{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENV / ATM 415: Climate Laboratory\n",
    "\n",
    "# Exercises on Radiative-Convective Equilibrium with `climlab`\n",
    "\n",
    "Tuesday April 5, 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as nc\n",
    "import climlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First, let's just repeat the calculations we did in the previous notebook `RadiativeConvectiveEquilibrium.ipynb`\n",
    "\n",
    "(Just by copying and pasting the relevant code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ncep_filename = 'air.mon.1981-2010.ltm.nc'\n",
    "# This will try to read the data over the internet.\n",
    "#ncep_url = \"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis.derived/\"\n",
    "#ncep_air = nc.Dataset( ncep_url + 'pressure/' + ncep_filename )\n",
    "# Or to read from local disk\n",
    "ncep_air = nc.Dataset( ncep_filename )\n",
    "\n",
    "level = ncep_air.variables['level'][:]\n",
    "lat = ncep_air.variables['lat'][:]\n",
    "# A log-pressure height coordinate\n",
    "zstar = -np.log(level/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Take averages of the temperature data\n",
    "Tzon = np.mean(ncep_air.variables['air'][:],axis=(0,3))\n",
    "Tglobal = np.average( Tzon , weights=np.cos(np.deg2rad(lat)), axis=1) + climlab.constants.tempCtoK\n",
    "#  Note the useful conversion factor. climlab.constants has lots of commonly used constant pre-defined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Q7d6f8MMgfcVgrXjb49kq099N/p3i7p+Wz3R3N7P3CUNXpF+PjQi/ROcB/zaz8lVLSl9u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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1122f5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Here we are plotting with respect to log(pressure) but labeling the axis in pressure units\n",
    "fig = plt.figure( figsize=(8,6) )\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot( Tglobal , zstar )\n",
    "yticks = np.array([1000., 750., 500., 250., 100., 50., 20., 10.])\n",
    "ax.set_yticks(-np.log(yticks/1000.))\n",
    "ax.set_yticklabels(yticks)\n",
    "ax.set_xlabel('Temperature (K)', fontsize=16)\n",
    "ax.set_ylabel('Pressure (hPa)', fontsize=16 )\n",
    "ax.set_title('Global, annual mean sounding from NCEP Reanalysis', fontsize = 24)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Repeating code from previous notebook ...  set up a column model with observed temperatures.\n",
    "\n",
    "#  initialize a grey radiation model with 30 levels\n",
    "col = climlab.GreyRadiationModel()\n",
    "# interpolate to 30 evenly spaced pressure levels\n",
    "lev = col.lev\n",
    "Tinterp = np.interp(lev, np.flipud(level), np.flipud(Tglobal))\n",
    "# Initialize model with observed temperatures\n",
    "col.Ts[:] = Tglobal[0]\n",
    "col.Tatm[:] = Tinterp\n",
    "#  The tuned value of absorptivity\n",
    "eps = 0.0534\n",
    "#  set it\n",
    "col.subprocess.LW.absorptivity = eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 730 steps, 730.4844 days, or 2.0 years.\n",
      "Total elapsed time is 1.99867375676 years.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -4.63536367e-07])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Pure radiative equilibrium\n",
    "\n",
    "#  Make a clone of our first model\n",
    "re = climlab.process_like(col)\n",
    "#  Run out to equilibrium\n",
    "re.integrate_years(2.)\n",
    "#  Check for energy balance\n",
    "re.ASR - re.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 730 steps, 730.4844 days, or 2.0 years.\n",
      "Total elapsed time is 1.99867375676 years.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  1.96076849e-06])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  And set up a RadiativeConvective model, \n",
    "rce = climlab.RadiativeConvectiveModel(adj_lapse_rate=6.)\n",
    "# Set our tuned absorptivity value\n",
    "rce.subprocess.LW.absorptivity = eps\n",
    "#  Run out to equilibrium\n",
    "rce.integrate_years(2.)\n",
    "#  Check for energy balance\n",
    "rce.ASR - rce.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  A handy re-usable routine for making a plot of the temperature profiles\n",
    "#  We will plot temperatures with respect to log(pressure) to get a height-like coordinate\n",
    "def plot_sounding(collist):\n",
    "    color_cycle=['r', 'g', 'b', 'y', 'm']\n",
    "    # col is either a column model object or a list of column model objects\n",
    "    if isinstance(collist, climlab.Process):\n",
    "        # make a list with a single item\n",
    "        collist = [collist]\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    for i, col in enumerate(collist):\n",
    "        zstar = -np.log(col.lev/climlab.constants.ps)\n",
    "        ax.plot(col.Tatm, zstar, color=color_cycle[i])\n",
    "        ax.plot(col.Ts, 0, 'o', markersize=12, color=color_cycle[i])\n",
    "    #ax.invert_yaxis()\n",
    "    yticks = np.array([1000., 750., 500., 250., 100., 50., 20., 10.])\n",
    "    ax.set_yticks(-np.log(yticks/1000.))\n",
    "    ax.set_yticklabels(yticks)\n",
    "    ax.set_xlabel('Temperature (K)')\n",
    "    ax.set_ylabel('Pressure (hPa)')\n",
    "    ax.grid()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1137c5690>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Fixzn7JY/2WqhxuQBWbbhiMinuMGes4H2wHpVvT9EZQuamG/DycratfDMM/DV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137d1dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Make a plot to compare observations and Radiative-Convective Equilibrium\n",
    "plot_sounding([col, re, rce])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#  Stratospheric ozone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  To read from internet\n",
    "#datapath = \"http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/Brian+Rose/CESM+runs/som_input/\"\n",
    "#endstr = \"/entry.das\"\n",
    "\n",
    "ozone_filename = 'ozone_1.9x2.5_L26_2000clim_c091112.nc'\n",
    "datapath = ''\n",
    "endstr = ''\n",
    "\n",
    "ozone = nc.Dataset( datapath + ozone_filename + endstr )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'netCDF4._netCDF4.Variable'>\n",
      "float32 O3(time, lev, lat, lon)\n",
      "    units: mol/mol\n",
      "    long_name: O3 concentration\n",
      "    cell_method: time: mean\n",
      "unlimited dimensions: time\n",
      "current shape = (12, 26, 96, 144)\n",
      "filling off\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print ozone.variables['O3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat = ozone.variables['lat'][:]\n",
    "lon = ozone.variables['lon'][:]\n",
    "lev = ozone.variables['lev'][:]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pressure levels in this dataset are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[   3.544638     7.3888135   13.967214    23.944625    37.23029     53.114605\n",
      "   70.05915     85.439115   100.514695   118.250335   139.115395   163.66207\n",
      "  192.539935   226.513265   266.481155   313.501265   368.81798    433.895225\n",
      "  510.455255   600.5242     696.79629    787.70206    867.16076    929.648875\n",
      "  970.55483    992.5561   ]\n"
     ]
    }
   ],
   "source": [
    "print lev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the global average of the ozone climatology, and plot it as a function of pressure (or height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "O3_zon = np.mean( ozone.variables['O3'],axis=(0,3) )\n",
    "O3_global = np.sum( O3_zon * np.cos(np.deg2rad(lat)), axis=1 ) / np.sum( np.cos(np.deg2rad(lat) ) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "O3_global.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vLZJ399+dfZ7J0WAdKQafnoTB4rKZbZ2Ummrg8AiAu481s3USk4cCg6L/rwFa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174a8590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,5))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "cax = ax1.contourf(lat, np.log(lev/climlab.constants.ps), O3_zon * 1.E6)\n",
    "ax1.invert_yaxis()\n",
    "ax1.set_xlabel('Latitude', fontsize=16)\n",
    "ax1.set_ylabel('Pressure (hPa)', fontsize=16 )\n",
    "yticks = np.array([1000., 500., 250., 100., 50., 20., 10., 5.])\n",
    "ax1.set_yticks( np.log(yticks/1000.) )\n",
    "ax1.set_yticklabels( yticks )\n",
    "ax1.set_title('Ozone concentration (annual mean)', fontsize = 16);\n",
    "plt.colorbar(cax)\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "ax2.plot( O3_global * 1.E6, np.log(lev/climlab.constants.ps) )\n",
    "ax2.invert_yaxis()\n",
    "ax2.set_xlabel('Ozone (ppm)', fontsize=16)\n",
    "ax2.set_ylabel('Pressure (hPa)', fontsize=16 )\n",
    "yticks = np.array([1000., 500., 250., 100., 50., 20., 10., 5.])\n",
    "ax2.set_yticks( np.log(yticks/1000.) )\n",
    "ax2.set_yticklabels( yticks )\n",
    "ax2.set_title('Global mean ozone concentration', fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that most of the ozone is indeed in the stratosphere, and peaks near the top of the stratosphere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The `BandRCModel`: a radiative model with individual spectral bands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a brief introduction to the `climlab.BandRCModel` process.\n",
    "\n",
    "This is a model that divides the spectrum into 7 distinct bands: three shortwave and four longwave.\n",
    "\n",
    "As we will see, the process works much like the familiar `climlab.RadiativeConvectiveModel`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About the spectra\n",
    "\n",
    "The shortwave is divided into three channels:\n",
    "\n",
    "- Channel 0 is the Hartley and Huggins band (extreme UV, 200 - 340 nm, 1% of total flux, strong ozone absorption)\n",
    "- Channel 1 is Chappuis band (450 - 800 nm, 27% of total flux, moderate ozone absorption)\n",
    "- Channel 2 is remaining radiation (72% of total flux, largely in the visible range, no ozone absorption)\n",
    "\n",
    "The longwave is divided into four bands:\n",
    "\n",
    "- Band 0 is the window region (between 8.5 and 11 $\\mu$m), 17% of total flux.\n",
    "- Band 1 is the CO2 absorption channel (the band of strong absorption by CO2 around 15 $\\mu$m), 15% of total flux\n",
    "- Band 2 is a weak water vapor absorption channel, 35% of total flux\n",
    "- Band 3 is a strong water vapor absorption channel, 33% of total flux\n",
    "\n",
    "The longwave decomposition is not as easily related to specific wavelengths, as in reality there is a lot of overlap between H$_2$O and CO$_2$ absorption features (as well as absorption by other greenhouse gases such as CH$_4$ and N$_2$O that we are not representing)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Create a Radiative-Convective model using these spectral bands, and put in some ozone\n",
    "\n",
    "Now create a new column model object on the **same pressure levels as the ozone data**. We are also going set an adjusted lapse rate of 6 K / km, and tune the longwave absorption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (26,) \n",
      "  Ts: (1,) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  Create the column with appropriate vertical coordinate, surface albedo and convective adjustment\n",
    "band1 = climlab.BandRCModel(lev=lev, adj_lapse_rate=6)\n",
    "print band1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out the list of subprocesses.\n",
    "\n",
    "We now have a process called `H2O`, in addition to things we've seen before.\n",
    "\n",
    "This model keeps track of water vapor. More on that later!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the radiatively active gases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Band model is aware of three different absorbing gases: O3 (ozone), CO2, and H2O (water vapor). The abundances of these gases are stored in a dictionary of arrays as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038]),\n",
       " 'H2O': Field([ 0.00041834,  0.00031609,  0.00025918,  0.00023079,  0.00022334,\n",
       "         0.00023235,  0.00025805,  0.00030617,  0.00037214,  0.00044727,\n",
       "         0.00053183,  0.00062593,  0.00072953,  0.00084236,  0.00096401,\n",
       "         0.00109382,  0.00123101,  0.00137459,  0.00152342,  0.00167623,\n",
       "         0.00185712,  0.00209789,  0.00241807,  0.00284451,  0.00341587,\n",
       "         0.00416384]),\n",
       " 'O3': Field([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
       "         0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band1.absorber_vmr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ozone and CO2 are both specified in the model. The default, as you see above, is zero ozone, and constant (well-mixed) CO2 at a volume mixing ratio of 3.8E-4 or 380 ppm.\n",
    "\n",
    "Water vapor is handled differently: it is determined by the model at each timestep. We make the following assumptions, following a classic paper on radiative-convective equilibrium by Manabe and Wetherald (J. Atmos. Sci. 1967):\n",
    "\n",
    "- the relative humidity just above the surface is fixed at 77% (can be changed of course... see the parameter `col1.relative_humidity`\n",
    "- water vapor drops off linearly with pressure\n",
    "- there is a small specified amount of water vapor in the stratosphere."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radiative-convective equilibrium in the band model without ozone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Tatm': Field([ 200.  ,  203.12,  206.24,  209.36,  212.48,  215.6 ,  218.72,\n",
       "         221.84,  224.96,  228.08,  231.2 ,  234.32,  237.44,  240.56,\n",
       "         243.68,  246.8 ,  249.92,  253.04,  256.16,  259.28,  262.4 ,\n",
       "         265.52,  268.64,  271.76,  274.88,  278.  ]), 'Ts': Field([ 288.])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band1.state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 730 steps, 730.4844 days, or 2 years.\n",
      "Total elapsed time is 1.99867375676 years.\n"
     ]
    }
   ],
   "source": [
    "band1.integrate_years(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00314361])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for energy balance\n",
    "band1.ASR - band1.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119b114d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JTzNmwKOPwrRp1G92iJQfDoW6RsYYl53pOZx2vrdDcZYMmACcnHtEVZcEtHYBEoo+HIDt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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117615ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Add another line to our graph!\n",
    "plot_sounding([col, re, rce, band1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now put in the ozone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (26,) \n",
      "  Ts: (1,) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "band2 = climlab.process_like(band1)\n",
    "print band2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038]),\n",
       " 'H2O': Field([  5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.72815749e-06,   8.48236360e-06,   1.57838825e-05,\n",
       "          3.35068061e-05,   6.90960430e-05,   1.38620761e-04,\n",
       "          2.70931380e-04,   5.16536496e-04,   9.13087173e-04,\n",
       "          1.43993837e-03,   2.03985499e-03,   2.61271071e-03,\n",
       "          3.03933387e-03,   3.28641981e-03]),\n",
       " 'O3': array([  7.82792878e-06,   8.64150531e-06,   7.58940016e-06,\n",
       "          5.24567137e-06,   3.17761577e-06,   1.82320007e-06,\n",
       "          9.80756956e-07,   6.22870520e-07,   4.47620550e-07,\n",
       "          3.34481165e-07,   2.62570301e-07,   2.07898124e-07,\n",
       "          1.57074554e-07,   1.12425544e-07,   8.06005002e-08,\n",
       "          6.27826493e-08,   5.42990557e-08,   4.99506094e-08,\n",
       "          4.60075681e-08,   4.22977788e-08,   3.80559070e-08,\n",
       "          3.38768568e-08,   3.12171617e-08,   2.97807122e-08,\n",
       "          2.87980968e-08,   2.75429934e-08])}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band2.absorber_vmr['O3'] = O3_global\n",
    "band2.absorber_vmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 730 steps, 730.4844 days, or 2.0 years.\n",
      "Total elapsed time is 3.99734751351 years.\n"
     ]
    }
   ],
   "source": [
    "#  Run the model out to equilibrium!\n",
    "band2.integrate_years(2.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x119c08d10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vq4cCvNMPPCChOUWn6nP4eHaldcvV/9df8O67MHMm+FXsGsvdlUvKNSm0/bgtdfuW+beH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\nUfABx3EcxykKPuA4juM4ReF/P3Cvlpbs5+QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174eac90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Add another line to our graph!\n",
    "plot_sounding([col, re, rce, band1, band2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we include ozone we get a well-defined stratosphere. \n",
    "\n",
    "Things to consider / try:\n",
    "\n",
    "- Here we used the global annual mean Q = 341.3 W m$^{-2}$. We might want to consider latitudinal or seasonal variations in Q.\n",
    "- We also used the global annual mean ozone profile! Ozone varies tremendously in latitude and by season. That information is all contained in the ozone data file we opened above. We might explore the effects of those variations.\n",
    "- We can calculate climate sensitivity in this model by doubling the CO2 concentration and re-running out to the new equilibrium. Does the amount of ozone affect the climate sensitivity?  (example below)\n",
    "- An important shortcoming of the model: there are no clouds! (that would be the next step in the hierarchy of column models)\n",
    "- Clouds would act both in the shortwave (increasing the albedo, cooling the climate) and in the longwave (greenhouse effect, warming the climate). Which effect is stronger depends on the vertical structure of the clouds (high or low clouds) and their optical properties (e.g. thin cirrus clouds are nearly transparent to solar radiation but are good longwave absorbers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Climate sensitivity to doubling CO2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now actually have a model that is aware of the amount of atmospheric CO2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "        0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "        0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "        0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "        0.00038,  0.00038])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "band2.absorber_vmr['CO2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "        0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "        0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "        0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "        0.00076,  0.00076])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's double CO2 and calculate radiative forcing\n",
    "band3 = climlab.process_like(band2)\n",
    "band3.absorber_vmr['CO2'] *= 2.\n",
    "band3.absorber_vmr['CO2']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've just increased CO2 from 380 ppm to 760 ppm.\n",
    "\n",
    "Radiative forcing is the instantaneous change in OLR..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The radiative forcing for doubling CO2 is 1.535000 W/m2.\n"
     ]
    }
   ],
   "source": [
    "band3.compute_diagnostics()\n",
    "print 'The radiative forcing for doubling CO2 is %f W/m2.' % (band2.OLR - band3.OLR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3 years.\n",
      "Total elapsed time is 6.99535814865 years.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([  8.36980604e-07])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  and make another copy, which we will integrate out to equilibrium\n",
    "band4 = climlab.process_like(band3)\n",
    "band4.integrate_years(3)\n",
    "band4.ASR - band4.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Equilibrium Climate Sensitivity is 2.829001 K.\n"
     ]
    }
   ],
   "source": [
    "DeltaT = band4.Ts - band2.Ts\n",
    "print 'The Equilibrium Climate Sensitivity is %f K.' % DeltaT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating the role of water vapor feedback in climate sensitivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make some plots of the vertical profiles of water vapor before and after the climate change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QRx91Hs2aweHDl792Z8k7WfDwAp6d9yxfrf3K5ms3xoQkb7qzflfVaiLyLHBa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\nrBExxhjjM2tEjDHG+Oz/AU+ELPu250U5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119da8510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  We multiply the H2O mixing ratio by 1000 to get units of g / kg\n",
    "#  (amount of water vapor per mass of air)\n",
    "plt.plot( band2.absorber_vmr['H2O'] *1000, lev, label='before 2xCO2')\n",
    "plt.plot( band4.absorber_vmr['H2O'] * 1000., lev, label='after 2xCO2')\n",
    "plt.xlabel('g/kg H2O')\n",
    "plt.ylabel('pressure (hPa)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "#  This reverses the axis so pressure decreases upward\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Water vapor decreases from the surface upward mostly because the temperature decreases!\n",
    "\n",
    "Our model uses a parameterization that holds the **relative humidity** to a fixed profile. Since the saturation point increases strongly with temperature, the actual amount of water vapor increases as the climate warms to preserve the relative humidity.\n",
    "\n",
    "We can see this increase in the graph above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing the fixed-relative-humidity water vapor subprocess\n",
    "\n",
    "Make a new model that does NOT have a water vapor feedback. Hold the H2O amount fixed as we warm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  First, make a new clone\n",
    "noh2o = climlab.process_like(band2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038]),\n",
       " 'H2O': Field([  5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.82819338e-06,   6.63658736e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.81052164e-06,   7.30667103e-06,\n",
       "          9.31673298e-06,   1.20405663e-05,   1.59719937e-05,\n",
       "          3.38913986e-05,   6.98600830e-05,   1.40097861e-04,\n",
       "          2.73714160e-04,   5.21651820e-04,   9.21834609e-04,\n",
       "          1.45336296e-03,   2.05847334e-03,   2.63619532e-03,\n",
       "          3.06639616e-03,   3.31553867e-03]),\n",
       " 'O3': array([  7.82792878e-06,   8.64150531e-06,   7.58940016e-06,\n",
       "          5.24567137e-06,   3.17761577e-06,   1.82320007e-06,\n",
       "          9.80756956e-07,   6.22870520e-07,   4.47620550e-07,\n",
       "          3.34481165e-07,   2.62570301e-07,   2.07898124e-07,\n",
       "          1.57074554e-07,   1.12425544e-07,   8.06005002e-08,\n",
       "          6.27826493e-08,   5.42990557e-08,   4.99506094e-08,\n",
       "          4.60075681e-08,   4.22977788e-08,   3.80559070e-08,\n",
       "          3.38768568e-08,   3.12171617e-08,   2.97807122e-08,\n",
       "          2.87980968e-08,   2.75429934e-08])}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  See what absorbing gases are currently present\n",
    "noh2o.absorber_vmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  double the CO2 !\n",
    "noh2o.absorber_vmr['CO2'] *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (26,) \n",
      "  Ts: (1,) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  Check out the list of subprocesses\n",
    "print noh2o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (26,) \n",
      "  Ts: (1,) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  Remove the process that changes the H2O\n",
    "noh2o.remove_subprocess('H2O')\n",
    "print noh2o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the subprocess labelled `'H2O'` is gone from the list.\n",
    "\n",
    "This was the process that modified the water vapor. But note that the model still contains H2O:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "         0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "         0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "         0.00076,  0.00076,  0.00076,  0.00076,  0.00076,  0.00076,\n",
       "         0.00076,  0.00076]),\n",
       " 'H2O': Field([  5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.82819338e-06,   6.63658736e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.81052164e-06,   7.30667103e-06,\n",
       "          9.31673298e-06,   1.20405663e-05,   1.59719937e-05,\n",
       "          3.38913986e-05,   6.98600830e-05,   1.40097861e-04,\n",
       "          2.73714160e-04,   5.21651820e-04,   9.21834609e-04,\n",
       "          1.45336296e-03,   2.05847334e-03,   2.63619532e-03,\n",
       "          3.06639616e-03,   3.31553867e-03]),\n",
       " 'O3': array([  7.82792878e-06,   8.64150531e-06,   7.58940016e-06,\n",
       "          5.24567137e-06,   3.17761577e-06,   1.82320007e-06,\n",
       "          9.80756956e-07,   6.22870520e-07,   4.47620550e-07,\n",
       "          3.34481165e-07,   2.62570301e-07,   2.07898124e-07,\n",
       "          1.57074554e-07,   1.12425544e-07,   8.06005002e-08,\n",
       "          6.27826493e-08,   5.42990557e-08,   4.99506094e-08,\n",
       "          4.60075681e-08,   4.22977788e-08,   3.80559070e-08,\n",
       "          3.38768568e-08,   3.12171617e-08,   2.97807122e-08,\n",
       "          2.87980968e-08,   2.75429934e-08])}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noh2o.absorber_vmr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But this will be held fixed now as the climate changes in `noh2o`.\n",
    "\n",
    "Let's go ahead and run it out to equilibrium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3 years.\n",
      "Total elapsed time is 6.99535814865 years.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ -4.80395101e-09])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noh2o.integrate_years(3)\n",
    "noh2o.ASR - noh2o.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VJ/76bel9fqWcJSQkhBkzZgDYPy9TwmWTUhljsgI/ACtFZIxt2z4gQETO2Oom60SkojFm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\ncRIxxhhTGycRY4wxtXESMcYYU5v/A0HUQ/GVJ0l9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119c67bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Repeat the same plot of water vapor profiles, but add a third curve\n",
    "#  We'll plot the new model as a dashed line to make it easier to see.\n",
    "plt.plot( band2.absorber_vmr['H2O'] *1000, lev, label='before 2xCO2')\n",
    "plt.plot( band4.absorber_vmr['H2O'] * 1000., lev, label='after 2xCO2')\n",
    "plt.plot( noh2o.absorber_vmr['H2O'] * 1000., lev, linestyle='--', label='No H2O feedback')\n",
    "plt.xlabel('g/kg H2O')\n",
    "plt.ylabel('pressure (hPa)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.grid()\n",
    "#  This reverses the axis so pressure decreases upward\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, the water vapor is identical in the new equilibrium climate to the old pre-CO2-increase model. \n",
    "\n",
    "So this model does NOT have a water vapor feedback.\n",
    "\n",
    "How does this affect the climate sensivity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Equilibrium Climate Sensitivity with water vapor feedback is 1.798071 K.\n"
     ]
    }
   ],
   "source": [
    "DeltaT_noh2o = noh2o.Ts - band2.Ts\n",
    "print 'The Equilibrium Climate Sensitivity with water vapor feedback is %f K.' % DeltaT_noh2o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "So the effect of the water vapor feedback on the climate sensitivity to doubled CO2 is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 1.03093029])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DeltaT - DeltaT_noh2o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get about an additional degree of warming from the water vapor increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MORE MESSING AROUND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (26,) \n",
      "  Ts: (1,) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  Create the column with appropriate vertical coordinate, surface albedo and convective adjustment\n",
    "tuneband = climlab.BandRCModel(lev=lev, adj_lapse_rate=6, albedo_sfc=0.1)\n",
    "print tuneband"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tuneband.absorber_vmr['O3'] = O3_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038]),\n",
       " 'H2O': Field([ 0.00041834,  0.00031609,  0.00025918,  0.00023079,  0.00022334,\n",
       "         0.00023235,  0.00025805,  0.00030617,  0.00037214,  0.00044727,\n",
       "         0.00053183,  0.00062593,  0.00072953,  0.00084236,  0.00096401,\n",
       "         0.00109382,  0.00123101,  0.00137459,  0.00152342,  0.00167623,\n",
       "         0.00185712,  0.00209789,  0.00241807,  0.00284451,  0.00341587,\n",
       "         0.00416384]),\n",
       " 'O3': array([  7.82792878e-06,   8.64150531e-06,   7.58940016e-06,\n",
       "          5.24567137e-06,   3.17761577e-06,   1.82320007e-06,\n",
       "          9.80756956e-07,   6.22870520e-07,   4.47620550e-07,\n",
       "          3.34481165e-07,   2.62570301e-07,   2.07898124e-07,\n",
       "          1.57074554e-07,   1.12425544e-07,   8.06005002e-08,\n",
       "          6.27826493e-08,   5.42990557e-08,   4.99506094e-08,\n",
       "          4.60075681e-08,   4.22977788e-08,   3.80559070e-08,\n",
       "          3.38768568e-08,   3.12171617e-08,   2.97807122e-08,\n",
       "          2.87980968e-08,   2.75429934e-08])}"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.absorber_vmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tuneband.compute_diagnostics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': Field([ 0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038,  0.00038,  0.00038,  0.00038,  0.00038,\n",
       "         0.00038,  0.00038]),\n",
       " 'H2O': Field([  5.00000000e-06,   5.00000000e-06,   5.00000000e-06,\n",
       "          5.00000000e-06,   5.00000000e-06,   7.85107006e-06,\n",
       "          1.31816258e-05,   2.04443889e-05,   3.05742708e-05,\n",
       "          4.48409701e-05,   6.46415045e-05,   9.17579776e-05,\n",
       "          1.28441493e-04,   1.77509570e-04,   2.42458332e-04,\n",
       "          3.27590801e-04,   4.38162609e-04,   5.80546339e-04,\n",
       "          7.62415731e-04,   9.92950889e-04,   1.28254570e-03,\n",
       "          1.64341929e-03,   2.09029713e-03,   2.64031177e-03,\n",
       "          3.31323455e-03,   4.13220797e-03]),\n",
       " 'O3': array([  7.82792878e-06,   8.64150531e-06,   7.58940016e-06,\n",
       "          5.24567137e-06,   3.17761577e-06,   1.82320007e-06,\n",
       "          9.80756956e-07,   6.22870520e-07,   4.47620550e-07,\n",
       "          3.34481165e-07,   2.62570301e-07,   2.07898124e-07,\n",
       "          1.57074554e-07,   1.12425544e-07,   8.06005002e-08,\n",
       "          6.27826493e-08,   5.42990557e-08,   4.99506094e-08,\n",
       "          4.60075681e-08,   4.22977788e-08,   3.80559070e-08,\n",
       "          3.38768568e-08,   3.12171617e-08,   2.97807122e-08,\n",
       "          2.87980968e-08,   2.75429934e-08])}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.absorber_vmr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'CO2': array([[ 0.        ],\n",
       "        [ 1.03157895],\n",
       "        [ 0.        ],\n",
       "        [ 0.        ]]), 'H2O': array([[ 0.    ],\n",
       "        [ 0.    ],\n",
       "        [ 0.0686],\n",
       "        [ 4.9   ]]), 'O3': array([[ 13.72],\n",
       "        [  0.  ],\n",
       "        [  0.  ],\n",
       "        [  0.  ]])}"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.subprocess.LW.absorption_cross_section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  This set of parameters gives correct Ts and ASR\n",
    "#   But seems weird... the radiative forcing for doubling CO2 is tiny\n",
    "#tuneband.subprocess.SW.reflectivity[15] = 0.255\n",
    "#tuneband.subprocess.LW.absorption_cross_section['CO2'][1] = 2.\n",
    "#tuneband.subprocess.LW.absorption_cross_section['H2O'][2] = 0.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Just tune the surface albedo, won't get correct ASR but get correct Ts\n",
    "tuneband = climlab.BandRCModel(lev=lev, adj_lapse_rate=6, albedo_sfc=0.22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total elapsed time is 4.99668439189 years.\n"
     ]
    }
   ],
   "source": [
    "tuneband.integrate_converge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 287.65936792])"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.Ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 270.01721362])"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.ASR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 270.01721362])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q': 341.3,\n",
       " 'abs_coeff': 0.0001229,\n",
       " 'adj_lapse_rate': 6,\n",
       " 'albedo_sfc': 0.22,\n",
       " 'timestep': 86400.0,\n",
       " 'water_depth': 1.0}"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tband2 = climlab.process_like(tuneband)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tband2.subprocess['LW'].absorber_vmr['CO2'] *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tband2.compute_diagnostics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 264.47795732])"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tband2.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tband2.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 264.47795732])"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tband2.OLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total elapsed time is 9.99610669304 years.\n"
     ]
    }
   ],
   "source": [
    "tband2.integrate_converge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 291.98764228])"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tband2.Ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<type 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF4_CLASSIC data model, file format UNDEFINED):\n",
       "    description:  Data from NCEP initialized reanalysis (4x/day).  These are interpolated to pressure surfaces from model (sigma) surfaces.\n",
       "    platform: Model\n",
       "    Conventions: COARDS\n",
       "    not_missing_threshold_percent: minimum 3% values input to have non-missing output value\n",
       "    history: Created 2011/07/12 by doMonthLTM\n",
       "Converted to chunked, deflated non-packed NetCDF4 2014/09\n",
       "    title: monthly ltm air from the NCEP Reanalysis\n",
       "    References: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.derived.html\n",
       "    dimensions(sizes): lon(144), lat(73), level(17), time(12), nbnds(2)\n",
       "    variables(dimensions): float32 \u001b[4mlevel\u001b[0m(level), float32 \u001b[4mlat\u001b[0m(lat), float32 \u001b[4mlon\u001b[0m(lon), float64 \u001b[4mtime\u001b[0m(time), float64 \u001b[4mclimatology_bounds\u001b[0m(time,nbnds), float32 \u001b[4mair\u001b[0m(time,level,lat,lon), float32 \u001b[4mvalid_yr_count\u001b[0m(time,level,lat,lon)\n",
       "    groups: "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ncep_air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'level',\n",
       " u'lat',\n",
       " u'lon',\n",
       " u'time',\n",
       " u'climatology_bounds',\n",
       " u'air',\n",
       " u'valid_yr_count']"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncep_air.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (96, 26) \n",
      "  Ts: (96, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.FixedInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "latmodel = climlab.BandRCModel(lat=lat, lev=lev, adj_lapse_rate=6, albedo_sfc=0.22)\n",
    "print latmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 26)"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latmodel.Tatm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 17, 73, 144)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncep_air.variables['air'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 287.65936792])"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.Ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tuneband.subprocess.SW.albedo_sfc = 0.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total elapsed time is 10.9927056622 years.\n"
     ]
    }
   ],
   "source": [
    "tuneband.integrate_converge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Field([ 260.08872516])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband.Ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<climlab.model.column.BandRCModel at 0x119f3d8d0>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuneband."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.BandRCModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (96, 26) \n",
      "  Ts: (96, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.BandRCModel'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#  make a model on the same grid as the ozone\n",
    "model = climlab.BandRCModel(lev=lev, lat=lat, albedo_sfc=0.22)\n",
    "insolation = climlab.radiation.insolation.AnnualMeanInsolation(domains=model.Ts.domain)\n",
    "model.add_subprocess('insolation', insolation)\n",
    "model.subprocess.SW.flux_from_space = model.subprocess.insolation.insolation\n",
    "print model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 173.71993914],\n",
       "       [ 173.92434149],\n",
       "       [ 174.53503258],\n",
       "       [ 175.5574257 ],\n",
       "       [ 176.99863623],\n",
       "       [ 178.86910604],\n",
       "       [ 181.18324939],\n",
       "       [ 183.96105065],\n",
       "       [ 187.22944147],\n",
       "       [ 191.02560147],\n",
       "       [ 195.40297286],\n",
       "       [ 200.4450058 ],\n",
       "       [ 206.30771663],\n",
       "       [ 213.44201356],\n",
       "       [ 221.5783636 ],\n",
       "       [ 230.2084302 ],\n",
       "       [ 239.12259251],\n",
       "       [ 248.190955  ],\n",
       "       [ 257.32240619],\n",
       "       [ 266.44858025],\n",
       "       [ 275.51580556],\n",
       "       [ 284.4805116 ],\n",
       "       [ 293.30639093],\n",
       "       [ 301.96254624],\n",
       "       [ 310.42223018],\n",
       "       [ 318.66196102],\n",
       "       [ 326.66088633],\n",
       "       [ 334.40031556],\n",
       "       [ 341.86337086],\n",
       "       [ 349.03472243],\n",
       "       [ 355.90038539],\n",
       "       [ 362.44756244],\n",
       "       [ 368.66452061],\n",
       "       [ 374.54049427],\n",
       "       [ 380.06560805],\n",
       "       [ 385.2308154 ],\n",
       "       [ 390.02784939],\n",
       "       [ 394.44918307],\n",
       "       [ 398.48799756],\n",
       "       [ 402.13815625],\n",
       "       [ 405.39418395],\n",
       "       [ 408.25125014],\n",
       "       [ 410.70515533],\n",
       "       [ 412.75232034],\n",
       "       [ 414.38977768],\n",
       "       [ 415.61516489],\n",
       "       [ 416.42671949],\n",
       "       [ 416.82327538],\n",
       "       [ 416.80426049],\n",
       "       [ 416.36969562],\n",
       "       [ 415.52019439],\n",
       "       [ 414.25696441],\n",
       "       [ 412.58180952],\n",
       "       [ 410.49713341],\n",
       "       [ 408.00594459],\n",
       "       [ 405.11186302],\n",
       "       [ 401.81912864],\n",
       "       [ 398.13261213],\n",
       "       [ 394.05782843],\n",
       "       [ 389.60095347],\n",
       "       [ 384.76884501],\n",
       "       [ 379.56906834],\n",
       "       [ 374.00992821],\n",
       "       [ 368.10050837],\n",
       "       [ 361.85072074],\n",
       "       [ 355.27136689],\n",
       "       [ 348.37421493],\n",
       "       [ 341.17209664],\n",
       "       [ 333.6790305 ],\n",
       "       [ 325.91037915],\n",
       "       [ 317.88305239],\n",
       "       [ 309.61577182],\n",
       "       [ 301.12942001],\n",
       "       [ 292.44750783],\n",
       "       [ 283.59681081],\n",
       "       [ 274.6082534 ],\n",
       "       [ 265.51816911],\n",
       "       [ 256.37015346],\n",
       "       [ 247.21790196],\n",
       "       [ 238.12980318],\n",
       "       [ 229.19699017],\n",
       "       [ 220.54937887],\n",
       "       [ 212.39660931],\n",
       "       [ 205.24703599],\n",
       "       [ 199.37020861],\n",
       "       [ 194.3152344 ],\n",
       "       [ 189.92611117],\n",
       "       [ 186.11940159],\n",
       "       [ 182.84167502],\n",
       "       [ 180.05576202],\n",
       "       [ 177.73473983],\n",
       "       [ 175.85863158],\n",
       "       [ 174.4130292 ],\n",
       "       [ 173.38749561],\n",
       "       [ 172.77491886],\n",
       "       [ 172.56988772]])"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.subprocess.insolation.insolation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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