Abstract
Through their role in buttressing upstream ice flow, Antarctic ice shelves play an important part in regulating future sea level change. Reduction in ice-shelf buttressing caused by increased ocean-induced melt along their undersides is now understood to be one of the key drivers of ice loss from the Antarctic Ice Sheet. However, despite the importance of this forcing mechanism most ice-sheet simulations currently rely on simple melt-parametrisations of this ocean-driven process, since a fully coupled ice-ocean modelling framework is prohibitively computationally expensive. Here, we provide an alternative approach that is able to capture the greatly improved physical description of this process provided by large-scale ocean-circulation models over currently employed melt-parameterisations but with trivial computational expense. We introduce a new approach that brings together deep learning and physical modelling to develop a deep neural network framework, MELTNET, that can emulate ocean model predictions of sub-ice shelf melt rates. We train MELTNET on synthetic geometries, using the NEMO ocean model as a ground-truth in lieu of observations to provide melt rates both for training and to evaluate the performance of the trained network. We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries and outperforms more traditional parameterisations for > 95 % of geometries tested. Furthermore, we find MELTNET's melt rate estimates show sensitivity to established physical relationships such as a changes in thermal forcing and ice shelf slope. This study demonstrates the potential for a deep learning framework to calculate melt rates with almost no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models.
Highlights
Ocean induced melting of ice shelves is currently the main driver of ice mass balance change in Antarctica and represents a major source of uncertainty for predictions of future sea level rise (Pritchard et al, 2012; Shepherd et al, 2018; Edwards 20 et al, 2021; IPCC, 2021)
We show that MELTNET can accurately predict melt rates for a wide range of complex synthetic geometries and outperforms more traditional parameterisations for >95% of geometries tested
This study demonstrates the potential for a deep learning framework to calculate melt rates with almost 15 no computational expense, that could in the future be used in conjunction with an ice sheet model to provide predictions for large-scale ice sheet models
Summary
Ocean induced melting of ice shelves is currently the main driver of ice mass balance change in Antarctica and represents a major source of uncertainty for predictions of future sea level rise (Pritchard et al, 2012; Shepherd et al, 2018; Edwards 20 et al, 2021; IPCC, 2021). Given the current gulf between ocean models and lower complexity parameterisations, and the aforementioned problems 60 with making long-term forecasts for the Antarctic ice sheet using a fully-coupled approach, there is a clear need for an alternative middle ground. This should retain the ability to predict complex spatial patterns of melting but be computationally efficient in order to be able to run it synchronously with an ice sheet model without inhibiting the size of the domain or the duration of the simulation. As a first step towards this goal, we demonstrate a deep learning framework that can accurately reproduce melt rate patterns as predicted by the NEMO ocean model and shows significantly better performance than existing intermediate complexity parameterisations without any increase in computational cost
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