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@spencerahill
Created March 14, 2019 17:48
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Latitude-by-latitude RCE solution from climlab using annual mean insolation
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{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import climlab\n",
"from climlab.convection import ConvectiveAdjustment\n",
"from climlab.radiation import AnnualMeanInsolation, DailyInsolation, FixedInsolation, RRTMG\n",
"from climlab.radiation.water_vapor import FixedRelativeHumidity\n",
"import matplotlib\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Getting ozone data from /Users/shill/Dropbox/miniconda3/lib/python3.5/site-packages/climlab/radiation/data/ozone/apeozone_cam3_5_54.nc\n"
]
}
],
"source": [
"ALBEDO = 0.3\n",
"ML_DEPTH = 0.1\n",
"LAPSE_RATE = 6.5\n",
"\n",
"state_ann = climlab.column_state(num_lev=100, num_lat=20, water_depth=ML_DEPTH)\n",
"rce_ann = climlab.TimeDependentProcess(state=state_ann)\n",
"insol_ann = AnnualMeanInsolation(domains=rce_ann.Ts.domain)\n",
"h2o_ann = FixedRelativeHumidity(state=state_ann)\n",
"conv_adj_ann = ConvectiveAdjustment(state=state_ann, adj_lapse_rate=LAPSE_RATE)\n",
"rad_ann = RRTMG(state=state_ann, specific_humidity=h2o_ann.q, \n",
" albedo=ALBEDO, S0=insol_ann.S0, \n",
" insolation=insol_ann.insolation, \n",
" coszen=insol_ann.coszen)\n",
"\n",
"rce_ann.add_subprocess('Insolation', insol_ann)\n",
"rce_ann.add_subprocess('Radiation', rad_ann)\n",
"rce_ann.add_subprocess('WaterVapor', h2o_ann)\n",
"rce_ann.add_subprocess('Convection', conv_adj_ann)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Integrating for 1826 steps, 1826.2110000000002 days, or 5 years.\n",
"Total elapsed time is 4.999422301147019 years.\n"
]
}
],
"source": [
"rce_ann.integrate_years(5)\n",
"ds_ann = rce_ann.to_xarray()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x11c3354e0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ds_ann['Tatm'].plot(ax=ax, yincrease=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11c4df5c0>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds_ann['Ts'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.DataArray 'Ts' (depth: 1, lat: 20)>\n",
"array([[218.145576, 221.785981, 229.917579, 245.748595, 263.441085,\n",
" 280.609627, 299.411882, 341.830896, 342.622627, 342.862874,\n",
" 342.860735, 342.614532, 341.798203, 298.934332, 280.254132,\n",
" 263.075624, 245.338987, 229.505305, 221.353993, 217.698376]])\n",
"Coordinates:\n",
" * depth (depth) float64 0.05\n",
" * lat (lat) float64 -85.5 -76.5 -67.5 -58.5 -49.5 ... 58.5 67.5 76.5 85.5\n"
]
}
],
"source": [
"print(ds_ann['Ts'])"
]
}
],
"metadata": {
"anaconda-cloud": {},
"gist": {
"data": {
"description": "climlab_annual_mean_rce.ipynb",
"public": true
},
"id": ""
},
"kernelspec": {
"display_name": "Python [default]",
"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": "3.5.5"
},
"toc": {
"base_numbering": 1,
"colors": {
"hover_highlight": "#DAA520",
"navigate_num": "#000000",
"navigate_text": "#333333",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700",
"sidebar_border": "#EEEEEE",
"wrapper_background": "#FFFFFF"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "105px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"threshold": 4,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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