Reducing uncertainty in climate models through improved parameterizations of small scale processes

The NEMO modelling framework finds application in numerous climate models. Simulating Earth’s climate and how it is changing means to solve a complex set of equations for a long period, usually hundreds of years. Given the small time scales of the processes involved and the limited available computational resources, this imposes numerical constraints on the spatial resolution of the simulation. Consequently, processes  with a smaller physical length scale than the model grid can not be explicitly resolved, for example mesoscale eddies. The effects of these subgrid-scale processes on the larger scale climate system need to be approximated through parameterisations. Recent studies propose new methods to find and formulate parameterisations using machine learning tools, which promise improvements in the predictive skill of the model. With the prospect of introducing these into future versions of NEMO, their potential benefit is yet to be determined. We propose a new configuration to be used as a test protocol for subgrid-scale parameterisations. The configuration is of intermediate complexity  and with an idealised basin geometry of the Atlantic and Southern Ocean. This allows for relatively cheap simulations even at very high horizontal resolution, while   crucial aspects of the system like the meridional overturning circulation (MOC) or the antarctic circumpolar current (ACC) are still maintained. Effects of the subgrid-scale processes on the large-scale circulation are then diagnosed to evaluate the performance of their parameterisation.