AI-empowered next-generation multiscale climate modelling for mitigation and adaptation

Abstract

Earth system models have been continously improved over the past decades, but systematic errors compared with observations and uncertainties in climate projections remain. This is due mainly to the imperfect representation of subgrid-scale or unknown processes. Here we propose a next-generation Earth system modelling approach with artificial intelligence that calls for accelerated models, machine-learning integration, systematic use of Earth observations and modernized infrastructures. The synergistic approach will allow faster and more accurate policy-relevant climate information delivery. We argue a multiscale approach is needed, making use of kilometre-scale climate models and improved coarser-resolution hybrid Earth system models that include essential Earth system processes and feedbacks yet are still fast enough to deliver large ensembles for better quantification of internal variability and extremes. Together, these can form a step change in the accuracy and utility of climate projections, meeting urgent mitigation and adaptation needs of society and ecosystems in a rapidly changing world. A multiscale Earth system modelling approach that integrates machine learning could pave the way for improved climate projections and support actionable climate science.