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arXiv · 2206.06649

A Simple Multiscale Intermediate Coupled Stochastic Model for El Ni\~no Diversity and Complexity

Nan Chen·Xianghui Fang
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Abstract

El Ni\~no-Southern Oscillation (ENSO) is the most prominent interannual climate variability in the tropics and exhibits diverse features in spatiotemporal patterns. In this paper, a simple multiscale intermediate coupled stochastic model is developed 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 distinct dominant linear solutions that represent the eastern Pacific (EP) and the central Pacific (CP) El Ni\~nos, 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, a cubic nonlinear damping is adopted to parameterize the relationship between subsurface temperature and thermocline depth. The model succeeds in reproducing the spatiotemporal dynamical evolution of different types of the 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 Ni\~no 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 Ni\~no events from the model simulation highly resemble those from the observations.

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Nan Chen, Xianghui Fang. 2022-06-14. A Simple Multiscale Intermediate Coupled Stochastic Model for El Ni\~no Diversity and Complexity. https://arxiv.org/abs/2206.06649

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