Temporospatial variability of snow's thermal conductivity on Arctic sea ice

• Published in: The Cryosphere
• Main Authors: A. R. Macfarlane, H. Löwe, L. Gimenes, D. N. Wagner, R. Dadic, R. Ottersberg, S. Hämmerle, M. Schneebeli
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Arctic; Sea ice; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-17-5417-2023; https://doaj.org/article/f21b527fbafb4b33850517f74faa7ad6

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parameterizations 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 dataset from the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, we have evaluated for the first time the seasonal evolution of the snow's and denser snow-ice interface layers' thermal conductivity above different ice ages (refrozen leads, first-year ice, and second-year ice) and topographic features (ridges). Our dataset has a density range of snow and ice between 50 and 900 kg m −3 , and corresponding anisotropy measurements, meaning we can test the current parameterizations of thermal conductivity for this density range. Combining different measurement parameterizations and assessing the robustness against spatial heterogeneity, we found the average thermal conductivity of snow ( <550 kg m −3 ) on sea ice remains approximately constant (0.26 ± 0.05 W K - 1 m - 1 <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="52pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="a2ccfb160aa81695eb97696ed766e91e"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-17-5417-2023-ie00001.svg" width="52pt" height="13pt" src="tc-17-5417-2023-ie00001.png"/></svg:svg> ) over time irrespective of underlying ice type, with substantial spatial and vertical variability. Due to this consistency, we can state that the thermal resistance is mainly influenced by snow height, resulting in a 2.7 times higher average thermal resistance on ridges (1.42 m 2 K W −1 ) compared to first-year level ice (0.51 m 2 K W −1 ). Our findings explain how the scatter of thermal conductivity values directly results from structural properties. Now, the only step is to find a quick method to measure snow anisotropy in the field. Suggestions ...