Ruelle-Pollicott Resonances of Stochastic Systems in Reduced State Space. Part III: Application to the Cane-Zebiak model of the El Ni{\~n}o-Southern Oscillation

TL;DR

This paper applies spectral analysis of Markov operators and reduced Ruelle-Pollicott resonances to a high-dimensional climate model, elucidating noise-induced oscillations and their predictability related to ENSO events.

Contribution

It demonstrates the use of reduced RP resonances to analyze stochastic low-frequency variability in a complex climate model, extending previous theoretical frameworks.

Findings

RP resonances organize along parabolas near bifurcation

Spectral gap quantifies phase diffusion and predictability

Resonance analysis links noise-induced oscillations to ENSO predictability

Abstract

The response of a low-frequency mode of climate variability, El Ni{\~n}o-Southern Oscillation, to stochastic forcing is studied in a high-dimensional model of intermediate complexity, the fully-coupled Cane-Zebiak model [ZC87], from the spectral analysis of Markov operators governing the decay of correlations and resonances in the power spectrum. Noise-induced oscillations excited before a supercritical Hopf bifurcation are examined by means of complex resonances , the reduced Ruelle-Pollicott (RP) resonances, via a numerical application of the reduction approach of the first part of this contribution [CTND19] to model simulations. The oscillations manifest themselves as peaks in the power spectrum which are associated with RP resonances organized along parabolas, as the bifurcation is neared. These resonances and the associated eigenvectors are furthermore well described by the small-noise expansion formulas obtained by [Gas02] and made explicit in the second part of this contribution [TCND19]. Beyond the bifurcation, the spectral gap between the imaginary axis and the real part of the leading resonances quantifies the diffusion of phase of the noise-induced oscillations and can be computed from the linearization of the model and from the diffusion matrix of the noise. In this model, the phase diffusion coefficient thus gives a measure of the predictability of oscillatory events representing ENSO. ENSO events being known to be locked to the seasonal cycle, these results should be extended to the non-autonomous case. More generally, the reduction approach theorized in [CTND19], complemented by our understanding of the spectral properties of reference systems such as the stochastic Hopf bifurcation, provides a promising methodology for the analysis of low-frequency variability in high-dimensional stochastic systems.