# Universal Early Warning Signals of Phase Transitions in Climate Systems

arXiv: https://arxiv.org/abs/2206.00060 · HTML: https://mlsanity.com/paper/2206.00060/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 40 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 1 of 1)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)

## Built on by (most cited first, 10 of 10)

- [Predicting discrete-time bifurcations with deep learning](https://mlsanity.com/paper/2303.09669/) (2023)
- [Deep learning for predicting the occurrence of tipping points](https://mlsanity.com/paper/2407.18693/) (2024)
- [An early warning indicator trained on stochastic disease-spreading models with different noises](https://mlsanity.com/paper/2403.16233/) (2024)
- [Neural models for prediction of spatially patterned phase transitions: methods and challenges](https://mlsanity.com/paper/2505.09718/) (2025)
- [Fold Bifurcation Identification through Scientific Machine Learning](https://mlsanity.com/paper/2312.14210/) (2023)
- [Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data](https://mlsanity.com/paper/2410.09707/) (2024)
- [Interpretable Early Warnings using Machine Learning in an Online Game-experiment](https://mlsanity.com/paper/2502.09880/) (2025)
- [Echoes Before Collapse: Deep Learning Detection of Flickering in Complex Systems](https://mlsanity.com/paper/2509.04683/) (2025)
- [Noise-robust Contrastive Learning for Critical Transition Detection in Dynamical Systems](https://mlsanity.com/paper/2512.12523/) (2025)
- [Rank Collapse Is Recoverable, Growing |Q| Is Not: Out-of-Sample Early Warning for Value Divergence in High-UTD Soft Actor-Critic](https://mlsanity.com/paper/2609.32819/) (2026)

## Abstract (arXiv)

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.