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

Authors :
Hames, Océane
JafariCRYOS, School of Architecture, Civil and EnvironmentalEngineering, EPFL, Lausanne, Switzerland;These authors contributed equally to this work;mahdi.jafari@epfl.ch, Mahdi
Wagner, David N.
Raphael, Ian
Clemens-Sewall, David
Polashenski, Chris
Shupe, Matthew D.
Schneebeli, Martin
Lehning, Michael
Source :
Geoscientific Model Development Discussions; 8/27/2021, p1-29, 29p
Publication Year :
2021

Abstract

The remoteness and extreme conditions of the Arctic make it a very difficult environment to investigate. In these regions, the wind has a substantial effect and redistributes a large part of the snow, which complicates precipitation estimates. 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 these uncertainties. 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 lidar observations of surface dynamics to simulate snow deposition on a piece of MOSAiC sea ice 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 terrestrial laser scans. However, the approximations imposed by the numerical framework together with potential measurement errors (precipitation) give rise to quantitative inaccuracies. The modelling 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19919611
Database :
Complementary Index
Journal :
Geoscientific Model Development Discussions
Publication Type :
Academic Journal
Accession number :
152302541
Full Text :
https://doi.org/10.5194/gmd-2021-254