气候变化领域集成服务门户(CCPortal): Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data

CCPortal

DOI	10.1016/j.atmosres.2019.104671
	Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data
	Lv Y.; Guo J.; Yim S.H.-L.; Yun Y.; Yin J.; Liu L.; Zhang Y.; Yang Y.; Yan Y.; Chen D.
发表日期	2020
ISSN	0169-8095
卷号	231
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. © 2019 Elsevier B.V.
学科领域	Atmospheric thermodynamics; Backpropagation; Climate change; Conformal mapping; Forecasting; Correlation coefficient; Interannual variability; Mapping algorithms; Monsoon precipitation; Multi-model ensemble; Precipitation predictions; Spatial and temporal distribution; Summer monsoon precipitation; Climate models; annual variation; climate modeling; CMIP; computer simulation; precipitation intensity; prediction; seasonal variation; spatiotemporal analysis; China
语种	英语
scopus关键词	Atmospheric thermodynamics; Backpropagation; Climate change; Conformal mapping; Forecasting; Correlation coefficient; Interannual variability; Mapping algorithms; Monsoon precipitation; Multi-model ensemble; Precipitation predictions; Spatial and temporal distribution; Summer monsoon precipitation; Climate models; annual variation; climate modeling; CMIP; computer simulation; precipitation intensity; prediction; seasonal variation; spatiotemporal analysis; China
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120620
作者单位	State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China; Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong; The Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin, N.T, Hong Kong; Centre for Environmental Policy and Resource Management, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong; Meteorological Observation Center, China Meteorological Administration, Beijing, China
推荐引用方式 GB/T 7714	Lv Y.,Guo J.,Yim S.H.-L.,et al. Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data[J],2020,231.
APA	Lv Y..,Guo J..,Yim S.H.-L..,Yun Y..,Yin J..,...&Chen D..(2020).Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data.Atmospheric Research,231.
MLA	Lv Y.,et al."Towards understanding multi-model precipitation predictions from CMIP5 based on China hourly merged precipitation analysis data".Atmospheric Research 231(2020).