关键词: 相空间重构, lyapunov指数, 全局函数拟合, 月平均海表温度异常, 厄尔尼诺-南方涛动, 预报

Abstract: Nonlinear time-sequence analysis is used to study the time evolution of El Nino-Southern Oscillation (ENSO) by combining the method of global function approximation with lyapunov exponent analysis. The method includes phase-space reconstruction, lyapunov exponent analysis, global function approximation, principal components analysis and least-square estimation. The data used here is monthly sea surface temperature anomalies (SSTA) from CZ (Cane & Zebiak) model. It differs from traditional time-sequence analysis methods in which nonlinear chaotic time-sequence prediction is used. The present method is proved to be a successful one with lesser data, and it provides an alternative method for ENSO prediction.

Key words: phase-space reconstruction, lyapunov exponents, global function approximation monthly, sea surface temperature anomaly (SSTA), El Nino-Southern Oscillation (ENSO), prediction