Observation and modelling of snow at a polygonal tundra permafrost site: spatial variability and thermal implications

• Published in: The Cryosphere
• Main Authors: Gouttevin, Isabelle, Langer, Moritz, Löwe, Henning, Boike, Julia, Proksch, Martin, Schneebeli, Martin
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2018
• Subjects: Arctic; permafrost; polar night; The Cryosphere; Tundra
• Online Access: https://epic.awi.de/id/eprint/48542/; https://doi.org/10.5194/tc-12-3693-2018; https://hdl.handle.net/10013/epic.c46d7e7c-8d11-4ee9-b339-7e8ec30bb978

The shortage of information on snow properties in high latitudes places a major limitation on permafrost and more generally climate modelling. A dedicated field program was therefore carried out to investigate snow properties and their spatial variability at a polygonal tundra permafrost site. Notably, snow samples were analysed for surface-normal thermal conductivity (Keff − z) based on X-ray microtomography. Also, the detailed snow model SNOWPACK was adapted to these Arctic conditions to enable relevant simulations of the ground thermal regime. Finally, the sensitivity of soil temperatures to snow spatial variability was analysed. Within a typical tundra snowpack composed of depth hoar overlain by wind slabs, depth hoar samples were found more conductive (Keff − z = 0.22±0.05Wm−1K−1) than in most previously published studies, which could be explained by their high density and microstructural anisotropy. Spatial variations in the thermal properties of the snowpack were well explained by the microtopography and ground surface conditions of the polygonal tundra, which control depth hoar growth and snow accumulation. Our adaptations to SNOWPACK, phenomenologically taking into account the effects of wind compaction, basal vegetation, and water vapour flux, yielded realistic density and Keff − z profiles that greatly improved simulations of the ground thermal regime. Also, a density- and anisotropy-based parameterization for Keff − z lead to further slight improvements. Soil temperatures were found to be particularly sensitive to snow conditions during the early winter and polar night, highlighting the need for improved snow characterization and modelling over this period.