Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS
Pongracz, Alexandra LU
; Wårlind, David
LU
; Miller, Paul A.
LU
and Parmentier, Frans Jan W.
LU
(2021)
In Biogeosciences
18(20).
p.5767-5787
- 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... (More)
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, where the overestimation of permafrost cover decreased from 10% to 5% using the new snow scheme. 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 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 more realistic simulation of arctic carbon-climate feedbacks.
(Less)
- author
- Pongracz, Alexandra
LU
; Wårlind, David
LU
; Miller, Paul A.
LU
and Parmentier, Frans Jan W.
LU
- organization
- publishing date
- 2021-10-26
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Biogeosciences
- volume
- 18
- issue
- 20
- pages
- 21 pages
- publisher
- Copernicus GmbH
- external identifiers
-
- scopus:85118242935
- ISSN
- 1726-4170
- DOI
- 10.5194/bg-18-5767-2021
- project
- WINTERGAP: Quantifying the impact of winter warming on the Arctic carbon cycling and associated climate feedbacks
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © Author(s) 2021.
- id
- 9e9bb945-e933-4958-98bb-98852d1c2938
- date added to LUP
- 2021-11-13 09:34:01
- date last changed
- 2023-02-19 06:00:09
@article{9e9bb945-e933-4958-98bb-98852d1c2938,
abstract = {{<p>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, where the overestimation of permafrost cover decreased from 10% to 5% using the new snow scheme. 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 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 more realistic simulation of arctic carbon-climate feedbacks. </p>}},
author = {{Pongracz, Alexandra and Wårlind, David and Miller, Paul A. and Parmentier, Frans Jan W.}},
issn = {{1726-4170}},
language = {{eng}},
month = {{10}},
number = {{20}},
pages = {{5767--5787}},
publisher = {{Copernicus GmbH}},
series = {{Biogeosciences}},
title = {{Model simulations of arctic biogeochemistry and permafrost extent are highly sensitive to the implemented snow scheme in LPJ-GUESS}},
url = {{http://dx.doi.org/10.5194/bg-18-5767-2021}},
doi = {{10.5194/bg-18-5767-2021}},
volume = {{18}},
year = {{2021}},
}