Sea ice thickness is a critical variable, both as a climate indicator and for forecasting sea ice conditions on seasonal and longer time scales. The lack of snow depth and density information is a major source of uncertainty in current thickness retrievals from laser and radar altimetry. In response to this data gap, a new Lagrangian snow evolution model (SnowModel‐LG) was developed to simulate snow depth, density, and grain size on a pan‐Arctic scale, daily from August 1980 through July 2018. In this study, we evaluate the results from this effort against various data sets, including those from Operation IceBridge, ice mass balance buoys, snow buoys, MagnaProbes, and rulers. We further compare modeled snow depths forced by two reanalysis products (Modern Era Retrospective‐Analysis for Research and Applications, Version 2 and European Centre for Medium‐Range Weather Forecasts Reanalysis, 5th Generation) with those from two historical climatologies, as well as estimates over first‐year and multiyear ice from satellite passive microwave observations. Our results highlight the ability of our SnowModel‐LG implementation to capture observed spatial and seasonal variability in Arctic snow depth and density, as well as the sensitivity to the choice of reanalysis system used to simulate snow depths. Since 1980, snow depth is found to decrease throughout most regions of the Arctic Ocean, with statistically significant trends during the cold season months in the marginal ice zones around the Arctic Ocean and slight positive trends north of Greenland and near the pole.

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the “KuKa” dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in “stare” nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r² values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r² 0.15 or lower. r² for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters.

The accurate knowledge of sea ice parameters, including sea ice thickness and snow depth over the sea ice cover, is key to both climate studies and data assimilation in operational forecasts. Large-scale active and passive remote sensing is the basis for the estimation of these parameters. In traditional altimetry or the retrieval of snow depth with passive microwave remote sensing, although the sea ice thickness and the snow depth are closely related, the retrieval of one parameter is usually carried out under assumptions over the other. For example, climatological snow depth data or as derived from reanalyses contain large or unconstrained uncertainty, which result in large uncertainty in the derived sea ice thickness and volume. In this study, we explore the potential of combined retrieval of both sea ice thickness and snow depth using the concurrent active altimetry and passive microwave remote sensing of the sea ice cover. Specifically, laser altimetry and L-band passive remote sensing data are combined using two forward models: the L-band radiation model and the isostatic relationship based on buoyancy model. Since the laser altimetry usually features much higher spatial resolution than L-band data from the Soil Moisture Ocean Salinity (SMOS) satellite, there is potentially covariability between the observed snow freeboard by altimetry and the retrieval target of snow depth on the spatial scale of altimetry samples. Statistically significant correlation is discovered based on high-resolution observations from Operation IceBridge (OIB), and with a nonlinear fitting the covariability is incorporated in the retrieval algorithm. By using fitting parameters derived from large-scale surveys, the retrievability is greatly improved compared with the retrieval that assumes flat snow cover (i.e., no covariability). Verifications with OIB data show good match between the observed and the retrieved parameters, including both sea ice thickness and snow depth. With detailed analysis, we show that the error of the retrieval mainly arises from the difference between the modeled and the observed (SMOS) L-band brightness temperature (TB). The narrow swath and the limited coverage of the sea ice cover by altimetry is the potential source of error associated with the modeling of L-band TB and retrieval. The proposed retrieval methodology can be applied to the basin-scale retrieval of sea ice thickness and snow depth, using concurrent passive remote sensing and active laser altimetry based on satellites such as ICESat-2 and WCOM.

What triggers Maud Rise polynya, a large opening in the winter Antarctic sea ice, is still debated. We show that the upcoming opening of all Maud Rise polynyas can be detected in early winter up to four months ahead, especially since the 2002 expansion in satellite observations. In all polynya years, continuous anomalous sea ice thinning begins in May, caused by atmospheric and oceanic forcings. Dynamically, an anomalous cyclonic circulation in the atmosphere and the ocean strengthens the Weddell Gyre and exerts anomalously intense stresses on the ice. Thermodynamically, the warm water advected by the intensified circulation, and most importantly entrained into the mixed layer, thins the ice from below at the beginning of the freezing season, preconditioning the region for a polynya event months later. This four‐month‐ahead pattern enables early predictions of the polynya, and improved expedition planning and sensor deployment.

Sea ice production within polynyas, an outcome of the atmosphere‐ice‐ocean interaction, is a major source of dense water and hence key to the global overturning circulation, but is poorly quantified over open‐ocean polynyas. Using the two recent extensive open‐ocean polynyas within the wider Maud Rise region of the Weddell Sea in 2016 and 2017, we here explore the sea ice energy budget and estimate their sea ice production based on satellite retrievals, in‐situ hydrographic observations and the Japanese 55‐year Reanalysis. We find that the oceanic heat flux amounts to 36.1 and 30.7 W m⁻² within the 2016 and 2017 polynyas, respectively. Especially the 2017 open‐ocean polynya produced nearly 200 km³ of new sea ice, which is comparable to the production in the largest Antarctic coastal polynyas. Finally, we determine that ice production is highly correlated with and sensitive to skin temperature and wind speed, which affect the turbulent fluxes. It is also strongly sensitive to uncertainties in the sea ice concentration and 1,000 hPa temperature, which all urgently need to be better monitored at high latitudes. Lastly, more process‐oriented campaigns are required to further elucidate the role of open‐ocean polynya on the local and global ocean circulations.