Empirical dynamical system modeling of ENSO using nonlinear inverse techniques

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 Nino-Southern Oscillation (ENSO) phenomenon. A set of reduced models is derived from an intermediate coupled atmosphere-ocean model of the tropical Pacific and from state-of-the-art coupled general circulation model simulations. The empirical technique presented in this study helps to identify key ENSO processes and to explain physical pecularities of ENSO simulations.