DOI: 10.1029/2018JD028267
Scopus记录号: 2-s2.0-85048948259
论文题名: Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China
作者: Zhao T. ; Chen X. ; Liu P. ; Zhang Y. ; Liu B. ; Lin K.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期: 11 起始页码: 6039
结束页码: 6052
语种: 英语
英文关键词: anomaly correlation
; global climate model
; precipitation
; seasonal forecasts
; spatial and temporal variation
Scopus关键词: anomaly
; climate modeling
; correlation
; eigenvalue
; forecasting method
; global climate
; precipitation (climatology)
; principal component analysis
; seasonal variation
; spatiotemporal analysis
; summer
; China
英文摘要: The skill of global climate model (GCM) forecasts is usually indicated by the anomaly correlation between ensemble mean and observation. For GCM forecasts, anomaly correlation does not steadily improve with decreasing lead time but oscillates instead. This paper aims to address the oscillation and illustrate the relationship between anomaly correlation and lead time. We formulate the anomaly correlation of forecasts at different initialization times as a vector and pool anomaly correlation vectors across grid cells in the analysis. We propose two patterns to characterize the spatial and temporal variation of anomaly correlation in the three-dimensional space of latitude, longitude, and initialization time. The first pattern suggests that the anomaly correlation at different initialization times is at a similar level. The second pattern indicates that the anomaly correlation linearly increases with decreasing lead time. These two patterns are tested using the eigenvectors through principal component analysis. They are first illustrated using the GFDL-CM2p1-aer04 forecasts of summer precipitation in China. They are further verified by another nine sets of North-American Multi-Model Ensemble (NMME) forecasts. Overall, the first pattern explains more variation than the second pattern. In total, the two patterns explain 42% of the variation of anomaly correlation for CanCM3, 59% for CanCM4, 42% for COLA-RSMAS-CCSM3), 45% for COLA-RSMAS-CCSM4, 59% for GFDL-CM2p1, 67% for GFDL-CM2p1-aer04, 65% for GFDL-CM2p5-FLOR-A06, 57% for GFDL-CM2p5-FLOR-B01, 48% for NCAR-CESM1, and 60% for NCEP-CFSv2. The percentage of explained variation demonstrates the effectiveness of the two patterns as exploratory tools to analyze the predictive performance of GCM forecasts. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/113717
Appears in Collections: 气候减缓与适应
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作者单位: Department of Water Resources and Environment, Sun Yat-Sen University, Guangzhou, China; State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China; Department of Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
Recommended Citation:
Zhao T.,Chen X.,Liu P.,et al. Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(11)