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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.

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [9];  [10];  [10]
  1. Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science; Univ. of Maryland, College Park, MD (United States). Earth System Science Interdisciplinary Center; Nanjing Univ. of Information Science and Technology, Nanjing (China). Climate, Environment and Sustainability Center, School of Atmospheric Science
  2. Univ. of Maryland, College Park, MD (United States). Dept. of Atmospheric and Oceanic Science; Univ. of Maryland, College Park, MD (United States). Earth System Science Interdisciplinary Center
  3. Univ. of Maryland, College Park, MD (United States). Earth System Science Interdisciplinary Center; Beijing Normal Univ., Beijing (China). College of Global Change and Earth System Science
  4. Sun Yat-Sen Univ., Guangzhou, (China). School of Atmospheric Sciences
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Climate Change Science Inst.
  6. Yeungnam Univ., Gyeongsan (South Korea). Dept. of Civil Engineering
  7. National Marine Environmental Forecasting Center, Beijing (China). Key Lab of Research on Marine Hazards Forecasting
  8. East China Normal Univ. (ECNU), Shanghai (China). Key Lab of Geographic Information Science, Ministry of Education
  9. Chinese Academy of Sciences (CAS), Beijing (China). Climate Change Research Center, Inst. of Atmospheric Physics
  10. China Meteorological Administration (China). National Climate Center
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE
OSTI Identifier:
1543489
Resource Type:
Journal Article
Journal Name:
Climate Dynamics
Additional Journal Information:
Journal Volume: 52; Journal Issue: 3-4; Journal ID: ISSN 0930-7575
Publisher:
Springer-Verlag
Country of Publication:
United States
Language:
English
Subject:
Meteorology & Atmospheric Sciences

Citation Formats

Liang, Xin-Zhong, Sun, Chao, Zheng, Xiaohui, Dai, Yongjiu, Xu, Min, Choi, Hyun I., Ling, Tiejun, Qiao, Fengxue, Kong, Xianghui, Bi, Xunqiang, Song, Lianchun, and Wang, Fang. CWRF performance at downscaling China climate characteristics. United States: N. p., 2018. Web. doi:10.1007/s00382-018-4257-5.
Liang, Xin-Zhong, Sun, Chao, Zheng, Xiaohui, Dai, Yongjiu, Xu, Min, Choi, Hyun I., Ling, Tiejun, Qiao, Fengxue, Kong, Xianghui, Bi, Xunqiang, Song, Lianchun, & Wang, Fang. CWRF performance at downscaling China climate characteristics. United States. doi:10.1007/s00382-018-4257-5.
Liang, Xin-Zhong, Sun, Chao, Zheng, Xiaohui, Dai, Yongjiu, Xu, Min, Choi, Hyun I., Ling, Tiejun, Qiao, Fengxue, Kong, Xianghui, Bi, Xunqiang, Song, Lianchun, and Wang, Fang. Mon . "CWRF performance at downscaling China climate characteristics". United States. doi:10.1007/s00382-018-4257-5.
@article{osti_1543489,
title = {CWRF performance at downscaling China climate characteristics},
author = {Liang, Xin-Zhong and Sun, Chao and Zheng, Xiaohui and Dai, Yongjiu and Xu, Min and Choi, Hyun I. and Ling, Tiejun and Qiao, Fengxue and Kong, Xianghui and Bi, Xunqiang and Song, Lianchun and Wang, Fang},
abstractNote = {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.},
doi = {10.1007/s00382-018-4257-5},
journal = {Climate Dynamics},
issn = {0930-7575},
number = 3-4,
volume = 52,
place = {United States},
year = {2018},
month = {5}
}