Coupling approaches for data-driven Earth system models

Machine learning models have emerged as powerful tools for simulating Earth system processes. Following their successful application in capturing atmospheric evolution for medium-range weather forecasts, attention has increasingly shifted towards other components of the Earth system, such as the marine and land environments. This interest is further driven by the potential to enhance forecasting capabilities beyond the medium range. Machine learning frameworks offer remarkable flexibility in integrating these model components to achieve a coherent Earth system representation. At one end of the spectrum, model components can be trained jointly within a unified framework optimised using a shared loss function. At the other end, components may be developed independently and coupled by exchanging physically relevant information at multiple interfaces, mirroring the traditional coupling strategies employed in numerical models. In this presentation, we will examine the advantages and challenges of these approaches, with a particular emphasis on coupling the atmospheric, land, and marine components within the deterministic AIFS model, the machine learning-based forecast system developed at ECMWF. Furthermore, we will compare the coupling strategies of data-driven models with those of traditional numerical models, highlighting their strengths and limitations.