A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover
In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights, which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of g...
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Language: | English |
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Copernicus Publications
2014
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Online Access: | https://doi.org/10.5194/tc-8-2063-2014 https://doaj.org/article/aa3f4a0a029d412e85ea58c497812dad |
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ftdoajarticles:oai:doaj.org/article:aa3f4a0a029d412e85ea58c497812dad 2023-05-15T17:55:53+02:00 A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover K. Gisnås S. Westermann T. V. Schuler T. Litherland K. Isaksen J. Boike B. Etzelmüller 2014-11-01T00:00:00Z https://doi.org/10.5194/tc-8-2063-2014 https://doaj.org/article/aa3f4a0a029d412e85ea58c497812dad EN eng Copernicus Publications http://www.the-cryosphere.net/8/2063/2014/tc-8-2063-2014.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 1994-0416 1994-0424 doi:10.5194/tc-8-2063-2014 https://doaj.org/article/aa3f4a0a029d412e85ea58c497812dad The Cryosphere, Vol 8, Iss 6, Pp 2063-2074 (2014) Environmental sciences GE1-350 Geology QE1-996.5 article 2014 ftdoajarticles https://doi.org/10.5194/tc-8-2063-2014 2022-12-31T06:07:52Z In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights, which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of ground surface temperatures within three study areas across a climatic gradient from continuous to sporadic permafrost in Norway. A variability of the mean annual ground surface temperature of up to 6°C was documented within areas of 0.5 km 2 . The observed variation can, to a large degree, be explained by variation in snow height. Permafrost models, employing averages of snow height for grid cells of, e.g., 1 km 2 , are not capable of representing such sub-grid variability. We propose a statistical representation of the sub-grid variability of ground surface temperatures and demonstrate that a simple equilibrium permafrost model can reproduce the temperature distribution within a grid cell based on the distribution of snow heights. Article in Journal/Newspaper permafrost The Cryosphere Directory of Open Access Journals: DOAJ Articles Norway The Cryosphere 8 6 2063 2074 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 K. Gisnås S. Westermann T. V. Schuler T. Litherland K. Isaksen J. Boike B. Etzelmüller A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
In permafrost environments exposed to strong winds, drifting snow can create a small-scale pattern of strongly variable snow heights, which has profound implications for the thermal regime of the ground. Arrays of 26 to more than 100 temperature loggers were installed to record the distribution of ground surface temperatures within three study areas across a climatic gradient from continuous to sporadic permafrost in Norway. A variability of the mean annual ground surface temperature of up to 6°C was documented within areas of 0.5 km 2 . The observed variation can, to a large degree, be explained by variation in snow height. Permafrost models, employing averages of snow height for grid cells of, e.g., 1 km 2 , are not capable of representing such sub-grid variability. We propose a statistical representation of the sub-grid variability of ground surface temperatures and demonstrate that a simple equilibrium permafrost model can reproduce the temperature distribution within a grid cell based on the distribution of snow heights. |
format |
Article in Journal/Newspaper |
author |
K. Gisnås S. Westermann T. V. Schuler T. Litherland K. Isaksen J. Boike B. Etzelmüller |
author_facet |
K. Gisnås S. Westermann T. V. Schuler T. Litherland K. Isaksen J. Boike B. Etzelmüller |
author_sort |
K. Gisnås |
title |
A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
title_short |
A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
title_full |
A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
title_fullStr |
A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
title_full_unstemmed |
A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
title_sort |
statistical approach to represent small-scale variability of permafrost temperatures due to snow cover |
publisher |
Copernicus Publications |
publishDate |
2014 |
url |
https://doi.org/10.5194/tc-8-2063-2014 https://doaj.org/article/aa3f4a0a029d412e85ea58c497812dad |
geographic |
Norway |
geographic_facet |
Norway |
genre |
permafrost The Cryosphere |
genre_facet |
permafrost The Cryosphere |
op_source |
The Cryosphere, Vol 8, Iss 6, Pp 2063-2074 (2014) |
op_relation |
http://www.the-cryosphere.net/8/2063/2014/tc-8-2063-2014.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 1994-0416 1994-0424 doi:10.5194/tc-8-2063-2014 https://doaj.org/article/aa3f4a0a029d412e85ea58c497812dad |
op_doi |
https://doi.org/10.5194/tc-8-2063-2014 |
container_title |
The Cryosphere |
container_volume |
8 |
container_issue |
6 |
container_start_page |
2063 |
op_container_end_page |
2074 |
_version_ |
1766163904669417472 |