Explainable AI for weather and climate, with a focus on extremes

Earth's climate is changing rapidly under the effect of global warming, leading to more frequent and severe extreme weather events [1,2]. These weather extremes, in turn, are exacting heavy socioeconomic and environmental tolls [3], prompting an urgent need for better understanding and predicting them. In this talk, we present some recent results obtained for the tropical Indo-Pacific region, using human-understandable methods (or taking a human view), namely dynamical system theory. In particular, we show that changes in weather patterns are leading to more weather extremes, namely heatwaves and extreme precipitation. We then present the use of explainable AI tools (i.e., machine view) to investigate the onset and precursors of these extremes. More specifically, we try to bridge existing human knowledge (human view) and “AI knowledge” (machine view) to better understand the behaviour and predictability of weather extremes. [1] "Changes in tropical Indo-Pacific weather patterns aggravate regional extremes”. C Dong, R Noyelle, G Messori, A Gualandi, L Fery, P Yiou, M Vrac, F D’Andrea, SJ Camargo, E Coppola, G Balsamo, C Chen, D Faranda, G Mengaldo (1st round of revisions) [2] "IPCC, 2021: Summary for Policymakers. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change”. VP Masson-Delmotte, et al. (2021) [3] "Evidence for sharp increase in the economic damages of extreme natural disasters”. M Coronese, F Lamperti, K Keller, F Chiaromonte, A Roventini, PNAS (2019).