sst_trends.ipynb

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    "# where on earth is SST changing the fastest?\n",
    "\n",
    "let's try to answer this question using NOAA's [Extended Reconstructed Sea Surface Temperature (ERSST) v5](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5)\n",
    "\n",
    "We will start by looking at a few specific spots, then look at the whole globe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 89, lon: 180, time: 2007)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-03-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI&#x27;s...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-04-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</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-5786898f-0764-473c-a2ab-40cf501fd50e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5786898f-0764-473c-a2ab-40cf501fd50e' 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>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 2007</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-44240108-11a2-4063-946a-835424f0d834' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44240108-11a2-4063-946a-835424f0d834' class='xr-section-summary' >Coordinates: <span>(3)</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'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-a1670023-c1e2-4c6e-be07-9c3d0f7377da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1670023-c1e2-4c6e-be07-9c3d0f7377da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa700c13-53a4-42f8-acb0-37e5ed440d45' class='xr-var-data-in' type='checkbox'><label for='data-aa700c13-53a4-42f8-acb0-37e5ed440d45' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
       "       -80., -82., -84., -86., -88.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-461fa28a-431c-4852-89a9-70c29c60a57d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-461fa28a-431c-4852-89a9-70c29c60a57d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff8aea4c-adf9-4676-b7ef-282cc61ad4b9' class='xr-var-data-in' type='checkbox'><label for='data-ff8aea4c-adf9-4676-b7ef-282cc61ad4b9' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
       "      dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1854-01-01 ... 2021-03-01</div><input id='attrs-eee9e7ef-8505-4ea0-b6b5-9fbed28a3012' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee9e7ef-8505-4ea0-b6b5-9fbed28a3012' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e2138ad-37d0-4c5e-b175-cd518333c875' class='xr-var-data-in' type='checkbox'><label for='data-0e2138ad-37d0-4c5e-b175-cd518333c875' 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>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80778.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1854-01-01T00:00:00.000000000&#x27;, &#x27;1854-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1854-03-01T00:00:00.000000000&#x27;, ..., &#x27;2021-01-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2021-02-01T00:00:00.000000000&#x27;, &#x27;2021-03-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b9bd3a8-417d-4208-9c6a-e9767a2a9b4c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b9bd3a8-417d-4208-9c6a-e9767a2a9b4c' 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>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7c49fa7-d7e1-4e23-b189-8c7dac800192' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7c49fa7-d7e1-4e23-b189-8c7dac800192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce55dc27-015d-4e87-afb7-3a112b54fe85' class='xr-var-data-in' type='checkbox'><label for='data-ce55dc27-015d-4e87-afb7-3a112b54fe85' 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>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>[32152140 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e784e81-743d-4de8-88a2-4698a03db206' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e784e81-743d-4de8-88a2-4698a03db206' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2021-04-07</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 89, lon: 180, time: 2007)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
       "  * time     (time) datetime64[ns] 1854-01-01 1854-02-01 ... 2021-03-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI's...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-04-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_dataset(url, drop_variables=['time_bnds'])\n",
    "\n",
    "print(ds.nbytes / 1e6, 'Mb')\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 17, lon: 30, time: 1452)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 34.0 32.0 30.0 28.0 26.0 ... 10.0 8.0 6.0 4.0 2.0\n",
       "  * lon      (lon) float32 258.0 260.0 262.0 264.0 ... 310.0 312.0 314.0 316.0\n",
       "  * time     (time) datetime64[ns] 1900-01-01 1900-02-01 ... 2020-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science &gt; Oceans &gt; Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI&#x27;s...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-04-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time</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-0a0cad45-0f35-47ce-87ff-2ac45cb036a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a0cad45-0f35-47ce-87ff-2ac45cb036a3' 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>: 17</li><li><span class='xr-has-index'>lon</span>: 30</li><li><span class='xr-has-index'>time</span>: 1452</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5ab2f81a-5dc9-4f56-9d3f-d65afb93793a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ab2f81a-5dc9-4f56-9d3f-d65afb93793a' class='xr-section-summary' >Coordinates: <span>(3)</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'>float32</div><div class='xr-var-preview xr-preview'>34.0 32.0 30.0 28.0 ... 6.0 4.0 2.0</div><input id='attrs-b08236d3-88a3-4312-8736-166be3dde7ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b08236d3-88a3-4312-8736-166be3dde7ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c934b5bd-e9cc-45c8-9763-a4ab97112a0b' class='xr-var-data-in' type='checkbox'><label for='data-c934b5bd-e9cc-45c8-9763-a4ab97112a0b' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([34., 32., 30., 28., 26., 24., 22., 20., 18., 16., 14., 12., 10.,  8.,\n",
       "        6.,  4.,  2.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>258.0 260.0 262.0 ... 314.0 316.0</div><input id='attrs-ed2e3426-ef23-4200-9198-374a66462739' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed2e3426-ef23-4200-9198-374a66462739' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df32a23a-e3cb-4f1c-969d-5dd51e6280b9' class='xr-var-data-in' type='checkbox'><label for='data-df32a23a-e3cb-4f1c-969d-5dd51e6280b9' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([258., 260., 262., 264., 266., 268., 270., 272., 274., 276., 278., 280.,\n",
       "       282., 284., 286., 288., 290., 292., 294., 296., 298., 300., 302., 304.,\n",
       "       306., 308., 310., 312., 314., 316.], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1900-01-01 ... 2020-12-01</div><input id='attrs-58057ad2-5b5b-40c6-9a2b-53caa205abe9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58057ad2-5b5b-40c6-9a2b-53caa205abe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cdcc49-62fb-416e-a66c-d1aafacfd850' class='xr-var-data-in' type='checkbox'><label for='data-f9cdcc49-62fb-416e-a66c-d1aafacfd850' 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>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80778.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1900-01-01T00:00:00.000000000&#x27;, &#x27;1900-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1900-03-01T00:00:00.000000000&#x27;, ..., &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-764fdad5-ed0e-46a2-98a9-baa9b2bee568' class='xr-section-summary-in' type='checkbox'  checked><label for='section-764fdad5-ed0e-46a2-98a9-baa9b2bee568' 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>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d197cb39-a083-4137-b493-f80c66a04b27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d197cb39-a083-4137-b493-f80c66a04b27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbefb480-334a-4814-ac6c-652de7168fc0' class='xr-var-data-in' type='checkbox'><label for='data-dbefb480-334a-4814-ac6c-652de7168fc0' 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>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd><dt><span>actual_range :</span></dt><dd>[-1.8     42.32636]</dd><dt><span>valid_range :</span></dt><dd>[-1.8 45. ]</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  89 180]</dd></dl></div><div class='xr-var-data'><pre>[740520 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffbab774-7a30-4c6f-9daf-0adec3505ca7' class='xr-section-summary-in' type='checkbox'  ><label for='section-ffbab774-7a30-4c6f-9daf-0adec3505ca7' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>-1.0</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.0</dd><dt><span>geospatial_laty_max :</span></dt><dd>89.0</dd><dt><span>geospatial_laty_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_max :</span></dt><dd>89.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-89.0</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>References :</span></dt><dd>https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v5 at NCEI and http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html</dd><dt><span>source :</span></dt><dd>In situ data: ICOADS R3.0 before 2015, NCEP in situ GTS from 2016 to present, and Argo SST from 1999 to present. Ice data: HadISST2 ice before 2015, and NCEP ice after 2015</dd><dt><span>title :</span></dt><dd>NOAA ERSSTv5 (in situ only)</dd><dt><span>history :</span></dt><dd>created 07/2017 by PSD data using NCEI&#x27;s ERSST V5 NetCDF values</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Centers for Environmental Information and time aggregated. Original Full Source: NOAA/NESDIS/NCEI/CCOG</dd><dt><span>citation :</span></dt><dd>Huang et al, 2017: Extended Reconstructed Sea Surface Temperatures Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. Journal of Climate, https://doi.org/10.1175/JCLI-D-16-0836.1</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS</dd><dt><span>standard_name_vocabulary :</span></dt><dd>CF Standard Name Table (v40, 25 January 2017)</dd><dt><span>processing_level :</span></dt><dd>NOAA Level 4</dd><dt><span>Conventions :</span></dt><dd>CF-1.6, ACDD-1.3</dd><dt><span>metadata_link :</span></dt><dd>:metadata_link = https://doi.org/10.7289/V5T72FNM (original format)</dd><dt><span>creator_name :</span></dt><dd>Boyin Huang (original)</dd><dt><span>date_created :</span></dt><dd>2017-06-30T12:18:00Z (original)</dd><dt><span>product_version :</span></dt><dd>Version 5</dd><dt><span>creator_url_original :</span></dt><dd>https://www.ncei.noaa.gov</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters</dd><dt><span>summary :</span></dt><dd>ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis</dd><dt><span>dataset_title :</span></dt><dd>NOAA Extended Reconstructed SST V5</dd><dt><span>data_modified :</span></dt><dd>2021-04-07</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>time</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 17, lon: 30, time: 1452)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 34.0 32.0 30.0 28.0 26.0 ... 10.0 8.0 6.0 4.0 2.0\n",
       "  * lon      (lon) float32 258.0 260.0 262.0 264.0 ... 310.0 312.0 314.0 316.0\n",
       "  * time     (time) datetime64[ns] 1900-01-01 1900-02-01 ... 2020-12-01\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    climatology:                     Climatology is based on 1971-2000 SST, X...\n",
       "    description:                     In situ data: ICOADS2.5 before 2007 and ...\n",
       "    keywords_vocabulary:             NASA Global Change Master Directory (GCM...\n",
       "    keywords:                        Earth Science > Oceans > Ocean Temperatu...\n",
       "    instrument:                      Conventional thermometers\n",
       "    source_comment:                  SSTs were observed by conventional therm...\n",
       "    geospatial_lon_min:              -1.0\n",
       "    geospatial_lon_max:              359.0\n",
       "    geospatial_laty_max:             89.0\n",
       "    geospatial_laty_min:             -89.0\n",
       "    geospatial_lat_max:              89.0\n",
       "    geospatial_lat_min:              -89.0\n",
       "    geospatial_lat_units:            degrees_north\n",
       "    geospatial_lon_units:            degrees_east\n",
       "    cdm_data_type:                   Grid\n",
       "    project:                         NOAA Extended Reconstructed Sea Surface ...\n",
       "    original_publisher_url:          http://www.ncdc.noaa.gov\n",
       "    References:                      https://www.ncdc.noaa.gov/data-access/ma...\n",
       "    source:                          In situ data: ICOADS R3.0 before 2015, N...\n",
       "    title:                           NOAA ERSSTv5 (in situ only)\n",
       "    history:                         created 07/2017 by PSD data using NCEI's...\n",
       "    institution:                     This version written at NOAA/ESRL PSD: o...\n",
       "    citation:                        Huang et al, 2017: Extended Reconstructe...\n",
       "    platform:                        Ship and Buoy SSTs from ICOADS R3.0 and ...\n",
       "    standard_name_vocabulary:        CF Standard Name Table (v40, 25 January ...\n",
       "    processing_level:                NOAA Level 4\n",
       "    Conventions:                     CF-1.6, ACDD-1.3\n",
       "    metadata_link:                   :metadata_link = https://doi.org/10.7289...\n",
       "    creator_name:                    Boyin Huang (original)\n",
       "    date_created:                    2017-06-30T12:18:00Z (original)\n",
       "    product_version:                 Version 5\n",
       "    creator_url_original:            https://www.ncei.noaa.gov\n",
       "    license:                         No constraints on data access or use\n",
       "    comment:                         SSTs were observed by conventional therm...\n",
       "    summary:                         ERSST.v5 is developed based on v4 after ...\n",
       "    dataset_title:                   NOAA Extended Reconstructed SST V5\n",
       "    data_modified:                   2021-04-07\n",
       "    DODS_EXTRA.Unlimited_Dimension:  time"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just grab the carriabian\n",
    "\n",
    "carib = ds.sel(lon=slice(360-102,360-44), \n",
    "               lat=slice(35,2)).sel(time=slice('1900','2020' ))\n",
    "\n",
    "carib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc97fff2580>"
      ]
     },
     "execution_count": 6,
     "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": [
    "carib.sst.sel(time='2020-09-01').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc980257d30>]"
      ]
     },
     "execution_count": 7,
     "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": [
    "carib.sst.sel(lon = 290, lat = 18, method = 'nearest').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 136 ms, sys: 93.1 ms, total: 229 ms\n",
      "Wall time: 6.23 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 17, lon: 30, time: 1452)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 34.0 32.0 30.0 28.0 26.0 ... 10.0 8.0 6.0 4.0 2.0\n",
       "  * lon      (lon) float32 258.0 260.0 262.0 264.0 ... 310.0 312.0 314.0 316.0\n",
       "  * time     (time) datetime64[ns] 1900-01-01 1900-02-01 ... 2020-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan ... 0.8248 0.8446 0.8607</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-0bdce1b6-1e1b-4d2d-bf1e-7c98757ffbe8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bdce1b6-1e1b-4d2d-bf1e-7c98757ffbe8' 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>: 17</li><li><span class='xr-has-index'>lon</span>: 30</li><li><span class='xr-has-index'>time</span>: 1452</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a5b8a546-7b92-4a69-b81a-096a1a47cfd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5b8a546-7b92-4a69-b81a-096a1a47cfd8' 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'>float32</div><div class='xr-var-preview xr-preview'>34.0 32.0 30.0 28.0 ... 6.0 4.0 2.0</div><input id='attrs-9ae40d79-2e28-4062-8c80-0e61c2f1c0d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ae40d79-2e28-4062-8c80-0e61c2f1c0d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74cb9d68-7055-499f-9615-f2cf5a479c2c' class='xr-var-data-in' type='checkbox'><label for='data-74cb9d68-7055-499f-9615-f2cf5a479c2c' 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>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([34., 32., 30., 28., 26., 24., 22., 20., 18., 16., 14., 12., 10.,  8.,\n",
       "        6.,  4.,  2.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>258.0 260.0 262.0 ... 314.0 316.0</div><input id='attrs-cf54dafa-9bf4-4ab1-8926-f61877ccfdf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf54dafa-9bf4-4ab1-8926-f61877ccfdf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73b6da9f-06e1-442b-a9d1-f32a63c5af03' class='xr-var-data-in' type='checkbox'><label for='data-73b6da9f-06e1-442b-a9d1-f32a63c5af03' 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>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([258., 260., 262., 264., 266., 268., 270., 272., 274., 276., 278., 280.,\n",
       "       282., 284., 286., 288., 290., 292., 294., 296., 298., 300., 302., 304.,\n",
       "       306., 308., 310., 312., 314., 316.], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1900-01-01 ... 2020-12-01</div><input id='attrs-0f460011-de48-4cae-9e49-2e72c2fa2f3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f460011-de48-4cae-9e49-2e72c2fa2f3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e49fa3b-776a-430c-9441-6adb8d162cfb' class='xr-var-data-in' type='checkbox'><label for='data-2e49fa3b-776a-430c-9441-6adb8d162cfb' 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>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80778.]</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1900-01-01T00:00:00.000000000&#x27;, &#x27;1900-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1900-03-01T00:00:00.000000000&#x27;, ..., &#x27;2020-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-11-01T00:00:00.000000000&#x27;, &#x27;2020-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-2ccf2165-3e31-46b6-8cab-df41167dd2ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2ccf2165-3e31-46b6-8cab-df41167dd2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c488a3-3d5d-4737-a10f-1dd32d0aa180' class='xr-var-data-in' type='checkbox'><label for='data-81c488a3-3d5d-4737-a10f-1dd32d0aa180' 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([ 1,  2,  3, ..., 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7edf6df5-2964-4a88-ae41-8bae01be767a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7edf6df5-2964-4a88-ae41-8bae01be767a' 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>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.8446 0.8607</div><input id='attrs-9f2df48f-c6f4-49c6-9782-14563f3a9db8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f2df48f-c6f4-49c6-9782-14563f3a9db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1da450b5-95e4-45ae-b589-34205673fc70' class='xr-var-data-in' type='checkbox'><label for='data-1da450b5-95e4-45ae-b589-34205673fc70' 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, ...,  0.05094337,\n",
       "         -0.0385704 , -0.12556076],\n",
       "        [        nan,         nan,         nan, ..., -0.01307869,\n",
       "         -0.1247673 , -0.2237854 ],\n",
       "        [        nan,         nan,         nan, ..., -0.12667656,\n",
       "         -0.21393394, -0.28930473],\n",
       "        ...,\n",
       "        [ 0.58244133,  0.5719242 ,  0.5851116 , ...,  0.70547676,\n",
       "          0.67539597,  0.5589485 ],\n",
       "        [ 1.0207138 ,  1.0238934 ,  1.0340462 , ...,  0.53188705,\n",
       "          0.46297455,  0.37509346],\n",
       "        [ 1.6180229 ,  1.6357422 ,  1.6204681 , ...,  0.33263588,\n",
       "          0.29175186,  0.2725544 ]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ..., -0.27661514,\n",
       "         -0.33237457, -0.3939762 ],\n",
       "        [        nan,         nan,         nan, ..., -0.4366703 ,\n",
       "         -0.5045929 , -0.5601654 ],\n",
       "        [        nan,         nan,         nan, ..., -0.5583687 ,\n",
       "         -0.62441254, -0.6709976 ],\n",
       "...\n",
       "        [ 0.06789017,  0.09943771,  0.12003899, ...,  0.6264744 ,\n",
       "          0.5289707 ,  0.4804535 ],\n",
       "        [-0.07554626, -0.0116024 ,  0.03470993, ...,  0.85604286,\n",
       "          0.82283974,  0.7837715 ],\n",
       "        [-0.2858963 , -0.17938995, -0.0924778 , ...,  1.092144  ,\n",
       "          1.1064186 ,  1.0981274 ]],\n",
       "\n",
       "       [[        nan,         nan,         nan, ...,  1.3160496 ,\n",
       "          1.2042942 ,  1.0448093 ],\n",
       "        [        nan,         nan,         nan, ...,  1.2107773 ,\n",
       "          1.1073322 ,  0.95679283],\n",
       "        [        nan,         nan,         nan, ...,  1.0266056 ,\n",
       "          0.9351063 ,  0.8080559 ],\n",
       "        ...,\n",
       "        [ 0.13433075,  0.1850071 ,  0.23415565, ...,  0.64375305,\n",
       "          0.60422516,  0.59187126],\n",
       "        [-0.00725174,  0.06012154,  0.11549377, ...,  0.74625206,\n",
       "          0.741251  ,  0.7276783 ],\n",
       "        [-0.18902588, -0.10355759, -0.02809906, ...,  0.8248272 ,\n",
       "          0.844635  ,  0.8606682 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3176758-c8e6-4161-8e7d-2ef81f75755d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3176758-c8e6-4161-8e7d-2ef81f75755d' 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:  (lat: 17, lon: 30, time: 1452)\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 34.0 32.0 30.0 28.0 26.0 ... 10.0 8.0 6.0 4.0 2.0\n",
       "  * lon      (lon) float32 258.0 260.0 262.0 264.0 ... 310.0 312.0 314.0 316.0\n",
       "  * time     (time) datetime64[ns] 1900-01-01 1900-02-01 ... 2020-12-01\n",
       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12\n",
       "Data variables:\n",
       "    sst      (time, lat, lon) float32 nan nan nan nan ... 0.8248 0.8446 0.8607"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# remove the seasonal cycle:\n",
    "\n",
    "gb = carib.groupby('time.month') \n",
    "carib_anom = gb - gb.mean(dim='time') \n",
    "carib_anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc981c17eb0>]"
      ]
     },
     "execution_count": 10,
     "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": [
    "# plot a single point\n",
    "# carib.sst.sel(lon = 290, lat = 18, method = 'nearest').plot()\n",
    "carib_anom.sst.sel(lon = 290, lat = 18, method = 'nearest').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc9822e94c0>]"
      ]
     },
     "execution_count": 11,
     "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": [
    "# average over space:\n",
    "\n",
    "carib_anom.sst.mean( dim=['lon', 'lat']).plot()\n",
    "\n",
    "# get a DECADE mean\n",
    "# decade_mean = carib_anom.sst.rolling(time=10*12, center=True, min_periods=5).mean()\n",
    "decade_mean = carib_anom.sst.coarsen(time=10*12, boundary='trim').mean()\n",
    "\n",
    "decade_mean.mean( dim=['lon', 'lat']).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# do the same for the gulf of maine:\n",
    "\n",
    "\n",
    "GOM = ds.sel(lon=slice(295,305), \n",
    "               lat=slice(50,44)).sel(time=slice('1900','2020' ))\n",
    "\n",
    "gb = GOM.groupby('time.month') \n",
    "GOM_anom = gb - gb.mean(dim='time') \n",
    "\n",
    "# get a DECADE mean\n",
    "# GOMdecade_mean = GOM_anom.sst.rolling(time=10*12, center=True, min_periods=5).mean()\n",
    "GOMdecade_mean = GOM_anom.sst.coarsen(time=10*12, boundary='trim').mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc982313e80>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decade_mean.mean( dim=['lon', 'lat']).plot(label='carib')\n",
    "GOMdecade_mean.mean( dim=['lon', 'lat']).plot(label='GOM')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# fit a linear trend to the data and plot the trends\n",
    "\n",
    "$ y = mx + b$\n",
    "\n",
    "we can use python to fin $m$ and $b$\n",
    "\n",
    "in our case $y$ is temperature and $x$ is time, so $m$ has units of degrees/time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\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",
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       "\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "\n",
       ".xr-var-preview {\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  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,\n",
       ".xr-attrs 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:               (degree: 2, lat: 17, lon: 30)\n",
       "Coordinates:\n",
       "  * degree                (degree) int64 1 0\n",
       "  * lat                   (lat) float64 34.0 32.0 30.0 28.0 ... 8.0 6.0 4.0 2.0\n",
       "  * lon                   (lon) float64 258.0 260.0 262.0 ... 312.0 314.0 316.0\n",
       "Data variables:\n",
       "    time_matrix_rank      int64 2\n",
       "    time_singular_values  (degree) float64 1.124 0.8582\n",
       "    polyfit_coefficients  (degree, lat, lon) float64 nan nan ... 0.0644 0.06686\n",
       "    polyfit_residuals     (lat, lon) float64 nan nan nan ... 187.9 184.1 172.3</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-f3037ec8-be5c-4798-baca-65958930e79f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3037ec8-be5c-4798-baca-65958930e79f' 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'>degree</span>: 2</li><li><span class='xr-has-index'>lat</span>: 17</li><li><span class='xr-has-index'>lon</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8c3eae79-3ee3-472c-8ffd-25fefb0fb269' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c3eae79-3ee3-472c-8ffd-25fefb0fb269' class='xr-section-summary' >Coordinates: <span>(3)</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'>degree</span></div><div class='xr-var-dims'>(degree)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 0</div><input id='attrs-2da55ed4-8623-43a7-bd3a-6d13db346453' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2da55ed4-8623-43a7-bd3a-6d13db346453' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-755807fa-ee0d-416b-85e4-985eead80991' class='xr-var-data-in' type='checkbox'><label for='data-755807fa-ee0d-416b-85e4-985eead80991' 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([1, 0])</pre></div></li><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'>34.0 32.0 30.0 28.0 ... 6.0 4.0 2.0</div><input id='attrs-fa0fed3e-01e9-47b6-9661-1e6c95fde3f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa0fed3e-01e9-47b6-9661-1e6c95fde3f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b62e85c5-7b7a-46d3-9561-5a6e6b15db11' class='xr-var-data-in' type='checkbox'><label for='data-b62e85c5-7b7a-46d3-9561-5a6e6b15db11' 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([34., 32., 30., 28., 26., 24., 22., 20., 18., 16., 14., 12., 10.,  8.,\n",
       "        6.,  4.,  2.])</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'>258.0 260.0 262.0 ... 314.0 316.0</div><input id='attrs-8463cd52-29a5-43cc-8305-91482a638c45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8463cd52-29a5-43cc-8305-91482a638c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c0d513e-b145-48de-8522-d80f3e8c3d9a' class='xr-var-data-in' type='checkbox'><label for='data-4c0d513e-b145-48de-8522-d80f3e8c3d9a' 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([258., 260., 262., 264., 266., 268., 270., 272., 274., 276., 278., 280.,\n",
       "       282., 284., 286., 288., 290., 292., 294., 296., 298., 300., 302., 304.,\n",
       "       306., 308., 310., 312., 314., 316.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f3c0402-ded2-47fd-8465-dc51a31b1b10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f3c0402-ded2-47fd-8465-dc51a31b1b10' class='xr-section-summary' >Data variables: <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>time_matrix_rank</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2</div><input id='attrs-0fa484fa-b8e9-4250-b22a-7e6ed7944ce8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fa484fa-b8e9-4250-b22a-7e6ed7944ce8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3a736d0-a6af-47dd-8c12-d5c61fdb20e2' class='xr-var-data-in' type='checkbox'><label for='data-c3a736d0-a6af-47dd-8c12-d5c61fdb20e2' 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(2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_singular_values</span></div><div class='xr-var-dims'>(degree)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.124 0.8582</div><input id='attrs-d77152b8-897c-43de-8487-471736b0684f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d77152b8-897c-43de-8487-471736b0684f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6869e6d7-ad4f-4f8d-a615-00f86b221602' class='xr-var-data-in' type='checkbox'><label for='data-6869e6d7-ad4f-4f8d-a615-00f86b221602' 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([1.12408663, 0.85815456])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyfit_coefficients</span></div><div class='xr-var-dims'>(degree, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.0644 0.06686</div><input id='attrs-530e5b95-7cc2-4119-93b7-bbc7b380be04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-530e5b95-7cc2-4119-93b7-bbc7b380be04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f07d42-404d-4b7c-86f0-49900a309680' class='xr-var-data-in' type='checkbox'><label for='data-43f07d42-404d-4b7c-86f0-49900a309680' 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, ...,\n",
       "         2.38128573e-19, 2.22810504e-19, 2.09869231e-19],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.39698356e-19, 2.25003000e-19, 2.11061109e-19],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         2.37742360e-19, 2.25156321e-19, 2.14469608e-19],\n",
       "        ...,\n",
       "        [1.84276428e-19, 1.88148485e-19, 1.93622283e-19, ...,\n",
       "         2.21651016e-19, 2.25276157e-19, 2.33041702e-19],\n",
       "        [1.71677252e-19, 1.82935592e-19, 1.89718368e-19, ...,\n",
       "         2.15454030e-19, 2.17638946e-19, 2.26389769e-19],\n",
       "        [1.53444534e-19, 1.71759047e-19, 1.81048214e-19, ...,\n",
       "         2.12151459e-19, 2.13818721e-19, 2.21988682e-19]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "         7.17213320e-02, 6.71061180e-02, 6.32070024e-02],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         7.21923124e-02, 6.77679644e-02, 6.35681575e-02],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "         7.16046316e-02, 6.78128007e-02, 6.45931143e-02],\n",
       "        ...,\n",
       "        [5.55014928e-02, 5.66686779e-02, 5.83148463e-02, ...,\n",
       "         6.67573716e-02, 6.78495358e-02, 7.01871727e-02],\n",
       "        [5.17043379e-02, 5.50964540e-02, 5.71390513e-02, ...,\n",
       "         6.48908088e-02, 6.55464327e-02, 6.81852020e-02],\n",
       "        [4.62133898e-02, 5.17306913e-02, 5.45284927e-02, ...,\n",
       "         6.38937296e-02, 6.43998934e-02, 6.68596081e-02]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>polyfit_residuals</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 187.9 184.1 172.3</div><input id='attrs-72e73e45-231d-4ce6-8801-1a7b5f5656c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-72e73e45-231d-4ce6-8801-1a7b5f5656c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8ca96b6-fdc9-4490-aac1-67e3e121e342' class='xr-var-data-in' type='checkbox'><label for='data-c8ca96b6-fdc9-4490-aac1-67e3e121e342' 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,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "         601.35661165,  618.61596625,  578.88572303,  522.45569831,\n",
       "         512.13639773,  509.43714726,  480.11031321,  430.32604587,\n",
       "         390.55289003,  380.27991521,  400.26757553,  428.39553423,\n",
       "         455.41078392,  464.19580643,  448.87185813,  413.96237426,\n",
       "         368.8118881 ,  326.5459529 ],\n",
       "       [          nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,  510.83122412,\n",
       "         488.91305716,  449.74507889,  414.33978114,  415.32286315,\n",
       "         433.03533498,  420.88626477,  382.06021276,  346.52208784,\n",
       "         331.44153079,  340.21874413,  362.97122013,  387.86469655,\n",
       "         404.09274567,  397.72108501,  372.16515519,  332.43410751,\n",
       "         291.38238764,  258.66275135],\n",
       "       [          nan,           nan,           nan,           nan,\n",
       "         842.32487107,  817.10276513,  761.45063242,  783.43562156,\n",
       "         917.97188576, 1016.58755088,           nan,  470.43405772,\n",
       "         408.24462814,  363.70802581,  353.60661047,  364.01511899,\n",
       "...\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,  165.41915048,  169.24639594,  173.4672009 ,\n",
       "         179.33075697,  174.4145538 ],\n",
       "       [ 616.08605462,  574.91282801,  546.93938267,  539.72298644,\n",
       "         549.53690196,  573.23083732,  590.04169659,  572.08802227,\n",
       "         515.86368249,  462.34753345,  458.76829504,  484.9131099 ,\n",
       "         503.20305691,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,  171.6657428 ,  172.18997282,\n",
       "         165.69986009,  156.53192258],\n",
       "       [1130.97420202, 1059.71919949, 1009.3414185 ,  991.92831878,\n",
       "        1003.92845359, 1038.64907356, 1066.32037858, 1038.34095337,\n",
       "         924.61549467,  772.34985631,  676.0806148 ,  636.72137168,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,           nan,\n",
       "                  nan,           nan,           nan,  187.94429628,\n",
       "         184.138232  ,  172.29849262]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0238aaf6-9786-4f0e-bbe6-9eabed9b806d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0238aaf6-9786-4f0e-bbe6-9eabed9b806d' 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:               (degree: 2, lat: 17, lon: 30)\n",
       "Coordinates:\n",
       "  * degree                (degree) int64 1 0\n",
       "  * lat                   (lat) float64 34.0 32.0 30.0 28.0 ... 8.0 6.0 4.0 2.0\n",
       "  * lon                   (lon) float64 258.0 260.0 262.0 ... 312.0 314.0 316.0\n",
       "Data variables:\n",
       "    time_matrix_rank      int64 2\n",
       "    time_singular_values  (degree) float64 1.124 0.8582\n",
       "    polyfit_coefficients  (degree, lat, lon) float64 nan nan ... 0.0644 0.06686\n",
       "    polyfit_residuals     (lat, lon) float64 nan nan nan ... 187.9 184.1 172.3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "out = carib_anom.sst.polyfit(dim=\"time\", deg=1, full=True)\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fc9822ddf70>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# plot the slope, or trend\n",
    "out.polyfit_coefficients.sel(degree=1).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# try for the globe? \n",
    "\n",
    "globe120 = ds.sel(time=slice('1900','2020' )).coarsen(time=10*12, boundary='trim').mean()\n",
    "\n",
    "out = globe120.sst.polyfit(dim=\"time\", deg=1, full=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(275.0, 345.0)"
      ]
     },
     "execution_count": 32,
     "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": [
    "out.polyfit_coefficients.sel(degree=1).plot( )\n",
    "plt.ylim([40,60])\n",
    "plt.xlim([275,345])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fed641596a0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.axes(projection = ccrs.Robinson())\n",
    "out.polyfit_coefficients.sel(degree=1).plot( transform = ccrs.PlateCarree(), cbar_kwargs={'shrink':0.4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fed63a465b0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nstodecade = (1e9)*60*60*24*365*10\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.axes(projection = ccrs.Robinson())\n",
    "\n",
    "plt.pcolormesh(out.lon, out.lat, nstodecade*out.polyfit_coefficients.sel(degree=1),\n",
    "              transform = ccrs.PlateCarree(), cmap='RdBu_r', vmin=-.2, vmax=.2)\n",
    "\n",
    "plt.colorbar(shrink=0.4, label= '$^\\circ$C / decade')"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}