Empirical Dynamical System Modeling of ENSO Using Nonlinear Inverse Techniques

Abstract

A statistical technique is presented that allows for the empirical derivation of dynamical system equations from data. It is based on multiple nonparametric regression analysis and is applicable to a broad class of physical systems. It is applied to differential delay equations as well as to ordinary differential equations. The aim of this paper is to illustrate this technique in the context of the El Niño–Southern Oscillation (ENSO) phenomenon. A set of reduced models is derived from an intermediate coupled atmosphere–ocean model of the tropical Pacific and from a state-of-the-art coupled general circulation model simulation. The analysis in this paper focuses on the dimensionality issue as well as on the role of nonlinearities. The empirical technique presented in this study helps to identify key ENSO processes and to explain physical peculiarities of ENSO simulations.