Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM
© 2016 American Meteorological Society. The location, timing, and intermittency of precipitation in California make the state integrally reliant on winter-season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter-season snowpack has shown...
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Language: | English |
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ftcdlib:qt0cz82359 2023-05-15T18:18:53+02:00 Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM Rhoades, Alan M Huang, Xingying Ullrich, Paul A Zarzycki, Colin M 173 - 196 2016-01-01 application/pdf http://www.escholarship.org/uc/item/0cz82359 english eng eScholarship, University of California qt0cz82359 http://www.escholarship.org/uc/item/0cz82359 public Rhoades, Alan M; Huang, Xingying; Ullrich, Paul A; & Zarzycki, Colin M. (2016). Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM. Journal of Applied Meteorology and Climatology, 55(1), 173 - 196. doi:10.1175/jamc-d-15-0156.1. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/0cz82359 Climate models Land surface model Model evaluation performance Multigrid models Meteorology & Atmospheric Sciences Atmospheric Sciences article 2016 ftcdlib https://doi.org/10.1175/jamc-d-15-0156.1 2019-04-19T22:52:10Z © 2016 American Meteorological Society. The location, timing, and intermittency of precipitation in California make the state integrally reliant on winter-season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter-season snowpack has shown a net decline across the western United States over the past 50 years, resulting in major uncertainty in water-resource management heading into the next century. Cutting-edge tools are available to help navigate and preemptively plan for these uncertainties. This paper uses a next-generation modeling technique-variable-resolution global climate modeling within the Community Earth System Model (VR-CESM)-at horizontal resolutions of 0.125° (14 km) and 0.25° (28 km). VR-CESM provides the means to include dynamically large-scale atmosphere-ocean drivers, to limit model bias, and to provide more accurate representations of regional topography while doing so in a more computationally efficient manner than can be achieved with conventional general circulation models. This paper validates VR-CESM at climatological and seasonal time scales for Sierra Nevada snowpack metrics by comparing them with the "Daymet," "Cal-Adapt," NARR, NCEP, and North American Land Data Assimilation System (NLDAS) reanalysis datasets, the MODIS remote sensing dataset, the SNOTEL observational dataset, a standard-practice global climate model (CESM), and a regional climate model (WRF Model) dataset. Overall, given California's complex terrain and intermittent precipitation and that both of the VR-CESM simulations were only constrained by prescribed sea surface temperatures and data on sea ice extent, a 0.68 centered Pearson product-moment correlation, a negative mean SWE bias of < 7 mm, an interquartile range well within the values exhibited in the reanalysis datasets, and a mean December-February extent of snow cover that is within 7% of the expected MODIS value together make apparent the efficacy of the VR-CESM framework. Article in Journal/Newspaper Sea ice University of California: eScholarship Journal of Applied Meteorology and Climatology 55 1 173 196 |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
English |
topic |
Climate models Land surface model Model evaluation performance Multigrid models Meteorology & Atmospheric Sciences Atmospheric Sciences |
spellingShingle |
Climate models Land surface model Model evaluation performance Multigrid models Meteorology & Atmospheric Sciences Atmospheric Sciences Rhoades, Alan M Huang, Xingying Ullrich, Paul A Zarzycki, Colin M Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
topic_facet |
Climate models Land surface model Model evaluation performance Multigrid models Meteorology & Atmospheric Sciences Atmospheric Sciences |
description |
© 2016 American Meteorological Society. The location, timing, and intermittency of precipitation in California make the state integrally reliant on winter-season snowpack accumulation to maintain its economic and agricultural livelihood. Of particular concern is that winter-season snowpack has shown a net decline across the western United States over the past 50 years, resulting in major uncertainty in water-resource management heading into the next century. Cutting-edge tools are available to help navigate and preemptively plan for these uncertainties. This paper uses a next-generation modeling technique-variable-resolution global climate modeling within the Community Earth System Model (VR-CESM)-at horizontal resolutions of 0.125° (14 km) and 0.25° (28 km). VR-CESM provides the means to include dynamically large-scale atmosphere-ocean drivers, to limit model bias, and to provide more accurate representations of regional topography while doing so in a more computationally efficient manner than can be achieved with conventional general circulation models. This paper validates VR-CESM at climatological and seasonal time scales for Sierra Nevada snowpack metrics by comparing them with the "Daymet," "Cal-Adapt," NARR, NCEP, and North American Land Data Assimilation System (NLDAS) reanalysis datasets, the MODIS remote sensing dataset, the SNOTEL observational dataset, a standard-practice global climate model (CESM), and a regional climate model (WRF Model) dataset. Overall, given California's complex terrain and intermittent precipitation and that both of the VR-CESM simulations were only constrained by prescribed sea surface temperatures and data on sea ice extent, a 0.68 centered Pearson product-moment correlation, a negative mean SWE bias of < 7 mm, an interquartile range well within the values exhibited in the reanalysis datasets, and a mean December-February extent of snow cover that is within 7% of the expected MODIS value together make apparent the efficacy of the VR-CESM framework. |
format |
Article in Journal/Newspaper |
author |
Rhoades, Alan M Huang, Xingying Ullrich, Paul A Zarzycki, Colin M |
author_facet |
Rhoades, Alan M Huang, Xingying Ullrich, Paul A Zarzycki, Colin M |
author_sort |
Rhoades, Alan M |
title |
Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
title_short |
Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
title_full |
Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
title_fullStr |
Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
title_full_unstemmed |
Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM |
title_sort |
characterizing sierra nevada snowpack using variable-resolution cesm |
publisher |
eScholarship, University of California |
publishDate |
2016 |
url |
http://www.escholarship.org/uc/item/0cz82359 |
op_coverage |
173 - 196 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Rhoades, Alan M; Huang, Xingying; Ullrich, Paul A; & Zarzycki, Colin M. (2016). Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM. Journal of Applied Meteorology and Climatology, 55(1), 173 - 196. doi:10.1175/jamc-d-15-0156.1. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/0cz82359 |
op_relation |
qt0cz82359 http://www.escholarship.org/uc/item/0cz82359 |
op_rights |
public |
op_doi |
https://doi.org/10.1175/jamc-d-15-0156.1 |
container_title |
Journal of Applied Meteorology and Climatology |
container_volume |
55 |
container_issue |
1 |
container_start_page |
173 |
op_container_end_page |
196 |
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1766195640124047360 |