Understanding the origin of extreme events in El Ni\~{n}o-Southern Oscillation

TL;DR

This paper uses a low-dimensional slow-fast model to analyze the dynamical mechanisms behind extreme El Niño events, revealing bifurcation-induced attractor expansions and global instabilities that lead to large amplitude events with multimodal height distributions.

Contribution

It introduces a novel bifurcation analysis of a low-dimensional model to explain the origin of extreme El Niño events and their statistical properties.

Findings

Sudden attractor expansion at critical parameters causes large events.

Multimodal distribution observed in event heights.

Interevent interval dependence affects predictability.

Abstract

We investigate a low-dimensional slow-fast model to understand the dynamical origin of El Ni\~no-Southern Oscillation. A close inspection of the system dynamics using several bifurcation plots reveals that a sudden large expansion of the attractor occurs at a critical system parameter via a type of interior crisis. This interior crisis evolves through merging of a cascade of period-doubling and period-adding bifurcations that leads to the origin of occasional amplitude-modulated extremely large events. More categorically, a situation similar to homoclinic chaos arises near the critical point, however, atypical global instability evolves as a channel-like structure in phase space of the system that modulates variability of amplitude and return time of the occasional large events and makes a difference from the homoclinic chaos. The slow-fast timescale of the low-dimensional model plays an important role on the onset of occasional extremely large events. Such extreme events are characterized by their heights when they exceed a threshold level measured by a mean-excess function. The probability density of events' height displays multimodal distribution with an upper-bounded tail. We identify the dependence structure of interevent intervals to understand the predictability of return time of such extreme events using autoregressive integrated moving average model and box plot analysis.