Spectral analysis of climate dynamics with operator-theoretic approaches

TL;DR

This paper applies spectral and operator-theoretic methods to analyze climate dynamics, successfully extracting coherent variability modes like ENSO from complex data without prefiltering, enhancing understanding and prediction of climate phenomena.

Contribution

It introduces a novel spectral approach combining dynamical systems theory and data science to identify climate variability modes directly from high-dimensional data.

Findings

Improved ENSO lifecycle characterization over traditional indices.

Identification of interaction modes between ENSO and seasonal cycles.

Enhanced understanding of climate variability timescales.

Abstract

The Earth's climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds in cloud microphysics, to thousands of kilometers and centuries in ocean dynamics. Yet, despite this dynamical complexity, climate dynamics is known to exhibit coherent modes of variability. A primary example is the El Ni\~no Southern Oscillation (ENSO), the dominant mode of interannual (3-5 yr) variability in the climate system. The objective and robust characterization of this and other important phenomena presents a long-standing challenge in Earth system science, the resolution of which would lead to improved scientific understanding and prediction of climate dynamics, as well as assessment of their impacts on human and natural systems. Here, we show that the spectral theory of dynamical systems, combined with techniques from data science, provides an effective means for extracting coherent modes of climate variability from high-dimensional model and observational data, requiring no frequency prefiltering, but recovering multiple timescales and their interactions. Lifecycle composites of ENSO are shown to improve upon results from conventional indices in terms of dynamical consistency and physical interpretability. In addition, the role of combination modes between ENSO and the annual cycle in ENSO diversity is elucidated.