In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time‐evolving snowfall rates over Arctic sea ice, and it can be difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences in spatial representativeness and temporal sampling are accounted for. This motivates the use of CloudSat's snowfall product to calibrate reanalysis snowfall. The calibration is carried out for four Arctic quadrants and combined to produce regionally resolved and consistent estimates of interannually varying snowfall. Calibrated reanalysis snowfall inputs are then used to drive the NASA Eulerian Snow On Sea Ice Model, reducing the interproduct spread in the resulting simulated snow depths across the Arctic.

<div> <div> <div> <div> <p>National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐ 2 (ICESat‐2) mission was launched in September 2018 and is now providing routine, very high‐resolution estimates of surface height/type (the ATL07 product) and freeboard (the ATL10 product) across the Arctic and Southern Oceans. In recent work we used snow depth and density estimates from the NASA Eulerian Snow on Sea Ice Model (NESOSIM) together with ATL10 freeboard data to estimate sea ice thickness across the entire Arctic Ocean. Here we provide an overview of updates made to both the underlying ATL10 freeboard product and the NESOSIM model, and the subsequent impacts on our estimates of sea ice thickness including updated comparisons to the original ICESat mission and ESA’s CryoSat-2. Finally we compare our Arctic ice thickness estimates from the 2018-2019 and 2019-2020 winters and discuss possible causes of these differences based on an analysis of atmospheric data (ERA5), ice drift (NSIDC) and ice type (OSI SAF).</p> </div> </div> </div> </div>

Abstract. NASA's ICESat-2 mission has provided near-continuous, high-resolution estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. This study provides an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NASA Eulerian Snow On Sea Ice Model (NESOSIM) snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ATL10, which generally results in an increase in freeboards compared to rel002 data. The thickness increases due to increased freeboards in ATL10 improved comparisons of Inner Arctic Ocean sea ice thickness with thickness estimates from ESA's CryoSat-2. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth and density which have a less significant impact on thickness compared to the rel002 to rel003 ATL10 freeboard changes. The updated monthly gridded thickness data are validated against ice draft measurements obtained by upward-looking sonar moorings deployed in the Beaufort Sea, showing strong agreement (r2 of 0.87, differences of 11 ± 20 cm). The seasonal cycle in winter monthly mean Arctic sea ice thickness shows good agreement with various CryoSat-2 products (and a merged ICESat-2–CryoSat-2 product) and PIOMAS (Pan-Arctic Ice-Ocean Modeling and Assimilation System). Finally, changes in Arctic sea ice conditions over the past three winter seasons of data collection (November 2018–April 2021) are presented and discussed, including a 50 cm decline in multiyear ice thickness and negligible interannual differences in first-year ice. Interannual changes in snow depth provide a notable impact on the thickness retrievals on regional and seasonal scales. Our monthly gridded thickness analysis is provided online in a Jupyter Book format to increase transparency and user engagement with our ICESat-2 winter Arctic sea ice thickness data.

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Abstract. Reliable basin-scale estimates of sea ice thickness are urgently needed to improve our understanding of recent changes and future projections of polar climate. Data collected by NASA’s ICESat-2 mission have provided new, high-resolution, estimates of sea ice freeboard across both hemispheres since data collection started in October 2018. These data have been used in recent work to produce estimates of winter Arctic sea ice thickness using snow loading estimates from the NASA Eulerian Snow On Sea Ice Model (NESOSIM). Here we provide an impact assessment of upgrades to both the ICESat-2 freeboard data (ATL10) and NESOSIM snow loading on estimates of winter Arctic sea ice thickness. Misclassified leads were removed from the freeboard algorithm in the third release (rel003) of ICESat-2 freeboard data, which increased freeboards in January and April 2019, and increased the fraction of low freeboards in November 2018, compared to rel002. These changes improved comparisons of sea ice thickness (lower mean biases and standard deviations, higher correlations) with monthly gridded thickness estimates produced from ESA’s CryoSat-2 (using the same input snow and ice density assumptions). Later releases (rel004 and rel005) of ICESat-2 ATL10 freeboards result in less significant changes in the freeboard distributions and thus thickness. The latest version of NESOSIM (version 1.1), forced by CloudSat-scaled ERA5 snowfall, has been re-calibrated using snow depth estimates obtained by NASA’s Operation IceBridge airborne mission. The upgrade from NESOSIM v1.0 to v1.1 results in only small changes in snow depth which have a less significant impact on thickness compared to the rel002 to rel003 freeboard changes. Finally, we present our updated monthly gridded winter Arctic sea ice thickness dataset and highlight key changes over the past three winter seasons of data collection (November 2018–April 2021). Strong differences in total winter Arctic thickness across the three winters are observed, linked to clear differences in the multiyear ice thickness at the start of each winter. Interannual changes in snow depth provide significant impacts on our thickness results on regional and seasonal scales. Our analysis of recent winter Arctic sea ice thickness variability is provided online in a novel Jupyter Book format to increase transparency and user engagement with our derived gridded monthly thickness dataset.