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.
Highlights
Sea ice figures prominently in a broad range of environmental, socioeconomic and geopolitical applications (Matthews et al, 15 2019; Yumashev et al, 2017; Huntington et al, 2016)
In this work we introduce snowBedFoam 1.0., a physics-based snow transport model implemented in the open source fluid dynamics software OpenFOAM
The so-called DPMFoam solver originally implemented in OpenFOAM version 2.3.0. was adapted to simulate the aeolian transport of snow particles. This multiphase flow solver handles coupled Eulerian-Lagrangian phases, which involves a finite number of particles spread in a continuous phase (OpenFOAM API Guide, 2006a). It is based on the Lagrangian particle tracking (LPT) technique called discrete particle method (DPM), which models the system at the micro-mechanical level and 140 tracks the motions of all the particles, or agglomerates of particles (Cleary and Prakash, 2004)
Summary
Sea ice figures prominently in a broad range of environmental, socioeconomic and geopolitical applications (Matthews et al, 15 2019; Yumashev et al, 2017; Huntington et al, 2016). Snow drifts were found to 45 form mostly behind the topographical obstacles, elongated along the dominant wind direction To our knowledge, such spatial observations of snow deposition are non-existent in the literature for Arctic sea ice. From a numerical perspective, Liston et al (2018) recently applied a snow-evolution modelling system (SnowModel, Liston and Elder (2006)) to simulate snowdrifts and snow-depth distributions around sea ice pressure ridges. We initiate the use of the physical model of snow transport based on CFD and LSM (Sharma et al, 2018; Comola et al, 2019) for sea ice applications, in addition to the integration of real snowfall and wind data as forcing 75 parameters.
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