Anticipating critical transitions in multidimensional systems driven by time- and state-dependent noise

TL;DR

This paper introduces a data-driven method to detect early warning signals of critical transitions in complex multidimensional systems with time- and state-dependent noise, overcoming limitations of traditional variance-based indicators.

Contribution

The authors propose a novel approach estimating a multidimensional Langevin equation, enabling reliable detection of stability changes without dimension reduction or noise assumptions.

Findings

Method effectively detects local stability changes in complex systems.

It discriminates between changes in dynamics and noise, reducing false alarms.

The approach outperforms traditional indicators in systems with time-dependent or multiplicative noise.

Abstract

Anticipating bifurcation-induced transitions in dynamical systems has gained relevance in various fields of the natural, social, and economic sciences. Before the annihilation of a system's equilibrium point by means of a bifurcation, the system's internal feedbacks that stabilize the initial state weaken and eventually vanish, a process referred to as critical slowing down (CSD). In one-dimensional systems, this motivates the use of variance and lag-1 autocorrelation as indicators of CSD. However, the applicability of variance is limited to time- and state-independent driving noise, strongly constraining the generality of this CSD indicator. In multidimensional systems, the use of these indicators is often preceded by a dimension reduction in order to obtain a one-dimensional time series. Many common techniques for such an extraction of a one-dimensional time series generally incur the risk of missing CSD in practice. Here, we propose a data-driven approach based on estimating a multidimensional Langevin equation to detect local stability changes and anticipate bifurcation-induced transitions in systems with generally time- and state-dependent noise. Our approach substantially generalizes the conditions under which CSD can reliably be detected, as demonstrated in a suite of examples. In contrast to existing approaches, changes in deterministic dynamics can be clearly discriminated from changes in the driving noise using our method. This substantially reduces the risk of false or missed alarms of conventional CSD indicators in settings with time-dependent or multiplicative noise. In multidimensional systems, our method can greatly advance the understanding of the coupling between system components and can avoid risks of missing CSD due to dimension reduction, which existing approaches suffer from.