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Spatio-Temporal Dynamics of Snow on Central Arctic Sea Ice and its Implications

David Nicholas Wagner

This thesis presents a comprehensive investigation of the interaction between precipitation and wind-induced erosion and deposition of snow on Arctic sea ice. The study uses observations from the 2019-2020 MOSAiC expedition and makes use of the 3D snow cover- and snow transport model ALPINE3D. Additionally, a series of related studies with my participation is presented. We describe the application of a snow depth-SWE (Snow-Water-Equivalent) function using SnowMicroPen force signals and its application to Magnaprobe snow depth data for MOSAiC transect lines. The model accurately reconstructed SWE compared to direct measurements, allowing for the estimation of the accumulated snow mass between October 2019 and May 2020. The study found that the net accumulation mass in spring was significantly lower than that observed during the earlier SHEBA or N-ICE2015 expeditions, likely due to climate change. The study also found that the snow accumulation rate on first year ice (FYI) was higher than that on second year ice (SYI), and the mass on both ice types approached a saturation point towards the end of February, although SYI had twice the snow mass at the beginning of the winter. In addition, we investigated the plausibility of precipitation sensors measurements installed during MOSAiC, finding that only a small fraction of the data was usable without post-processing. Snowfall sensors installed close to the ice at low altitude tended to overestimate the snowfall rate due to drifting snow falsely detected as precipitation, while sensors on the ship installed at over 20 m height showed almost no such behavior. The study calculated a plausible range of 72 to 107 mm for the total cumulated precipitation mass between October 2019 and May 2020. These values were compared with the accumulated snow amount on the ground to estimate the relative amount of snow that had disappeared after snowfall. We conclude that at least about 50 % of precipitated snow have been eroded and transported away, which is roughly in line with previous studies. Furthermore, the 3D snow cover and snow transport model ALPINE3 was adjusted, calibrated, and applied to simulate snow processes over Arctic sea ice. The model underwent several statistical and qualitative comparisons. ALPINE3D captured the statistical distribution of snow height differences well, but both the statistical and qualitative evaluations showed a lack of elongated snow bed forms such as dunes, which is likely due to the absence of a dynamic mesh. Regarding surface density, the model was compared to measurements, which showed excellent density reproduction up to a certain point but with a slight underestimation at later stages. Additionally, the model showed strong temporal variations in surface density, which were significantly reduced by increasing the fluid threshold at the surface for wind-induced snow transport. Furthermore, we used the model to investigate the deposition and erosion of snow in the area of pressure ridges based on cross-sections. These revealed a partially accurate modeling of the snow height difference, which suggests that the model can currently be used particularly well in these areas, for instance to investigate the thermal effect on the underlying ice or vice versa. Finally, we presented a series of related studies with co-authorship, including an overview paper on MOSAiC snow and ice measurements, the introduction of another snow transport model, an analysis of the impact of

EPFL2023

Thermal Conductivity of Snow on Arctic Sea Ice

Henning Löwe, Marc Schneebeli Zeugin, David Nicholas Wagner

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed.

2023

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

Océane Hames, Mahdi Jafari, Michael Lehning, Marc Schneebeli Zeugin, David Nicholas Wagner

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.

COPERNICUS GESELLSCHAFT MBH2022

Spatio-Temporal Dynamics of Snow on Central Arctic Sea Ice and its Implications

David Nicholas Wagner

EPFL2023