Preprints
https://doi.org/10.5194/bg-2021-121
https://doi.org/10.5194/bg-2021-121

  07 May 2021

07 May 2021

Review status: this preprint is currently under review for the journal BG.

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

Alexandra Pongracz1, David Wårlind1, Paul A. Miller1, and Frans-Jan W. Parmentier1,2 Alexandra Pongracz et al.
  • 1Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 2Centre for Biogeochemistry in the Anthropocene, Department of Geosciences, University of Oslo, Oslo, Norway

Abstract. 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 soil thermodynamics, and changes in its thickness and extent affect both permafrost thaw and soil biogeochemistry. Since soil respiration during the cold season potentially offsets carbon uptake during the growing season, it is essential to achieve a realistic simulation of the effect of snow cover on soil 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 and integrated a dynamic, multi-layer snow scheme in LPJ-GUESS. The new snow scheme performs well in simulating the insulation of snow at hundreds of locations across Russia compared to observations. We show that improving this single physical factor enhanced simulations of permafrost extent compared to an advanced permafrost product. Besides soil thermodynamics, the new snow scheme resulted in increased winter respiration and an overall lower soil carbon content due to warmer soil conditions. The Dynamic scheme also influenced vegetation dynamics, resulting in an improved vegetation distribution and tundra-taiga boundary simulation.

This study highlights the importance of a correct representation of snow in ecosystem models to project biogeochemical processes that govern climate feedbacks. The new dynamic snow scheme is an essential improvement in the simulation of cold season processes, which reduces the uncertainty of model projections. These developments contribute to a better understanding of the Arctic's role in the global climate system.

Alexandra Pongracz et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-121', Anonymous Referee #1, 12 Jul 2021
    • AC1: 'Reply on RC1', Alexandra Pongracz, 14 Sep 2021
  • RC2: 'Comment on bg-2021-121', Anonymous Referee #2, 26 Aug 2021
    • AC2: 'Reply on RC2', Alexandra Pongracz, 14 Sep 2021

Alexandra Pongracz et al.

Alexandra Pongracz et al.

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Short summary
This study shows that the introduction of a multi-layer snow scheme in the LPJ-GUESS DGVM improved simulations of high latitude soil temperature dynamics and permafrost extent compared to observations. In addition, these improvements led to shifts in carbon fluxes and vegetation distribution that contrasted within and outside of the permafrost region. Our results show that a realistic snow scheme is essential to accurately simulate snow-soil-vegetation relationships and carbon-climate feedbacks.
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