The Helmholtz Representation Model for Climate Science (HClimRep) proposal will develop a comprehensive foundation model for climate science, focusing on atmospheric dynamics, tracer transport and ocean coupling, and encompassing seasonal to decadal timescales. Building on the success of AtmoRep, a foundational model for atmospheric dynamics, HClimRep seeks to capture complex long-term interactions of atmosphere, ocean, and sea ice, extending the accuracy, robustness and efficiency of AI-based weather prediction to the climate system.

HClimRep will leverage large-scale transformer-based Artificial Intelligence (AI) models, optimised for graphics processing unit (GPU) efficiency, to emulate first-principle climate simulations at a fraction of the traditional computational cost. It incorporates cutting-edge AI methods, such as diffusion models, where they are beneficial. This proposal highlights the potential of foundation models to revolutionise Earth system science by overcoming computational limitations, improving cost-accuracy trade-offs, and facilitating extensive numerical experimentation. By integrating advancements in AI and high-performance computing (HPC), HClimRep aspires to provide a groundbreaking tool for climate research, offering new insights into the Earth's climate system, producing large climate projection ensembles for localised impact modeling, and supporting informed decision-making in climate adaptation and policy, for example in the context of digital twins.