Influence of the inter-annual variability of snow physical properties on the ground thermal regime - through observations and modelling (Samoylov Island, Siberia)
Automated measurements of snow physical properties in the remote Arctic are scarce, which poses a challenge not only for the investigation of the snow cover evolution throughout the season and years, but also for climate and permafrost modelling, as snow is a crucial parameter for the ground thermal...
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| Format: | Thesis |
| Language: | unknown |
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Department Geoscience
2022
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| Online Access: | https://epic.awi.de/id/eprint/57033/ https://epic.awi.de/id/eprint/57033/1/4523900_20220722_MJ_MSc.pdf https://hdl.handle.net/10013/epic.d9acdfe0-236d-4beb-8136-293c32e85d29 |
| Summary: | Automated measurements of snow physical properties in the remote Arctic are scarce, which poses a challenge not only for the investigation of the snow cover evolution throughout the season and years, but also for climate and permafrost modelling, as snow is a crucial parameter for the ground thermal regime. Here, I present the first-time analysis of a sophisticated automated snow measurement data set which was obtained in a Low Centre Polygon (LCP) complex on Samoylov Island, a permafrost site in the Lena River Delta, Siberia. My work focused on analysing the inter-annual variability and seasonal evolution of the snow physical properties depth, density and temperature. I investigated the influence of on the ground thermal regime as well as snow-soil interactions within a time period of four years from 2014 to 2018.Furthermore, I used the data to validate the new ‹CryoGrid (CG) Community› version of the CG permafrost model for Samoylov Island with regard to the snow physical properties. My processing routine included the quality check of the snow depth, snow temperatures as well as density time series and I compared the automated measurements with field observations to evaluate the sensor performances. Furthermore, I generated ‹CG Community› model runs with two versions of the Crocus snow scheme (Standard and Arctic) to validate the models’ ability to reproduce the snow and ground thermal regime for the time span of the observations (2014 to 2018). My results revealed great inter-annual variations in the snow cover extent and internal layering of the snowpack on Samoylov Island within the analysed time period. I found a great spatial variability of the snow depth, which was depended on the micro-topography of the LCP complex. The mean end-of-season (EOS) snow depth was the highest in the polygon centre (0.46 to 0.74 m) and the lowest on the polygon rims (0.32 and 0.53 m). The EOS density for the snowpack up to 0.3 m in the polygon centre was between 204 and 236 kg m−3. The snowpack was characterized by the ... |
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