CWRF performance at downscaling China climate characteristics

Xin Zhong Liang; Chao Sun; Xiaohui Zheng; Yongjiu Dai; Min Xu; Hyun I. Choi; Tiejun Ling; Fengxue Qiao; Xianghui Kong; Xunqiang Bi; Lianchun Song; Fang Wang

doi:10.1007/s00382-018-4257-5

CWRF performance at downscaling China climate characteristics

Xin Zhong Liang
, Chao Sun
, Xiaohui Zheng
, Yongjiu Dai
, Min Xu
, Hyun I. Choi
, Tiejun Ling
, Fengxue Qiao
, Xianghui Kong
, Xunqiang Bi
, Lianchun Song
, Fang Wang

Computational Earth Sciences Group

Research output: Contribution to journal › Article › peer-review

67 Scopus citations

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.

Original language	English
Pages (from-to)	2159-2184
Number of pages	26
Journal	Climate Dynamics
Volume	52
Issue number	3-4
DOIs	https://doi.org/10.1007/s00382-018-4257-5
State	Published - Feb 15 2019

Funding

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.

Keywords

CWRF
Diurnal temperature range
Downscaling performance
Extreme precipitation
Regional climate model
Surface wind

Access to Document

10.1007/s00382-018-4257-5

Cite this

@article{f77e8e8fe8c54f398e018ac82ce38180,

title = "CWRF performance at downscaling China climate characteristics",

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.",

keywords = "CWRF, Diurnal temperature range, Downscaling performance, Extreme precipitation, Regional climate model, Surface wind",

author = "Liang, \{Xin Zhong\} and Chao Sun and Xiaohui Zheng and Yongjiu Dai and Min Xu and Choi, \{Hyun I.\} and Tiejun Ling and Fengxue Qiao and Xianghui Kong and Xunqiang Bi and Lianchun Song and Fang Wang",

note = "Publisher Copyright: {\textcopyright} 2018, Springer-Verlag GmbH Germany, part of Springer Nature.",

year = "2019",

month = feb,

day = "15",

doi = "10.1007/s00382-018-4257-5",

language = "English",

volume = "52",

pages = "2159--2184",

journal = "Climate Dynamics",

issn = "0930-7575",

publisher = "Springer",

number = "3-4",

}

TY - JOUR

T1 - CWRF performance at downscaling China climate characteristics

AU - Liang, Xin Zhong

AU - Sun, Chao

AU - Zheng, Xiaohui

AU - Dai, Yongjiu

AU - Xu, Min

AU - Choi, Hyun I.

AU - Ling, Tiejun

AU - Qiao, Fengxue

AU - Kong, Xianghui

AU - Bi, Xunqiang

AU - Song, Lianchun

AU - Wang, Fang

PY - 2019/2/15

Y1 - 2019/2/15

N2 - 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.

AB - 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.

KW - CWRF

KW - Diurnal temperature range

KW - Downscaling performance

KW - Extreme precipitation

KW - Regional climate model

KW - Surface wind

UR - https://www.scopus.com/pages/publications/85047204101

U2 - 10.1007/s00382-018-4257-5

DO - 10.1007/s00382-018-4257-5

M3 - Article

AN - SCOPUS:85047204101

SN - 0930-7575

VL - 52

SP - 2159

EP - 2184

JO - Climate Dynamics

JF - Climate Dynamics

IS - 3-4

ER -

CWRF performance at downscaling China climate characteristics

Abstract

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this