Critical slowing down-based early warning signals (EWSs) are well-known indicators that precede an approaching collapse in complex systems. To date, the majority of studies on the predictability of critical transitions consider systems perturbed with temporally uncorrelated noise. In contrast, here we study catastrophic and non-catastrophic transitions, and the performance of associated EWSs in systems perturbed with correlated noise. We find that elevated noise correlation can advance the occurrence of a catastrophic transition, and simultaneously progresses the system’s recovery. However, noise correlation does not have a significant impact on the likelihood of non-catastrophic transitions. We show that depending upon the transition mechanism, the occurrence of weak to false signals increases with noise reddening. Imperfect data sampling, both spatial and temporal, further reduces the efficacy of EWSs. Spatially limited data has more impact on the efficacy of EWSs for negative noise correlation than that of positive. However, temporally imperfect data is more detrimental for positively correlated noise. Overall, our study suggests that performance of EWSs is critical to system-specific perturbations as well as data sampling.

基于关键减速的预警信号 (EWS) 是众所周知的指标，在​​复杂系统即将崩溃之前。迄今为止，大多数关于关键转换可预测性的研究都考虑了受到时间不相关噪声干扰的系统。相比之下，我们在这里研究灾难性和非灾难性转变，以及相关 EWS 在受相关噪声干扰的系统中的性能。我们发现提高的噪声相关性可以促进灾难性转变的发生，同时促进系统的恢复。然而，噪声相关性对非灾难性转变的可能性没有显着影响。我们表明，根据过渡机制，弱信号到错误信号的出现会随着噪声变红而增加。不完善的数据采样，在空间和时间上，进一步降低了 EWS 的功效。空间有限的数据对 EWS 对负噪声相关性的影响比对正噪声相关性的影响更大。然而，时间上不完美的数据对正相关噪声更有害。总体而言，我们的研究表明，EWS 的性能对于系统特定的扰动和数据采样至关重要。