Evaluating emergent climate behaviour in a hybrid machine learned atmosphere -- dynamical ocean model

Understanding how fast atmospheric variability shapes slow climate variability and sensitivity is a central challenge in Earth-system science. Recent advances in machine-learned (ML) atmospheric models have demonstrated remarkable skill on weather timescales, but their emergent behaviour in a fully coupled climate system is largely unexplored. We present results from a new hybrid modelling framework that couples a machine-learned atmosphere to a dynamical ocean model. We report on a set of 70-year coupled simulations (1950–2020 historical forcing and fixed-1950s control) in which the ACE2 ML climate emulator is interactively coupled to the NEMO ocean model. These experiments represent, to our knowledge, the first multi-decadal integrations of a machine-learned atmosphere interacting with a full-depth dynamical ocean. We assess the behaviour of the coupled system, with particular focus on low-frequency tropical variability and the climate response to greenhouse-gas forcing. Preliminary results indicate realistic emergent El Nino-like variability and a physically plausible climate sensitivity, suggesting that key atmosphere–ocean feedbacks can be captured within a hybrid ML–dynamical framework. These results evaluate the possible role of entirely machine-learned components in next-generation Earth-system models.