Coexistence of attractors in a coupled nonlinear delayed system modelling El Ni\~no Southern Oscillations

TL;DR

This paper models the coupled dynamics of sea surface temperature anomalies in two regions to understand El Niño Southern Oscillations, revealing complex attractor coexistence and basins of attraction that could improve forecasting methods.

Contribution

It introduces a coupled deterministic dynamical system model for ENSO, analyzing stability, attractors, and basins, contrasting with noise-based models and offering new insights for prediction.

Findings

Coexistence of multiple attractors including steady states and oscillations.

Coupling can induce oscillations even when one region is stable.

Basins of attraction help predict ENSO episodes based on initial conditions.

Abstract

We study the dynamics of the sea surface temperature (SST) anomaly using a model of the temporal patterns of two sub-regions, mimicking behaviour similar to El Ni\~no Southern Oscillations (ENSO). Specifically, we present the existence, stability, and basins of attraction of the solutions arising in the model system in the space of these parameters: self delay, delay and inter-region coupling strengths. The emergence or suppression of oscillations in our models is a dynamical feature of utmost relevance, as it signals the presence or absence of ENSO-like oscillations. In contrast to the well-known low order model of ENSO, where the influence of the neighbouring regions on the region of interest is modelled as external noise, we consider neighbouring regions as a coupled deterministic dynamical systems. Different parameters yield a rich variety of dynamical patterns in our model, ranging from steady states and homogeneous oscillations to irregular oscillations and coexistence of oscillatory attractors, without explicit inclusion of noise. Interestingly, if we take the self-delay coupling strengths of the two sub-regions to be such that the temperature of one region goes to a fixed point regime when uncoupled, while the other system is in the oscillatory regime, then on coupling both systems show oscillations. We explicitly obtain the basins of attraction for the different steady states and oscillatory states in the model. Our results might be helpful for forecasting of El Ni\~no (or La Ni\~na) progress, as it indicates the combination of initial SST anomalies in the sub-regions that can result in a El Ni\~no/La Ni\~na episodes. In particular, the result suggests using an interval as a criterion to estimate the El-Ni\~no or La-Ni\~na progress instead of the currently used the single value criterion.