A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

Journal Article · 11 November 2019 · Scientific Reports

DOI: https://doi.org/10.1038/s41598-019-52880-5 · OSTI ID:1619590

Xu, Yun  ^[1]; Jones, Andrew  ^[1]; Rhoades, Alan  ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

• 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); USDOE
• Grant/Contract Number: AC02-05CH11231; SC0016605
• OSTI ID: 1619590
• Alternate ID(s): OSTI ID: 1581388
• Journal Information: Scientific Reports, Vol. 9, Issue 1; ISSN 2045-2322
• Publisher: Nature Publishing GroupCopyright Statement
• Country of Publication: United States
• Language: English