{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# ENV/ATM 415: Climate Laboratory\n",
    "\n",
    "[Brian E. J. Rose](http://www.atmos.albany.edu/facstaff/brose/index.html), University at Albany\n",
    "\n",
    "# Lecture 4: Introducing the Community Earth System Model (CESM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "\n",
    "### What is it?\n",
    "\n",
    "- CESM is one of a handful of complex coupled GCMs that are used as part of the IPCC process.\n",
    "- Developed and maintained at NCAR (Boulder CO) by a group of climate scientists and software engineers.\n",
    "- “Community” refers to the fact that the code is open-source, with new pieces contributed by a wide variety of users. \n",
    "\n",
    "I use CESM in my own research. We are going to be using CESM in this course. Everyone should visit the website and learn about it.\n",
    "\n",
    "http://www2.cesm.ucar.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Key components of CESM:\n",
    "\n",
    "see http://www.cesm.ucar.edu/models/cesm1.2/ for more info\n",
    " \n",
    " - Atmospheric model (AGCM)\n",
    "     - Community Atmsophere Model (CAM)\n",
    " - Ocean model (OGCM)\n",
    "     - Parallel Ocean Program (POP)\n",
    " - Land surface model\n",
    "     - Community Land Model (CLM)\n",
    " - Sea ice model\n",
    "     - Community Ice CodE (CICE)\n",
    "     \n",
    "The software is somewhat modular, so different submodels can be combined together depending on the nature of the scientific problem at hand and the available computer power."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### The Slab Ocean Model\n",
    "\n",
    "Our experiments will use CESM in the so-called **Slab Ocean Model** mode, in which **the ocean is represented by a static layer of water with some fixed heat capacity** but no motion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Recall that we saw this schematic of different ways to represent the ocean in climate models:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![Figure 2.9: ocean model hierarchy](http://www.atmos.albany.edu/facstaff/brose/classes/ENV415_Spring2018/images/Primer_Figure2.9.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Using the slab ocean greatly reduced the *time required for the model to reach equilibrium*. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The net effect heat transport by ocean currents is prescribed through a so-called **q-flux**, which really just means we prescribe sources and sinks of heat at different locations. \n",
    "\n",
    "For (lots of) details, see <http://www2.cesm.ucar.edu/working-groups/pwg/documentation/cesm1-paleo-toolkit/ocean/som>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The key is that we **allow the sea surface temperature to change**, but we fix (prescribe) the net effect of ocean currents on the transport of energy.\n",
    "\n",
    "Why do this? \n",
    "- Because it takes thousands of years for the full ocean model to come into equilibrium!\n",
    "\n",
    "Why should we believe the results of the slab ocean model?\n",
    "- We shouldn’t! \n",
    "\n",
    "But experience with coupled models (meaning interactive ocean circulation) has shown that the circulation does not change radically under $2\\times CO_2$. \n",
    "\n",
    "So the slab ocean model gives us a decent first guess at climate sensitivity.\n",
    "And it makes it possible to do a lot of experimentation that we wouldn’t be able to do otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "____________\n",
    "<a id='section6'></a>\n",
    "## Our numerical experiments with CESM\n",
    "____________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Atmosphere\n",
    "\n",
    " - Horizontal resolution about 2º lat/lon\n",
    " - AGCM solves the fundamental equations:\n",
    "    - Conservation of momentum, mass, energy, water, equation of state\n",
    " - At 2º we resolve the **synoptic-scale dynamics**\n",
    "     - storm tracks and cyclones. \n",
    " - We do NOT resolve the mesoscale and smaller\n",
    "     - thunderstorms, individual convective events, clouds\n",
    " - These all must be parameterized.\n",
    " - Model also solves equations of radiative transfer. This takes account of\n",
    "     - composition of the atmosphere and the absorption properties of different gases\n",
    "     - radiative effects of clouds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Sea ice\n",
    "\n",
    "- Resolution of 1º.\n",
    "- Thermodynamics (conservation of energy, water and salt)\n",
    "    - determines freezing and melting\n",
    "- Dynamics (momentum equations) \n",
    "    - determine ice motion and deformation.\n",
    "- Complex! Sea ice is sort of a mixture of a fluid and a solid."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Land surface model\n",
    "\n",
    "- Same resolution as atmosphere. \n",
    "- Determines surface fluxes of heat, water, momentum (friction) based on prescribed vegetation types.\n",
    "- Don’t actually know much about how it works!\n",
    "- Great topic for someone to dig in to for their term project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ocean\n",
    "\n",
    "- Same grid as sea ice, 1º.\n",
    "- Sea surface temperature evolves based on:\n",
    "    - heat exchange with atmosphere\n",
    "    - prescribed “q-flux”."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Experimental setup\n",
    "\n",
    "Model is given realistic atmospheric composition, realistic solar radiation, etc.\n",
    "\n",
    "We perform a **control run** to get a baseline simulation, and take **averages of several years** (because the model has internal variability – every year is a little bit different)\n",
    "\n",
    "We then change something, e.g. $2\\times CO_2$!\n",
    "\n",
    "And allow the model to adjust to a new equilibrium, just as we did with the toy energy balance model.\n",
    "\n",
    "Once it has gotten close to its new equilibrium, we run it for several more years again to get the new climatology.\n",
    "\n",
    "Then we can look at the **differences in the climatologies before and after the perturbation**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Description of input\n",
    "\n",
    "First, let's take a look at some of the ingredients that go into the control run. **All of the necessary data will be served up by a special data server sitting in the department**, so you should be able to run this code to interact with the data on any computer that is connected to the internet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### You need to be connected to the internet to run the code in this notebook ###\n",
    "\n",
    "You can browse the available data through a web interface here:\n",
    "\n",
    "<http://ramadda.atmos.albany.edu:8080/repository/entry/show/Top/Users/BrianRose/CESM_runs>\n",
    "\n",
    "Within this folder called `CESM runs`, you will find another folder called `som_input` which contains all the input files.\n",
    "\n",
    "The data are all stored in `NetCDF` files, a standard file format for self-describing gridded data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to use a package called [xarray](http://xarray.pydata.org) (abbreviated here as `xr`) to work with the datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Boundary conditions: continents and topography\n",
    "\n",
    "Here we are going to load the input topography file and take a look at what's inside. \n",
    "\n",
    "In this case we are passing it a URL to our online dataserver. We'll put the URL in a string variable called `datapath` to simplify things later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/BrianRose/CESM_runs/som_input/USGS-gtopo30_1.9x2.5_remap_c050602.nc/entry.das\n"
     ]
    }
   ],
   "source": [
    "datapath = \"http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/BrianRose/CESM_runs/\"\n",
    "endstr = \"/entry.das\"\n",
    "\n",
    "#  Notice that in Python we can easily concatenate strings together just by `adding` them\n",
    "fullURL = datapath + 'som_input/USGS-gtopo30_1.9x2.5_remap_c050602.nc' + endstr\n",
    "print( fullURL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:       (lat: 96, lon: 144)\n",
      "Coordinates:\n",
      "  * lat           (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 ...\n",
      "  * lon           (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 ...\n",
      "Data variables:\n",
      "    PHIS          (lat, lon) float64 ...\n",
      "    SGH           (lat, lon) float64 ...\n",
      "    SGH30         (lat, lon) float64 ...\n",
      "    LANDFRAC      (lat, lon) float64 ...\n",
      "    LANDM_COSLAT  (lat, lon) float64 ...\n",
      "Attributes:\n",
      "    history:    Written on date: 20050602\\ndefinesurf -remap -t /fs/cgd/csm/i...\n",
      "    make_ross:  true\n",
      "    topofile:   /fs/cgd/csm/inputdata/atm/cam/topo/USGS-gtopo30_10min_c050419.nc\n",
      "    gridfile:   /fs/cgd/csm/inputdata/atm/cam/coords/fv_1.9x2.5.nc\n",
      "    landmask:   /fs/cgd/csm/inputdata/atm/cam2/hrtopo/landm_coslat.nc\n"
     ]
    }
   ],
   "source": [
    "#  Now we actually open the dataset\n",
    "topo = xr.open_dataset( fullURL )\n",
    "print(topo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Dataset` object has several important attributes. Much of this should look familiar if you have worked with `netCDF` data before. The `xarray` package gives a very powerful and easy to use interface to the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access individual variables within the `xarray.Dataset` object as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'PHIS' (lat: 96, lon: 144)>\n",
       "array([[  2.796465e+04,   2.796465e+04,   2.796465e+04, ...,   2.796465e+04,\n",
       "          2.796465e+04,   2.796465e+04],\n",
       "       [  2.718626e+04,   2.724515e+04,   2.730367e+04, ...,   2.699761e+04,\n",
       "          2.706319e+04,   2.712589e+04],\n",
       "       [  2.562017e+04,   2.579349e+04,   2.595301e+04, ...,   2.497548e+04,\n",
       "          2.521368e+04,   2.542803e+04],\n",
       "       ..., \n",
       "       [  1.720216e-02,   7.134989e-03,   2.748055e-03, ...,   1.569097e-01,\n",
       "          8.055879e-02,   3.857289e-02],\n",
       "       [  5.444020e-02,   3.951677e-02,   2.799566e-02, ...,   1.231596e-01,\n",
       "          9.609957e-02,   7.320690e-02],\n",
       "       [  0.000000e+00,   0.000000e+00,   0.000000e+00, ...,   0.000000e+00,\n",
       "          0.000000e+00,   0.000000e+00]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    long_name:   surface geopotential\n",
       "    units:       m2/s2\n",
       "    from_hires:  true\n",
       "    filter:      remap"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topo.PHIS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the topography\n",
    "\n",
    "We will now read the geopotential and make a plot of the topography of the Earth's surface as represented on the 2º grid. The code below makes a colorful plot of the topography. We also use the land-sea mask in order to plot nothing at grid points that are entirely ocean-covered. \n",
    "\n",
    "Execute this code exactly as written first, and then play around with it to see how you might customize the graph. \n",
    "\n",
    "Note that the function `pcolormesh` does most of the work here. It's a function that makes color 2D plots of an array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'height' (lat: 96, lon: 144)>\n",
       "array([[  2.853536e+00,   2.853536e+00,   2.853536e+00, ...,   2.853536e+00,\n",
       "          2.853536e+00,   2.853536e+00],\n",
       "       [  2.774108e+00,   2.780118e+00,   2.786089e+00, ...,   2.754858e+00,\n",
       "          2.761550e+00,   2.767948e+00],\n",
       "       [  2.614303e+00,   2.631989e+00,   2.648266e+00, ...,   2.548518e+00,\n",
       "          2.572824e+00,   2.594697e+00],\n",
       "       ..., \n",
       "       [  1.755323e-06,   7.280601e-07,   2.804138e-07, ...,   1.601120e-05,\n",
       "          8.220284e-06,   3.936009e-06],\n",
       "       [  5.555123e-06,   4.032323e-06,   2.856700e-06, ...,   1.256730e-05,\n",
       "          9.806078e-06,   7.470092e-06],\n",
       "       [  0.000000e+00,   0.000000e+00,   0.000000e+00, ...,   0.000000e+00,\n",
       "          0.000000e+00,   0.000000e+00]])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    units:    km"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = 9.8  # gravity in m/s2\n",
    "meters_per_kilometer = 1E3 \n",
    "height = topo.PHIS / g / meters_per_kilometer  # in kilometers\n",
    "#  Note that we have just created a new xarray.DataArray object that preserves the axis labels\n",
    "#  Let's go ahead and give it some useful metadata:\n",
    "height.attrs['units'] = 'km'\n",
    "height.name = 'height'\n",
    "height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a plot! `xarray` is able to automatically generate labeled plots. This is very handy for \"quick and dirty\" investigation of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1136ff6d8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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z7oBabjbsvWrC9BRByuR0beJchaxQQuhiJyiv0MTJ+l5J33wwWJLwjIK0GqrP1Vl4+9QJ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\nMAzYYGAYhmEA+P/h9CG6IQeUWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c2bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want more control over the appearance of the plot, we can use features of `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KXlxeGlXRNAbph2CGydOOWHMgThnHzeSppe3bH2UFmPQ2p090P1k37gliWAQn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\nDMMwMmCCaRiGYRgZMME0DMMwjAz8fwRRuTSm0JDMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138ef128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  A filled contour plot of topography with contours every 500 m\n",
    "lev = np.arange(0., 6., 0.5)\n",
    "fig1, ax1 = plt.subplots(figsize=(8,4))\n",
    "# Here we are masking the data to exclude points where the land fraction is zero (water only)\n",
    "cax1 = ax1.contourf( height.lon, height.lat, \n",
    "                    height.where(topo.LANDFRAC>0), levels=lev)\n",
    "ax1.set_title('Topography (km) and land-sea mask in CESM')\n",
    "ax1.set_xlabel('Longitude')\n",
    "ax1.set_ylabel('Latitude')\n",
    "cbar1 = fig1.colorbar(cax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that at 2º resolution we can see many smaller features (e.g. Pacific islands). The model is given a fractional land cover for each grid point. \n",
    "\n",
    "Here let's plot the land-sea mask itself so we can see where there is at least \"some\" water:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B9XOzhMsTktzKDNwtT7W82pGYL4MV1Ct+XhQFgjE24vkEVpT/Ho7+xDKRvnzu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\nNBkxsAdBEDQZMbAHQRA0Gf8HymM4cwzJQZ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113aa53c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 = plt.subplots()\n",
    "cax2 = ax2.pcolormesh( topo.lon, topo.lat, topo.LANDFRAC )\n",
    "ax2.set_title('Ocean mask in CESM')\n",
    "ax2.set_xlabel('Longitude'); ax2.set_ylabel('Latitude')\n",
    "cbar2 = fig2.colorbar(cax2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making nicer maps\n",
    "\n",
    "Notice that to make these plots we've just plotted the lat-lon array without using any map projection.\n",
    "\n",
    "There are nice tools available to make better maps. We'll leave that as a topic for another day. But if you're keen to read ahead, check out:\n",
    "\n",
    "<http://scitools.org.uk/cartopy/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ocean boundary conditions\n",
    "\n",
    "Another important input file contains information about the slab ocean. You can see this file in the data catalog here:\n",
    "\n",
    "<http://ramadda.atmos.albany.edu:8080/repository/entry/show/Top/Users/BrianRose/CESM_runs/som_input/pop_frc.1x1d.090130.nc>\n",
    "\n",
    "Let's load it and take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:  (lat: 180, lon: 360, time: 12)\n",
      "Coordinates:\n",
      "  * time     (time) float32 14.0 46.0 74.0 105.0 135.0 166.0 196.0 227.0 ...\n",
      "Dimensions without coordinates: lat, lon\n",
      "Data variables:\n",
      "    area     (lat, lon) float64 ...\n",
      "    mask     (lat, lon) int32 ...\n",
      "    yc       (lat) float32 ...\n",
      "    xc       (lon) float32 ...\n",
      "    S        (time, lat, lon) float64 ...\n",
      "    T        (time, lat, lon) float64 ...\n",
      "    U        (time, lat, lon) float64 ...\n",
      "    V        (time, lat, lon) float64 ...\n",
      "    dhdx     (time, lat, lon) float64 ...\n",
      "    dhdy     (time, lat, lon) float64 ...\n",
      "    hblt     (time, lat, lon) float64 ...\n",
      "    qdp      (time, lat, lon) float64 ...\n",
      "Attributes:\n",
      "    creation_date:  Fri Jan 30 10:22:53 MST 2009\n",
      "    comment:        This data is on a standard 1x1d grid.\n",
      "    calendar:       standard\n",
      "    author:         D. Bailey\n",
      "    note3:          qdp is computed from depth summed ocean column\n",
      "    note2:          all fields interpolated to T-grid\n",
      "    note1:          fields computed from 20-yr monthly means from pop\n",
      "    description:    Input data for DOCN7 mixed layer model from b40.999\n",
      "    source:         pop_frc.ncl\n",
      "    conventions:    CCSM data model domain description\n",
      "    title:          Monthly averaged ocean forcing from POP output\n"
     ]
    }
   ],
   "source": [
    "som_input = xr.open_dataset( datapath + 'som_input/pop_frc.1x1d.090130.nc' + endstr, decode_times=False )\n",
    "print(som_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ocean / sea ice models exist on different grids than the atmosphere (1º instead of 2º resolution)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to look at the **annual mean heat flux out of the ocean**, which is the prescribed 'q-flux' that we give to the slab ocean model.\n",
    "\n",
    "It is stored in the field `qdp` in the input file. \n",
    "\n",
    "The sign convention in CESM is that `qdp > 0` where **heat is going IN to the ocean**. We will change the sign to plot heat going OUT of the ocean INTO the atmosphere (a more atmosphere-centric viewpoint). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'qdp' (time: 12, lat: 180, lon: 360)>\n",
       "[777600 values with dtype=float64]\n",
       "Coordinates:\n",
       "  * time     (time) float32 14.0 46.0 74.0 105.0 135.0 166.0 196.0 227.0 ...\n",
       "Dimensions without coordinates: lat, lon\n",
       "Attributes:\n",
       "    spatial_op:  Bilinear remapping: 1st order: destarea: NCL: ./map_gx1v5_to..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "som_input.qdp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, here is a case in which the metadata are not very useful. There is no text description of what variable `qdp` actually is, or what its units are.  (It is actually in units of W/m2)\n",
    "\n",
    "We can see that there are 12 x 180 x 360 data points. One 180 x 360 grid for each calendar month!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to take the average over the year at each point. \n",
    "\n",
    "We will use the power of `xarray` here to take the average over the time dimension, leaving us with a single grid on 180 latitude points by 360 longitude points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x113c74438>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dg9766KwbPHgIdRwO58eCqoMgq2RM3VHV2iZBItLGKY1/rqo/4he/FFxQInID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\nGr8VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113760550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(-som_input.qdp.mean(dim='time')).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make a nice plot of the annual mean q-flux. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uXcrmzZuZM2cO3+jvDSkxZswY5s6dy5dffsn8+fNNsysRxYsV49sxY1JNFxwV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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31a930e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  We can always set a non-standard size for our figure window\n",
    "fig3, ax3 = plt.subplots(figsize=(10, 6))\n",
    "lev = np.arange(-700., 750., 50.)\n",
    "cax3 = ax3.contourf(som_input.xc, som_input.yc, \n",
    "                    -som_input.qdp.mean(dim='time'), \n",
    "                    levels=lev, cmap=plt.cm.bwr)\n",
    "cbar3 = fig3.colorbar(cax3)\n",
    "ax3.set_title( 'CESM: Prescribed heat flux out of ocean (W m$^{-2}$), annual mean', \n",
    "              fontsize=14 )\n",
    "ax3.set_xlabel('Longitude', fontsize=14)\n",
    "ax3.set_ylabel('Latitude', fontsize=14)\n",
    "ax3.text(65, 50, 'Annual', fontsize=16 )\n",
    "ax3.contour(topo.lon, topo.lat, topo.LANDFRAC, levels=[0.5], colors='k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice all the spatial structure here: \n",
    "\n",
    "- Lots of heat is going in to the oceans at the equator, particularly in the eastern Pacific Ocean.\n",
    "- The red hot spots show where lots of heat is coming out of the ocean.\n",
    "- Hot spots include the mid-latitudes off the eastern coasts of Asia and North America\n",
    "- And also the northern North Atlantic. \n",
    "\n",
    "**All this structure is determined by ocean circulation, which we are not modeling here.** Instead, we are prescribing these heat flux patterns as an input to the atmosphere.\n",
    "\n",
    "This pattern changes throughout the year. Recall that we just averaged over all months to make this plot. We might want to look at just one month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'qdp' (lat: 180, lon: 360)>\n",
       "array([[      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       ..., \n",
       "       [-5.75126 , -5.742314, -5.734764, ..., -5.792866, -5.774798, -5.761612],\n",
       "       [-5.533446, -5.537023, -5.540624, ..., -5.522859, -5.526363, -5.529893],\n",
       "       [-5.31631 , -5.314115, -5.311888, ..., -5.322703, -5.320605, -5.318473]])\n",
       "Coordinates:\n",
       "    time     float32 14.0\n",
       "Dimensions without coordinates: lat, lon\n",
       "Attributes:\n",
       "    spatial_op:  Bilinear remapping: 1st order: destarea: NCL: ./map_gx1v5_to..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select by month index (0 through 11)\n",
    "som_input.qdp.isel(time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'qdp' (lat: 180, lon: 360)>\n",
       "array([[      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       [      nan,       nan,       nan, ...,       nan,       nan,       nan],\n",
       "       ..., \n",
       "       [-5.75126 , -5.742314, -5.734764, ..., -5.792866, -5.774798, -5.761612],\n",
       "       [-5.533446, -5.537023, -5.540624, ..., -5.522859, -5.526363, -5.529893],\n",
       "       [-5.31631 , -5.314115, -5.311888, ..., -5.322703, -5.320605, -5.318473]])\n",
       "Coordinates:\n",
       "    time     float32 14.0\n",
       "Dimensions without coordinates: lat, lon\n",
       "Attributes:\n",
       "    spatial_op:  Bilinear remapping: 1st order: destarea: NCL: ./map_gx1v5_to..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  select by array slicing (but for this you have to know the axis order!)\n",
    "som_input.qdp[0,:,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we got just the first month (January) by specifying `[0,:,:]` after the variable name. This is called *slicing* or *indexing* an array. We are saying \"give me everything for month number 0\". Now make the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2NlhYWCClRKPRkJGRQUpKCikpKTx58oSYmBiio6OJjIzk/v373L9/n+DgYEJDQylbtizv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\n4orCZp9UAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31a9d5c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax4 = plt.subplots(figsize=(10,4)) \n",
    "cax4 = ax4.contourf( som_input.xc, som_input.yc, \n",
    "                    -som_input.qdp.isel(time=0), \n",
    "                      levels=lev, cmap=plt.cm.bwr)\n",
    "cbar4 = plt.colorbar(cax4)\n",
    "ax4.set_title( 'CESM: Prescribed heat flux out of ocean (W m$^{-2}$)', \n",
    "              fontsize=14 )\n",
    "ax3.set_xlabel('Longitude', fontsize=14)\n",
    "ax3.set_ylabel('Latitude', fontsize=14)\n",
    "ax4.text(65, 50, 'January', fontsize=12 );\n",
    "ax4.contour(topo.lon, topo.lat, topo.LANDFRAC, levels=[0.5], colors='k');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Just for fun: some interactive plotting\n",
    "\n",
    "`IPython` provides some really neat and easy-to-use tools to set up interactive graphics in your notebook.\n",
    "\n",
    "Here we're going to create a figure with a slider that lets of step through each month of the q-flux data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# A list of text labels for each month\n",
    "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', \n",
    "         'Sep', 'Oct', 'Nov', 'Dec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Nov', 'Dec']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# an example of slicing this list:\n",
    "months[-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  A function that takes a month index (0 - 11) and creates a plot just like above\n",
    "def sh(month):\n",
    "    fig, ax = plt.subplots(figsize=(10,4)) \n",
    "    cax = ax.contourf( som_input.xc, som_input.yc, \n",
    "                      -som_input.qdp.isel(time=month), \n",
    "                      levels=lev, cmap=plt.cm.bwr)\n",
    "    cbar = plt.colorbar(cax)\n",
    "    ax.set_title( 'CESM: Prescribed heat flux out of ocean (W m$^{-2}$)', \n",
    "              fontsize=14 )\n",
    "    ax.set_xlabel('Longitude', fontsize=14)\n",
    "    ax.set_ylabel('Latitude', fontsize=14)\n",
    "    ax.text(65, 50, months[month], fontsize=12 );\n",
    "    ax.contour(topo.lon, topo.lat, topo.LANDFRAC, levels=[0.5], colors='k');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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be/bsITX1yab1pUqVon79+jRq1AhnZ2dKly5N6dKlsbCwwMLCAnNzc4yNjZFSkpaWRmpq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31bfa6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calling this function with a single month index gives us a single plot:\n",
    "sh(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you execute the next cell, you should get a figure with a slider above it. Go ahead and play with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e6e1026ad9c4e148cded0dda5a97b28",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact\n",
    "interact(sh, month=(0,11,1));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The \"pre-industrial\" control run\n",
    "\n",
    "Our control run is set up to simulate the climate of the \"pre-industrial era\", meaning before significant human-induced changes to the composition of the atmosphere, nominally the year 1850.\n",
    "\n",
    "Output from the control run is available on the same data server as above. Look in the folder called `som_1850_f19` (Here `som` stands for \"slab ocean model\", 1850 indicated pre-industrial conditions, and `f19` is a code for the horizontal grid resolution).\n",
    "\n",
    "There are climatology files for each active model component:\n",
    "\n",
    "- atmosphere, \n",
    "- sea ice\n",
    "- land surface \n",
    "\n",
    "I created these files by **averaging over the last 10 years of the simulation**. Let's take a look at the atmosphere file. The file is called\n",
    "\n",
    "`som_1850_f19.cam.h0.clim.nc`\n",
    "\n",
    "(the file extension `.nc` is used to indicate NetCDF format)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:       (ilev: 27, lat: 96, lev: 26, lon: 144, nbnd: 2, slat: 95, slon: 144, time: 12)\n",
      "Coordinates:\n",
      "  * lev           (lev) float64 3.545 7.389 13.97 23.94 37.23 53.11 70.06 ...\n",
      "  * ilev          (ilev) float64 2.194 4.895 9.882 18.05 29.84 44.62 61.61 ...\n",
      "  * time          (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 ...\n",
      "  * lat           (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 ...\n",
      "  * lon           (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 ...\n",
      "  * slat          (slat) float64 -89.05 -87.16 -85.26 -83.37 -81.47 -79.58 ...\n",
      "  * slon          (slon) float64 -1.25 1.25 3.75 6.25 8.75 11.25 13.75 16.25 ...\n",
      "Dimensions without coordinates: nbnd\n",
      "Data variables:\n",
      "    hyam          (lev) float64 ...\n",
      "    hybm          (lev) float64 ...\n",
      "    hyai          (ilev) float64 ...\n",
      "    hybi          (ilev) float64 ...\n",
      "    P0            float64 ...\n",
      "    date          (time) int32 ...\n",
      "    datesec       (time) int32 ...\n",
      "    w_stag        (slat) float64 ...\n",
      "    time_bnds     (time, nbnd) float64 ...\n",
      "    date_written  (time) |S64 ...\n",
      "    time_written  (time) |S64 ...\n",
      "    ntrm          int32 ...\n",
      "    ntrn          int32 ...\n",
      "    ntrk          int32 ...\n",
      "    ndbase        int32 ...\n",
      "    nsbase        int32 ...\n",
      "    nbdate        int32 ...\n",
      "    nbsec         int32 ...\n",
      "    mdt           int32 ...\n",
      "    nlon          (lat) int32 ...\n",
      "    wnummax       (lat) int32 ...\n",
      "    gw            (lat) float64 ...\n",
      "    ndcur         (time) int32 ...\n",
      "    nscur         (time) int32 ...\n",
      "    co2vmr        (time) float64 ...\n",
      "    ch4vmr        (time) float64 ...\n",
      "    n2ovmr        (time) float64 ...\n",
      "    f11vmr        (time) float64 ...\n",
      "    f12vmr        (time) float64 ...\n",
      "    sol_tsi       (time) float64 ...\n",
      "    nsteph        (time) int32 ...\n",
      "    AEROD_v       (time, lat, lon) float64 ...\n",
      "    CLDHGH        (time, lat, lon) float32 ...\n",
      "    CLDICE        (time, lev, lat, lon) float32 ...\n",
      "    CLDLIQ        (time, lev, lat, lon) float32 ...\n",
      "    CLDLOW        (time, lat, lon) float32 ...\n",
      "    CLDMED        (time, lat, lon) float32 ...\n",
      "    CLDTOT        (time, lat, lon) float32 ...\n",
      "    CLOUD         (time, lev, lat, lon) float32 ...\n",
      "    CONCLD        (time, lev, lat, lon) float32 ...\n",
      "    DCQ           (time, lev, lat, lon) float32 ...\n",
      "    DTCOND        (time, lev, lat, lon) float32 ...\n",
      "    DTV           (time, lev, lat, lon) float32 ...\n",
      "    EMIS          (time, lev, lat, lon) float32 ...\n",
      "    FICE          (time, lev, lat, lon) float32 ...\n",
      "    FLDS          (time, lat, lon) float32 ...\n",
      "    FLDSC         (time, lat, lon) float32 ...\n",
      "    FLNS          (time, lat, lon) float32 ...\n",
      "    FLNSC         (time, lat, lon) float32 ...\n",
      "    FLNT          (time, lat, lon) float32 ...\n",
      "    FLNTC         (time, lat, lon) float32 ...\n",
      "    FLUT          (time, lat, lon) float32 ...\n",
      "    FLUTC         (time, lat, lon) float32 ...\n",
      "    FSDS          (time, lat, lon) float32 ...\n",
      "    FSDSC         (time, lat, lon) float32 ...\n",
      "    FSDTOA        (time, lat, lon) float32 ...\n",
      "    FSNS          (time, lat, lon) float32 ...\n",
      "    FSNSC         (time, lat, lon) float32 ...\n",
      "    FSNT          (time, lat, lon) float32 ...\n",
      "    FSNTC         (time, lat, lon) float32 ...\n",
      "    FSNTOA        (time, lat, lon) float32 ...\n",
      "    FSNTOAC       (time, lat, lon) float32 ...\n",
      "    FSUTOA        (time, lat, lon) float32 ...\n",
      "    ICEFRAC       (time, lat, lon) float32 ...\n",
      "    ICIMR         (time, lev, lat, lon) float32 ...\n",
      "    ICWMR         (time, lev, lat, lon) float32 ...\n",
      "    LANDFRAC      (time, lat, lon) float32 ...\n",
      "    LHFLX         (time, lat, lon) float32 ...\n",
      "    LWCF          (time, lat, lon) float32 ...\n",
      "    MSKtem        (time, lat, lon) float32 ...\n",
      "    OCNFRAC       (time, lat, lon) float32 ...\n",
      "    OMEGA         (time, lev, lat, lon) float32 ...\n",
      "    OMEGAT        (time, lev, lat, lon) float32 ...\n",
      "    PBLH          (time, lat, lon) float32 ...\n",
      "    PHIS          (time, lat, lon) float32 ...\n",
      "    PRECC         (time, lat, lon) float32 ...\n",
      "    PRECL         (time, lat, lon) float32 ...\n",
      "    PRECSC        (time, lat, lon) float32 ...\n",
      "    PRECSL        (time, lat, lon) float32 ...\n",
      "    PS            (time, lat, lon) float32 ...\n",
      "    PSL           (time, lat, lon) float32 ...\n",
      "    Q             (time, lev, lat, lon) float32 ...\n",
      "    QFLX          (time, lat, lon) float32 ...\n",
      "    QREFHT        (time, lat, lon) float32 ...\n",
      "    QRL           (time, lev, lat, lon) float32 ...\n",
      "    QRS           (time, lev, lat, lon) float32 ...\n",
      "    RELHUM        (time, lev, lat, lon) float32 ...\n",
      "    SFCLDICE      (time, lat, lon) float32 ...\n",
      "    SFCLDLIQ      (time, lat, lon) float32 ...\n",
      "    SHFLX         (time, lat, lon) float32 ...\n",
      "    SNOWHICE      (time, lat, lon) float32 ...\n",
      "    SNOWHLND      (time, lat, lon) float32 ...\n",
      "    SOLIN         (time, lat, lon) float32 ...\n",
      "    SWCF          (time, lat, lon) float32 ...\n",
      "    T             (time, lev, lat, lon) float32 ...\n",
      "    TAUX          (time, lat, lon) float32 ...\n",
      "    TAUY          (time, lat, lon) float32 ...\n",
      "    TGCLDCWP      (time, lat, lon) float32 ...\n",
      "    TGCLDIWP      (time, lat, lon) float32 ...\n",
      "    TGCLDLWP      (time, lat, lon) float32 ...\n",
      "    TH            (time, ilev, lat, lon) float32 ...\n",
      "    TH2d          (time, lat, lon) float32 ...\n",
      "    TMQ           (time, lat, lon) float32 ...\n",
      "    TREFHT        (time, lat, lon) float32 ...\n",
      "    TS            (time, lat, lon) float32 ...\n",
      "    TSMN          (time, lat, lon) float32 ...\n",
      "    TSMX          (time, lat, lon) float32 ...\n",
      "    U             (time, lev, lat, lon) float32 ...\n",
      "    U10           (time, lat, lon) float32 ...\n",
      "    U2d           (time, lat, lon) float32 ...\n",
      "    UTGWORO       (time, lev, lat, lon) float32 ...\n",
      "    UU            (time, lev, lat, lon) float32 ...\n",
      "    UV2d          (time, lat, lon) float32 ...\n",
      "    UV3d          (time, ilev, lat, lon) float32 ...\n",
      "    UW2d          (time, lat, lon) float32 ...\n",
      "    UW3d          (time, ilev, lat, lon) float32 ...\n",
      "    V             (time, lev, lat, lon) float32 ...\n",
      "    V2d           (time, lat, lon) float32 ...\n",
      "    VD01          (time, lev, lat, lon) float32 ...\n",
      "    VQ            (time, lev, lat, lon) float32 ...\n",
      "    VT            (time, lev, lat, lon) float32 ...\n",
      "    VTH2d         (time, lat, lon) float32 ...\n",
      "    VTH3d         (time, ilev, lat, lon) float32 ...\n",
      "    VU            (time, lev, lat, lon) float32 ...\n",
      "    VV            (time, lev, lat, lon) float32 ...\n",
      "    W2d           (time, lat, lon) float32 ...\n",
      "    WTH3d         (time, ilev, lat, lon) float32 ...\n",
      "    Z3            (time, lev, lat, lon) float32 ...\n",
      "Attributes:\n",
      "    Conventions:                     CF-1.0\n",
      "    source:                          CAM\n",
      "    case:                            som_1850_f19\n",
      "    title:                           UNSET\n",
      "    logname:                         br546577\n",
      "    host:                            snow-08.rit.alba\n",
      "    Version:                         $Name$\n",
      "    revision_Id:                     $Id$\n",
      "    initial_file:                    /data/rose_scr/cesm_inputdata/atm/cam/in...\n",
      "    topography_file:                 /data/rose_scr/cesm_inputdata/atm/cam/to...\n",
      "    history:                         Tue May 27 11:27:43 2014: ncrcat /networ...\n",
      "    nco_openmp_thread_number:        1\n",
      "    DODS.strlen:                     8\n",
      "    DODS.dimName:                    chars\n",
      "    DODS_EXTRA.Unlimited_Dimension:  time\n"
     ]
    }
   ],
   "source": [
    "atm_control = xr.open_dataset( datapath + 'som_1850_f19/som_1850_f19.cam.h0.clim.nc' + endstr, decode_times=False)\n",
    "print(atm_control)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the vertical coordinate -- pressure levels in units of hPa:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lev' (lev: 26)>\n",
       "array([   3.544638,    7.388814,   13.967214,   23.944625,   37.23029 ,\n",
       "         53.114605,   70.05915 ,   85.439115,  100.514695,  118.250335,\n",
       "        139.115395,  163.66207 ,  192.539935,  226.513265,  266.481155,\n",
       "        313.501265,  368.81798 ,  433.895225,  510.455255,  600.5242  ,\n",
       "        696.79629 ,  787.70206 ,  867.16076 ,  929.648875,  970.55483 ,\n",
       "        992.5561  ])\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 3.545 7.389 13.97 23.94 37.23 53.11 70.06 85.44 ...\n",
       "Attributes:\n",
       "    long_name:      hybrid level at midpoints (1000*(A+B))\n",
       "    units:          level\n",
       "    positive:       down\n",
       "    standard_name:  atmosphere_hybrid_sigma_pressure_coordinate\n",
       "    formula_terms:  a: hyam b: hybm p0: P0 ps: PS"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.lev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of different stuff! These are all the different quantities that are calculated as part of the model simulation. **Every quantity represents a long-term average for a particular month**. \n",
    "\n",
    "Want to get more information about a particular variable?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'co2vmr' (time: 12)>\n",
       "array([ 0.000285,  0.000285,  0.000285,  0.000285,  0.000285,  0.000285,\n",
       "        0.000285,  0.000285,  0.000285,  0.000285,  0.000285,  0.000285])\n",
       "Coordinates:\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ...\n",
       "Attributes:\n",
       "    long_name:     co2 volume mixing ratio\n",
       "    cell_methods:  time: mean"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.co2vmr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the (prescribed) amount of CO2 in the atmosphere (about 285 parts per million by volume).\n",
    "\n",
    "One nice thing about `xarray.DataArray` objects is that we can do simple arithmetic with them (already seen several examples of this in the notes above). For example, change the units of CO2 amount to ppm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'co2vmr' (time: 12)>\n",
       "array([ 284.7,  284.7,  284.7,  284.7,  284.7,  284.7,  284.7,  284.7,  284.7,\n",
       "        284.7,  284.7,  284.7])\n",
       "Coordinates:\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.co2vmr * 1E6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's another variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SOLIN' (time: 12, lat: 96, lon: 144)>\n",
       "[165888 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ...\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    Sampling_Sequence:  rad_lwsw\n",
       "    units:              W/m2\n",
       "    long_name:          Solar insolation\n",
       "    cell_methods:       time: mean time: mean"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.SOLIN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently this is the incoming solar radiation or **insolation**, with shape (12,96,144) meaning it's got 12 months, 96 latitude points and 144 longitude points. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________________\n",
    "## Exercise\n",
    "\n",
    "**Make two well-labeled plots of the insolation:** \n",
    "\n",
    "1. The annual mean\n",
    "2. The (June - December) difference\n",
    "____________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, note that we can't make a map of a three-dimensional dataset! We need to either select a single month, or average over months, to reduce the dimensionality of the data down to 2 (lat/lon)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two ways to average over months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'SOLIN' (lat: 96, lon: 144)>\n",
      "array([[ 172.817383,  172.817383,  172.817383, ...,  172.817383,  172.817383,\n",
      "         172.817383],\n",
      "       [ 173.020493,  173.02037 ,  173.020142, ...,  173.019943,  173.02002 ,\n",
      "         173.020279],\n",
      "       [ 173.62915 ,  173.62886 ,  173.628769, ...,  173.629318,  173.628906,\n",
      "         173.629028],\n",
      "       ..., \n",
      "       [ 172.122559,  172.123032,  172.123047, ...,  172.122635,  172.12294 ,\n",
      "         172.122757],\n",
      "       [ 171.51123 ,  171.511337,  171.51149 , ...,  171.511581,  171.511597,\n",
      "         171.511536],\n",
      "       [ 171.308182,  171.308182,  171.308182, ...,  171.308182,  171.308182,\n",
      "         171.308182]], dtype=float32)\n",
      "Coordinates:\n",
      "    time     float64 14.0\n",
      "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
      "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n"
     ]
    }
   ],
   "source": [
    "#  First, the explicit way. We loop over every month and add them up\n",
    "num_months = len(atm_control.SOLIN.time)\n",
    "average = 0. * atm_control.SOLIN[0,:,:]\n",
    "for n in range(0,num_months):\n",
    "    average += atm_control.SOLIN[n,:,:]\n",
    "average /= num_months\n",
    "print(average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'SOLIN' (lat: 96, lon: 144)>\n",
      "array([[ 172.817383,  172.817383,  172.817383, ...,  172.817383,  172.817383,\n",
      "         172.817383],\n",
      "       [ 173.020493,  173.02037 ,  173.020142, ...,  173.019943,  173.02002 ,\n",
      "         173.020279],\n",
      "       [ 173.62915 ,  173.62886 ,  173.628769, ...,  173.629318,  173.628906,\n",
      "         173.629028],\n",
      "       ..., \n",
      "       [ 172.122559,  172.123032,  172.123047, ...,  172.122635,  172.12294 ,\n",
      "         172.122757],\n",
      "       [ 171.51123 ,  171.511337,  171.51149 , ...,  171.511581,  171.511597,\n",
      "         171.511536],\n",
      "       [ 171.308182,  171.308182,  171.308182, ...,  171.308182,  171.308182,\n",
      "         171.308182]], dtype=float32)\n",
      "Coordinates:\n",
      "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
      "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n"
     ]
    }
   ],
   "source": [
    "#  Second, the automatic way using xarray's .mean() method\n",
    "average2 = atm_control.SOLIN.mean(dim='time')\n",
    "print(average2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'SOLIN' (lat: 96, lon: 144)>\n",
       "array([[ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       ..., \n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True],\n",
       "       [ True,  True,  True, ...,  True,  True,  True]], dtype=bool)\n",
       "Coordinates:\n",
       "    time     float64 14.0\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ..."
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Are they the same?\n",
    "average == average2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Looks like it. But this will test if every single element is the same, using numpy.all():\n",
    "np.all(average==average2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map of the annual mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GNuYQEZuATenxFknrgVXA0cAbUtgFwPXAqWn7hRERwI2SVkhamfYzNO45mJkN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\nbBYXBzMzm+X/A6KqYEDkWPmJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31ca42240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For the insolation, a \"quick and dirty\" plot will suffice!\n",
    "ax = average.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the annual average map of insolation, or incoming solar radiation.\n",
    "\n",
    "It is kind of a boring map because insolation is the same at every longitude point (once you average over a single day).\n",
    "\n",
    "But this figure shows that the earth receives greater than 400 W/m2 sunlight at the equator, and less than 200 W/m2 annually at the poles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map of the (June - December) difference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x31d36d668>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xC/AkxbroMxrt+YiTCPBAQjOr0uhXQFhJMUD8E8D+wBuBtwELgN2BGaercg3EzKwHk1Hu\nMcS+DOyIiGeAF1PMd/hliumr1k73wqYsaiCTAc8+N+SVRTPLRgRMjH4VZKxlUtxjKebougy4TNLG\nMidwDcTMrAcR5R5DbExSsxJxOHBNy75SlYtMaiDB089N1B2Gmc0RxTiQ4c4OJVwMfE/SduAZ4F8B\nJO1PcRtrRlkkEDOzqo16+oiIMyRdDSwBvhvP90tuAP+9zDmySCCugZhZ1Ya8gbyUiLi+TdlPyr4+\niwRiZla10b+DNXtOIGZmXYqIHHphzVoWCWQy8C0sM6tUDrewZiuLBGJmVqXAt7AgkwQSEewYnyw1\nO+ZYWr1uVI5tHt/NsTOde2zKUs5z+VqY9Wpy5PthzV4WCcTMrGqugWSSQNwGYmZVymQg4axlkUDM\nzKoUAc/53mgeCWQygl+4BmJmlXE3XqhpMkVJfyPpDkm3Srpc0qJUvq+kZyRtTI8v1RGfmdl0mrew\nyjxyVtdsvOuB10TEbwM/AU5t2Xd3RKxMj/fXE56Z2TQCJibLPXJWyy2siPhuy+b1wDGzOd9EBD9/\ndnx2QZmZleRG9MIwrAfyXuDbLdvLJd0i6XuSfreuoMzMOgngucko9cjZwGogkq4CXtZm12kRcUU6\n5jRgHLgo7dsGvDwiHpH0OuCfJR0QEU+2Of8aYA3AC/da4hqImVUnYCLz5FDGwBJIRLx5uv2STqBY\nwP3w5jz0EbED2JGe3yTpbuCVwIY2519LWrd3yYrX+JM0s8oE+TeQl1FLG4ikI4CTgf8SEU+3lO8F\nPBoRE5L2A1YA98x0vskInt7pbrxmVh0PA6lvHMgXgF2A9ZIArk89rt4AfFLSODABvL9l0Xczs6Hg\nRvRCXb2w9u9QfhlwWcXhmJl1x20gQCYj0Scmg5/vcCO6mVWj2QtrrhuGbrxmZiOlqpHonWbtSPtO\nlbRZ0p2Sfq+l/IhUtlnSKbMKYAZZ1EAigmd2ugZiZhWJYLKaGsh64NSIGJd0JsWsHSdLejVwHHAA\n8BvAVZJemV7zReAtwFbgRknrIuLfBxFcFgnEzKxKQTW9sKaZtWM1cEka+nCvpM3AwWnf5oi4B0DS\nJelYJ5BOJibhKQ8kNLMK1dAL673Apen5PhQJpWlrKgO4f0r5IYMKKIsEYmZWpWI9kNIzJS6W1DoY\nem0aCA30PGuH2hwftG/XHlimyyKBTEbwjAcSmllFuryFtT0iVnU8Vw+zdlDULJa1HLYUeDA971Te\nd+6FZWbWg4p6YTVn7Xh766wdwDrgOEm7SFpOMWvHDcCNwApJyyUtoGhoXzerIKaRRQ3EzKxKUd2K\nhG1n7YiI2yR9naJxfBz4QERMAEg6CfgOMAacHxG3DSq4LBJIRDDuJW3NrCoVjUTvNGtH2ncGcEab\n8iuBKwcZV1MWCcTMrEqBpzKBTBJIBIzvzHztSDMbGhGwc9zfOVkkEDOzKgXhGgi5JBC3gZhZlTwb\nL5BLAjEzq5DbQApZJJAIXAMxs8qEayBAJgnEzKxqTiBOIGZmXZuMYId7YeWRQDyQ0Myq5hpIJgnE\nzKxKbgMpZJFAPJDQzKpW0VxYQ62W2XglnS7pAUkb0+Ooln1t1/k1MxsWzYGEZR45q7MGck5EfK61\noNM6v81ZJjtxG4iZVclTmRSG7RZWp3V+/2+9YZmZPa8YSOgEUmcCOUnS8cAG4CMR8RjTr/PbWcCE\nfw2YWVUi/9tTZQysDUTSVZI2tXmsBs4FXgGsBLYBZzVf1uZUbT8lSWskbZC0YeKZJwfyN5iZtdOc\nysRtIAMy0zq/TZLOA76VNqdb53fq+dcCawEW7r1/3p+SmQ2VCBjPPDmUUcstLElLImJb2jwa2JSe\nrwO+Julsikb05jq/03IjuplVyZMpFupqA/mspJUUn8MW4H0A063za2Y2LCLCvbCoKYFExLum2dd2\nnd8ZTsjk+M7ZhmVmVpprIMPXjdfMbOh5KpNCFgkkIph8zjUQM6tOOIHkkUDMzKoUAZNOIE4gZmbd\nC8KTKWaSQGLSjehmVh3PfgHkkkDMzCoUQDh/5JFAAnfjtcGLyfZDktQYm3a/laPG2IzXeJj4FlYm\nCcTMrFJuRAdySSARTLgGYnVxzaM/pruOQ3eNw914ySWBmJlVKAImJtwIkkUCCU9lYmYVcw2kpjXR\nzcxGXUxGqUc/SPqopJC0OG1L0uclbZZ0q6SDWo49QdJd6XFCXwLoIIsaiJlZlSKiskZ0ScuAtwD3\ntRQfSbHcxQrgEIpF+g6RtCfwP4FVFL2Nb5K0Lq342nd5JBDPhWVmFauwG+85wMeAK1rKVgMXRhHE\n9ZIWSVoCHAasj4hHASStB44ALh5EYHkkEDOzinUxkHCxpA0t22vTiqozkvR24IGI+JH0Kyt+7wPc\n37K9NZV1Kh+IPBKIpzIxswpFd1OZbI+IVZ12SroKeFmbXacBHwfe2u5l7cKapnwg8kggZmZViv71\nwoqIN7crl/QfgeVAs/axFLhZ0sEUNYtlLYcvBR5M5YdNKb+uL4G2kUUCKealcZ9sM6tKMDngNpCI\n+DGwd3Nb0hZgVURsl7QOOEnSJRSN6E9ExDZJ3wH+WtKL08veCpw6qBizSCBmZlUqfrTWOg7kSuAo\nYDPwNPAegIh4VNKngBvTcZ9sNqgPQh4JxAMJzaxKfbyFVfotI/ZteR7ABzocdz5wfhUx5ZFAzMwq\n5skUa0ogki4FfittLgIej4iVkvYFbgfuTPuuj4j3Vx+hmVlnEcGk58KqJ4FExLHN55LOAp5o2X13\nRKzs8oRMDt1snWaWM9dAar6FpaJv2juAN9UZh5lZt7yAWP1tIL8LPBQRd7WULZd0C/Ak8D8i4l9n\nOkkAMeEP08wqEuEEwgATyHSjKyOiOafLO/nVOVq2AS+PiEckvQ74Z0kHRMSTbc6/BlgDoAV79Dd4\nM7NpBE4gMMAE0ml0ZZOkecAfAq9rec0OYEd6fpOku4FXAhumvj7NJbMWoLHb3uEP08wq4wlcgXpv\nYb0ZuCMitjYLJO0FPBoRE5L2o5iq+J66AjQza8u3sIB6E8hx/PoUw28APilpHJgA3l9uFKU/TDOr\nlr9zakwgEfHuNmWXAZdVH42ZWXluAynU3QvLzGz0hGsgkFEC8YdpZtXx4GXIKIGYmVUlPIEr4ARi\nZta9CA9exgnEzKwnvm2eSwJxn2wzq5K/c4BcEoiZWaWcQMAJxMysa8WStl4PxAnEzKxb7oUFOIGY\nmXXPi9gBTiBmZl3zGkQFJxAzs265FxbgBGJm1gMnEHACMTPrnhvRAVBE1B3DrEn6GfALYHvdsUyx\nmOGLCYYzLsdUjmMqr1NcvxkRe83mxJL+JZ2/jO0RccRs3m9YZZFAACRtiIhVdcfRahhjguGMyzGV\n45jKG9a4ctKoOwAzMxtNTiBmZtaTnBLI2roDaGMYY4LhjMsxleOYyhvWuLKRTRuImZlVK6caiJmZ\nVSiLBCLpCEl3Stos6ZQa49gi6ceSNkrakMr2lLRe0l3p3xcPOIbzJT0saVNLWdsYVPh8um63Sjqo\nwphOl/RAulYbJR3Vsu/UFNOdkn5vQDEtk3StpNsl3Sbpg6m87mvVKa7arpekhZJukPSjFNNfpfLl\nkn6YrtWlkhak8l3S9ua0f98KY/qqpHtbrtPKVF7J5zfnRMRIP4Ax4G5gP2AB8CPg1TXFsgVYPKXs\ns8Ap6fkpwJkDjuENwEHAppliAI4Cvg0IeD3wwwpjOh34aJtjX50+w12A5emzHRtATEuAg9LzPYCf\npPeu+1p1iqu265X+5t3T8/nAD9M1+DpwXCr/EvBn6fmfA19Kz48DLh3AdeoU01eBY9ocX8nnN9ce\nOdRADgY2R8Q9EbETuARYXXNMrVYDF6TnFwB/MMg3i4jvA4+WjGE1cGEUrgcWSVpSUUydrAYuiYgd\nEXEvsJniM+53TNsi4ub0/CngdmAf6r9WneLqZODXK/3NP0+b89MjgDcB30jlU69V8xp+AzhckiqK\nqZNKPr+5JocEsg9wf8v2Vqb/H26QAviupJskrUllL42IbVB8OQB71xBXpxjqvnYnpdsJ57fc2qs8\npnSL5bUUv2KH5lpNiQtqvF6SxiRtBB4G1lPUdB6PiPE27/vLmNL+J4CXDDqmiGhepzPSdTpH0i5T\nY2oTr/UohwTS7pdNXV3LDo2Ig4AjgQ9IekNNcZRV57U7F3gFsBLYBpxVR0ySdgcuAz4UEU9Od2ib\nsirjqvV6RcRERKwEllLUcP7DNO9bS0ySXgOcCrwK+E/AnsDJVcY01+SQQLYCy1q2lwIP1hFIRDyY\n/n0YuJzif7SHmlXl9O/DNYTWKYbarl1EPJS+ACaB83j+tktlMUmaT/ElfVFEfDMV136t2sU1DNcr\nxfE4cB1FO8IiSc0JWVvf95cxpf0vovwtzNnEdES6BRgRsQP4B2q6TnNFDgnkRmBF6hGygKLRbl3V\nQUjaTdIezefAW4FNKZYT0mEnAFdUHds0MawDjk89VF4PPNG8fTNoU+4/H01xrZoxHZd68iwHVgA3\nDOD9BXwFuD0izm7ZVeu16hRXnddL0l6SFqXnuwJvpmibuRY4Jh029Vo1r+ExwDUR0ddf+x1iuqMl\n+YuiTab1OtXy33rW6m7F78eDoofFTyjuy55WUwz7UfSG+RFwWzMOinu/VwN3pX/3HHAcF1Pc4niO\n4lfXiZ1ioKjWfzFdtx8DqyqM6R/Te95K8T/3kpbjT0sx3QkcOaCYfofiFsatwMb0OGoIrlWnuGq7\nXsBvA7ek994EfKLlv/kbKBru/wnYJZUvTNub0/79KozpmnSdNgH/h+d7alXy+c21h0eim5lZT3K4\nhWVmZjVwAjEzs544gZiZWU+cQMzMrCdOIGZm1hMnEBt5kn4+81Fm1m9OIGZm1hMnEMtGGmX8N5I2\nqViX5dhUfpik6yR9Q9Idki7q9+ywZnPRvJkPMRsZf0gx2eCBwGLgRknfT/teCxxAMf/RvwGHAj+o\nI0izXLgGYjn5HeDiKCYdfAj4HsWsrAA3RMTWKCYj3AjsW1OMZtlwArGcTHdbakfL8wlc+zabNScQ\ny8n3gWPTQkN7USyl2/eZfM2s4F9hlpPLgf9MMSNyAB+LiP8n6VX1hmWWJ8/Ga2ZmPfEtLDMz64kT\niJmZ9cQJxMzMeuIEYmZmPXECMTOznjiBmJlZT5xAzMysJ04gZmbWk/8P+j7N07tCKNMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31c96f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# June is the 6th month, which makes it index number 5 here\n",
    "insolation_june = atm_control.SOLIN.isel(time=5)\n",
    "#  And december is index 11\n",
    "insolation_dec = atm_control.SOLIN.isel(time=11)\n",
    "(insolation_june - insolation_dec).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are looking at the **difference** between the two solstice seasons. These differences are obviously largest at the poles. We'll look at the distribution of insolation in more detail later in the course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the control run with the observed energy budget\n",
    "\n",
    "Recall that our investigations so far have been guided by this figure of the observed **annual, global mean energy budget**:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Observed global energy budget](http://www.atmos.albany.edu/facstaff/brose/classes/ENV415_Spring2018/images/GlobalEnergyBudget.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The global average\n",
    "\n",
    "In order to compare these numbers with the control run, **we need to take global averages** of the data.\n",
    "\n",
    "A global average **must be weighted by the area of each grid cell**. We cannot simply average over each data point on a latitude-longitude grid.\n",
    "\n",
    "**WHY?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighting for global average\n",
    "\n",
    "The global average needs to weighted by the **cosine of latitude** (do you understand why?)\n",
    "\n",
    "We can implement this in `xarray` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  functions available in xarray for standard mathematical operations\n",
    "#  (but still preserving DataArray attributes and axes)\n",
    "from xarray.ufuncs import cos, deg2rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#  Take the cosine of latitude (first converting to radians)\n",
    "coslat = cos(deg2rad(atm_control.lat))\n",
    "#  And divide by its mean value\n",
    "weight_factor = coslat / coslat.mean(dim='lat') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'lat' (lat: 96)>\n",
       "array([  9.720485e-17,   5.248730e-02,   1.049172e-01,   1.572324e-01,\n",
       "         2.093756e-01,   2.612899e-01,   3.129185e-01,   3.642049e-01,\n",
       "         4.150931e-01,   4.655273e-01,   5.154525e-01,   5.648141e-01,\n",
       "         6.135580e-01,   6.616311e-01,   7.089806e-01,   7.555549e-01,\n",
       "         8.013030e-01,   8.461749e-01,   8.901215e-01,   9.330948e-01,\n",
       "         9.750478e-01,   1.015935e+00,   1.055710e+00,   1.094332e+00,\n",
       "         1.131757e+00,   1.167944e+00,   1.202854e+00,   1.236449e+00,\n",
       "         1.268692e+00,   1.299547e+00,   1.328981e+00,   1.356963e+00,\n",
       "         1.383460e+00,   1.408445e+00,   1.431889e+00,   1.453768e+00,\n",
       "         1.474057e+00,   1.492734e+00,   1.509779e+00,   1.525173e+00,\n",
       "         1.538899e+00,   1.550943e+00,   1.561290e+00,   1.569931e+00,\n",
       "         1.576854e+00,   1.582054e+00,   1.585523e+00,   1.587259e+00,\n",
       "         1.587259e+00,   1.585523e+00,   1.582054e+00,   1.576854e+00,\n",
       "         1.569931e+00,   1.561290e+00,   1.550943e+00,   1.538899e+00,\n",
       "         1.525173e+00,   1.509779e+00,   1.492734e+00,   1.474057e+00,\n",
       "         1.453768e+00,   1.431889e+00,   1.408445e+00,   1.383460e+00,\n",
       "         1.356963e+00,   1.328981e+00,   1.299547e+00,   1.268692e+00,\n",
       "         1.236449e+00,   1.202854e+00,   1.167944e+00,   1.131757e+00,\n",
       "         1.094332e+00,   1.055710e+00,   1.015935e+00,   9.750478e-01,\n",
       "         9.330948e-01,   8.901215e-01,   8.461749e-01,   8.013030e-01,\n",
       "         7.555549e-01,   7.089806e-01,   6.616311e-01,   6.135580e-01,\n",
       "         5.648141e-01,   5.154525e-01,   4.655273e-01,   4.150931e-01,\n",
       "         3.642049e-01,   3.129185e-01,   2.612899e-01,   2.093756e-01,\n",
       "         1.572324e-01,   1.049172e-01,   5.248730e-02,   9.720485e-17])\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ..."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Want to see what we just created?\n",
    "weight_factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An alternative: use weights already provided in the dataset\n",
    "\n",
    "You will find that many gridded datasets already provide a field that gives accurate area weighting.\n",
    "\n",
    "In the case of the CESM output, the field is called `gw`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weight_factor2 = atm_control.gw / atm_control.gw.mean(dim='lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(234.14572580253756)\n",
      "<xarray.DataArray ()>\n",
      "array(234.13552628165561)\n"
     ]
    }
   ],
   "source": [
    "#  Compare our two weights\n",
    "#   The field FLNT is actually the net longwave radiation flux at the top of the atmosphere --\n",
    "#  What we have called the OLR in this course\n",
    "print( (atm_control.FLNT * weight_factor).mean(dim=('time', 'lon', 'lat')))\n",
    "print( (atm_control.FLNT * weight_factor2).mean(dim=('time', 'lon', 'lat')))\n",
    "# Notice how we can average simultaneously over multiple dimensions here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These numbers should be very close to each other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___________________________\n",
    "## Exercise\n",
    "\n",
    "Make sure you can take a global average, testing on **surface temperature** in the control run.\n",
    "\n",
    "Surface temperature is called `'TS'` in the control run data file.\n",
    "\n",
    "**Calculate annual, global average `'TS'`**\n",
    "\n",
    "**Verify that you get something close to 288 K**\n",
    "\n",
    "If you don't, try to find and fix the errors.\n",
    "____________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, calculate the global average directly as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(287.6232949136381)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Take the area-weighted global average\n",
    "(atm_control.TS * weight_factor2).mean(dim=('time', 'lon', 'lat'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we want to repeat this operation, why not wrap it in a reusable function!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def global_average(field, weight=weight_factor2):\n",
    "    return (field * weight).mean(dim=('time', 'lon', 'lat'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look carefully at the function definition and make sure you understand it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray ()>\n",
       "array(287.6232949136381)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Of course this produces the same result as above\n",
    "global_average(atm_control.TS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "287.6232949136381"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  If we want to display the result as a plain floating-point number:\n",
    "float(global_average(atm_control.TS))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding the radiative fluxes in the model output\n",
    "\n",
    "Now that you have a working function to take global averages, we can compare some energy budget values against observations.\n",
    "\n",
    "The model output contains lots of diagnostics about the radiative fluxes. Here some CESM naming conventions to help you find the appropriate output fields:\n",
    "\n",
    "- All variables whose names being with `'F'` are an **energy flux** of some kind. \n",
    "- Most have a four-letter code, e.g. `'FLNT'`\n",
    "- `'FL'` means **longwave flux** (i.e. terrestrial)\n",
    "- `'FS'` means **shortwave flux** (i.e. solar)\n",
    "- The third letter indicates **direction** of the flux:\n",
    "    - `'U'` = up\n",
    "    - `'D'` = down\n",
    "    - `'N'` = net\n",
    "- The fourth letter indicates the **location** of the flux:\n",
    "    - `'T'` = top of atmosphere\n",
    "    - `'S'` = surface\n",
    "- So `'FLNT'` means 'net longwave flux at the top of atmosphere', i.e. the outgoing longwave radiation.\n",
    "\n",
    "You wil see that these are all 12 x 96 x 144 -- i.e. a two-dimensional grid for every calendar month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'FLNT' (time: 12, lat: 96, lon: 144)>\n",
       "[165888 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ...\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    Sampling_Sequence:  rad_lwsw\n",
       "    units:              W/m2\n",
       "    long_name:          Net longwave flux at top of model\n",
       "    cell_methods:       time: mean time: mean"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.FLNT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__________________________\n",
    "## Exercise\n",
    "\n",
    "Compute annual, global averages of the following quantities. \n",
    "\n",
    "1. Incoming solar radiation (or insolation)\n",
    "2. Absorbed solar radiation\n",
    "3. Outgoing longwave radiation\n",
    "\n",
    "Compare your results briefly to the observations.\n",
    "____________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(340.3058626212777)\n"
     ]
    }
   ],
   "source": [
    "# Insolation\n",
    "Q = global_average(atm_control.SOLIN)\n",
    "print(Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(234.03991728200748)\n"
     ]
    }
   ],
   "source": [
    "# Absorbed shortwave radiation\n",
    "ASR = global_average(atm_control.FSNT)\n",
    "print(ASR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(234.13552628165561)\n"
     ]
    }
   ],
   "source": [
    "# Outgoing longwave radiation\n",
    "OLR = global_average(atm_control.FLNT)\n",
    "print(OLR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing to the observations, it looks like **both the OLR and ASR** are a little lower than observed.\n",
    "\n",
    "But, what about the **net imbalance** at the top of the atmosphere?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(-0.0956089996481353)\n"
     ]
    }
   ],
   "source": [
    "print(ASR - OLR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The net imbalance is very small (less than 0.1 W/m2, or about 10x smaller than the observed imbalance).\n",
    "\n",
    "This means that the model is **very close to global energy balance** so we should not expect the climate to change if we keep running this simulation forward.\n",
    "\n",
    "The negative sign here means that the model is actually losing energy (OLR is greater than ASR) -- but again, it is small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A few more tidbits\n",
    "\n",
    "Feel free to keep exploring the data!\n",
    "\n",
    "Many other fields are four-dimensional (time, level, latitude, longitude). \n",
    "\n",
    "For example, here is the shape of the array that hold the **air temperature** at every point and every month:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 26, 96, 144)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control['T'].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An important *gotcha* with `xarray`\n",
    "\n",
    "Normally we can access a variable with the notation `Dataset.variable_name`\n",
    "\n",
    "But in `xarray` (and also in `numpy` and other packages), the notation `object.T` actually represents the **transpose** operator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 96, 144)\n",
      "(144, 96, 12)\n"
     ]
    }
   ],
   "source": [
    "print( atm_control.FLNT.shape)\n",
    "print( atm_control.FLNT.T.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here there is a name conflict because the air temperature variable is called `T`. One solution is to access it through the dictionary method, as I did above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'T' (time: 12, lev: 26, lat: 96, lon: 144)>\n",
       "[4313088 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 3.545 7.389 13.97 23.94 37.23 53.11 70.06 85.44 ...\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ...\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    mdims:         1\n",
       "    units:         K\n",
       "    long_name:     Temperature\n",
       "    cell_methods:  time: mean time: mean"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control['T']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another solution is just to rename the variable (here going from `T` to `Ta`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray 'Ta' (time: 12, lev: 26, lat: 96, lon: 144)>\n",
       "[4313088 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * lev      (lev) float64 3.545 7.389 13.97 23.94 37.23 53.11 70.06 85.44 ...\n",
       "  * time     (time) float64 14.0 45.0 73.0 104.0 134.0 165.0 195.0 226.0 ...\n",
       "  * lat      (lat) float64 -90.0 -88.11 -86.21 -84.32 -82.42 -80.53 -78.63 ...\n",
       "  * lon      (lon) float64 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 ...\n",
       "Attributes:\n",
       "    mdims:         1\n",
       "    units:         K\n",
       "    long_name:     Temperature\n",
       "    cell_methods:  time: mean time: mean"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atm_control.rename({'T': 'Ta'}, inplace=True)\n",
    "atm_control.Ta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is some code to plot the average sounding (temperature as function of pressure) at a particular point in the month of January."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x31ca83898>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KMxvfiM9d4u6nu/sC4JfufhowLnztfHd/iODu3o+5+3h3rzhOUyYA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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x31ca912e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot( atm_control.Ta[0,:,70,115], atm_control.lev )\n",
    "plt.gca().invert_yaxis()\n",
    "plt.ylabel('Pressure (hPa)')\n",
    "plt.xlabel('Temperature (K)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What was the location we just used for that plot? Let's check by indexing the latitude and longitude arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.63157894736841\n",
      "287.5\n"
     ]
    }
   ],
   "source": [
    "print( atm_control.lat[70].values)\n",
    "print( atm_control.lon[115].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are actually the coordinates of the Albany area (read longitude in degrees east)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So go ahead and mess around with the model output and see what you can do with it. And have fun.\n",
    "\n",
    "Thanks for playing!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}