GSTDTAP  > 地球科学
DOI10.1016/j.atmosres.2019.104671
Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data
Lv, Yanmin1; Guo, Jianping1; Yim, Steve Hung-Lam2,3,4; Yun, Yuxing1; Yin, Jinfang1; Liu, Lin1; Zhang, Yong5; Yang, Yuanjian3; Yan, Yan1; Chen, Dandan1
2020
发表期刊ATMOSPHERIC RESEARCH
ISSN0169-8095
EISSN1873-2895
出版年2020
卷号231
文章类型Article
语种英语
国家Peoples R China
英文摘要

Large uncertainties still exist in the simulation and projection of precipitation from current climate models. Here, the newly released state-of-the-art China Hourly Merged Precipitation Analysis (CHMPA) data has been used to evaluate the ten models from the fifth phase of the Coupled Models Intercomparison Project (CMIP5). Particularly, the precipitation predictions under the Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios in China are assessed for the period from 2008 to 2017. Interestingly, the ensemble mean precipitation under the two emission scenarios does not show systematic differences. Intercomparison analysis of precipitation between multi-model prediction and CHMPA yields a high correlation coefficient (0.85-0.95) on the annual timescale. However, most models tend to overestimate the precipitation in northern China but to underestimate that in southern China, due to the model-simulated monsoon precipitation extending to the north earlier. Relative to UKMO-HadGEM2AO model, other models overestimate precipitation at the southeastern edge of the Tibetan Plateau where the overestimation reaches up to 150%. In terms of the temporal evolution of predicted precipitation, the multi-model ensemble produces relatively small interannual variability except for more summer monsoon precipitation with biases over 0.3 mm/day, which indicates that models are not capable of reproducing the seasonal and meridional propagation of precipitation. Compared with the original model output, the precipitation corrected by quantile mapping algorithm better agrees with the observations for spatial and temporal distributions. The findings have great implications for better utilizing model-predicted precipitation in climate change studies.


领域地球科学
收录类别SCI-E
WOS记录号WOS:000513178000015
WOS关键词CLIMATE-CHANGE PROJECTIONS ; EARTH SYSTEM MODEL ; SUMMER MONSOON ; EASTERN CHINA ; BIAS CORRECTION ; RCP SCENARIOS ; AIR-QUALITY ; RAINFALL ; SIMULATIONS ; VARIABILITY
WOS类目Meteorology & Atmospheric Sciences
WOS研究方向Meteorology & Atmospheric Sciences
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/278768
专题地球科学
作者单位1.Chinese Acad Meteorol Sci, State Key Lab Severe Weather, Beijing, Peoples R China;
2.Chinese Univ Hong Kong, Dept Geog & Resource Management, Sha Tin, Hong Kong, Peoples R China;
3.Chinese Univ Hong Kong, Inst Environm Energy & Sustainabil, Sha Tin, Hong Kong, Peoples R China;
4.Chinese Univ Hong Kong, Ctr Environm Policy & Resource Management, Sha Tin, Hong Kong, Peoples R China;
5.China Meteorol Adm, Meteorol Observat Ctr, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Lv, Yanmin,Guo, Jianping,Yim, Steve Hung-Lam,et al. Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data[J]. ATMOSPHERIC RESEARCH,2020,231.
APA Lv, Yanmin.,Guo, Jianping.,Yim, Steve Hung-Lam.,Yun, Yuxing.,Yin, Jinfang.,...&Chen, Dandan.(2020).Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data.ATMOSPHERIC RESEARCH,231.
MLA Lv, Yanmin,et al."Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data".ATMOSPHERIC RESEARCH 231(2020).
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