Analysis and Predictability for Tipping Points with Leading-Order Nonlinear Terms

Abstract

Tipping points have been actively studied in various applications as well as from a mathematical viewpoint. A main technique to theoretically understand early-warning signs for tipping points is to use the framework of fast-slow stochastic differential equations. A key assumption in many arguments for the existence of variance and auto-correlation growth before a tipping point is to use a linearization argument, i.e., the leading-order term governing the deterministic (or drift) part of stochastic differential equation is linear. This assumption guarantees a local approximation via an Ornstein-Uhlenbeck process in the normally hyperbolic regime before, but sufficiently bounded away from, a bifurcation. In this paper, we generalize the situation to leading-order nonlinear terms for the setting of one fast variable. We work in the quasi-steady regime and prove that the fast variable has a well-defined stationary distribution and we calculate the scaling law for the variance as a bifurcation-induced tipping point is approached. We cross-validate the scaling law numerically. Furthermore, we provide a computational study for the predictability using early-warning signs for leading-order nonlinear terms based upon receiver-operator characteristic curves.

arXiv commentpreprint

References in corpus (4)

• Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system
• A mathematical framework for critical transitions: normal forms, variance and applications
• Early warning signs for saddle-escape transitions in complex networks
• Predictability of Critical Transitions

Cited by in corpus (5)

• No evidence for critical slowing down prior to human epileptic seizures
• Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems
• Sample Paths Estimates for Stochastic Fast-Slow Systems driven by Fractional Brownian Motion
• Precursors to Rare Events in Stochastic Resonance
• Network percolation provides early warnings of abrupt changes in coupled oscillatory systems: An explanatory analysis