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).
Amazon dieback as a tipping point is observed in some modelled climate change scenarios (1.3.2.1). One such study shows that temporal EWS, such as increases in AR(1) and variance, are not necessarily good indicators of Amazon dieback in a number of HadCM3 GCM runs (Boulton et al., 2013). This is most likely because the Amazon is forced too fast and non-linearly for these statistical measures to work. Because of this, a system-specific indicator was suggested, looking at the sensitivity of ecosystem productivity anomalies to temperature changes, and then as a real-world measurable signal, the sensitivity of atmospheric CO2 anomalies to these temperature anomalies. Both of these indicators worked well across the ensemble of runs.
Further work observes an increase in drying in the Amazon region across the recent CMIP6 model suite (Ritchie et al., 2022). An increase in the sensitivity of the temperature seasonal cycle amplitude to global warming is observed to be more prominent in locations that subsequently experience abrupt dieback shifts. The increasing sensitivity of the temperature seasonal cycle amplitude to global warming, therefore, has the potential to be used as a system-specific EWS for future dieback in the Amazon rainforest (Parry et al., 2022).
Real-world observational data has shown different results regarding the generic indicators discussed in this chapter, particularly the use of vegetation optical depth (VOD), a remotely sensed product that is strongly correlated with the amount of water content in the trees. Using this, increases in AR(1) and variance particularly since the early 2000s have shown a loss of resilience in the Amazon rainforest (Boulton et al., 2022). Using this same dataset, while modelling the water recycling network across the region (1.3.2.1), a network approach shows similar losses of resilience (Blaschke et al., 2023).