Submitted as: model description paper
27 Aug 2021
Submitted as: model description paper | 27 Aug 2021
Status: a revised version of this preprint is currently under review for the journal GMD.

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

Océane Hames1,2,, Mahdi Jafari2,, David N. Wagner1,2, Ian Raphael3, David Clemens-Sewall3, Chris Polashenski3,4, Matthew D. Shupe5,6, Martin Schneebeli1, and Michael Lehning1,2 Océane Hames et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, Lausanne, Switzerland
  • 3Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA
  • 4USACE-CRREL Alaska Projects Office, Fairbanks, Alaska, USA
  • 5NOAA Physical Science Laboratory, Boulder, Colorado, USA
  • 6Cooperative Institute for the Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado, USA
  • These authors contributed equally to this work.

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.

Océane Hames et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-254', Anonymous Referee #1, 05 Oct 2021
  • RC2: 'Comment on gmd-2021-254', Anonymous Referee #2, 03 Jan 2022
  • AC1: 'Comment on gmd-2021-254: responses to referee 1', Océane Hames, 11 Mar 2022
  • AC2: 'Comment on gmd-2021-254: responses to referee 2', Océane Hames, 11 Mar 2022

Océane Hames et al.

Data sets

MOSAiC Met City preliminary (Level 2) wind data Matthew D. Shupe et al.

Terrestrial Laser Scan Data David Clemens-Sewall

Model code and software

snowBedFoam 1.0. Océane Hames, Mahdi Jafari, Michael Lehning

Océane Hames et al.


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Short summary
This paper presents an Eulerian-Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.