Emulating Present and Future Simulations of Melt Rates at the Base of Antarctic Ice Shelves With Neural Networks

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Burgard, C., Jourdain, N, C, Mathiot, P., Smith, R., S., Schäfer, R., Caillet, J., Finn, T., S., Johnson, J., E.
• Other Authors: Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Processus et interactions de fine échelle océanique (PROTEO), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales Toulouse (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), NCAS-Climate Reading, Department of Meteorology Reading, University of Reading (UOR)-University of Reading (UOR), Physikalisch-Technische Bundesanstalt Braunschweig (PTB), Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA), École des Ponts ParisTech (ENPC)-EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), ANR-22-CE01-0014,AIAI,Intelligence artificielle pour améliorer le couplage de la calotte Antarctique avec le système océan/atmosphère(2022), European Project: 869304,PROTECT, European Project: 820575,TiPACCs, European Project: 101003536,ESM2025
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2023
• Subjects: Machine learning application to Earth system modeling; [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology; [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography; Antarctic; The Antarctic; Antarc*; Antarctica; Ice Sheet; Ice Shelf; Ice Shelves
• Online Access: https://hal.sorbonne-universite.fr/hal-04386163; https://hal.sorbonne-universite.fr/hal-04386163/document; https://hal.sorbonne-universite.fr/hal-04386163/file/J%20Adv%20Model%20Earth%20Syst%20-%202023%20-%20Burgard%20-%20Emulating%20Present%20and%20Future%20Simulations%20of%20Melt%20Rates%20at%20the%20Base%20of%20Antarctic.pdf; https://doi.org/10.1029/2023MS003829

International audience Melt rates at the base of Antarctic ice shelves are needed to drive projections of the Antarctic ice sheet mass loss. Current basal melt parameterizations struggle to link open ocean properties to ice‐shelf basal melt rates for the range of current sub‐shelf cavity geometries around Antarctica. We present a proof of concept exploring the potential of simple deep learning techniques to parameterize basal melt. We train a simple feedforward neural network, or multilayer perceptron, acting on each grid cell separately, to emulate the behavior of circum‐Antarctic cavity‐resolving ocean simulations. We find that this kind of emulator produces reasonable basal melt rates for our training ensemble, at least as close as or closer to the reference than traditional parameterizations. On an independent ensemble of simulations that was produced with the same ocean model but with different model parameters, cavity geometries and forcing, the neural network yields similar results to traditional parameterizations on present conditions. In much warmer conditions, both traditional parameterizations and neural network struggle, but the neural network tends to produce basal melt rates closer to the reference than a majority of traditional parameterizations. While this shows that such a neural network is at least as suitable for century‐scale Antarctic ice‐sheet projections as traditional parameterizations, it also highlights that tuning any parameterization on present‐like conditions can introduce biases and should be used with care. Nevertheless, this proof of concept is promising and provides a basis for further development of a deep learning basal melt parameterization.