DOI: 10.1007/s00382-011-1260-5
Scopus记录号: 2-s2.0-84868133564
论文题名: Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing
作者: Yang H. ; Wang B. ; Wang B.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2012
卷: 39, 期: 2017-09-10 起始页码: 2523
结束页码: 2532
语种: 英语
英文关键词: Bayesian model averaging
; Lateral boundary forcing
; Regional climate simulation
英文摘要: Reduction of uncertainty in large-scale lateral-boundary forcing in regional climate modeling is a critical issue for improving the performance of regional climate downscaling. Numerical simulations of 1998 East Asian summer monsoon were conducted using the Weather Research and Forecast model forced by four different reanalysis datasets, their equal-weight ensemble, and Bayesian model averaging (BMA) ensemble means. Large discrepancies were found among experiments forced by the four individual reanalysis datasets mainly due to the uncertainties in the moisture field of large-scale forcing over ocean. We used satellite water-vapor-path data as observed truth-and-training data to determine the posterior probability (weight) for each forcing dataset using the BMA method. The experiment forced by the equal-weight ensemble reduced the circulation biases significantly but reduced the precipitation biases only moderately. However, the experiment forced by the BMA ensemble outperformed not only the experiments forced by individual reanalysis datasets but also the equal-weight ensemble experiment in simulating the seasonal mean circulation and precipitation. These results suggest that the BMA ensemble method is an effective method for reducing the uncertainties in lateral-boundary forcing and improving model performance in regional climate downscaling. © 2011 Springer-Verlag. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/55111
Appears in Collections: 过去全球变化的重建
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作者单位: APEC Climate Center, Busan 612020, South Korea; Department of Meteorology, University of Hawaii at Manoa, Honolulu, HI 96822, United States; International Pacific Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, United States; LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Recommended Citation:
Yang H.,Wang B.,Wang B.. Reducing biases in regional climate downscaling by applying Bayesian model averaging on large-scale forcing[J]. Climate Dynamics,2012-01-01,39(2017-09-10)