EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Quantifying the effect of snow and sea ice interactions on SnowModel-LG snow depth and density product

Ioanna Merkouriadi1, Glen Liston2, and Heidi Salilla1
Ioanna Merkouriadi et al.
  • 1Finnish Meteorological Institute, Research, Finland (
  • 2Cooperative Institute for Research in the Atmosphere, Colorado State University

Snow is a crucial component of the Arctic sea ice system. It dominates the exchanges of heat and light between the atmosphere and the ocean, with important physical and biological implications. To address the imperative need for more realistic representation of snow on sea ice, recent efforts have focused on reanalysis-based snow depth and density reconstructions. However, none of the recent snow products account for snow losses due to snow and sea ice interactions.

This study quantifies the snow loss in snow-ice formation, and its effect in SnowModel-LG snow depth and density product. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice and thermal ice growth: SnowModel-LG_HS. We assumed that all negative freeboard would result in snow-ice formation. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2021, and they were guided by observations where available. In SnowModel-LG_HS, snow depth was lower (domain average: 18%), and snow density was higher (2.3%) compared to SnowModel-LG. The differences were much larger in the Atlantic sector. Our simulations suggest that when snow models do not account for snow and ice interactions, snow depth can be significantly overestimated. In this talk we will discuss the magnitude of this overestimation in relation to the sub-grid parameterization of sea ice dynamics and their effect in snow redistribution over the ice floes. Sea ice dynamics (e.g. deformed ice formation), are likely an additional important snow sink that is not yet accounted for in snow models.

Finally, we use our snow depth and density results from SnowModel-LG_HS to obtain sea ice thickness retrievals from CryoSat-2. A validation of these retrievals against Airborne Electromagnetic Measurements shows that SnowModel-LG_HS performed better when compared to SnowModel-LG and snow climatologies.



How to cite: Merkouriadi, I., Liston, G., and Salilla, H.: Quantifying the effect of snow and sea ice interactions on SnowModel-LG snow depth and density product, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12357,, 2023.