{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preconfigured Energy Balance Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this document the basic use of climlab's preconfigured EBM class is shown. \n",
    "\n",
    "Contents are how to\n",
    "\n",
    "  * setup an EBM model\n",
    "  * show and access subprocesses\n",
    "  * integrate the model\n",
    "  * access and plot various model variables\n",
    "  * calculate the global mean of the temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import climlab\n",
    "from climlab import constants as const"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The regular path for the EBM class is ``climlab.model.ebm.EBM`` but it can also be accessed through ``climlab.EBM``\n",
    "\n",
    "An EBM model instance is created through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model creation\n",
    "ebm_model = climlab.EBM(name='My EBM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default many parameters are set during initialization:\n",
    "\n",
    "``num_lat=90, S0=const.S0, A=210., B=2., D=0.55, water_depth=10., Tf=-10, a0=0.3, a2=0.078, ai=0.62, timestep=const.seconds_per_year/90., T0=12., T2=-40``\n",
    "\n",
    "For further details see the climlab documentation.\n",
    "\n",
    "Many of the input parameters are stored in the following dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'timestep': 350632.51200000005,\n",
       " 'S0': 1365.2,\n",
       " 's2': -0.48,\n",
       " 'A': 210.0,\n",
       " 'B': 2.0,\n",
       " 'D': 0.555,\n",
       " 'Tf': -10.0,\n",
       " 'water_depth': 10.0,\n",
       " 'a0': 0.3,\n",
       " 'a2': 0.078,\n",
       " 'ai': 0.62}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print model parameters\n",
    "ebm_model.param"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model consists of one state variable (surface temperature) and a couple of defined subprocesses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.ebm.EBM'>. \n",
      "State variables and domain shapes: \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "My EBM: <class 'climlab.model.ebm.EBM'>\n",
      "   LW: <class 'climlab.radiation.aplusbt.AplusBT'>\n",
      "   insolation: <class 'climlab.radiation.insolation.P2Insolation'>\n",
      "   albedo: <class 'climlab.surface.albedo.StepFunctionAlbedo'>\n",
      "      iceline: <class 'climlab.surface.albedo.Iceline'>\n",
      "      warm_albedo: <class 'climlab.surface.albedo.P2Albedo'>\n",
      "      cold_albedo: <class 'climlab.surface.albedo.ConstantAlbedo'>\n",
      "   SW: <class 'climlab.radiation.absorbed_shorwave.SimpleAbsorbedShortwave'>\n",
      "   diffusion: <class 'climlab.dynamics.meridional_heat_diffusion.MeridionalHeatDiffusion'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print model states and suprocesses\n",
    "print(ebm_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model subprocesses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The subprocesses are stored in a dictionary and can be accessed through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['LW', 'insolation', 'albedo', 'SW', 'diffusion'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access model subprocesses\n",
    "ebm_model.subprocess.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So to access the time type of the Longwave Radiation subprocess for example, type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'explicit'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# access specific subprocess through dictionary\n",
    "ebm_model.subprocess['LW'].time_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'explicit'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  For interactive convenience, you can also use attribute access for the same thing:\n",
    "ebm_model.subprocess.LW.time_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model integration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model time dictionary shows information about all the time related content and quantities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'timestep': 350632.51200000005,\n",
       " 'num_steps_per_year': 90.0,\n",
       " 'day_of_year_index': 0,\n",
       " 'steps': 0,\n",
       " 'days_elapsed': 0,\n",
       " 'years_elapsed': 0,\n",
       " 'days_of_year': array([  0.        ,   4.05824667,   8.11649333,  12.17474   ,\n",
       "         16.23298667,  20.29123333,  24.34948   ,  28.40772667,\n",
       "         32.46597333,  36.52422   ,  40.58246667,  44.64071333,\n",
       "         48.69896   ,  52.75720667,  56.81545333,  60.8737    ,\n",
       "         64.93194667,  68.99019333,  73.04844   ,  77.10668667,\n",
       "         81.16493333,  85.22318   ,  89.28142667,  93.33967333,\n",
       "         97.39792   , 101.45616667, 105.51441333, 109.57266   ,\n",
       "        113.63090667, 117.68915333, 121.7474    , 125.80564667,\n",
       "        129.86389333, 133.92214   , 137.98038667, 142.03863333,\n",
       "        146.09688   , 150.15512667, 154.21337333, 158.27162   ,\n",
       "        162.32986667, 166.38811333, 170.44636   , 174.50460667,\n",
       "        178.56285333, 182.6211    , 186.67934667, 190.73759333,\n",
       "        194.79584   , 198.85408667, 202.91233333, 206.97058   ,\n",
       "        211.02882667, 215.08707333, 219.14532   , 223.20356667,\n",
       "        227.26181333, 231.32006   , 235.37830667, 239.43655333,\n",
       "        243.4948    , 247.55304667, 251.61129333, 255.66954   ,\n",
       "        259.72778667, 263.78603333, 267.84428   , 271.90252667,\n",
       "        275.96077333, 280.01902   , 284.07726667, 288.13551333,\n",
       "        292.19376   , 296.25200667, 300.31025333, 304.3685    ,\n",
       "        308.42674667, 312.48499333, 316.54324   , 320.60148667,\n",
       "        324.65973333, 328.71798   , 332.77622667, 336.83447333,\n",
       "        340.89272   , 344.95096667, 349.00921333, 353.06746   ,\n",
       "        357.12570667, 361.18395333]),\n",
       " 'active_now': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accessing the model time dictionary\n",
    "ebm_model.time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To integrate the model forward in time different methods are availible: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integrate model for a single timestep\n",
    "ebm_model.step_forward()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model time step has increased from 0 to 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm_model.time['steps']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 12 steps, 50.0 days, or 0.1368954627915394 years.\n",
      "Total elapsed time is 0.1444444444444445 years.\n"
     ]
    }
   ],
   "source": [
    "# integrate model for a 50 days\n",
    "ebm_model.integrate_days(50.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 90 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 1.1444444444444433 years.\n"
     ]
    }
   ],
   "source": [
    "# integrate model for two years\n",
    "ebm_model.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total elapsed time is 9.144444444444344 years.\n"
     ]
    }
   ],
   "source": [
    "# integrate model until solution converges\n",
    "ebm_model.integrate_converge()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plotting model variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A couple of interesting model variables are stored in a dictionary named ``diagnostics``. It has following entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['OLR', 'insolation', 'coszen', 'icelat', 'ice_area', 'albedo', 'ASR', 'diffusive_flux', 'advective_flux', 'total_flux', 'flux_convergence', 'heat_transport', 'heat_transport_convergence', 'net_radiation'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm_model.diagnostics.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They can be accessed in two ways:\n",
    "\n",
    "- Through dictionary methods like ``ebm_model.diagnostics['ASR']``\n",
    "- As process attributes like ``ebm_model.ASR``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-70.,  70.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm_model.icelat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code does the plotting for some model variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating plot figure\n",
    "fig = plt.figure(figsize=(15,10))\n",
    "\n",
    "# Temperature plot\n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.plot(ebm_model.lat,ebm_model.Ts)\n",
    "\n",
    "ax1.set_xticks([-90,-60,-30,0,30,60,90])\n",
    "ax1.set_xlim([-90,90])\n",
    "ax1.set_title('Surface Temperature', fontsize=14)\n",
    "ax1.set_ylabel('(degC)', fontsize=12)\n",
    "ax1.grid()\n",
    "\n",
    "# Albedo plot\n",
    "ax2 = fig.add_subplot(223, sharex = ax1)\n",
    "ax2.plot(ebm_model.lat,ebm_model.albedo)\n",
    "\n",
    "ax2.set_title('Albedo', fontsize=14)\n",
    "ax2.set_xlabel('latitude', fontsize=10)\n",
    "ax2.set_ylim([0,1])\n",
    "ax2.grid()\n",
    "\n",
    "# Net Radiation plot\n",
    "ax3 = fig.add_subplot(222, sharex = ax1)\n",
    "ax3.plot(ebm_model.lat, ebm_model.OLR, label='OLR',\n",
    "                                       color='cyan')\n",
    "ax3.plot(ebm_model.lat, ebm_model.ASR, label='ASR',\n",
    "                                       color='magenta')\n",
    "ax3.plot(ebm_model.lat, ebm_model.ASR-ebm_model.OLR, \n",
    "                                       label='net radiation',\n",
    "                                       color='red')\n",
    "\n",
    "ax3.set_title('Net Radiation', fontsize=14)\n",
    "ax3.set_ylabel('(W/m$^2$)', fontsize=12)\n",
    "ax3.legend(loc='best')\n",
    "ax3.grid()\n",
    "\n",
    "# Energy Balance plot\n",
    "net_rad = np.squeeze(ebm_model.net_radiation)\n",
    "transport = np.squeeze(ebm_model.heat_transport_convergence)\n",
    "\n",
    "ax4 = fig.add_subplot(224, sharex = ax1)\n",
    "ax4.plot(ebm_model.lat, net_rad, label='net radiation', \n",
    "                                              color='red')\n",
    "ax4.plot(ebm_model.lat, transport, label='heat transport', \n",
    "                                              color='blue')\n",
    "ax4.plot(ebm_model.lat, net_rad+transport, label='balance',\n",
    "                                             color='black')\n",
    "\n",
    "ax4.set_title('Energy', fontsize=14)\n",
    "ax4.set_xlabel('latitude', fontsize=10)\n",
    "ax4.set_ylabel('(W/m$^2$)', fontsize=12)\n",
    "ax4.legend(loc='best')\n",
    "ax4.grid()\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The energy balance is zero at every latitude. That means the model is in equilibrium. Perfect!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global mean temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model's state dictionary has following entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['Ts'])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm_model.state.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like diagnostics, state variables can be accessed in two ways:\n",
    "\n",
    "- With dictionary methods, ``ebm_model.state['Ts']`` \n",
    "- As process attributes, ``ebm_model.Ts``\n",
    "\n",
    "These are entirely equivalent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm_model.Ts is ebm_model.state['Ts']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The global mean of the model's surface temperature can be calculated through"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The global mean temperature is 14.29 deg C.\n",
      "The modeled ice edge is at 70.00 deg latitude.\n"
     ]
    }
   ],
   "source": [
    "print('The global mean temperature is %.2f deg C.' %climlab.global_mean(ebm_model.Ts))\n",
    "print('The modeled ice edge is at %.2f deg latitude.' %np.max(ebm_model.icelat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}