1145 Understanding CWRF's Ability to Simulate U.S. Extreme Precipitation Characteristics

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Chao Sun, Univ. of Maryland, College Park, College Park, MD; and X. Z. Liang

A long-standing model problem is to realistically simulate extreme precipitation characteristics. Most global models tend to underestimate the extreme precipitation intensity, accompanying with more frequent drizzling events. To better understand this problem, we have conducted an ensemble of 24 regional climate simulations over the contiguous U.S. at 30-km grid spacing for 1980-2016 using the Climate-Weather Research and Forecasting (CWRF) model, each with a different physics configuration combining alternate schemes among cumulus, microphysics, cloud, aerosol, radiation, planetary boundary layer, and surface processes. These simulations were driven by the ECMWF-Interim reanalysis and thus best represent the CWRF ability in downscaling U.S. regional climate variations. We found that cumulus parameterization is the most sensitive model component for extreme precipitation simulation. Among the five major schemes tested, the multi-closure ensemble cumulus parameterization (ECP) can well capture the spatial patterns, seasonal variations and interannual trends of U.S. extreme precipitation, with better performance even than the driving reanalysis that has assimilated pseudo-observation of daily rainfall distributions. While capturing the observed characteristics of extreme precipitation is challenging, understanding the underlying physical mechanisms for model failure or success is even more difficult. To address the challenge, focusing on these five cumulus parameterization schemes, we conducted correlation, composite, and structural equation model (SEM) analyses among 22 major dynamic and thermodynamic fields most relevant to extreme precipitation. In particular, through machine learning based on the SEM framework, we discovered five distinct physical mechanisms underlying regional extreme precipitation biases, each involving interplays among water and energy supplies and surface and cloud forcings with varying degrees of relative importance. The choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, and thus determined CWRF’s ability to simulate U.S. extreme precipitation.
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