{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2fb55d1",
   "metadata": {},
   "source": [
    "(greenhouse:solution)=\n",
    "# Greenhouse model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb74f0eb",
   "metadata": {},
   "source": [
    "**Task 1**: Plug Eq. (7) into Eq. (6) and solve for the radiative equilibrium suface temperature $T_e$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "217ec79c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Radiative equilibrium temperature: 254.91\n"
     ]
    }
   ],
   "source": [
    "# Solve for the radiative equilibrium temperature\n",
    "\n",
    "sigma = 5.67e-8 # W m^-2 K^-4 \n",
    "Q = 342         # Incoming shortwave radiation W m^-2\n",
    "albedo = 0.3    # Albedo\n",
    "\n",
    "Te = (((1-0.3)*342)/5.67e-8)**(1/4)\n",
    "\n",
    "print('Radiative equilibrium temperature: {:.2f}'.format(Te))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9cd186",
   "metadata": {},
   "source": [
    "**Task 2**: Where in the atmosphere do we find $T_e$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfb827de",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Markdown, Latex, Math\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd0d37ac",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (level: 17, lat: 73, lon: 144, time: 12, nbnds: 2)\n",
       "Coordinates:\n",
       "  * level               (level) float32 1e+03 925.0 850.0 ... 30.0 20.0 10.0\n",
       "  * lat                 (lat) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
       "  * lon                 (lon) float32 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5\n",
       "  * time                (time) object 0001-01-01 00:00:00 ... 0001-12-01 00:0...\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    climatology_bounds  (time, nbnds) object 1981-01-01 00:00:00 ... 2010-12-...\n",
       "    air                 (time, level, lat, lon) float32 ...\n",
       "    valid_yr_count      (time, level, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    description:                     Data from NCEP initialized reanalysis (4...\n",
       "    platform:                       Model\n",
       "    Conventions:                    COARDS\n",
       "    not_missing_threshold_percent:  minimum 3% values input to have non-missi...\n",
       "    history:                        Created 2011/07/12 by doMonthLTM\\nConvert...\n",
       "    title:                          monthly ltm air from the NCEP Reanalysis\n",
       "    dataset_title:                  NCEP-NCAR Reanalysis 1\n",
       "    References:                     http://www.psl.noaa.gov/data/gridded/data...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-2fa086b5-a248-4902-9806-024638f6d022' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2fa086b5-a248-4902-9806-024638f6d022' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 17</li><li><span class='xr-has-index'>lat</span>: 73</li><li><span class='xr-has-index'>lon</span>: 144</li><li><span class='xr-has-index'>time</span>: 12</li><li><span>nbnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-11c15c14-62e5-4a66-9918-13fe229a67a8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11c15c14-62e5-4a66-9918-13fe229a67a8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1e+03 925.0 850.0 ... 20.0 10.0</div><input id='attrs-3baa1c1f-0e46-458e-92bb-cb342126678f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3baa1c1f-0e46-458e-92bb-cb342126678f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed21f2b-e509-41ec-9ec4-98a76fb688e9' class='xr-var-data-in' type='checkbox'><label for='data-bed21f2b-e509-41ec-9ec4-98a76fb688e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>millibar</dd><dt><span>long_name :</span></dt><dd>Level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>GRIB_id :</span></dt><dd>100</dd><dt><span>GRIB_name :</span></dt><dd>hPa</dd><dt><span>actual_range :</span></dt><dd>[1000.   10.]</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([1000.,  925.,  850.,  700.,  600.,  500.,  400.,  300.,  250.,  200.,\n",
       "        150.,  100.,   70.,   50.,   30.,   20.,   10.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>90.0 87.5 85.0 ... -87.5 -90.0</div><input id='attrs-8ac55f0a-6fd8-40bd-a237-00e2c6728283' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ac55f0a-6fd8-40bd-a237-00e2c6728283' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c528b9c-0316-4f67-985f-bb3fd98f8cd3' class='xr-var-data-in' type='checkbox'><label for='data-3c528b9c-0316-4f67-985f-bb3fd98f8cd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>actual_range :</span></dt><dd>[ 90. -90.]</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([ 90. ,  87.5,  85. ,  82.5,  80. ,  77.5,  75. ,  72.5,  70. ,  67.5,\n",
       "        65. ,  62.5,  60. ,  57.5,  55. ,  52.5,  50. ,  47.5,  45. ,  42.5,\n",
       "        40. ,  37.5,  35. ,  32.5,  30. ,  27.5,  25. ,  22.5,  20. ,  17.5,\n",
       "        15. ,  12.5,  10. ,   7.5,   5. ,   2.5,   0. ,  -2.5,  -5. ,  -7.5,\n",
       "       -10. , -12.5, -15. , -17.5, -20. , -22.5, -25. , -27.5, -30. , -32.5,\n",
       "       -35. , -37.5, -40. , -42.5, -45. , -47.5, -50. , -52.5, -55. , -57.5,\n",
       "       -60. , -62.5, -65. , -67.5, -70. , -72.5, -75. , -77.5, -80. , -82.5,\n",
       "       -85. , -87.5, -90. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.5 5.0 ... 352.5 355.0 357.5</div><input id='attrs-cb0e0acb-bd2e-47fd-ac37-ddb7aa25a083' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb0e0acb-bd2e-47fd-ac37-ddb7aa25a083' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40647bb7-3f09-4f89-b294-d156f63ee231' class='xr-var-data-in' type='checkbox'><label for='data-40647bb7-3f09-4f89-b294-d156f63ee231' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0.  357.5]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([  0. ,   2.5,   5. ,   7.5,  10. ,  12.5,  15. ,  17.5,  20. ,  22.5,\n",
       "        25. ,  27.5,  30. ,  32.5,  35. ,  37.5,  40. ,  42.5,  45. ,  47.5,\n",
       "        50. ,  52.5,  55. ,  57.5,  60. ,  62.5,  65. ,  67.5,  70. ,  72.5,\n",
       "        75. ,  77.5,  80. ,  82.5,  85. ,  87.5,  90. ,  92.5,  95. ,  97.5,\n",
       "       100. , 102.5, 105. , 107.5, 110. , 112.5, 115. , 117.5, 120. , 122.5,\n",
       "       125. , 127.5, 130. , 132.5, 135. , 137.5, 140. , 142.5, 145. , 147.5,\n",
       "       150. , 152.5, 155. , 157.5, 160. , 162.5, 165. , 167.5, 170. , 172.5,\n",
       "       175. , 177.5, 180. , 182.5, 185. , 187.5, 190. , 192.5, 195. , 197.5,\n",
       "       200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
       "       325. , 327.5, 330. , 332.5, 335. , 337.5, 340. , 342.5, 345. , 347.5,\n",
       "       350. , 352.5, 355. , 357.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0001-01-01 00:00:00 ... 0001-12-...</div><input id='attrs-3cb8ef17-4a22-45aa-8de5-484663479d35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cb8ef17-4a22-45aa-8de5-484663479d35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ce41357-c34f-4f2c-ba02-cf6a6a410b85' class='xr-var-data-in' type='checkbox'><label for='data-2ce41357-c34f-4f2c-ba02-cf6a6a410b85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0030-00-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>climatology :</span></dt><dd>climatology_bounds</dd><dt><span>climo_period :</span></dt><dd>1981/01/01 - 2010/12/31</dd><dt><span>interpreted_actual_range :</span></dt><dd>0001/01/01 00:00:00 - 0001/12/01 00:00:00</dd><dt><span>actual_range :</span></dt><dd>[-657073. -656739.]</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(1, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "       cftime.DatetimeGregorian(1, 12, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cea16099-208a-4e70-9105-5e323e8925e2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cea16099-208a-4e70-9105-5e323e8925e2' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>climatology_bounds</span></div><div class='xr-var-dims'>(time, nbnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3e22655-cf6a-4d23-8528-d86a8adb447d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3e22655-cf6a-4d23-8528-d86a8adb447d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27becaf0-8de3-485e-8ab5-6c9a64e9cc6a' class='xr-var-data-in' type='checkbox'><label for='data-27becaf0-8de3-485e-8ab5-6c9a64e9cc6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Climate Time Boundaries</dd></dl></div><div class='xr-var-data'><pre>array([[cftime.DatetimeGregorian(1981, 1, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 1, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 2, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 2, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 3, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 3, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 4, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 4, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 5, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 5, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 6, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 6, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 7, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 7, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 8, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 8, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 9, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 9, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 10, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 10, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 11, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 11, 1, 0, 0, 0, 0, has_year_zero=False)],\n",
       "       [cftime.DatetimeGregorian(1981, 12, 1, 0, 0, 0, 0, has_year_zero=False),\n",
       "        cftime.DatetimeGregorian(2010, 12, 1, 0, 0, 0, 0, has_year_zero=False)]],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2efecc51-9ab7-4c54-80e9-e8cb783b8a8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2efecc51-9ab7-4c54-80e9-e8cb783b8a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48ec51c0-0e65-46e3-9cdd-264fb5a9c18b' class='xr-var-data-in' type='checkbox'><label for='data-48ec51c0-0e65-46e3-9cdd-264fb5a9c18b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Long Term Mean of Air temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>var_desc :</span></dt><dd>Air Temperature</dd><dt><span>level_desc :</span></dt><dd>Multiple levels</dd><dt><span>statistic :</span></dt><dd>Long Term Mean</dd><dt><span>parent_stat :</span></dt><dd>Mean</dd><dt><span>valid_range :</span></dt><dd>[-200.  300.]</dd><dt><span>actual_range :</span></dt><dd>[-89.722336  41.616005]</dd><dt><span>dataset :</span></dt><dd>NCEP Reanalysis Derived Products</dd></dl></div><div class='xr-var-data'><pre>[2144448 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>valid_yr_count</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eef0ed2d-fed8-468d-b390-bdc98dc35359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eef0ed2d-fed8-468d-b390-bdc98dc35359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bacef7b1-494f-488e-98aa-df3003ae1c5b' class='xr-var-data-in' type='checkbox'><label for='data-bacef7b1-494f-488e-98aa-df3003ae1c5b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>count of non-missing values used in mean</dd></dl></div><div class='xr-var-data'><pre>[2144448 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e787be85-3664-4d19-9242-14b2cf0ce881' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e787be85-3664-4d19-9242-14b2cf0ce881' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd> Data from NCEP initialized reanalysis (4x/day).  These are interpolated to pressure surfaces from model (sigma) surfaces.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>not_missing_threshold_percent :</span></dt><dd>minimum 3% values input to have non-missing output value</dd><dt><span>history :</span></dt><dd>Created 2011/07/12 by doMonthLTM\n",
       "Converted to chunked, deflated non-packed NetCDF4 2014/09</dd><dt><span>title :</span></dt><dd>monthly ltm air from the NCEP Reanalysis</dd><dt><span>dataset_title :</span></dt><dd>NCEP-NCAR Reanalysis 1</dd><dt><span>References :</span></dt><dd>http://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.derived.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (level: 17, lat: 73, lon: 144, time: 12, nbnds: 2)\n",
       "Coordinates:\n",
       "  * level               (level) float32 1e+03 925.0 850.0 ... 30.0 20.0 10.0\n",
       "  * lat                 (lat) float32 90.0 87.5 85.0 82.5 ... -85.0 -87.5 -90.0\n",
       "  * lon                 (lon) float32 0.0 2.5 5.0 7.5 ... 352.5 355.0 357.5\n",
       "  * time                (time) object 0001-01-01 00:00:00 ... 0001-12-01 00:0...\n",
       "Dimensions without coordinates: nbnds\n",
       "Data variables:\n",
       "    climatology_bounds  (time, nbnds) object ...\n",
       "    air                 (time, level, lat, lon) float32 ...\n",
       "    valid_yr_count      (time, level, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    description:                     Data from NCEP initialized reanalysis (4...\n",
       "    platform:                       Model\n",
       "    Conventions:                    COARDS\n",
       "    not_missing_threshold_percent:  minimum 3% values input to have non-missi...\n",
       "    history:                        Created 2011/07/12 by doMonthLTM\\nConvert...\n",
       "    title:                          monthly ltm air from the NCEP Reanalysis\n",
       "    dataset_title:                  NCEP-NCAR Reanalysis 1\n",
       "    References:                     http://www.psl.noaa.gov/data/gridded/data..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The NOAA ESRL server is shutdown! January 2019\n",
    "ncep = xr.open_dataset('./files/air.mon.ltm.1981-2010.nc',use_cftime=True)\n",
    "\n",
    "ncep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4b8ea68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the area-weighted temperature over its domain. This dataset has a regular latitude/ longitude grid, \n",
    "# thus the grid cell area decreases towards the pole. For this grid we can use the cosine of the latitude as proxy \n",
    "# for the grid cell area.\n",
    "weights = np.cos(np.deg2rad(ncep.lat))\n",
    "\n",
    "# Use the xarray function to weight the air temperature array\n",
    "air_weighted = ncep.air.weighted(weights)\n",
    "\n",
    "# Take the mean over lat/lon/time to get a mean vertical profile\n",
    "weighted_mean = air_weighted.mean((\"lat\",\"lon\", \"time\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba5c0e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13ad4dab0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  a \"quick and dirty\" visualization of the data\n",
    "weighted_mean.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35e4f970",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the metpy library\n",
    "from metpy.plots import SkewT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f57ce26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Global, annual mean sounding from NCEP Reanalysis')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "\n",
    "# Create the skew-plot with the metpy module (see manual for details)\n",
    "skew = SkewT(fig, rotation=30)\n",
    "skew.plot(weighted_mean.level, weighted_mean, color='black', linestyle='-', linewidth=2, label='Observations')\n",
    "\n",
    "# Scale the x and y axis\n",
    "skew.ax.set_ylim(1050, 10)\n",
    "skew.ax.set_xlim(-75, 45)\n",
    "\n",
    "# Add the adiabats to the plot\n",
    "skew.plot_dry_adiabats(linewidth=0.5)\n",
    "skew.plot_moist_adiabats(linewidth=0.5)\n",
    "\n",
    "# Adde axis labels and legend\n",
    "skew.ax.legend()\n",
    "skew.ax.set_title('Global, annual mean sounding from NCEP Reanalysis', \n",
    "             fontsize = 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1866adcd",
   "metadata": {},
   "source": [
    "**Task 3**: What is the surface temperature with the single layer model? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55eceb48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface temperature: 303.14\n"
     ]
    }
   ],
   "source": [
    "# Solve for the atmospheric surface temperature\n",
    "\n",
    "# Calc surface temperature\n",
    "Ts = 2**(1/4) * Te\n",
    "print('Surface temperature: {:.2f}'.format(Ts))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "355993ad",
   "metadata": {},
   "source": [
    "Why does the model overestimate the surface temperature?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f64ed6e",
   "metadata": {},
   "source": [
    "**Task 5**: Write a Python function for $OLR = U_2 = (1-\\epsilon)^2 \\sigma T_s^4 + \\epsilon(1-\\epsilon)\\sigma T_0^4 + \\epsilon \\sigma T_1^4$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e17d7889",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function calculates the OLR of the two-layer model\n",
    "def two_layer_model(Ts, T0, T1, epsilon):\n",
    "    return ((1-epsilon)**2)*sigma*Ts**4 + epsilon*(1-epsilon)*sigma*T0**4 + epsilon*sigma*T1**4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31ad12d",
   "metadata": {},
   "source": [
    "**Task 6**: We will tune our model so that it reproduces the observed global mean OLR given observed global mean temperatures. Determine the temperatures for the two-layer model from the following sounding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ecf228",
   "metadata": {},
   "source": [
    "![alt text](pics/vertical_profile.png \"Sounding\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ca1797f",
   "metadata": {},
   "source": [
    "**Task 8**: Find graphically the best fit value of $\\epsilon$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "443de545",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimized transmissivity is: 0.59\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assignments\n",
    "OLR = []        # initialize array\n",
    "epsilons = []   # initialize array\n",
    "OLR_obs = 238.5 # observed outgoing long-wave radiation\n",
    "\n",
    "def find_nearest(array, value):\n",
    "    \"\"\" \n",
    "    Auxiliary function to find index of closest value in an array \n",
    "    array :: input array\n",
    "    value :: the value to find in the array\n",
    "    \"\"\"\n",
    "    # This searches the minimum between the values and all values in an array\n",
    "    # Basically, we enumerate over the array. The enumerator iterates over the array and returns a \n",
    "    # tuple (index, value) for each element. We take the value (x[1]) from this tuple and substract the value\n",
    "    # we are searching for. The element which has the smallest difference is what we are looking for. We finally\n",
    "    # return the index of this value.\n",
    "    idx,val = min(enumerate(array), key=lambda x: abs(x[1]-value))\n",
    "    return idx\n",
    "\n",
    "# Optimize epsilon\n",
    "# We define a range from 0 to 1 with a 0.01 step and calculate the OLR for each of these epsilon values\n",
    "for eps in np.arange(0, 1, 0.01):\n",
    "    OLR.append(OLR_obs - two_layer_model(288, 275, 230, eps))\n",
    "    # Store the results in the epsilon-array\n",
    "    epsilons.append(eps)\n",
    "\n",
    "# Now, find the closest value to the observed OLR using the previously defined function\n",
    "idx = find_nearest(OLR, 0)\n",
    "\n",
    "# Save the optimized epsilon in the epsilons-array\n",
    "epsilon = epsilons[idx]\n",
    "\n",
    "# Plot the results\n",
    "print('The optimized transmissivity is: {:.2f}'.format(epsilons[idx]))\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.plot(epsilons,OLR);\n",
    "plt.scatter(epsilons[idx], OLR[idx], s=50,  color='r')\n",
    "plt.hlines(0,0,1,linestyles='dotted',color='gray');\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df7658e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The modelled OLR is 237.63, while the observed value is 238.5\n"
     ]
    }
   ],
   "source": [
    "# Validate the result\n",
    "print('The modelled OLR is {:.2f}, while the observed value is 238.5'.format(two_layer_model(288, 275, 230, epsilon)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274ebcab",
   "metadata": {},
   "source": [
    "**Task 9**: Write a Python function to calculate each term in the OLR. Plug-in the observed temperatures and the tuned value for epsilon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ce7034f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_layer_terms(Ts, T0, T1, epsilon):\n",
    "    \"\"\"\n",
    "    This is the same as the two-layer model but instead of returning the OLR this function return the \n",
    "    individual terms of the two-layer equation\n",
    "    \"\"\"\n",
    "    return ( ((1-epsilon)**2)*sigma*Ts**4, epsilon*(1-epsilon)*sigma*T0**4, epsilon*sigma*T1**4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e4c7a227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "> **Term 1:** $65.57 ~ Wm^2$ <br> **Term 2:** $78.44 ~ Wm^2$ <br> **Term 3:** $93.62 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Total** (sum of all terms): $237.63 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculates the individual terms\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon)\n",
    "\n",
    "display(Markdown(r\"\"\"> **Term 1:** ${:.2f} ~ Wm^2$ <br> **Term 2:** ${:.2f} ~ Wm^2$ <br> **Term 3:** ${:.2f} ~ Wm^2$\"\"\".format(term1, term2, term3)))\n",
    "display(Markdown(r\"\"\"> **Total** (sum of all terms): ${:.2f} ~ Wm^2$\"\"\".format(term1+term2+term3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468d2b55",
   "metadata": {},
   "source": [
    "**Task 10**: Changing the level of emission by adding absorbers, e.g. by 10 %. \n",
    "Suppose further that this increase happens abruptly so that there is no time for the temperatures to respond to this change. We hold the temperatures fixed in the column and ask how the radiative fluxes change.\n",
    "\n",
    "Which terms in the OLR go up and which go down?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec0946f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "> **Term 1:** $37.49 ~ Wm^2$ <br> **Term 2:** $69.36 ~ Wm^2$ <br> **Term 3:** $109.48 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Total** (sum of all terms): $216.33 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculates the individual terms\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon+0.1)\n",
    "\n",
    "display(Markdown(r\"\"\"> **Term 1:** ${:.2f} ~ Wm^2$ <br> **Term 2:** ${:.2f} ~ Wm^2$ <br> **Term 3:** ${:.2f} ~ Wm^2$\"\"\".format(term1, term2, term3)))\n",
    "display(Markdown(r\"\"\"> **Total** (sum of all terms): ${:.2f} ~ Wm^2$\"\"\".format(term1+term2+term3)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb75e928",
   "metadata": {},
   "source": [
    "**Task 11**: Calculate the radiative forcing for the previous simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c1eed0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "> **RS**: $28.09 ~ Wm^2$ <br>\n",
       "                       **R0**: $9.08 ~ Wm^2$ <br>\n",
       "                       **R1**: $-15.87 ~ Wm^2$\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Radiative forcing**: $21.30 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First calculate the unperturbed terms\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon)\n",
    "# Now, add the perturbation to the epsilon values\n",
    "term1p, term2p, term3p = two_layer_terms(288, 275, 230, epsilon+0.1)\n",
    "\n",
    "# Print the results\n",
    "display(Markdown(r\"\"\"> **RS**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R0**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R1**: ${:.2f} ~ Wm^2$\n",
    "                    \"\"\".format(-(term1p-term1),\n",
    "                               -(term2p-term2),\n",
    "                               -(term3p-term3))))\n",
    "\n",
    "display(Markdown(r\"\"\"> **Radiative forcing**: ${:.2f} ~ Wm^2$\"\"\".format(-(term1p-term1)-(term2p-term2)-(term3p-term3))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "808791fc",
   "metadata": {},
   "source": [
    "**Task 12**: What is the greenhouse effect for an isothermal atmosphere?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "228fb83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "> **RS**: $28.09 ~ Wm^2$ <br>\n",
       "                       **R0**: $10.92 ~ Wm^2$ <br>\n",
       "                       **R1**: $-39.01 ~ Wm^2$\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Radiative forcing**: $-0.00 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First calculate the unperturbed terms\n",
    "term1, term2, term3 = two_layer_terms(288, 288, 288, epsilon)\n",
    "# Now, add the perturbation to the epsilon values\n",
    "term1p, term2p, term3p = two_layer_terms(288, 288, 288, epsilon+0.1)\n",
    "\n",
    "# Print the results\n",
    "display(Markdown(r\"\"\"> **RS**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R0**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R1**: ${:.2f} ~ Wm^2$\n",
    "                    \"\"\".format(-(term1p-term1),\n",
    "                               -(term2p-term2),\n",
    "                               -(term3p-term3))))\n",
    "\n",
    "display(Markdown(r\"\"\"> **Radiative forcing**: ${:.2f} ~ Wm^2$\"\"\".format(-(term1p-term1)-(term2p-term2)-(term3p-term3))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abd7541",
   "metadata": {},
   "source": [
    "**Task 13**: For a more realistic example of radiative forcing due to an increase in greenhouse absorbers, we use our observed temperatures and the tuned value for epsilon. Assume an increase of epsilon by 2 %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f538d06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The epsilon disturbance: 0.012 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "> **RS**: $3.72 ~ Wm^2$ <br>\n",
       "                       **R0**: $0.73 ~ Wm^2$ <br>\n",
       "                       **R1**: $-1.87 ~ Wm^2$\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Radiative forcing**: $2.58 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perturb the epsilon values by 2 %\n",
    "depsilon = epsilon * 0.02\n",
    "print('The epsilon disturbance: {:.3f} \\n'.format(depsilon))\n",
    "\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon)\n",
    "term1p, term2p, term3p = two_layer_terms(288, 275, 230, epsilon+depsilon)\n",
    "\n",
    "# Print the results\n",
    "display(Markdown(r\"\"\"> **RS**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R0**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R1**: ${:.2f} ~ Wm^2$\n",
    "                    \"\"\".format(-(term1p-term1),\n",
    "                               -(term2p-term2),\n",
    "                               -(term3p-term3))))\n",
    "\n",
    "display(Markdown(r\"\"\"> **Radiative forcing**: ${:.2f} ~ Wm^2$\"\"\".format(-(term1p-term1)-(term2p-term2)-(term3p-term3))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9b8a0680",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def warming_per_year(ASR=0.2):\n",
    "    '''ASR = absorbed shortwave radiation'''\n",
    "    # Surface of the earth\n",
    "    area_earth = 4*np.pi*6371000**2\n",
    "\n",
    "    # Pressure level\n",
    "    p = 5e4\n",
    "\n",
    "    # Total mass above 500 hPa [kg]\n",
    "    m_atmos = (area_earth*p)/9.81\n",
    "\n",
    "\n",
    "    # J\n",
    "    J_per_m2 = ASR*60*60*24\n",
    "    J = J_per_m2*area_earth\n",
    "\n",
    "    # J/kg\n",
    "    cp = 1004\n",
    "\n",
    "    # warming per day\n",
    "    warming = (J / m_atmos) / cp\n",
    "\n",
    "    #display(Markdown(r\"\"\"> Warming per year: ${:.2f} ~ K$ <br>\n",
    "    #                \"\"\".format(warming*365)))\n",
    "    \n",
    "    return warming*365"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23eab7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sigma = 5.67e-8\n",
    "epsilon = 0.59\n",
    "\n",
    "# The function calculates the OLR of the two-layer model\n",
    "def two_layer_model_aerosols(Ts, T0, T1, epsilon, asr):\n",
    "    T1 = T1 + warming_per_year(asr)\n",
    "    T0 = T0 + + warming_per_year(asr/10)\n",
    "    return ((1-epsilon)**2)*sigma*Ts**4 + epsilon*(1-epsilon)*sigma*T0**4 + epsilon*sigma*T1**4\n",
    "\n",
    "def two_layer_terms(Ts, T0, T1, epsilon, asr, lvl):\n",
    "    \"\"\"\n",
    "    This is the same as the two-layer model but instead of returning the OLR this function return the \n",
    "    individual terms of the two-layer equation\n",
    "    \"\"\"\n",
    "    if lvl == 1:\n",
    "        T1 = T1 + warming_per_year(asr)\n",
    "    elif lvl == 0:\n",
    "        T0 = T0 + warming_per_year(asr)\n",
    "    cp_soil = 1900\n",
    "    Ts = (((1-0.32)*(342-asr))/(0.59*sigma))**(1/4)\n",
    "    print(Ts,T0,T1)\n",
    "    return ( ((1-epsilon)**2)*sigma*Ts**4, epsilon*(1-epsilon)*sigma*T0**4, epsilon*sigma*T1**4)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "379d2eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "288.75199073610895 275.0 230\n",
      "288.1166689759792 293.4881370517928 230\n",
      "Term1 66.26 Term2 78.44 Term3: 93.62\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "> **RS**: $0.58 ~ Wm^2$ <br>\n",
       "                       **R0**: $-23.32 ~ Wm^2$ <br>\n",
       "                       **R1**: $-0.00 ~ Wm^2$\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Radiative forcing**: $-22.74 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown, Latex, Math\n",
    "\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon, asr=0.0, lvl=0)\n",
    "term1p, term2p, term3p = two_layer_terms(288, 275, 230, epsilon+0.0, asr=3.0, lvl=0)\n",
    "\n",
    "# Print the results\n",
    "print('Term1 {:.2f} Term2 {:.2f} Term3: {:.2f}'.format(term1, term2, term3))\n",
    "\n",
    "display(Markdown(r\"\"\"> **RS**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R0**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R1**: ${:.2f} ~ Wm^2$\n",
    "                    \"\"\".format(-(term1p-term1),\n",
    "                               -(term2p-term2),\n",
    "                               -(term3p-term3))))\n",
    "\n",
    "display(Markdown(r\"\"\"> **Radiative forcing**: ${:.2f} ~ Wm^2$\"\"\".format(-(term1p-term1)-(term2p-term2)-(term3p-term3))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68e9e99a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "288.75199073610895 275 230.0\n",
      "288.5406828809315 275 236.16271235059762\n",
      "Term1 66.26 Term2 78.44 Term3: 93.62\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "> **RS**: $0.19 ~ Wm^2$ <br>\n",
       "                       **R0**: $-0.00 ~ Wm^2$ <br>\n",
       "                       **R1**: $-10.44 ~ Wm^2$\n",
       "                    "
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "> **Radiative forcing**: $-10.25 ~ Wm^2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Markdown, Latex, Math\n",
    "\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, epsilon, asr=0.0, lvl=1)\n",
    "term1p, term2p, term3p = two_layer_terms(288, 275, 230, epsilon+0.0, asr=1.0, lvl=1)\n",
    "\n",
    "# Print the results\n",
    "print('Term1 {:.2f} Term2 {:.2f} Term3: {:.2f}'.format(term1, term2, term3))\n",
    "\n",
    "display(Markdown(r\"\"\"> **RS**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R0**: ${:.2f} ~ Wm^2$ <br>\n",
    "                       **R1**: ${:.2f} ~ Wm^2$\n",
    "                    \"\"\".format(-(term1p-term1),\n",
    "                               -(term2p-term2),\n",
    "                               -(term3p-term3))))\n",
    "\n",
    "display(Markdown(r\"\"\"> **Radiative forcing**: ${:.2f} ~ Wm^2$\"\"\".format(-(term1p-term1)-(term2p-term2)-(term3p-term3))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2b2b44ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288.75199073610895"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(((1-0.32)*342)/(epsilon*sigma))**(1/4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7d2229f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.599716982210949e+18"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "area_earth = 4*np.pi*6371000**2\n",
    "\n",
    "\n",
    "# Pressure level\n",
    "p = 5e4\n",
    "\n",
    "# Total mass above 500 hPa [kg]\n",
    "m_atmos = (area_earth*p)/9.81\n",
    "m_atmos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2493d25",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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