Towards model-independent machine learning parameterisations of meso-scale eddies

The integration of machine learning parameterisations within climate models is paving the way for the next generation of Earth System models. Machine learning parameterisations are being developed to represent ocean and atmosphere processes such as turbulence, vertical mixing, and cloud and precipitation. These parameterisations typically require large volumes of high-resolution data for their training. This training data is often derived from the same numerical model that the parameterisation is intended for. This has the advantage that the machine learning model is only exposed to one set of numerical discretisation schemes.Recently, global km-scale models have been introduced that simulate climate processes at remarkable detail. Explicitly resolving mesoscale and sub-mesoscale eddies and filaments enables these models to capture heat, carbon, and salt fluxes without the need for parameterisations. Global km-scale models are therefore promising training data sets for machine learning parameterisations.In this work we intend to examine two global km-scale models that could be employed for oceanic turbulence parameterisations: NEMO ORCA36 and ICON-O. The ORCA36 model uses the tripolar grid and ICON-O uses an icosahedral grid. The question is, can either model be used to inform new ML parameterisations that can be employed in any numerical model? Therefore, a key assessment of these models will be done by exploring and contrasting their energetics, as well as the heat, salt, and carbon transports. This work will take the first step towards model-independent machine learning parameterisation development, while facilitating further cross modelling centre collaboration.