Model Simulations of Arctic Biogeochemistry and Permafrost Extent Are Sensitive to the Implemented Snow Scheme

Abstract:

The amplified warming of the Arctic is strongest in winter, which is causing large-scale reductions in snow cover. Since snow is one of the main factors governing soil thermodynamics, changes in its thickness and extent also affects permafrost thaw and soil biogeochemistry. Soil respiration during the cold season may have the potential to offset carbon uptake during the growing season. Therefore, a correct simulation of snow cover is essential to project the direction of arctic carbon-climate feedbacks into the future. The Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model has used – up until now – a single layer snow scheme, which underestimated the insulation effect of snow leading to a cold bias in soil temperature. To address this shortcoming, we developed and integrated a dynamic, multi-layer snow scheme in LPJ-GUESS and show how improvement of this single physical parameter affects simulations of permafrost extent, vegetation distribution and greenhouse gas exchange. The new snow scheme performs well in simulating the insulation of snow across hundreds of locations across Russia and has a strongly improved correspondence in soil temperatures to a detailed permafrost model. Our analysis shows that the new snow scheme not only influences soil thermodynamics, but also affects soil carbon content, vegetation distribution and biogeochemistry. This study highlights the importance of a correct representation of snow in ecosystem models to realistically project biogeochemical processes that govern climate feedbacks. The implemented dynamic snow scheme prompts further model development in simulating cold season processes, which may reduce the uncertainty of future projections. Ultimately, these developments can contribute to a better understanding of the role of the Arctic in the global carbon cycle.