Advanced

Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region

Wang, Wenli; Rinke, Annette; Moore, John C.; Ji, Duoying; Cui, Xuefeng; Peng, Shushi; Lawrence, David M.; McGuire, A. David; Burke, Eleanor J. and Chen, Xiaodong, et al. (2016) In Cryosphere 10(4). p.1721-1737
Abstract

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a... (More)

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

(Less)
Please use this url to cite or link to this publication:
author
, et al. (More)
(Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Cryosphere
volume
10
issue
4
pages
17 pages
publisher
Copernicus Gesellschaft Mbh
external identifiers
  • scopus:84982132334
  • wos:000381218000015
ISSN
1994-0416
DOI
10.5194/tc-10-1721-2016
language
English
LU publication?
yes
id
46689c32-c35f-48d1-b171-4ea8f4f59e3c
date added to LUP
2016-10-11 17:21:59
date last changed
2017-10-16 15:09:48
@article{46689c32-c35f-48d1-b171-4ea8f4f59e3c,
  abstract     = {<p>A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C<sup>-1</sup>), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km<sup>2</sup>). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.</p>},
  author       = {Wang, Wenli and Rinke, Annette and Moore, John C. and Ji, Duoying and Cui, Xuefeng and Peng, Shushi and Lawrence, David M. and McGuire, A. David and Burke, Eleanor J. and Chen, Xiaodong and Decharme, Bertrand and Koven, Charles and MacDougall, Andrew and Saito, Kazuyuki and Zhang, Wenxin and Alkama, Ramdane and Bohn, Theodore J. and Ciais, Philippe and Delire, Christine and Gouttevin, Isabelle and Hajima, Tomohiro and Krinner, Gerhard and Lettenmaier, Dennis P. and Miller, Paul A. and Smith, Benjamin and Sueyoshi, Tetsuo and Sherstiukov, Artem B.},
  issn         = {1994-0416},
  language     = {eng},
  number       = {4},
  pages        = {1721--1737},
  publisher    = {Copernicus Gesellschaft Mbh},
  series       = {Cryosphere},
  title        = {Evaluation of air-soil temperature relationships simulated by land surface models during winter across the permafrost region},
  url          = {http://dx.doi.org/10.5194/tc-10-1721-2016},
  volume       = {10},
  year         = {2016},
}