Universal Early Warning Signals of Phase Transitions in Climate Systems

TL;DR

This paper demonstrates that deep learning models trained on synthetic phase transition data can effectively predict climate tipping points, outperforming traditional indicators and aiding early warning of critical climate shifts.

Contribution

It introduces a novel approach using deep neural networks trained on synthetic data to identify early warning signals of climate system phase transitions.

Findings

Deep neural networks outperform traditional statistical indicators.

Inclusion of spatial indicators improves prediction accuracy.

Models trained on Ising model data transfer effectively to climate systems.

Abstract

The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and often irreversible shift is well established, but prediction of these events using standard forecast modeling techniques is quite difficult. This has led to the development of an alternative suite of methods that seek to identify signatures of critical phenomena in data, which are expected to occur in advance of many classes of dynamical bifurcation. Crucially, the manifestations of these critical phenomena are generic across a variety of systems, meaning that data-intensive deep learning methods can be trained on (abundant) synthetic data and plausibly prove effective when transferred to (more limited) empirical data sets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on 2D Ising model phase transitions is tested on a number of real and simulated climate systems with considerable success. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators. Tools such as this may offer valuable insight into climate tipping events, as remote sensing measurements provide increasingly abundant data on complex geospatially-resolved Earth systems.