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

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If you have a question about this talk, please contact Dr. Shenjie Zhou .

The largest uncertainty when projecting the Antarctic contribution to sea-level rise comes from the ocean-induced melt at the base of Antarctic ice shelves. Current physics-based parameterisations used to link the ocean temperature and salinity in front of ice shelves to the melt at their base struggle to accurately simulate basal melt patterns. We explore the potential 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. This is work in progress and I am looking forward to discuss improvements and limitations of this approach.

This talk is part of the British Antarctic Survey - Polar Oceans seminar series series.

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Ray Dolby Centre Seminar Room B - B2.002 Webinar & LT2, Computer Laboratory, William Gates Building. LT1, Cambridge Judge Business School

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