Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past.

中文翻译：

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红噪声驱动的自然系统中接近关键转变的检测


检测关键减速 (CSD) 是预测噪声时间序列数据关键转变的主要途径。最常见的是，方差的变化和滞后 1 自相关 [AC(1)] 被用作 CSD 指标。然而，只有当驱动系统的噪声是白色且稳定的时，这些指标才会产生可靠的结果。在更现实的时间相关红噪声情况下，增加（减少）噪声的相关性将导致方差和 AC(1) 出现虚假（屏蔽）警报。在这里，我们提出了两种新方法，可以将真正的 CSD 与驾驶噪声特性的可能变化区分开来。我们专注于根据白噪声或红噪声驱动的朗之万型动力学估计线性恢复率的变化。我们评估了新估计器预测关键转变的能力，并表明它们对于连续时间和离散时间模型的性能明显优于其他现有方法。除了概念模型之外，我们还将我们的方法应用于非洲湿润期结束的气候模型模拟。这些估计排除了源自非平稳噪声特征的虚假信号，并揭示了非洲气候系统的不稳定，这是过去气候突变原型背后的动力机制。