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CWRF performance at downscaling China climate characteristics

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Abstract

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980–2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities.

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Acknowledgements

The RCM simulations were made using the Maryland Advanced Research Computing Center’s Bluecrab and the University of Maryland Deepthought HPC clusters for CWRF, and the Institute of Atmospheric Physics/Chinese Academy of Sciences supercomputing resources for RegCM4.6. The gridded analysis surface data as the observational reference were provided by China Meteorological Administration (CMA). Liang was partially supported by U.S. National Science Foundation Innovations at the Nexus of Food, Energy and Water Systems under Grant EAR-1639327 and CMA/National Climate Center research subcontract (2212031635601), Zheng by the Chinese Scholar Council fellowship, Dai by the National Key Research and Development Program of China (2017YFA0604303), Choi by the National Research Foundation of Korea (2017R1A2B4005232), Ling by the National Natural Science Foundation of China (NSFC) (41376016), Qiao by China Postdoctoral Science Foundation (2014M561437), Bin by NSFC (41575089), and Wang by NSFC (41275077). We thank Jennifer Kennedy for thorough editing and Qing Sun for GIS mapping.

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Authors and Affiliations

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

    Xin-Zhong Liang & Chao Sun

  2. Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Court, Suite 4001, College Park, MD, 20740, USA

    Xin-Zhong Liang, Chao Sun & Xiaohui Zheng

  3. Climate, Environment and Sustainability Center, School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing, China

    Xin-Zhong Liang

  4. College of Global Change and Earth System Science, Beijing Normal University, Beijing, China

    Xiaohui Zheng

  5. School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou, China

    Yongjiu Dai

  6. Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Min Xu

  7. Department of Civil Engineering, Yeungnam University, Gyeongsan, South Korea

    Hyun I. Choi

  8. Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing, China

    Tiejun Ling

  9. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China

    Fengxue Qiao

  10. Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Xianghui Kong & Xunqiang Bi

  11. National Climate Center, China Meteorological Administration, Beijing, China

    Lianchun Song & Fang Wang

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  1. Xin-Zhong Liang
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Correspondence to Xin-Zhong Liang or Yongjiu Dai.

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Appendix A: Acronyms and Abbreviations

Appendix A: Acronyms and Abbreviations

CAR:

Cloud aerosol radiation

CCLM:

COSMO (Consortium for Small Scale Modelling) Climate local model

CLM:

Community land model

CCM:

Community climate model

CSSP:

Conjunctive surface–subsurface process model

CAM:

Community atmosphere model

CMA:

China Meteorological Administration

CSSP:

Conjunctive surface–subsurface process model

CWRF:

Climate-weather research and forecasting model

ECP:

Ensemble cumulus parameterization

ECMWF:

European Centre for Medium-Range Weather Forecasts

ERI:

ECMWF reanalysis interim

GRIMs:

Global/regional integrated model system

GSFCLXZ:

Goddard Space Flight Center Liang, Xin-zhong radiation scheme

GSFCGCE:

Goddard Space Flight Center Goddard Cumulus Ensemble

IGBP:

International geosphere-biosphere programme

IPRC:

International Pacific Research Center model

JSM:

Japan spectral model

LMDZ:

Laboratoire de météorologie dynamique-zoom model

LISSS:

Lake ice snow and sediment simulator

MISR:

Multiangle imaging spectra radiometer

MM5:

Fifth-generation mesoscale model

MODIS:

Moderate resolution imaging spectroradiometer

PRECIS:

Providing regional climates for impacts studies model

RAMS:

Regional atmospheric modeling system

RegCM:

Regional climate modeling system

RIEMS:

Regional integrated environment model system

RMIP:

Regional model intercomparison project

RSM:

Regional spectral model

SUBEX:

Subgrid explicit moisture scheme

TDK:

Tiedtke convective scheme

UOM:

Upper ocean model

USGS:

United states geological survey

UW:

University of Washington moist turbulence parameterization

WRF:

Weather research and forecasting model

XRL:

Xu-Randall-Liang cloud cover parameterization

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Liang, XZ., Sun, C., Zheng, X. et al. CWRF performance at downscaling China climate characteristics. Clim Dyn 52, 2159–2184 (2019). https://doi.org/10.1007/s00382-018-4257-5

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