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. The Arctic is warming rapidly, especially in winter, which is causing large-scale reductions in snow cover. Snow is one of the main controls on soilthermodynamics, and changes in its thickness and extent affect both permafrost thaw and soil biogeochemistry. Since soil respiration during the coldseason potentially offsets carbon uptake during the growing season, it is essential to achieve a realistic simulation of the effect of snow cover onsoil conditions to more accurately project the direction of arctic carbon–climate feedbacks under continued winter warming. 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 andintegrated a dynamic, multi-layer snow scheme in LPJ-GUESS. The new snow scheme performs well in simulating the insulation of snow at hundreds oflocations across Russia compared to observations. We show that improving this single physical factor enhanced simulations of permafrost extentcompared to an advanced permafrost product, where the overestimation of permafrost cover decreased from 10 % to 5 % using the new snowscheme. Besides soil thermodynamics, the new snow scheme resulted in a doubled winter respiration and an overall higher vegetation carbon content. This study highlights the importance of a correct representation of snow in ecosystem models to project biogeochemical processes that govern climatefeedbacks. The new dynamic snow scheme is an essential improvement in the simulation of cold season processes, which reduces the uncertainty ofmodel projections. These developments contribute to a more realistic simulation of arctic carbon–climate feedbacks.

Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS