While AI has been disrupting conventional weatherforecasting, we are only beginning to witness theimpact of AI on long-term climate simulations. Thefidelity and reliability of climate models have beenlimited by computing capabilities. These limitationslead to inaccurate representations of key processessuch as convection, cloud, or mixing or restrict theensemble size of climate predictions. Therefore, theseissues are a significant hurdle in enhancing climatesimulations and their predictions.Here, I will discuss a new generation of climatemodels with AI representations of unresolved oceanphysics, learned from high-fidelity simulations, andtheir impact on reducing biases in climatesimulations. The simulations are performed withoperational ocean model components. I will furtherdemonstrate the potential of AI to accelerate climatepredictions and increase their reliability through thegeneration of fully AI-driven emulators, which canreproduce decades of climate model output in secondswith high accuracy
