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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Published in:The Cryosphere
Main Authors: 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
Format: Text
Language:English
Published: 2021
Subjects:
Online Access:https://doi.org/10.5194/tc-15-771-2021
https://tc.copernicus.org/articles/15/771/2021/
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spelling ftcopernicus:oai:publications.copernicus.org:tc89254 2023-05-15T18:30:56+02:00 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 2021-02-17 application/pdf https://doi.org/10.5194/tc-15-771-2021 https://tc.copernicus.org/articles/15/771/2021/ eng eng doi:10.5194/tc-15-771-2021 https://tc.copernicus.org/articles/15/771/2021/ eISSN: 1994-0424 Text 2021 ftcopernicus https://doi.org/10.5194/tc-15-771-2021 2021-02-22T17:22:14Z 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. Text taiga Tundra Copernicus Publications: E-Journals The Cryosphere 15 2 771 791
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
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 Text
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
spellingShingle 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
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
publishDate 2021
url https://doi.org/10.5194/tc-15-771-2021
https://tc.copernicus.org/articles/15/771/2021/
genre taiga
Tundra
genre_facet taiga
Tundra
op_source eISSN: 1994-0424
op_relation doi:10.5194/tc-15-771-2021
https://tc.copernicus.org/articles/15/771/2021/
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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