{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Experiments with surface heat flux looking at polar amplification"
   ]
  },
  {
   "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 climlab\n",
    "from climlab import constants as const"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  EBM with surface and atm layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 1) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ebm = climlab.GreyRadiationModel(num_lev=1, num_lat=90)\n",
    "insolation = climlab.radiation.insolation.AnnualMeanInsolation(domains=ebm.Ts.domain)\n",
    "ebm.add_subprocess('insolation', insolation)\n",
    "ebm.subprocess.SW.flux_from_space = ebm.subprocess.insolation.insolation\n",
    "print ebm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  add a fixed relative humidity process\n",
    "#  (will only affect surface evaporation)\n",
    "h2o = climlab.radiation.water_vapor.FixedRelativeHumidity(state=ebm.state, **ebm.param)\n",
    "ebm.add_subprocess('H2O', h2o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Add surface heat fluxes\n",
    "from climlab.surface.turbulent import SensibleHeatFlux, LatentHeatFlux\n",
    "shf = SensibleHeatFlux(state=ebm.state, Cd=3E-4)\n",
    "lhf = LatentHeatFlux(state=ebm.state, Cd=3E-4)\n",
    "# couple water vapor to latent heat flux process\n",
    "lhf.q = h2o.q\n",
    "ebm.add_subprocess('SHF', shf)\n",
    "ebm.add_subprocess('LHF', lhf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1 years.\n",
      "Total elapsed time is 0.999336878378 years.\n"
     ]
    }
   ],
   "source": [
    "ebm.integrate_years(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110de35d0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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56ejoUSha1LvXCP903acMWzeM1a1Xc9MNN7mOo9JZbFwsdcfV5Z489/DRQ945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\nv9bkaYp3uZMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110de3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ebm.lat, ebm.Ts)\n",
    "plt.plot(ebm.lat, ebm.Tatm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "co2ebm = climlab.process_like(ebm)\n",
    "co2ebm.subprocess['LW'].absorptivity = ebm.subprocess['LW'].absorptivity*1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3.0 years.\n",
      "Total elapsed time is 3.99734751351 years.\n"
     ]
    }
   ],
   "source": [
    "co2ebm.integrate_years(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110fcce50>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3w/DhfshkxYrwt7/5s+kiIqdryRI/9PLIEXj55cKf6FBLCeL73z/6CD780F8A0b69/6M0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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110e60a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  no heat transport but with evaporation -- no polar amplification\n",
    "plt.plot(ebm.lat, co2ebm.Ts - ebm.Ts)\n",
    "plt.plot(ebm.lat, co2ebm.Tatm - ebm.Tatm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now with meridional heat transport"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 1) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   LHF: <class 'climlab.surface.turbulent.LatentHeatFlux'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.FixedRelativeHumidity'>\n",
      "   SHF: <class 'climlab.surface.turbulent.SensibleHeatFlux'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "diffebm = climlab.process_like(ebm)\n",
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.6\n",
    "# meridional diffusivity in 1/s\n",
    "K = D / diffebm.Tatm.domain.heat_capacity\n",
    "d = climlab.dynamics.diffusion.MeridionalDiffusion(K=K, state={'Tatm': diffebm.state['Tatm']}, **diffebm.param)\n",
    "diffebm.add_subprocess('diffusion', d)\n",
    "print diffebm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 3.99734751351 years.\n"
     ]
    }
   ],
   "source": [
    "diffebm.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110f83790>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HRt5wrEiS668As13XywPhQHagJLAf15vLDY83nuDSJWPeesuYggWNmTzZmMRE\npxN5l4TEBDPixxGmwPACZuL2iSZRn0CPkJiYaKaETzEFhhcwwzcMN/EJ8U5H8hqJibYWFCxozODB\nxly96nSiv3LVzhTV7ptdUjJ6JwT4HogEjOsyEOiM7d9PBGKAHsaYE67HDAC6A3HY7qBVN/m+Jrmf\nnZHi42HaNLvZQp068PHHUKyYY3G80rGLx3j666e5FHeJmW1mUjKfbhfmaWLOxdD1q65ky5qNGW1m\nUCy3/pGn1JEjdm7OwYMwfjzUquV0Iktn5KZSXBwsWADvvmtXxhw+HGrWzPQYXm/ZvmU8t+Q5elTt\nwX/q/0dP1HqwhMQE3l//PmO2jmFCiwm0KNPC6UhewxiYPRveeMM2Dj/4AEqVcjaTFv0UOn0aJkyw\nyyGXKgX9+9uTN3qeMXWuJVxjwJoBzI+az6y2s6h7d12nI6kU2nBwA08tfIpWZVrxUeOPyJ41u9OR\nvMYff9jegJEjoUMHeOUV54q/LsPwD06etB/LmjSxyx9HR9tV9sLCoHlzLfipdeDcAepPqU/02WjC\ne4Rrwfcyde+uS3iPcA6cP0DdyXWJORfjdCSvcfvtdjz/7t12vZ7ate0Qz+++874hnj7R0r961W5M\nfvgwREXBpk2webP9ulkzOwyrWTMdb58eS/ctpfvi7rwR8gav1HpFh2J6MWMMozaNYtgPw5jQYgIt\ny7R0OpLXuXTJju4ZPRouXIC2baFdO9sFlNFbpvp9907z5nbH+yJFICgISpe2J1xq1oSKFSFAu5rT\nJSExgUFhg5i+czrz2s3TWbU+ZOOhjXSY34EulbowpOEQXfguDYyxrf/58+3l0CGoUcPWoGrVoEQJ\nW5fy53dfz4LfF/3z5+3HLd2Q3P1OXTpF54WdSTSJzHl8DoXuKOR0JOVmJy+dpOP8jmTNkpU5j8+h\nwO2672d6nDxpexk2b4YdO2xvw+HDcPmy3VbVHZsu+X3RVxlj+9HttP2iLZ0qdOK9Ru/p6BwfFp8Y\nz7+//Tfzds1jQfsFVC2mS8m626VLkD07ZHPD7p9a9JXbzdg5g36r+jG2+VjalW/ndByVSRbsXkDP\nZT35uMnHPFnpSafjqFvQoq/cJj4xnv6r+rNk3xK+7vg1FQpVcDqSymSRJyJpPa81rcu0Ztgjw/QT\nngfSoq/c4uzls3SY34EskoW5j88lX858TkdSDjl7+Swd53cEYF67efq34GF0nL5Kt6hTUdScWJNK\nhSqxrPM+lUywAAAP50lEQVQyfZH7ufw587P8yeWUL1ieWpNqse/MPqcjKTfSou/nVu5fSYOpDRhY\ndyAjmozQj/MKgIAsAXzc9GNeq/0adSfXZfUvq52OpNxEu3f8lDGG0VtGM3TDUL584ktC7g5xOpLy\nUN8f+J72X7ZnUINB9Krey+k4fk/79FWqxSXE8dKKl1h/cD1LOy8lOG+w05GUh/vl7C88NucxGt/T\nWD8ROkyLvkqVc1fO8cSXT5AtSzbmtptLntvyOB1JeQn92/EMeiJXpdhvv/9GnUl1KFegHIs7LdYX\nrUqVvDnysrzzcornKU69KfU4dP5Q8g9SHkeLvp/YeGgjdSbXoVe1Xnza7FP9eK7SJFvWbIx7bBxd\nKnWh9qTa7Di2w+lIKpW0e8cPfLnrS3ov783UVlNpfl9zp+MoH7EwaiE9lvZgcsvJujFLJkpv9442\n93yYMYaPfvyI0VtGs7rLaqoUqeJ0JOVD2pZrS1CeIFrPbc2B8wfoW6Ov05FUCmhL30fFJ8bTZ1kf\nNh/ZzNLOSwnKE+R0JOWjfvv9N5rNakbz0s35qPFHZBHtNc5IOnpH/c3FqxdpP789AF+0+4Lct+V2\nOJHydWcvn6XtvLbcdftdzGwzk5zZcjodyWfp6B31F0cuHKHelHqUuLMESzot0YKvMkX+nPn55qlv\nyBGQg4emP8SpS6ecjqRuQYu+D/npxE/UnlSbzhU7M7b5WB2hozLVbQG3MaPNDBoGN6TO5DpEn4l2\nOpK6Ce3e8RGrflnFUwuf4tNmn9KxQken4yg/N377eN5e+zYLOyykTvE6TsfxKdqnr5gcPpmB3w5k\nfvv51L27rtNxlAJgRfQKun7dlXHNx/F4+cedjuMztOj7MWMMg8IGMStyFss7L6dMgTJOR1LqL3Yc\n20GLOS3oX6c//6r1L6fj+AQt+n7qavxVnlvyHPvO7GNJpyW6abnyWAfOHeDR2Y/ycMmHGdlkJFmz\nZHU6klfTou+Hfr/8O23mtSF/zvzMbDuT27Pd7nQkpf7RuSvnaDuvLXfmuJNZbWfp32w66JBNP/Pb\n779RZ3IdqhatypdPfKkvHuUV8ubIy8qnVpI7e24aTWvEyUsnnY7kt5It+iISJCLficguEYkUkZdu\nuP1VEUkUkfxJjg0QkWgRiRKRxhkR3B9tOryJkMkh9K3elxFNRujHZOVVsmfNzrTW02h8b2NqT6rN\n3tN7nY7kl1IykDse6GeMiRCRXMB2EVlljNkjIkHAI8CB63cWkXJAe6AcEASsEZHS2peTPrpomvIF\nIsKQhkMombckDaY2YF67eTQIbuB0LL+SbEvfGHPcGBPhuh4LRAGBrptHAf1veEgrYK4xJt4YEwNE\nAzXcltjPGGMYtmEY/Vb1Y3WX1VrwlU949oFnmdV2Fu3nt2fmTzOdjuNXUjVlU0SCgSrAZhFpCRwy\nxkSK/OWcQiCwMcnXR/jfm4RKhWsJ1+i5tCfhx8PZ2H2jLpqmfMpD9zzE2qfX0nx2c/af3c+gBoO4\noZaoDJDiE7murp35wMtAAjAQGJRBufzemT/O0HhGY85ePsv6Z9drwVc+qXzB8mzsvpGV+1fy5MIn\nuRJ/xelIPi9FLX0RCcAW/BnGmEUiUgEIBnaKfWsOAnaISA1sy/7uJA8Pch37m8GDB/95PTQ0lNDQ\n0NT/Bj5oz+k9tJjTgjZl2zD04aG6VK3yaUVyFWHt02t5ZtEzNJrWiK87fq3zTpIICwsjLCzMbd8v\nReP0RWQ6cNoY0+8Wt/8GPGiM+V1EygOzgJrYbp3VwN9O5Oo4/ZtbuX8lXb/qytCHh9LtgW5Ox1Eq\n0ySaRAatHcTMyJks6riISoUrOR3JI2X4zlkiEgI8CUSKSDhggIHGmJVJ7mYAATDG7BaRL4DdQBzQ\nW6t78owxfLzpY4b/OJyFHRbqGjrK72SRLLzb6F3KFSzHQ9MfYlLLSbQs09LpWD5HZ+R6gCvxV+i1\nrBfhx8JZ1HERJfKWcDqSUo7acmQLbee1pW+NvrwR8oae4E1Cl2HwcocvHKbtvLaUzFeSSS0nkSt7\nLqcjKeURjlw4Qut5rbk3371MajmJO7Lf4XQkj6DLMHix9QfWU2NCDdqVb8fcx+dqwVcqicA8gXz/\nzPfcFnAbIZNDiDkX43Qkn6BF3wHGGEZtHEW7L9sxpdUUXg95XT++KnUTObPlZGqrqTxb5VlqTazF\nml/XOB3J62n3Tia7cPUC3RZ148D5A3z5xJcE5w12OpJSXiEsJozOCzrTp3ofBtQb4LdDmbV7x4vs\nPL6T6hOqU/D2gmx4doMWfKVSITQ4lK3Pb2X5/uW0nNOS3y//7nQkr6RFPxMYY/h8y+c8PONh3q7/\nNmMfG8ttAbc5HUsprxOYJ5Cwp8Monb80D/z3ATYe2pj8g9RfaPdOBjt7+SzdF3fn4PmDzH18LqXv\nKu10JKV8wqI9i3hh6Qv0q9WP/iH9/aa7R7t3PNjqX1ZTeVxlgu8M5sduP2rBV8qNWpVtxdbnt7Jk\n3xKazWrGsYvHnI7kFbToZ4A/4v6g7/K+dFvcjcktJzOq6SjtzlEqA9x9592EPRNGrcBaPPDfB1iw\ne4HTkTyedu+42foD63luyXNUK1aNz5p9Rr6c+ZyOpJRf2Hx4M12+6kKtoFp82uxT8ubI63SkDKHd\nOx7iwtUL9Frai44LOvLhQx8yq+0sLfhKZaKaQTUJ7xFO7uy5qTCmAov2LHI6kkfSln46GWNYGLWQ\nV755haalmjL8keE+28JQylt8f+B7nl/yPJULV2Z0s9EUzlXY6Uhuo2vvOCjqVBQvrXyJ47HH+azZ\nZ7rXp1Ie5HLcZYasG8Kk8Em83eBtelbrSUCWVG0W6JG06DvgzB9neH/9+8z4aQZv1X+L3tV7+8Qf\nk1K+aNfJXby44kXOXD7DZ80+o16Jek5HShct+pnoj7g/+GTTJ4zYOIL297dnUINBPvWxUSlfZYzh\ny91f8tqq16geWJ0PGn1AmQJlnI6VJnoiNxNcunaJjzd9TOnRpf/cpHxM8zFa8JXyEiJC+/vbs7fv\nXmoUq0HI5BB6Lu3J0YtHnY6W6bTo/4NzV84xdMNQ7v30XjYc3MCSTkv44okvdJKVUl4qZ7acvFH3\nDfb23Uuu7LmoMKYCfZb14cC5A05HyzTavXMTu0/tZvTm0czdNZfmpZszoO4A7i90v9OxlFJudvLS\nSUZtHMX4HeNpWaYlL9d8mSpFqjgd6x9pn76bXLx6kQVRC5i2cxp7Tu+hR9Ue9Kjag6K5izodTSmV\nwc5ePsu4beMYu20sJfOW5MUaL9KqbCuyZ83udLS/0aKfDrHXYln1yyoWRC1g2b5lNAhuQNdKXXns\nvsd02QSl/FBcQhxf7/maz7Z+xu5Tu+l4f0eervI0VYtW9ZiNjrTop0KiSeTnkz8TFhPGN798w/oD\n66kVVIvWZVvzRPknKHhHwUzNo5TyXL/+/iszds5g+k/TEYRWZVrRumxr6hSvQ9YsWR3LpUX/FuIS\n4jhw/gA7j+8k4ngEESci+PHQj9yV8y5Cg0N5qORDNC3VlDtz3JlhGZRS3s8YQ/jxcBbtWcSivYs4\ndOEQ9UvUJ7REKA2CG1ChUIVMnafj90V/SvgUok5Hce7KOc5dOceJSyeIORfD8djjFM1VlEqFK1Gl\nSBUqF65M7eK1KZa7mBvSK6X81dGLR1kXs46wmDDWHVjHoQuHKF+wPFUKV6FcwXIE5w0mOG8wxfMU\nJ1/OfG5/Q/D7oj9pxyTOXD5D3hx5ufO2Oyl0RyGC8wYTlCeIbFmzuSGpUkrd2sWrF4k8GUnE8Qj2\nndlHzLkYYs7FcOjCIc5fOU/ObDnJmyMvSzstpXKRyun+eX5f9JVSylMZY4i9Fsu5K+coeEdBcgTk\nSPf31KKvlFJ+RJdhUEoplWJa9JVSyo8kW/RFJEhEvhORXSISKSIvuo4PEZGdIhIuIitFpEiSxwwQ\nkWgRiRKRxhn5CyillEq5lLT044F+xpj7gdpAXxEpCww3xlQ2xjwALAMGAYhIeaA9UA5oBowRT5nK\n5sPCwsKcjuBT9Pl0H30uPUuyRd8Yc9wYE+G6HgtEAYGu69fdASS6rrcE5hpj4o0xMUA0UMOtqdXf\n6AvLvfT5dB99Lj1LqmYNiEgwUAXY7Pr6PaArcA5o6LpbILAxycOOuI4ppZRyWIpP5IpILmA+8PL1\nVr4x5j/GmLuBWcCLGRNRKaWUu6RonL6IBABLgRXGmE9ucntxYJkxppKIvAkYY8ww120rgUHGmM03\nPEYH6SulVBqkZ5x+Srt3JgO7kxZ8ESlljNnv+rI1sMd1fTEwS0RGYbt1SgFb3BlaKaVU2iRb9EUk\nBHgSiBSRcMAAA4HnRKQMkAAcAHoCGGN2i8gXwG4gDuitU2+VUsozOLYMg1JKqcyXKTNyRaSdiPws\nIgki8uANt910IpeIPCgiP4nIPhH5ODNyeiMRGSQih0Vkh+vSNMltOkkulUSkqYjscf3dveF0Hm8k\nIjFJJm5ucR3LJyKrRGSviHwjIrqRxS2IyCQROSEiPyU5dsvnL7Wv88xahiESaAOsS3pQRMpx64lc\nY4Huxpj7gPtEpEkmZfVGI40xD7ouKyHZ51bdhIhkAT4DmgD3A51cExFV6iQCocaYB4wx1+fovAms\nMcaUAb4DBjiWzvNNwf4NJnXT5y8tk2EzpegbY/YaY6KBG8O04iYTuVxLOuQ2xmx13W869mSxurmb\n/Sff9LnN1FTepwYQbYw5YIyJA+Zin0eVOsLfa0srYJrr+jT09XxLxpgNwO83HL7V85fqybBOL7gW\nCBxK8vX1iVyBwOEkxw+jE7z+SV8RiRCRiUk+9t3quVW3duNzpn93aWOA1SKyVUSecx0rbIw5AXaW\nP1DIsXTeqdAtnr9Uv87dto+XiKwGCic9hP3P/7cxZom7fo4/+qfnFhgDDDHGGNcM6RHAc3//Lkpl\nmhBjzDERKQisEpG92L/XpHQESfqk+flzW9E3xjyShocdAYon+TrIdexWx/1SKp7bCcD1N1h9DlPv\nCHB3kq/1OUsDY8wx17+nRORrbHfDCREpbIw54eq+PeloSO9zq+cv1a9zJ7p3kvY/LwY6ikh2ESmJ\nayKX6+PLeRGp4Top0RVY5EBWj5d0SWugLfCz6/pNn9vMzudltgKlRKSEiGQHOmKfR5VCInK7a8kW\nROQOoDF2IMdi4BnX3Z5GX8/JEf5eK59xXU/6/KX6de7ebdpvQURaA6OBAsBSEYkwxjRLZiJXH2Aq\nkANYfn1Uivqb4SJSBTtiIgboATpJLi2MMQki0hdYhW0QTTLGRDkcy9sUBr5yLbMSAMwyxqwSkW3A\nFyLSDTuZs72TIT2ZiMwGQoG7ROQgdtn6ocCXNz5/aXmd6+QspZTyI06P3lFKKZWJtOgrpZQf0aKv\nlFJ+RIu+Ukr5ES36SinlR7ToK6WUH9Gir5RSfkSLvlJK+ZH/Bwqf08uFfqJcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f834d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diffebm.lat, diffebm.Ts)\n",
    "plt.plot(diffebm.lat, diffebm.Tatm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def inferred_heat_transport( energy_in, lat_deg ):\n",
    "    '''Returns the inferred heat transport (in PW) by integrating the net energy imbalance from pole to pole.'''\n",
    "    from scipy import integrate\n",
    "    from climlab import constants as const\n",
    "    lat_rad = np.deg2rad( lat_deg )\n",
    "    return ( 1E-15 * 2 * np.math.pi * const.a**2 * integrate.cumtrapz( np.cos(lat_rad)*energy_in,\n",
    "            x=lat_rad, initial=0. ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113d89dd0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YaAU87JzbME4/GOjovW8ANHDOnZaDuJLiXu9945LbWCj32koZnHNbAYOA04CG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111032bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot the northward heat transport in this model\n",
    "Rtoa = np.squeeze(diffebm.timeave['ASR'] - diffebm.timeave['OLR'])\n",
    "plt.plot(diffebm.lat, inferred_heat_transport(Rtoa, diffebm.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  Now warm it up!\n",
    "co2diffebm = climlab.process_like(diffebm)\n",
    "co2diffebm.subprocess['LW'].absorptivity = diffebm.subprocess['LW'].absorptivity*1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.211 days, or 5 years.\n",
      "Total elapsed time is 8.99676981466 years.\n"
     ]
    }
   ],
   "source": [
    "co2diffebm.integrate_years(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113ca8bd0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N9J7Sm8Mph70uKWQp6AvYlNVT6D2lN6N7jebRTo9qBqBILlQqXYnYm2JpWqUp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\ngl5ExOcU9CIiPqegFxHxOQW9iIjP/X9HJyMmV5lxzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ca8290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  with heat transport and evaporation \n",
    "#   Get some modest polar amplifcation of surface warming\n",
    "#    but larger equatorial amplification of atmospheric warming\n",
    "#    Increased atmospheric gradient = increased poleward flux.\n",
    "plt.plot(diffebm.lat, co2diffebm.Ts - diffebm.Ts)\n",
    "plt.plot(diffebm.lat, co2diffebm.Tatm - diffebm.Tatm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x113decf10>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WRlQZwXm3WazbG4i/v+k04m/f+dyDojMYUEWGTQrxIinybyFNkjR8VXEg77br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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113dd3c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rtoa = np.squeeze(diffebm.timeave['ASR'] - diffebm.timeave['OLR'])\n",
    "Rtoa_co2 = np.squeeze(co2diffebm.timeave['ASR'] - co2diffebm.timeave['OLR'])\n",
    "plt.plot(diffebm.lat, inferred_heat_transport(Rtoa, diffebm.lat))\n",
    "plt.plot(diffebm.lat, inferred_heat_transport(Rtoa_co2, diffebm.lat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same thing but with NO EVAPORATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
      "Integrating for 1095 steps, 1095.7266 days, or 3 years.\n",
      "Total elapsed time is 11.9947804498 years.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116e1f850>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1PP2b9NeM2mQo6HOx6K3R9JvYj+aVmvNa19coW7Rs0CWJBGLZrmX0/bwvF0Zc\nyIjuIyhfvHzQJeUoWtQsFzpx+gSDvhlEj3E9eK7Dc4ztPVYhL3laowqNmD9wPvXPr0/jNxvzyc+f\nBF1SqKhFn82it0Zz+6TbqX9+fYZ3HU6FEppXJpLUvK3z6D+xP40qNGJ41+GcX/z8oEsKnFr0uUTs\nqVge+eoReo7rybORz/Lpnz9VyIsko3XV1iy+ezE1ImrQ8I2GfLT8I/J6A/FcqUWfDb5a9xX3TruX\ndtXbMbTTUC1EJpJG0VujGThlINVLV+f1bq9TrXS1oEsKhE7G5mDbDm/jsemPEb0tmreufYtONTsF\nXZJIrnMq/hQvzHmBl+a9xDPtnuH+lvdTIF+aJ/WHgrpucqC4+DhenPsijd9sTM0yNfn5vp8V8iIZ\nVCh/If7W7m/MuWMOk9dMpsWIFszdMjfosnIVtegz2Yz1M3h0+qNULFGR17q8Rt3z6gZdkkhoOOcY\nt2Icj894nC61uvDPK/+ZJ851qesmh1izbw2PT3+cn/f8zH+u/g+9Lu6liR8iWeTQiUP8Y9Y/GL10\nNE+0eYKHWz1M4QKFgy4ry6jrJmA7juzgvmn3cfmoy2lXvR0r71vJdfWuU8iLZKHSRUoz9Jqh/Djg\nR+ZsmUP91+szfsV4ElxC0KXlSGrRZ9D+4/t5Yc4LjFg0gtub3M6gtoM0mkYkIDP/O5NBMwcRnxDP\nvzr+i041O4WqsaWum2y2++huXpz7Im8vepve9Xrz9/Z/p2qpqkGXJZLnOef4LOYz/vbt3yhfvDzP\ntHuGqy66KhSBr6DPJuv3r+eV6Ff4YNkH3HTJTTzR5gmqR1QPuiwROcPphNOMWzGOf/7wT0oXLs3f\nrvgb3ep0y9XXdVDQZyHnHN9v+p6X5r3E7M2zGdB0AA+3fpjKJSsHXZqIpCI+IZ7PYj7j/2b/H8fj\njvOX1n+hb6O+ufLKVgr6LLDn6B7eX/Y+IxeNJMEl8HCrh+nXuF+ufIKI5HXOOWZtmsVL817ixy0/\n0q9RP+5sficXn3dx0KWlmYI+kxyPO87UNVP5aMVHfLvhW3rU7cGdze6kbbW2oejjExFYt38dIxeN\n5L0l71G7XG3uaHIH19e7ntJFSgdd2lkp6M/BgeMH+Hr910xZM4Vpa6ZxaZVL6dOgD73r9yaiSETQ\n5YlIFomLj2PKmimMWTqG7zZ+R8cLO9Lnkj50rtWZUoVLBV3eHyjo0+Hk6ZPM3zafqI1RzNwwk0U7\nFtG+Rnu05WrUAAAFpUlEQVS61e5Gr4t7UbFExcBqE5FgHDxxkM9jPmf8z+P5ccuPtK7ammvrXEun\nmp2oW65ujvhEr6BPwYnTJ1i3fx2Ldyxm4Y6FLNyxkCU7l3DxeRcTWT2SDhd2ILJGJMUKFsuWekQk\n54s9FcuM9TOYumYqMzfM5MTpE7Sv0Z4rql1B80rNaVyxcSCZkSeDfuWelRw4foAjp44QeyqWvcf2\nsv3IdrYf2c6Ww1tYs28NO47soEZEDZpUbEKzSs1oXqk5LSq3yPF9cSKSc2w8uJFZG2cxZ8scFu5Y\nSMyeGGqWrUm98+pRp1wd6pSrQ42IGlQqUYnKJStn2YCNPBn0nd7vxJFTRyhZqCQlC5ekXNFyVC5Z\nmUolKlG1VNVfD37B/AUzoWoREe/k6ZOs3LOSVXtXsXrfalbvW82WQ1vYEbuD7Ue2k9/yU7JwSUoW\nKkmJQiX4pt83mXKZ0DwZ9CIiOY1zjthTscSeiuXIqSMcOXmExhUbZ8ra+Qp6EZGQ0+qVIiJyVgp6\nEZGQU9CLiIScgl5EJOQU9CIiIZdq0JvZKDPbZWbLUvh+ezM7aGaLErenM79MERHJqLS06N8Frkll\nn++dc80St//NhLokDaKiooIuIVR0PDOPjmXOkmrQO+dmAwdS2S34VX/yIL2YMpeOZ+bRscxZMquP\n/jIzW2Jm08ysfiY9poiIZIJzn5sLC4FqzrljZtYFmAjUyYTHFRGRTJCmJRDMrDowxTnXKA37bgCa\nO+f2J/M9rX8gIpIB57IEQlpb9EYK/fBmVsE5tyvxdkv8m8cfQh7OrVAREcmYVIPezMYCkUA5M9sM\nDAYKAc45NwK4wczuBeKA48CNWVeuiIikV7auXikiItkvy2bGmtkNZrbCzOLNrNkZ33vSzNaaWYyZ\ndUpyfzMzW2Zma8zs5ayqLTczs8FmtjXJBLXOSb6X7HGVszOzzma2KvF59z9B15MbmdlGM1tqZovN\nbH7ifWXMbLqZrTazr81Ml3dLQXITU892/NL7Ws/KJRCWA9cBs5LeaWb1gD8D9YAuwOv229V33wAG\nOOfqAHXMLLWJWnnVi0kmqH0FqR5XSYGZ5QNew08KbADcZGYXB1tVrpQARDrnmjrnWibeNwj4xjlX\nF/gWeDKw6nK+5CamJnv8Eoewp+u1nmVB75xb7Zxbyx9P4vYExjnnTjvnNgJrgZZmVhEo6ZxbkLjf\nGKBXVtWXyyX3R032uGZrVblTS2Ctc26Tcy4OGIc/lpI+xh/zpCcwOvH2aPR6TlEKE1NTOn49SOdr\nPYhFzaoAW5J8vS3xvirA1iT3b028T/7ogcQJaiOTfJxL6bjK2Z153PS8yxgHzDCzBWY2MPG+X0fk\nOed2AuUDqy53Kp/C8Uv3a/2cJkyZ2QygQtK78H/wvznnppzLY+dlZzuuwOvAP5xzzsz+FxgKDPzj\no4hkqzbOuR1mdj4w3cxW45+zSWnkx7nJ8PE7p6B3zl2dgR/bBlyQ5OuqifeldH+ek47j+jbwyxuq\njl/GbAOqJflaxy0DnHM7Ev/dY2YT8V0Ju36ZZ5PYNbs70CJzn5SOX7pf69nVdZO0T3ky0MfMCpnZ\nhUAtYH7iR5NDZtYy8cRCP2BSNtWXayT+wX9xPbAi8XayxzW768uFFgC1zKy6mRUC+uCPpaSRmRUz\nsxKJt4sDnfCDMSYD/RN3uw29nlNz5sTUlI5ful/rmbHWTbLMrBfwKnAeMNXMljjnujjnVprZx8BK\n/CSr+9xvg/nvB94DigBf/DKiRH7neTNrgh/lsBG4GyCV4yopcM7Fm9kDwHR8w2eUcy4m4LJymwrA\n54lLnBQAPnTOTTezn4CPzewOYBN+pIgkI4WJqf8P+OTM45eR17omTImIhJwuJSgiEnIKehGRkFPQ\ni4iEnIJeRCTkFPQiIiGnoBcRCTkFvYhIyCnoRURC7v8D2uWHH6IbhcYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ab7450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vq59asXdzNnoXH07XrqbTxA/PnkHmmivJ/tlUjnXbJ3PGC4dmSxdDxctP2qiq\no3iU25v/TbjO/fum08QPkyaHoysMZVTNgVLghXiLmA6hrK+UugGUATYppbZaJ5b9yJwiM9+U6cb7\nzfozaJDpNI7v4UMYt/lHcmRMTs3cNU3HEcLmycVQVvAs+Bl5p+bj5cK17FpUmmLFTCdyXH36hTHb\nqQjL20ykRu4apuMIEetsqbsm3nJ1dmV4lWGkat6D7l9rWfw7lty+DdN++ZEcGVNQPVd103GEsAtS\n5K3kiw+/wDXVM26mWCPTEceSYSPCSFx9CKNqDJG+eCHekRR5K3FK4MSE6hMIqvg9vfsG8fy56USO\n5eJFWOy/mpwZ3aiWs5rpOELYDSnyVlQlZxVKZClIuro/MGqU6TSOpW+/MJLWHMKIap7SihciCqTI\nW9m4auO4mmU00xfc59Il02kcw8GDsPvBEvJmfl9a8UJEkYyuiQXdt3TniD+k8/di3TrTaeyb1lC2\nQhAXauRnbevFlMtaznQkIeKUjK6xQYPdB3Mx8QqOXj/Ljh2m09i39evhaprZlMpRQAq8ENEgLflY\nMuHABFb6+fDYeyMnT0LixKYT2Z+QECjw4XMetMzNrrZbKJZBLkAQ8Y+05G1Ut9LdeJDgd1KV2MWk\nSabT2KcZM0B/NJVqeSpIgRcimqQlH4vW/L6GATuH8Mfwoxw/mpAsWUwnsh/370P+Yn8S3iUvBzvu\nJ2/qvKYjCWFETFvyUuRjkdaaSgsr4Xq1GUl/+4rVq00nsh9du8Kvrt9TouyfzPaYbTqOEMZId40N\nU0oxueZkDrt64nfyET//bDqRfTh1ClZuv8rVVHMZWmmo6ThC2DVpyceBjhs7ci/AlTOTJ8pJ2LfQ\nGqpUgafVP6dOmXwMdh9sOpIQRklL3g4MrzycfU8XkbnoOUaPNp3Gtq1dC9fDDnMrkS+9PullOo4Q\ndk+KfBxImywtfcv1heo98PKC8+dNJ7JNz57Bt99pkjXoxdBKQ0nmnMx0JCHsnnTXxJHgsGCKeBeh\n3ItxXNlel59/BpmC5Z9694Yjz9Zzv8hAjnc6jlMCJ9ORhDBOumvshLOTM1NrTWV34m+4/yiQpUtN\nJ7ItJ0/CgiUvuZq3JxOqT5ACL4SVSEs+jjVa1YjUoUXY2HMwp09D6tSmE5kXHg5ly0L6JiPRGf1Y\n10wm/BHiLzJO3s5cf3ydYjOLUe/uEYLv5mDJEtOJzJs1C2auuMG1msXw6+BHzpQ5TUcSwmZId42d\nyfpeVno6rgRMAAAMbUlEQVSU6cG94t9y8CBs3Gg6kVm3bsHAgfD+573pWqqrFHghrExa8gYEhQZR\n2LswX2QYj/e3Hpw+DW5uplPFPa3BwwNSFvNhT6o2/N71d1wSuZiOJYRNkZa8HUqcMDEz6sxg5o1u\n1Kr/lJ49TScyY8kSuHo9hKPpuzOh+gQp8ELEAinyhlTOUZkqOauQqPogdu2CbdtMJ4pbt25Bz55Q\nsf94sryXmYYfNDQdSQiHJN01Bj148YCC0wvyvzwbGNGlNCdOQJo0plPFvr+6abIWu8BK14850vEI\n2d2ym44lhE2S7ho7ltolNROqT2DW7Y589nkI7dtbCqCjmz8frl3X/J6zEwPKD5ACL0QskiJv2OeF\nPyedazpS1h7PtWswZ47pRLHr7Fno0weajJjPs5CnfP3R16YjCeHQpLvGBlx7dI2Ss0syp9xu2nsU\nZv9+yOuAa2S8fAllykCLjncZ97wwO1rtoGj6oqZjCWHT5GIoBzHv2Dy8/LxoG3qIhfOc2b8fkiQx\nncq6vvkGAm5qQhrVo1DaQoysMtJ0JCFsntE+eaXUWKXUGaXUcaXUGqVUipjsLz5rW7QtmZJn4o8P\nhpMrF3TvbjqRdW3cCOvXQ4Vv5hLwJABPd0/TkYSIF2LUkldKVQV2a63DlVKjAa217veabaUl/xa3\nn96m6MyirKy3ic4epejVC7780nSqmDt/HsqVgx+WXaTrsY/xbeNLgfcLmI4lhF0w2pLXWv+stQ6P\neHgQyByT/cV3GZJnYErNKXTa3pLFK5/Rty/4+5tOFTNPnkC9ejBkWCiTr7VmYPmBUuCFiENW65NX\nSm0AVmitl73mdWnJv6N269sREh6CR+gievdWHD4M779vOlXUhYdD/fqQOTNk+GwYvtd82dFqBwmU\nDOoS4l3FekteKbVTKXUy0u1UxL91I20zAAh5XYEXUeNVy4ujt4/yNPd8WrSwXDgUGGg6VdQNHgyP\nH4PHt7vwPuLNwvoLpcALEccSvm0DrXW1N72ulGoDfApUftu+PD09/77v7u6Ou7v7274kXkrmnIzV\nTVZTcUFFdn9dmqtXC9GiBaxeDU52spbG7NmWuWnW7gqg1tqWLG24lEwpMpmOJYTN8/HxwcfHx2r7\ni+mJ15rABKCC1vrBW7aV7pooWnB8AWP2j2FvKz+a1EtO0aIwaZLpVG+3Zo1ldNDO3cG031+Revnq\nWda4FUJEmdFx8kqpC4Az8FeBP6i17vKabaXIR0OHDR24+/wu86qvpWIFJ9q0sayFaqt27YLmzWH7\ndph3uzvXn1xn7WdrpZtGiGiSi6EcXHBYMNUXV6dUxlJ8U2AclSpBhw7w/femk/3bwYOW8werV8Pv\nLt5MOjgJvw5+uCWJh5PlC2ElMS3yb+2TF2Y5OzmzpukayswtQ/40+fHx+ZJKlSAsDPr95xUJZuzc\nCZ9/DgsXwuP0Gxi2aRh72+6VAi+EYVLk7UBql9RsbL6RigsqkqNRDnx8KlO5MoSEwKBBoKL9O946\n1qyBzp3hp58gcU4/ai/7ks2fbyZXqlxmgwkhZBZKe5E/TX5WNl7JZz9+xtWwA/zyC6xdC23bQlCQ\nuVyzZllOsm7fDhkKXqT+ivrM85hH6UylzYUSQvxNirwdcc/uzuIGi6m/oj4B4YfZtw+ePYNKleDu\n3bjNEhhomXJh0iTw9QWXLOeotLASwyoNo26+um/fgRAiTkiRtzM1c9dkjscc6iyvw4Wnx1m1CqpV\ng9KlYc+euMlw8SJ8/DG8eAGHD0OI2+9UXlSZYZWG8WVxB5hsRwgHIkXeDnnk82Dap9OosaQG+27s\nYcgQ+OEHy9DFbt3g6dPYed/QUJg61VLgO3SAZcvg8vOTVF1UlbFVx9KmaJvYeWMhRLRJkbdTjQs0\nZkmDJTRe1ZglJ5dQty6cPm1pXRcuDOvWWeaOsZa9e6FECct0wb6+0LUrbDy/gaqLqjKl5hRaFGlh\nvTcTQliNjJO3c7/d+43ay2rTtmhb/lfxfyil2L7dMrwyJAT694emTaM3HUJ4uGVopJcXnDgBEyZA\nkyagCWf4nuHMPjqbH5v8yEeZP7L+NyaEAORiKAHceXaHBisbkNw5OXM95pLlvSxoDdu2wYgREBAA\nDRpYLlQqVw4SJXr9vkJD4dgx2L0b5s6FZMkso2eaN4ekSeH+i/t02NiBe8/vsabpGtK7po+7b1SI\neEiKvAAgNDyU0ftGM/XQVMZXH0+rIq0ifjgsrfANGyyrM128CPnzQ9askC0buLjAo0eW282b4Odn\nea1CBWjRwtL/rhRorVl0YhHf//w9LQu3ZGSVkSROmNj0ty2Ew5MiL/7h+J3jtF7bmnSu6fCs6EnZ\nrGX/8fq9e3DhAly7BtevW/rwU6YENzdIm9ay0Hbq1P/c54k7J+ixowePXj5iVp1ZlMhYIg6/IyHi\nNyny4l+Cw4JZdGIRI/aOIHeq3AwoP4AK2SpEaZKwcB3OtovbmPjrRM7cP0Ofsn3oUqoLCRPIRdJC\nxCUp8uK1/ir2Uw5N4Y/nf+CRzwOPfB58mO5DMiTP8I+CrbXm7vO7+F71ZdeVXey8vBO3JG70/Lgn\nTQs2xdnJ2eB3IkT8JUVevJNLDy+x/tx6Np3fxLkH57j/4j4ZXDPgksiFh4EPeRj4kGTOySiftTxV\nclShco7KFEpbCGV6Yhwh4jkp8iJagsOCCXgSQGBIIKldUpMySUo5kSqEDZIiL4QQDizWF/IWQghh\nv6TICyGEA5MiL4QQDkyKvBBCODAp8kII4cCkyAshhAOTIi+EEA5MirwQQjgwKfJCCOHApMgLIYQD\nkyIvhBAOTIq8EEI4sBgVeaXUUKXUCaXUMaXUNqWULPgphBA2JKYt+bFa6w+11sWAzcBgK2QS78DH\nx8d0BIchx9K65HjalhgVea31s0gPkwHhMYsj3pV8kKxHjqV1yfG0LTFesFMpNRxoDTwCKsU4kRBC\nCKt5a0teKbVTKXUy0u1UxL91AbTWA7XWWYGlQPfYDiyEEOLdWW1lKKVUFmCL1rrwa16XZaGEECIa\nYrIyVIy6a5RSubXWFyMe1gfOvG7bmIQUQggRPTFqySulfgTyYjnheg34Smt920rZhBBCxFCcLeQt\nhBAi7ln9ilelVGOl1GmlVJhSqvgrr/VTSl1QSp1RSlWP9HzxiJO555VSk62dyVEopQYrpQKUUkcj\nbjUjvfafx1a8mVKqplLqbMTPXh/TeeyNUupqpAsi/SKeS6mU2qGUOqeU2q6Ues90TlullJqrlLqr\nlDoZ6bnXHr/ofM5jY1qDU0ADwDfyk0qpD4CmwAdALWC6Uuqvfnpv4EutdV4gr1KqRizkchQTtdbF\nI27b4K3HVryGUioB8ANQAygINFdK5Tebyu6EA+5a62Ja69IRz/UFftZa5wN2A/2MpbN987H8/EX2\nn8dPKVWAaHzOrV7ktdbntNYXgFffvB6wQmsdqrW+ClwASkdMhZBca304YrtFWE7iiv/2X/+p/3ls\n4zSVfSoNXNBaX9NahwArsBxL8e4U/64j9YCFEfcXIp/n19Ja7wP+fOXp1x0/D6LxOY/LCcoyATci\nPb4Z8VwmICDS8wERz4n/1k0pdVwpNSfSn3GvO7bizV49bvKzF3Ua2KmUOqyUah/xXDqt9V0ArfUd\nIK2xdPYp7WuOX7Q+59EaQqmU2gmki/wUlv/sAVrrjdHZp7B407EFpgNDtdY64krjCUD7f+9FiDhT\nVmt9Wyn1PrBDKXUOy89rZDK6I2ZidPyiVeS11tWi8WU3gSyRHmeOeO51z8dLUTi2s4G/fqHKMYye\nm0DWSI/luEXRX0OmtdZ/KKXWYek+uKuUSqe1vhvRHXvPaEj787rjF63PeWx310TuP94ANFNKOSul\ncgC5Ab+IP0ceK6VKR5xEaA2sj+VcdumVqZwbAqcj7v/nsY3rfHboMJBbKZVNKeUMNMNyLMU7UEq5\nKKVcI+4nA6pjGXixAWgTsdkXyOf5bRT/rpVtIu5HPn7R+pzHeIKyVyml6gNeQBpgk1LquNa6ltb6\nd6XUKuB3IAToov9/kH5XYAGQBMvUCNusnctBjFVKFcUyouEq0AngLcdWvIbWOkwp1Q3YgaXBM1dr\n/dqrtsW/pAPWRkxZkhBYqrXeoZQ6AqxSSrXDcpFkU5MhbZlSahngDqRWSl3HMl37aGD1q8cvup9z\nuRhKCCEcmCz/J4QQDkyKvBBCODAp8kII4cCkyAshhAOTIi+EEA5MirwQQjgwKfJCCOHApMgLIYQD\n+z9r6UgcyKmbkgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116e13490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffebm2 = climlab.process_like(diffebm)\n",
    "diffebm2.remove_subprocess('LHF')\n",
    "diffebm2.integrate_years(3)\n",
    "co2diffebm2 = climlab.process_like(co2diffebm)\n",
    "co2diffebm2.remove_subprocess('LHF')\n",
    "co2diffebm2.integrate_years(3)\n",
    "#  With transport and no evaporation...\n",
    "#  No polar amplification, either of surface or air temperature!\n",
    "plt.plot(diffebm2.lat, co2diffebm2.Ts - diffebm2.Ts)\n",
    "plt.plot(diffebm2.lat, co2diffebm2.Tatm[:,0] - diffebm2.Tatm[:,0])\n",
    "plt.figure()\n",
    "#  And in this case, the lack of polar amplification is DESPITE an increase in the poleward heat transport.\n",
    "Rtoa = np.squeeze(diffebm2.timeave['ASR'] - diffebm2.timeave['OLR'])\n",
    "Rtoa_co2 = np.squeeze(co2diffebm2.timeave['ASR'] - co2diffebm2.timeave['OLR'])\n",
    "plt.plot(diffebm2.lat, inferred_heat_transport(Rtoa, diffebm2.lat))\n",
    "plt.plot(diffebm2.lat, inferred_heat_transport(Rtoa_co2, diffebm2.lat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A column model approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = climlab.GreyRadiationModel(num_lev=30, num_lat=90, abs_coeff=1.6E-4)\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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Convective adjustment for atmosphere only\n",
    "conv = climlab.convection.convadj.ConvectiveAdjustment(state={'Tatm':model.state['Tatm']}, adj_lapse_rate=6.5,\n",
    "                                                       **model.param)\n",
    "model.add_subprocess('convective adjustment', conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  add a fixed relative humidity process\n",
    "#  (will only affect surface evaporation)\n",
    "h2o = climlab.radiation.water_vapor.FixedRelativeHumidity(state=model.state, **model.param)\n",
    "model.add_subprocess('H2O', h2o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Add surface heat fluxes\n",
    "from climlab.surface.turbulent import SensibleHeatFlux, LatentHeatFlux\n",
    "shf = SensibleHeatFlux(state=model.state, Cd=1E-3)\n",
    "lhf = LatentHeatFlux(state=model.state, Cd=1E-3)\n",
    "lhf.q = model.subprocess.H2O.q\n",
    "model.add_subprocess('SHF', shf)\n",
    "model.add_subprocess('LHF', lhf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3.0 years.\n",
      "Total elapsed time is 2.99801063513 years.\n"
     ]
    }
   ],
   "source": [
    "model.integrate_years(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_temp_section(model, timeave=True):\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    if timeave:\n",
    "        field = model.timeave['Tatm'].transpose()\n",
    "    else:\n",
    "        field = model.Tatm.transpose()\n",
    "    cax = ax.contourf(model.lat, model.lev, field)\n",
    "    ax.invert_yaxis()\n",
    "    ax.set_xlim(-90,90)\n",
    "    ax.set_xticks([-90, -60, -30, 0, 30, 60, 90])\n",
    "    fig.colorbar(cax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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tkrS8bXpybZb0p/n63SDpAUnz2+5Lrr0tki6Q9DNJ/1tSch+1SzpQ0vp8vW6S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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113e0c450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model, timeave=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "co2model = climlab.process_like(model)\n",
    "co2model.subprocess['LW'].absorptivity = model.subprocess['LW'].absorptivity*1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 5.99602127027 years.\n"
     ]
    }
   ],
   "source": [
    "co2model.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C/lLSbOBfgD8GiIgNkm4HNgA7gIuiwHBEZUMWZmb2ZqV/qSdpiaS/l/SUpLsk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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117591a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(co2model, timeave=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117bd40d0>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117bd4a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Without transport, get equatorial amplification\n",
    "plt.plot(model.lat, co2model.Ts - model.Ts)\n",
    "plt.plot(model.lat, co2model.Tatm[:,0] - model.Tatm[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Now with meridional heat tranpsort!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "diffmodel = climlab.process_like(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.46414342629e-07\n"
     ]
    }
   ],
   "source": [
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.05\n",
    "# meridional diffusivity in 1/s\n",
    "K = D / diffmodel.Tatm.domain.heat_capacity[0]\n",
    "print K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   LHF: <class 'climlab.surface.turbulent.LatentHeatFlux'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.FixedRelativeHumidity'>\n",
      "   SHF: <class 'climlab.surface.turbulent.SensibleHeatFlux'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d = climlab.dynamics.diffusion.MeridionalDiffusion(K=K, state={'Tatm':diffmodel.state['Tatm']}, **diffmodel.param)\n",
    "diffmodel.add_subprocess('diffusion', d)\n",
    "print diffmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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 5.99602127027 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmZ4DOQS0pIUUbYeW4QocDtxd+n1HWtZS5VrIVk05h/F0o2g1j0RmIy+69YyG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179def90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(diffmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117958d90>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wSDnTg9cS0Ly5tybu0cMLbaEWMc2Y4TtxW7aEG29UgRdJIxX5ErHbbt7fZsgQ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\n2P8DACeqsXn/zYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c0e4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Plot the northward heat transport in this model\n",
    "Rtoa = np.squeeze(diffmodel.timeave['ASR'] - diffmodel.timeave['OLR'])\n",
    "plt.plot(diffmodel.lat, inferred_heat_transport(Rtoa, diffmodel.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  Now warm it up!\n",
    "co2diffmodel = climlab.process_like(diffmodel)\n",
    "co2diffmodel.subprocess['LW'].absorptivity = diffmodel.subprocess['LW'].absorptivity*1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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 8.9940319054 years.\n"
     ]
    }
   ],
   "source": [
    "co2diffmodel.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1174f6ed0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174f69d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  With transport, get polar amplification...\n",
    "#   of surface temperature, but not of air temperature!\n",
    "plt.plot(diffmodel.lat, co2diffmodel.Ts - diffmodel.Ts)\n",
    "plt.plot(diffmodel.lat, co2diffmodel.Tatm[:,0] - diffmodel.Tatm[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117546710>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gOooQxkiRf0ndvF9QPntJvl78A3fumE4joipCR7D8Tx9aF/KW5f2EU5Mi/wrz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117546690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rtoa = np.squeeze(diffmodel.timeave['ASR'] - diffmodel.timeave['OLR'])\n",
    "Rtoa_co2 = np.squeeze(co2diffmodel.timeave['ASR'] - co2diffmodel.timeave['OLR'])\n",
    "plt.plot(diffmodel.lat, inferred_heat_transport(Rtoa, diffmodel.lat))\n",
    "plt.plot(diffmodel.lat, inferred_heat_transport(Rtoa_co2, diffmodel.lat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same thing but with NO EVAPORATION\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "climlab Process of type <class 'climlab.model.column.GreyRadiationModel'>. \n",
      "State variables and domain shapes: \n",
      "  Tatm: (90, 30) \n",
      "  Ts: (90, 1) \n",
      "The subprocess tree: \n",
      "top: <class 'climlab.model.column.GreyRadiationModel'>\n",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   SW: <class 'climlab.radiation.greygas.GreyGasSW'>\n",
      "   LW: <class 'climlab.radiation.greygas.GreyGas'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.FixedRelativeHumidity'>\n",
      "   SHF: <class 'climlab.surface.turbulent.SensibleHeatFlux'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "diffmodel2 = climlab.process_like(diffmodel)\n",
    "diffmodel2.remove_subprocess('LHF')\n",
    "print diffmodel2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 8.9940319054 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel2.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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 11.9920425405 years.\n"
     ]
    }
   ],
   "source": [
    "co2diffmodel2 = climlab.process_like(co2diffmodel)\n",
    "co2diffmodel2.remove_subprocess('LHF')\n",
    "co2diffmodel2.integrate_years(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x117fcca90>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174f0f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  With transport and no evaporation...\n",
    "#  No polar amplification, either of surface or air temperature!\n",
    "plt.plot(diffmodel2.lat, co2diffmodel2.Ts - diffmodel2.Ts)\n",
    "plt.plot(diffmodel2.lat, co2diffmodel2.Tatm[:,0] - diffmodel2.Tatm[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x118e45e50>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l/ux9nEwpMpmOI0SSkiN5J/FcsucYWm0QoT692bXLff9nZlpsLAxf+xU1czaU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\nQgg3JiUvhBBuTEpeCCHcmJS8EEK4sf8D+7YgqAfqoHAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e45c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rtoa = np.squeeze(diffmodel2.timeave['ASR'] - diffmodel2.timeave['OLR'])\n",
    "Rtoa_co2 = np.squeeze(co2diffmodel2.timeave['ASR'] - co2diffmodel2.timeave['OLR'])\n",
    "plt.plot(diffmodel2.lat, inferred_heat_transport(Rtoa, diffmodel2.lat))\n",
    "plt.plot(diffmodel2.lat, inferred_heat_transport(Rtoa_co2, diffmodel2.lat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Warming effect of a DECREASE IN EVAPORATION EFFICIENCY\n",
    "\n",
    "Take a column model that includes evaporation and heat transport, and reduce the drag coefficient by a factor of 2.\n",
    "\n",
    "How does the surface temperature change?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.211 days, or 5.0 years.\n",
      "Total elapsed time is 10.9954435714 years.\n"
     ]
    }
   ],
   "source": [
    "diffmodel3 = climlab.process_like(diffmodel)\n",
    "diffmodel3.subprocess['LHF'].Cd *= 0.5\n",
    "diffmodel3.integrate_years(5.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119357f90>]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ar3wj9mzdk/i4+NAHIVKLwuJClmxawoKNC5i/cT4L8xayOG8xSU2Tql6flQmi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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119357a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Reduced evaporation gives equatorially enhanced warming of surface\n",
    "#  and cooling of near-surface air temperature\n",
    "plt.plot(diffmodel.lat, diffmodel3.Ts - diffmodel.Ts)\n",
    "plt.plot(diffmodel.lat, diffmodel3.Tatm[:,0] - diffmodel.Tatm[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same calculation in a two-layer EBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.211 days, or 5.0 years.\n",
      "Total elapsed time is 8.99676981466 years.\n"
     ]
    }
   ],
   "source": [
    "diffebm3 = climlab.process_like(diffebm)\n",
    "diffebm3.subprocess['LHF'].Cd *= 0.5\n",
    "diffebm3.integrate_years(5.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1194e79d0>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zGd93POf3PV/dStJsKqoqWLR1ETNyZjAzbyaLChYxLGVY7YXKmN5jNIsuBJQM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\nP/Y+CaajRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194e7a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Reduced evaporation gives equatorially enhanced warming of surface\n",
    "#  and cooling of near-surface air temperature\n",
    "plt.plot(diffebm.lat, diffebm3.Ts - diffebm.Ts)\n",
    "plt.plot(diffebm.lat, diffebm3.Tatm[:,0] - diffebm.Tatm[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretty much the same result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some stuff with Band models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Put in some ozone\n",
    "import netCDF4 as nc\n",
    "\n",
    "datapath = \"http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/Brian+Rose/CESM+runs/\"\n",
    "endstr = \"/entry.das\"\n",
    "\n",
    "topo = nc.Dataset( datapath + 'som_input/USGS-gtopo30_1.9x2.5_remap_c050602.nc' + endstr )\n",
    "ozone = nc.Dataset( datapath + 'som_input/ozone_1.9x2.5_L26_2000clim_c091112.nc' + endstr )\n",
    "\n",
    "#  Dimensions of the ozone file\n",
    "lat = ozone.variables['lat'][:]\n",
    "lon = ozone.variables['lon'][:]\n",
    "lev = ozone.variables['lev'][:]\n",
    "\n",
    "# Taking annual, zonal average of the ozone data\n",
    "O3_zon = np.mean( ozone.variables['O3'],axis=(0,3) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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",
    "model1 = climlab.BandRCModel(lev=lev, lat=lat)\n",
    "insolation = climlab.radiation.insolation.AnnualMeanInsolation(domains=model1.Ts.domain)\n",
    "model1.add_subprocess('insolation', insolation)\n",
    "model1.subprocess.SW.flux_from_space = model1.subprocess.insolation.insolation\n",
    "print model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Set the ozone mixing ratio\n",
    "O3_trans = np.transpose(O3_zon)\n",
    "# Put in the ozone\n",
    "model1.absorber_vmr['O3'] = O3_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q': 341.3,\n",
       " 'abs_coeff': 0.0001229,\n",
       " 'adj_lapse_rate': 6.5,\n",
       " 'albedo_sfc': 0.299,\n",
       " 'timestep': 86400.0,\n",
       " 'water_depth': 1.0}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.param"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Convective adjustment for atmosphere only\n",
    "model1.remove_subprocess('convective adjustment')\n",
    "conv = climlab.convection.convadj.ConvectiveAdjustment(state={'Tatm':model1.state['Tatm']}, **model1.param)\n",
    "model1.add_subprocess('convective adjustment', conv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Add surface heat fluxes\n",
    "from climlab.surface.turbulent import SensibleHeatFlux, LatentHeatFlux\n",
    "shf = SensibleHeatFlux(state=model1.state, Cd=0.5E-3)\n",
    "lhf = LatentHeatFlux(state=model1.state, Cd=0.5E-3)\n",
    "#  set the water vapor input field for LHF process\n",
    "lhf.q = model1.q\n",
    "model1.add_subprocess('SHF', shf)\n",
    "model1.add_subprocess('LHF', lhf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model1.step_forward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 1.00207478763 years.\n"
     ]
    }
   ],
   "source": [
    "model1.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 365 steps, 365.2422 days, or 1.0 years.\n",
      "Total elapsed time is 2.00141166601 years.\n"
     ]
    }
   ],
   "source": [
    "model1.integrate_years(1.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D2NugEHdshqY9geydyGokhwO0jV57AjljOTQ+9wzf4W1RgDs4Q9GOQPbOYwPKIZRzOFDb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\n8f8BDZK6We9AebMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b31210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(model1, timeave=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "co2model1 = climlab.process_like(model1)\n",
    "co2model1.absorber_vmr['CO2'] *= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3.0 years.\n",
      "Total elapsed time is 4.99942230115 years.\n"
     ]
    }
   ],
   "source": [
    "co2model1.integrate_years(3.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eUj9AZJSfTLJqgbl3ZB/IKbyTj3LjGXooZLS3gpueFLat3GQfyC4t/oWec51l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\n55xLhAeyc84lwgPZOecS8f8BqsxcmC43orkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117bd46d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_temp_section(co2model1, timeave=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model gets very very hot near equator. Very large equator-to-pole gradient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Band model with heat transport and evaporation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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",
      "   diffusion: <class 'climlab.dynamics.diffusion.MeridionalDiffusion'>\n",
      "   LHF: <class 'climlab.surface.turbulent.LatentHeatFlux'>\n",
      "   SW: <class 'climlab.radiation.nband.ThreeBandSW'>\n",
      "   LW: <class 'climlab.radiation.nband.FourBandLW'>\n",
      "   H2O: <class 'climlab.radiation.water_vapor.ManabeWaterVapor'>\n",
      "   SHF: <class 'climlab.surface.turbulent.SensibleHeatFlux'>\n",
      "   convective adjustment: <class 'climlab.convection.convadj.ConvectiveAdjustment'>\n",
      "   insolation: <class 'climlab.radiation.insolation.AnnualMeanInsolation'>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "diffmodel1 = climlab.process_like(model1)\n",
    "# thermal diffusivity in W/m**2/degC\n",
    "D = 0.01\n",
    "# meridional diffusivity in 1/s\n",
    "K = D / diffmodel1.Tatm.domain.heat_capacity[0]\n",
    "d = climlab.dynamics.diffusion.MeridionalDiffusion(K=K, state={'Tatm': diffmodel1.state['Tatm']}, **diffmodel1.param)\n",
    "diffmodel1.add_subprocess('diffusion', d)\n",
    "diffmodel1.absorber_vmr['CO2'] *= 4.\n",
    "print diffmodel1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1095 steps, 1095.7266 days, or 3.0 years.\n",
      "Total elapsed time is 4.99942230115 years.\n"
     ]
    },
    {
     "data": {
      "image/png": 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RcPVcR9Onazxw1Q9eXp4xdwYz585odhXj4iA2G1nZ88zNnIbWSxoJ5KeAnRGxPj3+JkUg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\nvKvmWVMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116f33190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffmodel1.integrate_years(3.)\n",
    "plot_temp_section(diffmodel1, timeave=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11bac20d0>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7rVjhp4f22cdX+9Svn933E5GNoySfQC1b+kPAX3vNV9589FF23ied9gn+nHP8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\nLS19r6wCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6f6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Rtoa = np.squeeze(diffmodel1.timeave['ASR'] - diffmodel1.timeave['OLR'])\n",
    "#Rtoa_co2 = np.squeeze(co2diffmodel1.timeave['ASR'] - co2diffmodel1.timeave['OLR'])\n",
    "plt.plot(diffmodel1.lat, inferred_heat_transport(Rtoa, diffmodel1.lat))\n",
    "#plt.plot(diffmodel1.lat, inferred_heat_transport(Rtoa_co2, diffmodel1.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c679290>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HIoXijjv8AoL//d/QkcSLkr+U27/+5VvzTp3q37KLZMI338DRR8OMGb4rrORGpMf8JVr6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\n/T9KHHVV/ZgrJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6ee810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diffmodel1.lat, diffmodel1.Ts-273.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrating for 1826 steps, 1826.211 days, or 5 years.\n",
      "Total elapsed time is 9.99884460229 years.\n"
     ]
    }
   ],
   "source": [
    "#  Now double CO2\n",
    "co2diffmodel1 = climlab.process_like(diffmodel1)\n",
    "co2diffmodel1.absorber_vmr['CO2'] *= 2.\n",
    "co2diffmodel1.integrate_years(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11d29d2d0>]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c6c6110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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CXHDm1hn2XzlMpayVZfKTiDPiXEv+b5ObdKZ0xlKUHlePS1fCTccRsWDu/rl4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\nEMKF/R/tN5++Vq9xAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d282ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  No polar amplification in this model!\n",
    "plt.plot(diffmodel1.lat, co2diffmodel1.Ts - diffmodel1.Ts)\n",
    "plt.plot(diffmodel1.lat, co2diffmodel1.Tatm[:,0] - diffmodel1.Tatm[:,0])\n",
    "plt.figure()\n",
    "Rtoa = np.squeeze(diffmodel1.timeave['ASR'] - diffmodel1.timeave['OLR'])\n",
    "Rtoa_co2 = np.squeeze(co2diffmodel1.timeave['ASR'] - co2diffmodel1.timeave['OLR'])\n",
    "plt.plot(diffmodel1.lat, inferred_heat_transport(Rtoa, diffmodel1.lat))\n",
    "plt.plot(diffmodel1.lat, inferred_heat_transport(Rtoa_co2, diffmodel1.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}