“Tipping points” in the climate system commonly refers to a critical threshold at which a tiny perturbation can qualitatively alter the state or development of a system.
“Tipping Elements” of the climate system refers to the large-scale components of the Earth System that may pass a tipping point.
Global assessments based on expert elicitation of tipping points are presented in Lenton et al. (2008). A quantitative assessment of the tipping points that are present in the state-of-the-art climate models used in the Intergovernmental Panel for Climate Change (IPCC) was provided by Drijfhout et al. (2015). The results of Drijfhout et al. (2015) focus attention on the high latitude North Atlantic and the Arctic ocean as regions containing a high number of tipping elements.
Blue-Action is focused on Arctic and high latitude changes. Therefore, the high number of tipping elements in this region and their potentially large impact on Northern Hemisphere climate is of particular interest and concern to Blue-Action scientists.
Early warning indicators of abrupt climate change are possible using mathematical analysis. Typical early warning signals arise from slowing oscillations prior to a change in state of the system that are indicated by increased autocorrelations (Livina and Lenton, 2007; Boulton et al., 2013).
Three areas of tipping point analysis were highlighted:
- detection, and
- forecasting of tipping (Livina et al., 2013).
Emerging techniques such as machine learning have also seen useful application to the detection of tipping points. In addition to mathematically-derived early warning signals, the physical precursors of tipping points were also discussed such as deep density changes for detection of AMOC change (Baehr et al., 2008).
The representation of abrupt changes in climate models was discussed. The state-of-the-art CMIP5 models are often thought of as too stable (Valdes, 2011) and the models that do show abrupt changes are often dismissed as unreliable. Emerging results from the next iteration of the coupled model intercomparison project, CMIP6, were thought to be promising for improving the simulation of instabilities in the climate system.