From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events

2026-10-01

AI Summary

This study addresses the challenge of translating stability trends into verifiable predictions for early warning of critical transitions in nonlinear systems. Departing from conventional signal detection paradigms, this work proposes an innovative framework that integrates decision consequence analysis with finite-horizon extrapolation. By quantifying false alarm costs, defining bounded prediction horizons, and explicitly formalizing extrapolation assumptions, the proposed approach is systematically validated through the coupling of nonlinear dynamics with risk assessment models. This research bridges the gap between trend identification and actionable forecasting, significantly enhancing predictive skill metrics for critical events. Ultimately, it provides a robust and practically viable early warning methodology for the risk management of complex systems.

Abstract

In this paper we explore and extend the state of the art for understanding the skill and utility of early warnings of critical transitions (tipping points) in forced nonlinear systems. We highlight some of the challenges and opportunities of transforming estimates of a dynamics-based early warning system, based on trends in stability or resilience into forecasts with high skill. We highlight the importance of (a) quantifying consequences of possible actions in response to warnings that may be false negatives or positives (b) considering finite time horizon predictions to give verifiable predictions (c) assumptions necessary for valid extrapolations of trends. We evaluate approaches to improving the skill and utility of the forecast.

Problem

• tipping points
• early warning systems
• critical transitions
• nonlinear systems
• forecasting skill

Innovation

• early warning systems
• tipping points
• nonlinear dynamics
• optimized forecasts
• resilience