Harmful tipping points in the natural world pose some of the gravest threats faced by humanity. Their triggering will severely damage our planet’s life-support systems and threaten the stability of our societies.
In the Summary Report:
• Narrative summary
• Global tipping points infographic
• Key messages
• Key Recommendations
Executive summary
• Section 1
• Section 2
• Section 3
• Section 4
This report is for all those concerned with tackling escalating Earth system change and mobilising transformative social change to alter that trajectory, achieve sustainability and promote social justice.
In this section:
• Foreword
• Introduction
• Key Concepts
• Approach
• References
Considers Earth system tipping points. These are reviewed and assessed across the three major domains of the cryosphere, biosphere and circulation of the oceans and atmosphere. We then consider the interactions and potential cascades of Earth system tipping points, followed by an assessment of early warning signals for Earth system tipping points.
Considers tipping point impacts. First we look at the human impacts of Earth system tipping points, then the potential couplings to negative tipping points in human systems. Next we assess the potential for cascading and compounding systemic risk, before considering the potential for early warning of impact tipping points.
Considers how to govern Earth system tipping points and their associated risks. We look at governance of mitigation, prevention and stabilisation then we focus on governance of impacts, including adaptation, vulnerability and loss and damage. Finally, we assess the need for knowledge generation at the science-policy interface.
Focuses on positive tipping points in technology, the economy and society. It provides a framework for understanding and acting on positive tipping points. We highlight illustrative case studies across energy, food and transport and mobility systems, with a focus on demand-side solutions (which have previously received limited attention).
As well as statistical and network methods that look for changing dynamics in a system, more complex methods can predict movement towards tipping points. One example is a generalised model approach, which integrates knowledge about the system into models and may allow us to estimate, for example, changes in the leading eigenvalue of the system once small model assumptions have been made (Lade and Gross, 2012). System-specific indicators can also be derived where understanding about processes in the system can help us to assess its resilience in novel ways (Boulton et al., 2013).
Machine learning (ML) techniques are now being applied to tipping point prediction. The documented success of neural networks for time series classification problems has inspired the development of similar ML methods specifically for EWS detection. There is a natural synergy to this approach in that the same CSD phenomena manifest across a wide range of systems approaching critical transitions, so the notoriously data-intensive task of training a neural network can be accomplished using plentiful synthetic data and still produce a result which can be applied to observational data (which is often more scarce and harder to label).
Deep learning models (which combine convolutional neural network layers with recurrent Long Short-Term Memory modules) have shown promise for EWS detection, outperforming methods using traditional statistical indicators (variance, AR(1), etc.) on a variety of test cases both real and simulated (Bury et al., 2021; Deb et. al., 2021). Furthermore, these models have exhibited success in inferring the type of oncoming bifurcation from observed pre-transition dynamics, and have performed well on rapid transitions in simple spatial models that evolve over time (Dylewsky et al., 2023).
Other ML techniques can also tell us something about how far systems are from tipping. For example, the ‘random forest’ method could be used to determine the factors that determine the AR(1) value in different areas of vegetation, and thus how close to tipping these areas could be, based on driving variables (Forzieri et al., 2022). Combining traditional EWS and ML techniques could provide some of the best prospects for monitoring systems that may be approaching tipping.