Parameterising melt at the base of Antarctic ice shelves with a feedforward neural network

One of the largest sources of uncertainty when projecting the Antarctic contribution to sea-level rise is the ocean-induced melt at the base of Antarctic ice shelves. This is because resolving the ocean circulation and the ice-ocean interactions occurring in the cavity below the ice shelves is computationally expensive.

Instead, for large ensembles and long-term projections of the ice-sheet evolution, ice-sheet models currently rely on parameterisations to link the ocean temperature and salinity in front of ice shelves to the melt at their base. However, current physics-based parameterisations struggle to accurately simulate basal melt patterns.

As an alternative approach, we explore the potential use of a deep feedforward neural network as a basal melt parameterisation. To do so, we train a neural network to emulate basal melt rates simulated by highly-resolved circum-Antarctic ocean simulations. We explore the influence of different input variables and show that the neural network struggles to generalise to ice-shelf geometries unseen during training, while it generalises better on timesteps unseen during training. We also test the parameterisation on separate coupled ocean-ice simulations to assess the neural network’s performance on independent data.