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 ``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- 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.

Deep Learning based Extreme Heatwave Forecast

Forecasting the occurrence of heatwaves constitutes a challenging issue, yet of major societal stake, because extreme events are not often observed and (very) costly to simulate from physics-driven numerical models. The present work aims to explore the use of Deep Learning architectures as alternative strategies to predict extreme heatwaves occurrences from a very limited amount of available relevant climate data. This implies addressing issues such as the aggregation of climate data of different natures, the class-size imbalance that is intrinsically associated with rare event prediction, and the potential benefits of transfer learning to address the nested nature of extreme events (naturally included in less extreme ones). Using 1000 years of state-of-the-art PlaSim Planete Simulator Climate Model data, it is shown that Convolutional Neural Network-based Deep Learning frameworks, with large-class undersampling and transfer learning achieve significant performance in forecasting the occurrence of extreme heatwaves, at three different levels of intensity, and as early as 15 days in advance from the restricted observation, for a single time (single snapshoot) of only two spatial fields of climate data, surface temperature and geopotential height.