Dynamical downscaling simulation of the East Asian summer monsoon in a regional Climate‐Weather Research and Forecasting model
Open Access
- 13 August 2020
- journal article
- research article
- Published by Wiley in International Journal of Climatology
- Vol. 41 (S1) , E1700-E1716
Abstract
A regional Climate‐Weather Research and Forecasting (CWRF) model with a 30‐km horizontal resolution was applied to simulate the East Asian summer monsoon (EASM) and climate in China from 1980 to 2016. As compared with observations and reanalysis data, the model can reasonably reproduce the spatial distributions of the climatological mean atmospheric circulation and water vapour transport in East Asia, as well as the seasonal advance and retreat of EASM and rain bands. The correlation coefficient between the EASM circulation index in the simulation and reanalysis is .97. The model can well represent the geographic distributions of summer mean temperature and precipitation over most of China. However, model biases still exist, in particular the skill in the Yangtze–Huaihe River basin is relatively low. The simulated climatological mean temperature and precipitation deviations from observations may be related to the model's systematic circulation biases, with a thicker lower troposphere (i.e., higher temperature between 500 and 1,000 hPa) over most of China (except for the south and southwest) and a thinner one over the coastal oceans in summertime. Compared with the reanalysis, the model overestimates the land‐ocean thermal contrast, which causes the South Asian High shifted to the north and east, as well as the subtropical high and tropical convective activity located further north and persisted longer. Consequently, more water vapour transports northward, leading to more precipitation over North China–Northeast China and less precipitation over the Yangtze and Huaihe River basin. Therefore, the enhanced land‐ocean thermal contrast may be a major factor for the stronger EASM in the model, and corresponding precipitation and temperature biases in China. This analysis provides important information for further improving model performance in EASM simulation.Keywords
Funding Information
- National Natural Science Foundation of China (41790471)
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