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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Main Author: Marshall, Hans-Peter
Format: Text
Language:unknown
Published: ScholarWorks 2021
Subjects:
Online Access:https://scholarworks.boisestate.edu/geo_facpubs/577
https://scholarworks.boisestate.edu/context/geo_facpubs/article/1582/viewcontent/Marshall__Hans_Peter__2021__Snow_ensemble_uncertainty___PUB.pdf
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spelling ftboisestateu:oai:scholarworks.boisestate.edu:geo_facpubs-1582 2023-10-29T02:40:39+01:00 Snow Ensemble Uncertainty Project (SEUP): Quantification of Snow Water Equivalent Uncertainty Across North America via Ensemble Land Surface Modeling Marshall, Hans-Peter 2021-02-17T08:00:00Z application/pdf https://scholarworks.boisestate.edu/geo_facpubs/577 https://scholarworks.boisestate.edu/context/geo_facpubs/article/1582/viewcontent/Marshall__Hans_Peter__2021__Snow_ensemble_uncertainty___PUB.pdf unknown ScholarWorks https://scholarworks.boisestate.edu/geo_facpubs/577 https://scholarworks.boisestate.edu/context/geo_facpubs/article/1582/viewcontent/Marshall__Hans_Peter__2021__Snow_ensemble_uncertainty___PUB.pdf http://creativecommons.org/licenses/by/4.0/ Geosciences Faculty Publications and Presentations CGISS Earth Sciences Geophysics and Seismology text 2021 ftboisestateu 2023-09-29T15:21:38Z 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 Boise State University: Scholar Works
institution Open Polar
collection Boise State University: Scholar Works
op_collection_id ftboisestateu
language unknown
topic CGISS
Earth Sciences
Geophysics and Seismology
spellingShingle CGISS
Earth Sciences
Geophysics and Seismology
Marshall, Hans-Peter
Snow Ensemble Uncertainty Project (SEUP): Quantification of Snow Water Equivalent Uncertainty Across North America via Ensemble Land Surface Modeling
topic_facet CGISS
Earth Sciences
Geophysics and Seismology
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 Marshall, Hans-Peter
author_facet Marshall, Hans-Peter
author_sort Marshall, Hans-Peter
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 ScholarWorks
publishDate 2021
url https://scholarworks.boisestate.edu/geo_facpubs/577
https://scholarworks.boisestate.edu/context/geo_facpubs/article/1582/viewcontent/Marshall__Hans_Peter__2021__Snow_ensemble_uncertainty___PUB.pdf
genre taiga
Tundra
genre_facet taiga
Tundra
op_source Geosciences Faculty Publications and Presentations
op_relation https://scholarworks.boisestate.edu/geo_facpubs/577
https://scholarworks.boisestate.edu/context/geo_facpubs/article/1582/viewcontent/Marshall__Hans_Peter__2021__Snow_ensemble_uncertainty___PUB.pdf
op_rights http://creativecommons.org/licenses/by/4.0/
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