气候变化领域集成服务门户(CCPortal): Significant spatial patterns from the GCM seasonal forecasts of global precipitation

CCPortal

DOI	10.5194/hess-24-1-2020
	Significant spatial patterns from the GCM seasonal forecasts of global precipitation
	Zhao T.; Zhang W.; Zhang Y.; Liu Z.; Chen X.
发表日期	2020
ISSN	1027-5606
起始页码	1
结束页码	16
卷号	24 期号:1
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. © 2020 Copernicus GmbH. All rights reserved.
语种	英语
scopus关键词	Cells; Climate models; Cytology; Precipitation (meteorology); Anomaly correlations; Clustering approach; Global precipitation; Multi-model ensemble; Predictive performance; Seasonal forecasts; Spatial patterns; Spatial variability; Forecasting; climate modeling; ensemble forecasting; global climate; precipitation (climatology); spatial variation; weather forecasting; North America
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159536
作者单位	Zhao, T., Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China; Zhang, W., IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, 52242, United States; Zhang, Y., Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Liu, Z., Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China; Chen, X., Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
推荐引用方式 GB/T 7714	Zhao T.,Zhang W.,Zhang Y.,et al. Significant spatial patterns from the GCM seasonal forecasts of global precipitation[J],2020,24(1).
APA	Zhao T.,Zhang W.,Zhang Y.,Liu Z.,&Chen X..(2020).Significant spatial patterns from the GCM seasonal forecasts of global precipitation.Hydrology and Earth System Sciences,24(1).
MLA	Zhao T.,et al."Significant spatial patterns from the GCM seasonal forecasts of global precipitation".Hydrology and Earth System Sciences 24.1(2020).