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Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective
Zhao, Tongtiegang1; Zhang, Yongyong2; Chen, Xiaohong1
2019-03-01
Source PublicationJOURNAL OF HYDROLOGY
ISSN0022-1694
Volume570Pages:17-25
Corresponding AuthorChen, Xiaohong(eescxh@mail.sysu.edu.cn)
AbstractGlobal climate models (GCMs) produce informative seasonal forecasts of global precipitation months ahead of the occurrence for hydrological forecasting. Meanwhile, the skill of GCM forecasts varies by location and initialization time. In this paper, we investigate the anomaly correlation, which indicates the correspondence between forecasts and observations, for 10 sets of global precipitation forecasts in the North American Multi-Model Ensemble (NMME) project. We propose to use principal component analysis to characterize the variation of anomaly correlation. We identify the existence of spatial and temporal patterns at the global scale. The spatial pattern reveals that high (low) anomaly correlation at one initialization time coincides with high (low) anomaly correlation at other initialization times. In other words, for a grid cell, the anomaly correlation at different initialization times tends to be similarly high, or low. It is observed that some of the regions where grid cells are with overall high anomaly correlation tend to exhibit tele-connections with global climate drivers. On the other hand, the temporal pattern suggests that the anomaly correlation tends to improve with initialization time. This pattern is attributable to data assimilation that bases forecasts at a later initialization time on more global observations and simulations. Generally, the two patterns are effective and explain 50% to 70% of the variation of anomaly correlation for the 10 sets of NMME forecasts. The projections of anomaly correlation vectors onto the two patterns help illustrate where and when the NMME precipitation forecasts are skillful.
KeywordGlobal climate model Seasonal forecasts Precipitation Anomaly correlation North American Multi-Model Ensemble
DOI10.1016/j.jhydrol.2018.12.036
WOS KeywordSYSTEM ; MODEL ; TEMPERATURE ; RAINFALL ; SKILL ; ENSO
Indexed BySCI
Language英语
Funding ProjectNatural Science Foundation of China (NSFC)[91547202] ; Natural Science Foundation of China (NSFC)[51479216] ; Natural Science Foundation of China (NSFC)[41671024] ; Natural Science Foundation of China (NSFC)[91547108] ; Natural Science Foundation of China (NSFC)[51779279] ; Natural Science Foundation of China (NSFC)[51822908] ; Ministry of Science and Technology of China (MSTC)
Funding OrganizationNatural Science Foundation of China (NSFC) ; Ministry of Science and Technology of China (MSTC)
WOS Research AreaEngineering ; Geology ; Water Resources
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000460709400002
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49234
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Xiaohong
Affiliation1.Sun Yat Sen Univ, Ctr Water Resources & Environm, Guangzhou, Guangdong, Peoples R China
2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Tongtiegang,Zhang, Yongyong,Chen, Xiaohong. Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective[J]. JOURNAL OF HYDROLOGY,2019,570:17-25.
APA Zhao, Tongtiegang,Zhang, Yongyong,&Chen, Xiaohong.(2019).Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective.JOURNAL OF HYDROLOGY,570,17-25.
MLA Zhao, Tongtiegang,et al."Predictive performance of NMME seasonal forecasts of global precipitation: A spatial-temporal perspective".JOURNAL OF HYDROLOGY 570(2019):17-25.
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