PreprintCC BY 4.0arXiv
From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events
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.
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BibTeX
@misc{plessis2026from,
title = {{From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events}},
author = {Franco Du Plessis and Victoria Volodina and Chris A. Boulton and Muhammed Fadera and Sneha Kachhara and Timothy M. Lenton and Paul D. L. Ritchie and Peter Ashwin},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.01677},
eprint = {2610.01677},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.01677},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events
AU - Franco Du Plessis
AU - Victoria Volodina
AU - Chris A. Boulton
AU - Muhammed Fadera
AU - Sneha Kachhara
AU - Timothy M. Lenton
AU - Paul D. L. Ritchie
AU - Peter Ashwin
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.01677
UR - https://arxiv.org/abs/2610.01677
AB - 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.
ER -