ArticleJournal of the Royal Society, Interface2023
Universal early warning signals of phase transitions in climate systems.
Article in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Interpretable early warnings using machine learning in an online game-experiment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- FatePredictor: Cell fate decision-making prediction with an ensemble deep learning model.Innovation (Cambridge (Mass.)) · 2025Article
- Identifying dispersal events of red foxes (Vulpes vulpes) using early warning signals.Movement ecology · 2025Article
- Deep learning for predicting the occurrence of tipping points.Royal Society open science · 2025Article
- FateNet: an integration of dynamical systems and deep learning for cell fate prediction.Bioinformatics (Oxford, England) · 2024Article
- Early warning signals for bifurcations embedded in high dimensions.Scientific reports · 2024Article
- An early warning indicator trained on stochastic disease-spreading models with different noises.Journal of the Royal Society, Interface · 2024Article
- Optimal sampling of spatial patterns improves deep learning-based early warning signals of critical transitions.Royal Society open science · 2024Article
- Remotely sensing potential climate change tipping points across scales.Nature communications · 2024Review
- Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.Frontiers in network physiology · 2024Article
- Transition Temperature of Color Change in Thermochromic Systems and Its Description Using Sigmoidal Models.Materials (Basel, Switzerland) · 2023Article
- Early warning signals have limited applicability to empirical lake data.Nature communications · 2023Article
- Predicting discrete-time bifurcations with deep learning.Nature communications · 2023Article
- Universal early warning signals of phase transitions in climate systems.Journal of the Royal Society, Interface · 2023Article
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Authors and funding
7 authors.
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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 modelling 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 datasets. This paper provides a proof of concept for this approach as applied to lattice phase transitions: a deep neural network trained exclusively on two-dimensional 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.
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