1.6 Early warning signals of Earth system tipping points

Chris A. Boulton, Joshua E. Buxton, Beatriz Arellano-Nava, Sebastian Battiany, Lana Blaschke, Niklas Boers, Vasilis Dakos, Daniel Dylewsky, Sonia Kefi, Carlos Lopez-Martinez, Isobel Parry, Paul Ritchie, Bregje van der Bolt, Larissa van der Laan, Els Weinans

Key Messages

  • Early warning signals (EWS) can be used to detect the potential movement of Earth’s systems towards tipping points.
  • The central western Greenland Ice Sheet, Atlantic Meridional Overturning Circulation, and Amazon rainforest all show evidence of loss of resilience consistent with moving towards tipping points.

Recommendations

  • EWS can provide an indication of a tipping point approaching, and should be taken as a chance to prevent it from happening.
  • Results from models need to be leveraged to identify which specific variables are most likely to display EWS as tipping points approach, so that these can be monitored with empirical data.
  • Further investigation is required to explore the utility of machine learning for EWS and to detect the drivers of conventional EWS.
  • Openly available datasets from on-the-ground sensors and measurements as well as remote sensing products provide an avenue for this EWS detection, however careful consideration is required to ascertain which variables are most appropriate and the limitations of existing remote sensing data.
  • Future work should look to design remote sensing studies and data acquisition strategies that minimise the potential for biassing EWS such that false indications occur.

Summary

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.

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