Machine learning for hybrid Earth system modelling

Earth system models (ESMs) are key tools in projecting the reponse of the Earth's climate and ecosystems to anthropogenic forcing in terms of increasing greenhouse gas concentrations and resulting temperature increases, as well as land use change. However, ESMs continue to suffer from prononced biases when compared to observations, and exhibit limited horizontal resolution due to computational constraints, mking reliable impact assessment challenging. Generative machine learning methods, such as Generative Adversarial Networks or Diffusion models, have shown great success in bias correcting and downscaling Earth system model output [1,2]. However, so far these approaches have been applied only as a postprocessing. After summarizing advances in this context, I will present recent work addressing conceptual and technical challenges in incorporating (generative) machine learning inside the architectures of process-based ESMs. These include the need for automatic differentiability of all ESM components [3], as well as physical constraints to assure that dynamics learned by machine learning components fulfills, for example, physical conservation laws [4].  

[1] P. Hess, M. Drüke, F. Strnad, S. Petri, N. Boers: Physically constrained generative adversarial networks for improving precipitation fields from Earth system models, Nature Machine Intelligence 4, 828-839 (2022)

[2] P. Hess, M. Aich, B. Pan, N. Boers: Fast, scale-Adaptive, and uncertainty-aware downscaling of Earth system model fields with generative machine learning, Nature Machine Intelligence 7, 363–373 (2025)

[3] M. Gelbrecht, A. White, S. Bathiany, N. Boers: Differentiable Programming for Earth System Modelling, Geoscientific Model Development 16, 3123–3135 (2023)

[4] A. White, N. Kilbertus, M. Gelbrecht, N. Boers: Stabilized Neural Differential Equations, NeurIPS (2023)