Potential for Machine Learning Emulators to Augment Regional Climate Simulations in Provision of Local Climate Change Information
Potentiel des émulateurs d'apprentissage automatique pour améliorer les simulations climatiques régionales et fournir des informations sur les changements climatiques locaux
Kendon, Elizabeth J. ; Addison, Henry ; Doury, Antoine ; Somot, Samuel ; Watson, Peter A. G. ; Booth, Ben B. B. ; Coppola, Erika ; Gutiérrez, José Manuel ; Murphy, James ; Scullion, Calum
Année de publication
2025
High-resolution regional climate simulations provide detailed information on future climate change to support decision-making. Ensembles of simulations, including at kilometer-scale resolution, are becoming available from international coordinated initiatives, but these do not effectively sample the full range of uncertainties. Machine learning (ML) has already been used for statistical downscaling but has the potential to augment high-resolution simulations, via emulators, enabling rapid production of local climate information at a fraction of the cost. Here, we explore skill in ML-based emulators sampling a range of architectures and identify remaining scientific issues that need to be addressed before such emulators can be considered ready for application in climate services. This includes the ability to capture extremes, produce coherent multivariate predictions, account for memory in the climate system, and robustly downscale other (out of sample) global climate models. Climate expertise needs to be integrated into the development and evaluation of ML emulators, and here, we provide recommendations on validation methods. If skillful, ML emulation has implications for how we coordinate and perform regional climate simulations. We should focus on running at the highest resolution and greatest Earth system complexity affordable, to give the best representation of processes at the local scale, for subsequent training of ML emulators. Emphasis should be on sampling the full range of conditions, including high-end scenarios. Overall, ML has promise to augment our production of regional-to-local climate projection information over the next 5-10 years, and as a climate community, we need to come together to address the relevant scientific issues.</div>
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