Implementation of an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into a sea ice model (SI3): 1D thermodynamic coupling and validation

Abstract

Snow plays a crucial role in the formation and sustainability of sea ice. Due to its thermal properties, snow acts as an insulating layer, shielding the ice from the air above. This insulation reduces the heat transfer between the sea ice and the atmosphere. Due to its reflective properties, the snow cover also strongly contributes to albedo over ice-covered regions, which gives it a significant role in the Earth's climate system. Current state-of-art climate models use overly simple representations of the snow cover overlaying the sea ice. The snow cover is often represented with a one-layer scheme, assuming a constant density, no wet or dry metamorphism, or that no liquid water is stored in the snow. Here we implemented an intermediate-complexity snow-physics scheme (ISBA-Explicit Snow) into a sea ice model (SI3), which serves as the sea ice component for upcoming versions of the CNRM-CM climate model. This is, to our knowledge, the first time that a snow model with such a level of complexity has been incorporated into a sea ice model designed for global to regional applications. We validated our model by comparing 1D simulations with data from the Surface Heat Budget of the Arctic Ocean (SHEBA) but also simulations from another advanced snow-on-sea ice model (SnowModel-LG), as well as simulations with the previous SI3 snow scheme. Our model simulates realistic snow thicknesses, densities, and temperatures, aligning well with SHEBA observations and SnowModel-LG outputs, while capturing their temporal variability. We show that the thickness, density, and thermal conductivity of the snowpack are significantly affected by the choices made in parameterization for calculating snowfall density and wind-induced snow compaction and by the choice of the atmospheric forcing. Unlike the previous SI3 snow scheme that assumed constant density and thermal conductivity, our model realistically simulates the evolution of these properties, resulting in more accurate temperatures at the snow-ice interface. Ultimately, our study shows that modeling the temporal changes in the density and thermal conductivity of the snow layers leads to more realistic upper sea ice temperatures and therefore to a more accurate representation of heat transfer between the underlying sea ice and the atmosphere.