Neural network emulators of high resolution melt processes under Antarctic ice shelves

To make accurate projections of future sea level rise, small-scale ice-sheet and ice-shelf processes must be included in global climate models. Since high-resolution fully-coupled ice-sheet--ocean models are computationally expensive, multi-centennial simulations use lower resolution grids combined with simple parameterizations of the ice-ocean interface. However, these simple parameterizations do not fully reproduce observed melt patterns and have low sensitivity to warmer conditions. Instead, neural networks can be used to improve models by emulating the ice-ocean interactions simulated by high resolution models. We present a framework for training neural networks to emulate small-scale Antarctic basal melt processes within a global low-resolution model (here the NEMO ocean model). We employ a multi-layer perceptron which is trained with a variety of model simulations on a grid with quarter degree resolution, and aim to assess the performance of the neural network, particularly in warmer conditions representative of potential future climate states. This simple framework provides a springboard for future work using more complex architectures, and offers the potential to run computationally affordable long-period global simulations while still capturing crucial ice-shelf--ocean interactions.