loading page

A Simple Multiscale Intermediate Coupled Stochastic Model for El Niño Diversity and Complexity
  • Nan Chen,
  • Xianghui Fang
Nan Chen
University of Wisconsin-Madison
Author Profile
Xianghui Fang
Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University

Corresponding Author:fangxh@fudan.edu.cn

Author Profile


El Niño-Southern Oscillation (ENSO) is the most prominent interannual climate variability in the tropics and exhibits diverse features in spatiotemporal patterns. This paper develops a simple multiscale intermediate coupled stochastic model to capture the ENSO diversity and complexity. The model starts with a deterministic and linear coupled interannual atmosphere, ocean, and sea surface temperature (SST) system. It can generate two dominant linear solutions representing the eastern Pacific (EP) and the central Pacific (CP) El Niños, respectively. In addition to adopting a stochastic model for characterizing the intraseasonal wind bursts, another simple stochastic process is developed to describe the decadal variation of the background Walker circulation. The latter links the two dominant modes in a simple nonlinear fashion and advances the modulation of the strength and occurrence frequency of the EP and the CP events. Finally, cubic nonlinear damping is adopted to parameterize the relationship between subsurface temperatures and thermocline depth. The model succeeds in reproducing the spatiotemporal dynamical evolution of different types of ENSO events. It also accurately recovers the strongly non-Gaussian probability density function, the seasonal phase locking, the power spectrum, and the temporal autocorrelation function of the SST anomalies in all the three Niño regions (3, 3.4 and 4) across the equatorial Pacific. Furthermore, both the composites of the SST anomalies for various ENSO events and the strength-location bivariate distribution of equatorial Pacific SST maxima for the El Niño events from the model simulation highly resemble those from the observations.