Coupling functions in climate

TL;DR

This paper extends the use of coupling functions from one-dimensional to two-dimensional dynamical systems to analyze interactions between climate subsystems, demonstrated through ENSO and IOD indices, revealing their mutual influence.

Contribution

The paper introduces a method to construct coupling functions from climate data to analyze interactions between two climate subsystems, expanding previous models.

Findings

ENSO controls seasonal variability of IOD during its mature phase

Coupling functions can interpret mutual climate subsystem interactions

Network models for climate variability are feasible using this approach

Abstract

We examine how coupling functions in the theory of dynamical systems provide a quantitative window into climate dynamics. Previously we have shown that a one-dimensional periodic non-autonomous stochastic dynamical system can simulate the monthly statistics of surface air temperature data. Here we expand this approach to two-dimensional dynamical systems to include interactions between two sub-systems of the climate. The relevant coupling functions are constructed from the covariance of the data from the two sub-systems. We demonstrate the method on two tropical climate indices; The El-Ni\~{n}o--Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), to interpret the mutual interactions between these two air-sea interaction phenomena in the Pacific and Indian Oceans. The coupling function reveals that ENSO mainly controls the seasonal variability of the IOD during its mature phase. This demonstrates the plausibility of constructing a network model for the seasonal variability of climate systems based on such coupling functions.