Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm

• URL: http://arxiv.org/abs/2401.10800v3
• Date: Tue, 2 Jul 2024 10:06:43 GMT
• Title: Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm
• Authors: Valérian Jacques-Dumas, René M. van Westen, Henk A. Dijkstra,
• Abstract summary: The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate. This study computes the probability that the AMOC collapses within a specified time window. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 ``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 F(ast)-transitions and S(low)-transitions. Results for the F-transtions 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.

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