This study enhances a snow model for Arctic environments by improving the heat exchanges within the snowpack and at its interfaces, revising the compaction scheme, and adding consideration of blowing snow sublimation losses. Simulations at ten Arctic, mid-latitude, and Alpine sites show significant reductions in simulated soil and snow temperature biases and improved simulated snow depth and density, which are key features to improve simulated energy, water, and carbon budgets in the Arctic.
This study shows that an alternate snow cover fraction parameterization significantly improves snow simulation by CLASSIC in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and snow cover fraction.
Current snow models – including the most sophisticated ones, such as CROCUS and SNOWPACK – struggle to properly simulate Arctic snowpack characteristics such as density profiles. Indeed, those models have been developed and designed for Alpine …
This talk describes the preliminary work intended to serve as a basis for the development of a multilayer snow model adapted for the Arctic region in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). Ten sites – including …