Slowing down as an early warning signal for abrupt climate change

By Vasilis Dakos, Marten Scheffer, E. V. van Nes, V. Brovkin, V. Petoukhov, Hermann Held. Proceedings of the National Academy of Sciences of the United States of America, 2008

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Machine learning and artificial intelligence to aid climate change research and preparedness (2019, background): “We suggest ML can improve identification of tipping elements and advance precursor analyses (Dakos et al 2008).”
Do early warning indicators consistently predict nonlinear change in long‐term ecological data? (2016, background): “These models demonstrate that ecological time series show characteristic behaviours as a consequence of this process, notably an increase in variance and autocorrelation over time, and these behaviours have been suggested as potential EWIs (Wissel 1984; Carpenter & Brock 2006; van Nes & Scheffer 200”
Interacting regime shifts in ecosystems: implication for early warnings (2010, background): “These indicators may serve as diagnostics of critical 904 transitions in long-term observed data (Dakos et al. 2008).”
Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data (2012, methodology): “For instance, the choice of the rolling window size depends on a tradeoff between availability of data and reliability of the estimation of the indicators [8].”
Tipping points: Early warning and wishful thinking (2010, result): “This is contrary to what was recently claimed [Dakos et al., 2008; Scheffer et al., 2009].”