Modeling the small-scale deposition of snow onto structured Arctic sea ice during a MOSAiC storm using snowBedFoam 1.0.

• Published in: Geoscientific Model Development
• Main Authors: Hames, Oceane, Jafari, Mahdi, Wagner, David Nicholas, Raphael, Ian, Clemens-Sewall, David, Polashenski, Chris, Shupe, Matthew D., Schneebeli, Martin, Lehning, Michael
• Format: Text
• Language: unknown
• Published: Gottingen, COPERNICUS GESELLSCHAFT MBH 2022
• Subjects: Arctic; Sea ice
• Online Access: https://doi.org/10.5194/gmd-15-6429-2022; https://infoscience.epfl.ch/record/296669/files/gmd-15-6429-2022.pdf; http://infoscience.epfl.ch/record/296669

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these polar regions covered by sea ice, the wind is relatively strong due to the absence of obstructions and redistributes a large part of the deposited snow mass, which complicates estimates for precipitation hardly distinguishable from blowing or drifting snow. Moreover, the snow mass balance in the sea ice system is still poorly understood, notably due to the complex structure of its surface. Quantitatively assessing the snow distribution on sea ice and its connection to the sea ice surface features is an important step to remove the snow mass balance uncertainties (i.e., snow transport contribution) in the Arctic environment. In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open-source fluid dynamics software OpenFOAM. We combine the numerical simulations with terrestrial laser scan observations of surface dynamics to simulate snow deposition in a MOSAiC (Multidisciplinary Drifting Observatory for the Study of Arctic Climate) sea ice domain with a complicated structure typical for pressure ridges. The results demonstrate that a large fraction of snow accumulates in their vicinity, which compares favorably against scanner measurements. However, the approximations imposed by the numerical framework, together with potential measurement errors (precipitation), give rise to quantitative inaccuracies, which should be addressed in future work. The modeling of snow distribution on sea ice should help to better constrain precipitation estimates and more generally assess and predict snow and ice dynamics in the Arctic.