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Contribution to Collected Edition

Early warning signals of Earth system tipping points


Boulton,  Chris A.
External Organizations;

Buxton,  Joshua E.
External Organizations;

Arellano-Nava,  Beatriz
External Organizations;

Battiany,  Sebastian
Potsdam Institute for Climate Impact Research;


Blaschke,  Lana
Potsdam Institute for Climate Impact Research;


Boers,  Niklas
Potsdam Institute for Climate Impact Research;

Dakos,  Vasilis
External Organizations;

Dylewsky,  Daniel
External Organizations;

Kefi,  Sonia
External Organizations;

Lopez-Martinez,  Carlos
External Organizations;

Parry,  Isobel
External Organizations;

Ritchie,  Paul
External Organizations;

van der Bolt,  Bregje
External Organizations;

van der Laan,  Larissa
External Organizations;

Weinans,  Els
External Organizations;

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Boulton, C. A., Buxton, J. E., Arellano-Nava, B., Battiany, S., Blaschke, L., Boers, N., Dakos, V., Dylewsky, D., Kefi, S., Lopez-Martinez, C., Parry, I., Ritchie, P., van der Bolt, B., van der Laan, L., Weinans, E. (2023): Early warning signals of Earth system tipping points. - In: Lenton, T., Armstrong McKay, D. I., Loriani, S., Abrams, J., Lade, S. J., Donges, J. F., Buxton, J. E., Milkoreit, M., Powell, T., Smith, S. R., Zimm, C., Bailey, E., Dyke, J. G., Ghadiali, A., Laybourn, L. (Eds.), The Global Tipping Points Report 2023, Exeter : University of Exeter, 155-163.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_29437
This chapter focuses on the methods used to predict the movement of parts of the Earth system towards tipping points. It begins by introducing the theory of critical slowing down (CSD), a general phenomenon of slowing recovery from perturbations that happens in many systems being forced slowly towards a tipping point. Then, it describes the various methods that can be used to estimate the occurrence of CSD and the approach of a tipping point, beginning with methods based on changes over time in the system, spatial changes, or changes in network structure, up to more advanced modelling techniques, including AI. These ‘early warning signals’ (EWS) can be used on data from a number of different sources, be these models, field experiments or remotely sensed data from satellites. The chapter considers various case studies that use real-world observations, to show how these methods are being used to predict losses in resilience in these systems. Finally, it explores limitations and potential solutions in the field of EWS, looking ahead to advances in data availability and what this could mean for predicting the movement towards tipping in these Earth systems in the future.