Spatial association of anomaly correlation for GCM seasonal forecasts of global precipitation
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Spatial association of anomaly correlation for GCM seasonal forecasts of global precipitation Tongtiegang Zhao1 · Haoling Chen1 · Weixin Xu2 · Huayang Cai3 · Denghua Yan4 · Xiaohong Chen1 Received: 11 March 2020 / Accepted: 13 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Global climate models (GCMs) are used by major climate centers worldwide for global climate forecasting, and predictive performance is one of the most important issues in GCM forecast applications. In addition to spatial plotting that illustrates anomaly correlation at individual grid cells, this study proposes a novel local indicator of spatial association (LISA) of anomaly correlation (herein, LISAAC) for GCM seasonal forecasts of global precipitation. LISAAC is built upon local Moran’s I by relating anomaly correlation at neighboring grid cells to one another. While local Moran’s I takes the grand mean of anomaly correlation as the benchmark, LISSAC considers the original value of anomaly correlation in the mathematical formulation. A case study is devised for the Climate Forecast System version 2 (CFSv2) seasonal forecasts, which are initialized in January, February,…, and June, of the global precipitation in June, July, and August. Three metrics—LISAAC, local Moran’s I, and original anomaly correlation—are applied to investigate the predictive performance. In comparison with local Moran’s I, LISAAC can identify clusters of positive, neutral, and negative anomaly correlations. In comparison with anomaly correlation, LISAAC can capture outliers of positive (negative) anomaly correlation surrounded by negative (positive) anomaly correlation. Overall, the results highlight that LISAAC can serve as a useful tool for evaluating the predictive performance of GCM seasonal forecasts of global precipitation.
1 Introduction Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00382-020-05384-2) contains supplementary material, which is available to authorized users. * Tongtiegang Zhao [email protected] Denghua Yan [email protected] Xiaohong Chen [email protected] 1
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
2
School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou 510275, China
3
Institute of Estuarine and Coastal Research, School of Marine Engineering and Technology, Sun Yat-Sen University, Guangzhou 510275, China
4
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China
Global climate models (GCMs) coupling land–ocean–atmosphere processes have been steadily improved over the past decades with the advances in supercomputing and global observational/data assimilation systems (Molteni et al. 1996; Smith et al. 2007; Bauer et al. 2015). At present, GCMs are used by major climate cen
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