Winter Arctic sea ice state variability (updates through to April 2022)

Summary: In this notebook, we provide updates to the original winter Arctic sea ice thickness notebooks with an additional winter (2021-2022) of data collection. Here we read the IS2SITMOGR4 files from our AWS S3 bucket, instead of our aggregated Jupyter Book dataset. I generally focus on showing the ‘’smoothed/interpolated’ variables, whereas Petty et al., (2023) focussed more on the non-interpolated gridded data. Once we produce an updated aggregated Jupyter Book dataset we can update this notebook with those additional variables.

Version history: Version 1 (01/23/2023)

Import notebook dependencies

# For working with gridded climate data 
import xarray as xr 
# Helper function for reading the data from the bucket
from utils.read_data_utils import read_IS2SITMOGR4 
from utils.plotting_utils import static_winter_comparison_lineplot, staticArcticMaps, staticArcticMaps_overlayDrifts, interactiveArcticMaps, compute_gridcell_winter_means, interactive_winter_comparison_lineplot # Plotting utils 
import numpy as np
# Plotting dependencies
#%config InlineBackend.figure_format = 'retina'
import matplotlib as mpl
# Sets figure size in the notebook
mpl.rcParams['figure.dpi'] = 150 

# Remove warnings to improve display
import warnings 
warnings.filterwarnings('ignore') 

# Read in the raw monthly gridded winter Arctic sea ice data from S3 (still using V2)
IS2SITMOGR4_all = read_IS2SITMOGR4(data_type='zarr-s3', 
                                   local_data_path='./data/IS2SITMOGR4/', version='V2',persist=True) 
IS2SITMOGR4_all

load zarr from S3 bucket:  icesat-2-sea-ice-us-west-2
zarr_path: s3://icesat-2-sea-ice-us-west-2/IS2SITMOGR4_V2/zarr/IS2SITMOGR4_V2_201811-202204.zarr/all/

<xarray.Dataset>
Dimensions:            (time: 30, y: 448, x: 304)
Coordinates:
    latitude           (y, x) float32 31.1 31.2 31.3 31.39 ... 34.68 34.58 34.47
    longitude          (y, x) float32 168.3 168.1 168.0 ... -10.36 -10.18 -9.999
  * time               (time) datetime64[ns] 2018-11-01 ... 2022-04-01
    xgrid              (y, x) float32 dask.array<chunksize=(224, 304), meta=np.ndarray>
    ygrid              (y, x) float32 dask.array<chunksize=(224, 304), meta=np.ndarray>
Dimensions without coordinates: y, x
Data variables: (12/15)
    freeboard          (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    freeboard_int      (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    ice_density        (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    ice_thickness      (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    ice_thickness_int  (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    ice_thickness_unc  (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    ...                 ...
    projection         (time) float64 dask.array<chunksize=(30,), meta=np.ndarray>
    region_mask        (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    sea_ice_conc       (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    snow_density       (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    snow_depth         (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>
    snow_depth_int     (time, y, x) float32 dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • time: 30
  • y: 448
  • x: 304
• Coordinates: (5)
  • latitude
    (y, x)
    float32
    31.1 31.2 31.3 ... 34.58 34.47

    array([[31.102673, 31.199413, 31.295801, ..., 31.677736, 31.582802,
            31.4875  ],
           [31.25011 , 31.34728 , 31.444096, ..., 31.82774 , 31.73238 ,
            31.63665 ],
           [31.397564, 31.495165, 31.592411, ..., 31.977772, 31.881985,
            31.785826],
           ...,
           [34.345894, 34.45253 , 34.55881 , ..., 34.980267, 34.87546 ,
            34.77028 ],
           [34.198696, 34.304863, 34.41067 , ..., 34.830242, 34.725906,
            34.621197],
           [34.05146 , 34.157154, 34.262493, ..., 34.680187, 34.576324,
            34.472084]], dtype=float32)

  • longitude
    (y, x)
    float32
    168.3 168.1 168.0 ... -10.18 -9.999

    array([[168.32042 , 168.14876 , 167.97641 , ..., 102.71974 , 102.544685,
            102.370316],
           [168.4334  , 168.26143 , 168.08879 , ..., 102.608604, 102.433235,
            102.258545],
           [168.54704 , 168.37479 , 168.20184 , ..., 102.49679 , 102.3211  ,
            102.146095],
           ...,
           [-80.970955, -80.79312 , -80.61448 , ..., -10.108099,  -9.926246,
             -9.745198],
           [-80.842575, -80.665   , -80.48663 , ..., -10.234803, -10.053245,
             -9.872486],
           [-80.71499 , -80.53768 , -80.35958 , ..., -10.360703, -10.179442,
             -9.998976]], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2018-11-01 ... 2022-04-01

    array(['2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000',
           '2019-01-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000',
           '2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000',
           '2019-09-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000',
           '2019-11-01T00:00:00.000000000', '2019-12-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000', '2020-02-01T00:00:00.000000000',
           '2020-03-01T00:00:00.000000000', '2020-04-01T00:00:00.000000000',
           '2020-09-01T00:00:00.000000000', '2020-10-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000',
           '2021-01-01T00:00:00.000000000', '2021-02-01T00:00:00.000000000',
           '2021-03-01T00:00:00.000000000', '2021-04-01T00:00:00.000000000',
           '2021-09-01T00:00:00.000000000', '2021-10-01T00:00:00.000000000',
           '2021-11-01T00:00:00.000000000', '2021-12-01T00:00:00.000000000',
           '2022-01-01T00:00:00.000000000', '2022-02-01T00:00:00.000000000',
           '2022-03-01T00:00:00.000000000', '2022-04-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • xgrid
    (y, x)
    float32
    dask.array<chunksize=(224, 304), meta=np.ndarray>

    long_name :

    projection grid x values

    units :

    meters

    Array Chunk Bytes 532.00 kiB 266.00 kiB Shape (448, 304) (224, 304) Count 2 Tasks 2 Chunks Type float32 numpy.ndarray

  • ygrid
    (y, x)
    float32
    dask.array<chunksize=(224, 304), meta=np.ndarray>

    long_name :

    projection grid y values

    units :

    meters

    Array Chunk Bytes 532.00 kiB 266.00 kiB Shape (448, 304) (224, 304) Count 2 Tasks 2 Chunks Type float32 numpy.ndarray

• Data variables: (15)
  • freeboard
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean freeboard from rel005 ATL10

    long_name :

    sea ice freeboard

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • freeboard_int
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean interpolated freeboard from rel005 ATL10

    long_name :

    sea ice freeboard interpolated

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • ice_density
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Bulk sea ice density

    long_name :

    bulk sea ice density

    units :

    kilograms per meters cubed

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • ice_thickness
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean sea ice thickness from redistributed (piecewise) NESOSIM data.

    long_name :

    sea ice thickness

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • ice_thickness_int
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean interpolated sea ice thickness from redistributed (piecewise) NESOSIM data.

    long_name :

    sea ice thickness interpolated

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • ice_thickness_unc
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean ice thickness uncertainty

    long_name :

    sea ice thickness uncertainty

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • ice_type
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean ice type from OSI SAF (sub-sampled by ICESat-2 along-track prior to gridding)

    long_name :

    OSI SAF sea ice type classification (sub-sampled)

    source :

    http://www.osi-saf.org/?q=content/global-sea-ice-type-c

    units :

    ice type flag (0 = first-year ice, 1 = multi-year ice)

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • mean_day_of_month
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean day of the month represented by a given grid-cell based on the date of the along-track data

    long_name :

    day of month

    units :

    day

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • num_segments
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Number of valid freeboard/thickness segments in the given monthly grid-cell

    long_name :

    number of segments

    units :

    counts

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • projection
    (time)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    GeoTransform :

    -3850000.0 25000.0 0 5850000.0 0 25000.0

    false_easting :

    0.0

    false_northing :

    0.0

    grid_boundary_bottom_projected_y :

    -5350000.0

    grid_boundary_left_projected_x :

    -3850000.0

    grid_boundary_right_projected_x :

    3750000.0

    grid_boundary_top_projected_y :

    5850000.0

    grid_mapping_name :

    polar_stereographic

    latitude_of_projection_origin :

    90.0

    long_name :

    NSIDC Sea Ice Polar Stereographic North

    longitude_of_projection_origin :

    -45.0

    parent_grid_cell_column_subset_end :

    304.0

    parent_grid_cell_column_subset_start :

    0.0

    parent_grid_cell_row_subset_end :

    448.0

    parent_grid_cell_row_subset_start :

    0.0

    proj4text :

    +proj=stere +lat_0=90 +lat_ts=70 +lon_0=-45 +k=1 +x_0=0 +y_0=0 +a=6378273 +b=6356889.449 +units=m +no_defs

    scaling_factor :

    1.0

    semimajor_radius :

    6378273.0

    semiminor_radius :

    6356889.449

    spatial_ref :

    PROJCS["NSIDC Sea Ice Polar Stereographic North",GEOGCS["Unspecified datum based upon the Hughes 1980 ellipsoid",DATUM["Not_specified_based_on_Hughes_1980_ellipsoid",SPHEROID["Hughes 1980",6378273,298.279411123061,AUTHORITY["EPSG","7058"]],AUTHORITY["EPSG","6054"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4054"]],UNIT["metre",1,AUTHORITY["EPSG","9001"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["scale_factor",1],PARAMETER["false_easting",0],PARAMETER["false_northing",0],AUTHORITY["EPSG","3411"],AXIS["X",UNKNOWN],AXIS["Y",UNKNOWN]]

    srid :

    urn:ogc:def:crs:EPSG::3411

    standard_parallel :

    70.0

    units :

    meters

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Count 1 Tasks 1 Chunks Type float64 numpy.ndarray

  • region_mask
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    NSIDC Northern Hemisphere region mask (updated v2 NSIDC mask, preliminary)

    long_name :

    NSIDC Arctic region mask

    source :

    W. Meier and S. Stewart, National Snow and Ice Data Center (NSIDC)

    units :

    0: Outside of defined regions, 1: Central Arctic, 2: Beaufort Sea, 3: Chukchi Sea, 4: East Siberian Sea, 5: Laptev Sea, 6: Kara Sea, 7: Barents Sea, 8: East Greenland Sea, 9: Baffin Bay and Davis Strait, 10: Gulf of St. Lawrence, 11: Hudson Bay, 12: Canadian Archipelago, 13: Bering Sea 14: Sea of Okhotsk, 15: Sea of Japan, 16: Bohai Sea, 17: Gulf of Bothnia, Baltic Sea, 18: Gulf of Alaska, 30: Land, 31: Coast, 32: Lakes

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • sea_ice_conc
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean monthly ice concentration from CDR passive microwave data (raw data not sub-sampled by ICESat-2)

    long_name :

    CDR sea ice concentration

    source :

    https://nsidc.org/data/G02202/

    units :

    sea ice concentration (0 to 1, data masked below 0.15)

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • snow_density
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean snow density from NESOSIM

    long_name :

    snow density

    units :

    kilograms per meters cubed

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • snow_depth
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean snow depth from redistributed (piecewise) NESOSIM data

    long_name :

    snow depth

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

  • snow_depth_int
    (time, y, x)
    float32
    dask.array<chunksize=(8, 112, 152), meta=np.ndarray>

    description :

    Mean interpolated snow depth from redistributed (piecewise) NESOSIM data

    long_name :

    snow depth interpolated

    units :

    meters

    Array Chunk Bytes 15.59 MiB 532.00 kiB Shape (30, 448, 304) (8, 112, 152) Count 32 Tasks 32 Chunks Type float32 numpy.ndarray

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-11-01', '2018-12-01', '2019-01-01', '2019-02-01',
                   '2019-03-01', '2019-04-01', '2019-09-01', '2019-10-01',
                   '2019-11-01', '2019-12-01', '2020-01-01', '2020-02-01',
                   '2020-03-01', '2020-04-01', '2020-09-01', '2020-10-01',
                   '2020-11-01', '2020-12-01', '2021-01-01', '2021-02-01',
                   '2021-03-01', '2021-04-01', '2021-09-01', '2021-10-01',
                   '2021-11-01', '2021-12-01', '2022-01-01', '2022-02-01',
                   '2022-03-01', '2022-04-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (0)

# Years over which to perform analysis
years = [2018,2019,2020, 2021]

Winter mean Arctic maps (static)

Compute and map (static) mean winter sea ice conditions across the years of analysis chosen

freeboard_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.freeboard_int, years=years)
staticArcticMaps(freeboard_winter_means, dates=freeboard_winter_means.time.values, set_cbarlabel = "Sea ice freeboard (m)", cmap="YlOrRd", col_wrap=4, vmin=0, vmax=0.8, out_str='freeboard_winter_2018_2022')

snow_depth_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_depth_int, years=years)
staticArcticMaps(snow_depth_winter_means, dates=snow_depth_winter_means.time.values,set_cbarlabel = "Snow depth (m)", cmap="inferno", col_wrap=4, vmin=0, vmax=0.5, out_str='snowdepth_winter_2018_2022')

thickness_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_thickness_int, years=years)
staticArcticMaps(thickness_winter_means, dates=thickness_winter_means.time.values,title="", set_cbarlabel = "Sea ice thickness (m)", col_wrap=4, cmap="viridis", vmin=0, vmax=5, out_str='thickness_winter_2018_2022')

snow_density_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_density, years=years)
staticArcticMaps(snow_density_winter_means, dates=snow_density_winter_means.time.values,set_cbarlabel = "Snow density (kg/m3)", cmap="GnBu", col_wrap=4, vmin=240, vmax=330, out_str='snowdensity_winter_2018_2022')

ice_type_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_type, years=years)
staticArcticMaps(ice_type_winter_means, dates=ice_type_winter_means.time.values, set_cbarlabel = "Sea ice type (0 = FYI, 1 = MYI)", col_wrap=4, cmap="YlOrRd", vmin=0, vmax=1, out_str='icetype_winter_2018_2022')

Winter anomaly plots

Compute and map (static) winter anomaly maps (relative to the mean across the three winters by default) for given variables across the four winters

freeboard_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.freeboard, years=years)
staticArcticMaps(freeboard_winter_means-freeboard_winter_means.mean(axis=0), dates=freeboard_winter_means.time.values,title="", col_wrap=4, set_cbarlabel = "Sea ice freeboard anomaly (m)", cmap="BrBG", vmin=-0.2, vmax=0.2, out_str='freeboard_winter_anomaly_2018_2022')

snow_depth_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.snow_depth, years=years)
staticArcticMaps(snow_depth_winter_means-snow_depth_winter_means.mean(axis=0), dates=snow_depth_winter_means.time.values,col_wrap=4, set_cbarlabel = "Snow depth anomaly (m)", cmap="PRGn", vmin=-0.2, vmax=0.2, out_str='snowdepth_winter_anomaly_2018_2022')

thickness_winter_means = compute_gridcell_winter_means(IS2SITMOGR4_all.ice_thickness_int, years=years)
staticArcticMaps(thickness_winter_means-thickness_winter_means.mean(axis=0), dates=thickness_winter_means.time.values,col_wrap=4, set_cbarlabel = "Sea ice thickness anomaly (m)", cmap="RdBu", vmin=-1.5, vmax=1.5, out_str='thickness_winter_anomaly_2018_2022')

Monthly mean timeseries

Next we’ll compute monthly means by averaging over all gridcells within a given region. We’ll use this to generate a lineplot to compare across the three winter seasons for each variable.

# Here is where we might also want to set a region mask, e.g. to avoid including some of the more uncertain data in the peripheral seas
innerArctic = [1,2,3,4,5,6]
IS2SITMOGR4_all_innerArctic = IS2SITMOGR4_all.where(IS2SITMOGR4_all.region_mask.isin(innerArctic))

# Uncomment out to set an additional ice type mask too and change the save_label accordingly (0 = FYI, 1 = MYI)
#IS2SITMOGR4_all_innerArctic = IS2SITMOGR4_all_innerArctic.where(IS2SITMOGR4_all_innerArctic.ice_type==1)

save_label='Inner_Arctic'

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.freeboard, years=years, start_month="Sep", figsize=(5,3), annotation='(a)', set_ylabel=r'Total freeboard (m)', save_label=save_label, loc_pos=4, legend=True)

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.snow_depth, years=years, start_month="Sep", figsize=(5,3), annotation='(b)',set_ylabel='Snow depth (m)', save_label=save_label, legend=False)

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.snow_density, years=years, start_month="Sep", figsize=(5,3), annotation='(c)',set_ylabel=r'Snow density (kg/m$^3$)', save_label=save_label, legend=False)

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.ice_thickness, 
                                  da_unc = IS2SITMOGR4_all_innerArctic.ice_thickness_unc, 
                                  years=years, start_month="Sep", figsize=(5,3), set_ylabel='Sea ice thickness (m)', save_label=save_label, annotation='', legend=True)

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.sea_ice_conc, years=years, start_month="Sep", figsize=(5,3), annotation='(e)', set_ylabel='Sea ice concentration', save_label=save_label, legend=False)

static_winter_comparison_lineplot(IS2SITMOGR4_all_innerArctic.ice_type, years=years, start_month="Sep", figsize=(5,3), annotation='(f)',set_ylabel='Multi-year ice fraction', save_label=save_label, legend=False)

Winter mean Arctic maps (interactive, preliminary)

Interactively map the mean winter sea ice conditions across the most recent winter months.
NB: interactive plotting is slow but can be useful for the zoom functionality and for looking at exacty grid-cell/regional values. Plotting all months in the dataset pushes the notebook above 100 Mb which triggers a GitHubt warning

interactiveArcticMaps(IS2SITMOGR4_all.ice_thickness_int.sel(time=slice('2021-09-01', '2022-04-01')), title='IS2SITMOGR4', frame_width=600)

#interactive_winter_comparison_lineplot(IS2SITMOGR4_all.ice_thickness, frame_width=800, title="Arctic Ocean mean winter ICESat-2 sea ice thickness")