Note

Go to the end to download the full example code.

Time Scale Average#

Before proceeding with all the steps, first import some necessary libraries and packages

import easyclimate as ecl
import xarray as xr

Example data are sea ice concentration (SIC) data for the Barents-Kara Sea (30°-90°E, 65°-85°N).

sic_data_Barents_Sea = ecl.tutorial.open_tutorial_dataset("mini_HadISST_ice").sic
sic_data_Barents_Sea

<xarray.DataArray 'sic' (time: 508, lat: 20, lon: 60)> Size: 2MB
[609600 values with dtype=float32]
Coordinates:
  * time     (time) datetime64[ns] 4kB 1981-01-31 1981-02-28 ... 2023-04-30
  * lat      (lat) float32 80B 65.5 66.5 67.5 68.5 69.5 ... 81.5 82.5 83.5 84.5
  * lon      (lon) float32 240B 30.5 31.5 32.5 33.5 34.5 ... 86.5 87.5 88.5 89.5
Attributes:
    standard_name:  sea_ice_area_fraction
    long_name:      Monthly 1 degree resolution sea ice concentration
    units:          1
    cell_methods:   time: lat: lon: median

xarray.DataArray

'sic'

time: 508
lat: 20
lon: 60

...
```
[609600 values with dtype=float32]
```

Coordinates: (3)

time

(time)

datetime64[ns]

1981-01-31 ... 2023-04-30

array(['1981-01-31T00:00:00.000000000', '1981-02-28T00:00:00.000000000',
       '1981-03-31T00:00:00.000000000', ..., '2023-02-28T00:00:00.000000000',
       '2023-03-31T00:00:00.000000000', '2023-04-30T00:00:00.000000000'],
      dtype='datetime64[ns]')

lat

(lat)

float32

65.5 66.5 67.5 ... 82.5 83.5 84.5

units :: degrees_north
long_name :: Latitude
standard_name :: latitude

array([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5], dtype=float32)

lon

(lon)

float32

30.5 31.5 32.5 ... 87.5 88.5 89.5

units :: degrees_east
long_name :: Longitude
standard_name :: longitude

array([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype=float32)

Indexes: (3)

time

PandasIndex

PandasIndex(DatetimeIndex(['1981-01-31', '1981-02-28', '1981-03-31', '1981-04-30',
               '1981-05-31', '1981-06-30', '1981-07-31', '1981-08-31',
               '1981-09-30', '1981-10-31',
               ...
               '2022-07-31', '2022-08-31', '2022-09-30', '2022-10-31',
               '2022-11-30', '2022-12-31', '2023-01-31', '2023-02-28',
               '2023-03-31', '2023-04-30'],
              dtype='datetime64[ns]', name='time', length=508, freq=None))

lat

PandasIndex

PandasIndex(Index([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5],
      dtype='float32', name='lat'))

lon

PandasIndex

PandasIndex(Index([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype='float32', name='lon'))

Attributes: (4)
standard_name :
sea_ice_area_fraction
long_name :
Monthly 1 degree resolution sea ice concentration
units :
1
cell_methods :
time: lat: lon: median

Mean States#

Solving for the overall climatological mean state was solved using easyclimate.calc_all_climatological_mean.

ecl.calc_all_climatological_mean(sic_data_Barents_Sea, dim="time")

<xarray.DataArray 'sic' (lat: 20, lon: 60)> Size: 5kB
array([[       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       ...,
       [0.877244  , 0.8792713 , 0.87023586, ..., 0.9318309 , 0.9357874 ,
        0.93395644],
       [0.93192923, 0.9332087 , 0.93057084, ..., 0.95885825, 0.9580121 ,
        0.95854366],
       [0.94389784, 0.94403535, 0.9427558 , ..., 0.9602362 , 0.9616337 ,
        0.9612792 ]], dtype=float32)
Coordinates:
  * lat      (lat) float32 80B 65.5 66.5 67.5 68.5 69.5 ... 81.5 82.5 83.5 84.5
  * lon      (lon) float32 240B 30.5 31.5 32.5 33.5 34.5 ... 86.5 87.5 88.5 89.5
Attributes:
    standard_name:  sea_ice_area_fraction
    long_name:      Monthly 1 degree resolution sea ice concentration
    units:          1
    cell_methods:   time: lat: lon: median

xarray.DataArray

'sic'

lat: 20
lon: 60

nan nan nan nan nan 0.2485 ... 0.9608 0.9607 0.9602 0.9616 0.9613

array([[       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       [       nan,        nan,        nan, ...,        nan,        nan,
               nan],
       ...,
       [0.877244  , 0.8792713 , 0.87023586, ..., 0.9318309 , 0.9357874 ,
        0.93395644],
       [0.93192923, 0.9332087 , 0.93057084, ..., 0.95885825, 0.9580121 ,
        0.95854366],
       [0.94389784, 0.94403535, 0.9427558 , ..., 0.9602362 , 0.9616337 ,
        0.9612792 ]], dtype=float32)

Coordinates: (2)

lat

(lat)

float32

65.5 66.5 67.5 ... 82.5 83.5 84.5

units :: degrees_north
long_name :: Latitude
standard_name :: latitude

array([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5], dtype=float32)

lon

(lon)

float32

30.5 31.5 32.5 ... 87.5 88.5 89.5

units :: degrees_east
long_name :: Longitude
standard_name :: longitude

array([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype=float32)

Indexes: (2)

lat

PandasIndex

PandasIndex(Index([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5],
      dtype='float32', name='lat'))

lon

PandasIndex

PandasIndex(Index([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype='float32', name='lon'))

Attributes: (4)
standard_name :
sea_ice_area_fraction
long_name :
Monthly 1 degree resolution sea ice concentration
units :
1
cell_methods :
time: lat: lon: median

If the climate state is for each season, the results are solved using easyclimate.calc_seasonal_climatological_mean.

ecl.calc_seasonal_climatological_mean(sic_data_Barents_Sea, dim="time")

<xarray.DataArray 'sic' (season: 4, lat: 20, lon: 60)> Size: 19kB
array([[[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.92078125, 0.9253906 , 0.9153125 , ..., 0.989375  ,
         0.9890625 , 0.98664063],
        [0.9571094 , 0.95734376, 0.958125  , ..., 0.9867188 ,
         0.98578125, 0.9857031 ],
        [0.960625  , 0.9615625 , 0.96070313, ..., 0.98398435,
         0.98515624, 0.9838281 ]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
...
        [0.95984375, 0.9634375 , 0.9628125 , ..., 0.99210936,
         0.99109375, 0.988125  ],
        [0.96867186, 0.9694531 , 0.97015625, ..., 0.9910156 ,
         0.9909375 , 0.9903906 ],
        [0.9719531 , 0.971875  , 0.9717187 , ..., 0.9882031 ,
         0.9895312 , 0.98828125]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.7614286 , 0.7625397 , 0.7449206 , ..., 0.8363492 ,
         0.844127  , 0.8452381 ],
        [0.88079363, 0.8851587 , 0.87738097, ..., 0.91642857,
         0.91515875, 0.91642857],
        [0.9123016 , 0.9113492 , 0.9086508 , ..., 0.92579365,
         0.9272222 , 0.9281746 ]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 80B 65.5 66.5 67.5 68.5 69.5 ... 81.5 82.5 83.5 84.5
  * lon      (lon) float32 240B 30.5 31.5 32.5 33.5 34.5 ... 86.5 87.5 88.5 89.5
  * season   (season) object 32B 'DJF' 'JJA' 'MAM' 'SON'
Attributes:
    standard_name:  sea_ice_area_fraction
    long_name:      Monthly 1 degree resolution sea ice concentration
    units:          1
    cell_methods:   time: lat: lon: median

xarray.DataArray

'sic'

season: 4
lat: 20
lon: 60

nan nan nan nan nan 0.5057 ... 0.9271 0.926 0.925 0.9258 0.9272 0.9282

array([[[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.92078125, 0.9253906 , 0.9153125 , ..., 0.989375  ,
         0.9890625 , 0.98664063],
        [0.9571094 , 0.95734376, 0.958125  , ..., 0.9867188 ,
         0.98578125, 0.9857031 ],
        [0.960625  , 0.9615625 , 0.96070313, ..., 0.98398435,
         0.98515624, 0.9838281 ]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
...
        [0.95984375, 0.9634375 , 0.9628125 , ..., 0.99210936,
         0.99109375, 0.988125  ],
        [0.96867186, 0.9694531 , 0.97015625, ..., 0.9910156 ,
         0.9909375 , 0.9903906 ],
        [0.9719531 , 0.971875  , 0.9717187 , ..., 0.9882031 ,
         0.9895312 , 0.98828125]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.7614286 , 0.7625397 , 0.7449206 , ..., 0.8363492 ,
         0.844127  , 0.8452381 ],
        [0.88079363, 0.8851587 , 0.87738097, ..., 0.91642857,
         0.91515875, 0.91642857],
        [0.9123016 , 0.9113492 , 0.9086508 , ..., 0.92579365,
         0.9272222 , 0.9281746 ]]], dtype=float32)

Coordinates: (3)

lat

(lat)

float32

65.5 66.5 67.5 ... 82.5 83.5 84.5

units :: degrees_north
long_name :: Latitude
standard_name :: latitude

array([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5], dtype=float32)

lon

(lon)

float32

30.5 31.5 32.5 ... 87.5 88.5 89.5

units :: degrees_east
long_name :: Longitude
standard_name :: longitude

array([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype=float32)

season

(season)

object

'DJF' 'JJA' 'MAM' 'SON'

array(['DJF', 'JJA', 'MAM', 'SON'], dtype=object)

Indexes: (3)

lat

PandasIndex

PandasIndex(Index([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5],
      dtype='float32', name='lat'))

lon

PandasIndex

PandasIndex(Index([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype='float32', name='lon'))

season

PandasIndex

PandasIndex(Index(['DJF', 'JJA', 'MAM', 'SON'], dtype='object', name='season'))

Attributes: (4)
standard_name :
sea_ice_area_fraction
long_name :
Monthly 1 degree resolution sea ice concentration
units :
1
cell_methods :
time: lat: lon: median

However, if the climate state is for each month, the results are solved using easyclimate.calc_seasonal_cycle_mean.

ecl.calc_seasonal_cycle_mean(sic_data_Barents_Sea, dim="time")

<xarray.DataArray 'sic' (month: 12, lat: 20, lon: 60)> Size: 58kB
array([[[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.9181395 , 0.9183721 , 0.9062791 , ..., 0.9906977 ,
         0.9902325 , 0.9872093 ],
        [0.96255815, 0.9618605 , 0.9623256 , ..., 0.9883721 ,
         0.9872093 , 0.987907  ],
        [0.9632558 , 0.96465117, 0.96279067, ..., 0.9851163 ,
         0.98697674, 0.9860465 ]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
...
        [0.8907143 , 0.8957143 , 0.882619  , ..., 0.96428573,
         0.9659524 , 0.9628571 ],
        [0.93523806, 0.9342857 , 0.93714285, ..., 0.97309524,
         0.9714286 , 0.97166663],
        [0.9411905 , 0.9407143 , 0.9388095 , ..., 0.96690476,
         0.9671428 , 0.96619046]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.91714287, 0.9292857 , 0.9211905 , ..., 0.9842857 ,
         0.9847619 , 0.98190475],
        [0.9438095 , 0.9445238 , 0.9454762 , ..., 0.9809524 ,
         0.98      , 0.9792857 ],
        [0.95095235, 0.9511905 , 0.9519048 , ..., 0.97761905,
         0.9802381 , 0.97761905]]], dtype=float32)
Coordinates:
  * lat      (lat) float32 80B 65.5 66.5 67.5 68.5 69.5 ... 81.5 82.5 83.5 84.5
  * lon      (lon) float32 240B 30.5 31.5 32.5 33.5 34.5 ... 86.5 87.5 88.5 89.5
  * month    (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
    standard_name:  sea_ice_area_fraction
    long_name:      Monthly 1 degree resolution sea ice concentration
    units:          1
    cell_methods:   time: lat: lon: median

xarray.DataArray

'sic'

month: 12
lat: 20
lon: 60

nan nan nan nan nan 0.5602 ... 0.9788 0.9779 0.9776 0.9802 0.9776

array([[[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.9181395 , 0.9183721 , 0.9062791 , ..., 0.9906977 ,
         0.9902325 , 0.9872093 ],
        [0.96255815, 0.9618605 , 0.9623256 , ..., 0.9883721 ,
         0.9872093 , 0.987907  ],
        [0.9632558 , 0.96465117, 0.96279067, ..., 0.9851163 ,
         0.98697674, 0.9860465 ]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
...
        [0.8907143 , 0.8957143 , 0.882619  , ..., 0.96428573,
         0.9659524 , 0.9628571 ],
        [0.93523806, 0.9342857 , 0.93714285, ..., 0.97309524,
         0.9714286 , 0.97166663],
        [0.9411905 , 0.9407143 , 0.9388095 , ..., 0.96690476,
         0.9671428 , 0.96619046]],

       [[       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        [       nan,        nan,        nan, ...,        nan,
                nan,        nan],
        ...,
        [0.91714287, 0.9292857 , 0.9211905 , ..., 0.9842857 ,
         0.9847619 , 0.98190475],
        [0.9438095 , 0.9445238 , 0.9454762 , ..., 0.9809524 ,
         0.98      , 0.9792857 ],
        [0.95095235, 0.9511905 , 0.9519048 , ..., 0.97761905,
         0.9802381 , 0.97761905]]], dtype=float32)

Coordinates: (3)

lat

(lat)

float32

65.5 66.5 67.5 ... 82.5 83.5 84.5

units :: degrees_north
long_name :: Latitude
standard_name :: latitude

array([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5], dtype=float32)

lon

(lon)

float32

30.5 31.5 32.5 ... 87.5 88.5 89.5

units :: degrees_east
long_name :: Longitude
standard_name :: longitude

array([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      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([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5],
      dtype='float32', name='lat'))

lon

PandasIndex

PandasIndex(Index([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype='float32', name='lon'))

month

PandasIndex

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

Attributes: (4)
standard_name :
sea_ice_area_fraction
long_name :
Monthly 1 degree resolution sea ice concentration
units :
1
cell_methods :
time: lat: lon: median

Remove Seasonal Cycle#

easyclimate.remove_seasonal_cycle_mean helps us to remove seasonal cycles (annual cycles) from the data in order to obtain monthly average anomalies.

sic_data_Barents_Sea_remove_seasonal_cycle = ecl.remove_seasonal_cycle_mean(
    sic_data_Barents_Sea, dim="time"
)
sic_data_Barents_Sea_remove_seasonal_cycle

<xarray.DataArray 'sic' (time: 508, lat: 20, lon: 60)> Size: 2MB
array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [ 0.03186047,  0.05162793,  0.05372089, ..., -0.01069766,
         -0.01023251, -0.01720929],
        [-0.01255816, -0.01186049, -0.01232558, ..., -0.00837207,
         -0.0072093 , -0.01790696],
        [-0.02325583, -0.01465118, -0.01279068, ..., -0.01511627,
         -0.00697672, -0.01604646]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
...
        [ 0.04511625,  0.04116273,  0.03116274, ...,  0.00813955,
          0.00860465,  0.0004651 ],
        [ 0.02302319,  0.02255809,  0.02069765, ...,  0.00999999,
         -0.00023258,  0.00976747],
        [ 0.01930231,  0.0197674 ,  0.01906973, ...,  0.00116277,
          0.0095349 ,  0.00093019]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [ 0.02116275,  0.01837206,  0.01883715, ...,  0.00744188,
          0.00790697,  0.01069766],
        [ 0.02069765,  0.01953483,  0.01837206, ..., -0.00116283,
         -0.00093025, -0.00093025],
        [ 0.01906973,  0.01906973,  0.0197674 , ...,  0.00255811,
          0.00139529,  0.00255811]]], dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 4kB 1981-01-31 1981-02-28 ... 2023-04-30
  * lat      (lat) float32 80B 65.5 66.5 67.5 68.5 69.5 ... 81.5 82.5 83.5 84.5
  * lon      (lon) float32 240B 30.5 31.5 32.5 33.5 34.5 ... 86.5 87.5 88.5 89.5
    month    (time) int64 4kB 1 2 3 4 5 6 7 8 9 10 11 ... 7 8 9 10 11 12 1 2 3 4
Attributes:
    standard_name:  sea_ice_area_fraction
    long_name:      Monthly 1 degree resolution sea ice concentration
    units:          1
    cell_methods:   time: lat: lon: median

xarray.DataArray

'sic'

time: 508
lat: 20
lon: 60

nan nan nan nan nan ... 0.0004651 0.001395 0.002558 0.001395 0.002558

array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [ 0.03186047,  0.05162793,  0.05372089, ..., -0.01069766,
         -0.01023251, -0.01720929],
        [-0.01255816, -0.01186049, -0.01232558, ..., -0.00837207,
         -0.0072093 , -0.01790696],
        [-0.02325583, -0.01465118, -0.01279068, ..., -0.01511627,
         -0.00697672, -0.01604646]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
...
        [ 0.04511625,  0.04116273,  0.03116274, ...,  0.00813955,
          0.00860465,  0.0004651 ],
        [ 0.02302319,  0.02255809,  0.02069765, ...,  0.00999999,
         -0.00023258,  0.00976747],
        [ 0.01930231,  0.0197674 ,  0.01906973, ...,  0.00116277,
          0.0095349 ,  0.00093019]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [ 0.02116275,  0.01837206,  0.01883715, ...,  0.00744188,
          0.00790697,  0.01069766],
        [ 0.02069765,  0.01953483,  0.01837206, ..., -0.00116283,
         -0.00093025, -0.00093025],
        [ 0.01906973,  0.01906973,  0.0197674 , ...,  0.00255811,
          0.00139529,  0.00255811]]], dtype=float32)

Coordinates: (4)

time

(time)

datetime64[ns]

1981-01-31 ... 2023-04-30

array(['1981-01-31T00:00:00.000000000', '1981-02-28T00:00:00.000000000',
       '1981-03-31T00:00:00.000000000', ..., '2023-02-28T00:00:00.000000000',
       '2023-03-31T00:00:00.000000000', '2023-04-30T00:00:00.000000000'],
      dtype='datetime64[ns]')

lat

(lat)

float32

65.5 66.5 67.5 ... 82.5 83.5 84.5

units :: degrees_north
long_name :: Latitude
standard_name :: latitude

array([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5], dtype=float32)

lon

(lon)

float32

30.5 31.5 32.5 ... 87.5 88.5 89.5

units :: degrees_east
long_name :: Longitude
standard_name :: longitude

array([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype=float32)

month
(time)
int64
1 2 3 4 5 6 7 ... 10 11 12 1 2 3 4
```
array([1, 2, 3, ..., 2, 3, 4])
```

Indexes: (3)

time

PandasIndex

PandasIndex(DatetimeIndex(['1981-01-31', '1981-02-28', '1981-03-31', '1981-04-30',
               '1981-05-31', '1981-06-30', '1981-07-31', '1981-08-31',
               '1981-09-30', '1981-10-31',
               ...
               '2022-07-31', '2022-08-31', '2022-09-30', '2022-10-31',
               '2022-11-30', '2022-12-31', '2023-01-31', '2023-02-28',
               '2023-03-31', '2023-04-30'],
              dtype='datetime64[ns]', name='time', length=508, freq=None))

lat

PandasIndex

PandasIndex(Index([65.5, 66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5,
       77.5, 78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5],
      dtype='float32', name='lat'))

lon

PandasIndex

PandasIndex(Index([30.5, 31.5, 32.5, 33.5, 34.5, 35.5, 36.5, 37.5, 38.5, 39.5, 40.5, 41.5,
       42.5, 43.5, 44.5, 45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5,
       54.5, 55.5, 56.5, 57.5, 58.5, 59.5, 60.5, 61.5, 62.5, 63.5, 64.5, 65.5,
       66.5, 67.5, 68.5, 69.5, 70.5, 71.5, 72.5, 73.5, 74.5, 75.5, 76.5, 77.5,
       78.5, 79.5, 80.5, 81.5, 82.5, 83.5, 84.5, 85.5, 86.5, 87.5, 88.5, 89.5],
      dtype='float32', name='lon'))

Attributes: (4)
standard_name :
sea_ice_area_fraction
long_name :
Monthly 1 degree resolution sea ice concentration
units :
1
cell_methods :
time: lat: lon: median

We can visualize the results by plotting.

sic_data_Barents_Sea_remove_seasonal_cycle.mean(dim=("lat", "lon")).sel(
    time=slice("2010-01-01", "2015-12-31")
).plot(
    figsize=(10, 3),
    marker=".",
)

[<matplotlib.lines.Line2D object at 0x7f80527130d0>]

Total running time of the script: (0 minutes 0.897 seconds)

Download Jupyter notebook: plot_time_scale_average.ipynb

Download Python source code: plot_time_scale_average.py

Download zipped: plot_time_scale_average.zip

Time Scale Average

Contents

Time Scale Average#

Mean States#

Remove Seasonal Cycle#