Data-Driven Koopman Analysis of Tropical Climate Space-Time Variability

TL;DR

This paper applies Koopman operator-based feature extraction to tropical climate data, revealing multiscale spatiotemporal patterns and their relation to lower-frequency dynamics, with implications for predictability and understanding climate variability.

Contribution

It introduces a Koopman analysis approach to tropical climate data, uncovering multiscale patterns without prefiltering, and explores their predictability and dynamical properties.

Findings

Detected multiscale spatiotemporal modes in climate data

Identified propagation of organized convection on intraseasonal timescales

Linked traveling patterns to lower-frequency climate variability

Abstract

We study nonlinear dynamics of the Earth's tropical climate system. For that, we apply a recently developed technique for feature extraction and mode decomposition of spatiotemporal data generated by ergodic dynamical systems. The method relies on constructing low-dimensional representations (temporal patterns) of signals using eigenfunctions of Koopman operators governing the evolution of observables in ergodic dynamical systems. We apply this technique to a variety of tropical climate datasets and extract a multiscale hierarchy of spatiotemporal patterns on diurnal to interannual timescales. In particular, we detect without prefiltering the input data modes operating on intraseasonal and shorter timescales that correspond to propagation of organized convection. We discuss the salient properties of these propagating features and in particular we focus on how the activity of certain types of these traveling patterns is related to lower-frequency dynamics. As an extension of this work, we discuss their potential predictability based on a range of nonparametric techniques and potential advances related to understanding the deterministic and stochastic aspects of the variability of these modes.