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CO2Amp_Problems.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#Use miniconda-analysis environment\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"import numpy as np\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"# import co2_timeseries_tools as co2tt\n",
"from scipy import stats\n",
"from scipy import signal\n",
"from ctsm_py import utils"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Obs Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"path='/glade/work/dll/CTSM_py/notebooks/'\n",
"brw={'name':'Barrow', 'acronym': 'brw', 'lat': 71.3, 'lon':360-156.61, 'z': 11.0}\n",
"mlo={'name':'Mauna Loa', 'acronym': 'mlo', 'lat': 19.5, 'lon':360-155.6, 'z':3397.0}\n",
"alt={'name':'Alert', 'acronym': 'alt', 'lat': 82.5, 'lon':360-62.5, 'z':200.0}\n",
"azr={'name': 'Azores', 'acronym':'azr','lat':38.8, 'lon':360-27.4, 'z':19.0}\n",
"cba={'name': 'Cold Bay', 'acronym':'cba', 'lat':55.2, 'lon':360-162.7, 'z':21.3}\n",
"kum={'name':'Kumukahi', 'acronym':'kum', 'lat':19.7, 'lon':360-155.0, 'z':0.3}\n",
"ESRL=[brw, mlo, alt, azr, cba, kum]\n",
"lats=np.array([71.3,19.5,82.5,38.8,55.2,19.7])\n",
"lons=np.array([360-156.61,360-155.6,360-62.5,360-27.4,360-162.7,360-155.0])\n",
"#note that the 'lev' variable only goes from 0 to 1000, not sure how to translate from 'alt' to 'lev'\n",
"alt=np.array([11.0,3397.0,200.0,19.0,21.3,0.3])\n",
"cesm1levs=np.array([25,20,25,25,25,25])\n",
"cesm2levs=np.array([31,22,31,31,31,31])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>acronym</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>z</th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>co2</th>\n",
" <th>time</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Barrow</td>\n",
" <td>brw</td>\n",
" <td>71.3</td>\n",
" <td>203.39</td>\n",
" <td>11.0</td>\n",
" <td>1971</td>\n",
" <td>5</td>\n",
" <td>332.34</td>\n",
" <td>1971-05-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Barrow</td>\n",
" <td>brw</td>\n",
" <td>71.3</td>\n",
" <td>203.39</td>\n",
" <td>11.0</td>\n",
" <td>1971</td>\n",
" <td>6</td>\n",
" <td>330.29</td>\n",
" <td>1971-06-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Barrow</td>\n",
" <td>brw</td>\n",
" <td>71.3</td>\n",
" <td>203.39</td>\n",
" <td>11.0</td>\n",
" <td>1971</td>\n",
" <td>7</td>\n",
" <td>323.78</td>\n",
" <td>1971-07-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Barrow</td>\n",
" <td>brw</td>\n",
" <td>71.3</td>\n",
" <td>203.39</td>\n",
" <td>11.0</td>\n",
" <td>1971</td>\n",
" <td>8</td>\n",
" <td>317.71</td>\n",
" <td>1971-08-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Barrow</td>\n",
" <td>brw</td>\n",
" <td>71.3</td>\n",
" <td>203.39</td>\n",
" <td>11.0</td>\n",
" <td>1971</td>\n",
" <td>9</td>\n",
" <td>318.74</td>\n",
" <td>1971-09-01</td>\n",
" </tr>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503</th>\n",
" <td>Kumukahi</td>\n",
" <td>kum</td>\n",
" <td>19.7</td>\n",
" <td>205.00</td>\n",
" <td>0.3</td>\n",
" <td>2018</td>\n",
" <td>8</td>\n",
" <td>405.42</td>\n",
" <td>2018-08-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>504</th>\n",
" <td>Kumukahi</td>\n",
" <td>kum</td>\n",
" <td>19.7</td>\n",
" <td>205.00</td>\n",
" <td>0.3</td>\n",
" <td>2018</td>\n",
" <td>9</td>\n",
" <td>404.39</td>\n",
" <td>2018-09-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>505</th>\n",
" <td>Kumukahi</td>\n",
" <td>kum</td>\n",
" <td>19.7</td>\n",
" <td>205.00</td>\n",
" <td>0.3</td>\n",
" <td>2018</td>\n",
" <td>10</td>\n",
" <td>405.83</td>\n",
" <td>2018-10-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>506</th>\n",
" <td>Kumukahi</td>\n",
" <td>kum</td>\n",
" <td>19.7</td>\n",
" <td>205.00</td>\n",
" <td>0.3</td>\n",
" <td>2018</td>\n",
" <td>11</td>\n",
" <td>408.37</td>\n",
" <td>2018-11-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>507</th>\n",
" <td>Kumukahi</td>\n",
" <td>kum</td>\n",
" <td>19.7</td>\n",
" <td>205.00</td>\n",
" <td>0.3</td>\n",
" <td>2018</td>\n",
" <td>12</td>\n",
" <td>409.96</td>\n",
" <td>2018-12-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2795 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" name acronym lat lon z year month co2 time\n",
"0 Barrow brw 71.3 203.39 11.0 1971 5 332.34 1971-05-01\n",
"1 Barrow brw 71.3 203.39 11.0 1971 6 330.29 1971-06-01\n",
"2 Barrow brw 71.3 203.39 11.0 1971 7 323.78 1971-07-01\n",
"3 Barrow brw 71.3 203.39 11.0 1971 8 317.71 1971-08-01\n",
"4 Barrow brw 71.3 203.39 11.0 1971 9 318.74 1971-09-01\n",
".. ... ... ... ... ... ... ... ... ...\n",
"503 Kumukahi kum 19.7 205.00 0.3 2018 8 405.42 2018-08-01\n",
"504 Kumukahi kum 19.7 205.00 0.3 2018 9 404.39 2018-09-01\n",
"505 Kumukahi kum 19.7 205.00 0.3 2018 10 405.83 2018-10-01\n",
"506 Kumukahi kum 19.7 205.00 0.3 2018 11 408.37 2018-11-01\n",
"507 Kumukahi kum 19.7 205.00 0.3 2018 12 409.96 2018-12-01\n",
"\n",
"[2795 rows x 9 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"minYear = 1981 # minimum year for an 'early' trend, used later for plotting\n",
"dfs = []\n",
"for site in ESRL:\n",
"# print(site)\n",
" filename=path+'co2_'+site['acronym']+'_surface-flask_1_ccgg_month.txt'\n",
" #import glob\n",
" #filename=glob.glob(partialname+ '*co2')\n",
" with open(filename, 'r') as fid:\n",
" first_line=fid.readline()\n",
" nheader=first_line[-3:-1]\n",
" nheader=np.int(np.float(nheader))\n",
" data=np.loadtxt(filename, usecols=(1,2,3), skiprows=nheader)\n",
" time=data[:,0]+data[:,1]/12\n",
" co2=data[:,2]\n",
" month=data[:,1]\n",
" year=data[:,0]\n",
" site['year']=year.astype(int)\n",
" site['month']=month.astype(int)\n",
" site['co2']=co2\n",
" dfs.append(pd.DataFrame(site)) # Turn dictionary into a pandas.DataFrame \n",
" \n",
"df = pd.concat(dfs)\n",
"df['time'] = pd.to_datetime(df.year.astype('str') + '-' + df.month.astype('str'))\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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"\n",
".xr-attrs dt {\n",
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" grid-column: 1;\n",
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"\n",
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" display: inline-block;\n",
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"\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (name: 6, time: 589)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1969-08-01 1969-09-01 ... 2018-12-01\n",
" * name (name) object &#x27;Alert&#x27; &#x27;Azores&#x27; &#x27;Barrow&#x27; ... &#x27;Kumukahi&#x27; &#x27;Mauna Loa&#x27;\n",
"Data variables:\n",
" co2 (time, name) float64 nan nan nan nan ... 415.1 414.2 410.0 409.3</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a94feef7-fa23-4fa7-9eb2-cb0584284e3e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a94feef7-fa23-4fa7-9eb2-cb0584284e3e' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>name</span>: 6</li><li><span class='xr-has-index'>time</span>: 589</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a7c1da1e-5e09-4ace-b01f-bd08d0f219ca' class='xr-section-summary-in' type='checkbox' checked><label for='section-a7c1da1e-5e09-4ace-b01f-bd08d0f219ca' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1969-08-01 ... 2018-12-01</div><input id='attrs-146a7ee4-45ba-4224-9552-d86c823c8921' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-146a7ee4-45ba-4224-9552-d86c823c8921' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83934fce-6846-42ef-a65a-f3c84474d641' class='xr-var-data-in' type='checkbox'><label for='data-83934fce-6846-42ef-a65a-f3c84474d641' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1969-08-01T00:00:00.000000000&#x27;, &#x27;1969-09-01T00:00:00.000000000&#x27;,\n",
" &#x27;1969-10-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
" &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>name</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Alert&#x27; &#x27;Azores&#x27; ... &#x27;Mauna Loa&#x27;</div><input id='attrs-761c04e7-2d22-40e9-be46-74551a15b36d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-761c04e7-2d22-40e9-be46-74551a15b36d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93031364-25bb-47bb-bc5b-11f300088b0b' class='xr-var-data-in' type='checkbox'><label for='data-93031364-25bb-47bb-bc5b-11f300088b0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Alert&#x27;, &#x27;Azores&#x27;, &#x27;Barrow&#x27;, &#x27;Cold Bay&#x27;, &#x27;Kumukahi&#x27;, &#x27;Mauna Loa&#x27;],\n",
" dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-137b3738-4a75-42ee-8735-a98d99c9cd48' class='xr-section-summary-in' type='checkbox' checked><label for='section-137b3738-4a75-42ee-8735-a98d99c9cd48' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>co2</span></div><div class='xr-var-dims'>(time, name)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 414.2 410.0 409.3</div><input id='attrs-c80b60ff-1b7c-4931-92ff-95ed606977d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c80b60ff-1b7c-4931-92ff-95ed606977d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2eb57f23-e35e-4f4c-90b7-5471d84884d6' class='xr-var-data-in' type='checkbox'><label for='data-2eb57f23-e35e-4f4c-90b7-5471d84884d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, nan, nan, 322.51],\n",
" [ nan, nan, nan, nan, nan, 321.36],\n",
" [ nan, nan, nan, nan, nan, 320.74],\n",
" ...,\n",
" [404.58, 406.86, 405.94, 409.53, 405.83, 406.1 ],\n",
" [409.63, 409.91, 411.5 , 412.97, 408.37, 407.98],\n",
" [413.34, 412.04, 415.08, 414.19, 409.96, 409.27]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eaaeef10-e798-4c53-810d-3b550f2ba28e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eaaeef10-e798-4c53-810d-3b550f2ba28e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (name: 6, time: 589)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 1969-08-01 1969-09-01 ... 2018-12-01\n",
" * name (name) object 'Alert' 'Azores' 'Barrow' ... 'Kumukahi' 'Mauna Loa'\n",
"Data variables:\n",
" co2 (time, name) float64 nan nan nan nan ... 415.1 414.2 410.0 409.3"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = df.set_index(['time', 'name'])[['co2']].to_xarray()\n",
"ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1\n",
"I would like to calculate max - min for each year but can't figure out how to loop over the years.\n",
"The code below calculates absolute min and max across all years"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
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"Dimensions: (name: 6, year: 50)\n",
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" * name (name) object &#x27;Alert&#x27; &#x27;Azores&#x27; &#x27;Barrow&#x27; ... &#x27;Kumukahi&#x27; &#x27;Mauna Loa&#x27;\n",
" * year (year) int64 1969 1970 1971 1972 1973 ... 2014 2015 2016 2017 2018\n",
"Data variables:\n",
" co2 (name, year) float64 nan nan nan nan nan ... 6.9 6.51 7.64 6.69 6.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1164dc92-9246-45f7-b0f8-1825fbcd75b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1164dc92-9246-45f7-b0f8-1825fbcd75b1' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>name</span>: 6</li><li><span class='xr-has-index'>year</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cf181664-9b6d-483a-aeef-b7aaabd06929' class='xr-section-summary-in' type='checkbox' checked><label for='section-cf181664-9b6d-483a-aeef-b7aaabd06929' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>name</span></div><div class='xr-var-dims'>(name)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Alert&#x27; &#x27;Azores&#x27; ... &#x27;Mauna Loa&#x27;</div><input id='attrs-1e7fb892-0edc-4aba-bde3-1f758f9dee71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e7fb892-0edc-4aba-bde3-1f758f9dee71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-398889d8-c071-43a6-a30a-c583580f60f1' class='xr-var-data-in' type='checkbox'><label for='data-398889d8-c071-43a6-a30a-c583580f60f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Alert&#x27;, &#x27;Azores&#x27;, &#x27;Barrow&#x27;, &#x27;Cold Bay&#x27;, &#x27;Kumukahi&#x27;, &#x27;Mauna Loa&#x27;],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1969 1970 1971 ... 2016 2017 2018</div><input id='attrs-dc785d6e-163a-4423-a19a-c182c8b9920f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc785d6e-163a-4423-a19a-c182c8b9920f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f1878bf-7fb7-4ce9-a54b-a49a86c2f22d' class='xr-var-data-in' type='checkbox'><label for='data-2f1878bf-7fb7-4ce9-a54b-a49a86c2f22d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980,\n",
" 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992,\n",
" 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004,\n",
" 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016,\n",
" 2017, 2018])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9af99e28-086c-482a-bbfc-f7f8a161d6ff' class='xr-section-summary-in' type='checkbox' checked><label for='section-9af99e28-086c-482a-bbfc-f7f8a161d6ff' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>co2</span></div><div class='xr-var-dims'>(name, year)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 7.64 6.69 6.0</div><input id='attrs-79dc2b78-b2c1-4928-aab8-a2bbf541bae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79dc2b78-b2c1-4928-aab8-a2bbf541bae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1913e94f-df91-4779-b895-033f8097ffd6' class='xr-var-data-in' type='checkbox'><label for='data-1913e94f-df91-4779-b895-033f8097ffd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, nan, nan, nan, nan, nan, nan, 11.99, 13.39,\n",
" 12.45, 12.66, 14.67, 14.38, 13.85, 16.22, 14.99, 14.38, 13.71,\n",
" 13.65, 13.75, 13.28, 15.15, 14.58, 15.67, 14.16, 14.22, 15. ,\n",
" 14.32, 14.69, 16.57, 15.49, 16.12, 14.67, 16.41, 16.83, 14.6 ,\n",
" 15.64, 16.29, 15.83, 16.72, 16.27],\n",
" [ nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" nan, 0. , 8.75, 10.25, 13.77, 8.08, 9.14, 12.14, 5.39,\n",
" 10.76, 8.15, 4.47, 5.06, 10.89, 0. , nan, nan, 10.25,\n",
" 7.86, 10.57, 9.83, 7.8 , 8. , 9.79, 6.63, 10.2 , 10.25,\n",
" 10.94, 9.38, 11.71, 10.55, 10.11, 0.87, nan, 11.4 , 7.64,\n",
" 10.43, 10.8 , 9.43, 6.84, 11.55],\n",
" [ nan, nan, 14.63, 16.8 , 14.64, 15.49, 12.03, 14.03, 13.25,\n",
" 13.97, 16.16, 12.79, 15.63, 14.61, 14.4 , 14.63, 15.02, 14.78,\n",
" 14.85, 14.8 , 17.56, 17.17, 15.29, 15.55, 15.79, 16.84, 15.24,\n",
" 14.76, 16.06, 15.7 , 16.39, 16.44, 16.6 , 15.6 , 15.48, 17.49,\n",
" 15.93, 15.68, 16.77, 16.75, 18.11, 17.18, 18.74, 19.42, 15.76,\n",
" 16.33, 17.53, 17.83, 18.19, 17.78],\n",
" [ nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
" 4.33, 14.95, 14.17, 15.31, 17.32, 14.19, 11.75, 14.54, 14.93,\n",
" 15.17, 16.24, 17.86, 15.97, 14.1 , 16.54, 14.13, 16.88, 15.42,\n",
" 14.45, 4.26, nan, nan, 0. , 16.95, 13.99, 14.84, 15.19,\n",
" 15.76, 16.71, 16.66, nan, 17.32, 17.05, 18.12, 15.94, 14.32,\n",
" 14.31, 16.68, 14.16, 13.82, 13.63],\n",
" [ nan, nan, nan, nan, nan, nan, nan, 7.85, 7. ,\n",
" 8.04, 7.91, 4.5 , 7.34, 8.1 , 7.87, 7.85, 8.49, 6.77,\n",
" 7.34, 7.39, 7.28, 6.99, 8.41, 8.04, 8.86, 7.11, 7.46,\n",
" 7.19, 8.5 , 7.59, 8.69, 7.3 , 7.48, 7.51, 7.36, 6.1 ,\n",
" 8.02, 9.07, 6.17, 8.07, 7.83, 8.16, 8.45, 7.26, 8. ,\n",
" 7.41, 7.16, 8.46, 8.12, 8.6 ],\n",
" [ 3.04, 5.03, nan, nan, nan, nan, nan, 2.97, 5.94,\n",
" 5.83, 5.06, 6.3 , 6.19, 6.5 , 6.32, 6.09, 5.75, 6.14,\n",
" 5.33, 5.58, 6.65, 5.96, 6.92, 6.39, 6.63, 6.41, 5.59,\n",
" 6.06, 6.17, 5.26, 5.78, 5.01, 6. , 5.83, 5.93, 6.56,\n",
" 5.7 , 6.71, 6.58, 5.93, 5.68, 6.82, 5.66, 6.14, 5.88,\n",
" 6.9 , 6.51, 7.64, 6.69, 6. ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5afd89df-8c77-4972-88d1-f38644f6a42e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5afd89df-8c77-4972-88d1-f38644f6a42e' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (name: 6, year: 50)\n",
"Coordinates:\n",
" * name (name) object 'Alert' 'Azores' 'Barrow' ... 'Kumukahi' 'Mauna Loa'\n",
" * year (year) int64 1969 1970 1971 1972 1973 ... 2014 2015 2016 2017 2018\n",
"Data variables:\n",
" co2 (name, year) float64 nan nan nan nan nan ... 6.9 6.51 7.64 6.69 6.0"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped = ds.groupby('name')\n",
"dsets = []\n",
"for site, dataset in grouped:\n",
" g = dataset.groupby('time.year')\n",
" r = (g.max() - g.min())\n",
" dsets.append(r)\n",
"x = xr.concat(dsets, dim='name')\n",
"x"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2b13481861d0>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x.sel(name='Alert').co2.plot()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x2b13499afd90>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x.co2.plot(col='name', col_wrap=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"simyrs = \"185001-201412\"\n",
"var = \"CO2\"\n",
"\n",
"datadir = \"/glade/p/cesm/lmwg_dev/dll/\"\n",
"subdir = \"/atm/proc/tseries/month_1/\"\n",
"Mod1dir = \"CESM2_Coupled_NoCrop/\"\n",
"\n",
"sim = \"b.e21.BHIST_BPRP.f09_g17.CMIP6-esm-hist.001\"\n",
"sim2 = \"b40.20th.1deg.coup.001\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def time_set_mid(ds, time_bounds_name, dim, time_name='time'):\n",
" #with xr.set_options(keep_attrs=True):\n",
" attrs = ds.time.attrs\n",
" ds = ds.assign({time_name: ds[time_bounds_name].mean(dim)})\n",
" ds.time.attrs = attrs\n",
" return xr.decode_cf(ds)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (lat: 192, lev: 26, lon: 288, time: 1872)\n",
"Coordinates:\n",
" * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
" * lev (lev) float64 3.545 7.389 13.97 23.94 ... 867.2 929.6 970.6 992.6\n",
" * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
" * time (time) object 1850-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
"Data variables:\n",
" CO2 (time, lev, lat, lon) float32 dask.array&lt;chunksize=(20, 26, 192, 288), meta=np.ndarray&gt;\n",
"Attributes:\n",
" Conventions: CF-1.0\n",
" source: CAM\n",
" case: b40.20th.1deg.coup.001\n",
" title: UNSET\n",
" logname: klindsay\n",
" host: be0504en.ucar.ed\n",
" Version: $Name$\n",
" revision_Id: $Id$\n",
" initial_file: b40.coup_carb.004.cam2.i.0351-01-01-00000.nc\n",
" topography_file: /fis/cgd/cseg/csm/inputdata/atm/cam/topo/USGS-...\n",
" history: Fri Nov 11 07:38:21 2011: /fs/local/bin/ncrcat...\n",
" NCO: 4.0.8\n",
" nco_openmp_thread_number: 1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7659798b-f2e2-4d6c-9fd8-fe301b86889b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7659798b-f2e2-4d6c-9fd8-fe301b86889b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lev</span>: 26</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>time</span>: 1872</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-95e5af8e-05b4-4a89-82a7-ce6abc772145' class='xr-section-summary-in' type='checkbox' checked><label for='section-95e5af8e-05b4-4a89-82a7-ce6abc772145' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-0ed53463-ba5e-4697-aa09-e246af412051' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed53463-ba5e-4697-aa09-e246af412051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06efe49-d042-4585-8747-845d4ca9a02d' class='xr-var-data-in' type='checkbox'><label for='data-d06efe49-d042-4585-8747-845d4ca9a02d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90. , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
" -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
" -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
" -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
" -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
" -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
" -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
" -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
" -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
" -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
" -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
" -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
" -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
" -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
" -10.837696, -9.895288, -8.95288 , -8.010471, -7.068063, -6.125654,\n",
" -5.183246, -4.240838, -3.298429, -2.356021, -1.413613, -0.471204,\n",
" 0.471204, 1.413613, 2.356021, 3.298429, 4.240838, 5.183246,\n",
" 6.125654, 7.068063, 8.010471, 8.95288 , 9.895288, 10.837696,\n",
" 11.780105, 12.722513, 13.664921, 14.60733 , 15.549738, 16.492147,\n",
" 17.434555, 18.376963, 19.319372, 20.26178 , 21.204188, 22.146597,\n",
" 23.089005, 24.031414, 24.973822, 25.91623 , 26.858639, 27.801047,\n",
" 28.743455, 29.685864, 30.628272, 31.570681, 32.513089, 33.455497,\n",
" 34.397906, 35.340314, 36.282723, 37.225131, 38.167539, 39.109948,\n",
" 40.052356, 40.994764, 41.937173, 42.879581, 43.82199 , 44.764398,\n",
" 45.706806, 46.649215, 47.591623, 48.534031, 49.47644 , 50.418848,\n",
" 51.361257, 52.303665, 53.246073, 54.188482, 55.13089 , 56.073298,\n",
" 57.015707, 57.958115, 58.900524, 59.842932, 60.78534 , 61.727749,\n",
" 62.670157, 63.612565, 64.554974, 65.497382, 66.439791, 67.382199,\n",
" 68.324607, 69.267016, 70.209424, 71.151832, 72.094241, 73.036649,\n",
" 73.979058, 74.921466, 75.863874, 76.806283, 77.748691, 78.691099,\n",
" 79.633508, 80.575916, 81.518325, 82.460733, 83.403141, 84.34555 ,\n",
" 85.287958, 86.230366, 87.172775, 88.115183, 89.057592, 90. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.545 7.389 13.97 ... 970.6 992.6</div><input id='attrs-ea6316b7-432d-4248-9339-1f6d13a6dba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6316b7-432d-4248-9339-1f6d13a6dba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00e63077-b1d3-4bf0-8f9e-0dbfbe87a7c9' class='xr-var-data-in' type='checkbox'><label for='data-00e63077-b1d3-4bf0-8f9e-0dbfbe87a7c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([ 3.544638, 7.388814, 13.967214, 23.944625, 37.23029 , 53.114605,\n",
" 70.05915 , 85.439115, 100.514695, 118.250335, 139.115395, 163.66207 ,\n",
" 192.539935, 226.513265, 266.481155, 313.501265, 368.81798 , 433.895225,\n",
" 510.455255, 600.5242 , 696.79629 , 787.70206 , 867.16076 , 929.648875,\n",
" 970.55483 , 992.5561 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-ec294704-84fc-4a46-8e94-770b510b8672' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec294704-84fc-4a46-8e94-770b510b8672' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35ea2daf-384a-4a68-973f-45e5caa45e22' class='xr-var-data-in' type='checkbox'><label for='data-35ea2daf-384a-4a68-973f-45e5caa45e22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 1.25, 2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-fd3fc779-39e3-47b2-8cfb-a453680380ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd3fc779-39e3-47b2-8cfb-a453680380ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16d8747e-ebed-48d2-afd9-25b0d49c45a3' class='xr-var-data-in' type='checkbox'><label for='data-16d8747e-ebed-48d2-afd9-25b0d49c45a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0), ...,\n",
" cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4034fdb6-7ff5-4575-95f5-cb9c77064844' class='xr-section-summary-in' type='checkbox' checked><label for='section-4034fdb6-7ff5-4575-95f5-cb9c77064844' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CO2</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 26, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-0342e937-e850-4ea4-ac92-b54a52a28670' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0342e937-e850-4ea4-ac92-b54a52a28670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-312838c7-91e7-4e3e-959b-b7443bf9f0c4' class='xr-var-data-in' type='checkbox'><label for='data-312838c7-91e7-4e3e-959b-b7443bf9f0c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg/kg</dd><dt><span>long_name :</span></dt><dd>CO2</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd></dl></div><div class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 10.77 GB </td> <td> 115.02 MB </td></tr>\n",
" <tr><th> Shape </th><td> (1872, 26, 192, 288) </td> <td> (20, 26, 192, 288) </td></tr>\n",
" <tr><th> Count </th><td> 95 Tasks </td><td> 94 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"486\" height=\"104\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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" <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6a8f2ae-a825-4d0e-872b-fe8a506d43b7' class='xr-section-summary-in' type='checkbox' ><label for='section-e6a8f2ae-a825-4d0e-872b-fe8a506d43b7' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>case :</span></dt><dd>b40.20th.1deg.coup.001</dd><dt><span>title :</span></dt><dd>UNSET</dd><dt><span>logname :</span></dt><dd>klindsay</dd><dt><span>host :</span></dt><dd>be0504en.ucar.ed</dd><dt><span>Version :</span></dt><dd>$Name$</dd><dt><span>revision_Id :</span></dt><dd>$Id$</dd><dt><span>initial_file :</span></dt><dd>b40.coup_carb.004.cam2.i.0351-01-01-00000.nc</dd><dt><span>topography_file :</span></dt><dd>/fis/cgd/cseg/csm/inputdata/atm/cam/topo/USGS-gtopo30_0.9x1.25_remap_c051027.nc</dd><dt><span>history :</span></dt><dd>Fri Nov 11 07:38:21 2011: /fs/local/bin/ncrcat -O b40.20th.1deg.coup.001.cam2.h0.CO2.1850.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1851.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1852.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1853.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1854.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1855.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1856.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1857.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1858.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1859.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1860.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1861.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1862.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1863.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1864.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1865.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1866.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1867.nc 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CAT.b40.20th.1deg.coup.001.cam2.h0.CO2.185001-200512.nc\n",
"Fri Nov 11 07:14:56 2011: /fs/local/bin/ncrcat -O b40.20th.1deg.coup.001.cam2.h0.CO2.1850-01.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-02.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-03.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-04.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-05.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-06.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-07.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-08.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-09.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-10.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-11.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850-12.nc b40.20th.1deg.coup.001.cam2.h0.CO2.1850.nc\n",
"Fri Nov 11 07:11:08 2011: /fs/local/bin/ncks -O -v P0,mdt,nbdate,nbsec,ndbase,nsbase,ntrk,ntrm,ntrn,ch4vmr,co2vmr,date,datesec,f11vmr,f12vmr,gw,hyai,hyam,hybi,hybm,ilev,isccp_prs,isccp_prstau,isccp_tau,lat,lev,lon,n2ovmr,ndcur,nlon,nscur,nsteph,slat,slon,sol_tsi,time,w_stag,wnummax,date_written,time_bnds,time_written,CO2 b40.20th.1deg.coup.001.cam2.h0.1850-01.nc CO2_4d.d/b40.20th.1deg.coup.001.cam2.h0.CO2.1850-01.nc</dd><dt><span>NCO :</span></dt><dd>4.0.8</dd><dt><span>nco_openmp_thread_number :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 192, lev: 26, lon: 288, time: 1872)\n",
"Coordinates:\n",
" * lat (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0\n",
" * lev (lev) float64 3.545 7.389 13.97 23.94 ... 867.2 929.6 970.6 992.6\n",
" * lon (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
" * time (time) object 1850-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
"Data variables:\n",
" CO2 (time, lev, lat, lon) float32 dask.array<chunksize=(20, 26, 192, 288), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: CF-1.0\n",
" source: CAM\n",
" case: b40.20th.1deg.coup.001\n",
" title: UNSET\n",
" logname: klindsay\n",
" host: be0504en.ucar.ed\n",
" Version: $Name$\n",
" revision_Id: $Id$\n",
" initial_file: b40.coup_carb.004.cam2.i.0351-01-01-00000.nc\n",
" topography_file: /fis/cgd/cseg/csm/inputdata/atm/cam/topo/USGS-...\n",
" history: Fri Nov 11 07:38:21 2011: /fs/local/bin/ncrcat...\n",
" NCO: 4.0.8\n",
" nco_openmp_thread_number: 1"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1 = time_set_mid(xr.open_dataset(datadir+Mod1dir+sim+\".cam.h0.\"+var+\".\"+simyrs+\".nc\", \n",
" decode_times=False, chunks={'time': 20}),\n",
" time_bounds_name='time_bnds', dim='nbnd')[['CO2']]\n",
"\n",
"data2 = time_set_mid(xr.open_dataset(datadir+Mod1dir+sim2+\".cam2.h0.\"+var+\".185001-200512.nc\", \n",
" decode_times=False, chunks={'time': 20}),\n",
" time_bounds_name='time_bnds', dim='tbnd')[['CO2']]\n",
"data2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Converting CO2 units to ppm"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"#conversion of CESM CO2 to ppm\n",
"convert = 10.0**6 * 28.966/44.0"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 11.3 ms, sys: 0 ns, total: 11.3 ms\n",
"Wall time: 130 ms\n"
]
}
],
"source": [
"%%time\n",
"CESM1ppm = data2.CO2.sel(time=slice('1950','2014')) * convert\n",
"CESM2ppm = data1.CO2.sel(time=slice('1950','2014')) * convert\n",
"\n",
"CESM1ppm.attrs['units'] = 'ppm'\n",
"CESM2ppm.attrs['units'] = 'ppm'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selecting sites for comparison to observations"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"#initialize list using '[]' and dictionary using '{}'\n",
"CESM1points = []\n",
"CESM2points = []\n",
"\n",
"#lat and lon are actual values to pull out, level refers to a specific index, not a value, so requires 'isel'\n",
"for x in range(6):\n",
" CESM1pointloop = CESM1ppm.sel(lat=[lats[x]], lon=[lons[x]], method=\"nearest\")\n",
" CESM2pointloop = CESM2ppm.sel(lat=[lats[x]], lon=[lons[x]], method=\"nearest\")\n",
" CESM1pointloop = CESM1pointloop.isel(lev=[cesm1levs[x]])\n",
" CESM2pointloop = CESM2pointloop.isel(lev=[cesm2levs[x]])\n",
" CESM1pointloop['name'] = xr.DataArray(np.array([ESRL[x]['name']]).reshape(1, 1), dims=['lat', 'lon'])\n",
" CESM2pointloop['name'] = xr.DataArray(np.array([ESRL[x]['name']]).reshape(1, 1), dims=['lat', 'lon'])\n",
" CESM1points.append(CESM1pointloop)\n",
" CESM2points.append(CESM2pointloop)\n",
" \n",
"CESM1points = xr.merge(CESM1points)\n",
"CESM2points = xr.merge(CESM2points)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (lat: 5, lev: 2, lon: 5, time: 672)\n",
"Coordinates:\n",
" * lat (lat) float64 19.32 39.11 55.13 71.15 82.46\n",
" * lev (lev) float64 696.8 992.6\n",
" * lon (lon) float64 197.5 203.8 205.0 297.5 332.5\n",
" * time (time) object 1950-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
" name (lat, lon) object nan nan &#x27;Mauna Loa&#x27; nan ... nan nan &#x27;Alert&#x27; nan\n",
"Data variables:\n",
" CO2 (time, lev, lat, lon) float32 dask.array&lt;chunksize=(20, 2, 5, 5), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-62df074d-53f1-417e-ad3e-13aa802b6a2e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-62df074d-53f1-417e-ad3e-13aa802b6a2e' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 5</li><li><span class='xr-has-index'>lev</span>: 2</li><li><span class='xr-has-index'>lon</span>: 5</li><li><span class='xr-has-index'>time</span>: 672</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39c3c51b-42d5-4fe1-8b2b-494d46576737' class='xr-section-summary-in' type='checkbox' checked><label for='section-39c3c51b-42d5-4fe1-8b2b-494d46576737' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.32 39.11 55.13 71.15 82.46</div><input id='attrs-84ca0a14-4a28-4a2d-8a33-1ca1fb4f4407' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84ca0a14-4a28-4a2d-8a33-1ca1fb4f4407' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd8febb1-d159-4ec7-b05c-fd01286841c7' class='xr-var-data-in' type='checkbox'><label for='data-dd8febb1-d159-4ec7-b05c-fd01286841c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([19.319372, 39.109948, 55.13089 , 71.151832, 82.460733])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>696.8 992.6</div><input id='attrs-85ccc700-fef3-44a4-9aa9-92144ff08ae6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85ccc700-fef3-44a4-9aa9-92144ff08ae6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae2a8f2a-c939-4cff-b238-6174a65ace7f' class='xr-var-data-in' type='checkbox'><label for='data-ae2a8f2a-c939-4cff-b238-6174a65ace7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>hybrid level at midpoints (1000*(A+B))</dd><dt><span>units :</span></dt><dd>level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula_terms :</span></dt><dd>a: hyam b: hybm p0: P0 ps: PS</dd></dl></div><div class='xr-var-data'><pre>array([696.79629, 992.5561 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>197.5 203.8 205.0 297.5 332.5</div><input id='attrs-80da30bd-fb84-4764-93b6-8e2f0eb6fdef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80da30bd-fb84-4764-93b6-8e2f0eb6fdef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7d8dd4-f6e3-48b9-b096-65aef7e175d6' class='xr-var-data-in' type='checkbox'><label for='data-3b7d8dd4-f6e3-48b9-b096-65aef7e175d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([197.5 , 203.75, 205. , 297.5 , 332.5 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1950-01-16 12:00:00 ... 2005-12-...</div><input id='attrs-ea64809d-3b2b-42b4-afa6-9c1b2d391a1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea64809d-3b2b-42b4-afa6-9c1b2d391a1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a80b40bd-beb6-470e-ac49-dfaaff8e0084' class='xr-var-data-in' type='checkbox'><label for='data-a80b40bd-beb6-470e-ac49-dfaaff8e0084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1950, 1, 16, 12, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(1950, 2, 15, 0, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(1950, 3, 16, 12, 0, 0, 0), ...,\n",
" cftime.DatetimeNoLeap(2005, 10, 16, 12, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(2005, 11, 16, 0, 0, 0, 0),\n",
" cftime.DatetimeNoLeap(2005, 12, 16, 12, 0, 0, 0)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>name</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>nan nan &#x27;Mauna Loa&#x27; ... &#x27;Alert&#x27; nan</div><input id='attrs-0074e3f6-46b6-4a7b-955e-356d08030189' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0074e3f6-46b6-4a7b-955e-356d08030189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23e674a-b9d8-4f9f-a0ad-07152ff37198' class='xr-var-data-in' type='checkbox'><label for='data-a23e674a-b9d8-4f9f-a0ad-07152ff37198' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, &#x27;Mauna Loa&#x27;, nan, nan],\n",
" [nan, nan, nan, nan, &#x27;Azores&#x27;],\n",
" [&#x27;Cold Bay&#x27;, nan, nan, nan, nan],\n",
" [nan, &#x27;Barrow&#x27;, nan, nan, nan],\n",
" [nan, nan, nan, &#x27;Alert&#x27;, nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8872d0e1-e4ef-4024-bbdd-b754b87651ad' class='xr-section-summary-in' type='checkbox' checked><label for='section-8872d0e1-e4ef-4024-bbdd-b754b87651ad' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>CO2</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(20, 2, 5, 5), meta=np.ndarray&gt;</div><input id='attrs-eedb3e32-9e8d-4c8c-8669-71cfc5e1b57b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eedb3e32-9e8d-4c8c-8669-71cfc5e1b57b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-436195da-d53b-45e4-bc5b-b71ff6bf2957' class='xr-var-data-in' type='checkbox'><label for='data-436195da-d53b-45e4-bc5b-b71ff6bf2957' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>ppm</dd></dl></div><div class='xr-var-data'><table>\n",
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],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (lat: 5, lev: 2, lon: 5, time: 672)\n",
"Coordinates:\n",
" * lat (lat) float64 19.32 39.11 55.13 71.15 82.46\n",
" * lev (lev) float64 696.8 992.6\n",
" * lon (lon) float64 197.5 203.8 205.0 297.5 332.5\n",
" * time (time) object 1950-01-16 12:00:00 ... 2005-12-16 12:00:00\n",
" name (lat, lon) object nan nan 'Mauna Loa' nan ... nan nan 'Alert' nan\n",
"Data variables:\n",
" CO2 (time, lev, lat, lon) float32 dask.array<chunksize=(20, 2, 5, 5), meta=np.ndarray>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"CESM1points"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculating detrended annual cycle for early and late time periods"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def detrend_dim(da, dim, deg=1):\n",
" # detrend along a single dimension\n",
" p = da.polyfit(dim=dim, deg=deg) # requires xarray v0.16 and later\n",
" fit = xr.polyval(da.coords[dim], p.polyfit_coefficients)\n",
" return da - fit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def calculate_annual_cycle(ds):\n",
" \n",
" grouped = ds.groupby('name')\n",
"\n",
" ann_late = []\n",
" ann_late_detrend = []\n",
" ann_early = []\n",
" ann_early_detrend = []\n",
"\n",
" for key, group in grouped:\n",
" late = group.sel(time=slice('2000','2005'))\n",
" x = late.groupby('time.year').mean().unstack()['CO2']\n",
" x.name = 'CO2'\n",
" ann_late.append(x)\n",
" x_detrended = detrend_dim(x, 'year')\n",
" x_detrended.name = 'CO2'\n",
" ann_late_detrend.append(x_detrended)\n",
"\n",
" early = group.sel(time=slice('1980','1985')) \n",
" y = early.groupby('time.year').mean().unstack()['CO2']\n",
" y.name = 'CO2'\n",
" ann_early.append(y)\n",
" y_detrended = detrend_dim(y, 'year')\n",
" y_detrended.name = 'CO2'\n",
" ann_early_detrend.append(y_detrended)\n",
" \n",
" ann_late = xr.merge(ann_late)\n",
" ann_late_detrend = xr.merge(ann_late_detrend)\n",
" ann_early = xr.merge(ann_early)\n",
" ann_early_detrend = xr.merge(ann_early_detrend)\n",
" \n",
" return ann_late, ann_early, ann_late_detrend, ann_early_detrend\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"CESM1_ann_late, CESM1_ann_early, CESM1_ann_late_detrend, CESM1_ann_early_detrend = calculate_annual_cycle(CESM1points)\n",
"CESM2_ann_late, CESM2_ann_early, CESM2_ann_late_detrend, CESM2_ann_early_detrend = calculate_annual_cycle(CESM2points)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x2abab856d150>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
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\n",
"text/plain": [
"<Figure size 504x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"CESM1_ann_late.where(CESM1_ann_late.name=='Alert', drop=True).CO2.plot(col='lev')\n",
"CESM1_ann_early.where(CESM1_ann_early.name=='Mauna Loa', drop=True).CO2.plot(col='lev') "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x2abab8c45a90>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"CESM1_ann_late_detrend.where(CESM1_ann_late_detrend.name=='Alert', drop=True).CO2.plot(col='lev')\n",
"CESM1_ann_early_detrend.where(CESM1_ann_early_detrend.name=='Mauna Loa', drop=True).CO2.plot(col='lev')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "array must not contain infs or NaNs",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-fc0a564e8a52>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mCESM1_ann_late\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msite\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCESM1late\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"time.month\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mCESM1_ann_early\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msite\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCESM1early\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"time.month\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mCESM1_ann_early_detrend\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msite\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCESM1early\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"time.month\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mCESM1_ann_late_detrend\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msite\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetrend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCESM1late\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"time.month\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCESM1_ann_early_detrend\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/glade/work/abanihi/softwares/miniconda3/envs/playground/lib/python3.7/site-packages/scipy/signal/signaltools.py\u001b[0m in \u001b[0;36mdetrend\u001b[0;34m(data, axis, type, bp, overwrite_data)\u001b[0m\n\u001b[1;32m 3324\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNpts\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.0\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mNpts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3325\u001b[0m \u001b[0msl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3326\u001b[0;31m \u001b[0mcoef\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstsq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3327\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnewdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msl\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3328\u001b[0m \u001b[0;31m# Put data back in original shape.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/glade/work/abanihi/softwares/miniconda3/envs/playground/lib/python3.7/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36mlstsq\u001b[0;34m(a, b, cond, overwrite_a, overwrite_b, check_finite, lapack_driver)\u001b[0m\n\u001b[1;32m 1154\u001b[0m \"\"\"\n\u001b[1;32m 1155\u001b[0m \u001b[0ma1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1156\u001b[0;31m \u001b[0mb1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_validated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_finite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_finite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1157\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Input array a should be 2D'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/glade/work/abanihi/softwares/miniconda3/envs/playground/lib/python3.7/site-packages/scipy/_lib/_util.py\u001b[0m in \u001b[0;36m_asarray_validated\u001b[0;34m(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'masked arrays are not supported'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0mtoarray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray_chkfinite\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck_finite\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 273\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mobjects_ok\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'O'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/glade/work/abanihi/softwares/miniconda3/envs/playground/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36masarray_chkfinite\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m 484\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypecodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'AllFloat'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 485\u001b[0m raise ValueError(\n\u001b[0;32m--> 486\u001b[0;31m \"array must not contain infs or NaNs\")\n\u001b[0m\u001b[1;32m 487\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: array must not contain infs or NaNs"
]
}
],
"source": [
"CESM1_ann_early = {}\n",
"CESM1_ann_late = {}\n",
"CESM2_ann_early = {}\n",
"CESM2_ann_late = {}\n",
"CESM1_ann_early_detrend = {}\n",
"CESM1_ann_late_detrend = {}\n",
"CESM2_ann_early_detrend = {}\n",
"CESM2_ann_late_detrend = {}\n",
"\n",
"for site, data in CESM1points.items():\n",
" CESM1late = data.sel(time=slice('2000','2005'))\n",
" CESM1early = data.sel(time=slice('1980','1985'))\n",
" CESM1_ann_late[site] = CESM1late.groupby(\"time.month\").mean()\n",
" CESM1_ann_early[site] = CESM1early.groupby(\"time.month\").mean()\n",
" CESM1_ann_early_detrend[site] = signal.detrend(CESM1early.groupby(\"time.month\").mean())\n",
" CESM1_ann_late_detrend[site] = signal.detrend(CESM1late.groupby(\"time.month\").mean())\n",
"print(CESM1_ann_early_detrend[1])\n",
"print(type(CESM1_ann_early_detrend))\n",
"\n",
"for site, data in CESM2points.items():\n",
" CESM2late = data.sel(time=slice('2000','2005'))\n",
" CESM2early = data.sel(time=slice('1980','1985'))\n",
" CESM2_ann_late[site] = CESM2late.groupby(\"time.month\").mean()\n",
" CESM2_ann_early[site] = CESM2early.groupby(\"time.month\").mean()\n",
" CESM2_ann_early_detrend[site] = signal.detrend(CESM2early.groupby(\"time.month\").mean())\n",
" CESM2_ann_late_detrend[site] = signal.detrend(CESM2late.groupby(\"time.month\").mean())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem #2\n",
"### Detrended plot has oddly high late-season values\n",
"Bottom plot is detrended. Should I be using a different function? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Absolute value plot"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2adea7dd5cf8>]"
]
},
"execution_count": 12,
"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": [
"CESM1_ann_early[0].plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Detrended Plot"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2adea7e7bb38>]"
]
},
"execution_count": 13,
"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": [
"plt.plot(CESM1_ann_early_detrend[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.8"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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