In the climate system, extreme events or transitions between climate attractors are of primarily importance for understanding the impact of climate change. Recent extreme heat waves with huge impact, or period very low production of renewable energy in the electricity system are striking examples. However, a key challenge is the lack of data, because these events are too rare and realistic models are too complex. This lack of data issue drastically challenges any available approaches either based on physics or statistics.

I will discuss new algorithms and theoretical approaches, based on rare event simulations, emulators of climate models, and machine learning for stochastic processes, which we have specifically designed for the prediction of the committor function (the probability of the extreme event to occur). To illustrate the performance of these tools, I will discuss results for the study of midlatitude extreme heat waves and the extremes of renewable energy production in relation with the resilience of the electricity system.

References:

[1] V. Mascolo, A. Lovo, C. Herbert, and F. Bouchet, 2024, Gaussian Framework and Optimal Projection of Weather Fields for Prediction of Extreme Events, arXiv:2405.20903, [pdf]

[2] A. Lovo, A. Lancelin, C. Herbert, and F. Bouchet, 2024, Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme Heatwaves, arXiv:2410.00984, [pdf].

[3] F. Ragone, J. Wouters, and F. Bouchet, Proceedings of the National Academy of Sciences, vol 115, no 1, pages 24-29, https://doi.org/10.1073/pnas.1712645115, and arXiv:1709.03757, [pdf] (2018)

[4] G. Miloshevich, B. Cozian, P. Abry, P. Borgnat, and F. Bouchet, Phys. Rev. Fluids 8, 040501, doi.org/10.1103/PhysRevFluids.8.040501 and arXiv:2208.00971, [pdf] (2023)

[5] B. Cozian, C. Herbert, and F. Bouchet, 2023, Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales, Environmental Research Letters, 2024, vol. 19, no 4, p. 044046, arXiv:2311.13526, [pdf].

[6] C. Le Priol, J.M. Monteiro, and F. Bouchet, 2024, Using rare event algorithms to understand the statistics and dynamics of extreme heatwave seasons in South Asia Authors, arXiv:2404.07791, [pdf]