Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM

Journal Article · Mon Jan 25 00:00:00 EST 2016 · Journal of Applied Meteorology and Climatology

DOI:https://doi.org/10.1175/jamc-d-15-0156.1· OSTI ID:1571055

Rhoades, Alan M. ^[1]; Huang, Xingying ^[1]; Ullrich, Paul A. ^[1]; Zarzycki, Colin M. ^[2]

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1. Univ. of California, Davis, CA (United States)
2. National Center for Atmospheric Research, Boulder, CO (United States)

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.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), Climate and Environmental Sciences Division (SC-23.1 )
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1571055
• Journal Information: Journal of Applied Meteorology and Climatology, Journal Name: Journal of Applied Meteorology and Climatology Journal Issue: 1 Vol. 55; ISSN 1558-8424
• Publisher: American Meteorological Society
• Country of Publication: United States
• Language: English

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54 ENVIRONMENTAL SCIENCES