Winter Arctic sea ice state variability (updates through to April 2025)
Contents
Winter Arctic sea ice state variability (updates through to April 2025)#
Summary: In this notebook, we provide the fourth update to the original winter Arctic sea ice thickness notebooks with the addition of the 2024 to 2025 winter from the Version 4 monthly gridded winter Arctic sea ice thickness dataset IS2SITMOGR4 (based on new Release 007 ATL10 freeboards).
Author: Alek Petty
Version history: Version 1 (11/13/2025)
### Import notebook dependencies# Regular Python library imports importxarrayasxrimportnumpyasnpimportholoviewsashvimportpandasaspdimporthvplot.pandas# noqa# Helper functions for reading the data from the bucket and plottingfromutils.read_data_utilsimportread_IS2SITMOGR4,read_book_datafromutils.plotting_utilsimportstatic_winter_comparison_lineplot,staticArcticMaps,staticArcticMaps_2025,interactiveArcticMaps,compute_gridcell_winter_means,interactive_winter_comparison_lineplot# Plotting utils # Plotting dependencies#%config InlineBackend.figure_format = 'retina'importmatplotlibasmpl# Sets figure size in the notebookmpl.rcParams['figure.dpi']=200# Remove warnings to improve displayimportwarningswarnings.filterwarnings('ignore')
# Set some plotting parametersmpl.rcParams.update({"text.usetex":False,# Use LaTeX for rendering"font.family":"sans-serif",'mathtext.fontset':'stixsans',"lines.linewidth":1.,"font.size":8,#"lines.alpha": 0.8,"axes.labelsize":8,"xtick.labelsize":8,"ytick.labelsize":8,"legend.fontsize":8})mpl.rcParams['font.sans-serif']=['Arial']
Read in the Version 4 monthly gridded winter Arctic sea ice data#
Here I just load the data using the original netcdf files stored locally.
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",6378273,298.279411123064,AUTHORITY["EPSG","7058"]],AUTHORITY["EPSG","6054"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4054"]],PROJECTION["Polar_Stereographic"],PARAMETER["latitude_of_origin",70],PARAMETER["central_meridian",-45],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AUTHORITY["EPSG","3411"]]
Monthly mean gridded sea ice thickness calculated using redistributed NESOSIM v1.1 snow loading and experimental J22 ice density (Jutila et al., 2022).
Mean ice type from Ocean and Sea Ice Satellite Application Facility (OSI SAF) subsampled by ICESat-2. Ice type in September is not available from OSI SAF, so all grid cells were prescribed as multi-year ice.
units :
ice type flag (0 = first-year ice, 1 = multi-year ice)
Monthly mean gridded sea ice thickness calculated using redistributed SnowModel-LG E5 reanalysis snow loading (Liston et al., 2021). Only available up to July 2021 (public data availability).
Monthly mean gridded sea ice thickness calculated using redistributed SnowModel-LG M2 reanalysis snow loading (Liston et al., 2021). Only available up to July 2021 (public data availability).
Monthly mean gridded and redistributed SnowModel-LG E5 reanalysis snow depths (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
source :
Liston, G. E., J. Stroeve, and P. Itkin. (2021). Lagrangian Snow Distributions for Sea-Ice Applications, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/27A0P5M6LZBI.
Monthly mean gridded SnowModel-LG E5 reanalysis snow density (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
source :
Liston, G. E., J. Stroeve, and P. Itkin. (2021). Lagrangian Snow Distributions for Sea-Ice Applications, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/27A0P5M6LZBI.
Monthly mean gridded and redistributed SnowModel-LG M2 reanalysis snow depths (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
source :
Liston, G. E., J. Stroeve, and P. Itkin. (2021). Lagrangian Snow Distributions for Sea-Ice Applications, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/27A0P5M6LZBI.
Monthly mean gridded SnowModel-LG M2 reanalysis snow density (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
source :
Liston, G. E., J. Stroeve, and P. Itkin. (2021). Lagrangian Snow Distributions for Sea-Ice Applications, Version 1 [Data Set]. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/27A0P5M6LZBI.
Monthly mean gridded and interpolated/smoothed sea ice thickness using redistributed SnowModel-LG E5 reanalysis snow loading. Only available up to July 2021 (public data availability).
Monthly mean gridded and interpolated/smoothed sea ice thickness using redistributed SnowModel-LG M2 reanalysis snow loading. Only available up to July 2021 (public data availability).
Monthly mean gridded and interpolated/smoothed sea ice thickness using redistributed NESOSIM v1.1 snow loading and experimental J22 ice density (Jutila et al., 2022).
Monthly mean gridded, redistributed and smoothed/interpolated SnowModel-LG E5 reanalysis snow depth (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
Monthly mean gridded, redistributed and smoothed/interpolated SnowModel-LG M2 reanalysis snow depth (sub-sampled daily by ICESat-2 prior to monthly binning). Only available up to July 2021 (public data availability).
Monthly mean gridded and redistributed modified Warren 99 (Warren et al., 1999) snow depths (sub-sampled daily by ICESat-2 prior to monthly binning). We use inner Arctic monthly W99 means following Tilling et al., (2017).
source :
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R, (1999). Snow Depth on Arctic Sea Ice, Journal of Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2 & Tilling, R. L., A. Ridout, and A. Shepherd, (2017). Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data, Advances in Space Research, 0273-1177, doi: 10.1016/j.asr.2017.10.051.
Monthly mean gridded Warren 99 snow density (Warren et al., 1999). We use inner Arctic monthly W99 means following Tilling et al., (2017).
source :
Warren, S. G., Rigor, I. G., Untersteiner, N., Radionov, V. F., Bryazgin, N. N., Aleksandrov, Y. I., and Colony, R, (1999). Snow Depth on Arctic Sea Ice, Journal of Climate, 12, 1814–1829, https://doi.org/10.1175/1520-0442(1999)012<1814:SDOASI>2.0.CO;2 & Tilling, R. L., A. Ridout, and A. Shepherd, (2017). Estimating Arctic sea ice thickness and volume using CryoSat-2 radar altimeter data, Advances in Space Research, 0273-1177, doi: 10.1016/j.asr.2017.10.051.
Monthly mean experimental gridded bulk ice density estimates calculated based on along-track ice freeboard following Jutila et al., (2022). To calculate ice freeboard we remove the redistributed SnowModel-LG snow depth from the total ATL10 freeboard.
source :
Jutila, A., Hendricks, S., Ricker, R., von Albedyll, L., Krumpen, T., and Haas, C. (2022). Retrieval and parameterisation of sea-ice bulk density from airborne multi-sensor measurements, The Cryosphere, 16, 259–275, doi:10.5194/tc-16-259-2022.
Monthly mean gridded and redistributed sea ice thickness calculated using modified Warren 99 snow loading (Warren et al., 1999). We use inner Arctic monthly W99 means following Tilling et al., (2017).
Monthly mean gridded, redistributed and smoothed/interpolated modified Warren (Warren et al., 1999) snow depths (sub-sampled daily by ICESat-2 prior to monthly binning). We use inner Arctic monthly W99 means following Tilling et al., (2017).
Monthly mean gridded and interpolated/smoothed sea ice thickness using redistributed modified Warren 99 snow loading (Warren et al., 1999). We use inner Arctic monthly W99 means following Tilling et al., (2017).
Monthly mean gridded total sea ice thickness uncertainty at this 25 km grid-scale, calculated as an average of the systematic uncertainty contributions in the underlying along-track IS2SITDAT4 dataset. Random uncertainties in the along-track data are assumed to not contribute to the total uncertainty at this 25 km grid-scale.
Monthly mean gridded ice concentration from the NOAA/NSIDC Climate Data Record (CDR) of Passive Microwave Sea Ice Concentration, Version 4 (based on monthly input data, not sub-sampled by ICESat-2). Data masked below 0.15
Official NSIDC data doi: 10.5067/CV6JEXEE31HF. Derived data analysis guide: https://www.icesat-2-sea-ice-state.info. Peer reviewed data methodology: 'Petty A. A., N. Keeney, A. Cabaj, P. Kushner, M. Bagnardi (2023), Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection, The Cryosphere, 17, 127–156, doi: 10.5194/tc-17-127-2023' and 'Petty, A. A., N. T. Kurtz, R. Kwok, T. Markus, T. A. Neumann (2020), Winter Arctic sea ice thickness from ICESat‐2 freeboards, Journal of Geophysical Research: Oceans, 125, e2019JC015764. doi:10.1029/2019JC015764'
history :
Created 20/10/25
Winter mean maps, extended and focussing in on the new 2024-2025 winter data#
# Years over which to perform analysis (start year of that winter period)years=[xforxinrange(2018,2024+1)]freeboard_winter_means=compute_gridcell_winter_means(IS2SITMOGR4_v4.freeboard_int,years=years)snow_depth_winter_means=compute_gridcell_winter_means(IS2SITMOGR4_v4.snow_depth_int,years=years)thickness_winter_means=compute_gridcell_winter_means(IS2SITMOGR4_v4.ice_thickness_int,years=years)#staticArcticMaps(thickness_winter_means, dates=thickness_winter_means.time.values,title="", set_cbarlabel = "Sea ice thickness (m)", col_wrap=3, cmap="viridis", vmin=0, vmax=5, out_str='thickness_winter_2018_2025')
# Set a region mask, e.g. to avoid including some of the more uncertain data in the peripheral seasinnerArctic=[1,2,3,4,5,6]IS2SITMOGR4_v4_innerArctic=IS2SITMOGR4_v4.where(IS2SITMOGR4_v4.region_mask.isin(innerArctic))# Drop Sep and October as coverage issues means they are hard to interpretIS2SITMOGR4_v4_innerArctic=IS2SITMOGR4_v4_innerArctic.where(((IS2SITMOGR4_v4_innerArctic['time.month']>10)|(IS2SITMOGR4_v4_innerArctic['time.month']<5)),drop=True)# 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_v4_innerArctic.snow_density,years=years,start_month="Sep",figsize=(4.3,2.7),annotation='(d)',set_ylabel=r'Snow density (kg/m$^3$)',fmts=['m.--','c.--','y.--','r.--','g.--','b.--','ko-'],save_label=save_label,legend=False)
Petty, A. A., Keeney, N., Cabaj, A., Kushner, P., & Bagnardi, M. (2023). Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection. The Cryosphere,17, 127–156, https://doi.org/10.5194/tc-17-127-2023
