Estimation of AMOC Transition Probabilities Using a Machine Learning–Based Rare-Event Algorithm

The Atlantic meridional overturning circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called trajectory-adaptive multilevel splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called the “committor function,” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a next-generation reservoir computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called fast (F) and slow (S) transitions. Results for the F transitions compare favorably with those in the literature where a physically informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S transitions in the same model. In both cases of F transitions and S transitions, we also show how the next-generation reservoir computing technique can be interpreted to retrieve an analytical estimate of the committor function.