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Climate reddening increases the chance of critical transitions

Climate change research often focuses on trends in the mean and variance. However, analyses of palaeoclimatic and contemporary dynamics reveal that climate memory — as measured for instance by temporal autocorrelation — may also change substantially over time. Here, we show that elevated temporal autocorrelation in climatic variables should be expected to increase the chance of critical transitions in climate-sensitive systems with tipping points. We demonstrate that this prediction is consistent with evidence from forests, coral reefs, poverty traps, violent conflict and ice sheet instability. In each example, the duration of anomalous dry or warm events elevates chances of invoking a critical transition. Understanding the effects of climate variability thus requires research not only on variance, but also on climate memory.

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This work was carried out under the programme of the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture, and Science (OCW) and received funding from the European Union’s Horizon 2020 research and innovation Programme under the Marie Sklodowska-Curie grant. We thank A. Staal for the fruitful discussions and all the recommendations for literature to include in our tropical forest example, and I. van de Leemput for her input on the coral reef model.

Author information

All authors contributed to the design of the study. B.v.d.B. was responsible for executing the study, with contributions to the code from E.H.v.N., to the Supplementary Methods section from S.B. and to the main paper from M.S. All authors discussed the results and implications and commented on the manuscript at all stages.

Correspondence to Bregje van der Bolt.

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    Supplementary Methods and Supplementary Results, including Supplementary Figures 1–4, Supplementary Table 1 and Supplementary References

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Fig. 1: Schematic representation of how the duration of a climate event may determine whether it invokes a transition to an alternative attractor.
Fig. 2: The relationship between the duration and size of a perturbation in the conditions for invoking a state shift.
Fig. 3: The simulated fate of biomass subject to a gradual slow increase of the harvest rate under a white noise versus a time-correlated stochastic forcing regime in a classical overexploitation model.
Fig. 4: The effect of temporal autocorrelation of a stochastic forcing regime on the likelihood of collapse of the biomass system.
Fig. 5: Combined effects of the autocorrelation and standard deviation of the fluctuations on the value of the driver at which the system shifts.