeOp7vxAVV90Xni6SJyQv1vRaReRJ4Qke3O34wt5SKyUkReE5EdInJz2vb/EJFXRWSTiDx0ovGYkSUHAqYnl5EGTRYrVeXpp59m69at7N69m+3bt/Poo4+WxABJSPQM3LRpEy+99NKgZX4nukgkwiWXXEJXVxfBYJCenh5WrVqV16l4CrhX2L8BFwDbVHUuibaWP7k5cczEIiLfSbt/IbCVRBZ7WUROZCm2m4GnVHUB8JTzeOhr+4Fvk1hO8zTgahFJdkB/AjjDWZBmG/C5E4jFjCIajXL48GGi0SiqSjwe5+jRo3R1dXkdWt709/ezfft2tmzZwhtvvMErr7zCnj17OHy4aKfLS+np6WHZsmUsX76cFStWsHTpUo4ePep1WK48/fTTg5Z98Pl8xONx/uu//itvMRRwYok4q1D6RMSnqr8HznJzopsSywVp9/8NuEJVlwMXM/oqZGO5nES/aJy/V2Q4ZimwQ1V3qmoYuN85D1V93FmEBuA5inR6mYnA5/MRi8U4dOgQLS0ttLa2prrclopoNMru3btTiVVVaWlpKZgL7In4whe+wMsvv0xPTw/d3d28/vrr3HTTTV6H5cpIn0++xh+JCH6/39VtAjomItUkljD+iYj8J29O7TWq8Vb+1TptLsnFY07kX2OqqrY6z9UKnJThmBnA3rTH+5xtQ/0vYM0JxGJG4ff7h82TFggESmo2gvRR90kiUhIJdtOmTYOqjsLhMJs2bfIwIvfOO+88YrFYquo2Ho8Tj8e56KKL8hZDAZdYNgK9JKbuehR4HZeN924SyyKnHeNlYGGyLUREfDjLYo5ERJ4Ukc0Zbpe7CY7M0zIP+naLyK1AlMS0/iPFcZ2IrBORdaVQdZELvb299PX10dnZSU9PD5FIpKQa7zMNEI3H4yXxb3D22WcPWsCtrKyMs85yVSPiuebmZi699FJ8Ph/RaBQR4W1vexsLFizIWwwFnFiWq2pcVaOq+kNVvYPEMJMxuelufOqQxz3O33oSayyPSFUvGWmfiBwUkSZVbXX6Rx/KcNg+El2bk2aSmFU5+RzXkFgf5u06ysxyqnoPcA/AkiVLbOm7cRoYGKCjoyP16zwSiXDs2DE2btxIJBJhxowZXHHFFUXd9Ti5uFn6BUBKZA65W2+9lWeffZYNGzYgIsyfP5+vfOUrXoflis/n49///d+pra3ljTfeYNasWdxyyy15n9KlkIjIDcD/BuaLSHrRtAaXjfduuhvvHmF7G/CgmxcZwcMkFgu7zfn76wzHvAAsEJG5wH5gNfABSPQWA/4BuNjpb21yKL3KJxKJpNobAHbt2sV9993HRz/6Ua/Cy7mamhoWLVrEoUOHBq0iOWfOHG8Dy4OKigqefPJJtm/fTiwW45RTTpmobQIZ3X333Tz77LOEw2FaW1v55je/ya235m+F9UJLLMBPSTQt/DuDO1V1qeoRN0/gplfYyrT7k0Tke07V2E9F5EQ68t8GrBCR7cAK5zEiMl1EHgFwGudvBB4DXgEeUNUtzvnfIpFBnxCRDSJy1wnEYkYx9Nfd0Mkn4/E4Bw8eJBwO5zOsvEpOwllZWUl5eTnl5eXU1taWRFUYJH75n3LKKZx22mkFlVQOHTrEr371K/r7+4nH4/T39/PYY4+xZ8+evLx+ITbeO7PY71LVq1V1d9rNVVIBd20sX0q7/1WgFXg3idLE3eML+U2q2q6qb1fVBc7fI872FlVdlXbcI6q6UFXnq+oX07afrKqzVPUs53b98cZiRhcIBGhqakr98hppwFc+qxfyrbu7e1j3alXl4MGDHkVk3GhpaRk27iYSieQtsUD22lhE5F4ROSQim0fYLyJyhzPmb5OkTXE/0njAXBlvr7AlqvqPTvb6OjAnBzGZCej888+nrq4Ov99PdXX1sC9CZWVlwY4wdiPZxgJvzkRQKksTF7JAIDCo40VyfrsCnTb/ByTm7hrJpcAC53YdzhT3Y4wHzAk3/7onicinSfTQqhURSWsoL94riRnkv//7v+no6EBV6e7uJhgMpgZMJnvcDAwMFO1U+uXl5cyePZvnn38+tUzxySefzLRp07wOzYxi5syZhMPhVCJJTkU0b968vLy+ZHFKF1V9RkTmjHLI5cCPnOvzc07TRROJAsAOZ4gIIpIcD7g1K4Fl4OYd/18SbRnVJAYyNjrBTQM25CowM3EMDAzQ3t6e+pIkv6TJ1SR9Pl9qxuNiFYvF2LhxY2oKF1Vl165dJTX7QCHq7u7G7/cTiUQIh8NEIhH8fj+dnZ15iyGP3Y1HGvfndjxg1rjpFfYvI2w/AHw46xFNEPF4nJ6eRM/qUlyGN93AwEAqeUCi2quiooL+/v7Ul+Ltb397Uf8bdXZ2Duuc4Pf7aW1ttTVZJrD+/v5U6TopFArlda6wcWgUkXVpj+9xhkq4NdK4vzHHA2abmxUkPz3aflX9WvbCmRii0SivvfZa6kISCARYtGhRUf8iH83QHisiwtSpU6mqqmLq1KlMnz6dpqYmj6LLj8rKymETTobDYWpraz2KyLgxe/ZsGhoaaG1tJRaLpdoI8zlAchw/uNpUdckJvNRI4/5CI2zPGTfvuMa5LQFu4M2i1fUkGoKKTktLCx0dHRw4cIADBw7Q0dHB3r17xz6xSAWDwWEXVRGhqqqKc889t+iTCiTeb21t7aCqi4qKipL9sVEogsEg3/3ud/mzP/szqqurOfXUU7n33nvz1hbothosS1VhDwMfdnqHXQB0ONNlpcYDikiIxHjAh7PxgiNxXRUmIo8D56hql/P488DPcxmcV9ra2gYtbDUwMEAwGMxbg99E09vby+bNmznjjDNSjfX79+8viVHnSf39/dTW1hIMBlP19DU1NfT09DBlyhSvwzOjeOKJJ3jppZcIBAK8/PLLrFmzho985CN5e/1sDZAUkfuAZSSqzPYB/4wzrZaq3gU8AqwCdpCY4+sjzr6oiCTHA/qBe9PGA+bEePrcNQPplcxhirS78bFjxwbNC6WqdHR0eBiRt2pqamhra+OZZ56hpqaG/v5+uru7Wb58udeh5U1VVVWqrj5ZSonH4zQ2NnocmRnNkSNH+MpXvjKofeyOO+5g5cqVeStpZyuxqOrVY+xX4O9G2PcIicSTF+Npbf0xsFZEPi8i/wysBX6Um7C8FQqFhm0r5sF/Y+nr6yMSidDf38/hw4fp6urC5/PR0pLTatoJxe/3c/nll1NVVUVZWRnl5eWsXLmS6upqr0Mzozh48OCwRelisRgHDuRvAdw8VoVNGK6vlqr6RRFZA7yTRI+Ca1X1pZxF5qH6+nra2tpSpRYRoaGhweOovBMOh4fN7uv3+3nppZfYuXMnixYtYtmyZUXdKwwS04MkZ/kVEfbv38+cOXOK7qJQTAKBAJFIZNBnlO8VMEvx/4frK4GIfILEOJYAiV4GPxSRj+cqMC9VVlYyffp0KioqqKiooKmpqaR/mQaDQaZNmzasd1hLSwtbtmzhN7/5DT/96U89ii4/wuEwGzZsIBaLEYvFiEaj7NmzhyNHXE+fZDxw+PDhYT94fD4fhw5lmkw9+/LceD9hjKd+52+AC1S1B0BEvgz8D/DNXATmpdraWnp7e6mqqgLe7BFUqkSEpUuXsnXrVlpaWgiFQvT19aV6ioXDYZ5//nne//73F20vqWSpLZ3P55uo4yGMIxgMZrxw27T5uTWeugsB0vucxsg88KbgTZ48mcbGxtSgwOTjUlVWVkYgEODUU09lxYoVnHzyyRmPG2VJnIJXWVk5rO1NVamvr/coIuNG+iDe9Fs+V/60Esvovg88LyIPOY+vAL6X/ZC8JyI0NjZSUVEBJC4qxfbBj0fyV19yBb5Jkybh9/tTkzEGg0EWLVqUsdNDsfD5fKxYsYLHH3+c/v5+/H4/b3vb21L/R8zE5Pf7U13Ek4LBYF6nqS/Fa8d4Gu+/JiJ/AC4kUVL5SLE23kciEdauXUtbWxuQKMFccMEFRX3hHE00GqWjo4NQKITf76e8vJxzzz2XAwcOEA6HWbhwIZdf7na16cK1Zs0aHn30USBRWtm/fz+f/exnPY7KjGbSpEmIyLDvbj7HYFliGYOqrgfW5yiWCWPr1q309vYye/ZsRITDhw+zceNGzjvP1XLPRaevr4/y8nJCoVCqd1hDQwMLFy5k6dKlXoeXF11dXaxZs2bQts2bN7Nhw4aCWf+9VPX396d680FiwHO+2liyObtxISndwRmj6O7uZuHChani8qRJk0p6SpeysrJUUoE3vywTadW7XDt48OCwLtciwt69ey2xTGCBQIB4PE5vb++gz68UL/b5ZP+6GTQ2Ng66aPr9fk466SQPI/LW0HEAScXaAyyTmTNnDtumqixatMiDaIxbgUCAsrKyVHWYiKRK3/lijfcGSDTWD5VelC415eXl+Hy+QSOYRaSkekSVl5ezePFiNm7cmNo2bdo05s+f72FUZizNzc1UV1czZ86c1OqRb7zxxog9G3Oh2JKGG1ZiyaC8vHzYXGGl2nAPiRJbcs2R5JK8ZWVlJbV6Yk9PD62trdTX11NXV8fkyZMREXbt2uV1aGYUmzZtYs6cOalF6fx+P3PnzuXZZ5/NWwylWGKxxJJBfX39oA862f24VMXjcbq6upg2bRrNzc2paqF8jV6eCCKRSGpcU3LVTJ/Pl/fpQcz4bN26ddj4KlVl69acrco7SKmOvLfEkoHf72fKlCmUl5dTXl5OY2NjSU9CmZwaPjmeJfnvk+yOXQpqa2sHtb0l/x2am5s9jsyMZunSpcMu2iLCBRdckLcYkj9CxroVk+J6N1kSiUQ4duxYal6ojo6OYcvSlpJAIEAgEBj2BS2l6kGfz8cNN9zAokWLqK2tZfbs2Xz84x+3AZIT3MKFC5k6dWpqfrdYLEZdXR3nnntu3mIoxRJL6f4MH0Vyrft03d3dJdVYnW6k//Sl1N0YoKKigoULF1JWVsaUKVNsrfsCcfvtt/OHP/yBDRs2cMYZZ3DJJZfk9fWLLWm4YYklg2Ke8+p4xOPx1HrhSao6bLniYvfzn/+c1157jUgkwvbt29m2bRs33HBDSVeTFoply5axbNkyr8MoGfaNyKCiomJY1VcpdzcOhUJ0dHQMmuG5t7eXgYEB+vr6aGpqKvoeYj09PbzyyiupZBqLxTh69Ch79uwp2SWrzdiKsZrLDUssGWT6JT50FbpSEo/H6enpIRqNEggEiMVivPLKK/T09KS+NG9/+9tZvHixx5HmTiwWy9gIXMr/L4w7xdYw70bpvWMXkl1L02+l3q3U7/czadIkysrKCIfDdHd3pxpEo9EoTz75ZFFfZGtqamhqahrUKywUCjFr1iyPIzMTnTXeGyBxER2aSErxV0dSPB5nxowZ+Hw+Kioq6OzsHPZFUFUikQhlZWUeRZlbIsK1117Lb3/7W/bu3UtDQwPvfve7i/b9muwptqThhiWWDKqqqgiHw8Tj8dR/ilJemnhoUp08efKgxyJCXV1d0V9ky8rKuPLKK70OwxSQbJdGRGQl8J+AH/iuqt42ZP9ngQ86DwPAqcAUVT0iIruALhKLNEZVdUnWAhuidH+Gj8Ln81FfX09tbS01NTXU19eXXNfadMmFkpJVXVVVVZxzzjmpRNLQ0MD73vc+L0M0puiJiB/4NnApcBpwtYicln6Mqv6Hqp6lqmcBnwOeVtUjaYcsd/bnLKmAlVhGJCJF/wvcrWg0yhtvvEFjYyPxeByfz0dfXx9XXHEFM2fOLOlqQmPGksUSy1Jgh6rudJ73fuByYKT5aa4G7svWi4+HXRHMmGKxWKoLdjweJxKJUFFRQVdXlyUVY8aQxcb7GUD6wlD7nG2ZXrMSWAn8Mm2zAo+LyIsict1xvh1XrMRixpS+lkXycXKte2PM6MZRYmkUkXVpj+9R1XvSnyrDOSON5n438Kch1WBvVdUWETkJeEJEXlXVZ9wGNx6WWMyYRkogVVVVeY7EmMIzjsTSNkbbxz4gvX/7TKBlhGNXM6QaTFVbnL+HROQhElVrOUksntVjiEi9iDwhItudv5NHOG6liLwmIjtE5OYM+/+PiKiIlO689jkWjUbp7e1FVfH5fKgq8Xic/v5+r0MzZkLL8rT5LwALRGSuiIRIJI+HM7xmHXAx8Ou0bVUiUpO8D/wlsDkLbzEjLyvIbwaeUtUFwFPO40HG6gUhIrOAFcCevERcwqLRKFVVVZSXl6dKKqXYP98Yr6hqFLgReAx4BXhAVbeIyPUicn3aoe8BHlfV9Nl0pwJ/FJGNwFrgt6r6aK5i9bIq7HJgmXP/h8AfgH8YcsxYvSC+DtxEWmY22RcIBGhqahqUSGprawfNHWaMySybP8BU9RHgkSHb7hry+AfAD4Zs2wmcmbVAxuBliWWqqrYCOH9PynDMiL0gROQyYL+qbsxwnsmiTGvRxOPxklroy5jjZVO6ZJmIPAlkmvb2VrdPkWGbOl3pbiVRT+gmjuuA6wBb8e84DAwMEI/HBw0SFRFLLMa4UGxJw42cJhZVHXFFHRE5KCJNqtoqIk1ApgXUR+oFMR+YC2x0PrSZwHoRWaqqBzLEcQ9wD8CSJUtssZVxCoVCtLS0MH36dCDxRWlvby/Zhc+MGQ9LLPn1MHANcJvzN1M7SaoXBLCfRC+ID6jqFtKqzpw5cJaoqv2EzoGKigpEhB07dqQWQfP7/Vx44YUeR2bMxFaM1VxueNnGchuwQkS2k+jZdRuAiEwXkUdg5F4QHsVb0qZNm0ZlZSWNjY2pCScrKyu9DssYMwF5VmJR1Xbg7Rm2twCr0h4P6wWR4Zw52Y7PvCkWi9HW1sakSZPw+XyUl5cTi8XYu3cvs2fP9jo8Y8a0du1adu7cyezZs7ngggvyWoooxWmPbOS9GVM4HKa6unrQlC4+n4/u7m6PIzNmbHfccQe/+MUviMViBAIB3vnOd3LTTTfl7fWtKsyYDEKhUMbtkyZNynMkxozPoUOHeOCBB+jv7ycSidDX18dvfvMb9u7dO/bJ5rhZYjFj8vv91NfXpxruVZVQKMTUqVM9jsyY0R07doxAYHDFTCAQ4OjRo3mLwcaxGDOKSCSSmitMVYvuy2CKT3Nz87BJVEWEefPm5eX1izFpuGElFjOmWCzGnj17GBgYoK+vj/7+fvr6+myApJnwysvLufPOO5kxYwY+n49p06bxne98p6SXGs8HK7GYMcXj8WEllFgsRk9PzyhnGTMxnHzyyTz00EOelbKtV5gxGcTj8dSSxMlFvowpNF5VSZViVZglFjOmYDBIOBwmEAjg9/uJx+NEo1Fqamq8Ds0YMwGVXhnNjJvP52Pp0qVEo1HC4TCRSIRZs2bR2GhrqxkzFusVZswImpubmTx5MkePHqWyspKGhoai+zIYk23FmDTcsMRiXKupqbHqL2PMmCyxGGNMDpVir7DSe8fGGGNyykosxhiTQ6XYxmIlFmOMMVllicUYY3LEbVdjt6UaEVkpIq+JyA4RuTnD/mUi0iEiG5zbP7k9N5usKswYY3IoW1VhIuIHvk1ixd19wAsi8rCqbh1y6H+r6ruO89yssBKLMcYUhqXADlXdqaph4H7g8jycO26WWIwxJoeyWBU2A0hfoWyfs22ot4jIRhFZIyKnj/PcrLCqMGOMmRgaRWRd2uN7VPWetMeZss/QGWHXA7NVtVtEVgG/Aha4PDdrLLEYY0wOjaONpU1Vl4yyfx8wK+3xTKAl/QBV7Uy7/4iIfEdEGt2cm01WFWaMMYXhBWCBiMwVkRCwGng4/QARmSZOJhORpSSu8e1uzs0mK7EYY0wOZatXmKpGReRG4DHAD9yrqltE5Hpn/13Ae4EbRCQK9AGrNbGAUsZzsxJYBpZYjDEmR7I9u7GqPgI8MmTbXWn3vwV8y+25uWJVYcYYY7LKEosxxpissqowY4zJIZuE0hhjjDlBVmIxxpgcKsUSiyUWY4zJoVJMLFYVZowxJqusxGKMMTlkJRZjjDHmBFmJxRhjciTbI+8LhWclFhGpF5EnRGS783fyCMeNuJymiHzc2bdFRL6Sn8iNMca9bC5NXCi8rAq7GXhKVRcATzmPB0lbTvNS4DTgahE5zdm3nMQKaItV9XTgq/kK3BhjzMi8TCyXAz907v8QuCLDMaMtp3kDcJuqDgCo6qEcx2uMMeNmJZb8mqqqrQDO35MyHDPacpoLgb8QkedF5GkROS+n0RpjjHElp433IvIkMC3DrlvdPkWGbcnlNAPAZOAC4DzgARGZ56w9MDSO64DrAJqbm12+tDHGnLhiK424kdPEoqqXjLRPRA6KSJOqtopIE5CpKmu05TT3AQ86iWStiMSBRuBwhjjuAe5xXvewiOw+rjfkjUagzesgPFCq7xtK971PxPc9+0ROfvHFFx9zlgZ2Y6K99+PmZXfjh4FrgNucv7/OcExqOU1gP4nlND/g7PsV8DbgDyKyEAjh4oNR1SknHnr+iMi6MdbBLkql+r6hdN97Mb5vVV3pdQxe8LKN5TZghYhsB1Y4jxGR6SLyCCSW4gSSy2m+AjyQtpzmvcA8EdlMolH/mkzVYMYYY/JL7Fo8sRXjrzg3SvV9Q+m+91J938XIpnSZ+O7xOgCPlOr7htJ976X6vouOlViMMcZklZVYjDHGZJUlljwTkXtF5JDT6SC57fMisl9ENji3VWn7PufMk/aaiLwjbfu5IvKys+8OmeCd5UVkloj8XkReceZ2+6SzfcQ540rgvRf15y4i5SKyVkQ2Ou/7X5ztRf+ZlzxVtVseb8BFwDnA5rRtnwf+T4ZjTwM2AmXAXOB1wO/sWwu8hcQg0jXApV6/tzHedxNwjnO/BtjmvL+vADc7228GvlxC772oP3cnxmrnfhB4nsSA5qL/zEv9ZiWWPFPVZ4AjLg+/HLhfVQdU9Q1gB7DUGVBaq6r/o4lv3Y/IPNfahKGqraq63rnfRaL7+AxGnjOuFN77SIrivWtCt/Mw6NyUEvjMS50llonjRhHZ5FSVJasGRporbYZzf+j2giAic4CzSfyCHWnOuFJ471Dkn7uI+EVkA4mZNZ5Q1ZL7zEuRJZaJ4U5gPnAW0Arc7mwfaa600eZQm9BEpBr4JfD3qto52qEZthXbey/6z11VY6p6FonpmJaKyBmjHF4077vUWWKZAFT1oPMFjAP/l8RyATDyXGn7nPtDt09oIhIkcWH9iao+6Gw+6FR1MGTOuKJ/76XyuQOo6jHgD8BKSuQzL2WWWCaA5JfM8R4g2WPsYWC1iJQ586UtANY61QddInKB0zvmw2Sea23CcOL8HvCKqn4tbVdyzjgYPGdc0b/3Yv/cRWSKiExy7lcAlwCvUgKfecnzuvdAqd2A+0hUe0RI/BL7KPBj4GVgE4kvV1Pa8beS6B3zGmk9YYAlJC5ErwPfwhnsOlFvwIUkqi82ARuc2yqggcQKotudv/Ul9N6L+nMHFgMvOe9vM/BPzvai/8xL/WYj740xxmSVVYUZY4zJKkssxhhjssoSizHGmKyyxGKMMSarLLEYY4zJKkssxhhjssoSiyk6IvJpEdnqzMH1lIjMHuXYs0Tkf5xp3TeJyPvT9s0Vkeed6d1/JiIhZ/syEelIm+7+n9LOWelM+b5DRG7O7Ts1ZmKycSym6IjIcuB5Ve0VkRuAZar6/hGOXUhiIt7tIjIdeBE4VVWPicgDwIOqer+I3AVsVNU7RWQZienu3zXkufwkpsRfQWLw6wvA1aq6NVfv1ZiJyEospmCIyIedUsVGEfmxiMx2SiTJkkkzgKr+XlV7ndOeY/A8U4Oo6jZV3e7cbyExb9UUZ+qQtwG/cA5Nn959JEuBHaq6U1XDwP0kpoI3pqRYYjEFQUROJzHdx9tU9UzgkySm9viRqi4GfgLckeHUj5JYGMrNaywFQiSmDWkAjqlq1Nk9dKr2tzgJbo0TG4w87bsxJSXgdQDGuPQ24Beq2gagqkdE5C3Alc7+H5NYmTBFRP6axBxTF4/15M6EkD8GrlHV+AhL3ybrjdcDs1W1WxLLCf+KxISJNr27MViJxRQOYeyLdGq/iFxCooRzmaoOjPrEIrXAb4F/VNXnnM1twCQRSf74Sk3Vrqqd6qyMqKqPAEERaWTkad+NKSmWWEyheAq4SkQaAESkHngWWO3s/yDwR2ff2cDdJJLKoQzPleL09HqIRJXaz5PbNdGr5ffAe51NqendRWRaskTjVJ/5gHYSjfULnN5kISe2h0/wfRtTcKxXmCkYInIN8FkgRmI69s8D9wKNwGHgI6q6R0SeBP6MxPIEAHtU9bIRnvOvge8DW9I2X6uqG0RkHokG+Hrn9f5aVQdE5EbgBiAK9L8W3MYAAABqSURBVAGfVtVnnedbBXwD8AP3quoXs/X+jSkUlliMMcZklVWFGWOMySrrFWZKgoj8GYleX+kGVPV8L+IxpphZVZgxxpissqowY4wxWWWJxRhjTFZZYjHGGJNVlliMMcZklSUWY4wxWfX/ANMKPZuDXdyEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.plot.scatter(x='co2_2050', y='od550aer_2050', c='tas_2050')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = df.plot.scatter(x='co2_2050', y='ch4_2050', c='tas_2050')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>tas_2050</th>\n",
       "      <th>od550aer_2050</th>\n",
       "      <th>tas_2100</th>\n",
       "      <th>od550aer_2100</th>\n",
       "      <th>co2_2050</th>\n",
       "      <th>co2_2100</th>\n",
       "      <th>so2_2050</th>\n",
       "      <th>so2_2100</th>\n",
       "      <th>ch4_2050</th>\n",
       "      <th>ch4_2100</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th>scenario</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ACCESS-CM2</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>1.000582</td>\n",
       "      <td>-0.025191</td>\n",
       "      <td>1.407198</td>\n",
       "      <td>-0.038552</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.187015</td>\n",
       "      <td>-0.012222</td>\n",
       "      <td>2.489487</td>\n",
       "      <td>-0.027589</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.103402</td>\n",
       "      <td>0.006077</td>\n",
       "      <td>3.791109</td>\n",
       "      <td>0.001188</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.478602</td>\n",
       "      <td>-0.008842</td>\n",
       "      <td>5.016310</td>\n",
       "      <td>-0.016805</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">ACCESS-ESM1-5</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.819904</td>\n",
       "      <td>-0.013947</td>\n",
       "      <td>0.967981</td>\n",
       "      <td>-0.015895</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.078853</td>\n",
       "      <td>-0.004727</td>\n",
       "      <td>2.052867</td>\n",
       "      <td>-0.009796</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.047218</td>\n",
       "      <td>0.003862</td>\n",
       "      <td>3.422980</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.412020</td>\n",
       "      <td>-0.002210</td>\n",
       "      <td>4.096812</td>\n",
       "      <td>-0.001356</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">CESM2</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.974787</td>\n",
       "      <td>-0.003323</td>\n",
       "      <td>1.134402</td>\n",
       "      <td>-0.010735</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.119462</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>2.303586</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.154434</td>\n",
       "      <td>0.007554</td>\n",
       "      <td>3.501974</td>\n",
       "      <td>0.016237</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.453782</td>\n",
       "      <td>0.002178</td>\n",
       "      <td>4.980513</td>\n",
       "      <td>0.016340</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">CESM2-WACCM</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.770853</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>1.053462</td>\n",
       "      <td>-0.006278</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.118329</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>2.252389</td>\n",
       "      <td>0.006330</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.022903</td>\n",
       "      <td>0.011563</td>\n",
       "      <td>3.526784</td>\n",
       "      <td>0.035894</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.311378</td>\n",
       "      <td>0.005453</td>\n",
       "      <td>5.010599</td>\n",
       "      <td>0.038768</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">CanESM5</th>\n",
       "      <th>ssp119</th>\n",
       "      <td>0.641977</td>\n",
       "      <td>-0.030857</td>\n",
       "      <td>0.333723</td>\n",
       "      <td>-0.036624</td>\n",
       "      <td>1247.788346</td>\n",
       "      <td>905.867767</td>\n",
       "      <td>-0.069425</td>\n",
       "      <td>-0.080790</td>\n",
       "      <td>-0.201275</td>\n",
       "      <td>-0.257405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.989167</td>\n",
       "      <td>-0.029769</td>\n",
       "      <td>0.953098</td>\n",
       "      <td>-0.041451</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.318495</td>\n",
       "      <td>-0.016138</td>\n",
       "      <td>2.454336</td>\n",
       "      <td>-0.033573</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.724648</td>\n",
       "      <td>-0.004288</td>\n",
       "      <td>4.744037</td>\n",
       "      <td>-0.021374</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp434</th>\n",
       "      <td>1.304015</td>\n",
       "      <td>-0.023741</td>\n",
       "      <td>1.863815</td>\n",
       "      <td>-0.040477</td>\n",
       "      <td>1825.355018</td>\n",
       "      <td>1612.733274</td>\n",
       "      <td>-0.055372</td>\n",
       "      <td>-0.077382</td>\n",
       "      <td>0.002907</td>\n",
       "      <td>-0.032744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.877571</td>\n",
       "      <td>-0.025367</td>\n",
       "      <td>5.984793</td>\n",
       "      <td>-0.040852</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">HadGEM3-GC31-LL</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.768553</td>\n",
       "      <td>-0.026508</td>\n",
       "      <td>1.314000</td>\n",
       "      <td>-0.037692</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.262740</td>\n",
       "      <td>-0.014397</td>\n",
       "      <td>2.651430</td>\n",
       "      <td>-0.024341</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.606117</td>\n",
       "      <td>-0.007902</td>\n",
       "      <td>5.547793</td>\n",
       "      <td>-0.015396</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">MIROC-ES2L</th>\n",
       "      <th>ssp119</th>\n",
       "      <td>0.703733</td>\n",
       "      <td>-0.014334</td>\n",
       "      <td>0.557255</td>\n",
       "      <td>-0.020002</td>\n",
       "      <td>1247.788346</td>\n",
       "      <td>905.867767</td>\n",
       "      <td>-0.069425</td>\n",
       "      <td>-0.080790</td>\n",
       "      <td>-0.201275</td>\n",
       "      <td>-0.257405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.785037</td>\n",
       "      <td>-0.017767</td>\n",
       "      <td>0.572429</td>\n",
       "      <td>-0.028761</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>0.789146</td>\n",
       "      <td>-0.010066</td>\n",
       "      <td>1.589075</td>\n",
       "      <td>-0.019236</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.131194</td>\n",
       "      <td>0.001314</td>\n",
       "      <td>2.623115</td>\n",
       "      <td>0.006749</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.117971</td>\n",
       "      <td>-0.003265</td>\n",
       "      <td>3.748485</td>\n",
       "      <td>-0.003236</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MPI-ESM1-2-HR</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.370221</td>\n",
       "      <td>-0.009666</td>\n",
       "      <td>0.351424</td>\n",
       "      <td>-0.014088</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>0.735819</td>\n",
       "      <td>-0.001871</td>\n",
       "      <td>1.352010</td>\n",
       "      <td>-0.008150</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>0.819827</td>\n",
       "      <td>0.008950</td>\n",
       "      <td>2.616101</td>\n",
       "      <td>0.003430</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>0.946618</td>\n",
       "      <td>0.004603</td>\n",
       "      <td>3.153594</td>\n",
       "      <td>-0.004407</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MPI-ESM1-2-LR</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.518146</td>\n",
       "      <td>-0.009654</td>\n",
       "      <td>0.358184</td>\n",
       "      <td>-0.014067</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>0.877299</td>\n",
       "      <td>0.008940</td>\n",
       "      <td>2.610222</td>\n",
       "      <td>0.003437</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>1.029724</td>\n",
       "      <td>0.004613</td>\n",
       "      <td>3.278956</td>\n",
       "      <td>-0.004377</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">NorESM2-LM</th>\n",
       "      <th>ssp126</th>\n",
       "      <td>0.331449</td>\n",
       "      <td>-0.011588</td>\n",
       "      <td>0.376486</td>\n",
       "      <td>-0.017219</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>0.533042</td>\n",
       "      <td>-0.001380</td>\n",
       "      <td>1.295670</td>\n",
       "      <td>-0.005419</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>0.704050</td>\n",
       "      <td>0.007691</td>\n",
       "      <td>2.552758</td>\n",
       "      <td>0.022834</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>0.983765</td>\n",
       "      <td>0.007667</td>\n",
       "      <td>2.957163</td>\n",
       "      <td>0.009652</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">UKESM1-0-LL</th>\n",
       "      <th>ssp119</th>\n",
       "      <td>0.918189</td>\n",
       "      <td>-0.027682</td>\n",
       "      <td>0.970074</td>\n",
       "      <td>-0.035182</td>\n",
       "      <td>1247.788346</td>\n",
       "      <td>905.867767</td>\n",
       "      <td>-0.069425</td>\n",
       "      <td>-0.080790</td>\n",
       "      <td>-0.201275</td>\n",
       "      <td>-0.257405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp126</th>\n",
       "      <td>1.284645</td>\n",
       "      <td>-0.024166</td>\n",
       "      <td>1.563342</td>\n",
       "      <td>-0.036149</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>1848.864201</td>\n",
       "      <td>-0.064020</td>\n",
       "      <td>-0.082066</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.241390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp245</th>\n",
       "      <td>1.604553</td>\n",
       "      <td>-0.010934</td>\n",
       "      <td>3.040434</td>\n",
       "      <td>-0.022142</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>3932.717046</td>\n",
       "      <td>-0.035997</td>\n",
       "      <td>-0.059775</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.092845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp370</th>\n",
       "      <td>1.749074</td>\n",
       "      <td>0.004618</td>\n",
       "      <td>5.046036</td>\n",
       "      <td>0.007180</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>6912.965613</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>-0.020179</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>0.368859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp434</th>\n",
       "      <td>1.511831</td>\n",
       "      <td>-0.009184</td>\n",
       "      <td>2.371976</td>\n",
       "      <td>-0.027848</td>\n",
       "      <td>1825.355018</td>\n",
       "      <td>1612.733274</td>\n",
       "      <td>-0.055372</td>\n",
       "      <td>-0.077382</td>\n",
       "      <td>0.002907</td>\n",
       "      <td>-0.032744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssp585</th>\n",
       "      <td>2.028151</td>\n",
       "      <td>-0.007564</td>\n",
       "      <td>6.038747</td>\n",
       "      <td>-0.006429</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>10283.292188</td>\n",
       "      <td>-0.028185</td>\n",
       "      <td>-0.059536</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.104140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          tas_2050  od550aer_2050  tas_2100  od550aer_2100  \\\n",
       "model           scenario                                                     \n",
       "ACCESS-CM2      ssp126    1.000582      -0.025191  1.407198      -0.038552   \n",
       "                ssp245    1.187015      -0.012222  2.489487      -0.027589   \n",
       "                ssp370    1.103402       0.006077  3.791109       0.001188   \n",
       "                ssp585    1.478602      -0.008842  5.016310      -0.016805   \n",
       "ACCESS-ESM1-5   ssp126    0.819904      -0.013947  0.967981      -0.015895   \n",
       "                ssp245    1.078853      -0.004727  2.052867      -0.009796   \n",
       "                ssp370    1.047218       0.003862  3.422980       0.000495   \n",
       "                ssp585    1.412020      -0.002210  4.096812      -0.001356   \n",
       "CESM2           ssp126    0.974787      -0.003323  1.134402      -0.010735   \n",
       "                ssp245    1.119462       0.000371  2.303586       0.000121   \n",
       "                ssp370    1.154434       0.007554  3.501974       0.016237   \n",
       "                ssp585    1.453782       0.002178  4.980513       0.016340   \n",
       "CESM2-WACCM     ssp126    0.770853       0.000561  1.053462      -0.006278   \n",
       "                ssp245    1.118329       0.000520  2.252389       0.006330   \n",
       "                ssp370    1.022903       0.011563  3.526784       0.035894   \n",
       "                ssp585    1.311378       0.005453  5.010599       0.038768   \n",
       "CanESM5         ssp119    0.641977      -0.030857  0.333723      -0.036624   \n",
       "                ssp126    0.989167      -0.029769  0.953098      -0.041451   \n",
       "                ssp245    1.318495      -0.016138  2.454336      -0.033573   \n",
       "                ssp370    1.724648      -0.004288  4.744037      -0.021374   \n",
       "                ssp434    1.304015      -0.023741  1.863815      -0.040477   \n",
       "                ssp585    1.877571      -0.025367  5.984793      -0.040852   \n",
       "HadGEM3-GC31-LL ssp126    0.768553      -0.026508  1.314000      -0.037692   \n",
       "                ssp245    1.262740      -0.014397  2.651430      -0.024341   \n",
       "                ssp585    1.606117      -0.007902  5.547793      -0.015396   \n",
       "MIROC-ES2L      ssp119    0.703733      -0.014334  0.557255      -0.020002   \n",
       "                ssp126    0.785037      -0.017767  0.572429      -0.028761   \n",
       "                ssp245    0.789146      -0.010066  1.589075      -0.019236   \n",
       "                ssp370    1.131194       0.001314  2.623115       0.006749   \n",
       "                ssp585    1.117971      -0.003265  3.748485      -0.003236   \n",
       "MPI-ESM1-2-HR   ssp126    0.370221      -0.009666  0.351424      -0.014088   \n",
       "                ssp245    0.735819      -0.001871  1.352010      -0.008150   \n",
       "                ssp370    0.819827       0.008950  2.616101       0.003430   \n",
       "                ssp585    0.946618       0.004603  3.153594      -0.004407   \n",
       "MPI-ESM1-2-LR   ssp126    0.518146      -0.009654  0.358184      -0.014067   \n",
       "                ssp370    0.877299       0.008940  2.610222       0.003437   \n",
       "                ssp585    1.029724       0.004613  3.278956      -0.004377   \n",
       "NorESM2-LM      ssp126    0.331449      -0.011588  0.376486      -0.017219   \n",
       "                ssp245    0.533042      -0.001380  1.295670      -0.005419   \n",
       "                ssp370    0.704050       0.007691  2.552758       0.022834   \n",
       "                ssp585    0.983765       0.007667  2.957163       0.009652   \n",
       "UKESM1-0-LL     ssp119    0.918189      -0.027682  0.970074      -0.035182   \n",
       "                ssp126    1.284645      -0.024166  1.563342      -0.036149   \n",
       "                ssp245    1.604553      -0.010934  3.040434      -0.022142   \n",
       "                ssp370    1.749074       0.004618  5.046036       0.007180   \n",
       "                ssp434    1.511831      -0.009184  2.371976      -0.027848   \n",
       "                ssp585    2.028151      -0.007564  6.038747      -0.006429   \n",
       "\n",
       "                             co2_2050      co2_2100  so2_2050  so2_2100  \\\n",
       "model           scenario                                                  \n",
       "ACCESS-CM2      ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "ACCESS-ESM1-5   ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "CESM2           ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "CESM2-WACCM     ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "CanESM5         ssp119    1247.788346    905.867767 -0.069425 -0.080790   \n",
       "                ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp434    1825.355018   1612.733274 -0.055372 -0.077382   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "HadGEM3-GC31-LL ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "MIROC-ES2L      ssp119    1247.788346    905.867767 -0.069425 -0.080790   \n",
       "                ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "MPI-ESM1-2-HR   ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "MPI-ESM1-2-LR   ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "NorESM2-LM      ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "UKESM1-0-LL     ssp119    1247.788346    905.867767 -0.069425 -0.080790   \n",
       "                ssp126    1795.710867   1848.864201 -0.064020 -0.082066   \n",
       "                ssp245    2314.385253   3932.717046 -0.035997 -0.059775   \n",
       "                ssp370    2813.146604   6912.965613  0.004142 -0.020179   \n",
       "                ssp434    1825.355018   1612.733274 -0.055372 -0.077382   \n",
       "                ssp585    3192.373467  10283.292188 -0.028185 -0.059536   \n",
       "\n",
       "                          ch4_2050  ch4_2100  \n",
       "model           scenario                      \n",
       "ACCESS-CM2      ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "ACCESS-ESM1-5   ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "CESM2           ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "CESM2-WACCM     ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "CanESM5         ssp119   -0.201275 -0.257405  \n",
       "                ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp434    0.002907 -0.032744  \n",
       "                ssp585    0.205365  0.104140  \n",
       "HadGEM3-GC31-LL ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp585    0.205365  0.104140  \n",
       "MIROC-ES2L      ssp119   -0.201275 -0.257405  \n",
       "                ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "MPI-ESM1-2-HR   ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "MPI-ESM1-2-LR   ssp126   -0.153999 -0.241390  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "NorESM2-LM      ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp585    0.205365  0.104140  \n",
       "UKESM1-0-LL     ssp119   -0.201275 -0.257405  \n",
       "                ssp126   -0.153999 -0.241390  \n",
       "                ssp245   -0.034673 -0.092845  \n",
       "                ssp370    0.154481  0.368859  \n",
       "                ssp434    0.002907 -0.032744  \n",
       "                ssp585    0.205365  0.104140  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collapse ensemble members\n",
    "df = df.groupby(['model', 'scenario']).mean()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = df.query(\"model == 'MIROC-ES2L'\").plot.scatter(x='co2_2050', y='tas_2050', c='od550aer_2050')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import normalize\n",
    "# Merge the year columns in to a long df \n",
    "df=pd.wide_to_long(df.reset_index(), [\"tas\", \"od550aer\", \"co2\", \"ch4\", \"so2\"], i=['model', 'scenario'], j=\"year\", suffix='_(\\d+)')\n",
    "# Choose only the 2050 data since the aerosol signal is pretty non-existent by 2100\n",
    "df = df[df.index.isin([\"_2050\"], level=2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tas</th>\n",
       "      <th>od550aer</th>\n",
       "      <th>co2</th>\n",
       "      <th>ch4</th>\n",
       "      <th>so2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.085538</td>\n",
       "      <td>-0.006851</td>\n",
       "      <td>2415.708964</td>\n",
       "      <td>0.024789</td>\n",
       "      <td>-0.035145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.381735</td>\n",
       "      <td>0.011775</td>\n",
       "      <td>613.880074</td>\n",
       "      <td>0.152382</td>\n",
       "      <td>0.025320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.331449</td>\n",
       "      <td>-0.030857</td>\n",
       "      <td>1247.788346</td>\n",
       "      <td>-0.201275</td>\n",
       "      <td>-0.069425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.804487</td>\n",
       "      <td>-0.014140</td>\n",
       "      <td>1795.710867</td>\n",
       "      <td>-0.153999</td>\n",
       "      <td>-0.064020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.047218</td>\n",
       "      <td>-0.004727</td>\n",
       "      <td>2314.385253</td>\n",
       "      <td>-0.034673</td>\n",
       "      <td>-0.035997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.307697</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>2813.146604</td>\n",
       "      <td>0.154481</td>\n",
       "      <td>-0.028185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.028151</td>\n",
       "      <td>0.011563</td>\n",
       "      <td>3192.373467</td>\n",
       "      <td>0.205365</td>\n",
       "      <td>0.004142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tas   od550aer          co2        ch4        so2\n",
       "count  47.000000  47.000000    47.000000  47.000000  47.000000\n",
       "mean    1.085538  -0.006851  2415.708964   0.024789  -0.035145\n",
       "std     0.381735   0.011775   613.880074   0.152382   0.025320\n",
       "min     0.331449  -0.030857  1247.788346  -0.201275  -0.069425\n",
       "25%     0.804487  -0.014140  1795.710867  -0.153999  -0.064020\n",
       "50%     1.047218  -0.004727  2314.385253  -0.034673  -0.035997\n",
       "75%     1.307697   0.003020  2813.146604   0.154481  -0.028185\n",
       "max     2.028151   0.011563  3192.373467   0.205365   0.004142"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do a 20/80 split of the data for test and training \n",
    "msk = np.random.rand(len(df)) < 0.8\n",
    "train, test = df[msk], df[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try a few different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.25\n",
      "Proportion of 'Bad' estimates : 2.13%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from esem.utils import leave_one_out, prediction_within_ci\n",
    "from scipy import stats\n",
    "\n",
    "# Try just modelling the temperature based on cumulative CO2\n",
    "res = leave_one_out(df[['co2']], df[['tas']].values, model='GaussianProcess', kernel=['Linear'])\n",
    "\n",
    "r2_values = stats.linregress(*np.squeeze(np.asarray(res, dtype=float)).T[0:2])[2]**2\n",
    "print(\"R^2: {:.2f}\".format(r2_values))\n",
    "validation_plot(*np.squeeze(np.asarray(res, dtype=float)).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.40\n",
      "Proportion of 'Bad' estimates : 8.51%\n"
     ]
    },
    {
     "data": {
      "image/png": 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lrrvuYty4cXh6ehpdligmYSiEENdhwIABREdH07lz50qPOXnyJJMmTSIxMZERI0bw+OOPy/tBByNhKIQQNrRt2zZmzpyJu7s7s2fPpkuXLta9Qckam3Z4ZL9//37Onj2Ll5cXWutaFegShkIIYQNms5mlS5eycuVKOnToQFhYGA0aNDC6rGuSk5PDgw8+yI4dO8jJyQHg1ltv5ZdffiEwMNDg6qxD5vkRQggrS09P5+2332blypXcd999zJ07t8YGIUBYWBjbtm0jKysLs9mM2WzmyJEjvPrqq0aXZjUShkJYScm7I2fijD/zlcTFxRESEsL+/fsZO3Ys48aNw93d3eiyrsvSpUtLW4Ql8vPzWb9+Pbm5uQZVZV3ymFQIUW0ypKRqP//8M7Nnz8bPz4+5c+dy0003GV2SVVQWeFprCgsL7VyNbUjLUAghrOD06dNMnz6d9u3bExkZWWuCEOCBBx6wuKhw586d8fb2NqAi65MwFEKI61BQUEB2djYpKSk89thjRERE1LqFeN9//33q169fGnwmkwl/f3+WLl1qcGXWI2EohEEGDBhQ+thR1EwxMTEcOXIEs9lM8+bNef311y22oGq6oKAgjhw5wgcffEDDhg1p3rw5sbGx3HLLLUaXZjUShkIIcQ3WrVvHqFGjMJlMeHl51brW4OX8/PwYMWIEHTp0oFmzZtSvX9/okqyq9v0JI4QQNpSXl8fHH3/M+vXr6d69Ozk5ORw4cMDossR1kpahEEJUU3JyMqNGjWL9+vU8++yzzJo1CxcXF6PLElYgLUMhhKiG6OhowsLCyMvLY+rUqfTt29fokoQVSRgKIUQVtNZ88803LF68mODgYMLDw2nWrJnRZQkrkzAUViWDskVtkpOTw+zZs9m0aRN9+/bl7bffrjXj6kR5EobCMNVZ+kYIoyQlJTFp0iSOHz/OK6+8wtNPP12rVmkQ5UkYOjhpaQlhf7t372batGkopXjvvffo3r270SUJG5MwFEKIYlpr/vGPf7B8+XJatWpFeHg4jRs3NrosYQcShkIIAWRmZjJz5ky2b9/OPffcw1tvvYWHh4fRZQk7kTB0cvIY1rFFR0fLlG12cOLECd59912SkpIYOXIkjzzyiLwfdDIy6F4IB5aRkSHrBdrYli1bGD58OBkZGXz00Uc8+uijEoROSFqGQginZDab+eyzz1i9ejU33ngjU6dOJTAw0OiyhEEkDIUQTictLY3w8HCioqJ48MEHGTlyJG5ubkaXJQxks8ekSqmlSqlkpdQflewfoJS6pJSKLv5MLrNvkFLqiFIqVin1tq1qFEI4n2PHjjF06FCio6MZN24cb775pgShsGnLcDkwH1hRxTFbtdYPlN2glHIBFgB/ARKBvUqp77XWMbYqVAjhHDZs2EBERAR16tRh3rx5dOjQweiShIOwWRhqrbcopVpcw6k9gFitdTyAUuoLYDAgYehkpOOIsJaCggIWLlzI2rVr6dy5M6GhoQQEBBhdlnAgRr8z7K2U+g1IAt7SWh8EmgCnyhyTCPSs7AJKqaHAUEAmzxVCVHD+/HmmTJnCgQMHeOKJJwgJCZFll0QFRobhr0BzrXWGUuo+4J9AW8BSn2Zd2UW01pFAJEC3bt0qPU4I4XwOHjxIaGgoGRkZTJo0ibvuusvokoSDMmycodY6TWudUfz1D4CbUiqQopZg0zKHBlPUchRCiGpLTU1l9OjRuLu7s3DhQglCUSXDwlAp1UgVj2xVSvUoruUcsBdoq5RqqZRyB4YA3xtVpxCiZsnLy+PUqVMkJibStWtXPvnkE1q1amV0WcLB2ewxqVJqNTAACFRKJQKhgBuA1nox8DgwXClVAGQDQ7TWGihQSr0O/AS4AEuL3yUKIUSVkpOTmTRpEufOnaNRo0bMnDlTZpMR1WLL3qRPXWH/fIqGXlja9wPwgy3qEkLUTvv37ycsLIz8/HxatWqFv7+/BKGoNpmbVAhR43355ZeMHTuWgIAAFi9ejL+/v9EliRrG+cJwwICij3BKAwYMkFUgahGz2UxCQgKLFy+mb9++hIeHM3fuXHbv3k1UVBTLli2j6O2LEFUzepyhEEJck8TERI4dO0ZOTg4hISHcd9993HLLLZw9e5a8vDwARo4cyd69e1m4cKHB1QpH53wtwxpAWi9CVG3nzp0MGzas9P3gkCFDWLZsGampqaVBCEUL9i5btoxTp05VcTUhJAwtkjASwjFprfn73//OhAkTCAoKol27dvj5+QHw888/k5WVVeEcd3d3oqKibFLPjz/+SFRUFJcuXWL37t2sWrXKJvcRtidhKISoETIyMpg4cSLLly9n4MCBzJs3D3d399L9LVu2xNW14psfs9lMkyZNrF7PTz/9xGOPPUZmZiYAOTk5vPrqq3z22WdWv5ewPQlDIYTDO378OMOGDWPPnj2MGjWKt99+Gw8Pj3LHvPbaa+XCEcDFxYXmzZvTvXt3q9f09ttvV2iJZmVlMXHiROm0UwNJGAohHNrmzZsZMWIE2dnZfPTRRzz88MMWxw+2b9+eNWvW0KhRI0wmE0opevfuzcaNG20y3vDo0aMWt58/f97i41phBcePQ3w8HDoEy5ZBTo7VLi1hKIRwSFprkpKSCAsLo3Xr1kRGRnLLLbdUec6gQYM4ffo03bp1o1evXmzdupXGjRvbpL7mzZtb3O7v74+3t7dN7unUNmyAjh0hMRGSk2HkSLjtNkhLs8rlJQyFEA7n4sWLxMfHk5yczODBg5kzZw716tWr1rkmkwkvL68Kj0xLWWms8fTp0yuEnre3N++++67MfGNthYXwt79BVhaUPILOzCxqKX70kVVuIWEohHAoR48eJSQkhMzMTJo1a8bo0aMtdowx2iOPPEJkZGRp6Lq6ujJz5kxGjx5tcGW10JEjReF3uZwc+PJLq9xCwlAIB7Rx40b2799PYWEhmZmZpKenG12SXfz444+8/vrrALRt25YbbrjB4Iqq9swzz9C7d2/q1KlDnz59eOONN6RVaAve3mA2W97n62uVW0gYCuFgUlJSePjhh0krfhdSUFBAdHR06fe1UUFBAXPmzOG9996jY8eOfPLJJ3h5eRldlnAULVpA+/ZguiyyfHxgxAir3MLxnj0I4eRiY2PLzaICRWPl4uPjDarIts6dO0doaCgHDx5kyJAhvPLKK7i4uBhdlnA0335b9K739Omi94bu7jBkCDz3nFUuL2EohAMpLCysEIQlauOj0gMHDjBlyhSysrIIDQ2VmZ9E5Vq2LBpWcdttkJcH69ZB69ZWu7w8JhXCgZhMJkyXPwoqVmnvyBpIa83atWsZM2YMXl5eLFy4sFwQnjhxgvj4eGJiYliwYAEZGRnGFSsch4sL1K0LDRtaNQhBwlAIh6KUIjg4uEKXfZPJRIsWLYwpyspyc3OZNWsWH3/8MT169GDx4sW0bNmydP/WrVu5+eabSUxMJCUlhfHjx9OxY0dSU1MNrFrUdhKGQlTBiEnbW7RowbBhw8q1EFu2bEnDhg3tWoctnD17lpEjR7JhwwZeeOEFpk+fjm+Z3oBaa5577jkyMzNLpzTLysrizJkzTJs2zaiyhROQMBTCwSiliIiIoE+fPri4uFCnTh2Cg4ONLuu6RUVFERISQlJSEjNmzOD555+vMAwhMTGRP//8s8K5eXl5rFmzxl6lCickHWiEcFCVvTusabTWfPHFF3z66ac0b96c8PDwSsPdy8sLcyXjyXytNJ5MCEtqx39tQgiHlJWVxZQpU4iMjKR///4sXLiwylZuYGAgvXr1qjDjjLe3N6+99pqtyxVOTMJQCGETp06dYsSIEWzdupVhw4YxefLkag2kX716Na1bt8ZkMuHi4oKnpycPP/www4cPt0PVwlnJY1IhhNXt2LGD6dOn4+bmRkREBF26dKn2uY0bN+bQoUPcdttt5Obm8t1339G2bVsbViuEhKEQwsqWLVvGihUraNeuHeHh4TRo0OCqr6GUok6dOgAShMIu5DGpsKvo6GiZZaSWKiwsJD4+nhUrVnDvvfcyb968awpCIYwgLUMhxHXLycnh+PHj5OXlMWbMGB588EGbr96Qn59Pamoq2dnZbNy4kbvvvrvW9MAV9if/zxFO7+TJk5w8eZL4+Hh++eWX0sHeonoKCgo4evQoZrOZNm3a8NBDD9k8CJOSkmjbti2HDx/m+PHjPProo3Tr1q1Wzt8q7EPCUDi1b7/9lg4dOpCQkMCpU6d48MEHefTRRysd61aVCxcukJmZSVRUFO+++y7nzp2zQcWOo7CwkKSkJHJycvD29qZ9+/b4+PjY5d4vv/wyiYmJFBYWorUmIyODmJgYJk6caJf7i9pHwlA4raysLJ577jmys7NLW4OZmZls3LiRb7/99qqu9dFHH/HHH39QUFBAZmYms2fPpnPnzhQUFNiidMNdvHiRt956i+TkZNzc3GjdurXdVqPPzc3l559/prCwsML2lStX2qUGUftIGAqntWXLFovr5mVmZvL5559X+zoZGRlMnDixXGsyNzeXlJQUEhMTrVKrI8nKymLo0KHExMTQvHlzPDw87Lq6u9a60kfZlwekENUlYSicVlUtmatZLik6Otri8bm5uZw/f/6aanNU58+f59ixY5hMJubPn0/dunXtXoOnpyd9+vSp0FnGzc2Nxx57zO71iNrhimGolDIppW63RzFC2FO/fv0s9j708fHhxRdfrPZ1GjRoQH5+vsV9tWUNwvz8fCIiIjh58iS+vr5ERkaWjv/TWnPp0qVKFyW2haVLl1KvXr3S//18fX1p1qwZ7733nt1qELXLFcNQa20GIuxQi6ghoqOjiY6ONrqM6+bu7s53332Hr68vJpMJpRReXl68+OKLDBo0qNrXadeuHTfffHOF7d7e3jRt2tSaJRsiJSWFUaNGsW7dOho2bEjr1q3x9/cnLy+PmJgY0tLSOHDgALt27eLw4cN2eU/aunVrjh8/Ttu2bWnatCmffvopMTExBAYG2vzeonaq7mPSDUqpx5Q9XwwIYQf9+vUjKSmJdu3a0bp1a/bv38+8efPKvQM7e/Yse/fuJS0tjUOHDnH8+PEK1/nXv/6Fn58fAC4uLvj6+vLhhx+WzqJSU2VmZjJ06FASEhIICwujcePGpfsmTJhQ2mO2pFdnSkoK06dPt0ttPj4+NGrUiFatWjFkyJBa0woXxqhuGL4JfA3kKaXSlFLpSqm0qk5QSi1VSiUrpf6oZP8zSqnfiz87lFKdyuxLUEodUEpFK6Wiqv3TCHEN/Pz8aNiwIU2aNKF9+/bl9h0/fpxjx46RlZWF1prk5GS6du1Kbm5uueMaNmzIbbfdhp+fH506dSI5OZmQkBB7/hhWpbUmNTWV2NhYfH19WbRoEf369Su3f/HixRWGoJjNZubNm2fvcoW4btXqC6219ruGay8H5gMrKtl/HOivtb6glLoXiAR6ltl/p9Y69RruK4RVXLx4kVOnTlXouZiRkcGpU6do06ZNhXNMJhN+fn7VWp3BUeXm5jJ79mwSExOpU6cOixcvrjB+UGtNVlaWxfPT0qr8O1kIh1TtgUFKqYeAkj8NN2ut11V1vNZ6i1KqRRX7d5T5dhdQ85fyFrXKwYMHMZlMFbrr5+fnc+nSJYOqsq0zZ84wadIk4uPjady4MQ0bNrQ4kN5kMtGlSxd+/fXXCvt69eplj1KFsKpqPSZVSs0CRgExxZ9Rxdus5WXg32W+1xS9p9ynlBp6hdqGKqWilFJRKSkpVixJOLumTZtanImmpKNNbbNnzx5CQkL4888/mTlzJg0bNqzy+AULFlTojWsymZg7d64tyxTCJqrbMrwP6FzcsxSl1N+B/cDb11uAUupOisLwjjKb+2itk5RSDYCNSqnDWustls7XWkdS9IiVbt26yaSSwmqaNWtGQEAAFy9eLPeo1MvLq1b0Ei2htWbVqlUsWbKEli1bEh4eTlBQ0BXP69WrF127diU6OhoPDw/8/Pxo2rTpVa1dKISjuJpB9wFlvrZKFzml1K3AZ8BgrXXpRI5a66TifyYDa4Ee1rifEFfr5ptvJjAwsLR3qbu7O6tXry7tOVrTZWVlERoaymeffcZdd93FggULqhWEJby9vfH29qZr1660a9euVrbRNqZEAAAgAElEQVSYhXOobhjOBPYrpZYXtwr3FW+7ZkqpZsC3wN+01kfLbPdRSvmVfA0MBCz2SBXC1lxcXLjpppvo06cPfn5+9O7dm4ceesjosqzi5MmTDB8+nO3btzNixAgmTpyIp6en0WUJC7Zs2cKBAweIiopi9OjRJCUlGV1SrVPd3qSrlVKbge6AAv5Pa322qnOUUquBAUCgUioRCAXciq+3GJgM1AMWFv/VXaC17gY0BNYWb3MFVmmtf7zqn0wIK3JxcalVa+Vt3bqVWbNm4e7uTkREBJ07dza6JFGJFStWMHz48NLeuwsXLuTzzz8nOjqa4GDpd2gt1QpDpdQmrfXdwPcWtlmktX6qqmtqrV8BXrGwPR7oVPEMIcT1MpvNLF26lJUrV9KhQwemTp1K/fr1jS5LVCI/P59Ro0aVG8ZS0pt5+vTpLFq0yMDqapcqw1Ap5Ql4U9S6q0tRqxDAH6j+iwUhhOHS09MJDw9n79693H///bzxxhsya4uDi42NtTi9XUFBAT/99JMBFdVeV2oZhgCjKQq+ffwvDNOABTasSwhhRbGxsUyePJmUlBTGjh3LAw88YHRJohrq1atX6STwZafGE9evypcgWuu5WuuWwFta61Za65bFn05a6/l2qlEIcR1+/vlnXn/9dfLz85k7d64EYQ3SoEED7r777goteG9vb8aPH29QVbVTdTvQzFNKdQRuAjzLbK9sqjUhhMG01syfP581a9Zw6623MmXKFEPWHxTXZ9WqVTzxxBP8/PPPmEwm3N3dCQ0NZfDgwUaXVqtUtwNNKEU9Q28CfgDuBbZR+byjQggDFRQUkJCQwJo1a3j88ccJCQmpcjFj4bjq1KnDhg0b6N27N3l5efz3v//F19fX6LJqner+1/E4RT0892utX1RKNaRosLwQwsHExMRw5MgRCgsLmThxIvfcc4/RJQkr8PDwwMPDQ4LQRqo7cCq7eCq2AqWUP5AMtLJdWUKIslJTU0lKSrriosrr1q1j1KhRmEwm2rZta7cgzMnJ4dSpU5w8eZKjR49e+QQhHEx1wzBKKRUAfEpRr9JfgT02q0oIUSotLY3Dhw8TGxtLnz59eOyxxyqspJGXl8fs2bOJiIigS5cudp0aLTc3lz179nD8+HESEhLo3LkzM2bMsMu9hbCWaoWh1nqE1vpi8cwxfwGe11q/aNvShBAlK8iX/DMrK4sff/yRTz/9tPSY5ORkRo0axfr163n22WeZNWsWLi4udqkvJyeHnJwctNaln+zsbKZNm8ahQ4fsUoMQ1lBlGCqlbrv8A9wAuBZ/LYSwkZycHIvbs7KyiIyMBIoWGg4JCeHkyZNMnTqVl19+2SrTxmmtycnJIS8vr8rjzp07Z3F7fn4+33zzzXXXIYS9XKkDTUQV+zRwlxVrEUKUYWktxRK5ubmkpKSQlJTEzTffTHh4OM2aNbPKfXfu3MkzzzzDiRMn0FrTs2dPvvrqK5o3b35V1ylZ6UOImqDKMNRa32mvQoRwZGfOnCEtLY3//ve/tG/fHnd3d+rVq1flORcuXODPP/9Ea83Zs2dp1KjRVd3T29vb4nZPT0+CgoLYu3cvAQEBLFq0qNJjr9aZM2cYOHAgGRkZpduioqLo168f8fHxFY4PDAwkNja2wnY3Nzcef/xxq9QkhD1Ud5zhc5a2y6B74QxOnz5NfHx86QK/R48exWQycfPNN1d6zrfffsuzzz5Lbm4uAC1btiQiIoIRI0Zc1b1NJhNmsxmlFFprfHx88PT0JC8vj8aNG9OwYUOrBSHAsmXLKsyFaTabuXDhAhs3bqxwvIeHB15eXuUe6Xp4eDBlyhQ6dOhgtbqEsLXqjjPsXuZrT+BuinqUShiKWi8hIaHCI0uz2WyxpQRF78ueffZZsrOzS7fl5OTw1ltvcc8999CuXbtq31sphb+/P4GBgaSnp9OsWTMaNWpEaGgo48aNu7YfqArx8fEW31UWFhaSmJho8Rx3d3duvfVWUlNT0VqzYcMGWrdubfXahLCl6vYmHVnm8yrQBZDp7kWtV1hYWGEYQ4myYVdWamqqxU4s+fn5rF69+qprUErh7u5Ofn4+Xbp04dNPP6V79+6VHr9hwwZ+/fVXduzYwT333MPu3burfa/+/fvj4+NTYXvJu8PKeHp6EhwcTNOmTSUIRY10rd3OsoC21ixE1G6FhYWcP3+e3NxcLl68WPrI0dG5uLhUOkyhsnF8WmuLnV8KCwsr7SFalZycHM6cOUPdunWZP39+lasVfP311zzyyCOkp6eTn5/Ppk2buOuuu9i2bVu17vXEE08QHByMh4dH6TZvb28GDRrELbfcctW1C1FTVCsMlVL/Ukp9X/xZBxwBvrNtacJRlYx7K9vJoiopKSncfPPNxMTEkJOTw4EDB+jatavFddocUfPmzSu09EwmE61aWZ6E6YYbbrAY9l5eXjzyyCNV3quwsJC8vDzMZjM5OTmYzWYKCgoIDg6mWbNm5ULqclpr3nzzzXILwULRUIzqPlL19PRk9+7djB49Gk9PT7y8vJgxYwZfffVVtc4Xoqaq7jvD2WW+LgBOaK0tv0AQtVZ+fj6vvfYaaWlpQNHyMmPGjGHatGlVdqMfMWIE8fHxpY8bzWYzf/zxB+7u7mRlZbFr1y7Cw8MZP358lb/sjRIcHIzJZCI2NhatNW3atMHDw4MbbrjB4vGenp6EhoYyderU0kep3t7evPjii/To0cPiORkZGbz66qts374dgPr165OXl4fWGi8vLwIDA69YZ1ZWFmfPnrW47/fff6/OjwoUTQw9a9Ysdu3aBcCoUaOqfa4QNVV13xn+V2v9X2A/cAjIUkpZ/k0gaq0JEyawcuXK0u+zs7OZM2cOCxcurPQcs9nMP//5zwoLlObn55OZmYnWmtzcXGbOnMmgQYMc9vFpUFAQ/v7+9O/fn2PHjl0xnN5++2127txJ06ZNadKkCRs3bmT+/MqXAB0yZAhr164tncXl/PnzZGRkYDKZqj2bjJeXV6WPbmUhWNvIzc0lNTWVvLy8a3oEfl20hl274PRpSE2FShYBFtVT3cekQ5VSfwK/A1EUzU8aZcvChGMxm80sWrTI4iO49957r9LzSn65X0l2djZ79+5l586d112ro+jUqROtWrWiTZs23H777ZUel5iYyKZNm0qHYZRV1cD7y5lMJt58880KQy28vb2ZNGlS9QsX1bJ//36CgoI4fPgw2dnZ7Nmzh7Fjx9rnD7rcXBg4EO65B+Li4PBhaNECjh+3/b1rqep2oBkH3Ky1blFmxXtZtcKJ5ObmVvqXb2VTckFRB5S77rqrWlOEFRQUsHfv3tKpvEqmA7t06dI1110TnDp16qrXGtRak56eTmpqKidOnCjdPnnyZEaOHInJZMJkMlGnTh2mTZvG888/b+2ynZrZbOaBBx7g/PnzpY//tdZ88skn/PDDD7YvICICtm+HzMyiFmJhIZw9C089Zft711LVDcM4inqQCifl5eVV6XRc3bp1q/LcTz75hMDAwNJAVEpZfMfo7u5OYGAgnTt35sUXXyQ3N5fs7GxatGjBH3/8Uen1MzIyeO2119i2bRtbtmzhoYceIiEhofo/nMHOnj1bocUNlf97Sk5OpnPnzkRHR3P48GE6dOjASy+9hNlsxmQyMWvWLPr06UOPHj1ISUlhzJgx9vgxnMqePXtIT0+vsD0zM5NPPvnE9gUsWQKXD+0xmyE6GlJSbH//Wqi6YfgOsEMp9YlS6uOSjy0LE45nwYIF5R7BmUwmfHx8iIioagrbotlX4uLiaNOmDe7u7rRu3Ro/P79yx5hMJvz8/IiKiiIuLq5cT9VLly7xt7/9zeK1tdYMGjSIJUuWlK7ssH79erp3787Fixev46etXFJSEmfPnuXPP/+8rlar1prExEQ+/vhjunbtWuHfbcnncs888wyHDh3CbDaXDtf48ssvy/0SNplMeHh44Obmds31icplZ2dX2mksMzPT9gVU9X6whvTSdjTVDcNPgP8Auyh6X1jyEU5k0KBB/Oc//8HV1RWlFI8//ji7du26YssQwNfXl8aNG+Pl5UWTJk3YsmVL6S9/pRQ9evRg27ZtfPXVVxXenWmtiYmJITU1tcJ1o6KiiI6OLneO2WwmKyuL5cuXX98PbEFiYiKtW7fm2LFjHD16lKCgIP79739f9XXOnTtHXFwcqampPPnkk+zYsaP0jw03NzeefPJJunbtWuG88+fPs2XLlgodkrKyspg3b941/1zi6vTu3dviu0EfHx+efvpp2xcwZAhY6nndqhVIZ6lrUt0wLNBav6m1Xqa1/nvJx6aVCYfUs2dPfHx88Pf358svv6Rjx47XdJ1OnTrRvXt3/P396d27Nzt37qRly5ZV9py01Eo6ePCgxWOzsrLYt8+6f68VFhZy/Pjx0vF/JaH7+OOPlw43qY6DBw8SEhJCVlYWLVq0YPjw4bi6uvLCCy/QvXt3br/9dlavXm2xZ2hmZmal718tPbYTtuHp6cmSJUvw8vIqbSGaTCZuu+22Sp9iWNW770Lr1uDrS/HNwd8fyvT2FlenumH4S3GP0sZKqRtKPjatTDgFpVS5R3kvvvginp6e5Y4xmUx069bN4ri+9u3bW7yul5cXnTp1smqtJYPhL+fi4sK6deuueL7Wmu+++47Ro0fj4eFBu3btCAgIuKoagoODqV+/foXtbm5uDB48+KquJa7PE088wW+//UZwcDBubm7ceOON/Oc//8Hd3Q4zVfr7w/79Re8OmzQpahEePw5dutj+3rVUdcPwaYrfG/K/R6QytEJY3dtvv023bt3wLfmLF2jYsCGff/65xeN79erFjTfeWG6wvlIKT09PXnrpJZvXC0WPZS0NiygrNzeX999/nzlz5tC1a1cWL15cIfSrQynF8uXL8fb2Lm2ReHl5Ub9+fRk+YYC2bdvSqlUrvL29CQwMvOpewdfF3R2efBLatCkKxEomgRDVU91B9y0tfGRohbA6Ly8vtmzZwvr16/H09MTb25uEhARatmxp8XilFD///DN//etfS8PhzjvvZNeuXZXOEHOt3NzcLD6iLCwsZNCgQZWel5eXxxtvvMGPP/7Ic889x8yZMyt0ILoad911F7/99htBQUHccMMNTJs2jZiYGBo2bHjN17wWJ0+e5MyZM+Tn51c6mbkQNUWVf8YopcZrrd8v/voJrfXXZfbN0FpPsHWBwvkopejXr19pa+9Kj53q1KnD3//+99Lxdps2bbJJXa6urtSvX5+MjIzSHoNeXl6Eh4ezbds2jhw5Qk5OTrnVLDIyMkhISKBu3bpMnz69ysH3V6NNmza0adMGgDfffNMq17yS9PR00tLS8PDwYPr06UybNq300fGOHTvo2LEjdevWtUstQljblVqGQ8p8/c5l+yr/U1iIWqpDhw6sX7+eoKAgmjZtytatW/nmm2946aWXOHv2LLm5uURFRfHtt9/yxRdfEBcXh6urK4sXL64QhPn5+TVmsvIZM2bQsGFDfv/9d3bv3s3kyZNLOxLB/+ablRaiqKmu9IBbVfK1pe+FcAr9+/enbduiFcz27NnD77//Xm7QvNls5umnn6Znz57UqVOHZs2a0bRp09L90dHRPPvss8TExADQt29fVq5cSbNmzez7g1TTmjVrmDFjRrkWr6VhBUopLly4YM/ShLCaK7UMdSVfW/peCKezatUqi7PHFBQUcOedd9KiRYty7xnPnTtH//79OXjwYOm8rTt37qRv374O20p8//33qz2Q/GrmUhXCkVwpDDsppdKUUunArcVfl3wvK30Kp2dpVXgADw8PHnzwwQrbV6xYUWHAfGFhIRcuXOCnn36ySY3XKzk5uVrHmc1meWcoaqwqw1Br7aK19tda+2mtXYu/Lvle5nkSTm/48OEWA7FevXrcdtttFbbHxsaWe9xYIj8/32HnUx04cOAVhwyYTCbatm0r0785gc2bN7N582ajy7C66o4zvGpKqaVKqWSllMUZllWRj5VSsUqp35VSt5XZN0gpdaR439u2qlFYn9aa/Px8h12X0NruuusuOnTogMlkKh3a4ebmxvr16y3OXXn77beXG0NZwsXFhe7du9u83msxadIk6tSpU65Xr7e3N++88w5NmjTBw8ODrl27ypqJokazWRgCy6m6x+m9QNviz1BgEYBSygVYULz/JuAppdRNNqzTYW3bto2DBw/y66+/Eh4e7tCdE/Lz8xkzZgzbtm1j586dBAUF8cUXXxhdlk3l5OQwfPhwAgICWLhwIe3atcPb25vevXtzyy2W3yI8/vjjNG7cuFyweHl50bt3b4cNw+DgYA4cOMDIkSPx8fGhXr16bNiwgRkzZtCmTZvS8aBC1GQ2C0Ot9RbgfBWHDAZW6CK7gAClVGOgBxCrtY7XWucBXxQf61TOnDnD//t//4/U1FTS09OZMWMGnTp1qnLtQCONHj2ayMhIzGYzWmvOnj3Lyy+/zM8//2x0aTZx8eJFjh49SnZ2NnPmzCEkJIRGjRrh5uZW6WoGUPQucffu3YwYMQJ3d3c8PDyYMGEC69atq/I8ozVu3JjZs2fTrVs3OnbsSJ8+fYwuSQirsmXL8EqaAKfKfJ9YvK2y7RYVz5kapZSKSqkl63iZzWZiY2PL9VLMyckhOTmZOXPmGFiZZRkZGSxdurRCr8qsrCymTp1a6XklSy7VJFprFi9eTEJCAl5eXkRGRl71ZOV169blo48+onfv3vTq1Yt333233HRyQgj7MzIMLf0ZrKvYbpHWOlJr3U1r3c3SBMY1UWZmpsVWQm5ubrUmhLa35OTkSlebiI+Pr7AtNzeX/v37s337dtLS0ti3b1+lq084Eq018fHxfPnllwQGBtKmTRvq1atndFlCCCswMgwTgaZlvg8GkqrY7jRcXV0rbTHZe/7J6ggODrY4Z6dSyuKafPv372f79u2lP2NGRgZ9+/a9roVybS0rK4vs7GwyMzP5v//7P4KDgx36saYQ4uoYGYbfA88V9yrtBVzSWp8B9gJtlVItlVLuFE0J972Bddqdl5cXPj4+Fbqze3t7M3r0aIOqqpy7uzuhoaEVhhiUzNtZVskUZJdP25Wbm8tKB12L7fz588TGxgJFqxRUNSm3EKJmsuXQitXATqC9UipRKfWyUmqYUmpY8SE/APFALPApMAJAa10AvA78BBwCvtJaO/4zNCvr2LEjnTp1wmQy4eLigpeXF2FhYQ77i3js2LFERkbi7e2Ni4sL99xzD1u3buXWW28td1zJoriXy8rKKg0cR1FQUEBiYiInT57Ex8cHLy8viwvu2sLRo0eJjo6msLCQtLQ0zpw5Y5f7CuGsbLb4ltb6qSvs18Brlez7gaKwdFru7u7s2LGDHj16kJeXx5YtW/D39ze6rCo9/fTTREZGArBx40aLx7i4uKCUqvAY2NfXlx49eti8xuo6d+4coaGhpKam0qBBAxo3bsxvv/1ml3tnZGRw4403lv7RoLXm6NGjZGRk2OX+QjgjIx+Timrw9vYmICDA4YOwulxdXfH19S23sK1SiqCgIB599FEDK/ufAwcO8OqrrxIXF0eLFi0ICgqy6/vBmJgYi63npKQkzp+varSSEOJaSRg6saSkJM6cOcPZs2ftOn6xU6dOjB07Fnd399Ig3LVr1xXXLbSHtWvXMmbMGHx8fFi0aBEBAQF2r8HSdG0lDhw4YMdKhHAeEoZOasGCBbRu3ZrY2FiOHTtG06ZN+eqrr+xyb5PJxLRp0+jduzf+/v60adPG8AmezWYzJ0+e5OOPP6ZHjx4sWrSIFi1aGFqTJcHBwUaXIEStJGHohI4cOcK4ceNKF2c1m81kZ2fzwgsv4CgTFxQUFNhtOaCzZ88SGxvLhQsXePHFF5k+fbrF+UPt5YYbbrC43c3NjdatW9u5GiGcg4ShE1q9enWFZYSg6N3dP//5TwMqKu+zzz6jQYMGpKenc+nSJYYNG0ZeXp5N7hUVFcXQoUPJzc2lZcuWPPfcc4aPH7zxxhsrtABNJpPFVTCEENYhYeiE8vPzLba6tNY2C53qWrduHaNGjSo3KfmKFSt44403rHofrTWrVq1i/PjxBAYG0q5dO4fppOTq6kpCQgIdO3ZEKYW3tzd33HFHuU5HQgjrkjB0Qo888ojFX6xaax544AEDKvqf8PDwCnOcZmdn8/e//73aq61fidlsZsqUKXz66af079+fBQsWONzcoC4uLtSrVw+TyXTFyb+FENfPucLwwAE4dAj27YNhw8DCvJnOoFu3bgwdOrTcsjslg/qbN29uYGVw8uRJi9tNJhOpqanXff3c3FyOHj3K1q1bGT58OJMnT7bbQHohhONynjDcvBl69YLkZMjIgCVLoHNn+MPi2sO13kcffcQvv/xCcHAwzZo1Y/fu3YwfP97osujRo4fFVpC7uztBQUHXde3t27dz9OhRCgoKiIiI4Mknn5QWlxACcKYwHDYMyj5+KyiA9HR4803jajJYjx49aN26NS1btqx0MVp7mz59Ot7e3uVCytvbm5kzZ+Lm5nZN1zSbzSxdurR0qaT27dvTpUsXa5UshKgFnCMMs7Kgsnkvt2+3by2iSh07dmTnzp08+OCDKKUwmUysWrWKYcOGXflkC9LT05kwYQL/+Mc/uPfee2nbtu01h6oQovZyjjD08IDKZjexMNi7ZLV2YYxbbrmF7777Dn9/f/z8/Bg8ePA1XSc+Pp6QkBD27dvHmDFjGDdunDwWFUJY5Bxh6OICL74Il3eU8PaGMWNKv/3999/p3r07W7duZevWrTzxxBN2nwsyOjqa+Ph44uLi2LNnj13vXZts2rSJESNGkJeXx5w5c3jooYckCIUQlbLZqhUOJyICUlLgm2/AZAJXV3jhhdIwPHv2LH379iUtLQ0oGmbw/fffExcXx759++zyi3T69OnMmDGjdGjBnXfeyQ033CCzjlwFrTULFy7k66+/5pZbbmHKlCmVzugihBAlnKNlCODpCV99VdSj9NZbITERFiwoCkaKZj25fMB5Xl4eR48eZffu3TYvLz4+nmnTppUbY5eVlUVSUpIs3VNNBQUFxMXF8fXXX/Poo4/y4YcfShCK/7l4EV5+GbZuhS1b4LHH4PRpo6sSDsJ5WoYl3N2LPoGB5Tb/8ccf5OTkVDhcKUVsbCy9evWyaVnr1q2z+J7SbDZbZXxdbXf48OHSYRPvvPMOAwcOrHDMiRMnSExMLP3a6DGVwo7MZujfHw4fLvoa4LvvYNcuOHas6JWJcGrO0zK8gl69epUbhF6isLCwwmrttuDm5obJVPF/jpIelaJy69evZ+TIkQC0bdvWYhDOmzePDh06EB8fT3x8PB06dGD+/Pn2LlUY5ZdfiibZKPv0p7AQ0tLgyy+Nq0s4DPktW+yFF17A19cXFxeX0m2enp707dvXLmFY1cK29evXt/n9a6L8/HwiIiKYPXs2nTt3pn379hZnkzl+/Djjx48nJycHrTVaa3Jychg3bhzHjx83oHJhdwcPgoXJ6cnIgN9+s389wuFIGBYLCAggKiqKJ554AhcXF9zc3BgzZgzff/+9Xe7fsGFDlixZgqenJyaTCZPJhKenJ23atJHpwixISUlh1KhRrFu3jqeffpr33nuv3B8yZX377bcWJyY3m82sWbPG1qUKR9Chg+XhVT4+4CATTghjSRiW0bRpU1avXs0dd9zB7bffzowZM+w6gfNTTz1FYmIibdu2pU2bNhw/fvy6pyCrjX777TeGDh1KQkICYWFhvPrqq1U+Si5pDVa2TziBe+6BJk2g7IQLLi5FYThkiHF1CYchYehg6tWrR6NGjWjcuDGNGjUyuhyHorVmzZo1vPnmm/j6+rJo0SL69et3xfMefvhhi61Gk8nEI488YotShcE2b97M5s2b/7fBZIJt24p6kJYMkxo4EPbsKQpE4fScrzepqJFyc3P54IMP2LRpE3369OGdd97Bp5q/xNq0aUNYWBhTpkwhOzsbKFqlIzQ0lDZt2tiybOFI6tWD1avhzJmi73/4wdh6xLUp+0eOFUkYCod35swZJk2aRHx8PC+99BLPPvvsVU+CMH78eB5++GEGDRoEwL///W/at29vi3KFEDWQhKFwaIWFhYSEhKC1ZubMmfTs2fOar9WuXTuaNWsGIEEorKpz587lH8uKGkfCUDgkrTV5eXnk5eVRv359wsPDpTOREMJmJAyFw8nKymLmzJnk5eXh6urKggUL8PT0NLosIUQtJmEoHMqJEyeYNGkSp0+fxsPDAzc3NwlCIYTNydAK4TC2bt3K8OHDSU9PJyIiwvBFePfu3cuhQ4fYv38/J06ckDGJQtRi0jIUDuGzzz5j5cqVdOjQgalTpxo+Bd3q1at55ZVXSlcRSU9PB4qmgBNC1D4ShsJQWmvi4uJYuXIl999/P6NGjTK8RZifn8+IESPKLadV0io8efKk3erYvHkzAQEBdrufEM5MwlAYJjs7m+zsbEwmE2PHjuWBBx4wuiSgaDmowsJCi/vOnTtn52qEEPYg7wyFITZu3MixY8fQWtO2bVuHCUKAunXrVvo41OhWqxDCNiQMhd2dPn2aGTNm4O3tXfpxJMHBwXTv3t1i8DVt2tSAioQQtiZhKOymoKCA7OxsUlJSePzxx2nduvVVT6tmL9988w2dOnXCZDLh4uKCUgoPDw8CAwONLk0IYQM2fWeolBoEzAVcgM+01rMu2z8OeKZMLTcC9bXW55VSCUA6UAgUaK272bJWYVsxMTEcOXIEs9lM8+bNee211/j666+NLqtSDRo0YO/evfTo0YPc3Fz8/Pz4448/jC7Lqcj0ZsKebBaGSikXYAHwFyAR2KuU+l5rHVNyjNb6A+CD4uMfBMZorc+XucydWutUW9UobE9rzbp165gzZw45OTmYzWYSEhJYvnx5jRi354iPcYUQ1mfLlmEPIFZrHQ+glPoCGAzEVHL8U8BqG7ynONcAABUnSURBVNYj7CwvL4+5c+eyfv16EhISyMrKwmw2k5aWxuuvv46Xl5fRJQohBGDbd4ZNgFNlvk8s3laBUsobGASsKbNZAxuUUvuUUkMru4lSaqhSKkopFZWSkmKFsoU1JCcnM2rUKH744Qe6du1KamoqZrO5dH9mZibnz5+vdAiDEELYky3D0FLPiMqeiz0IbL/sEWkfrfVtwL3Aa0opi0uaa60jtdbdtNbdjJ61RBTJyMggJCSEkydPEh4ejpeXF5mZmRWO01pTUFBgQIVCCFGeLcMwESjbDz0YSKrk2CFc9ohUa51U/M9kYC1Fj12FA9Nak5KSQlxcHP7+/ixatIg77riDBg0aWHwkqpRy2N6kQgjnYssw3Au0VUq1VEq5UxR4319+kFKqDtAf+K7MNh+llF/J18BAQLryObCcnBymTZvG6dOnqVOnDosWLSpdSPepp57CxcWlwjlKKRnELoRwCDYLQ611AfA68BNwCPhKa31QKTVMKTWszKGPABu01mWfozUEtimlfgP2AOu11j/aqlZxfZKSknjttdf45ZdfCAoKokWLFuV6YNavX5/169eXBp/JZKJJkyZ06tRJWoZCCIdg00H3WusftNbttNattdbTi7ct1lovLnPMcq31kMvOi9dadyr+3FxyrnAcWmvMZjO7d+8mJCSElJQU3n//fRo0aGDx+H79+tG7d298fX3p0qULp06dws/Pz85VCyGEZTJRt6i2goICxo8fz6VLlwAICAigSZMm3HHHHUydOpXGjRtXeb5SChcXF3x9faVFKIRwKDIdm6i2t956i08++aT0+/T0dOLi4vj/7d17kFT1mcbx78tVEJF7mMIBgULJYgHiLNldZSUSlhCpoEgihjWbRBMV0KBYAclcYBgVgSKzBCJQLiIWhkqQsEQhhiJMYGEJuiIYECJyc9BCrsotwOC7f3RrtWPDNDrd53Sf51M1Rffpc2aeHubXT5/T3b9z66231liEIiJhpj1DScnp06eZO3cup0+f/szyc+fO8cQTT3DbbbcFlKx2aOqv7KT/N6kt2jOUlBw+fPgzH5pPtHfv3gynCa+ePXvqAVokC6kMpUbnz59n2bJlSWeLMTNuuOGGAFKJiNQelaFc1IcffsjYsWP57W9/y+DBgz83aXWjRo0oKysLKJ2ISO1QGcoFvf3229x3331s2bKFn/3sZyxevJjnnnuOOnVifzbf+MY3WLNmDb169Qo4qYjIl6M30EhSr7zyCtOnT6dZs2bMmDGDrl27AjB06NBPPx+4cuXKICOKiNQalaF8RlVVFbNmzWLp0qVcf/31FBcX06xZs6BjiYiklQ6TyqcOHz7Mww8/zNKlS/nud7/L1KlTVYRxFRUV9OzZM+gYoaR30Eou0J6hALB161ZKSko4ceIERUVF3HLLLUFHEhHJGJWhcOjQIUaPHk2bNm2YMmUKnTp1CjqSiEhGqQwj7MyZM+zbt48jR44waNAgfv7zn2vybJGQ0qHo9NJrhhF14MABHnroIY4cOULbtm158sknVYQiElnaM4yg119/nYkTJ1JVVUWnTp1o2rSpziIhIpGmPcMIcXcWLVrEo48+SvPmzZkzZw5NmzYNOpaISOC0ZxgRp0+fZsqUKVRUVHDzzTczduxYGjVqFHQsEZFQUBlGQGVlJYWFhezbt4/77ruPO++8U4dFRUQSqAxz3Pr163n88cepV68eU6dO1RkmRESSUBnmKHdn/vz5LFiwgC5dulBaWkrbtm2DjiUiEkoqwxx04sQJHn/8cTZs2MCAAQN4+OGHadiwYdCxRERCS2WYY3bv3k1hYSEHDhxg9OjRfPvb39brgyIiNVAZ5pDVq1fz1FNPcfnll1NeXs51110XdCQRkaygMswB58+fZ+7cufzmN7+hW7duTJw4kZYtWwYdS0Qka6gMs9yxY8coLS1l06ZN3HbbbYwcOZJ69fTfKiJyKfSomcV27NhBcXExR48eZdy4cQwYMCDoSJIm2TBJczZkFLkQlWGWWrFiBb/4xS9o3rw5M2fO5Jprrgk6kohI1lIZZpmqqip++ctfsmzZMnr16kVxcTFXXnll0LFERLKayjCLHD58mOLiYrZt28awYcO49957qVu3btCxJEfpsKdEicowS2zZsoUJEyZw+vRpSkpK6Nu3b9CRRERyhsowCxw6dIhHHnmEvLw8pk+fztVXXx10JBGRnKIyDLEzZ86wb98+jhw5wuDBg3nsscdo0qRJ0LHkEuhQo0h2UBmG1NmzZxk1ahRHjx4lLy+PsrIyTasmIp+nJ1y1QmUYQsePH2fv3r20aNGCjh070rRpUxVhCGTTXl42ZRUJgzrp/OZm9k0z22FmO81sXJLb+5rZh2b2RvyrONVtc5G788ILL7Br1y7q16/PnDlzaNq0adCx0qpnz5564JasVlFRob/hHJC2PUMzqwvMAvoDlcCrZrbM3bdVW3Wtuw/6gtvmjFOnTjF58mTWrl1Ls2bNyM/Pp127dkHHSqpnz55faLuKigq9C1ZEQimde4a9gZ3uvsvdzwKLgMEZ2DbrvPvuu4wYMYJ169YxYsQIOnToQJ06ad1pl5DSXoZIMNL5iNsOeDfhemV8WXX/bGabzWyFmXW7xG0xs5+Y2Wtm9trBgwdrI3dGrVu3jvvvv59jx44xbdo0vvOd7wQdSUQkctL5Bppk7/jwatdfBzq4+wkz+xawFOiS4raxhe5zgbkABQUFSdcJo48//pj58+fz/PPPc+2111JaWkqbNm2CjiUiEknpLMNKID/h+lXAe4kruPtHCZeXm9mvzKxVKttms+PHj1NWVsbGjRsZOHAgo0ePpkGDBkHHEhGJrHSW4atAFzPrCOwHhgHfS1zBzNoCB9zdzaw3scO2h4FjNW2brd555x2Kioo4ePAgjzzyCIMGDdLHJi7gUt+oE7XX2qJ2f0XSKW1l6O5VZjYKeAWoC8xz961mdn/89tnAUOABM6sCTgPD3N2BpNumK2umrFq1iqlTp9KkSRPKy8vp1q1bzRuJiEjapfVD9+6+HFhebdnshMszgZmpbputqqqqmDNnDosXL6Z79+6UlJTQokWLoGOJiEicZqBJs6NHjzJx4kQ2b97MkCFDeOCBB6hXT7/2bKdDlCK5RY/KabR9+3aKior46KOPGD9+PP379w86koiIJKEyTJOXX36Z8vJyWrVqxcyZM+nSpUvQkURE5AJUhrXs3LlzzJgxg5deeomCggKKiopyfn5REZFspzKsRQcPHqS4uJjt27czfPhwfvSjH2laNclpeu1UcoXKsJZs3ryZCRMmcObMGUpLS+nTp0/QkSTkVCQi4aEy/JLcnRdffJGnn36adu3aUV5eTocOHYKOJQGIQrlF4T5KNKkMv4QzZ84wdepUVq1axY033sj48eNp3Lhx0LFEROQSqQy/oPfff5/CwkJ2797NPffcw/Dhw9MyrZqeiYuIpJ/K8AvYuHEjZWVlAEyePJnevXsHnCg8VN4iko1UhpfA3Vm4cCHz5s2jY8eOlJWVkZeXF3QsERH5klSGKTp16hRPPPEE69ato1+/fjz66KNcdtllQccSEZFaoDJMwd69eykqKmL//v2MHDmSO+64Q6ddEhHJISrDGqxdu5Ynn3yShg0bMn36dHr06BF0pJyh1xdFJCxUhhfxzDPPsHDhQrp27UppaSmtW7cOOpKIiKSByjCJ8+fPs2fPHhYuXMigQYN46KGHqF+/ftCxREQkTVSG1bz99tvs2LGDc+fOMWbMGAYNGhR0JImwY8eOBR1BJBI0i3SClStXMmrUKNydLl26qAhFRCJCe4ZAVVUVTz/9NEuWLKFHjx6cPHlSZ6MXEYmQyO8ZHjlyhDFjxrBkyRKGDh3KtGnTVIQiIhET6Uf9rVu3UlJSwokTJygsLKRfv35BRxIRkQBErwwrKnB3Xvr975kxYwatW7dm1qxZdO7cOehkIiISkMiV4dmzZykvL2fFihX07t2bwsJCrrjiiqBjSRL6UL6IZEqkyvCDDz6guLiYHTt2cPfdd/ODH/yAOnWi/bKpCkdEJEJluGnTJiZOnMi5c+eYNGkSN910U9CRREQkJHK+DN2dxYsXM3v2bPLz85k0aRL5+flBxxIRkRDJ6TL8+9//zpQpU1i9ejV9+vRh3LhxNG7cuMbtdOhQRCRacrYM9+/fT1FREXv27OHHP/4xd911l067JCKgJ7uSRE6W4YYNGygrK6NOnTpMmTKFgoKCoCOJiEiI5VwZLliwgPnz59O5c2dKS0vJy8sLOtIl02FaEZHMyqky3L9/P88++yz9+/dnzJgxNGzYMOhIIiKSBXKqDE+ePMmDDz7I7bffrtcHRUQkZTlVhvn5+QwZMiToGCIikmVyavqVRo0aBR1BRESyUFrL0My+aWY7zGynmY1LcvtwM9sS/1pvZj0SbttjZm+a2Rtm9lo6c4qISLSl7TCpmdUFZgH9gUrgVTNb5u7bElbbDdzs7kfNbCAwF/hawu1fd/dD6cooIiIC6d0z7A3sdPdd7n4WWAQMTlzB3de7+9H41Q3AVWnMIyIiklQ630DTDng34Xoln93rq+4eYEXCdQf+aGYOzHH3uck2MrOfAD8BaN++/ZcKLLlPn+EUkWTSWYbJPtvgSVc0+zqxMkw8lcSN7v6embUBVprZdndf87lvGCvJuQAFBQVJv7+IiMjFpPMwaSWQeHqIq4D3qq9kZt2BZ4DB7n74k+Xu/l783w+A3xE77CoiIlLr0lmGrwJdzKyjmTUAhgHLElcws/bAEuBud/9bwvLLzeyKTy4D/wb8NY1ZRUQkwtJ2mNTdq8xsFPAKUBeY5+5bzez++O2zgWKgJfCr+IwxVe5eAHwF+F18WT3gBXf/Q7qyiohItKV1Bhp3Xw4sr7ZsdsLle4F7k2y3C+hRfbmIiEg65NQMNCIiIl+EylBERCIvpybqFqlt+lyiSDRoz1BERCJPZSgiIpGnMhQRkchTGYqISOSpDEVEJPJUhiIiEnkqQxERiTx9zlAySp/bE5Ew0p6hiIhEnspQREQiT2UoIiKRpzIUEZHIUxmKiEjkqQxFRCTyVIYiIhJ5+pyhXDJ9VlBEco32DEVEJPJUhiIiEnkqQxERiTyVoYiIRJ7KUEREIk9lKCIikacyFBGRyFMZiohI5Jm7B52h1pjZQWBvmn9MK+BQmn9GbcqmvNmUFbIrbzZlhezKm01ZIbvy1kbWDu7euqaVcqoMM8HMXnP3gqBzpCqb8mZTVsiuvNmUFbIrbzZlhezKm8msOkwqIiKRpzIUEZHIUxleurlBB7hE2ZQ3m7JCduXNpqyQXXmzKStkV96MZdVrhiIiEnnaMxQRkchTGV6AmX3TzHaY2U4zG3eR9f7RzM6b2dBM5quWocasZtbXzN4ws61m9udMZ6yW5aJ5zexKM/u9mW2O5/1hEDnjWeaZ2Qdm9tcL3G5mNiN+X7aYWa9MZ0zIUlPW4fGMW8xsvZn1yHTGankumjdhvTCMsRqzhmyM1fS3EKYxlm9mq83srXiWnyZZJ/3jzN31Ve0LqAu8A3QCGgCbgX+4wHp/ApYDQ8OaFWgGbAPax6+3CfPvFhgPPBW/3Bo4AjQIKO+/Ar2Av17g9m8BKwAD/gn4S4C/25qy/gvQPH55YJBZU8mb8PcS6BhL8XcbmjGWYt4wjbE8oFf88hXA35I8JqR9nGnPMLnewE533+XuZ4FFwOAk6z0IvAh8kMlw1aSS9XvAEnffB+DuYc/rwBVmZkATYgO1KrMx40Hc18R//oUMBhZ4zAagmZnlZSbdZ9WU1d3Xu/vR+NUNwFUZCXbhPDX9biEcYyyVrGEaY6nkDdMYe9/dX49fPg68BbSrtlrax5nKMLl2wLsJ1yup9p9jZu2A24HZGcyVTI1ZgWuA5mZWYWb/Z2bfz1i6z0sl70zgq8B7wJvAT93948zEu2Sp3J8wuofYM+3QCtEYS0WYxlgqQjnGzOxq4HrgL9VuSvs4q1eb3yyHWJJl1d92Ww6MdffzsSdXgUklaz3gBqAf0Aj4XzPb4O5/S3e4JFLJOwB4A7gF6AysNLO17v5RusN9Aancn1Axs68TK8Obgs5Sg7CMsVSEaYylInRjzMyaEDsKMDpJjrSPM5VhcpVAfsL1q4g9g0pUACyKD9JWwLfMrMrdl2Ym4qdSyVoJHHL3k8BJM1sD9CB2bD7TUsn7Q2Cyx14s2Glmu4GuwMbMRLwkqdyf0DCz7sAzwEB3Pxx0nhqEZYylIkxjLBWhGmNmVp9YES509yVJVkn7ONNh0uReBbqYWUczawAMA5YlruDuHd39ane/GlgMjAhokNaYFfhvoI+Z1TOzxsDXiB2XD0IqefcRe4aNmX0FuBbYldGUqVsGfD/+brd/Aj509/eDDpWMmbUHlgB3h3iP5VMhGmOpCNMYS0Voxlj8dcv/At5y9+kXWC3t40x7hkm4e5WZjQJeIfZutnnuvtXM7o/fHprXMFLJ6u5vmdkfgC3Ax8Az7n7Rt7MHmReYBMw3szeJHR4Z6+6BzLJvZr8G+gKtzKwSKAHqJ2RdTuydbjuBU8SecQcihazFQEvgV/G9rSoPcMLmFPKGRk1ZwzTGUslLiMYYcCNwN/Cmmb0RXzYeaA+ZG2eagUZERCJPh0lFRCTyVIYiIhJ5KkMREYk8laGIiESeylBERCJPZSgSYmbmZvZ8wvV6ZnbQzF66xO+zx8xafdl1RHKVylAk3E4C15lZo/j1/sD+APOI5CSVoUj4rQBujV++C/j1JzeYWQszWxo/x9uG+HRrmFlLM/ujmW0yszkkzO1oZv9uZhvj596bY2Z1M3lnRMJIZSgSfouAYWZ2GdCdz87oPxHY5O7dic3asSC+vAT4H3e/nthUVu0BzOyrwJ3Aje7eEzgPDM/IvRAJMU3HJhJy7r4lfmqbu4hNS5XoJuCO+Hp/iu8RXkns5K5D4stfNrNPzmPYj9jZFV6NT8nWiIDPFSgSBipDkeywDJhGbL7JlgnLL3Zqm2RzLRrwnLs/VqvpRLKcDpOKZId5QKm7v1lt+RrihznNrC+x0wh9VG35QKB5fP1VwFAzaxO/rYWZdUh/fJFw056hSBZw90rgP5PcNAF41sy2EJvN/z/iyycCvzaz14E/EztlD+6+zcwKgT+aWR3gHDAS2JveeyASbjprhYiIRJ4Ok4qISOSpDEVEJPJUhiIiEnkqQxERiTyVoYiIRJ7KUEREIk9lKCIikacyFBGRyPt/tcbdEy2IjM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This model still doesn't do brilliantly, but it's better than just CO2\n",
    "res = leave_one_out(df[['co2', 'od550aer']], df[['tas']].values, model='GaussianProcess', kernel=['Linear'])\n",
    "\n",
    "r2_values = stats.linregress(*np.squeeze(np.asarray(res, dtype=float)).T[0:2])[2]**2\n",
    "print(\"R^2: {:.2f}\".format(r2_values))\n",
    "validation_plot(*np.squeeze(np.asarray(res, dtype=float)).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.40\n",
      "Proportion of 'Bad' estimates : 8.51%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adding Methane doesn't seem to improve the picture\n",
    "res = leave_one_out(df[['co2', 'od550aer', 'ch4']], df[['tas']].values, model='GaussianProcess', kernel=['Linear', 'Bias'])\n",
    "\n",
    "r2_values = stats.linregress(*np.squeeze(np.asarray(res, dtype=float)).T[0:2])[2]**2\n",
    "print(\"R^2: {:.2f}\".format(r2_values))\n",
    "validation_plot(*np.squeeze(np.asarray(res, dtype=float)).T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = gp_model(df[['co2', 'od550aer']], df[['tas']].values, kernel=['Linear'])\n",
    "m.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample a large AOD/CO2 space using the emulator\n",
    "xx, yy = np.meshgrid(np.linspace(0, 4000, 25), np.linspace(-.05, 0.05, 20))\n",
    "X_new = np.stack([xx.flat, yy.flat], axis=1)\n",
    "Y_new, Y_new_sigma = m.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the scnario mean values for comparison\n",
    "scn_mean = train.groupby(['scenario']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "scale = 1.5\n",
    "matplotlib.rcParams['font.size'] = 12 * scale\n",
    "matplotlib.rcParams['lines.linewidth'] = 1.5 * scale\n",
    "matplotlib.rcParams['lines.markersize'] = 6 * scale\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "norm = matplotlib.colors.Normalize(vmin=-2.5,vmax=2.5)\n",
    "p = plt.contourf(xx, yy, Y_new.reshape(xx.shape), norm=norm, levels=30, cmap='RdBu_r')\n",
    "\n",
    "plt.scatter(train.co2, train.od550aer, c=train.tas, norm=norm, edgecolors='k', cmap='RdBu_r', marker='x')\n",
    "plt.scatter(scn_mean.co2, scn_mean.od550aer, c=scn_mean.tas, norm=norm, edgecolors='k', cmap='RdBu_r', marker='s')\n",
    "\n",
    "c = plt.contour(xx, yy, np.sqrt(Y_new_sigma.reshape(xx.shape)), cmap='viridis', levels=6)\n",
    "\n",
    "plt.setp(plt.gca(), xlabel='Cumulative CO$_2$ (GtCO$_2$)', ylabel='$\\Delta$AOD')\n",
    "\n",
    "plt.colorbar(c, label='$\\sigma_{\\Delta T(K)}$')\n",
    "plt.colorbar(p, label='$\\Delta$T(K)')\n",
    "\n",
    "# Cumulative CO2, delta T and delta AOD all relative to a 2015-2020 average. Each point represents a single model integration for different scenarios in the CMIP6 archive. \n",
    "plt.savefig('CMIP6_emulator_paper_v1.1.png', transparent=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample emissions for a particular temperature target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acceptance rate: 0.9614173964786951\n"
     ]
    }
   ],
   "source": [
    "from esem.sampler import MCMCSampler\n",
    "\n",
    "# The MCMC algorithm works much better with a normalised parameter range, so recreate the model\n",
    "m = gp_model(pd.concat([df[['co2']]/4000, (df[['od550aer']]+0.05)/0.1], axis=1), df[['tas']].values, kernel=['Linear'])\n",
    "m.train()\n",
    "\n",
    "# Target 1.2 degrees above present day (roughly 2 degrees above pre-industrial)\n",
    "sampler = MCMCSampler(m, np.asarray([1.2], dtype=np.float64))\n",
    "samples = sampler.sample(n_samples=8000, mcmc_kwargs=dict(num_burnin_steps=1000) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the emulated temperatures for these samples\n",
    "new_samples = pd.DataFrame(data=samples, columns=['co2', 'od550aer'])\n",
    "Z, _ = m.predict(new_samples.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 6))\n",
    "\n",
    "cl = plt.contour(xx, yy, Y_new.reshape(xx.shape), levels = [1.2],\n",
    "             colors=('k',),linestyles=('-',),linewidths=(2,))\n",
    "\n",
    "cl=plt.hexbin(new_samples.co2*4000, new_samples.od550aer*0.1-0.05, gridsize=20)\n",
    "\n",
    "plt.setp(plt.gca(), xlabel='Cumulative CO$_2$ (GtCO$_2$)', ylabel='$\\Delta$AOD')\n",
    "\n",
    "plt.colorbar(cl, label='N samples')\n",
    "plt.setp(plt.gca(), ylim=[-0.05, 0.05], xlim=[0, 4000])\n",
    "\n",
    "plt.savefig('CMIP6_emulator_sampled.png', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:gcem_dev]",
   "language": "python",
   "name": "conda-env-gcem_dev-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}