DOI: 10.1175/JCLI-D-14-00239.1
Scopus记录号: 2-s2.0-84961290585
论文题名: Predicting critical transitions in ENSO Models. Part I: Methodology and simple models with memory
作者: Mukhin D. ; Loskutov E. ; Mukhina A. ; Feigin A. ; Zaliapin I. ; Ghil M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 5 起始页码: 1940
结束页码: 1961
语种: 英语
Scopus关键词: Atmospheric pressure
; Atmospheric temperature
; Climate models
; Climatology
; Differential equations
; Dynamical systems
; Forecasting
; Neural networks
; Oceanography
; Stochastic control systems
; Stochastic systems
; Surface waters
; Time series
; Climate prediction
; Delay differential equations
; Discrete random dynamical systems
; ENSO
; Nonlinear stochastic model
; Sea surface temperature anomalies
; Southern oscillation
; Time-dependent evolutions
; Stochastic models
; artificial neural network
; climate prediction
; El Nino-Southern Oscillation
; sea surface temperature
; stochasticity
; temperature anomaly
; time series analysis
英文摘要: A new empirical approach is proposed for predicting critical transitions in the climate system based on a time series alone. This approach relies on nonlinear stochastic modeling of the system's time-dependent evolution operator by the analysis of observed behavior. Empirical models that take the form of a discrete random dynamical system are constructed using artificial neural networks; these models include statedependent stochastic components. To demonstrate the usefulness of such models in predicting critical climate transitions, they are applied here to time series generated by a number of delay-differential equation (DDE) models of sea surface temperature anomalies. These DDE models take into account the main conceptual elements responsible for the El Niño-Southern Oscillation phenomenon. The DDE models used here have been modified to include slow trends in the control parameters in such a way that critical transitions occur beyond the learning interval in the time series. Numerical results suggest that the empirical models proposed herein are able to forecast sequences of critical transitions that manifest themselves in future abrupt changes of the climate system's statistics. © 2015 American Meteorological Society. 资助项目: NSF, National Science Foundation
; 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/50640
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 Mathematics and Statistics, University of Nevada-Reno, Reno, NV, 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.,Loskutov E.,Mukhina A.,et al. Predicting critical transitions in ENSO Models. Part I: Methodology and simple models with memory[J]. Journal of Climate,2015-01-01,28(5)