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).
The EWS methods described in this chapter could be invaluable in understanding how climate and ecological systems are moving towards tipping points. A variety of different data types can be used to monitor the changing resilience of these systems, whether this be temporal, spatial, or more complex data. While many studies use models to test EWS, a number of studies already make use of real-world observations to do this. However, advances in machine learning techniques, in addition to longer and higher spatial resolution remote sensing products, can improve results. With these, we will be able to better determine with higher statistical accuracy if there are losses of resilience in systems, and in more systems than have been analysed so far.