A delay differential model of ENSO variability: Parametric instability and the distribution of extremes

TL;DR

This paper analyzes a delay differential equation model for ENSO variability, revealing complex stability regimes, potential chaos, and extreme event distributions driven by key physical parameters.

Contribution

It introduces a delay differential model capturing ENSO dynamics with detailed stability analysis and reveals complex, possibly fractal, stability boundaries and chaotic behavior.

Findings

Stable and unstable regimes separated by a fractal-like neutral curve

Unstable regime exhibits complex, chaotic trajectories

Model reproduces ENSO extreme variability and transitions

Abstract

We consider a delay differential equation (DDE) model for El-Nino Southern Oscillation (ENSO) variability. The model combines two key mechanisms that participate in ENSO dynamics: delayed negative feedback and seasonal forcing. We perform stability analyses of the model in the three-dimensional space of its physically relevant parameters. Our results illustrate the role of these three parameters: strength of seasonal forcing $b$, atmosphere-ocean coupling $\kappa$, and propagation period $\tau$ of oceanic waves across the Tropical Pacific. Two regimes of variability, stable and unstable, are separated by a sharp neutral curve in the $(b,\tau)$ plane at constant $\kappa$. The detailed structure of the neutral curve becomes very irregular and possibly fractal, while individual trajectories within the unstable region become highly complex and possibly chaotic, as the atmosphere-ocean coupling $\kappa$ increases. In the unstable regime, spontaneous transitions occur in the mean ``temperature'' ({\it i.e.}, thermocline depth), period, and extreme annual values, for purely periodic, seasonal forcing. The model reproduces the Devil's bleachers characterizing other ENSO models, such as nonlinear, coupled systems of partial differential equations; some of the features of this behavior have been documented in general circulation models, as well as in observations. We expect, therefore, similar behavior in much more detailed and realistic models, where it is harder to describe its causes as completely.