In this preprint , M²LInES demonstrates the power of AI driven methods in producing reliable climate simulations. We introduce a new framework that brings physics- and scale-aware machine learning into climate models. Traditional parameterizations of physical processes often produce significant biases, but AI can now learn these processes directly from data. Our team implements a suite of data-driven parameterizations in the ocean and sea-ice components of a state-of-the-art model, ranging from deep learning to interpretable equation-based methods. Our results demonstrate that AI-driven parameterizations can run effectively in operational climate simulations, enabling hybrid atmosphere–ocean–sea-ice modeling. All tools are open source and available to the community.