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: open (until 05 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Alexandra Pongracz et al.

Alexandra Pongracz et al.


Total article views: 244 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
192 47 5 244 17 2 4
  • HTML: 192
  • PDF: 47
  • XML: 5
  • Total: 244
  • Supplement: 17
  • BibTeX: 2
  • EndNote: 4
Views and downloads (calculated since 07 May 2021)
Cumulative views and downloads (calculated since 07 May 2021)

Viewed (geographical distribution)

Total article views: 218 (including HTML, PDF, and XML) Thereof 218 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 18 Jun 2021
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.