Le programme de recherche TRACCS – Transformer la modélisation du climat pour les services climatiques – vous donne rendez-vous vendredi 31 janvier 2025 de 11h à 12h pour son webinaire « Combining physics and machine learning in hybrid climate models » présenté par Julien Le Sommer (CNRS).

In recent years, scientific machine learning has emerged as a transformative approach in climate science and geoscientific modeling. Among its most impactful applications are emulators for weather forecasting, which now rival traditional physics-based models in accuracy and efficiency. Beyond emulation, this paradigm shift has led to the development of hybrid climate models, where physics-based frameworks are augmented with trainable components, offering a new frontier in climate model design. 

After a quick review of the progress and challenges in this rapidly evolving field, with a focus on ocean climate models, this webinar will explore the benefits of hybrid modelling, from parameterisations to bias correction, and provide insights into the practical design of trainable components. Additionally, it will address the technical complexities of integrating these components with existing model architectures. 

Looking ahead, Julien Le Sommer will argue that current hybrid models foreshadow a new generation of AI-native climate models, powered by differentiable programming. These models promise seamless integration of trainable components and transformative capabilities, but they also pose significant challenges. This presentation will highlight the expected benefits of this emerging class of models and discuss the roadmap to harnessing their full potential in advancing climate modelling.

Ce webinaire est accessible via ce lien :
https://meteo.webex.com/meteo/j.php?MTID=m2ccb9d71ee3a9743a8d5539c08d31183

Pour visionner les précédents webinaires, rendez-vous sur la page YouTube :
https://www.youtube.com/@pepr.traccs/playlists