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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Copernicus Publications
2021
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00055633 2024-09-15T18:38:41+00:00 Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling Kim, Rhae Sung Kumar, Sujay Vuyovich, Carrie Houser, Paul Lundquist, Jessica Mudryk, Lawrence Durand, Michael Barros, Ana Kim, Edward J. Forman, Barton A. Gutmann, Ethan D. Wrzesien, Melissa L. Garnaud, Camille Sandells, Melody Marshall, Hans-Peter Cristea, Nicoleta Pflug, Justin M. Johnston, Jeremy Cao, Yueqian Mocko, David Wang, Shugong 2021-02 electronic https://doi.org/10.5194/tc-15-771-2021 https://noa.gwlb.de/receive/cop_mods_00055633 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00055284/tc-15-771-2021.pdf https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-15-771-2021 https://noa.gwlb.de/receive/cop_mods_00055633 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00055284/tc-15-771-2021.pdf https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2021 ftnonlinearchiv https://doi.org/10.5194/tc-15-771-2021 2024-06-26T04:41:37Z 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 Niedersächsisches Online-Archiv NOA The Cryosphere 15 2 771 791 |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Kim, Rhae Sung Kumar, Sujay Vuyovich, Carrie Houser, Paul Lundquist, Jessica Mudryk, Lawrence Durand, Michael Barros, Ana Kim, Edward J. Forman, Barton A. Gutmann, Ethan D. Wrzesien, Melissa L. Garnaud, Camille Sandells, Melody Marshall, Hans-Peter Cristea, Nicoleta Pflug, Justin M. Johnston, Jeremy Cao, Yueqian Mocko, David Wang, Shugong Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling |
topic_facet |
article Verlagsveröffentlichung |
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 |
Kim, Rhae Sung Kumar, Sujay Vuyovich, Carrie Houser, Paul Lundquist, Jessica Mudryk, Lawrence Durand, Michael Barros, Ana Kim, Edward J. Forman, Barton A. Gutmann, Ethan D. Wrzesien, Melissa L. Garnaud, Camille Sandells, Melody Marshall, Hans-Peter Cristea, Nicoleta Pflug, Justin M. Johnston, Jeremy Cao, Yueqian Mocko, David Wang, Shugong |
author_facet |
Kim, Rhae Sung Kumar, Sujay Vuyovich, Carrie Houser, Paul Lundquist, Jessica Mudryk, Lawrence Durand, Michael Barros, Ana Kim, Edward J. Forman, Barton A. Gutmann, Ethan D. Wrzesien, Melissa L. Garnaud, Camille Sandells, Melody Marshall, Hans-Peter Cristea, Nicoleta Pflug, Justin M. Johnston, Jeremy Cao, Yueqian Mocko, David Wang, Shugong |
author_sort |
Kim, Rhae Sung |
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://noa.gwlb.de/receive/cop_mods_00055633 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00055284/tc-15-771-2021.pdf https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf |
genre |
taiga The Cryosphere Tundra |
genre_facet |
taiga The Cryosphere Tundra |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-15-771-2021 https://noa.gwlb.de/receive/cop_mods_00055633 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00055284/tc-15-771-2021.pdf https://tc.copernicus.org/articles/15/771/2021/tc-15-771-2021.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
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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1810483089343774720 |