21. Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen

Attribution: This notebook is a revision of the Xarray Interpolation, Groupby, Resample, Rolling, and Coarsen notebook by Ryan Abernathey from An Introduction to Earth and Environmental Data Science. Thanks to Aiyin Zhang for preparing this notebook.

In this lesson, we cover some more advanced aspects of xarray.

You can access this notebook (in a Docker image) on this GitHub repo.

import numpy as np
import xarray as xr
from matplotlib import pyplot as plt

21.1. Interpolation

In the previous lesson on xarray, we learned how to select data based on its dimension coordinates and align data with dimension different coordinates. But what if we want to estimate the value of the data variables at different coordinates. This is where interpolation comes in.

# we write it out explicitly so we can see each point.
x_data = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
f = xr.DataArray(x_data**2, dims=['x'], coords={'x': x_data})
f

<xarray.DataArray (x: 11)> Size: 88B
array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])
Coordinates:
  * x        (x) int64 88B 0 1 2 3 4 5 6 7 8 9 10

xarray.DataArray

• x: 11

• 0 1 4 9 16 25 36 49 64 81 100

  array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100])

• Coordinates: (1)
  • x
    (x)
    int64
    0 1 2 3 4 5 6 7 8 9 10

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

• Indexes: (1)
  • x
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype='int64', name='x'))

• Attributes: (0)

f.plot(marker='o')

[<matplotlib.lines.Line2D at 0xffff5c6420d0>]

f.sel(x=3)

<xarray.DataArray ()> Size: 8B
array(9)
Coordinates:
    x        int64 8B 3

xarray.DataArray

• 9

  array(9)

• Coordinates: (1)
  • x
    ()
    int64
    3

    array(3)

• Indexes: (0)
• Attributes: (0)

We only have data on the integer points in x. But what if we wanted to estimate the value at, say, 4.5?

f.sel(x=4.5)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/pandas/core/indexes/base.py:3805, in Index.get_loc(self, key)
   3804 try:
-> 3805     return self._engine.get_loc(casted_key)
   3806 except KeyError as err:

File index.pyx:167, in pandas._libs.index.IndexEngine.get_loc()

File index.pyx:175, in pandas._libs.index.IndexEngine.get_loc()

File pandas/_libs/index_class_helper.pxi:70, in pandas._libs.index.Int64Engine._check_type()

KeyError: 4.5

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/indexes.py:789, in PandasIndex.sel(self, labels, method, tolerance)
    788 try:
--> 789     indexer = self.index.get_loc(label_value)
    790 except KeyError as e:

File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/pandas/core/indexes/base.py:3812, in Index.get_loc(self, key)
   3811         raise InvalidIndexError(key)
-> 3812     raise KeyError(key) from err
   3813 except TypeError:
   3814     # If we have a listlike key, _check_indexing_error will raise
   3815     #  InvalidIndexError. Otherwise we fall through and re-raise
   3816     #  the TypeError.

KeyError: 4.5

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
Cell In[5], line 1
----> 1 f.sel(x=4.5)

File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/dataarray.py:1675, in DataArray.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   1559 def sel(
   1560     self,
   1561     indexers: Mapping[Any, Any] | None = None,
   (...)
   1565     **indexers_kwargs: Any,
   1566 ) -> Self:
   1567     """Return a new DataArray whose data is given by selecting index
   1568     labels along the specified dimension(s).
   1569 
   (...)
   1673     Dimensions without coordinates: points
   1674     """
-> 1675     ds = self._to_temp_dataset().sel(
   1676         indexers=indexers,
   1677         drop=drop,
   1678         method=method,
   1679         tolerance=tolerance,
   1680         **indexers_kwargs,
   1681     )
   1682     return self._from_temp_dataset(ds)

File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/dataset.py:3223, in Dataset.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
   3155 """Returns a new dataset with each array indexed by tick labels
   3156 along the specified dimension(s).
   3157 
   (...)
   3220 
   3221 """
   3222 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel")
-> 3223 query_results = map_index_queries(
   3224     self, indexers=indexers, method=method, tolerance=tolerance
   3225 )
   3227 if drop:
   3228     no_scalar_variables = {}

File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/indexing.py:194, in map_index_queries(obj, indexers, method, tolerance, **indexers_kwargs)
    192         results.append(IndexSelResult(labels))
    193     else:
--> 194         results.append(index.sel(labels, **options))
    196 merged = merge_sel_results(results)
    198 # drop dimension coordinates found in dimension indexers
    199 # (also drop multi-index if any)
    200 # (.sel() already ensures alignment)

File /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/indexes.py:791, in PandasIndex.sel(self, labels, method, tolerance)
    789                 indexer = self.index.get_loc(label_value)
    790             except KeyError as e:
--> 791                 raise KeyError(
    792                     f"not all values found in index {coord_name!r}. "
    793                     "Try setting the `method` keyword argument (example: method='nearest')."
    794                 ) from e
    796 elif label_array.dtype.kind == "b":
    797     indexer = label_array

KeyError: "not all values found in index 'x'. Try setting the `method` keyword argument (example: method='nearest')."

Interpolation to the rescue!

f.interp(x=4.5)

<xarray.DataArray ()> Size: 8B
array(20.5)
Coordinates:
    x        float64 8B 4.5

xarray.DataArray

• 20.5

  array(20.5)

• Coordinates: (1)
  • x
    ()
    float64
    4.5

    array(4.5)

• Indexes: (0)
• Attributes: (0)

Interpolation uses scipy.interpolate under the hood. There are different modes of interpolation.

f.interp(x=4.5, method='linear').values

array(20.5)

f.interp(x=4.5, method='nearest').values

array(16.)

f.interp(x=4.5, method='cubic').values

array(20.25)

We can interpolate to a whole new set of coordinates at once:

x_new = x_data + 0.5
f_interp_linear = f.interp(x=x_new, method='linear')
f_interp_cubic = f.interp(x=x_new, method='cubic')
f.plot(marker='o', label='original')
f_interp_linear.plot(marker='o', label='linear')
f_interp_cubic.plot(marker='o', label='cubic')
plt.legend()

<matplotlib.legend.Legend at 0xffff545501a0>

Note that values outside of the original range are not supported:

f_interp_cubic.values

array([ 0.25,  2.25,  6.25, 12.25, 20.25, 30.25, 42.25, 56.25, 72.25,
       90.25,   nan])

Note

You can apply interpolation to any dimension, and even to multiple dimensions at a time. (Multidimensional interpolation only supports mode='nearest' and mode='linear'.) But keep in mind that xarray has no built-in understanding of geography. If you use interp on lat / lon coordinates, it will just perform naive interpolation of the lat / lon values. More sophisticated treatment of spherical geometry requires another package such as xesmf.

21.2. Loading Data from NetCDF Files

NetCDF (Network Common Data Format) is the most widely used format for distributing geoscience data. NetCDF is maintained by the Unidata organization.

Below we quote from the NetCDF website:

NetCDF (network Common Data Form) is a set of interfaces for array-oriented data access and a freely distributed collection of data access libraries for C, Fortran, C++, Java, and other languages. The netCDF libraries support a machine-independent format for representing scientific data. Together, the interfaces, libraries, and format support the creation, access, and sharing of scientific data.

NetCDF data is:

• Self-Describing. A netCDF file includes information about the data it contains.

• Portable. A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers.

• Scalable. A small subset of a large dataset may be accessed efficiently.

• Appendable. Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure.

• Sharable. One writer and multiple readers may simultaneously access the same netCDF file.

• Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software.

xarray is designed to make reading netCDF files in python as easy, powerful, and flexible as possible. (See xarray netCDF docs for more details.)

Below we download and load some the NASA GISSTemp global temperature anomaly dataset. The original file is located at https://data.giss.nasa.gov/pub/gistemp/gistemp1200_GHCNv4_ERSSTv5.nc.gz, but their server is non-responsive at the time of publication of this notebook. So we will use the mirror server from Zenodo.

import pooch

gistemp_file = pooch.retrieve(
    'https://zenodo.org/records/13963679/files/gistemp1200_GHCNv4_ERSSTv5.nc',
    known_hash=None
)

Downloading data from 'https://zenodo.org/records/13963679/files/gistemp1200_GHCNv4_ERSSTv5.nc' to file '/home/gisuser/.cache/pooch/0e25c9375a619f2b3a461407656b0edc-gistemp1200_GHCNv4_ERSSTv5.nc'.
SHA256 hash of downloaded file: f24422f16fcf86e194308603c989c5a9727d506a6f06125c9abf54b901b004e7
Use this value as the 'known_hash' argument of 'pooch.retrieve' to ensure that the file hasn't changed if it is downloaded again in the future.

ds = xr.open_dataset(gistemp_file)
ds

<xarray.Dataset> Size: 113MB
Dimensions:      (lat: 90, lon: 180, time: 1737, nv: 2)
Coordinates:
  * lat          (lat) float32 360B -89.0 -87.0 -85.0 -83.0 ... 85.0 87.0 89.0
  * lon          (lon) float32 720B -179.0 -177.0 -175.0 ... 175.0 177.0 179.0
  * time         (time) datetime64[ns] 14kB 1880-01-15 1880-02-15 ... 2024-09-15
Dimensions without coordinates: nv
Data variables:
    time_bnds    (time, nv) datetime64[ns] 28kB ...
    tempanomaly  (time, lat, lon) float32 113MB ...
Attributes:
    title:        GISTEMP Surface Temperature Analysis
    institution:  NASA Goddard Institute for Space Studies
    source:       http://data.giss.nasa.gov/gistemp/
    Conventions:  CF-1.6
    history:      Created 2024-10-18 17:43:48 by SBBX_to_nc 2.0 - ILAND=1200,...

xarray.Dataset

• Dimensions:
  • lat: 90
  • lon: 180
  • time: 1737
  • nv: 2
• Coordinates: (3)
  • lat
    (lat)
    float32
    -89.0 -87.0 -85.0 ... 87.0 89.0

    standard_name :

    latitude

    long_name :

    Latitude

    units :

    degrees_north

    array([-89., -87., -85., -83., -81., -79., -77., -75., -73., -71., -69., -67.,
           -65., -63., -61., -59., -57., -55., -53., -51., -49., -47., -45., -43.,
           -41., -39., -37., -35., -33., -31., -29., -27., -25., -23., -21., -19.,
           -17., -15., -13., -11.,  -9.,  -7.,  -5.,  -3.,  -1.,   1.,   3.,   5.,
             7.,   9.,  11.,  13.,  15.,  17.,  19.,  21.,  23.,  25.,  27.,  29.,
            31.,  33.,  35.,  37.,  39.,  41.,  43.,  45.,  47.,  49.,  51.,  53.,
            55.,  57.,  59.,  61.,  63.,  65.,  67.,  69.,  71.,  73.,  75.,  77.,
            79.,  81.,  83.,  85.,  87.,  89.], dtype=float32)

  • lon
    (lon)
    float32
    -179.0 -177.0 ... 177.0 179.0

    standard_name :

    longitude

    long_name :

    Longitude

    units :

    degrees_east

    array([-179., -177., -175., -173., -171., -169., -167., -165., -163., -161.,
           -159., -157., -155., -153., -151., -149., -147., -145., -143., -141.,
           -139., -137., -135., -133., -131., -129., -127., -125., -123., -121.,
           -119., -117., -115., -113., -111., -109., -107., -105., -103., -101.,
            -99.,  -97.,  -95.,  -93.,  -91.,  -89.,  -87.,  -85.,  -83.,  -81.,
            -79.,  -77.,  -75.,  -73.,  -71.,  -69.,  -67.,  -65.,  -63.,  -61.,
            -59.,  -57.,  -55.,  -53.,  -51.,  -49.,  -47.,  -45.,  -43.,  -41.,
            -39.,  -37.,  -35.,  -33.,  -31.,  -29.,  -27.,  -25.,  -23.,  -21.,
            -19.,  -17.,  -15.,  -13.,  -11.,   -9.,   -7.,   -5.,   -3.,   -1.,
              1.,    3.,    5.,    7.,    9.,   11.,   13.,   15.,   17.,   19.,
             21.,   23.,   25.,   27.,   29.,   31.,   33.,   35.,   37.,   39.,
             41.,   43.,   45.,   47.,   49.,   51.,   53.,   55.,   57.,   59.,
             61.,   63.,   65.,   67.,   69.,   71.,   73.,   75.,   77.,   79.,
             81.,   83.,   85.,   87.,   89.,   91.,   93.,   95.,   97.,   99.,
            101.,  103.,  105.,  107.,  109.,  111.,  113.,  115.,  117.,  119.,
            121.,  123.,  125.,  127.,  129.,  131.,  133.,  135.,  137.,  139.,
            141.,  143.,  145.,  147.,  149.,  151.,  153.,  155.,  157.,  159.,
            161.,  163.,  165.,  167.,  169.,  171.,  173.,  175.,  177.,  179.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1880-01-15 ... 2024-09-15

    long_name :

    time

    bounds :

    time_bnds

    array(['1880-01-15T00:00:00.000000000', '1880-02-15T00:00:00.000000000',
           '1880-03-15T00:00:00.000000000', ..., '2024-07-15T00:00:00.000000000',
           '2024-08-15T00:00:00.000000000', '2024-09-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (2)
  • time_bnds
    (time, nv)
    datetime64[ns]
    ...

    [3474 values with dtype=datetime64[ns]]

  • tempanomaly
    (time, lat, lon)
    float32
    ...

    long_name :

    Surface temperature anomaly

    units :

    K

    cell_methods :

    time: mean

    [28139400 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.0, -87.0, -85.0, -83.0, -81.0, -79.0, -77.0, -75.0, -73.0, -71.0,
           -69.0, -67.0, -65.0, -63.0, -61.0, -59.0, -57.0, -55.0, -53.0, -51.0,
           -49.0, -47.0, -45.0, -43.0, -41.0, -39.0, -37.0, -35.0, -33.0, -31.0,
           -29.0, -27.0, -25.0, -23.0, -21.0, -19.0, -17.0, -15.0, -13.0, -11.0,
            -9.0,  -7.0,  -5.0,  -3.0,  -1.0,   1.0,   3.0,   5.0,   7.0,   9.0,
            11.0,  13.0,  15.0,  17.0,  19.0,  21.0,  23.0,  25.0,  27.0,  29.0,
            31.0,  33.0,  35.0,  37.0,  39.0,  41.0,  43.0,  45.0,  47.0,  49.0,
            51.0,  53.0,  55.0,  57.0,  59.0,  61.0,  63.0,  65.0,  67.0,  69.0,
            71.0,  73.0,  75.0,  77.0,  79.0,  81.0,  83.0,  85.0,  87.0,  89.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([-179.0, -177.0, -175.0, -173.0, -171.0, -169.0, -167.0, -165.0, -163.0,
           -161.0,
           ...
            161.0,  163.0,  165.0,  167.0,  169.0,  171.0,  173.0,  175.0,  177.0,
            179.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1880-01-15', '1880-02-15', '1880-03-15', '1880-04-15',
                   '1880-05-15', '1880-06-15', '1880-07-15', '1880-08-15',
                   '1880-09-15', '1880-10-15',
                   ...
                   '2023-12-15', '2024-01-15', '2024-02-15', '2024-03-15',
                   '2024-04-15', '2024-05-15', '2024-06-15', '2024-07-15',
                   '2024-08-15', '2024-09-15'],
                  dtype='datetime64[ns]', name='time', length=1737, freq=None))

• Attributes: (5)

  title :

  GISTEMP Surface Temperature Analysis

  institution :

  NASA Goddard Institute for Space Studies

  source :

  http://data.giss.nasa.gov/gistemp/

  Conventions :

  CF-1.6

  history :

  Created 2024-10-18 17:43:48 by SBBX_to_nc 2.0 - ILAND=1200, IOCEAN=NCDC/ER5, Base: 1951-1980

ds.tempanomaly.isel(time=-1).plot()
plt.show()

ds.tempanomaly.mean(dim=('lon', 'lat')).plot()
plt.show()

Let’s visualize the temperature anomally for the grid near Worcester. (Worcester’s coordinates: latitude: 42.2626; longtitude: -71.8023)

ds.tempanomaly.sel(lat=42.2626, lon=-71.8023, method='nearest').plot()
plt.show()

Note that you need to define method since the corresponding values for Worcester latitude and longitude do not exist in the DataArray’s coordinates.

21.3. Groupby

xarray copies pandas' very useful groupby functionality, enabling the “split / apply / combine” workflow on xarray DataArrays and Datasets. In the first part of the lesson, we will learn to use groupby by analyzing sea-surface temperature data.

First we load a dataset. We will use the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) v5 product, a widely used and trusted gridded compilation of historical data going back to 1854.

Since the data is provided via an OPeNDAP server, we can load it directly without downloading anything:

url = 'http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/noaa.ersst.v5/sst.mnmean.nc'
ds = xr.open_dataset(url, drop_variables=['time_bnds'])

ds = ds.sel(time=slice('1960', '2022'))
ds

<xarray.Dataset> Size: 48MB
Dimensions:  (lat: 89, lon: 180, time: 756)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
Data variables:
    sst      (time, lat, lon) float32 48MB ...
Attributes: (12/39)
    climatology:                     Climatology is based on 1971-2000 SST, X...
    description:                     In situ data: ICOADS2.5 before 2007 and ...
    keywords_vocabulary:             NASA Global Change Master Directory (GCM...
    keywords:                        Earth Science > Oceans > Ocean Temperatu...
    instrument:                      Conventional thermometers
    source_comment:                  SSTs were observed by conventional therm...
    ...                              ...
    comment:                         SSTs were observed by conventional therm...
    summary:                         ERSST.v5 is developed based on v4 after ...
    dataset_title:                   NOAA Extended Reconstructed SST V5
    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5
    data_modified:                   2024-11-03
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • lat: 89
  • lon: 180
  • time: 756
• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    ...

    long_name :

    Monthly Means of Sea Surface Temperature

    units :

    degC

    var_desc :

    Sea Surface Temperature

    level_desc :

    Surface

    statistic :

    Mean

    dataset :

    NOAA Extended Reconstructed SST V5

    parent_stat :

    Individual Values

    actual_range :

    [-1.8 42.32636]

    valid_range :

    [-1.8 45. ]

    _ChunkSizes :

    [ 1 89 180]

    [12111120 values with dtype=float32]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (39)

  climatology :

  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.

  description :

  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.

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  keywords :

  Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature >

  instrument :

  Conventional thermometers

  source_comment :

  SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker

  geospatial_lon_min :

  -1.0

  geospatial_lon_max :

  359.0

  geospatial_laty_max :

  89.0

  geospatial_laty_min :

  -89.0

  geospatial_lat_max :

  89.0

  geospatial_lat_min :

  -89.0

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_east

  cdm_data_type :

  Grid

  project :

  NOAA Extended Reconstructed Sea Surface Temperature (ERSST)

  original_publisher_url :

  http://www.ncdc.noaa.gov

  References :

  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

  source :

  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

  title :

  NOAA ERSSTv5 (in situ only)

  history :

  created 07/2017 by PSD data using NCEI's ERSST V5 NetCDF values

  institution :

  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

  citation :

  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

  platform :

  Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS

  standard_name_vocabulary :

  CF Standard Name Table (v40, 25 January 2017)

  processing_level :

  NOAA Level 4

  Conventions :

  CF-1.6, ACDD-1.3

  metadata_link :

  :metadata_link = https://doi.org/10.7289/V5T72FNM (original format)

  creator_name :

  Boyin Huang (original)

  date_created :

  2017-06-30T12:18:00Z (original)

  product_version :

  Version 5

  creator_url_original :

  https://www.ncei.noaa.gov

  license :

  No constraints on data access or use

  comment :

  SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters

  summary :

  ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis

  dataset_title :

  NOAA Extended Reconstructed SST V5

  _NCProperties :

  version=2,netcdf=4.6.3,hdf5=1.10.5

  data_modified :

  2024-11-03

  DODS_EXTRA.Unlimited_Dimension :

  time

Let’s do some basic visualizations of the data, just to make sure it looks reasonable.

ds.sst.sel(time = '1960-01-01').plot(vmin=-2, vmax=30)
plt.show()

Note that xarray correctly parsed the time index, resulting in a Pandas datetime index on the time dimension.

ds.time

<xarray.DataArray 'time' (time: 756)> Size: 6kB
array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
       '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
       '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
      dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
Attributes:
    long_name:        Time
    delta_t:          0000-01-00 00:00:00
    avg_period:       0000-01-00 00:00:00
    prev_avg_period:  0000-00-07 00:00:00
    standard_name:    time
    axis:             T
    actual_range:     [19723. 82088.]
    _ChunkSizes:      1

xarray.DataArray

'time'

• time: 756

• 1960-01-01 1960-02-01 1960-03-01 ... 2022-10-01 2022-11-01 2022-12-01

  array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
         '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
         '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
        dtype='datetime64[ns]')

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (8)

  long_name :

  Time

  delta_t :

  0000-01-00 00:00:00

  avg_period :

  0000-01-00 00:00:00

  prev_avg_period :

  0000-00-07 00:00:00

  standard_name :

  time

  axis :

  T

  actual_range :

  [19723. 82088.]

  _ChunkSizes :

  1

ds.sst.sel(lon=300, lat=50).plot()
plt.show()

As we can see from the plot, the timeseries at any one point is totally dominated by the seasonal cycle. We would like to remove this seasonal cycle (called the “climatology”) in order to better see the long-term variaitions in temperature. We will accomplish this using groupby.

The syntax of xarray’s groupby is almost identical to pandas. We will first apply groupby to a single DataArray.

21.3.1. Split Step

The most important argument is group: this defines the unique values we will use to “split” the data for grouped analysis. We can pass either a DataArray or a name of a variable in the dataset. Lets first use a DataArray. Just like with pandas, we can use the time index to extract specific components of dates and times. xarray uses a special syntax for this .dt, called the DatetimeAccessor.

See a list of datatime properties you can access through .dt here

ds.time.dt

<xarray.core.accessor_dt.DatetimeAccessor at 0xffff46029bd0>

ds.time.dt.month

<xarray.DataArray 'month' (time: 756)> Size: 6kB
array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
...
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
        5,  6,  7,  8,  9, 10, 11, 12])
Coordinates:
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
Attributes:
    long_name:        Time
    delta_t:          0000-01-00 00:00:00
    avg_period:       0000-01-00 00:00:00
    prev_avg_period:  0000-00-07 00:00:00
    standard_name:    time
    axis:             T
    actual_range:     [19723. 82088.]
    _ChunkSizes:      1

xarray.DataArray

'month'

• time: 756

• 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 ... 11 12 1 2 3 4 5 6 7 8 9 10 11 12

  array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
          6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
         11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
          4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
          9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
          2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
          7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
         12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
          5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
         10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
          3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
          8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
          1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
          6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
         11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
          4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
          9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
          2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
          7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
         12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
  ...
          6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
         11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
          4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
          9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
          2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
          7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
         12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
          5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,
         10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,
          3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,
          8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,
          1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,
          6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,
         11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,
          4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,
          9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,
          2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,
          7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,
         12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,
          5,  6,  7,  8,  9, 10, 11, 12])

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (8)

  long_name :

  Time

  delta_t :

  0000-01-00 00:00:00

  avg_period :

  0000-01-00 00:00:00

  prev_avg_period :

  0000-00-07 00:00:00

  standard_name :

  time

  axis :

  T

  actual_range :

  [19723. 82088.]

  _ChunkSizes :

  1

ds.time.dt.year

<xarray.DataArray 'year' (time: 756)> Size: 6kB
array([1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960,
       1960, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961,
       1961, 1961, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962,
       1962, 1962, 1962, 1963, 1963, 1963, 1963, 1963, 1963, 1963, 1963,
       1963, 1963, 1963, 1963, 1964, 1964, 1964, 1964, 1964, 1964, 1964,
       1964, 1964, 1964, 1964, 1964, 1965, 1965, 1965, 1965, 1965, 1965,
       1965, 1965, 1965, 1965, 1965, 1965, 1966, 1966, 1966, 1966, 1966,
       1966, 1966, 1966, 1966, 1966, 1966, 1966, 1967, 1967, 1967, 1967,
       1967, 1967, 1967, 1967, 1967, 1967, 1967, 1967, 1968, 1968, 1968,
       1968, 1968, 1968, 1968, 1968, 1968, 1968, 1968, 1968, 1969, 1969,
       1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1970,
       1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970,
       1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971,
       1971, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
       1972, 1972, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973,
       1973, 1973, 1973, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974,
       1974, 1974, 1974, 1974, 1975, 1975, 1975, 1975, 1975, 1975, 1975,
       1975, 1975, 1975, 1975, 1975, 1976, 1976, 1976, 1976, 1976, 1976,
       1976, 1976, 1976, 1976, 1976, 1976, 1977, 1977, 1977, 1977, 1977,
       1977, 1977, 1977, 1977, 1977, 1977, 1977, 1978, 1978, 1978, 1978,
...
       2004, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005,
       2005, 2005, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006,
       2006, 2006, 2006, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007,
       2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
       2008, 2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009,
       2009, 2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010,
       2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 2011,
       2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012,
       2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013,
       2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014,
       2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,
       2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,
       2015, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
       2016, 2016, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
       2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
       2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
       2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020,
       2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021,
       2021, 2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022, 2022,
       2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022])
Coordinates:
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
Attributes:
    long_name:        Time
    delta_t:          0000-01-00 00:00:00
    avg_period:       0000-01-00 00:00:00
    prev_avg_period:  0000-00-07 00:00:00
    standard_name:    time
    axis:             T
    actual_range:     [19723. 82088.]
    _ChunkSizes:      1

xarray.DataArray

'year'

• time: 756

• 1960 1960 1960 1960 1960 1960 1960 ... 2022 2022 2022 2022 2022 2022

  array([1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960, 1960,
         1960, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961, 1961,
         1961, 1961, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962, 1962,
         1962, 1962, 1962, 1963, 1963, 1963, 1963, 1963, 1963, 1963, 1963,
         1963, 1963, 1963, 1963, 1964, 1964, 1964, 1964, 1964, 1964, 1964,
         1964, 1964, 1964, 1964, 1964, 1965, 1965, 1965, 1965, 1965, 1965,
         1965, 1965, 1965, 1965, 1965, 1965, 1966, 1966, 1966, 1966, 1966,
         1966, 1966, 1966, 1966, 1966, 1966, 1966, 1967, 1967, 1967, 1967,
         1967, 1967, 1967, 1967, 1967, 1967, 1967, 1967, 1968, 1968, 1968,
         1968, 1968, 1968, 1968, 1968, 1968, 1968, 1968, 1968, 1969, 1969,
         1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1969, 1970,
         1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970, 1970,
         1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971, 1971,
         1971, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972, 1972,
         1972, 1972, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973, 1973,
         1973, 1973, 1973, 1974, 1974, 1974, 1974, 1974, 1974, 1974, 1974,
         1974, 1974, 1974, 1974, 1975, 1975, 1975, 1975, 1975, 1975, 1975,
         1975, 1975, 1975, 1975, 1975, 1976, 1976, 1976, 1976, 1976, 1976,
         1976, 1976, 1976, 1976, 1976, 1976, 1977, 1977, 1977, 1977, 1977,
         1977, 1977, 1977, 1977, 1977, 1977, 1977, 1978, 1978, 1978, 1978,
  ...
         2004, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005, 2005,
         2005, 2005, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006, 2006,
         2006, 2006, 2006, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007,
         2007, 2007, 2007, 2007, 2008, 2008, 2008, 2008, 2008, 2008, 2008,
         2008, 2008, 2008, 2008, 2008, 2009, 2009, 2009, 2009, 2009, 2009,
         2009, 2009, 2009, 2009, 2009, 2009, 2010, 2010, 2010, 2010, 2010,
         2010, 2010, 2010, 2010, 2010, 2010, 2010, 2011, 2011, 2011, 2011,
         2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2012, 2012, 2012,
         2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2012, 2013, 2013,
         2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2014,
         2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014,
         2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,
         2015, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016,
         2016, 2016, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,
         2017, 2017, 2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,
         2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019, 2019, 2019, 2019,
         2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020, 2020, 2020, 2020,
         2020, 2020, 2020, 2020, 2020, 2020, 2021, 2021, 2021, 2021, 2021,
         2021, 2021, 2021, 2021, 2021, 2021, 2021, 2022, 2022, 2022, 2022,
         2022, 2022, 2022, 2022, 2022, 2022, 2022, 2022])

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (8)

  long_name :

  Time

  delta_t :

  0000-01-00 00:00:00

  avg_period :

  0000-01-00 00:00:00

  prev_avg_period :

  0000-00-07 00:00:00

  standard_name :

  time

  axis :

  T

  actual_range :

  [19723. 82088.]

  _ChunkSizes :

  1

We can use these arrays in a groupby operation:

gb = ds.sst.groupby(ds.time.dt.month)
gb

<DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
    'month': 12/12 groups present with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>

xarray also offers a more concise syntax when the variable you’re grouping on is already present in the dataset. This is identical to the previous line:

gb = ds.sst.groupby('time.month')
gb

<DataArrayGroupBy, grouped over 1 grouper(s), 12 groups in total:
    'month': 12/12 groups present with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>

Now that the data are split, we can manually iterate over the group. The iterator returns the key (group name) and the value (the actual dataset corresponding to that group) for each group.

for group_name, group_da in gb:
    print(group_name) # stop after first iteration
    break
group_da

1

<xarray.DataArray 'sst' (time: 63, lat: 89, lon: 180)> Size: 4MB
[1009260 values with dtype=float32]
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 504B 1960-01-01 1961-01-01 ... 2022-01-01
Attributes:
    long_name:     Monthly Means of Sea Surface Temperature
    units:         degC
    var_desc:      Sea Surface Temperature
    level_desc:    Surface
    statistic:     Mean
    dataset:       NOAA Extended Reconstructed SST V5
    parent_stat:   Individual Values
    actual_range:  [-1.8     42.32636]
    valid_range:   [-1.8 45. ]
    _ChunkSizes:   [  1  89 180]

xarray.DataArray

'sst'

• time: 63
• lat: 89
• lon: 180

• ...

  [1009260 values with dtype=float32]

• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-01-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1961-01-01T00:00:00.000000000',
           '1962-01-01T00:00:00.000000000', '1963-01-01T00:00:00.000000000',
           '1964-01-01T00:00:00.000000000', '1965-01-01T00:00:00.000000000',
           '1966-01-01T00:00:00.000000000', '1967-01-01T00:00:00.000000000',
           '1968-01-01T00:00:00.000000000', '1969-01-01T00:00:00.000000000',
           '1970-01-01T00:00:00.000000000', '1971-01-01T00:00:00.000000000',
           '1972-01-01T00:00:00.000000000', '1973-01-01T00:00:00.000000000',
           '1974-01-01T00:00:00.000000000', '1975-01-01T00:00:00.000000000',
           '1976-01-01T00:00:00.000000000', '1977-01-01T00:00:00.000000000',
           '1978-01-01T00:00:00.000000000', '1979-01-01T00:00:00.000000000',
           '1980-01-01T00:00:00.000000000', '1981-01-01T00:00:00.000000000',
           '1982-01-01T00:00:00.000000000', '1983-01-01T00:00:00.000000000',
           '1984-01-01T00:00:00.000000000', '1985-01-01T00:00:00.000000000',
           '1986-01-01T00:00:00.000000000', '1987-01-01T00:00:00.000000000',
           '1988-01-01T00:00:00.000000000', '1989-01-01T00:00:00.000000000',
           '1990-01-01T00:00:00.000000000', '1991-01-01T00:00:00.000000000',
           '1992-01-01T00:00:00.000000000', '1993-01-01T00:00:00.000000000',
           '1994-01-01T00:00:00.000000000', '1995-01-01T00:00:00.000000000',
           '1996-01-01T00:00:00.000000000', '1997-01-01T00:00:00.000000000',
           '1998-01-01T00:00:00.000000000', '1999-01-01T00:00:00.000000000',
           '2000-01-01T00:00:00.000000000', '2001-01-01T00:00:00.000000000',
           '2002-01-01T00:00:00.000000000', '2003-01-01T00:00:00.000000000',
           '2004-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',
           '2006-01-01T00:00:00.000000000', '2007-01-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2009-01-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',
           '2012-01-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',
           '2014-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000',
           '2016-01-01T00:00:00.000000000', '2017-01-01T00:00:00.000000000',
           '2018-01-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000', '2021-01-01T00:00:00.000000000',
           '2022-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1961-01-01', '1962-01-01', '1963-01-01',
                   '1964-01-01', '1965-01-01', '1966-01-01', '1967-01-01',
                   '1968-01-01', '1969-01-01', '1970-01-01', '1971-01-01',
                   '1972-01-01', '1973-01-01', '1974-01-01', '1975-01-01',
                   '1976-01-01', '1977-01-01', '1978-01-01', '1979-01-01',
                   '1980-01-01', '1981-01-01', '1982-01-01', '1983-01-01',
                   '1984-01-01', '1985-01-01', '1986-01-01', '1987-01-01',
                   '1988-01-01', '1989-01-01', '1990-01-01', '1991-01-01',
                   '1992-01-01', '1993-01-01', '1994-01-01', '1995-01-01',
                   '1996-01-01', '1997-01-01', '1998-01-01', '1999-01-01',
                   '2000-01-01', '2001-01-01', '2002-01-01', '2003-01-01',
                   '2004-01-01', '2005-01-01', '2006-01-01', '2007-01-01',
                   '2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01',
                   '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01',
                   '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01',
                   '2020-01-01', '2021-01-01', '2022-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (10)

  long_name :

  Monthly Means of Sea Surface Temperature

  units :

  degC

  var_desc :

  Sea Surface Temperature

  level_desc :

  Surface

  statistic :

  Mean

  dataset :

  NOAA Extended Reconstructed SST V5

  parent_stat :

  Individual Values

  actual_range :

  [-1.8 42.32636]

  valid_range :

  [-1.8 45. ]

  _ChunkSizes :

  [ 1 89 180]

21.3.2. Map & Combine

Now that we have groups defined, it’s time to “apply” a calculation to the group. Like in pandas, these calculations can either be:

• aggregation: reduces the size of the group

• transformation: preserves the group’s full size

At then end of the apply step, xarray will automatically combine the aggregated / transformed groups back into a single object.

Warning

Xarray calls the “apply” step map. This is different from Pandas!

gb.map?

Signature:
gb.map(
    func: 'Callable[..., DataArray]',
    args: 'tuple[Any, ...]' = (),
    shortcut: 'bool | None' = None,
    **kwargs: 'Any',
) -> 'DataArray'
Docstring:
Apply a function to each array in the group and concatenate them
together into a new array.

`func` is called like `func(ar, *args, **kwargs)` for each array `ar`
in this group.

Apply uses heuristics (like `pandas.GroupBy.apply`) to figure out how
to stack together the array. The rule is:

1. If the dimension along which the group coordinate is defined is
   still in the first grouped array after applying `func`, then stack
   over this dimension.
2. Otherwise, stack over the new dimension given by name of this
   grouping (the argument to the `groupby` function).

Parameters
----------
func : callable
    Callable to apply to each array.
shortcut : bool, optional
    Whether or not to shortcut evaluation under the assumptions that:

    (1) The action of `func` does not depend on any of the array
        metadata (attributes or coordinates) but only on the data and
        dimensions.
    (2) The action of `func` creates arrays with homogeneous metadata,
        that is, with the same dimensions and attributes.

    If these conditions are satisfied `shortcut` provides significant
    speedup. This should be the case for many common groupby operations
    (e.g., applying numpy ufuncs).
*args : tuple, optional
    Positional arguments passed to `func`.
**kwargs
    Used to call `func(ar, **kwargs)` for each array `ar`.

Returns
-------
applied : DataArray
    The result of splitting, applying and combining this array.
File:      /opt/conda/envs/xarray_tutorial/lib/python3.13/site-packages/xarray/core/groupby.py
Type:      method

21.3.2.1. Aggregations

Like pandas, xarray’s groupby object has many built-in aggregation operations (e.g. mean, min, max, std, etc):

sst_mm = gb.mean(dim='time')
sst_mm

<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)> Size: 769kB
array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        ...,
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],

       [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
...
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],

       [[-1.7995719, -1.7996585, -1.7998686, ..., -1.7998053,
         -1.7996899, -1.7995679],
        [-1.799625 , -1.7997937, -1.800001 , ..., -1.800001 ,
         -1.7998347, -1.7996482],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        ...,
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    long_name:     Monthly Means of Sea Surface Temperature
    units:         degC
    var_desc:      Sea Surface Temperature
    level_desc:    Surface
    statistic:     Mean
    dataset:       NOAA Extended Reconstructed SST V5
    parent_stat:   Individual Values
    actual_range:  [-1.8     42.32636]
    valid_range:   [-1.8 45. ]
    _ChunkSizes:   [  1  89 180]

xarray.DataArray

'sst'

• month: 12
• lat: 89
• lon: 180

• -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan

  array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          ...,
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]],
  
         [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
  ...
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]],
  
         [[-1.7995719, -1.7996585, -1.7998686, ..., -1.7998053,
           -1.7996899, -1.7995679],
          [-1.799625 , -1.7997937, -1.800001 , ..., -1.800001 ,
           -1.7998347, -1.7996482],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          ...,
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]]], dtype=float32)

• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (10)

  long_name :

  Monthly Means of Sea Surface Temperature

  units :

  degC

  var_desc :

  Sea Surface Temperature

  level_desc :

  Surface

  statistic :

  Mean

  dataset :

  NOAA Extended Reconstructed SST V5

  parent_stat :

  Individual Values

  actual_range :

  [-1.8 42.32636]

  valid_range :

  [-1.8 45. ]

  _ChunkSizes :

  [ 1 89 180]

.map accepts as its argument a function. We can pass an existing function:

gb.map(np.mean)

<xarray.DataArray 'sst' (month: 12)> Size: 48B
array([13.679474, 13.787702, 13.784417, 13.704169, 13.662362, 13.736686,
       13.950283, 14.123172, 14.008736, 13.715602, 13.528554, 13.548446],
      dtype=float32)
Coordinates:
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12

xarray.DataArray

'sst'

• month: 12

• 13.68 13.79 13.78 13.7 13.66 13.74 13.95 14.12 14.01 13.72 13.53 13.55

  array([13.679474, 13.787702, 13.784417, 13.704169, 13.662362, 13.736686,
         13.950283, 14.123172, 14.008736, 13.715602, 13.528554, 13.548446],
        dtype=float32)

• Coordinates: (1)
  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (1)
  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (0)

Because we specified no extra arguments (like axis) the function was applied over all space and time dimensions. This is not what we wanted. Instead, we could define a custom function. This function takes a single argument –the group dataset– and returns a new dataset to be combined:

def time_mean(a):
    return a.mean(dim='time')

sst_mm = gb.map(time_mean)
sst_mm

<xarray.DataArray 'sst' (month: 12, lat: 89, lon: 180)> Size: 769kB
array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        ...,
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],

       [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
...
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]],

       [[-1.7995719, -1.7996585, -1.7998686, ..., -1.7998053,
         -1.7996899, -1.7995679],
        [-1.799625 , -1.7997937, -1.800001 , ..., -1.800001 ,
         -1.7998347, -1.7996482],
        [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
         -1.800001 , -1.800001 ],
        ...,
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12

xarray.DataArray

'sst'

• month: 12
• lat: 89
• lon: 180

• -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan

  array([[[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          ...,
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]],
  
         [[-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
  ...
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]],
  
         [[-1.7995719, -1.7996585, -1.7998686, ..., -1.7998053,
           -1.7996899, -1.7995679],
          [-1.799625 , -1.7997937, -1.800001 , ..., -1.800001 ,
           -1.7998347, -1.7996482],
          [-1.800001 , -1.800001 , -1.800001 , ..., -1.800001 ,
           -1.800001 , -1.800001 ],
          ...,
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan],
          [       nan,        nan,        nan, ...,        nan,
                  nan,        nan]]], dtype=float32)

• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • month
    (month)
    int64
    1 2 3 4 5 6 7 8 9 10 11 12

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • month
    PandasIndex

    PandasIndex(Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))

• Attributes: (0)

So we did what we wanted to do: calculate the climatology at every point in the dataset. Let’s look at the data a bit.

Climatlogy at a specific point in the North Atlantic

sst_mm.sel(lon=300, lat=-50).plot()
plt.show()

Zonal Mean Climatology

sst_mm.mean(dim='lon').transpose().plot.contourf(levels=12, vmin=-2, vmax=30)
plt.show()

Difference between January and July Climatology

(sst_mm.sel(month=1) - sst_mm.sel(month=7)).plot(vmax=10)
plt.show()

21.3.2.2. Transformations

Now we want to remove this climatology from the dataset, to examine the residual, called the anomaly, which is the interesting part from a climate perspective. Removing the seasonal climatology is a perfect example of a transformation: it operates over a group, but doesn’t change the size of the dataset. Here is one way to code it.

def remove_time_mean(x):
    return x - x.mean(dim='time')

ds_anom = ds.groupby('time.month').map(remove_time_mean)
ds_anom

<xarray.Dataset> Size: 48MB
Dimensions:  (time: 756, lat: 89, lon: 180)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
Data variables:
    sst      (time, lat, lon) float32 48MB 1.073e-06 1.073e-06 ... nan nan

xarray.Dataset

• Dimensions:
  • time: 756
  • lat: 89
  • lon: 180
• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    1.073e-06 1.073e-06 ... nan nan

    array([[[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
    ...
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[-4.2808056e-04, -3.4141541e-04, -1.3136864e-04, ...,
             -1.9466877e-04, -3.1006336e-04, -4.3201447e-04],
            [-3.7491322e-04, -2.0623207e-04,  1.0728836e-06, ...,
              1.0728836e-06, -1.6522408e-04, -3.5178661e-04],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (0)

Note

In the above example, we applied groupby to a Dataset instead of a DataArray.

Xarray makes these sorts of transformations easy by supporting groupby arithmetic. This concept is easiest explained with an example:

gb = ds.groupby('time.month')
ds_anom = gb - gb.mean(dim='time')
ds_anom

<xarray.Dataset> Size: 48MB
Dimensions:  (lat: 89, lon: 180, time: 756)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
    month    (time) int64 6kB 1 2 3 4 5 6 7 8 9 10 11 ... 3 4 5 6 7 8 9 10 11 12
Data variables:
    sst      (time, lat, lon) float32 48MB 1.073e-06 1.073e-06 ... nan nan

xarray.Dataset

• Dimensions:
  • lat: 89
  • lon: 180
  • time: 756
• Coordinates: (4)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • month
    (time)
    int64
    1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12

    array([ 1,  2,  3, ..., 10, 11, 12])

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    1.073e-06 1.073e-06 ... nan nan

    array([[[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
    ...
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[-4.2808056e-04, -3.4141541e-04, -1.3136864e-04, ...,
             -1.9466877e-04, -3.1006336e-04, -4.3201447e-04],
            [-3.7491322e-04, -2.0623207e-04,  1.0728836e-06, ...,
              1.0728836e-06, -1.6522408e-04, -3.5178661e-04],
            [ 1.0728836e-06,  1.0728836e-06,  1.0728836e-06, ...,
              1.0728836e-06,  1.0728836e-06,  1.0728836e-06],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (0)

Now we can view the climate signal without the overwhelming influence of the seasonal cycle.

Timeseries at a single point in the North Atlantic

ds_anom.sst.sel(lon=300, lat=50).plot()
plt.show()

Difference between Jan. 1 2022 and Jan. 1 1960

(ds_anom.sel(time='2022-01-01') - ds_anom.sel(time='1960-01-01')).sst.plot()
plt.show()

21.4. Grouby-Related: Resample, Rolling, Coarsen

21.4.1. Resample

Resample in xarray is nearly identical to pandas. It can be applied only to time-index dimensions. Here we compute the five-year mean. It is effectively a group-by operation, and uses the same basic syntax. Note that resampling changes the length of the the output array.

See the list of string aliases for time series frequencies

ds_anom_resample = ds_anom.resample(time='5YE').mean(dim='time')
ds_anom_resample

<xarray.Dataset> Size: 898kB
Dimensions:  (time: 14, lat: 89, lon: 180)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 112B 1960-12-31 1965-12-31 ... 2025-12-31
Data variables:
    sst      (time, lat, lon) float32 897kB -0.0004908 -0.0004653 ... nan nan

xarray.Dataset

• Dimensions:
  • time: 14
  • lat: 89
  • lon: 180
• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-12-31 ... 2025-12-31

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-12-31T00:00:00.000000000', '1965-12-31T00:00:00.000000000',
           '1970-12-31T00:00:00.000000000', '1975-12-31T00:00:00.000000000',
           '1980-12-31T00:00:00.000000000', '1985-12-31T00:00:00.000000000',
           '1990-12-31T00:00:00.000000000', '1995-12-31T00:00:00.000000000',
           '2000-12-31T00:00:00.000000000', '2005-12-31T00:00:00.000000000',
           '2010-12-31T00:00:00.000000000', '2015-12-31T00:00:00.000000000',
           '2020-12-31T00:00:00.000000000', '2025-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    -0.0004908 -0.0004653 ... nan nan

    array([[[-4.9077469e-04, -4.6533346e-04, -4.4634938e-04, ...,
             -4.0059289e-04, -4.6346584e-04, -5.0854683e-04],
            [-4.6831369e-04, -5.1384169e-04, -5.4075319e-04, ...,
             -1.3466676e-04, -3.2770634e-04, -3.9959946e-04],
            [-1.2606382e-05, -1.6719103e-04, -2.6898584e-04, ...,
             -1.1458993e-04, -7.1207680e-05, -3.1044085e-05],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[-4.9077469e-04, -4.6533346e-04, -4.4634938e-04, ...,
             -4.0059289e-04, -4.6346584e-04, -5.0854683e-04],
            [-4.6831369e-04, -5.1384169e-04, -5.4075319e-04, ...,
             -1.3466676e-04, -3.2770634e-04, -3.9959946e-04],
            [-1.2606382e-05, -1.6719103e-04, -2.6898584e-04, ...,
             -1.1458993e-04, -7.1207680e-05, -3.1044085e-05],
    ...
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]],
    
           [[-4.9077469e-04, -4.6533346e-04, -4.4634938e-04, ...,
             -4.0059289e-04, -4.6346584e-04, -5.0854683e-04],
            [-4.6831369e-04, -5.1384169e-04, -5.4075319e-04, ...,
             -1.3466676e-04, -3.2770634e-04, -3.9959946e-04],
            [-1.2606382e-05, -1.6719103e-04, -2.6898584e-04, ...,
             -1.1458993e-04, -7.1207680e-05, -3.1044085e-05],
            ...,
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan],
            [           nan,            nan,            nan, ...,
                        nan,            nan,            nan]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-12-31', '1965-12-31', '1970-12-31', '1975-12-31',
                   '1980-12-31', '1985-12-31', '1990-12-31', '1995-12-31',
                   '2000-12-31', '2005-12-31', '2010-12-31', '2015-12-31',
                   '2020-12-31', '2025-12-31'],
                  dtype='datetime64[ns]', name='time', freq='5YE-DEC'))

• Attributes: (0)

ds_anom.sst.sel(lon=300, lat=50).plot()
ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o')
plt.show()

(ds_anom_resample.sel(time='2015-01-01', method='nearest') -
 ds_anom_resample.sel(time='1965-01-01', method='nearest')).sst.plot()
plt.show()

21.4.2. Rolling

Rolling is also similar to pandas. It does not change the length of arrays. Instead, it allows a moving window to be applied to the data at each point.

ds_anom_rolling = ds_anom.rolling(time=12, center=True).mean()
ds_anom_rolling

<xarray.Dataset> Size: 48MB
Dimensions:  (lat: 89, lon: 180, time: 756)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 6kB 1960-01-01 1960-02-01 ... 2022-12-01
    month    (time) int64 6kB 1 2 3 4 5 6 7 8 9 10 11 ... 3 4 5 6 7 8 9 10 11 12
Data variables:
    sst      (time, lat, lon) float32 48MB nan nan nan nan ... nan nan nan nan

xarray.Dataset

• Dimensions:
  • lat: 89
  • lon: 180
  • time: 756
• Coordinates: (4)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-01-01 ... 2022-12-01

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-01-01T00:00:00.000000000', '1960-02-01T00:00:00.000000000',
           '1960-03-01T00:00:00.000000000', ..., '2022-10-01T00:00:00.000000000',
           '2022-11-01T00:00:00.000000000', '2022-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • month
    (time)
    int64
    1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12

    array([ 1,  2,  3, ..., 10, 11, 12])

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    nan nan nan nan ... nan nan nan nan

    array([[[nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            ...,
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan]],
    
           [[nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            ...,
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan]],
    
           [[nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            ...,
    ...
            ...,
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan]],
    
           [[nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            ...,
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan]],
    
           [[nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            ...,
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan],
            [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-01-01', '1960-02-01', '1960-03-01', '1960-04-01',
                   '1960-05-01', '1960-06-01', '1960-07-01', '1960-08-01',
                   '1960-09-01', '1960-10-01',
                   ...
                   '2022-03-01', '2022-04-01', '2022-05-01', '2022-06-01',
                   '2022-07-01', '2022-08-01', '2022-09-01', '2022-10-01',
                   '2022-11-01', '2022-12-01'],
                  dtype='datetime64[ns]', name='time', length=756, freq=None))

• Attributes: (0)

ds_anom.sst.sel(lon=300, lat=50).plot(label='monthly anom')
ds_anom_resample.sst.sel(lon=300, lat=50).plot(marker='o', label='5 year resample')
ds_anom_rolling.sst.sel(lon=300, lat=50).plot(label='12 month rolling mean', color='k')
plt.legend()
plt.show()

21.5. Coarsen

Coarsen is a simple way to reduce the size of your data along one or more axes. It is very similar to resample when operating on time dimensions; the key difference is that coarsen only operates on fixed blocks of data, irrespective of the coordinate values, while resample actually looks at the coordinates to figure out, e.g. what month a particular data point is in.

For regularly-spaced monthly data beginning in January, the following should be equivalent to annual resampling. However, results would be different for irregularly-spaced data.

ds.coarsen(time=12, boundary = 'exact').mean()

<xarray.Dataset> Size: 4MB
Dimensions:  (time: 63, lat: 89, lon: 180)
Coordinates:
  * lat      (lat) float32 356B 88.0 86.0 84.0 82.0 ... -82.0 -84.0 -86.0 -88.0
  * lon      (lon) float32 720B 0.0 2.0 4.0 6.0 8.0 ... 352.0 354.0 356.0 358.0
  * time     (time) datetime64[ns] 504B 1960-06-16T08:00:00 ... 2022-06-16T12...
Data variables:
    sst      (time, lat, lon) float32 4MB -1.8 -1.8 -1.8 -1.8 ... nan nan nan
Attributes: (12/39)
    climatology:                     Climatology is based on 1971-2000 SST, X...
    description:                     In situ data: ICOADS2.5 before 2007 and ...
    keywords_vocabulary:             NASA Global Change Master Directory (GCM...
    keywords:                        Earth Science > Oceans > Ocean Temperatu...
    instrument:                      Conventional thermometers
    source_comment:                  SSTs were observed by conventional therm...
    ...                              ...
    comment:                         SSTs were observed by conventional therm...
    summary:                         ERSST.v5 is developed based on v4 after ...
    dataset_title:                   NOAA Extended Reconstructed SST V5
    _NCProperties:                   version=2,netcdf=4.6.3,hdf5=1.10.5
    data_modified:                   2024-11-03
    DODS_EXTRA.Unlimited_Dimension:  time

xarray.Dataset

• Dimensions:
  • time: 63
  • lat: 89
  • lon: 180
• Coordinates: (3)
  • lat
    (lat)
    float32
    88.0 86.0 84.0 ... -86.0 -88.0

    units :

    degrees_north

    long_name :

    Latitude

    actual_range :

    [ 88. -88.]

    standard_name :

    latitude

    axis :

    Y

    coordinate_defines :

    center

    array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,
            64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,
            40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,
            16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,
            -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,
           -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,
           -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,
           -80., -82., -84., -86., -88.], dtype=float32)

  • lon
    (lon)
    float32
    0.0 2.0 4.0 ... 354.0 356.0 358.0

    units :

    degrees_east

    long_name :

    Longitude

    actual_range :

    [ 0. 358.]

    standard_name :

    longitude

    axis :

    X

    coordinate_defines :

    center

    array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,
            24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,
            48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,
            72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,
            96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,
           120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,
           144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,
           168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,
           192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,
           216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,
           240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,
           264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,
           288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,
           312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,
           336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    1960-06-16T08:00:00 ... 2022-06-...

    long_name :

    Time

    delta_t :

    0000-01-00 00:00:00

    avg_period :

    0000-01-00 00:00:00

    prev_avg_period :

    0000-00-07 00:00:00

    standard_name :

    time

    axis :

    T

    actual_range :

    [19723. 82088.]

    _ChunkSizes :

    1

    array(['1960-06-16T08:00:00.000000000', '1961-06-16T12:00:00.000000000',
           '1962-06-16T12:00:00.000000000', '1963-06-16T12:00:00.000000000',
           '1964-06-16T08:00:00.000000000', '1965-06-16T12:00:00.000000000',
           '1966-06-16T12:00:00.000000000', '1967-06-16T12:00:00.000000000',
           '1968-06-16T08:00:00.000000000', '1969-06-16T12:00:00.000000000',
           '1970-06-16T12:00:00.000000000', '1971-06-16T12:00:00.000000000',
           '1972-06-16T08:00:00.000000000', '1973-06-16T12:00:00.000000000',
           '1974-06-16T12:00:00.000000000', '1975-06-16T12:00:00.000000000',
           '1976-06-16T08:00:00.000000000', '1977-06-16T12:00:00.000000000',
           '1978-06-16T12:00:00.000000000', '1979-06-16T12:00:00.000000000',
           '1980-06-16T08:00:00.000000000', '1981-06-16T12:00:00.000000000',
           '1982-06-16T12:00:00.000000000', '1983-06-16T12:00:00.000000000',
           '1984-06-16T08:00:00.000000000', '1985-06-16T12:00:00.000000000',
           '1986-06-16T12:00:00.000000000', '1987-06-16T12:00:00.000000000',
           '1988-06-16T08:00:00.000000000', '1989-06-16T12:00:00.000000000',
           '1990-06-16T12:00:00.000000000', '1991-06-16T12:00:00.000000000',
           '1992-06-16T08:00:00.000000000', '1993-06-16T12:00:00.000000000',
           '1994-06-16T12:00:00.000000000', '1995-06-16T12:00:00.000000000',
           '1996-06-16T08:00:00.000000000', '1997-06-16T12:00:00.000000000',
           '1998-06-16T12:00:00.000000000', '1999-06-16T12:00:00.000000000',
           '2000-06-16T08:00:00.000000000', '2001-06-16T12:00:00.000000000',
           '2002-06-16T12:00:00.000000000', '2003-06-16T12:00:00.000000000',
           '2004-06-16T08:00:00.000000000', '2005-06-16T12:00:00.000000000',
           '2006-06-16T12:00:00.000000000', '2007-06-16T12:00:00.000000000',
           '2008-06-16T08:00:00.000000000', '2009-06-16T12:00:00.000000000',
           '2010-06-16T12:00:00.000000000', '2011-06-16T12:00:00.000000000',
           '2012-06-16T08:00:00.000000000', '2013-06-16T12:00:00.000000000',
           '2014-06-16T12:00:00.000000000', '2015-06-16T12:00:00.000000000',
           '2016-06-16T08:00:00.000000000', '2017-06-16T12:00:00.000000000',
           '2018-06-16T12:00:00.000000000', '2019-06-16T12:00:00.000000000',
           '2020-06-16T08:00:00.000000000', '2021-06-16T12:00:00.000000000',
           '2022-06-16T12:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (1)
  • sst
    (time, lat, lon)
    float32
    -1.8 -1.8 -1.8 -1.8 ... nan nan nan

    long_name :

    Monthly Means of Sea Surface Temperature

    units :

    degC

    var_desc :

    Sea Surface Temperature

    level_desc :

    Surface

    statistic :

    Mean

    dataset :

    NOAA Extended Reconstructed SST V5

    parent_stat :

    Individual Values

    actual_range :

    [-1.8 42.32636]

    valid_range :

    [-1.8 45. ]

    _ChunkSizes :

    [ 1 89 180]

    array([[[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            ...,
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan]],
    
           [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
    ...
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan]],
    
           [[-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            [-1.7999998, -1.7999998, -1.7999998, ..., -1.7999998,
             -1.7999998, -1.7999998],
            ...,
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan],
            [       nan,        nan,        nan, ...,        nan,
                    nan,        nan]]], dtype=float32)

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 88.0,  86.0,  84.0,  82.0,  80.0,  78.0,  76.0,  74.0,  72.0,  70.0,
            68.0,  66.0,  64.0,  62.0,  60.0,  58.0,  56.0,  54.0,  52.0,  50.0,
            48.0,  46.0,  44.0,  42.0,  40.0,  38.0,  36.0,  34.0,  32.0,  30.0,
            28.0,  26.0,  24.0,  22.0,  20.0,  18.0,  16.0,  14.0,  12.0,  10.0,
             8.0,   6.0,   4.0,   2.0,   0.0,  -2.0,  -4.0,  -6.0,  -8.0, -10.0,
           -12.0, -14.0, -16.0, -18.0, -20.0, -22.0, -24.0, -26.0, -28.0, -30.0,
           -32.0, -34.0, -36.0, -38.0, -40.0, -42.0, -44.0, -46.0, -48.0, -50.0,
           -52.0, -54.0, -56.0, -58.0, -60.0, -62.0, -64.0, -66.0, -68.0, -70.0,
           -72.0, -74.0, -76.0, -78.0, -80.0, -82.0, -84.0, -86.0, -88.0],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([  0.0,   2.0,   4.0,   6.0,   8.0,  10.0,  12.0,  14.0,  16.0,  18.0,
           ...
           340.0, 342.0, 344.0, 346.0, 348.0, 350.0, 352.0, 354.0, 356.0, 358.0],
          dtype='float32', name='lon', length=180))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1960-06-16 08:00:00', '1961-06-16 12:00:00',
                   '1962-06-16 12:00:00', '1963-06-16 12:00:00',
                   '1964-06-16 08:00:00', '1965-06-16 12:00:00',
                   '1966-06-16 12:00:00', '1967-06-16 12:00:00',
                   '1968-06-16 08:00:00', '1969-06-16 12:00:00',
                   '1970-06-16 12:00:00', '1971-06-16 12:00:00',
                   '1972-06-16 08:00:00', '1973-06-16 12:00:00',
                   '1974-06-16 12:00:00', '1975-06-16 12:00:00',
                   '1976-06-16 08:00:00', '1977-06-16 12:00:00',
                   '1978-06-16 12:00:00', '1979-06-16 12:00:00',
                   '1980-06-16 08:00:00', '1981-06-16 12:00:00',
                   '1982-06-16 12:00:00', '1983-06-16 12:00:00',
                   '1984-06-16 08:00:00', '1985-06-16 12:00:00',
                   '1986-06-16 12:00:00', '1987-06-16 12:00:00',
                   '1988-06-16 08:00:00', '1989-06-16 12:00:00',
                   '1990-06-16 12:00:00', '1991-06-16 12:00:00',
                   '1992-06-16 08:00:00', '1993-06-16 12:00:00',
                   '1994-06-16 12:00:00', '1995-06-16 12:00:00',
                   '1996-06-16 08:00:00', '1997-06-16 12:00:00',
                   '1998-06-16 12:00:00', '1999-06-16 12:00:00',
                   '2000-06-16 08:00:00', '2001-06-16 12:00:00',
                   '2002-06-16 12:00:00', '2003-06-16 12:00:00',
                   '2004-06-16 08:00:00', '2005-06-16 12:00:00',
                   '2006-06-16 12:00:00', '2007-06-16 12:00:00',
                   '2008-06-16 08:00:00', '2009-06-16 12:00:00',
                   '2010-06-16 12:00:00', '2011-06-16 12:00:00',
                   '2012-06-16 08:00:00', '2013-06-16 12:00:00',
                   '2014-06-16 12:00:00', '2015-06-16 12:00:00',
                   '2016-06-16 08:00:00', '2017-06-16 12:00:00',
                   '2018-06-16 12:00:00', '2019-06-16 12:00:00',
                   '2020-06-16 08:00:00', '2021-06-16 12:00:00',
                   '2022-06-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (39)

  climatology :

  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.

  description :

  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.

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  keywords :

  Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature >

  instrument :

  Conventional thermometers

  source_comment :

  SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker

  geospatial_lon_min :

  -1.0

  geospatial_lon_max :

  359.0

  geospatial_laty_max :

  89.0

  geospatial_laty_min :

  -89.0

  geospatial_lat_max :

  89.0

  geospatial_lat_min :

  -89.0

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_east

  cdm_data_type :

  Grid

  project :

  NOAA Extended Reconstructed Sea Surface Temperature (ERSST)

  original_publisher_url :

  http://www.ncdc.noaa.gov

  References :

  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

  source :

  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

  title :

  NOAA ERSSTv5 (in situ only)

  history :

  created 07/2017 by PSD data using NCEI's ERSST V5 NetCDF values

  institution :

  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

  citation :

  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

  platform :

  Ship and Buoy SSTs from ICOADS R3.0 and NCEP GTS

  standard_name_vocabulary :

  CF Standard Name Table (v40, 25 January 2017)

  processing_level :

  NOAA Level 4

  Conventions :

  CF-1.6, ACDD-1.3

  metadata_link :

  :metadata_link = https://doi.org/10.7289/V5T72FNM (original format)

  creator_name :

  Boyin Huang (original)

  date_created :

  2017-06-30T12:18:00Z (original)

  product_version :

  Version 5

  creator_url_original :

  https://www.ncei.noaa.gov

  license :

  No constraints on data access or use

  comment :

  SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets), Engine Room Intakers, or floats and drifters

  summary :

  ERSST.v5 is developed based on v4 after revisions of 8 parameters using updated data sets and advanced knowledge of ERSST analysis

  dataset_title :

  NOAA Extended Reconstructed SST V5

  _NCProperties :

  version=2,netcdf=4.6.3,hdf5=1.10.5

  data_modified :

  2024-11-03

  DODS_EXTRA.Unlimited_Dimension :

  time

Coarsen also works on spatial coordinates (or any coordiantes).

ds_coarse = ds.coarsen(lon=4, lat=4, boundary='pad').mean()
ds_coarse.sst.isel(time=0).plot(vmin=2, vmax=30, figsize=(12, 5), edgecolor='k')
plt.show()