California receives half of its total annual precipitation in five to 15 days of the year, making its precipitation patterns some of the most intermittent in the United States. Importantly, most of this precipitation falls during the winter months and largely in the northern and mountainous parts of the state as snow, which acts like a natural surface water reservoir and is released during dry portions of the year. Thus, the integrity of California’s economy, and agricultural identity, is largely dependent on ample snowpack accumulation in the Sierra Nevada. Unfortunately, over the past 50 years, numerous observational studies have shown that snowpack has been in steady decline throughout much of the western United States, including the northern Sierra Nevada.
A recent study analyzed the efficacy of a new cutting-edge modeling technique, variable-resolution modeling using the Community Earth System Model (VR-CESM), at horizontal resolutions of 14 km and 28 km (and three topographic characterizations) in representing Sierra Nevada snowpack [i.e., snow water equivalent (SWE) and snow cover (SNOWC)]. VR-CESM was compared with a suite of observational, reanalysis, and dynamically downscaled model results. Overall, considering California’s complex terrain, intermittent precipitation, and that the VR-CESM simulations were only constrained by prescribed sea surface temperatures and sea ice extent data, a 0.68 centered Pearson product-moment correlation, negative mean winter SWE bias of <7 mm, interquartile range well within the values exhibited in the reanalysis datasets, and mean winter SNOWC within 7% of the expected satellite derived value, the efficacy of the VR-CESM framework was shown.
VR-CESM is a novel tool for modeling the climate system and represents a hybrid of global and regional climate models. It is envisioned that this new modeling framework will bring added value to the snowpack modeling community with the benefit of a global solution, accounting for major teleconnections and regional high-resolution, with better representation of winter storms and orographic forcings. Additionally, VR-CESM can be run for a fraction of the cost of a high-resolution global climate model run, on a local server (<1000 processors), with 20 to 40 day turnarounds on 25-year simulation periods, and provide model resolutions (28 km to 14 km), which decision makers (especially in the western United States water sector), may find more useful in regional planning endeavors. The enhanced representation of snowpack and relative computational efficiency of VR-CESM lends itself well to future investigations of other snowpack-dependent regions of the western United States, as well as ensemble-based climate change scenario analysis. This research is underway.
Reference: Rhoades, A. M., X. Huang, P. A. Ullrich, and C. M. Zarzycki. 2015. “Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM,” Journal of Applied Meteorology and Climatology 55, 173–96. DOI: 10.1175/JAMC-D-15-0156.1. (Reference link)
Contact: Dorothy Koch, SC-23.1, (301) 903-0105
SC-33.1 Earth and Environmental Sciences Division, BER
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