Abstract: Understanding how fast atmospheric variability shapes slow climate variability and change 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 the ACE2 ML atmosphere to the NEMO dynamical ocean model. We assess the behaviour of the coupled system, and show that the hybrid model can generate a stable mean climate; however, emergent low-frequency variability is too weak, and the response to CO2 forcing deviates over the 20th century. These results provide a unique test of physical realism for atmospheric emulators, and evaluate the possible role of entirely machine-learned components in next-generation Earth system models.