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Improving US extreme precipitation simulation: sensitivity to physics parameterizations

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Abstract

Climate models tend to underestimate rainfall intensity while producing more frequent light events, leading to significant bias in extreme precipitation simulation. To reduce this bias and better understand its underlying causes, we tested an ensemble of 25 physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF). All configurations were driven by the ECMWF-Interim reanalysis and continuously integrated during 1980–2015 over the contiguous United States with 30-km grid spacing. Together they represent CWRF’s ability to simulate characteristics of US extreme precipitation, and their spread depicts the structural uncertainty from alternate physics parameterizations. The US extreme precipitation simulation was most sensitive to cumulus parameterization among all physics configurations. The ensemble cumulus parameterization (ECP) was overall the most skilled at reproducing seasonal mean spatial patterns of daily 95th percentile precipitation (P95). Other cumulus schemes severely underestimated P95, especially over the Gulf States and the Central-Midwest States in convective prevailing seasons. CWRF with ECP outperformed the driving reanalysis, which substantially underestimated P95 despite its daily atmospheric moisture data assimilation. The CWRF improvement over ERI is much larger in warm than cold seasons. Changing alone ECP closure assumptions produced two distinct clusters of convective heating/drying effects: one altered P95 mainly by changing total precipitation intensity and another by changing rainy-day frequency. Microphysics, radiation, boundary layer, and land surface processes also impacted the result, especially under mixed synoptic and convective forcings, and some of their parameterization schemes worked with ECP to further improve P95.

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Acknowledgements

The CWRF simulations and analyses were conducted on the supercomputers, including the University of Illinois’ Blue Water, the Maryland Advanced Research Computing Center, the Computational and Information Systems Lab of the National Center for Atmospheric Research, and the National Energy Research Scientific Computing Center of U.S. Department of Energy. We thank Kenneth Kunkel for providing the Cooperative Observer network station data. The research was supported by U.S. National Science Foundation Innovations at the Nexus of Food, Energy and Water Systems under Grants EAR-1639327 and EAR1903249, U.S. Department of Agriculture UV-B Monitoring and Research Program at Colorado State University under the National Institute of Food and Agriculture Grant 2015-34263-24070, and U.S. Environmental Protection Agency Science to Achieve Results under Assistance Agreement No. RD83587601. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the funding Agency. We sincerely thank Jennifer Kennedy for thorough editing.

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  1. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, USA

    Chao Sun & Xin-Zhong Liang

  2. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Chao Sun & Xin-Zhong Liang

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  1. Chao Sun
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Correspondence to Xin-Zhong Liang.

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Sun, C., Liang, XZ. Improving US extreme precipitation simulation: sensitivity to physics parameterizations. Clim Dyn 54, 4891–4918 (2020). https://doi.org/10.1007/s00382-020-05267-6

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