Many recent studies have discussed whether future climate change will be punctuated by abrupt shifts, so-called tipping points. As it would be difficult for societies and ecosystems to cope with such events, it is important to assess the associated risk. However, the list of climate tipping points put forward in scientific studies mainly results from idealized models, qualitative arguments and visual inspection. We will explore the possibility of future abrupt climate change more systematically by using the vastly increasing amount of climate model data.
As a crucial first step toward this goal we will explore the potential of change-point detection and edge detection algorithms to detect and interpret abrupt changes in large model ensembles from existing projects. Moreover, we will scan these datasets for a change in climate variability in order to learn if these changes can predict if, where and why abrupt shifts will occur.
Our approach will update our knowledge on abrupt climate change and will allow a systematic assessment of the feasibility of data mining tools. We thereby set the scene for a larger project with new perturbed-physics ensembles that will allow us to quantify the risk of future abrupt climate change in complex models.
Image: Asian Development Bank