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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-398
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-398
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Sep 2019

Submitted as: research article | 05 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Significant spatial patterns from the GCM seasonal forecasts of global precipitation

Tongtiegang Zhao1, Wei Zhang2, Yongyong Zhang3, and Xiaohong Chen1 Tongtiegang Zhao et al.
  • 1Center of Water Resources and Environment, Sun Yat-Sen University, Guangzhou 510275, China
  • 2IIHR-Hydroscience & Engineering, University of Iowa, Iowa City 52242, United States
  • 3Key Laboratory of Water Cycle and Related Land Surface Processes, Chinese Academy of Sciences, Beijing 100101, China

Abstract. 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 and derive significant spatial patterns to illustrate the predictive performance. For the anomaly correlation derived from the ten 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 correlation at neighbouring grid cells to one another. The global Moran's I indicates that at the global scale anomaly correlation at one grid cell relates significantly and positively to anomaly correlation at surrounding grid cells, while the local Moran's I 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 center 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 one of the ten 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.

Tongtiegang Zhao et al.
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