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Reduction of systematic biases in regional climate downscaling through ensemble forcing

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Abstract

Simulations of the East Asian summer monsoon for the period of 1979–2001 were carried out using the Weather Research and Forecast (WRF) model forced by three reanalysis datasets (NCEP-R2, ERA-40, and JRA-25). The experiments forced by different reanalysis data exhibited remarkable differences, primarily caused by uncertainties in the lateral boundary (LB) moisture fluxes over the Bay of Bengal and the Philippine Sea. The climatological mean water vapor convergence into the model domain computed from ERA-40 was about 24% higher than that from the NCEP-R2 reanalysis. We demonstrate that using the ensemble mean of NCEP-R2, ERA-40, and JRA-25 as LB forcing considerably reduced the biases in the model simulation. The use of ensemble forcing improved the performance in simulated mean circulation and precipitation, inter-annual variation in seasonal precipitation, and daily precipitation. The model simulated precipitation was superior to that in the reanalysis in both climatology and year-to-year variations, indicating the added value of dynamic downscaling. The results suggest that models having better performance under one set of LB forcing might worsen when another set of reanalysis data is used as LB forcing. Use of ensemble mean LB forcing for assessing regional climate model performance is recommended.

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Acknowledgments

Hongwei Yang acknowledges joint support from the 973 Project (2010CB428403), 863 Project (2010AA012301), and LASG State Key Laboratory Special fund. Bin Wang at the University of Hawaii acknowledges support by APEC Climate Center (APCC) and the Korean meteorological Administration Research and Development Program under Grant RACS 2010-2017. The authors would also like to thank two anonymous reviewers of this paper for their useful and insightful comments.

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Authors and Affiliations

  1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Hongwei Yang

  2. Department of Meteorology, University of Hawaii at Manoa, Honolulu, Hawaii

    Bin Wang

  3. International Pacific Research Center, University of Hawaii at Manoa, Honolulu, Hawaii

    Bin Wang

  4. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Bin Wang

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  1. Hongwei Yang
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  2. Bin Wang
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Correspondence to Hongwei Yang.

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Yang, H., Wang, B. & Wang, B. Reduction of systematic biases in regional climate downscaling through ensemble forcing. Clim Dyn 38, 655–665 (2012). https://doi.org/10.1007/s00382-011-1006-4

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