DOI: 10.1175/JCLI-D-15-0372.1
Scopus记录号: 2-s2.0-84960915929
论文题名: Diversity, nonlinearity, seasonality, and memory effect in ENSO simulation and prediction using empirical model reduction
作者: Chen C. ; Cane M.A. ; Henderson N. ; Lee D.E. ; Chapman D. ; Kondrashov D. ; Chekroun M.D.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 5 起始页码: 1809
结束页码: 1830
语种: 英语
Scopus关键词: Atmospheric temperature
; Climate change
; Locks (fasteners)
; Oceanography
; Probability density function
; Regression analysis
; Surface waters
; Time delay
; ENSO
; History informations
; Multivariate models
; Sea surface temperature anomalies
; Seasonal phase locking
; Skewed probability density function
; Statistical forecasting
; Subsurface information
; Forecasting
; atmospheric circulation
; atmospheric dynamics
; computer simulation
; El Nino-Southern Oscillation
; empirical analysis
; nonlinearity
; sea surface temperature
; seasonality
; Pacific Ocean
; Pacific Ocean (Tropical)
英文摘要: A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value. © 2016 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50190
Appears in Collections: 气候变化事实与影响
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作者单位: Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY, United States; Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, United States
Recommended Citation:
Chen C.,Cane M.A.,Henderson N.,et al. Diversity, nonlinearity, seasonality, and memory effect in ENSO simulation and prediction using empirical model reduction[J]. Journal of Climate,2016-01-01,29(5)