{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2fb55d1",
   "metadata": {},
   "source": [
    "(greenhouse:exercise)=\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 Te\n",
    "# Put you own code here\n",
    "sigma = 5.67e-8\n",
    "Q = 342\n",
    "albedo = 0.3\n",
    "\n",
    "Te = (((1-albedo)*Q)/sigma)**(1/4)\n",
    "\n",
    "print('Radiative equilibrium temperature: {:.2f}'.format(Te))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec37719",
   "metadata": {},
   "source": [
    "**Task 2**: Where in the atmosphere do we find $T_e$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c80a19b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "263016e7",
   "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-13348c80-c098-40f9-a716-d55a85af2add' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13348c80-c098-40f9-a716-d55a85af2add' 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-e8eaa2ba-d2d5-4b44-bfc8-8fa4a66cb5be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8eaa2ba-d2d5-4b44-bfc8-8fa4a66cb5be' 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-12da761a-3311-448d-bcd5-ecfcbf5a9a29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12da761a-3311-448d-bcd5-ecfcbf5a9a29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54bc87e6-a3cf-4d8b-b46d-1bb77091d692' class='xr-var-data-in' type='checkbox'><label for='data-54bc87e6-a3cf-4d8b-b46d-1bb77091d692' 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-6cf7cd3a-27ea-4d17-a4ba-adc2ab190bb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cf7cd3a-27ea-4d17-a4ba-adc2ab190bb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95968fc4-a054-4d78-ad8f-76cb20385326' class='xr-var-data-in' type='checkbox'><label for='data-95968fc4-a054-4d78-ad8f-76cb20385326' 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-1665d58a-1f28-4d42-8dc6-6acbc56a381e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1665d58a-1f28-4d42-8dc6-6acbc56a381e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7a3a488-21f7-42b6-920c-fae1c124dab8' class='xr-var-data-in' type='checkbox'><label for='data-f7a3a488-21f7-42b6-920c-fae1c124dab8' 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-03736222-11a2-4afb-8778-65a5cb782880' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03736222-11a2-4afb-8778-65a5cb782880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0b6b92b-f71c-45e4-8dd8-8e0e6fb84757' class='xr-var-data-in' type='checkbox'><label for='data-c0b6b92b-f71c-45e4-8dd8-8e0e6fb84757' 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-4c764b9a-e9dc-4bb5-afca-a89294f58cd1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c764b9a-e9dc-4bb5-afca-a89294f58cd1' 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-c4d65f4d-aa9d-46e8-a955-a9804196a767' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4d65f4d-aa9d-46e8-a955-a9804196a767' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d6afebc-fe33-4377-a12f-1bd005e83822' class='xr-var-data-in' type='checkbox'><label for='data-1d6afebc-fe33-4377-a12f-1bd005e83822' 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-6f31512a-1178-49cd-8268-fd7f851795e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f31512a-1178-49cd-8268-fd7f851795e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1596fa87-bd23-40ed-9661-5a3638764f3e' class='xr-var-data-in' type='checkbox'><label for='data-1596fa87-bd23-40ed-9661-5a3638764f3e' 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-c88bd6da-5672-4fc0-9377-dde656130e2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c88bd6da-5672-4fc0-9377-dde656130e2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd012b71-aa37-4cd2-80d2-f4558c1ee5d1' class='xr-var-data-in' type='checkbox'><label for='data-fd012b71-aa37-4cd2-80d2-f4558c1ee5d1' 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-65ddf817-c800-46ee-bac4-3880ce3cfb35' class='xr-section-summary-in' type='checkbox'  checked><label for='section-65ddf817-c800-46ee-bac4-3880ce3cfb35' 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": [
    "## NCAR data\n",
    "url = 'air.mon.ltm.1981-2010.nc'\n",
    "nc = xr.open_dataset('./files/air.mon.ltm.1981-2010.nc', use_cftime = True)\n",
    "nc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94b52260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x11fa27940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Take global, annual average and convert to Kelvin\n",
    "nc.air.sel(time='0001-06-01 00:00:00',level=1000).plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "332b4f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c0a7130>]"
      ]
     },
     "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",
    "weights = np.cos(np.deg2rad(nc.lat))\n",
    "air_weighted = nc.air.weighted(weights)\n",
    "\n",
    "weighted_mean = air_weighted.mean((\"lat\",'lon','time'))\n",
    "weighted_mean.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbeab6ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "97a1c94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x28562e620>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the skew-plot with the metpy module\n",
    "from metpy.plots import SkewT\n",
    "\n",
    "fig = plt.figure(figsize=(10,7))\n",
    "\n",
    "skew = SkewT(fig, rotation=30)\n",
    "skew.plot(weighted_mean.level, weighted_mean, color='black', linestyle='-')\n",
    "\n",
    "skew.plot_dry_adiabats(linewidth=0.5)\n",
    "skew.plot_moist_adiabats(linewidth=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1866adcd",
   "metadata": {},
   "source": [
    "**Task 3**: What is the surface temperature with the single layer model? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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 Ts\n",
    "# Put you own code here\n",
    "Ts = 2**(1/4) * Te\n",
    "\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": 17,
   "id": "e17d7889",
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_layer_model(Ts, T0, T1, epsilon):\n",
    "    return ((1-epsilon)**2)*sigma*Ts**4 + epsilon*(1-epsilon)*sigma*T0**4 \\\n",
    "+ epsilon*sigma*T1**4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c31ad12d",
   "metadata": {},
   "source": [
    "**Task 7**: 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": 20,
   "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",
    "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",
    "# 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",
    "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",
    "# 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": 21,
   "id": "df7658e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237.629744608674"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_layer_model(288, 275, 230, 0.59)"
   ]
  },
  {
   "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": 22,
   "id": "ce7034f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def two_layer_terms(Ts, T1, T0, epsilon):\n",
    "    return ( ((1-epsilon)**2)*sigma*Ts**4, \\\n",
    "            epsilon*(1-epsilon)*sigma*T0**4, \\\n",
    "            epsilon*sigma*T1**4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e4c7a227",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Term Ts: 65.57\n",
      "Term T0: 38.38\n",
      "Term T1: 191.32\n"
     ]
    }
   ],
   "source": [
    "# Calculate terms\n",
    "term1, term2, term3 = two_layer_terms(288, 275, 230, 0.59)\n",
    "\n",
    "print('Term Ts: {:.2f}'.format(term1))\n",
    "print('Term T0: {:.2f}'.format(term2))\n",
    "print('Term T1: {:.2f}'.format(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": [],
   "source": [
    "# Make simulation here"
   ]
  },
  {
   "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": [],
   "source": [
    "# Calculate radiative forcing"
   ]
  },
  {
   "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": [],
   "source": [
    "# Make simulation here"
   ]
  },
  {
   "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": [],
   "source": [
    "# Make simulation here"
   ]
  }
 ],
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