Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operatio...
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Online Access: | https://doi.org/10.5194/tc-15-771-2021 https://doaj.org/article/e5567893cba84c9c880ad4c8c81c14e9 |
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ftdoajarticles:oai:doaj.org/article:e5567893cba84c9c880ad4c8c81c14e9 2023-05-15T18:30:59+02:00 Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling R. S. Kim S. Kumar C. Vuyovich P. Houser J. Lundquist L. Mudryk M. Durand A. Barros E. J. Kim B. A. Forman E. D. Gutmann M. L. Wrzesien C. Garnaud M. Sandells H.-P. Marshall N. Cristea J. M. Pflug J. Johnston Y. Cao D. Mocko S. Wang 2021-02-01T00:00:00Z https://doi.org/10.5194/tc-15-771-2021 https://doaj.org/article/e5567893cba84c9c880ad4c8c81c14e9 EN eng Copernicus Publications https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-771-2021 1994-0416 1994-0424 https://doaj.org/article/e5567893cba84c9c880ad4c8c81c14e9 The Cryosphere, Vol 15, Pp 771-791 (2021) Environmental sciences GE1-350 Geology QE1-996.5 article 2021 ftdoajarticles https://doi.org/10.5194/tc-15-771-2021 2022-12-31T10:47:16Z The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes. Article in Journal/Newspaper taiga The Cryosphere Tundra Directory of Open Access Journals: DOAJ Articles The Cryosphere 15 2 771 791 |
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 R. S. Kim S. Kumar C. Vuyovich P. Houser J. Lundquist L. Mudryk M. Durand A. Barros E. J. Kim B. A. Forman E. D. Gutmann M. L. Wrzesien C. Garnaud M. Sandells H.-P. Marshall N. Cristea J. M. Pflug J. Johnston Y. Cao D. Mocko S. Wang Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009–2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In midlatitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation–snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty, and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the midlatitudes. |
format |
Article in Journal/Newspaper |
author |
R. S. Kim S. Kumar C. Vuyovich P. Houser J. Lundquist L. Mudryk M. Durand A. Barros E. J. Kim B. A. Forman E. D. Gutmann M. L. Wrzesien C. Garnaud M. Sandells H.-P. Marshall N. Cristea J. M. Pflug J. Johnston Y. Cao D. Mocko S. Wang |
author_facet |
R. S. Kim S. Kumar C. Vuyovich P. Houser J. Lundquist L. Mudryk M. Durand A. Barros E. J. Kim B. A. Forman E. D. Gutmann M. L. Wrzesien C. Garnaud M. Sandells H.-P. Marshall N. Cristea J. M. Pflug J. Johnston Y. Cao D. Mocko S. Wang |
author_sort |
R. S. Kim |
title |
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
title_short |
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
title_full |
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
title_fullStr |
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
title_full_unstemmed |
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
title_sort |
snow ensemble uncertainty project (seup): quantification of snow water equivalent uncertainty across north america via ensemble land surface modeling |
publisher |
Copernicus Publications |
publishDate |
2021 |
url |
https://doi.org/10.5194/tc-15-771-2021 https://doaj.org/article/e5567893cba84c9c880ad4c8c81c14e9 |
genre |
taiga The Cryosphere Tundra |
genre_facet |
taiga The Cryosphere Tundra |
op_source |
The Cryosphere, Vol 15, Pp 771-791 (2021) |
op_relation |
https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-15-771-2021 1994-0416 1994-0424 https://doaj.org/article/e5567893cba84c9c880ad4c8c81c14e9 |
op_doi |
https://doi.org/10.5194/tc-15-771-2021 |
container_title |
The Cryosphere |
container_volume |
15 |
container_issue |
2 |
container_start_page |
771 |
op_container_end_page |
791 |
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1766214618837942272 |