Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-499-2023
https://doi.org/10.5194/tc-17-499-2023
Research article
 | 
07 Feb 2023
Research article |  | 07 Feb 2023

Predicting ocean-induced ice-shelf melt rates using deep learning

Sebastian H. R. Rosier, Christopher Y. S. Bull, Wai L. Woo, and G. Hilmar Gudmundsson

Related authors

Quantifying the potential future contribution to global mean sea level from the Filchner–Ronne basin, Antarctica
Emily A. Hill, Sebastian H. R. Rosier, G. Hilmar Gudmundsson, and Matthew Collins
The Cryosphere, 15, 4675–4702, https://doi.org/10.5194/tc-15-4675-2021,https://doi.org/10.5194/tc-15-4675-2021, 2021
Short summary
The tipping points and early warning indicators for Pine Island Glacier, West Antarctica
Sebastian H. R. Rosier, Ronja Reese, Jonathan F. Donges, Jan De Rydt, G. Hilmar Gudmundsson, and Ricarda Winkelmann
The Cryosphere, 15, 1501–1516, https://doi.org/10.5194/tc-15-1501-2021,https://doi.org/10.5194/tc-15-1501-2021, 2021
Short summary
Exploring mechanisms responsible for tidal modulation in flow of the Filchner–Ronne Ice Shelf
Sebastian H. R. Rosier and G. Hilmar Gudmundsson
The Cryosphere, 14, 17–37, https://doi.org/10.5194/tc-14-17-2020,https://doi.org/10.5194/tc-14-17-2020, 2020
Short summary
Tidal bending of ice shelves as a mechanism for large-scale temporal variations in ice flow
Sebastian H. R. Rosier and G. Hilmar Gudmundsson
The Cryosphere, 12, 1699–1713, https://doi.org/10.5194/tc-12-1699-2018,https://doi.org/10.5194/tc-12-1699-2018, 2018
Short summary
Strong tidal variations in ice flow observed across the entire Ronne Ice Shelf and adjoining ice streams
Sebastian H. R. Rosier, G. Hilmar Gudmundsson, Matt A. King, Keith W. Nicholls, Keith Makinson, and Hugh F. J. Corr
Earth Syst. Sci. Data, 9, 849–860, https://doi.org/10.5194/essd-9-849-2017,https://doi.org/10.5194/essd-9-849-2017, 2017
Short summary

Related subject area

Discipline: Ice sheets | Subject: Ice Shelf
Unveiling spatial variability within the Dotson Melt Channel through high-resolution basal melt rates from the Reference Elevation Model of Antarctica
Ann-Sofie Priergaard Zinck, Bert Wouters, Erwin Lambert, and Stef Lhermitte
The Cryosphere, 17, 3785–3801, https://doi.org/10.5194/tc-17-3785-2023,https://doi.org/10.5194/tc-17-3785-2023, 2023
Short summary
Brief communication: Is vertical shear in an ice shelf (still) negligible?
Chris Miele, Timothy C. Bartholomaus, and Ellyn M. Enderlin
The Cryosphere, 17, 2701–2704, https://doi.org/10.5194/tc-17-2701-2023,https://doi.org/10.5194/tc-17-2701-2023, 2023
Short summary
Change in Antarctic ice shelf area from 2009 to 2019
Julia R. Andreasen, Anna E. Hogg, and Heather L. Selley
The Cryosphere, 17, 2059–2072, https://doi.org/10.5194/tc-17-2059-2023,https://doi.org/10.5194/tc-17-2059-2023, 2023
Short summary
Glaciological history and structural evolution of the Shackleton Ice Shelf system, East Antarctica, over the past 60 years
Sarah S. Thompson, Bernd Kulessa, Adrian Luckman, Jacqueline A. Halpin, Jamin S. Greenbaum, Tyler Pelle, Feras Habbal, Jingxue Guo, Lenneke M. Jong, Jason L. Roberts, Bo Sun, and Donald D. Blankenship
The Cryosphere, 17, 157–174, https://doi.org/10.5194/tc-17-157-2023,https://doi.org/10.5194/tc-17-157-2023, 2023
Short summary
An assessment of basal melt parameterisations for Antarctic ice shelves
Clara Burgard, Nicolas C. Jourdain, Ronja Reese, Adrian Jenkins, and Pierre Mathiot
The Cryosphere, 16, 4931–4975, https://doi.org/10.5194/tc-16-4931-2022,https://doi.org/10.5194/tc-16-4931-2022, 2022
Short summary

Cited articles

Asay-Davis, X. S., Cornford, S. L., Durand, G., Galton-Fenzi, B. K., Gladstone, R. M., Gudmundsson, G. H., Hattermann, T., Holland, D. M., Holland, D., Holland, P. R., Martin, D. F., Mathiot, P., Pattyn, F., and Seroussi, H.: Experimental design for three interrelated marine ice sheet and ocean model intercomparison projects: MISMIP v. 3 (MISMIP +), ISOMIP v. 2 (ISOMIP +) and MISOMIP v. 1 (MISOMIP1), Geosci. Model Dev., 9, 2471–2497, https://doi.org/10.5194/gmd-9-2471-2016, 2016. a, b
Barnier, B., Madec, G., Penduff, T., Molines, J., Treguier, A.-M., Le Sommer, J., Beckmann, A., Biastoch, A., Boning, C., Dengg, J., Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S., Maltrud, M., McClean, J., and de Cuevas, B.: Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution, Ocean Dynam., 56, 543–567, https://doi.org/10.1007/s10236-006-0082-1, 2006. a
Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., and Gentine, P.: Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems, Phys. Rev. Lett., 126, 098302, https://doi.org/10.1103/PhysRevLett.126.098302, 2021. a
Boyer, T. P., García, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Reagan, J. R., Weathers, K. A., Baranova, O. K., Paver, C. R., Seidov, D., Smolyar, I. V.: World Ocean Atlas 2018, decav, NOAA National Centers for Environmental Information [data set], https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18 (last access: 10 June 2021), 2018. a, b
Brenowitz, N. D. and Bretherton, C. S.: Prognostic Validation of a Neural Network Unified Physics Parameterization, Geophys. Res. Lett., 45, 6289–6298, https://doi.org/10.1029/2018GL078510, 2018. a
Download
Short summary
Future ice loss from Antarctica could raise sea levels by several metres, and key to this is the rate at which the ocean melts the ice sheet from below. Existing methods for modelling this process are either computationally expensive or very simplified. We present a new approach using machine learning to mimic the melt rates calculated by an ocean model but in a fraction of the time. This approach may provide a powerful alternative to existing methods, without compromising on accuracy or speed.