Significant spatial patterns from the GCM seasonal forecasts of global precipitation

doi:10.5194/hess-24-1-2020

	Significant spatial patterns from the GCM seasonal forecasts of global precipitation
	Zhao, Tongtiegang 1; Zhang, Wei 2; Zhang, Yongyong 3; Liu, Zhiyong 1; Chen, Xiaohong 1
刊名	HYDROLOGY AND EARTH SYSTEM SCIENCES
	2020-01-03
卷号	24 期号:1 页码:1-16
ISSN号	1027-5606
DOI	10.5194/hess-24-1-2020
通讯作者	Zhao, Tongtiegang(zhaottg@mail.sysu.edu.cn) ; Chen, Xiaohong(eescxh@mail.sysu.edu.cn)
英文摘要	Fully coupled global climate models (GCMs) generate a vast amount of high-dimensional forecast data of the global climate; therefore, interpreting and understanding the predictive performance is a critical issue in applying GCM forecasts. Spatial plotting is a powerful tool to identify where forecasts perform well and where forecasts are not satisfactory. Here we build upon the spatial plotting of anomaly correlation between forecast ensemble mean and observations to derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the 10 sets of forecasts archived in the North America Multi-Model Ensemble (NMME) experiment, the global and local Moran's I are calculated to associate anomaly correlations at neighbouring grid cells with one another. The global Moran's I associates anomaly correlation at the global scale and indicates that anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells. The local Moran's I links anomaly correlation at one grid cell with its spatial lag and reveals clusters of grid cells with high, neutral, and low anomaly correlation. Overall, the forecasts produced by GCMs of similar settings and at the same climate centre exhibit similar clustering of anomaly correlation. In the meantime, the forecasts in NMME show complementary performances. About 80% of grid cells across the globe fall into the cluster of high anomaly correlation under at least 1 of the 10 sets of forecasts. While anomaly correlation exhibits substantial spatial variability, the clustering approach serves as a filter of noise to identify spatial patterns and yields insights into the predictive performance of GCM seasonal forecasts of global precipitation.
资助项目	Ministry of Science and Technology of China[2017YFC0405900] ; Ministry of Science and Technology of China[2016YFC0400902] ; Natural Science Foundation of China[51979295] ; Natural Science Foundation of China[51861125203] ; Natural Science Foundation of China[U191120010] ; Guangdong Provincial Department of Science and Technology[2019ZT08G090]
WOS关键词	MORANS I ; CLIMATE ; TEMPERATURE ; PREDICTION ; MODEL ; SKILL ; PREDICTABILITY ; CALIBRATION ; RAINFALL ; SYSTEM
WOS研究方向	Geology ; Water Resources
语种	英语
出版者	COPERNICUS GESELLSCHAFT MBH
WOS记录号	WOS:000505677200001
资助机构	Ministry of Science and Technology of China ; Natural Science Foundation of China ; Guangdong Provincial Department of Science and Technology
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/131217]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhao, Tongtiegang; Chen, Xiaohong
作者单位	1.Sun Yat Sen Univ, Sch Civil Engn, Southern Marine Sci & Engn Guangdong Lab Zhuhai, Ctr Water Resources & Environm, Guangzhou 510275, Peoples R China 2.Univ Iowa, IIHR Hydrosci & Engn, Iowa City, IA 52242 USA 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Tongtiegang,Zhang, Wei,Zhang, Yongyong,et al. Significant spatial patterns from the GCM seasonal forecasts of global precipitation[J]. HYDROLOGY AND EARTH SYSTEM SCIENCES,2020,24(1):1-16.
APA	Zhao, Tongtiegang,Zhang, Wei,Zhang, Yongyong,Liu, Zhiyong,&Chen, Xiaohong.(2020).Significant spatial patterns from the GCM seasonal forecasts of global precipitation.HYDROLOGY AND EARTH SYSTEM SCIENCES,24(1),1-16.
MLA	Zhao, Tongtiegang,et al."Significant spatial patterns from the GCM seasonal forecasts of global precipitation".HYDROLOGY AND EARTH SYSTEM SCIENCES 24.1(2020):1-16.