We examined the effect of snow-ice formation on SnowModel-LG snow depth and density products. We coupled SnowModel-LG, a modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D thermodynamic sea ice model, to create SnowModel-LG_HS. Pan-Arctic model simulations spanned from 1 August 1980 through 31 July 2022. In SnowModel-LG_HS, domain average snow depth decreased by 20%, and snow density increased by 2% when compared to SnowModel-LG, with largest differences in the Atlantic sector. Averaged across the CryoSat-2 era (2011–2022), domain average April sea ice thickness retrievals from CryoSat-2 decreased by 7.7% when snow-ice was accounted for. Evaluation of SnowModel-LG HS against snow depth, snow-ice, and sea ice thickness observations highlighted the importance of snow redistribution over deformed sea ice. The findings suggest that neglecting snow and sea ice interactions in models can lead to substantial overestimation of snow depth over level ice.

This study quantified the effect of snow-ice formation on SnowModel-LG snow depth and density products. We coupled SnowModel-LG, a modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. We compared snow depth and density from the coupled product (SnowModel-LG_HS) to the original outputs of SnowModel-LG. In SnowModel-LG_HS, domain average snow depth decreased by 22%, and snow density increased by 2% when compared to SnowModel-LG. The differences were much larger in the Atlantic sector. Our simulations suggest that when snow-on-sea-ice models account for snow-ice formation, snow depth can be remarkably reduced. Sea ice thickness retrievals from CryoSat-2 were guided by both snow products. Averaged across the CryoSat-2 era (2011-2022), domain average sea ice thickness retrievals decreased by 10% when snow-ice was accounted for.