Universal Early Warning Signals of Phase Transitions in Climate Systems

• URL: http://arxiv.org/abs/2206.00060v1
• Date: Tue, 31 May 2022 19:07:15 GMT
• Title: Universal Early Warning Signals of Phase Transitions in Climate Systems
• Authors: Daniel Dylewsky, Timothy M. Lenton, Marten Scheffer, Thomas M. Bury, Christopher G. Fletcher, Madhur Anand, Chris T. Bauch
• Abstract summary: A deep neural network trained exclusively on 2D Ising model phase transitions is tested on a number of real and simulated climate systems. Its accuracy frequently surpasses that of conventional statistical indicators, with performance shown to be consistently improved by the inclusion of spatial indicators.
• Score: 0.586336038845426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The potential for complex systems to exhibit tipping points in which an equilibrium state undergoes a sudden and potentially 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.

Related papers

• Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data [3.9617282900065853]
  Complex systems can undergo critical transitions, where slowly changing environmental conditions trigger a sudden shift to a new, potentially catastrophic state. Early warning signals for these events are crucial for decision-making in fields such as ecology, biology and climate science. We introduce an approach that trains machine learning classifiers directly on surrogate data of past transitions.
  arXiv  Detail & Related papers  (2024-10-13T03:25:49Z)
• A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data [12.566163525039558]
  We present a strategy for training neural network models to non-intrusively correct under-resolved long-time simulations of chaotic systems. We demonstrate its ability to accurately predict the anisotropic statistics over time horizons more than 30 times longer than the data seen in training.
  arXiv  Detail & Related papers  (2024-08-02T18:34:30Z)
• SFANet: Spatial-Frequency Attention Network for Weather Forecasting [54.470205739015434]
  Weather forecasting plays a critical role in various sectors, driving decision-making and risk management. Traditional methods often struggle to capture the complex dynamics of meteorological systems. We propose a novel framework designed to address these challenges and enhance the accuracy of weather prediction.
  arXiv  Detail & Related papers  (2024-05-29T08:00:15Z)
• Early warning indicators via latent stochastic dynamical systems [0.0]
  We develop an anisotropic diffusion map that captures the latent evolutionary dynamics in the low-dimensional manifold. Three effective warning signals are derived through the latent coordinates and the latent dynamical systems. We find that our early warning indicators are capable of detecting the tipping point during state transition.
  arXiv  Detail & Related papers  (2023-09-07T16:55:33Z)
• Clustering-based Identification of Precursors of Extreme Events in Chaotic Systems [0.0]
  Abrupt and rapid high-amplitude changes in a dynamical system's states known as extreme event appear in many processes occurring in nature. The applicability of a data-driven modularity-based clustering technique to identify precursors of rare and extreme events in chaotic systems is explored.
  arXiv  Detail & Related papers  (2023-06-20T12:38:38Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Stochastic embeddings of dynamical phenomena through variational autoencoders [1.7205106391379026]
  We use a recognition network to increase the observed space dimensionality during the reconstruction of the phase space. Our validation shows that this approach not only recovers a state space that resembles the original one, but it is also able to synthetize new time series.
  arXiv  Detail & Related papers  (2020-10-13T10:10:24Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)
• Unsupervised machine learning of quantum phase transitions using diffusion maps [77.34726150561087]
  We show that the diffusion map method, which performs nonlinear dimensionality reduction and spectral clustering of the measurement data, has significant potential for learning complex phase transitions unsupervised. This method works for measurements of local observables in a single basis and is thus readily applicable to many experimental quantum simulators.
  arXiv  Detail & Related papers  (2020-03-16T18:40:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.