.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_gallery/plot_time_scale_average.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_gallery_plot_time_scale_average.py: Time Scale Average =================================== Before proceeding with all the steps, first import some necessary libraries and packages .. GENERATED FROM PYTHON SOURCE LINES 8-11 .. code-block:: Python import easyclimate as ecl import xarray as xr .. GENERATED FROM PYTHON SOURCE LINES 12-13 Example data are sea ice concentration (SIC) data for the Barents-Kara Sea (30°-90°E, 65°-85°N). .. GENERATED FROM PYTHON SOURCE LINES 13-16 .. code-block:: Python sic_data_Barents_Sea = ecl.tutorial.open_tutorial_dataset("mini_HadISST_ice").sic sic_data_Barents_Sea .. raw:: html 
<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


.. GENERATED FROM PYTHON SOURCE LINES 17-21 Mean States ------------------------------------ Solving for the overall climatological mean state was solved using :py:func:`easyclimate.calc_all_climatological_mean `. .. GENERATED FROM PYTHON SOURCE LINES 21-23 .. code-block:: Python ecl.calc_all_climatological_mean(sic_data_Barents_Sea, dim="time") .. raw:: html 
<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 ]], shape=(20, 60), 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


.. GENERATED FROM PYTHON SOURCE LINES 24-25 If the climate state is for each season, the results are solved using :py:func:`easyclimate.calc_seasonal_climatological_mean `. .. GENERATED FROM PYTHON SOURCE LINES 25-27 .. code-block:: Python ecl.calc_seasonal_climatological_mean(sic_data_Barents_Sea, dim="time") .. raw:: html 
<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 ]]], shape=(4, 20, 60), 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


.. GENERATED FROM PYTHON SOURCE LINES 28-29 However, if the climate state is for each month, the results are solved using :py:func:`easyclimate.calc_seasonal_cycle_mean `. .. GENERATED FROM PYTHON SOURCE LINES 29-31 .. code-block:: Python ecl.calc_seasonal_cycle_mean(sic_data_Barents_Sea, dim="time") .. raw:: html 
<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]]], shape=(12, 20, 60), 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


.. GENERATED FROM PYTHON SOURCE LINES 32-35 Remove Seasonal Cycle ------------------------------------ :py:func:`easyclimate.remove_seasonal_cycle_mean ` helps us to remove seasonal cycles (annual cycles) from the data in order to obtain monthly average anomalies. .. GENERATED FROM PYTHON SOURCE LINES 35-40 .. code-block:: Python 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 .. raw:: html 
<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]]], shape=(508, 20, 60), 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


.. GENERATED FROM PYTHON SOURCE LINES 41-42 We can visualize the results by plotting. .. GENERATED FROM PYTHON SOURCE LINES 42-48 .. code-block:: Python 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=".", ) .. image-sg:: /auto_gallery/images/sphx_glr_plot_time_scale_average_001.png :alt: plot time scale average :srcset: /auto_gallery/images/sphx_glr_plot_time_scale_average_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [] .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 3.551 seconds) .. _sphx_glr_download_auto_gallery_plot_time_scale_average.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_time_scale_average.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_time_scale_average.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_time_scale_average.zip `