Snow‐depth distributions on sea ice have substantial impacts on winter ice growth and summer ice melt. There are two types of wind‐related snow distributions in this environment: snowdrifts that form around ice pressure ridges; and snow dunes and other snow bedforms that form on relatively level, undeformed ice. A snow‐evolution modeling system (SnowModel) was tested against winter snow observations collected during the Norwegian young sea ICE expedition (N‐ICE2015) north of Svalbard, with an emphasis on reproducing these two types of snow distributions. The SnowModel simulation spanned 1 year, summer 2014 through summer 2015, over a 1.5 km by 1.5 km domain, using a 1 m horizontal grid increment and 3 h time step. Existing SnowModel components, created originally for terrestrial applications, performed energy balance, snow property, snowdrift, and data assimilation calculations in response to prescribed meteorological forcings. A new SnowModel component, SnowDunes, was created to simulate snow‐bedform distributions on level, undeformed ice. Model results reproduced observed snowdrift profiles around pressure ridges and snow‐depth distributions over level, undeformed ice, resulting in physically realistic snow heterogeneity and property information. We conclude that the SnowModel toolkit contains the components required to simulate most processes driving the seasonal distribution of snow on sea ice. SnowModel can evolve snow on sea ice over local to pan‐Arctic areas, using grid increments ranging from 1 m to tens of km, and time increments of subhourly to daily.

During the Norwegian young sea ICE (N‐ICE2015) campaign in early 2015, a deep snowpack was observed, almost double the climatology for the region north of Svalbard. There were significant amounts of snow‐ice in second‐year ice (SYI), while much less in first‐year ice (FYI). Here we use a 1‐D snow/ice thermodynamic model, forced with reanalyses, to show that snow‐ice contributes to thickness growth of SYI in absence of any bottom growth, due to the thick snow. Growth of FYI is tightly controlled by the timing of growth onset relative to precipitation events. A later growth onset can be favorable for FYI growth due to less snow accumulation, which limits snow‐ice formation. We surmise these findings are related to a phenomenon in the Atlantic sector of the Arctic, where frequent storm events bring heavy precipitation during autumn and winter, in a region with a thinning ice cover.

Snow is a crucial component of the Arctic sea ice system. Its thickness and thermal properties control heat conduction and radiative fluxes across the ocean, ice, and atmosphere interfaces. Hence, observations of the evolution of snow depth, density, thermal conductivity, and stratigraphy are crucial for the development of detailed snow numerical models predicting energy transfer through the snow pack. Snow depth is also a major uncertainty in predicting ice thickness using remote sensing algorithms. Here we examine the winter spatial and temporal evolution of snow physical properties on first‐year (FYI) and second‐year ice (SYI) in the Atlantic sector of the Arctic Ocean, during the Norwegian young sea ICE (N‐ICE2015) expedition (January to March 2015). During N‐ICE2015, the snow pack consisted of faceted grains (47%), depth hoar (28%), and wind slab (13%), indicating very different snow stratigraphy compared to what was observed in the Pacific sector of the Arctic Ocean during the SHEBA campaign (1997–1998). Average snow bulk density was 345 kg m⁻³ and it varied with ice type. Snow depth was 41 ± 19 cm in January and 56 ± 17 cm in February, which is significantly greater than earlier suggestions for this region. The snow water equivalent was 14.5 ± 5.3 cm over first‐year ice and 19 ± 5.4 cm over second‐year ice.

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research.

Global monitoring of seasonal snow water equivalent (SWE) has advanced significantly over the past decades. However, challenges remain when estimating SWE from passive and active microwave signatures, because a priori characterization of snow properties is required for SWE retrievals. Numerical experiments have shown that utilizing physical snow models to acquire snowpack characterization can potentially improve microwave‐based SWE retrievals. This study aims to identify the challenges of assimilating active and passive microwave signatures with physical snow models, and to examine solutions to those challenges. Guided by observations from a point‐based study, we designed a sensitivity experiment to quantify the effects of changes in the physically modeled SWE—and of corresponding changes to other snowpack properties—to the microwave‐based SWE retrievals. The results indicate that assimilating microwave signatures with physical snow models face some critical challenges associated with the physical relationship between SWE and snow microstructure. We demonstrate these challenges can be overcome if the microwave algorithms account for these relationships.

We examine the regional variations and long‐term changes of the potential for snow‐ice formation for level Arctic sea ice from 1980 to 2016. We use daily sea ice motion data and implement a 1‐D snow/ice thermodynamic model that follows the ice trajectories while forcing the simulations with Modern‐Era Retrospective analysis for Research and Applications, Version 2 and ERA‐Interim reanalyses. We find there is potential for snow‐ice formation in level ice over most of the Arctic Ocean; this is true since the 1980s. In addition, the regional variations are very strong. The largest potential is typically found in the Atlantic sector of the Arctic Ocean, particularly in the Greenland Sea, where precipitation is highest. We surmise that, in addition to the annual amount of solid precipitation, potential for snow‐ice formation is controlled by two main factors: the initial second‐year/multiyear ice thickness in the autumn and the timing of first‐year ice formation.