Constraining safe and unsafe overshoots in saddle-node bifurcations

TL;DR

This paper analyzes how overshoot parameters in saddle-node bifurcations affect system stability, establishing new power law relationships for safe and unsafe overshoot thresholds with implications for risk assessment in complex systems.

Contribution

It introduces a generalized framework for understanding overshoot dynamics in saddle-node bifurcations, extending previous models to larger overshoots with variable power law exponents.

Findings

Safe overshoot threshold follows a R^{-1} power law for finite support overshoots.

Large overshoots exhibit a crossover to different power law exponents depending on parameter asymptotics.

Numerical simulations validate the analytical power law relationships.

Abstract

We consider a dynamical system undergoing a saddle-node bifurcation with an explicitly time dependent parameter~$p(t)$. The combined dynamics can be considered as a dynamical systems where $p$ is a slowly evolving parameter. Here, we investigate settings where the parameter features an overshoot. It crosses the bifurcation threshold for some finite duration $t_e$ and up to an amplitude $R$, before it returns to its initial value. We denote the overshoot as safe when the dynamical system returns to its initial state. Otherwise, one encounters runaway trajectories (tipping), and the overshoot is unsafe. For shallow overshoots (small $R$) safe and unsafe overshoots are discriminated by an inverse square-root border, $t_e \propto R^{-1/2}$, as reported in earlier literature. However, for larger overshoots we here establish a crossover to another power law with an exponent that depends on the asymptotics of $p(t)$. For overshoots with a finite support we find that $t_e \propto R^{-1}$, and we provide examples for overshoots with exponents in the range $[-1, -1/2]$. All results are substantiated by numerical simulations, and it is discussed how the analytic and numeric results pave the way towards improved risks assessments separating safe from unsafe overshoots in climate, ecology and nonlinear dynamics.