{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ENV / ATM 415: Climate Laboratory\n", "\n", "# Introducing the Community Earth System Model\n", "\n", "_____________________________________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## About the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are using a version of the Community Earth System Model (CESM) which is developed and maintained at the National Center for Atmospheric Research in Boulder, CO. See the CESM website here: http://www2.cesm.ucar.edu\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 heat capacity but no motion. This greatly simplifies the necessary calculations, particularly the time required for the model to reach equilibrium. The net effect heat transport by ocean currents is prescribed through a so-called \"q-flux\", which really just means we prescribed sources and sinks of heat at different locations. For (lots of) details, see http://www2.cesm.ucar.edu/working-groups/pwg/documentation/cesm1-paleo-toolkit/ocean/som\n", "\n", "\n", "### Atmosphere\n", " - Horizontal resolution about 2º lat/lon\n", " - AGCM solves the fundamental equations:\n", " - Conservation of momentum, mass, energy, water, equation of state\n", " - Model also solves equations of **radiative transfer**. This takes account of\n", " - composition of the atmosphere\n", " - absorption properties of different gases\n", " - radiative effects of **clouds**.\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", "\n", "\n", "### Sea ice\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.\n", "\n", "### Land surface model\n", "- Same resolution as atmosphere. \n", "- Determines surface fluxes of heat, water, momentum (friction) based on prescribed vegetation types.\n", "\n", "### Ocean\n", "- Same grid as sea ice, 1º.\n", "- Sea surface temperature evolves based on:\n", " - heat exchange with atmosphere\n", " - prescribed “q-flux”.\n", "\n", "### 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've done with our various simple models.\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**.\n", "\n", "The model runs on one of our local compute clusters here at U. Albany. We can simulate about 5 years per day by running the model on 32 cores. Equilibration time for the slab ocean model is roughly 20 years. Thus it takes a few days to run any particularly scenario out to equilibrium. The atmospheric GCM uses about half of the cpu time, the sea ice uses about one quarter, and the rest goes to the land model, the coupler, and various other bits and pieces.\n", "\n", "We will begin with a \"control run\", i.e. we will set up the model with (approximately) realistic conditions and run it out to equilibrium. We can then measure how well our simulated climate agrees with observations.\n", "\n", "_______________________________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What can we resolve with a 2º atmosphere?\n", "____________\n", "\n", "The following animation shows contours of sea level pressure in the control simulation. It is based on 6-hourly output from the numerical model.\n", "\n", "The atmosphere is simulated with a 2º finite volume dynamical core." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/jpeg": 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aO6uRol0Qxn0gjEsR1Z3Yrmx2C7PvNE80/QT6QrpIyKsm/K4BwoO7Hbu79Np2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\nG853DceXGtAb2HdpS4X6WdDergfZWu0NOquhVgC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"text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('As85L34fKYQ')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discussion point:\n", "How well does this represent the true general circulation of the atmosphere?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____________________________________\n", "## 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.\n", "\n", "### 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", "\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. Python has some nice interfaces for working with `NetCDF` data files, including accessing files remotely over the internet. To begin, we need to import the Python package `netCDF4` to read the data files.\n", "\n", "We also set the notebook to `inline` graphics mode to display figures right here in the notebook." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import netCDF4 as nc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You are encouraged to experiment and tinker with all the code below." ] }, { "cell_type": "markdown", "metadata": {}, "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", "We use the `Dataset` object from the `netCDF4` module as our basic container for any `netCDF` data. `Dataset()` requires at least one argument telling it what file to open. This can either be a file on your local disk or a URL.\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": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://ramadda.atmos.albany.edu:8080/repository/opendap/latest/Top/Users/Brian+Rose/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/Brian+Rose/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": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "root group (NETCDF3_CLASSIC data model, file format UNDEFINED):\n", " history: Written on date: 20050602\n", "definesurf -remap -t /fs/cgd/csm/inputdata/atm/cam/topo/USGS-gtopo30_10min_c050419.nc -g /fs/cgd/csm/inputdata/atm/cam/coords/fv_1.9x2.5.nc -l /fs/cgd/csm/inputdata/atm/cam2/hrtopo/landm_coslat.nc USGS-gtopo30_1.9x2.5_remap_c05060\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", " dimensions(sizes): lat(96), lon(144)\n", " variables(dimensions): float64 \u001b[4mlat\u001b[0m(lat), float64 \u001b[4mlon\u001b[0m(lon), float64 \u001b[4mPHIS\u001b[0m(lat,lon), float64 \u001b[4mSGH\u001b[0m(lat,lon), float64 \u001b[4mSGH30\u001b[0m(lat,lon), float64 \u001b[4mLANDFRAC\u001b[0m(lat,lon), float64 \u001b[4mLANDM_COSLAT\u001b[0m(lat,lon)\n", " groups: \n", "\n" ] } ], "source": [ "# Now we actually open the file\n", "topo = nc.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. This Python interface to `netCDF` stores most of the data in dictionaries. For example, the actual variables contained in this dataset are:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lat\n", "lon\n", "PHIS\n", "SGH\n", "SGH30\n", "LANDFRAC\n", "LANDM_COSLAT\n" ] } ], "source": [ "for v in topo.variables: print(v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The output of our little for loop is a list of variable names contained in the file we just opened -- actually the keys to the dictionary called `variables`. Each variables contains some data along with a bunch of descriptive information about the data. Here's an example:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "float64 PHIS(lat, lon)\n", " _FillValue: 1e+36\n", " missing_value: 1e+36\n", " long_name: surface geopotential\n", " units: m2/s2\n", " from_hires: true\n", " filter: remap\n", "unlimited dimensions: \n", "current shape = (96, 144)\n", "filling off\n", "\n" ] } ], "source": [ "print topo.variables['PHIS']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting the actual data\n", "\n", "This shows the syntax for reading a particular variable, which in this code is the surface geopotential (from which we can get surface elevation). It will be handy to store the grid information (latitude and longitude) in some local arrays. **The example below shows that by adding `[:]` at the end of the expression, we reference just the data itself (which is stored as a `numpy` array) and not the accompanying decription.** In the Python world this is called *slicing*. You can get subsets of the data by taking a smaller slice, using array indexing." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(96,) (144,)\n" ] } ], "source": [ "lat = topo.variables['lat'][:]\n", "lon = topo.variables['lon'][:]\n", "print lat.shape, lon.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have made local copies of these two arrays that contain coordinate information for the actual data in the file. The `.shape` command used above (which works for any `numpy` array) gives us information about the dimensions of the arrays, in this case, 96 latitude points and 144 longitude points. If you like can also try just typing \n", "\n", "`print lat` \n", "\n", "or \n", "\n", "`print lon` \n", "\n", "and see the coordinates themselves." ] }, { "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": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Az6jbnwOwCmFHZuIUosRrx5HNrv+iOWiiuVy4Xj4e7x8lqiorRUXyVSTplEgS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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "height = topo.variables['PHIS'][:] / 9.8 / 1000 # in kilometers\n", "\n", "# This makes use of the `masked array`, part of the numpy module\n", "# essentially a numpy array with a mask of the same size\n", "height_masked = np.ma.MaskedArray(height, topo.variables['LANDFRAC'][:] == 0. )\n", "\n", "fig1 = plt.figure(figsize=(10,5))\n", "ax1 = fig1.add_subplot(1, 1, 1)\n", "cax1 = ax1.pcolormesh( lon, lat, height_masked )\n", "ax1.set_title('Topography (km) and land-sea mask in CESM')\n", "ax1.axis([0, 360, -90, 90])\n", "ax1.set_xlabel('Longitude'); ax1.set_ylabel('Latitude');\n", "cbar1 = plt.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": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/iH1PG70CoXkySROEgniK0GSeaSLSKbyvlqquBABV/RnALnmzjhBCCKnoVE7j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\nv4gcDmAzgEUAOgOAqn4nIqMAfIfQ4NQ1zFgihBB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ocean_mask = np.ma.MaskedArray( topo.variables['LANDFRAC'][:], topo.variables['LANDFRAC'][:] == 1. )\n", "\n", "fig2 = plt.figure(figsize=(10,5))\n", "ax2 = fig2.add_subplot(1, 1, 1)\n", "cax2 = ax2.pcolormesh( lon, lat, ocean_mask )\n", "ax2.set_title('Ocean mask in CESM')\n", "ax2.axis([0, 360, -90, 90])\n", "ax2.set_xlabel('Longitude'); ax2.set_ylabel('Latitude')\n", "cbar2 = plt.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. If you're keen to learn about this, check out:\n", "\n", "\n", "\n", "and\n", "\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", "\n", "\n", "Let's load it and take a look." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "area\n", "mask\n", "yc\n", "xc\n", "time\n", "S\n", "T\n", "U\n", "V\n", "dhdx\n", "dhdy\n", "hblt\n", "qdp\n" ] } ], "source": [ "som_input = nc.Dataset( datapath + 'som_input/pop_frc.1x1d.090130.nc' + endstr )\n", "for v in som_input.variables: print v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ocean / sea ice models exist on different grids than the atmosphere (1º instead of 2º resolution). Let's store the new coordinate information just like we did for the atmosphere grid." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(360,) (180,)\n" ] } ], "source": [ "lon_som = som_input.variables['xc'][:]\n", "lat_som = som_input.variables['yc'][:]\n", "print lon_som.shape, lat_som.shape" ] }, { "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). \n", "\n", "First, let's just look at the size of the data array:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(12, 180, 360)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape(som_input.variables['qdp'][:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This means 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. We will use a very convenient array function np.mean(), which just computes the mean point-by-point. This leaves us with a single grid on 180 latitude points by 360 longitude points:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(180, 360)\n" ] } ], "source": [ "qdp_an = np.mean( som_input.variables['qdp'][:], axis=0 )\n", "# Note that we have to tell numpy which axis to average over\n", "# By default it will average over all axes simultaneously \n", "print qdp_an.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now make a nice plot of the annual mean q-flux. This code also overlays a contour of the land-sea mask for reference. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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PU7FixUyLIoDftm9n4cKFXL58+ZX4SPPz86Njx4506dKFhQsXYqE/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v610BPtwIEDjBs3jsOH/8Dd3cOsWaiv69Th199+s0oocnNz482bN/rpMaVhuOIpbirYXiy1v/JB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can always set a non-standard size for our figure window\n", "fig3 = plt.figure(figsize=(10, 6))\n", "ax3 = fig3.add_subplot(111)\n", "cax3 = ax3.pcolormesh( lon_som, lat_som, -qdp_an, cmap=plt.cm.seismic, vmin=-700., vmax=700. )\n", "cbar3 = plt.colorbar(cax3)\n", "ax3.set_title( 'CESM: Prescribed heat flux out of ocean (W m$^{-2}$), annual mean', fontsize=14 )\n", "ax3.axis([0, 360, -90, 90], fontsize=12)\n", "ax3.set_xlabel('Longitude', fontsize=14)\n", "ax3.set_ylabel('Latitude', fontsize=14)\n", "ax3.contour( lon, lat, topo.variables['LANDFRAC'][:], [0.5], colors='k');\n", "ax3.text(65, 50, 'Annual', fontsize=16 );" ] }, { "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": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(180, 360)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qdp_jan = som_input.variables['qdp'][0,:,:]\n", "qdp_jan.shape" ] }, { "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 1\". Now make the plot:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JFC5cmODgYOb/+CNGo5HRn3/O15MmsW37dk6dPIldBmVNmzaNy5cvs3Tp0iT3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\nHjN9D+Dj48OSJUto164dT5Urh0WxVv/UqVO0+uYbOlapwgfdu+MbHu4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1jJh6/5Xx1Bif\n0YChNyHRGzz0BgMlw1Wro1aY2sCgRpNUNzMDgMR7r1w5SL58hb3isbJUqVK0adOG1Rs28K82bRyu\nuiMisFiDnAsC/AF/giMcy5zly1a13l9SQvow+WRBWd/Gjbty4EBrgoPzuqXTOX3Uc+TyJZtmxa9R\nsnsr0BmwacT1GgRBaAV8goOz/UcUxXTuASVVg4R69VoyduyHDBzYkdOnNY588yJSU1MZMmQIL774\nKkWLliFRsV1dDVo2Kse9j+HCBaVQ797x0z6iQoWK0K1bV6pXr86AAQOIi4tD8PNz2Xl9khIhwUbc\nxYu0792baR99xJNPPgnA3r17qV6tGiC1sY9DnWOz4QNYnAObVJaSRglGUrBaWjPqGk/iGoUb5SWH\n2js1GpiUz8wwfeUzedkak0M3qNnC1QaerVs30bbtU+kzyCCeffZZvpo3j3916uTSIflYrSQk+Lh9\nEw56fFz802jVr9l7R95506l/vKpiz2RmZvhaVsKUIVcUxT2ya7soiiNwnP6SZRAEwQeYBTwNVAB6\nCIIQYyZt/frNSEi4l5XkufDOO+/w4EEKI0emP6buUaNDh+eYP/9rxo8fT/HixVXjxF28yFNt2vDi\niy/St29fV3ixYsU4f+FCdpH6GI8AoiiyffvX9OjRwziySTRv3pxtO3eSkpLitTxzHDJhyM0MX/MW\nzKp35BYeH6AGEJolFKWhFnBCFMVzThq+BZ4FjsojKSV9qxVu3oynYMGsP1Vn1apVLFy4iC1b9pCa\n6ueiR02ak/4rJXOzfcaTKb9036rVc4wbN4p9+45SrVoMgiBgf/jQpV+1371Llz59GDBoECNHjnQr\nr3bt2uzatYv791MJCHDs0gwL80mnxtGqq/JeC3qSt1EaM5K5p3nppVdKjkZ1NtMuWu9ObfagN6NQ\nqnikezm9yjhnzuxAFFNo1KgR3kJ4eDjlypdn+4kTNKlSJc1iTIirDtL+EGnGCequnOR1VNZFD2oz\nTq8ic5K+Kb6WlTBL/R7SdubagDPAwKwiyomigFzUjMPRYLqw2Rz+QiIispbpJyYm8sILL/Pll0sI\nDi6gqmeVd1h5p5bolJ7JP0blYVRq0GIUynurNYD+/Qcxf/5c5syZQYECBbhy7ZqjvKQkUhIT2bN/\nP7/v3JmujJ07d1KkcGFCQnwIDEzLW/lB6emRjWDE8PXaNDMftafqGL17PcaupaYxKl/J/MykVWt/\npdt7ZZz16z9i9OgXPTqYxwxefvllBrz+Or98/z2lnO6QIyJcm2gBXJv+5EKDmgAjQdnHtPpbJtYN\nmEfmMs8QX/MmzFIfK4qim9ZNEISALKDHY8ydOx7pkKonn2xMo0aNuXLlEpGRRbO03ClTplCzZj2e\neKJhutkGpP9o1aQXNZ2jGgPVM0YqGbFy1tO792AaNarOhAnvU6pUKa7fuEFi4l38g6xYIyLIkycP\nt2/fdluuJ4oiY8aMYeqUKeQJFLGTxhTkOlmp7GXLFlCiRFGaN2+ZjnEpBztlG6jBSOrWk5TNfI9K\nZiqvixmJXy0veT5mB0FPVwFpMT29fKRjZJX94vr1Mxw9upnBg782JtRD9OvXj8TERBp36MDGH36g\nfNmy+JOCJdjfNQBJ/UJayCN9Q2pCkZJ+I4YvYdeuzezevdmrddMjYPPmzWzessX75XkZZpn+7zjc\nKsvxh0qYN3ERh78fCVHOMDe88sp4wL3D2O3JCELWufu/dOkSM2fOZPXqg67OKr/UPnq1D9oTxqDF\n+PUYlsUCpUqVoEGDRixZspBXX32BWrVqsXLZUgb27w9WK2XLlOHPP/+kVatWrnS7d+8mOTmZ9u3b\nExcXR9FixRAEQVVd8ODBA4YMGeCwAZw/74rj7w+JieaFIuXApdVeaoOcp0xfbYA0M3PSy0v52xMV\nlB7UmJ2e8VaNdmkGKbWdxQLbtq2hWbNnyZMnT8YIM8CLL75IYGAgDVu35vPPP6fjs8+6aJFmIPK2\nkXsNUevHavdGaNiwMXXrNnb9/uyzCRmoSXpouflo1LgpjRqnLWicMHGiWjRTfC0roWvIFQQhUhCE\nGkCgIAjVBEGo7rwaA0F6ab2AXUAZQRBKOE/p6g78aCbh008/w4YNq0hOTs4Swj7//HM6duxB4cJR\nWZK/t9G370CWL18CwMcff8yYt9/mnHOt8YhXX2X06NEkJia64m/fvp2mTZpw/fp1ypQrx+eff66Z\nd6BT95M/PJz79+8zbdqHlChRjBo1ajB69GvcuPFod0k/hjoCAgIRxaw9B6l///6sXr2aESNG0Kp1\nazZv3uzxzt+cCElNZXRpIMN8zVsw8rLZF+gH1AR2yx7dA74SRfH7LCXOsbRpBmlLmyYrnounTolu\n02pp+vjcc03p0WMgL7/8vFdpSkpKIrpECX7472aKFo11SSVyvama9Ke23E4ueYH7bEGphzVSh6hJ\nR5L0lJKSQsmSRfntt9+IjY3lgw8+YMeOHaxauhTR15fefftyPzGRBbNmERISQovOnenfuzc3b95k\n0aJFHDp6lPPnz5M/f3rbBYDw8D77Dx6kVr16NGnShI8++ogHDx6wbNkyVqxYwWefLaF27adU6+8t\naOm2taRGPf29kSpKb6aRUf27Mn893bReenlfkDtNlUv7ERHw44/LWb16OWvWrNAnxgtITk5m0aJF\nfDh1KoGBgQx7dSTduvUmNVUwtHd4Cj31XOnS3tmcJW0INUJoqHp5Rnwtq6Hb/URR/Br4WhCEzqIo\nrswmmuTlrwd03f9prXQZOnQUU6aM46WXenrVULVw4UJqVq9OjRqx3L6d5pBMa9WNnE411YQaw1fC\nyMAopddCUJA/777zDoMGDWLLli3UrFmTTZs2gc2GYLPxxZw5jH33XfKVKEFMuXKEhYXR5Zln+Gvv\nXl7dv59OnTqR33lGgFpb+wbmoUKVarRr156YmBiqOdf316tXj7Zt29K3b3fatn2W8ePfJyQk3GXE\nk9fDrKrLSE1mNh815q9lODWyscjjmglTo0fZJ4wMklo2Ij0mKr+/fPkcRYtmz0w1ICCAQYMGMWDA\nADZu3Mibb77Jr79uZNq0eQhCoFtcvUHbrMo0o6pUs8jswGSGr2UljCT9XqIofiMIwkjUd+ROz0ri\njCAIgnjlShpZ8imV3W6nZctKzJ37Kc0k3/aZhCiKVK1cmY/ee4/m7du7/M1rSeXy//LNImodWetj\nz0hnVc3DbqNd+/bkCwmheatW/LJ+Pd/MnevGbRJTUth74AAVK1UiLCwMgHPnz1MgMpKgkBDXLlwt\nPfq5c+eoWbM6xw8dIr/MKdvt27cZN24cGzdu5M9Nm/AJK+7G+OUOSc3US+u3t2HG+OypMdZsWVp1\nNWNjUBrRlYiIgFGj+lK/fiNGjszqRXjpkZiYyMCBAzl37hzLlm3E1zePx+/SE4YvoWhR70j6cp6j\nh0KFMl9eVsBoc5Zk5QkG8iquYK1E2QktlYi/vw/9+g1l4cKFXivrwYMHHD1+nGZ16oDN5maU0nKd\nr6RVSbPRpVZPrRmCVrgPdnzsdlZ+8w2X4uMZMGAATRo2TEdkUHg4DZo0IUw6ltFmo0Tx4ulOB1OT\nHn2wU7JoYQYPGEDbDh148OCBK05YWBizZ8+mbt26fDR/PiEhUKSI45If0qKmptKDp/E9hZm29uSS\n+oj8XB3pktzPS5daHGU85TOtMtWEEkGwe32pplkEBQWxZMkSihcvzowZ76rOcowGAaN+rxbfW5D3\nVb0rp8Ksl836oihuNwrLbgiCIN64kUa/srGvXLlA06ZVuXo1Ps2HTCZw/fp1ypcrx42jRyEsjESb\nfzq9vEQHuOvxjQ69NprWa6l99Kb/0jO5U7a7d++ye8cOChQqxKJvv2Vwjx6ULVUK1wgmz1xeOYu7\nl0N5NKs1rQxRFOnQpQu1q1Rh7HvvucU9deoUtWvXZu/ek9hsYZp1MKPSMBPuTWh9xJ5I+tLglpnZ\nm15ZSqajdbZNWBgsXz6To0cP8c032kb6rMbVq1epVKkSCxasp2bNal6bNamltVi8I3kLgiCeO2dO\n0i9RImdK+maZ/l5RFKsbhWU3lFMttU7Srl1tpk79N82bN890eZcuXaJ6tWrE//03BAeTQtruVLkU\npfzotKbaWqocT/SSagxTbeBwc4Nss9GhVy9WrVtH1QoV2Pe//zm8IToPWAGUTlIAVJm+FMUHu2vZ\nwqkzZ6jVogWnTp92qYkk9OrVi4oVa9K163A3GqU2Us5ulHXSagcJGWWqyvpklUHR6JlUvpkwebia\noCEXguS/rVY4e/YgQ4a0YePGo1Sq9Ogm7R9++CG7dh1m2rSv3JZtmhnkjMLlsFi8p945dcoc0/eG\n4TgroPt5CIJQF6gHFBAEYYTsUQjgq54qe2Gkd61duzG///67V5h+aGgod+/dcy2DkDxbysvW+tjk\n0GJgajp/+dQ3M0ZMi8UHH2n5RlISs6dPp1///jRp0MClUPe/fdUhBjq/OslbKDZ35qImkVksPo7D\n0q1WSsfG0qZNGz6bO5c3x4wBYOPGPWzdupmmTbvwn//MoGfP4enyUOYv3cvbQA1qfUA58HnKaNXS\nZfWMQu9dGtVDaif5enx531OO4TExlalevS7ffjuPSpVGqGeaDQgLC8Nut7hoM5q9ymH0bSj7kreg\nsxwzV8CoG/vj0N1bcOjxJdwFumQVUZ5AjUFKsFigevW6/PDDPK+UFRQURGpqKreSRfLl8XOVqaRB\nTa/niaQq/6+8l0NNoleevy595A7duQ8Wiz+WYH+KlgumaKlS6Qm9fdvFOXys4O/vDykpgMWZ3t19\ns5xGi8UfLP74YOf1UaNo1qIFxUvEkidPMAMH9qRkybLY7Xb++msPSUkPsFrdV27I6dXMm1RAAAAg\nAElEQVRqFyVj8HRGpGQqWT1rUIPRgKJHn/LoY/m9cqaoZIZyxg8wZMhYhg5tz6RJw/HxMeV70eu4\nefMmQUF5DYUj0H6u1h+Ug503kZP19Wag26VFUdwCbBEE4SvJQVBOg5oUKH8pUVGlOXnyvFfKEgSB\nevXqsWHDL3Tq9Aw2AxcDatKmHrNS+1CVnVhZT6UkLFeRKBmk/L/V6vDD72+1pOeoIF/k70aj2lmv\n6aV0H2KeqMrcuV8yadK7HDy4j++++42qVevz2mvPExQURtmyVgQBjh9Pr5LypI2MGIUSnthMlGVq\nPTNCZtR1Ws/MDEbytnJOwtzoKFeuGv7+ARw9epQnnnjCOMMswLffruSFF95Nd5y0GvQkfbW4StcO\n3sI/munLkCgIwoc4XIG6FtflhENUtBiuxIzz5y/EjRtXvFZe586d+fHHb2nZ8hm3ciQoJW0Jnhji\n5L+1prx6v5USpJrqxCUBWR3Sv8vYq8PdfBQFqdVd/qxly/Y0adKO1NRUEhIc6aZNW0Jycqpr5Yj8\nw9U7XdKoPaT4ZlUDauFmBwwl3fLfWnl5MmMwo64wk7ecNumSGKH0/itVqs3OnTsfCdPft28f165d\noUWLVprvztMZnQRlW3mTUed2pm92TrcYh+vPksAE4CyO7cQ5HkFBeUhMvO+1/Nq3b8+vv+Z8p0o5\nBQ6fPZZ0vw8ehL17dRI+RragQYNW/Oc/ix9J2e+99x79+7+Mr2+OMA+ahlKFq3XlVJiVP/KLovgf\nQRBelal8chTTV2tkqxWSkx+4/MN4A0WLFuXGjWs8ePAw3TJQ6WVL0r70OyPSp9ZxcJ5Cz8gJaZKf\n4xQjE+6UFLMIH9td3nj7bU6dOk2lSpV44YVhREREukmVDx8+BNRtIHI9s9YsSUval0MZ5mmbaUmZ\nGW1/NVrUZmBqZegxDDMSr1JFJu+L4O75tGvXHsya9RZ79uyhRo0a5iuYSWzfvp2dO3fy/vuLTMX3\nVE2TFbYYCTmZoZuBWUn/ofP/ZUEQ2gqCUA0I10uQXdB7uVYrXLx4iXz5wrQjeVyehQIFCnDt2hXX\nxyVtlAkOhoIF3RmYmUu5aUe692QTjtolh5rqSGLMEhOQO4uSfmtJMBYL+InJ9OzTh7i4OAYM6M/F\ni3GMHDnMTW++b98OIiP9SU1NdoUp9fd6dUrf/u5plfHM5KOXv1Z5avd6ZSn/q5VrFCcjjEstb2Vf\nk2CzQWCgH337vsjMmV94Xlgm8NZbb/HGG5MJDEwTNLzBTLXetzePtf3/Ium/JwhCKDASmIljyeZw\n/STZAyMJYNWqpXTq1MmrZQYHB3P9+n0KFpR+O+gICQGbzYayWbXo02MoStuEPB+lrlatk4P65hz5\nvVLiVqNH+RFJdRXv32Pi5MmcOnOGP//8k4CAAKKjoxkwYIDbgLJ69RoA1q9fxdNPdwXSGxSVdVJC\nWV9lPC3JVi0PLZiRrrXaSI1etfdiZtZgllnozQzk9VcOAvI62GyOVV316zdi3Lhh5gr2ApKSkti1\naxfz5q1O11fVBkKj2Y2W/UMZz1vI7Us2zZ6R+5MoindEUTwkimITURRrAKWzmLZMwdHxbXz33df0\n69fPq3lbrVYePEh062znzp1l+PDh+Pn5cfHiOdVVLkr65P+le6XUq7UdX0/yV5Yh3WsNDqAvnUgM\nXNqwu2vXTuo3acJvv/3G6tWrCQhwnKdTokQJzpw5g91ud+X/228/M3ToUFasmE3ZsqKz/TLmQsBb\nl1Z7GEGP4ZvJ30hA8RaUg6NygFTOIitXrsLx40e4dy97zpXeu3cvMTEx5MmTflOYclDX65NyoUer\n72eF1J3bJf3MLM59dDs6TOLkyaPkyZOXChUqeC3P5ORkTp8+TfHipVxhR48eoWnTWvj7+xMSEuJV\nw3FOwv379xkyZAAdO3bkX337sumHH9wOXM+fPz9lypRh06afXWHnzp3i7bff5vr16/zwww+PguzH\nMECePHl49tmuDB/+draUd+PGDSKlI71yIXI708/M5CdHbC9W26wi4eDB/dSsWdWr5f3+++8UL/4E\nfn6h2GyQmHiD7t2fYfr0j+jVqxezZ88mKqqYiya1DqAlcasZ+qR7pZSoVDWoSUhKtYdWXHme8vLk\n8YOD4bXX3uTOnVscPXqUkJAQ1fYZOnQo8+Z9Rr16rbDZbNy9e4dChQoxa9YsBg4cyO+/t3Ns+NKh\nQ0/VI6dJS1KXt5vRx2dWdaAmMculej0VgzJPNejRqqyr0raiRb9WO0ibroODHeqd69fhjTem0a5d\nDWbOrM0rr/TQJ9bLUNZbS52ZmTy9iZzM0M0gM0w/RxyBo8bYpLDr1+PcJNHM4vLly/Tr9wJdu77q\nKnPcuJE8+2x7evfuzenTpwkNDSNv3rwu5Y6ZjqsVR48R6B0vZ8Qs9eJr6aEPHNjGhg3fc+TIIU2G\nD9CjRw+mTJnCsGG9KV/+CapWrYGvry9NmjQhPDyc/ft3Uq9ew3T5y2HGRmFGdeWJ/l0vjjIP5cBq\nxmajVp4ceu9MbxBUo90MU1J+K/7+EXz55U88/3xzChQIoXv3tsaZZBD3799P57lVD0bvJyv193rl\neRuCIHQBxgOxwJOiKO51hpcA/saxbB5ghyiKLzqfVQe+wrF/aq0oioa2Vt1mEgThHurMXQC8tw4y\nk1BKwmkd2UJiYqpXyoiPj6d27ca0bt2X9u1fwGaDy5fj2bRpFefOnQHgjz/+oFatOi4duJYhVQ45\n0zNjTJQg12MqnWzJ46h9EFr6Tq1Zyf37SbzxxhBmzpxBvnz5tInEYeTev38/kyZNYtasaWzbts31\nrFq1avz992EaNWqoycCVZcuhZgDWS2PEJPTu9WZjZuwBysFMyezNCAFKmGH+yryN6JSv2IqMrMQX\nX/zIoEHtqRC7kUpVqugnziAuXryIv38USUnpV9Wo9WEzMJrVeZNRZ6Gk/xfQEVBzfXpSw8HlXGCg\nKIq7BEFYKwjC06IobtArRFenL4piXlEUQ1SuvKIoZtO4qg+9FxAcnJcrVzK/G1cURfr06UPjxs/x\n/PNjXR/977//QIsW7V2eJHft2kXNmrXT0WdGxaD1IWsxJTnUDJV68bWglkdYGHz11QRiY2Pp3Lmz\nqXyCg4OZMmUK169fJzY21hVerVo1/vjjf27lKJcQmtWHqtVZzpDVdvdqpTU7K5DDrF7Xkzp5Unez\n+ajRIV+KK+Un5VmpUm2mTZ3CoCFDjAnJIE6dOkVkZHEXfUqa1epi1K6etHNmkckzcjUhiuIxURRP\noK46Vzt2MRLIK4qitGdqIdDBqJxH42Upm9CuXSfWrFnLtWvXMpXPypUrOX36Mv36jXcLFwTBzWnY\niRMnKFfukZ2CliU4cuQAy5b9hzlz5nh86IYyfp8+fdi+fTu7d+eofX2PoUCPHj04dOgQd+/ezZL8\nf/vtN6pWbZAleWcHvDXIe4hoQRD2CoLwqyAIUuMVBeJkceKcYbrIEdJ6ZqClN7VYICysAO3bP8fc\nuXN55513MpR/amoqr746kvHjFxIWZnGTJkNC8pKYmLbM7dSpUxQtWpqEBH29uXzqb1aqVEsrh15+\nemoGeb7Kzhoff4zRo/swZcoHFCpUyJhQAwQHBzN58mQGDerH1q378fPzU7XHKGlWSqRqbSDXr8v/\nG6lRzOjJlWoyNRWYWpgafVrPtH6bVW+YsQmotY+kMrt9O006vX7TSoUK1di1a5fXjhqVcOnSJS5e\nvEjp0tUM1VTyMCP1jRG8qe/XouPEic2cPLlZN60gCBsB+cck4FChjxNFcbVGsktAcVEUbzl1+P8V\nBCHDzpJyPdPXYwBWK/To8Tzjx4/OMNMHuHbtMtWr13P7eKxWuHHjEpGRjh1aqampnD17lqio0qZV\nOmZhxCTk8czYB7QYpxR+//49Zs4cx8aNS3nzzTcZMGCAeWIN0KtXL+bPn8/mzT/SokVnF9NRqhzk\nNEnQG/wy+lHrMWwzg6VES0bfp1paT5mbVrtoMUu9/iP9L1y4aKZnyGpYsWIF9eo9Q548vpp9VY1u\ns22S2cHBDLTyL1myMSVLNnb9Xr9+Qro4oii28LQ8URQfArec93sFQTgFlAMuAsVkUaOcYbrI9eod\nudsA5csIDob69Wtx+PAh7t/P2Np5X19fChYsyL17lwF3iXjfvoM8+aTD2HXx4kXCwvIjCIGa9EjQ\nmg5quT3QCldjVvIPV+u31qYogO3bt9GpUyWs1gSOHj3KyJEjvXqWqiAIDB06lLlz5xISIvf9k15H\nK6+XPJ7SEO0JU1O2iRqMpuhG5WvRoKZ/VjI2tbpIfUDPLYZavlp9Ry1fqV5pLo4tpKZ6ZxGEHF98\n8S3Nm3dz7exWwtNZjlp9le5EpHBvQetb1Ps2MwDXRycIQoQgCD7O+1JAGeC0KIrxwB1BEGoJjo+0\nD7DKKON/hKQvv5ekBIlJBAYGEhMTy19//UWdOnUyVEZkZCTXr8cTHl7cTZK6fPkCJUqUAODIkSOU\nLh3j9lF5qrqRp5PC1YyUavWVp7FazR3PKN1L0vbdu3Y++KAvM2dOo0uXrDsjp3PnzowdO5aff/6Z\nBg1apqNJS8LTYu5mpDs1qVJv5mAEvQFd7bmeVGuUVhnfjCFXD2bqmZyc5NpP4S0sWbKEW7eu0qhR\nc0B74PJUytcbnLNC6s+qmYQgCB1wuLmJAH4SBGG/KIqtgUbAREEQUgA78C9RFG87k72E+5LN9Ubl\n5HqmbwalSpXmzJkzGWb6oaGh3L+ffou6v3+A04Mk7Nixg2rVaqeLk5uwe/dWAgODTK/SySgCAgKY\nOXMmw4cPZ8eOg/w/6Ya5CnFx57y2x2XdunVMnDiLv//eyezZ/8PPz7uDSXYjq5i+KIr/Bf6rEv49\n8L1Gmj1AJU/KyfXqHeWUTj6lBcdoX7p0KU6dOpWh/O12Ozdv3kQQHqZTD+TNG8q5c+e4ceMGK1as\noGrVeq50akZEteWBRlNEPdWOcqquLEN5qdEid2e8bt33tGrV06vqHC20a9eOyMhIli6ej48tBX+L\nPZ0qSo1erXoZTfPlagwt1ZiyTfWW4OmpmdTeizxc+dtTtYCn/UUZplYPSd0XFub4f/lyHMWKFVMn\nwAPMnz+fvn0H06RJN1asOEvJkpUJC1NXO2oZ3rXC1aBVb28iq5ZsZhdyvYil9qFZLNKZsI7fxYqV\nZeXKLbz1luf5f/zxxwQG5qF16xZcu+auCx848BU6dWrExIkTadGiJw0atDFFq9k66aWTT4OVKiAw\n95Eo4wYGpuLjk94JVlZAEASmTp1K+/btef755wm2WrHZ3N0zqNEqQe29K+ObZaDK9Fpqn4yqVsww\nfqMwT2BWHaLGTKXv5v79BPLmzZs+sQeYNWsWH3zwEbNmbSY6uoybzcAsrWpxPGkfNXVRZpFVkn52\nIdczfTmUH7B0xndISDni4tQ2uenj2LFjTJ48mS1b/iQ01Jdbt9yNn/XrN+Tddz8EQujUaQgpKeDv\nr72qRI1Ra9GupeNWptGSjORp9BiVXMJKSsqDxZKgHdnLqFmzJi1atGDie+8x9b33dD9Os3pbZRsr\n29NTpmzWXmCG0erRoMxPix4zDEetj8nbQa1t5DOdhw9FkpKSsGbCCf1ff/3Fu+9OZPHiXRQsWMJt\nVmlEnxbNOQU5iZaMINczfS3GZ7M5pJaEBAgPr8b580e4c+cOoaGhpvNet24dXTt14okyRUm0uatC\npOlbz56jdCVAPalKGUetXkqGpFdftXL0GL5yZmCx5CEhIXs9hL788su8+OKLkJSExeKv+0GZHRCM\nGLcZo6pamJJReiJRa+WvF+4plH1Gb6BUo0tKe/v2PQICrOlOhvMEv//+Oy1adKBcuRKu/PWkfLU2\n0mpvTwcBMzM0T5DbmX6u1+mbQWBgHkqXrsPmzZs9Snfw4EGqVPLIRpKrYbXmISkpMVvLLFq0KBcv\nGi4tfoxsxLVrVyhQIHOb8W7evEm+fPm9RFHOgp5NRct2kpPwj2L6agahpCSHtF+27NN8+OEa03kl\nJiaydu1aGsTEAI6XqLauXTlt1VK3eFoHPahJ/3LpTtnp1GYBamGFCkVy48Z588R6AQULFuTq1auQ\nlORmL9FCRj4mNYlerQ09ycesmkXL2GsmnVp/1usfZlWC8nevPKITHM4F8+fPHNM/fvw4pUoVczvu\n04h2ted6Bl8jA7Ac3tbpP2b6ORBKFUf16p04cOC/2Ey+jUWLFlG7dm2eKF4cLGnuF+R5am1wUkJN\n3aD3Wx5mplMrp/NaHVCtM8rDWrZ8igMHNrNtm/6pX96ExWLBbrdjt9tNfcyZHVQzCqO2NUO3XJ+t\n9kxeljJv+X9lflq0KsPM0h4fH090dGFzDaMCu93OunXraNmyleZAricgGZ2sJsV7VHjM9HMA1BpY\n2ZEjI0sRHl6Mfv02G+Znt9uZMW0ar7ZuDRYLdnxUO6lZna50r9UhvNFBjPI2M/DExkaRN28EJ07s\nzzxBHsDHx0eT6Uu0qbW90QemJZln5OP0FpPRGjy04qnRYUSLpwOjvL2tVrhz50qmfC1t3LiRwMD8\nlCxZyhStWvR6ItVnJ5N9vGTzEcNISpZLCrGxfTlwYAHQXDfP9evXE+DvT5Mnn4TISBJkC1rk03Uj\nyd4Mo1BTuSiNWZJ0qCYlKvPSgxbN8vybNm3F4cPrATXX3d5HfHw8ERERDolfRo8SyvprDcB6Kg5P\nGLwRIzZSsSjb2kyf0Otbyj6Smb4nH2ykusrrdv/+XZe7cE/x8OFDhg8fztChU1w7w43oMYJen5Xg\nqeotM8jJUrwZ/CMkfbOoVKkXJ0+uIz4+XjOOKIpMnjyZkYMHZ8smpZyGevWasWPHr9lW3smTJylV\nqpRxxMfINty/n0CePHkylHbevHmEhxejQYP2XqYq5+CxeieHQU3KDwtzXIULhxMdXZtp03Zrpp85\ncyZJiYl0a90abDbsVvVj3cyoU8x2AKWeUkvFIf+vBy2VjhENFguUL1+W+PgLmvFEUWTv3r28/vq/\nqVOnCRUrPknLlgOZOXOf+cJkmDdvHq2bNk1Hhxk1hhn1SEbUKJ5+sBlpb630WvdmoacW01KRKZ89\nfJhCQECA+UKdSElJ4b33PuC11yZToICQIWaYk5mlhNzO9P9x6h0tBhoV5fC6GRVVkuvXz6rmJR3z\nt+PXX/FLTYWoKJdvfHl+Nlv6o/uk5/LpbEY/Vq2PUa6GkUNLbZPmMVFb7WG1gg92sNkIslrIm9cX\nu12d8K1bt9K9ey/8/f1p2rQtgwePws8vH7/99gdvv/00qamrGD68ruk6nz9/njU//cSZgwcBBx0W\ni0+6djWj2pLHl6eT6ivPQ48RKvMyo9KRypD/z0heanQZ0Sv1RWXZZtRP8vzl/1NTM3b89cKFCylb\n9gkaNaru+j70VFWeQvlutVR8j9U7+vjHMn35vcUCkZEOpl+jxpOsWjWXpKRBBAQEcOxYIvHxN/ju\nu69ZtmwGsz75hNJFigCQaA3HpmGQyYjEohaWEaObMk8ju4Zp2GwUKJCf27dvcvLkScqUKcPVq0mc\nPXuFLVt+ZsqUsUydupBmzVoRGOiQ5JKSoGLFekRHxzJxYkeCgtYzZEhVU8UtXryYHl26EOrrq2r5\n0pN8tRic2gCt9sxkc2QZPJlhGPUbo3qZHXCk8ISEOwQHl9XOUAXHjh1j3LhxLFz4k8u3zu3bxunU\n6mF2cFfG1xoMvI3HTP8R4//aO/M4KYq7/79rdhiGZViWZZddBAFxEcEDLwhI5CHxioZ48KBCnmjU\nXF5k9SGJShKNt+Z8jCYmUaMx3sQDDRqjP8R4gAeCqIjAAnKzLrDAsgy7w9Tvj+6eqamp7umBvUb6\n83rNa2aqq6u+XV31qW99q+pbMcVVjJs261To0lK46qoL+fjjFxk6dBgNDVtpbm6mR49SRo06kTff\nnMvQvpXQ2Mj22AEZhK9r3nplc8IdDcdNC9GhmzPUEUOuhppLizJp+vrwMxxWAuNx+pdF+fnVV3PR\nRRcx8phjuP/BB4nFejJgwEHMmjWHgw46LCu/aBROP/10tm+/jenTT+fZZ4/ikUceyXmI+pw5c7j0\n7LMtdigtJelibdSJ2+lsnE95eWY9UMvBRCK5SMGtrPOF33xN9cgUZ2/kVq+ZRo8mLFz4Hlde+QPv\nSAqeeWYpP/zhBC699FZOOGEkkfh2iEYJh9PeNPV3aPqtymr6rcP0PH6fcV9Q6KQvpNy7oVxngBBC\n7twpXYd1TkNxiCkSTpIkxObNu/ngg0X063cQlZXlxGIQodkin3AYYjHqGrLdv3p1Knq43kh17VRv\n6I6MOunrmqv+fCYZ1efWOxOV9MNhKI4ms3qBFlHE5PPOYUD//lxRcw29eh3gmp8ja2OjVXwbNuzm\nn/+8hOLiYv7whz+Yb8Ba5dG7d29W/utf9O7fH6qqaEpECIczOzw3DT8eT2uSsVj6o5etKqNaRqby\n3Fu4EbJfcmgPEnHrgJw655TPhg2bGTduIFu2bPZl13/ooYeoqZnGxRf/nG9/+4ccWd0EiQTJWIlR\n089XE8/VUeq/Tfc5v3v2FEgp92l1hhBCjhnjjzPnzt33/NoCBa/p7w26du3KMceMLPgeu61QVFTE\nU//4ByQSNCUiea057tKlK+ec8yumTBnKiSdezcSJZp/sb731FkOHDqX3Xi4NDNA2ePbZhznllLN9\nT+R+8MEHfPvbV3PeeT9sY8k6D9qKN4QQNwJnYh2Usgm40D4dCyHEtcDFQAKokVL+2w4/hsxDVK7M\nlU/Bk34xTRC1VNkkoQwtNhJOptXGBou5QkBJSv3Fvma/xdJSmokYtUIducJNk6emUYCu6ZvSceDm\n9DCXFudM1AJEwmGcydKUaUfLKIQ1IiIcyTBxuT2zakay0i3njDO+zwMP3MDEifcb73nqqac49dRT\nMzZSqOLoz6NriNGoZdZx3tXgwbBlS+57/UwqmuzFXnAbgXnZ3f1Ouu5N/m55eM0jON///Oej3HHH\nTbkztRGPx+nTJ8KgQXY9iMchGk2dXGeSw0v2fNsbZI/i9Lan1v/WQBsqi7+UUl4HIISYClwPXGof\ngn4uMAzrHNxXhBBDpGWmuQf4jpTyXSHEC0KIU6WUL3llUvCkr7J8KBwmEg4TCWPVhPqG7N0heg20\n13MmCdHYmHnQRi47qKky65NKXmJ7hZls72pD0uPpUIfs4XAobV/VOp9o1GB3TWTm79ap6CQcDlur\npOJxuPjiH3PhhaO5+OJp/OUvdxC2IyeTSS67bDovv/wCr74wy7oxFiNJKCWv+g502fQyj0SS/OUv\ntzN16hR69jzIWJZ6eXlBfW+53pFfeKXnla5u3tPj5SJVk7nT7b5EwjKXLVnyEWPG+FuBtWzZMp5+\n+mmef34WfcptBYswyWgxCW1Do5uM+c5TZJE5QDRkLL/UdbdGspdoK9KXUqp+zbtDar/iGcDjUsoE\nsEoIsQwYJYT4DOghpXzXjvcQcBbwBSd9lakhbdiNx6G+Pn2tvNxcg2zCgTRxeWmbDvzaJk0VXK+D\nbiTqJ55Xfn7jOI3DtFzS7X6353LkLi0tY8aMeUybNoXDDx9BLNaHXbu2s3r1cg4//FjmzHmbA3t3\ns+y/0WLPfNUOWB3FgDUZ/PDDv+ehh37Ltddey0UXTfMkFb+apRtydf657Mv6feo7dZuTUtPIR151\nZGiqr/r/oiLJ7t27cm7MeusteOONf3PHHedzyy23cNwBVbB2rdXW+vfPmFNS8zchH9JPkbiWYASI\nRNMj/VRcfXKoldCWZmEhxM1YB5w3AF+xg/sBc5Vo6+ywBLBWCV9rh3ui8Em/tJTUYnpVlXHOfXM6\nBcd+YmuWTjx9xYiX9qSuh/Y7XNbTUP+7afJ+Got63Y10/AyvrTD3VTNez6gTlp72oEFlPPnkC8yf\n/ybr1u2hZ8/uDB5czajD7fdSXw9VVa4jimw5ySKU999/nf/5nymMGTOG8847j29964cpP/B+Gmd7\nzev4HXm41R+94/PTOftRTNT8duzYSdeu3QiFvPdsvvTSw9x114/41a/+wfnnnwBLFmVU3nw6KLdO\n1GQac9pqiGTWMNy5li6XEJF9OATGC27vsqlpDrt2zfG8VwjxMqA6NhKABH4qpXxeSvkz4GdCiKuB\nqcAv9l3iTBQ+6Qfo1CgqKuL448exapW6Oqn1fPaPGTOOs876Ko888gjPPffcPh38sb+joaGe0tJy\nzzj33nsv99xzA7/+9WyOOmp4O0nWueBG+pHIeCKR8an/W7bckBVHSnmyz2weBWZhkf46QD2wuL8d\n5hbuicInfVX9VlXQcJhkeR9Czmyfc/inqiYmEoSUeyLhtEdNZ2So3uJMCEXCpLQLXUNVd+rqNln9\nO5Fw98bnpi3lOwpwy9eUXq7RiNezmEcQ6QHWoEHp4yupr0+vtywvJ2zPK5jmRbzSTCTguOO+zEkn\nnc7NN1/HkCFfMlYH/X4Tcs3B+IGbZq2G5zIRmrR50zvaF1u4W/iuXZs9Dz65667Z3HTTDdxzzxz6\n9rXOvI3Et1umHbBG1w0Nqbk1NYNILuEz2mE62GmPmXXWHomEI4QSzRlJqHU2kQjlbb7zg7byoCmE\nqJZSLrf/ngUssX8/BzwihPgdlvmmGnhHSimFENuEEKOAd7HMQr/PlU/Bk/72xhDhcDFhzR6fSEC8\nwXrxUEw0WpyuAPZIVK2YzkSic79pRUA0GrLusXcFhcJhewiZXg2TWenSsqgwrdJx4JfIvezIOql7\nkahbx2BKS+3E9G8vsgslmimjkbJonGU7d7Lwvfc4ql+/1I1eZaXL5nyKRAs1NZdxzTXXcP/9s1Jl\nqm6O0ztfr/TzMS95IZcZx+td5Yrrdp+fDsCrDjjo2rUbu3fvYv16sDelZ2DGjN9z0UU3cOCB1WnP\ns84GDafgnQtOz5yrxzJVOCUslGi2TUbZ7TMchlgsQhh3870fE1e+aEOT4O1CiJi0CNgAACAASURB\nVEOwJnA/Ay4BkFIuFkI8CSwGWoDLZHqD1eVkLtn8V65MCp707RViQCbZ6o3YUfRBIaloelWL6V4T\neUajIYqjUUtbTSVYnBFPt3XrgxA9ntuIALL9quTSXt20SvW/HzuqnrZX48k1WgAgHqdh2zZO+upX\n6derF2/dfLOlGSrxvOzV4TAUh5vtUVucO+66ixkzZrBhwzb++Mcns+LqnZG+wznXc+RDqnoaXvm4\nvRsdpvriJZPXqMjtuopoFPr1601DQ73x+saNG1m48DVuueXvxGKWcp9SXurr03/KFfOQ1WC8NQmT\nYKpWZGtfoXCYaLQ49R5Vpaws1kw0Gknt0NZHpq1N0m1F+lLKSR7XbgNuM4TPB/I607XgSR+yNTkT\naev1TP14paEiFksPG0tsByNJQsQbMxupm+boRu6mTsHULry0e7dwN00yH+J3gxNfX0qqP6fTSpvX\nrSMSDtOvqsoKLy+nmYhreTtIvSc7nWdeeIE777+f8847j5UrN2aUj372gRuR+3k2Jw0/mrfpXq8w\nP1q8n/ftRxa/71SILiQSLcZrd999N6ecMpmePXukdkOHw9BUegDFDtvGYhbpO6ycHiKbBXMTGLJ7\nu3icEFAcDtMcjmQQf1nUMtMWR6NYI/vMg0wKSNNvF3whSD9AYaBPWRnLnnrKajUeZxp44e8zZvDj\nG27g1ttv5yc/uZrnn5/fylLuv9i5cwexWIkhfCd/+tOfuP/+tztAqs6HgPQ7GF6asQ6T5uTXBqtq\n75YGEcmwL6r3KcpJ6l5llWgKjsbkxCsttX0AAeFoJCNvx2+QrhV7acduz67K6QduJgk304EaHqHZ\nGv5/9FF6aW1VlTWzO2iQLw3fef7dewQ/+PGP+dvf/sZtt93G9df/moMPHpA1ajPBr3nMK8wv/IzW\nvMw8fkxEbiYwt3fk9l/Fxo3r6NWrIsue//jjjzN27FgOOuhgEol0nQ2H4b33YNxRR1nvdtAgmmJ9\nLHfdjduzzTt+bWZqITnHbykVJRINk0ikN/Ml+xen1vCXxJKp+bn6esOosxUQkH4ng5vZwrmmE6Y+\n8jR1DCZzjOpMykRcTj1V67xq+mlsTC9Vd65ZWwgixGJWo4mEwzQ78wUJawgbCYeJRMNg2MGqy+9l\n23cjFv2Z1fimuGo6en6JBDSHI0TKyzM3x1VXQ//+NCUiGXHV9LLs8okEXYuK+POf/8y5557LiBHH\nMXHiBRlzOpBtQjaZ+/yY0kzl5hbmBb9mOLdrfqwgud6nF+E51xobYe7clxg37sSsOA8//DDnnvvD\nrHedSMCqVVA1+pR021rlbHIvIawvWAhnOzHM2HDlCGzquQxzA0472rjRmmsDS7mKhJMUR60wP3M5\n+aKlJZk7UidGwZO+ak9WNWynnuh1SG8ceqNwazwqQZjSUMPV/85+MEgruvrhyepOYCdOWcwSPkIz\nJMg0WEejRO1huE74fgkj13N7kYWpTZo0bedZo9FiSkaPTr2YpkSExobsctW/nTRVYjj22PP585/7\nctRRQ9i5U6RIXz0LORy2Vn0kw9nzBbmeUX+/pg4zX/gleK9wr3j5arF6XXbq6euvv8hNN/0yI25d\nXR0LFizgj388naKi9P1qGu+9ly7/qqp0HdZde5uVkPSySitOhFBY2zSphDl1wcnPUZycAUFpKZSX\nhyiOJpU5uNYm/j2tmVi7o8NIXwhxPfA9oM4Omu4sN3LzKGeCWul17TAWM6/aMA15TRqeTt5qPvqq\nGj1v9Xc4nN4YrMqjm32c/ynHVTpbKUuQQtEoxdEw0WjmvgK3NcR+tXq3e9xGAM63+qxqGaRlspbO\nJrQyUInaTeN2Alassohg9OiTgMxO1GngKa0/HicUtUjEy4Gem2Zv6gz8Ip+4uUw5fuP5IX/Ts0ej\nVge5Z+dOPv74I449dmRGnGg0SktLC0OHdmH58uy2BdmnxeluwfU6ocuSrYCEssKdTsAhfueglmjU\nGnU7I2eniRRXWW7DEzF9p25rICD9fcFvpZS/VQOEEMNw9ygXIECANsCKVauoqOiT5XenpKSEsrIy\nVq9eDQzqENk6HwLS3xeYDhg4E4NHOcC4dEDVbnVNLRJOkgind/S5mTBMpglnuOikpX47v91sqW4a\nr6q468PdxkZLe3LkzlCXVPVJPSIqkbDXL2fuWjR9u5l9/Gj5prRV2VWNTNXcdc1PLQtTPnr+6TIO\npdJRzyzWy7W0ND15V1YaSyWerzatv6N8TT1tbQIyvRs3LdoLKdNZPM6Wzz+nsncZxcWZcebM+ZB4\nPM727cWpdxuLWWWtmimdOuyYWpx4eltw8jW1KbU96s+W+dyhjFGyWi8cv28NDaGM1aOtq+kHNv19\nwRVCiPOB94BpUsptuHuUM0I3r2QPFbN3wKoNW7UHO9BJSr1X/XazD5sqtNMQVDcNeqewcaPzP2T5\n/Nd7HXWyQtn5GIlGU14GVZnVzlDvAEwE4UYWpud1i6suz3Z83pmG+2o6uUjKMY05Q3fTHIj625I1\nRCyWnjj0ysPUmavhauel32ciZLW8TGVnulfPV0/DjbTczFVeJJtRP23W7hWJsHXrVpJJcPyttbS0\nUFNzITfeeAfl5X0yqmP//ukyUVfJ6O5LdE+fzv2mCXdT+1Hnc3QvsGCl4+zxU2Vw9k767fDzQ6Dp\nu8LLoxzwR+BG23/EzcBvgO/mm8edd/4iVUlHjRrP2LHjUy+7KR7KmDB1YKo4kbBVuZKEsiojmMnJ\n1AmY7lXjmOAQZCKRXhUULy0jWlrGRx+lfdeUrXo/zX7aJEOz7WekOJr5HDrJ6/9N5eKm4eodrOma\nHuY0epUM1Ak+vdxMROgQuk4kujymSW2vjlqFW0eux3ErOxV+RzL5jL7cyMtU53QN2SRb6potSJ/S\nUjbW19Pc3Jw6Y+GZZ56he/funHvuRWzbllm+qrsLteNV373aSejvQfcom4vwdT9X+kBYL7dEAl5/\nfQ7vvz/Hur9VlfOA9F2Rh0e5e4Hn7d95eY67+upfZDRYx3Oy6lXZq4JlNIxEwppuVHxzq5VbJwY3\ngneDroE7UNN1PmvXWh1AfX36/Ncyx3mclrF6AElJzH6OeJzicNh6w9EwScXdhD6ZbHoGE5mbRgUq\nueqN3tm1aeosTaMxnRAgTfD6aEx9Jyq56OYlPT+3jlv9b3p2PR29EzV1Lqb7dflN92sWvL2GWz3P\nKB9bgNLmZoYPHMgbb/yHk046CYDly5dz7LHHs3u3yOpQGxvhyCOt/+oIS5VZJ2RdHvWjjoRDiWaI\nZz54KBxGdZni9jyOSSkahRNOGM9XvjI+JcOf/pTt9XLv0Jw7SieGt+PsNoQQokr5OxH4yP79HDBZ\nCBERQhyE7VGuveULEGB/w5njxvHUU0+l/q9du5aKikrX+C+8YB2osv8h6fPTOdGmmn4O/FIIcRRW\n6awCfgA5Pcplwc104diSdXsyZGtc8TjgeNC0I1uaRab3TD0PPS3nugovs44qt65hqvk2NsKSJbA2\nlnnIuGMWKlc043jcciIXDkeIRdND5PRQOZTxLCZZTZqam+ym/6pJxjTKUk00qlYfi2Xm7SBEkmg0\nlLFMUJdZ1xydtNVrJqj3m+qR10hHfU4nzOvcDpM27LWc1EvL9zMC0AeFutzhMJbzQHtr+IUnnMDh\nl1/OdddcQ9+BA/nKV77CL35xB2eeeSW7dwujjDrc2oo6HaXL7/x2RCkOk/liIMMLrv7xM3JtXRS2\neUcU8kpIIYT87LNM+dWGr1YMt+F+SSyZvYAe0uNEDaYNP06afiuZabWRk4YT7hCmeuKjY7ZyzD1p\n97LmiTHVdh5KNGcUhH5imElG1S6ry6fCRPiOecfU4VZVpU0YDmE6z6SXRyjelLlyyZa/OZGer1En\neA0OGjNk13+bnktXFtR4zrOo5KWaN1TzkgrThKZJRr3jMNUpr/LXn8tk6lJRHE06h+MCcNXjj9Ow\nbRt33nU/sViII488hssvv4YTT5xsfH7nGXTSdcxuahnqZaPKoi6oCDVuz34I0m3PeWaV9E31Vc9v\n4ECBlNK0YtA3hBASVviMPXif82sLdKSm3ypwawCmhm3SCo1qnce5iOoIwJSvm2ZoiudHi1afxyHG\n8nLLnazaAZmgaqCxWAR9G7zXqMSkAZvimWT1eibH35CTjon01NFBsd4T2szQ2BjJcHVh6vRyyeT2\nDOrEpGkRgKmDcUvPeS71W0/L+Z2PzH7ydYujriBriocIx8osVxkNDfzsG99g0q13MHLk4RxSVcmn\nn37M+tr3OOTrx1MXHWBsX04dUd+bU1/1clTvV5uXWs7FTu+vZeZVrs4owavNtR7aVtMXQkwDfgWU\nSym3CCEGAp+QPlRlnpTyMjvuMWT6078yV/oFT/omVwsO1Aqo76h1TsHKmO11Lnot7I1bh6cUR6PW\nBKnh9Cw1f/W3WsndGrhKBM7zOd/l5ZY5pySWhPoGQlgHwURsDVjdpu52KEw4nHbq5pwUBkp52IiE\nrROMotHsDk6V2/QcqnavamCqLA5/6+WVpSXr9rlolGbrHKbU/fqgzDQS0/Nxex71ei6N2hHJNELR\nn8etE3WrC346CvW/Hq7n75ZmqmirDyFUX0fvVauYffMN/L+PP2bNxo387y/+yPgjq2DnTmKxzFGN\naTStTqTqMropFTrUeqwWkHrmrXqus8oB+uginxG4f7Qd6Qsh+gMnYx2iomK5lPIYwy33AN+RUr4r\nhHhBCHGqlPIlrzwKnvQDBAjQuhBCcNLo0QCsLz8MIbZ0sESdDW06Sfs74MdYC1pUZJmJ7MUwPaSU\n79pBD2Eds/jFJn0vDdtN00pptblUQreEPWQxDV9NcrppfI62ou48VeVPJKA5EbK0eyUx1TeJm1yp\nMohGsid4deGVtNy0TZOWqf52O69W1cicuKptXo0XLS0hZNsJkuFMk45TLiHSbqe9ylhN162M9Cqh\nx9U38zky6Fq66eMG9X7Da3CFX3OQ+p7c0NAAsdI+RKrD6fXC8TgHNDTAoYeyIjGAJXPSaejzSI6W\nr74X3dZveh69faTrhbUgIeJSBuoa/kg4U/PPt1nnj7ZZsimEOANYI6X8UIgsjh8khHgf2Ab8XEr5\nBtam1bVKnLV4bGR1UPCk7zZ01U06zuarjNpgWt7jNdZWf9vXQ+EwKORoiqpDtanqZK+moz6DSi6W\nacTaFq+7ptUbmU7GqU7Pgcd22QyTj8LQziHXqmMsPX+3IjWRgJtZwkGSkF3OabK1JnjTJh/nPufZ\nImH3DtDrvy6PaqZwdhgbvPxmpKd2eqkyjCvlp9yskpU6B+N8q+/drXN1k8WrLur3pppCtIzEoLJU\nHg0NUL/KWzaH7PWy0fP36iBdCVp9eBVavXRKsSQWTZk23RZM7Dv23rzjsWH1Z8B0LNOOeg1gPTBA\nSrnVtuE/K4QYvrcyFDzp6/ZUyK6YRpjUQjejo8dvr1UwXpVN15DVuq3yr6pR6n1VNJr2I27K29To\nsiKZegk3lUwJ9yJUU3+p2l31EYypwzZ2UGgdkamT0uMa5PXqpEzavonc9LxM5WH0Fa+/eINMOvZG\nc9VfoZ6lqX444eo8jONiIRwm5dkSMv3q6PZ7t3rg1kl5PZfa4Wc9oKnnS5j9UbUu3Eh/vv1xh9uG\nVSHE4Vge7T4QlprfH5gvhBglpawDttr3vy+EqAUOIc+NrA4KnvS91kWD2nDtRmn75jaO4/VK5MZG\nSliIJOFwKKsiq3nrMCWrZ206aUu/3+2/KX6GGcd0s86EhhaqTrA5JhU9KQOfZWmreqemk3/a/Ib6\nAs3dqzPi0pkmqxPP9MOjRnMjQMjUYEtLld2i2rKjkH6zqVwN/1Xf8X4081xwSyPXwNUkbjicudfC\nrwM1U+fi9jx+SDlF/G5114AQySyzT+vBzaZ/tP1xcJ/vFKWUHwGpDatCiJXAMbZ2Xw5skVImhRCD\nsTasrpBSNgghtgkhRgHvAhcAv8+VV8GTvnOMoAluPJaqRF5DAY8KpeenNxSTJumWhakem8wHblpa\nrkaTJjV7lY6aqYcq5qYZY7CdqzBdy6OtZsNNHVV/mwhfuUdfyeQmv67dg7Z+XJ8L8pJZv55r8b32\niLpc+cT3c59bfLfRjp6n2lmaRmhOem6diV/ZVKUt63AVG1kjKp/X9h7tsjlLkjbvjANuFEI0Y/U4\nP5BSOmf3XU7mks1/5Uq44Ek/QIAAAdoXbU/6UsrByu+ngadd4s0Hjsgn7S806XtpQan16YZIuXar\n+pkMNIV72TIdrcjkQVLHPgxSMiO4jf9Ja0iq11E/Wrxbdl5mn6x83UYibgZjj4z1fRR+Rh364CI1\n8jKpwF6TJ272O00QZ8Ofm5avD85Mr88EL5OOqTwcO756Ipb+OCZf+W7bWkyP62XCyvVKnXZpqkvt\ni8J2w/CFJv1ccBv6+Zmkc4PJRKBeM4U7yGXH9WowapjpudQlneYGGskgepPcbmYrN3LRr7ldV8My\n7LcmE46akBeTeBB+LouRl7y+IpgI3sOsppe3n/mSXLZ7/Vm94oDZVTFkKiHq/Ivjjnz9xlBG52Ba\nWKHCi7D9mn3U+9vGfJMLLR2QZ+vhC036OSuEGyNpjVJfBeJFxkZNVVGXdNL1Ik83BVJHxnPqvY59\nk76uOWMnriaAPvpxmwxzGqmXuToXabpeNzFAPkbqRCJDg/bqmLySD4eVMmp0cfLiZcj2GiYpcqpw\nUzJMAwY/CoQfu7+pfNT81INrUsQeCnHAAVBX5356nUkuk3LitifEhI4hehWBpt/h8F0J3Bperlah\nXDdpYRkTfCYnI5Cxo8ePlUI/aCRFPgZ1NcuVgkGTzLgWDmcSTQ5ydZ5W9dzjbJqJRDPLTu1Q3Do0\nU1aZirstr5tdSL/ZlIgju32v2yYfFanJbkNHmKEGe9kzVFbTj2xTv22odclPPXYz7Tj10q2v9BqB\nmaxozm9nmWrqCM8S7TxFG16rzfTwdH5mk5YJHU/0Kgqb9DvMn36AAAEKCx8vXszRRx/Nv/71L9av\nhy1bYPv23Pd98bDH56dz4guh6ftGPjNfHuFZPaVmUqmPx3nllVeQQnDggQcy9ogjEHbejtaVa/it\naltZJwk56phjjgHLEVkiRH2DpYkdcIAVNR6Ps3nzZpYvX86KFSsoKytj4AEHUD3sMIqLi0kkoLEx\nyauvvkI0Kjj11FMJJ5NZeRjLJxxmz5491NbWsqulhe7du1NSXExpaSmRSCS98zSRsDRt+39zIpSh\nQJsU+pQGrMw1uMqhhnuNYLygmvG87nNenmmewSSPc4/bgxqgjiZ9mfS0MJM5LsdrzLBCqTtrQwnb\n5UACnn/lFRYuXMhzzz3HkUd+LXV/riXGe4POpdnr6Myy5UbB+9OXe7J7VKO92nDdgWsF80kajk+Y\nZcuW8qtf3cisWbMYP3480WiUBQsWUFpayjXXTOfwwyeQTIYyjnTMWP+twsUsQySClJI33niDRYsW\nUVtby/r161m/fj0bNmxg+/btFBUV0dzczI4dOwAoKytj8ODBHHzwwWzdupWVK1dSW1tLWVkZ3bp1\no76+nqOOOordu3ezevVqzj77bE488UQGDBhAUVERH3zwAe+88w4ff/wxGzduZPDgwfTo0YOlS5ey\ndOlS+vTpQ0lJCTt27GDHjh00NDTQs2dPDjzwQAYNGsTRRx/NVVddRY8ePSz1UPEKqr4D970BqSJx\nf1/5mu7ynXxQzTtuZjyVPXN928jwKKnBawGBn8UGfvs8PV7GjmP7AOr169dTUVHBpk2b6N27Ny0t\n3YjFMt+bnykMB/vSKZie3U8nIYqKWsmf/j98xp7UKf3pfyFJ3y88O4cc2l6SEKEQtLS08PbbbzNj\nxgweeeQRpk2bxiWXXEKvXr0A2LNnD08//TS33347TU1NTJ48merqaiorKyktLaWqqoqqqirCTito\nasraZtycsFfeJOO8/vrr3HrrrWzYsIHx48dTXV1Nv3796Nu3L3379qWkpAQpJeFwmB49ehCNRjE4\nbyKRSLBhwwbi8TilpaVUVFQAsHjxYmbNmsXs2bPZtGkTzc3NHH744YwePZojjjiCyspKVq5cyfbt\n2znkkEMYOnQoJSUlmeWaTFJfX8/q1atZuXIlzz//PK+99hq///3vmTBhAkVFRfaDNftbleOXIfIl\n8nyh2/f1PN2I3wW+lQ89fy/o5efVe2KeU1AXG2zbuolzJ09mzpw5/N///R81NTXWhebmbAf6hmdq\nK+zNSKD1SP8Jn7HPC0i/tZGT9HNUeNf4iQTJaDHLl1t/D6lOV7DmRIhdu3Zx112/YebMmSxevJhD\nDz2UU089lZqaGiorzWeKSimZPXs2s2fPpra2ls8//5wtW7awceNGmpqaeOGFFxg7dqzVmBR16f2P\nIixaNJd//OMWXn31VYYPH86ll17KBRdckO4oCgD//ve/mT59OnV1dXz1q1+lpKSEsrIyKisr6dWr\nF7169WL06HH07NEVcCej1oYp7X0mXw8CzGU5am842nnWiBKQRUV87bTTOPyII/jmN7/JN77xDZYv\nX05xcTEk06PTpOLiIh+Lmh94l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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig4 = plt.figure(); \n", "ax4 = fig4.add_subplot(111)\n", "cax4 = ax4.pcolormesh( lon_som, lat_som, -qdp_jan, cmap=plt.cm.seismic, vmin=-700., vmax=700. )\n", "cbar4 = plt.colorbar(cax3)\n", "ax4.set_title( 'CESM: Prescribed heat flux out of ocean (W m$^{-2}$)' )\n", "ax4.axis([0, 360, -90, 90])\n", "ax4.set_xlabel('Longitude')\n", "ax4.set_ylabel('Latitude')\n", "ax4.contour( lon, lat, topo.variables['LANDFRAC'][:], [0.5], colors='k');\n", "ax4.text(65, 50, 'January', fontsize=12 );" ] }, { "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": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# The variable we want to plot\n", "qdp = som_input.variables['qdp'][:]\n", "# A function that makes a plot for a given month (which we pass as an argument)\n", "# note... imshow is an alternate way to generate an image from a data array\n", "# Faster than pcolormesh (which is why I use it here)\n", "# but not as flexible\n", "def sh(month):\n", " plt.imshow(np.flipud(-qdp[month,:,:]), cmap=plt.cm.seismic, vmin=-700., vmax=700. )\n", " plt.colorbar()" ] }, { "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": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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J69YRr6whMg8FM0Nw/Tps28ZUcSPnL57kG9/oAFQO7crKypUh7H37llb20qWX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"text/plain": [ "" ] }, "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": "markdown", "metadata": {}, "source": [ "Output from the control run is available on the same data server as above. Look in the folder called som_control.\n", "\n", "There are climatology files for the atmosphere, sea ice, and land surface. I created these files by averaging over the last 10 years of the simulation. Let's take a look at the atmosphere file. You can look up the necessary URL by following the procedure outlined above. The file is called\n", "\n", "som_control.cam.h0.clim.nc\n", "\n", "(the file extension .nc is used to indicate NetCDF format).\n", "\n", "Or just copy the following:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "atm_control = nc.Dataset( datapath + 'som_1850_f19/som_1850_f19.cam.h0.clim.nc' + endstr )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we would like to see what's in this file, same as before:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lev\n", "hyam\n", "hybm\n", "ilev\n", "hyai\n", "hybi\n", "P0\n", "time\n", "date\n", "datesec\n", "lat\n", "lon\n", "slat\n", "slon\n", "w_stag\n", "time_bnds\n", "date_written\n", "time_written\n", "ntrm\n", "ntrn\n", "ntrk\n", "ndbase\n", "nsbase\n", "nbdate\n", "nbsec\n", "mdt\n", "nlon\n", "wnummax\n", "gw\n", "ndcur\n", "nscur\n", "co2vmr\n", "ch4vmr\n", "n2ovmr\n", "f11vmr\n", "f12vmr\n", "sol_tsi\n", "nsteph\n", "AEROD_v\n", "CLDHGH\n", "CLDICE\n", "CLDLIQ\n", "CLDLOW\n", "CLDMED\n", "CLDTOT\n", "CLOUD\n", "CONCLD\n", "DCQ\n", "DTCOND\n", "DTV\n", "EMIS\n", "FICE\n", "FLDS\n", "FLDSC\n", "FLNS\n", "FLNSC\n", "FLNT\n", "FLNTC\n", "FLUT\n", "FLUTC\n", "FSDS\n", "FSDSC\n", "FSDTOA\n", "FSNS\n", "FSNSC\n", "FSNT\n", "FSNTC\n", "FSNTOA\n", "FSNTOAC\n", "FSUTOA\n", "ICEFRAC\n", "ICIMR\n", "ICWMR\n", "LANDFRAC\n", "LHFLX\n", "LWCF\n", "MSKtem\n", "OCNFRAC\n", "OMEGA\n", "OMEGAT\n", "PBLH\n", "PHIS\n", "PRECC\n", "PRECL\n", "PRECSC\n", "PRECSL\n", "PS\n", "PSL\n", "Q\n", "QFLX\n", "QREFHT\n", "QRL\n", "QRS\n", "RELHUM\n", "SFCLDICE\n", "SFCLDLIQ\n", "SHFLX\n", "SNOWHICE\n", "SNOWHLND\n", "SOLIN\n", "SWCF\n", "T\n", "TAUX\n", "TAUY\n", "TGCLDCWP\n", "TGCLDIWP\n", "TGCLDLWP\n", "TH\n", "TH2d\n", "TMQ\n", "TREFHT\n", "TS\n", "TSMN\n", "TSMX\n", "U\n", "U10\n", "U2d\n", "UTGWORO\n", "UU\n", "UV2d\n", "UV3d\n", "UW2d\n", "UW3d\n", "V\n", "V2d\n", "VD01\n", "VQ\n", "VT\n", "VTH2d\n", "VTH3d\n", "VU\n", "VV\n", "W2d\n", "WTH3d\n", "Z3\n" ] } ], "source": [ "for v in atm_control.variables: print v" ] }, { "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": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "float32 SOLIN(time, lat, lon)\n", " Sampling_Sequence: rad_lwsw\n", " units: W/m2\n", " long_name: Solar insolation\n", " cell_methods: time: mean time: mean\n", "unlimited dimensions: time\n", "current shape = (12, 96, 144)\n", "filling off\n", "\n" ] } ], "source": [ "print atm_control.variables['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": [ "### Your task, for now...." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Is just to play with the data in this file. Try to figure out what some of the different fields are. Get familiar with the data and with making plots.\n", "\n", "Some fields you might want to look at:\n", "\n", "- surface temperature, 'TS'\n", "\n", "- Outgoing longwave radiation, 'FLNT'\n", "\n", "- Net absorbed shortwave, 'FSNT'\n", "\n", "These are all 12 x 96 x 144 -- i.e. a two-dimensional grid for every calendar month.\n", "\n", "Many other fields are three-dimensional. 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": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(12, 26, 96, 144)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.shape( atm_control.variables['T'] )" ] }, { "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": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot( atm_control.variables['T'][1,:,70,115], atm_control.variables['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 we previously stored:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(42.631578947368418, 287.5)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lat[70], lon[115]" ] }, { "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thanks for playing!" ] } ], "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 }