Deterministic Collapse and Stochastic Recovery in a Data-Driven Model of the Atlantic Meridional Overturning Circulation

During the last ice-age, temperatures in Greenland have frequently increased and decreased by 10°C. Detailed studies with climate models suggest that this is caused by collapses and recoveries of the Atlantic Meridional Overturning Circulation (AMOC). The causes of these AMOC transitions are still debated, though. Here we describe the development of a neural-network based surrogate model of the AMOC. It is trained on 32,000 years of climate model integrations to build a set of stochastic differential equations that emulate the climate models' AMOC behavior. In particular it reproduces the spectra and the asymmetry in the times it takes for the AMOC to recover and collapse, which makes it more realistic than previously published sets of coupled differential equations to study past AMOC transitions. Monte Carlo simulations with this model show that collapses are deterministic, but recoveries are stochastically forced, in partial support of the leading hypotheses surrounding the AMOC transitions.