Tipping points characterize the situation when a system experiences abrupt, rapid, and sometimes irreversible changes in response to only a gradual change in environmental conditions. Given that such events are in most cases undesirable, numerous approaches have been proposed to identify if a system is approaching a tipping point. Such approaches have been termed early warning signals and represent a set of methods for identifying statistical changes in the underlying behaviour of a system across time or space that would be indicative of an approaching tipping point. Although the idea of early warnings for a class of tipping points is not new, in the last 2 decades, the topic has generated an enormous amount of interest, mainly theoretical. At the same time, the unprecedented amount of data originating from remote sensing systems, field measurements, surveys, and simulated data, coupled with innovative models and cutting-edge computing, has made possible the development of a multitude of tools and approaches for detecting tipping points in a variety of scientific fields. However, we miss a complete picture of where, how, and which early warnings have been used so far in real-world case studies. Here we review the literature of the last 20 years to show how the use of these indicators has spread from ecology and climate to many other disciplines. We document what metrics have been used; their success; and the field, system, and tipping points involved. We find that, despite acknowledged limitations and challenges, in the majority of the case studies we reviewed, the performance of most early warnings was positive in detecting tipping points. Overall, the generality of the approaches employed – the fact that most early warnings can in theory be observed in many dynamical systems – explains the continuous multitude and diversification in their application across scientific domains.

Amazon forest dieback is seen as a potential tipping point under climate change. These concerns are partly based on an early coupled climate–carbon cycle simulation that produced unusually strong drying and warming in Amazonia. In contrast, the fifth-generation Earth system models (Phase 5 of the Coupled Model Intercomparison Project, CMIP5) produced few examples of Amazon dieback under climate change. Here we examine results from seven sixth-generation models (Phase 6 of the Coupled Model Intercomparison Project, CMIP6), which include interactive vegetation carbon and in some cases interactive forest fires. Although these models typically project increases in area-mean forest carbon across Amazonia under CO₂-induced climate change, five of the seven models also produce abrupt reductions in vegetation carbon, which indicate localised dieback events. The northern South America (NSA) region, which contains most of the rainforest, is especially vulnerable in the models. These dieback events, some of which are mediated by fire, are preceded by an increase in the amplitude of the seasonal cycle in near-surface temperature, which is consistent with more extreme dry seasons. Based on the ensemble mean of the detected dieback events we estimate that 7±5 % of the NSA region will experience abrupt downward shifts in vegetation carbon for every degree of global warming past 1.5 ^∘C.

Over the last 2 decades, tipping points in open systems subject to changing external conditions have become a topic of a heated scientific debate due to the devastating consequences that they may have on natural and human systems. Tipping points are generally believed to be associated with a system bifurcation at some critical level of external conditions. When changing external conditions across a critical level, the system undergoes an abrupt transition to an alternative, and often less desirable, state. The main message of this paper is that the rate of change in external conditions is arguably of even greater relevance in the human-dominated Anthropocene but is rarely examined as a potential sole mechanism for tipping points. Thus, we address the related phenomenon of rate-induced tipping: an instability that occurs when external conditions vary faster, or sometimes slower, than some critical rate, usually without crossing any critical levels (bifurcations). First, we explain when to expect rate-induced tipping. Then, we use three illustrative and distinctive examples of differing complexity to highlight the universal and generic properties of rate-induced tipping in a range of natural and human systems.

Climate change is expected to impact agricultural land use. Steadily accumulating changes in temperature and water availability can alter the relative profitability of different farming activities and promote land-use changes. There is also potential for high-impact ‘climate tipping points’, where abrupt, nonlinear change in climate occurs, such as the potential collapse of the Atlantic Meridional Overturning Circulation (AMOC). Here, using data from Great Britain, we develop a methodology to analyse the impacts of a climate tipping point on land use and economic outcomes for agriculture. We show that economic and land-use impacts of such a tipping point are likely to include widespread cessation of arable farming with losses of agricultural output that are an order of magnitude larger than the impacts of climate change without an AMOC collapse. The agricultural effects of AMOC collapse could be ameliorated by technological adaptations such as widespread irrigation, but the amount of water required and the costs appear to be prohibitive in this instance.