Climate/06_global_climate_shoebox_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Shoebox Model </b><br>\n",
    "Reference: Walter A. Robinson, Modeling Dynamic Climate Systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('seaborn-dark')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step size          \n",
    "dt = 10\n",
    "\n",
    "# Set up simulation time\n",
    "tmin = 0\n",
    "tmax = 10800\n",
    "t = np.arange(tmin, tmax + dt, dt)\n",
    "n = len(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Incoming radiation (lightbulb)\n",
    "sun = 60 # W\n",
    "\n",
    "# Shoebox height, width, length, area\n",
    "height = 0.15 # m\n",
    "width = 0.20 # m\n",
    "length = 0.40 # m\n",
    "area = length * width # m^2\n",
    "\n",
    "# Cloud area\n",
    "cloud_area = 0.05 # m^2\n",
    "\n",
    "# Cloud thickness\n",
    "cloud_thickness = 0.001 # m\n",
    "\n",
    "# Cloud temperature\n",
    "cloud_temperature = 273.15 # K\n",
    "\n",
    "# Specific heat capacity of atmosphere\n",
    "spec_heat_atm = 1004 # J/kg*K\n",
    "\n",
    "# Specific heat capacity of water\n",
    "spec_heat_water = 4218 # J/kg*K\n",
    "\n",
    "# Air density\n",
    "air_density = 1.25 # kg/m^3\n",
    "\n",
    "# Density of water\n",
    "water_density = 1000 # kg/m^3\n",
    "\n",
    "# Ocean\n",
    "ocean_depth = 0.02 # m\n",
    "ocean_fraction = 0.5\n",
    "\n",
    "ocean_area = area * ocean_fraction # m^2\n",
    "ocean_heat_capacity = water_density * spec_heat_water * ocean_area * ocean_depth # J/K\n",
    "\n",
    "# Air mass and cloud capacity\n",
    "air_volume = area * height # m^3\n",
    "air_mass = air_density * air_volume # kg\n",
    "cloud_capacity = cloud_area * cloud_thickness # m^3\n",
    "\n",
    "# Latent heat of vaporization\n",
    "lv = 2500000 # J/kg\n",
    "\n",
    "#  Water-vapor gas constant is Rv = 461 J/K*kg\n",
    "rv = 461 # J/K*Kg\n",
    "\n",
    "# Diffusion velocity\n",
    "diffusion_velocity = 1.5 * 10 ** -2 # m/s\n",
    "\n",
    "# Cloud saturation humidity (Clausius-Clapeyron Equation)\n",
    "cloud_saturation_humidity = 0.0038 * np.exp((lv/rv) * (1/273.15 - 1/cloud_temperature))\n",
    "\n",
    "# Cloud flux constant\n",
    "cloud_flux_constant = air_density * diffusion_velocity * cloud_area # kg/s\n",
    "\n",
    "# Ocean flux constant\n",
    "ocean_flux_constant = ocean_area * air_density * diffusion_velocity # kg/s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Air\n",
    "air = np.zeros((n, ))\n",
    "\n",
    "# Cloud\n",
    "cloud = np.zeros((n, ))\n",
    "\n",
    "# Surface temperature\n",
    "temperature = np.zeros((n, ))\n",
    "temperature[0] = 280\n",
    "\n",
    "# Humidity\n",
    "humidity = np.zeros((n, ))\n",
    "humidity[0] = air[0]/air_mass\n",
    "\n",
    "# Saturation humidity\n",
    "saturation_humidity = np.zeros((n, ))\n",
    "saturation_humidity[0] = 0.0038 * np.exp((lv/rv) * (1/273.15 - 1/temperature[0]))\n",
    "\n",
    "# Evaporation\n",
    "evaporation = np.zeros((n, )) \n",
    "evaporation[0] =  ocean_flux_constant * (saturation_humidity[0] - humidity[0]) \n",
    "\n",
    "# Condensation - Sensible heat\n",
    "condensation = np.zeros((n, )) \n",
    "condensation[0] = cloud_flux_constant * (humidity[0] - cloud_saturation_humidity)\n",
    "\n",
    "# Conduction\n",
    "conduction = np.zeros((n, ))\n",
    "conduction[0] = air_density * spec_heat_atm * (temperature[0] - cloud_temperature) * cloud_area * diffusion_velocity\n",
    "\n",
    "# Precipitation\n",
    "precip = np.zeros((n, ))\n",
    "precip[0] = conduction[0]\n",
    "\n",
    "# Latent Heat of Evaporation\n",
    "l_evaporation = np.zeros((n, ))\n",
    "l_evaporation[0] = lv * evaporation[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in range(1, n):\n",
    "    \n",
    "    # Air\n",
    "    air[k] = air[k-1] + (evaporation[k-1] - condensation[k-1]) * dt\n",
    "    \n",
    "    # Cloud\n",
    "    cloud[k] = cloud[k-1] + (condensation[k-1] - precip[k-1]) * dt\n",
    "    \n",
    "    # Surface temperature\n",
    "    temperature[k] = temperature[k-1] + (sun - l_evaporation[k-1] - conduction[k-1]) * dt/ocean_heat_capacity\n",
    "    \n",
    "    # Humidity\n",
    "    humidity[k] = air[k]/air_mass\n",
    "    \n",
    "    # Saturation humidity\n",
    "    saturation_humidity[k] = 0.0038 * np.exp((lv/rv) * (1/273.15 - 1/temperature[k]))\n",
    "\n",
    "    # Evaporation\n",
    "    evaporation[k] = ocean_flux_constant * (saturation_humidity[k] - humidity[k]) \n",
    "    \n",
    "    # Condensation\n",
    "    condensation[k] =  cloud_flux_constant * (humidity[k] - cloud_saturation_humidity)\n",
    "    \n",
    "    # Conduction - Sensible heat\n",
    "    conduction[k] = air_density * spec_heat_atm * (temperature[k] - cloud_temperature) * cloud_area * diffusion_velocity\n",
    "    \n",
    "    # Latent Heat of Evaporation\n",
    "    l_evaporation[k] = lv * evaporation[k]\n",
    "    \n",
    "    # Precipitation\n",
    "    if cloud[k] > cloud_capacity:\n",
    "        precip[k] = condensation[k]\n",
    "    else:\n",
    "        precip[k] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the result of the shoebox model\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.set_xlabel(\"Seconds\")\n",
    "ax.set_ylabel(\"Temperature (°C)\")\n",
    "ax.set_title(\"Temperature versus time in shoebox model\")\n",
    "ax.set_xlim(0, t.max())\n",
    "ax.plot(t, temperature - 273.15, c=\"tab:red\")\n",
    "ax.grid()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Energy fluxes\n",
    "sun_np = sun * np.ones(n)\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.set_xlabel(\"Seconds\")\n",
    "ax.set_ylabel(\"Energy Fluxes\")\n",
    "ax.set_xlim(0, t.max())\n",
    "ax.plot(t, l_evaporation, c=\"tab:green\")\n",
    "ax.plot(t, conduction, c=\"tab:blue\")\n",
    "ax.plot(t, sun_np, c=\"tab:orange\")\n",
    "ax.legend((\"Latent Heat\",\"Sensible Heat\", \"Sun\"))\n",
    "ax.grid()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Water fluxes\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.set_xlabel(\"Seconds\")\n",
    "ax.set_ylabel(\"Water Fluxes\")\n",
    "ax.set_xlim(0, t.max())\n",
    "ax.plot(t, evaporation, c=\"tab:green\")\n",
    "ax.plot(t, condensation, c=\"tab:blue\", linestyle=\"dashed\")\n",
    "ax.legend((\"Evaporation\",\"Condensation\"))\n",
    "ax.grid()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Temperature, T=33°C\n"
     ]
    }
   ],
   "source": [
    "print(f\"Final Temperature, T={round(temperature[-1] - 273.15)}°C\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7b11ec4e63c99d8162a346e44061eab97ff71e663e3c205d951a4ad2f12c3cfa"
  },
  "kernelspec": {
   "display_name": "Python 3.7.10 64-bit (system)",
   "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": "3.7.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}