Inferring likelihoods and climate system characteristics from climate models and multiple tracers

Characterizing the risks of anthropogenic climate change poses considerable statistical challenges. A key problem is how to combine the information contained in large-scale observational data sets with simulations of Earth system models in a statistically sound and computationally tractable manner. Here, we describe a statistical approach for improving projections of the North Atlantic meridional overturning circulation (AMOC). The AMOC is part of the global ocean conveyor belt circulation and transfers heat between low and high latitudes in the Atlantic basin. The AMOC might collapse in a “tipping point” response to anthropogenic climate forcings. Assessing the risk of an AMOC collapse is of considerable interest because it may result [...]