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Quantifying limits to detection of early warning for critical transitions

机译:量化关键过渡期的预警检测极限

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摘要

Catastrophic regime shifts in complex natural systems may be averted through advanced detection. Recent work has provided a proof-of-principle that many systems approaching a catastrophic transition may be identified through the lens of early warning indicators such as rising variance or increased return times. Despite widespread appreciation of the difficulties and uncertainty involved in such forecasts, proposed methods hardly ever characterize their expected error rates. Without the benefits of replicates, controls or hindsight, applications of these approaches must quantify how reliable different indicators are in avoiding false alarms, and how sensitive they are to missing subtle warning signs. We propose a model-based approach to quantify this trade-off between reliability and sensitivity and allow comparisons between different indicators. We show these error rates can be quite severe for common indicators even under favourable assumptions, and also illustrate how a model-based indicator can improve this performance. We demonstrate how the performance of an early warning indicator varies in different datasets, and suggest that uncertainty quantification become a more central part of early warning predictions.
机译:复杂的自然系统中的灾难性政权转移可以通过先进的检测来避免。最近的工作提供了一个原理证明,可以通过预警指标(例如方差增加或返回时间增加)来识别许多正在发生灾难性转变的系统。尽管人们普遍认识到此类预测所涉及的困难和不确定性,但所提出的方法几乎无法表征其预期的错误率。在没有复制,控制或事后观察的好处的情况下,这些方法的应用必须量化不同指标在避免错误警报方面的可靠性,以及它们对于丢失细微警告信号的敏感程度。我们提出了一种基于模型的方法来量化可靠性和灵敏度之间的折衷,并允许在不同指标之间进行比较。我们显示,即使在有利的假设下,这些误差率对于常见指标而言也可能非常严重,并且还说明了基于模型的指标如何改善此性能。我们演示了预警指标的性能在不同数据集中如何变化,并建议不确定性量化成为预警预测的核心部分。

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