DOI: 10.1175/JCLI-D-14-00240.1
Scopus记录号: 2-s2.0-84961288270
论文题名: Predicting critical transitions in ENSO models. Part II: Spatially dependent models
作者: Mukhin D. ; Kondrashov D. ; Loskutov E. ; Gavrilov A. ; Feigin A. ; Ghil M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 5 起始页码: 1962
结束页码: 1976
语种: 英语
Scopus关键词: Atmospheric pressure
; Climate models
; Climatology
; Data compression
; Forecasting
; Neural networks
; Orthogonal functions
; Principal component analysis
; Spatial distribution
; Spectrum analysis
; Stochastic systems
; Time series
; Time series analysis
; Climate prediction
; Empirical Orthogonal Function
; ENSO
; Multichannel singular spectrum analysis
; Non-stationary time series
; Ocean-atmosphere models
; Prediction methodology
; Principal components analysis
; Stochastic models
; air-sea interaction
; artificial neural network
; atmosphere-ocean coupling
; climate modeling
; El Nino-Southern Oscillation
; principal component analysis
; weather forecasting
英文摘要: The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin-Neelin-Ghil (JNG) hybrid seasonally forced coupled ocean-atmosphere model of El Niño- Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample. © 2015 American Meteorological Society. 资助项目: NSF, National Science Foundation
; NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50522
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Applied Physics, Russian Academy of Sciences, Lobachevsky State University of Nizhni Novgorod, Nizhny Novgorod, Russian Federation; Department of Atmospheric and Oceanic Sciences, University of California-Los Angeles, Los Angeles, CA, United States; Geosciences Department and Laboratoire de Météorologie Dynamique, École Normale Supérieure, CNRS and IPSL, Paris, France; Department of Atmospheric and Oceanic Sciences, Institute of Geophysics and Planetary Physics, University of California-Los Angeles, Los Angeles, CA, United States
Recommended Citation:
Mukhin D.,Kondrashov D.,Loskutov E.,et al. Predicting critical transitions in ENSO models. Part II: Spatially dependent models[J]. Journal of Climate,2015-01-01,28(5)