the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2026
DeepMelt-GL v1: A neural network emulator of ice-shelf melt rates for use in ocean models which partially resolve ice-shelf cavities
Abstract. The spatial pattern of melting beneath Antarctic ice shelves influences ice flow and retreat, and the resulting fresh-water input into the Southern Ocean influences global carbon storage and primary productivity. It is therefore crucial that interactions at the ice-ocean interface are adequately represented in global climate models. However, due to computational limitations, existing climate models are forced to choose between either high resolution or long period simulations, and struggle to resolve melt rates in ice shelf cavities. Here, we show that a simple multilayer perceptron can be used to emulate sub-shelf melt rates in the parts of cavities closest to the grounding zone. We find that the melt rates produced by applying neural network emulators are a good match for melt rates from high resolution simulations, provided that similar conditions were included in the neural network training. We also find that neural networks are particularly sensitive to temperatures and ice-draft slopes outside those used in the training dataset. However, if we train multiple neural networks on the same input data, we demonstrate that the ensemble spread of the neural networks is a good indicator of the reliability of the emulator in any given conditions. The neural network emulator of sub-shelf melt which is presented here, DeepMelt-GL, can be used to improve the representation of ice shelf cavities in both ocean and global climate models.
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Status: open (until 12 May 2026)
- RC1: 'Comment on egusphere-2025-6314', Anonymous Referee #1, 19 Apr 2026 reply
Data sets
Data which accompanies the manuscript "A neural network emulator of ice-shelf melt rates for use in ocean models which partially resolve ice-shelf cavities" Helen Ockenden, Clara Burgard, Pierre Mathiot, and Christoph Kittel https://doi.org/10.5281/zenodo.17358229
Model code and software
Code which accompanies the manuscript "A neural network emulator of ice-shelf melt rates for use in ocean models which partially resolve ice-shelf cavities" Helen Ockenden and Clara Burgard https://doi.org/10.5281/zenodo.17358195
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- 1
The study looks at ways of improving Antarctic ice shelf melt rate estimation in climate models with particular focus on the landward margins of the models. The authors describe a comprehensive study examining how fine resolution models might be used to improve coarser models to enable some form of cross talk between small scales and those that large/long scale models need to work at if they are to provide useful climate insights.
This bridging of timescales is a major challenge for climate science and so studies like this are important in helping “speed up” simulations in ways that still brings the needed physics along.
I find some of the technical terminology in machine learning articles as a little challenging but probably OK from a GMD perspective.
“Partially resolve” in the title is somewhat broad brush given that the actual advance is to examine the thin water column margins of cavities. The frontal terminal face and major crevasses also likely are resolution induced challenges – can they be examined in a similar way?
A critical point that stands out to me is the training is happening from model to model with no reference to any in situ evidence base. There are studies that describe processes in these marginal regions - all that in red in Fig 2. As far as I could tell there was no appeal to the insight provided from this important evidence base (see references). They point to issues like stratification, basal crevasses, tidal variability, even tsunamis - as all being present (lead authors – Begeman, Davis, Lawrence, Schmidt, Stewart & Washam). It would seem useful to have a reality check as to if these observed scales are reflected at some level in the high-resolution modelling. Is there a way of walking through the various likely processes in this unique environment and identifying how each process might fare in the upscaling?
Martin et al provide a useful insight into the connection between modelling and observations and point to (i) the need for approaches such as this manuscript but also (ii) ways to better connect with observations. I am not suggesting including these direct observations in the training but I think a few sentences connecting between the challenge at hand and the in situ evidence – and what extra evidence might be gathered – would help the impact of the work.
I also thought the final paragraph on the NEMO application was somewhat abrupt as it is an important future aspect of the work if it is to help improve ocean models more generally. The authors are proposing a significant shift in how future ocean models might operate and it raises lots of questions around how this might play out in terms tracking the ML enhancements within the general ocean model evolution. Can this be expanded a little in terms of speculation or next steps?
References
Begeman, C.B., Tulaczyk, S.M., Marsh, O.J., Mikucki, J.A., Stanton, T.P., Hodson, T.O., Siegfried, M.R., Powell, R.D., Christianson, K. and King, M.A., 2018. Ocean stratification and low melt rates at the Ross Ice Shelf grounding zone. Journal of Geophysical Research: Oceans, 123(10), pp.7438-7452.
Davis, P.E., Nicholls, K.W., Holland, D.M., Schmidt, B.E., Washam, P., Riverman, K.L., Arthern, R.J., Vaňková, I., Eayrs, C., Smith, J.A. and Anker, P.G., 2023. Suppressed basal melting in the eastern Thwaites Glacier grounding zone. Nature, 614(7948), pp.479-485.
Lawrence, J.D., Washam, P.M., Stevens, C., Hulbe, C., Horgan, H.J., Dunbar, G., Calkin, T., Stewart, C., Robinson, N., Mullen, A.D. and Meister, M.R., 2023. Crevasse refreezing and signatures of retreat observed at Kamb Ice Stream grounding zone. Nature Geoscience, 16(3), pp.238-243.
Martin, T., Dufour, C. O., Meijers, A. J. S., and Hancock, A. M.: Opinion: Status, Plans and Needs of Southern Ocean Modelling, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-213, 2026.
Schmidt, B.E., Washam, P., Davis, P.E., Nicholls, K.W., Holland, D.M., Lawrence, J.D., Riverman, K.L., Smith, J.A., Spears, A., Dichek, D.J.G. and Mullen, A.D., 2023. Heterogeneous melting near the Thwaites Glacier grounding line. Nature, 614(7948), pp.471-478.
Stewart, C., Horgan, H. and Stevens, C., 2024. Short Note: 2022 Hunga Tonga-Hunga Ha'apai tsunami measured beneath the Ross Ice Shelf. Antarctic Science, 36(3), pp.181-183.
Washam, P., Lawrence, J.D., Stevens, C.L., Hulbe, C.L., Horgan, H.J., Robinson, N.J., Stewart, C.L., Spears, A., Quartini, E., Hurwitz, B. and Meister, M.R., 2023. Direct observations of melting, freezing, and ocean circulation in an ice shelf basal crevasse. Science Advances, 9(43), p.eadi7638.