气候变化领域集成服务门户(CCPortal): Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods

CCPortal

DOI	10.1007/s10584-020-02841-z
	Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods
	Guo L.; Jiang Z.; Chen D.; Le Treut H.; Li L.
发表日期	2020
ISSN	0165-0009
起始页码	623
结束页码	643
卷号	162 期号:2
英文摘要	Four bias correction methods, i.e., gamma cumulative distribution function (GamCDF), quantile–quantile adjustment (QQadj), equidistant cumulative probability distribution function (CDF) matching (EDCDF), and transform CDF (CDF-t), to read are applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four bias correction methods, which helps understanding their natures and essences for identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias correction method based on a comprehensive evaluation of different precipitation indices. Future precipitation projections corresponding to the global warming levels of 1.5 °C and 2 °C under RCP8.5 were obtained using the bias correction methods. The multi-method and multi-model ensemble characteristics allow to explore the spreading of projections, considered a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias correction methods is smaller than that among dynamical downscaling simulations. The four bias correction methods, with CDF-t at the top, all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility. © 2020, Springer Nature B.V.
英文关键词	1.5 °C and 2 °C global warming; Bias correction; Climate downscaling; Climate projection uncertainty; Daily precipitation
语种	英语
scopus关键词	Distribution functions; Global warming; Bias-correction methods; Comprehensive evaluation; Cumulative distribution function; Cumulative probability distribution function; Dynamical downscaling; Mathematical frameworks; Precipitation indices; Regional climate simulation; Climate models; climate modeling; climate prediction; correction; downscaling; global warming; precipitation assessment; probability; regional climate; sampling bias; China
来源期刊	Climatic Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/147072
作者单位	Key Laboratory of Meteorological Disaster of Ministry of Education, Joint International Research Laboratory of Climate and Environment Change, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China; Regional Climate Group, Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden; Laboratoire de Météorologie Dynamique, CNRS, Sorbonne Université, Ecole Normale Supérieure, Ecole Polytechnique, Paris, France
推荐引用方式 GB/T 7714	Guo L.,Jiang Z.,Chen D.,et al. Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods[J],2020,162(2).
APA	Guo L.,Jiang Z.,Chen D.,Le Treut H.,&Li L..(2020).Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods.Climatic Change,162(2).
MLA	Guo L.,et al."Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods".Climatic Change 162.2(2020).