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Climate tipping points occur when change in a part of the climate system becomes self-perpetuating beyond a forcing threshold, leading to abrupt and/or irreversible impacts. Synthesizing paleoclimate, observational, and model-based studies, we provide a revised shortlist of global ‘core’ tipping elements and regional ‘impact’ tipping elements and their temperature thresholds. Current global warming of ~1.1°C above pre-industrial already lies within the lower end of some tipping point uncertainty ranges. Several more tipping points may be triggered in the Paris Agreement range of 1.5-2°C global warming, with many more likely at the 2-3°C of warming expected on current policy trajectories. In further work we use these estimates to test the potential for and impact of tipping cascades in response to global warming scenarios using a stylised model. This strengthens the evidence base for urgent action to mitigate climate change and to develop improved tipping point risk assessment, early warning capability, and adaptation strategies.Preprint: https://doi.org/10.1002/essoar.10509769.1

The assessment of potential tipping elements in the Earth system and their associated tipping thresholds is essential for understanding long-term Earth system change and describing a safe operating space. However, their identification in model outputs and observational data typically requires making assumptions about the spatial extent of individual elements. While the resulting regional to continental aggregates allow for the study of collective time series, they are potentially based on subjective judgement and could mask non-linear behaviour on smaller scales. In this work, we present a novel method based on a timescale- and variable-independent metric to automatically identify potential tipping elements in the Earth system with a few or no free parameters. Gridded datasets are scanned for abrupt shifts on the grid-cell level, which are subsequently automatically clustered in space and time. This allows for the creation of maps with areas grouped and classified by their dynamical behaviour without an a-priori definition of connected regions. Applying the presented method to various Earth System model outputs, we detect clusters with different nonlinear responses to future emission scenarios which are otherwise masked. Consequently, our bottom-up approach provides insight into the spatial structures and temporal processes of large-scale tipping elements, and sheds light on ‘hidden’ tipping of their subsystems.