Review of “Learning to melt: Emulating Greenland surface melt from a polar RCM with machine learning” by Elke Schlager et al. 

The Cryosphere (TC): egusphere-2026-7

General comments

This paper introduces a newly developed neural network-based emulator that predicts the temporal evolution of Greenland ice sheet surface melt. The emulator was trained on the output from the polar regional climate model HIRHAM5 and its firn model DMIHH, forced by the ERA-Interim reanalysis. It is clearly shown that the Modular NN configuration of the emulator, the standard setting developed in this study, can provide realistic information on the spatiotemporal evolution of ice-sheet surface melt, along with the daily melt amount. My impression is that this is a unique study that can provide useful information on the synergy between machine learning and cryosphere science. Although I think the information provided, in particular on the methods, can be improved, the results and discussion sound reasonable and sufficient to me. Therefore, I suggest that this paper can be published after revisions. I list some specific comments below. 

Specific comments 

L. 9 “mean absolute error below 0.23 mm w.e.”: Compared to what? What is the reference data for this comparison? Please explain.

L. 45 ~ 58: It is worth reviewing and citing the paper by Hu et al. (https://doi.org/10.5194/tc-15-5639-2021) in this part.

L. 59 “high temporal variability”: Can the authors explain this point quantitatively and add a reference for this argument if possible?

L. 60 “temporal context”: I don’t think this technical term is widely recognized in the cryosphere community. Can the authors introduce additional explanations about the term so that more readers can easily understand?

L. 60 “While the models predicting annual ~”: Do the authors mean that the models refer to “ML” emulator? Or RCMs? Please clarify.

L. 63: What do the authors mean by “lag effects”? Please explain in more detail.

L. 67: What do the authors mean by “model generalization”? Please explain.

L. 73 “Our model can be re-trained on data for future scenarios ~”: If the NN will be used for the future simulations of the ice sheet surface melt, do the authors have to train the NN using the output from the future climate simulations by an RCM such as HIRHAM5? Please explain more explicitly.

L. 78 ~ 79: Please explain all the properties included in the daily output of the polar RCM HIRHAM5 with its firn model DMIHH.

L. 79: What is the total snow and ice model layer thickness that DMIHH considers with the 32 model layers?

L. 84: It is better to explain how bare ice is determined in the DMIHH model.

L. 85: Atmospheric forcing for what? For DMIHH? Or for the newly developed emulator? Please clarify. In addition, please list all the properties included in the atmospheric forcing.

L. 90: It is unclear what the “input data” are. Input data for DMIHH? Or input data for the emulator?

L. 97 “they can be problematic when training ML models.”: Please explain the reason for this argument in more detail.

L. 107: Does the negative sensible heat flux mean that the heat flux directs from the ice sheet surface to the atmosphere? Or opposite? Please explain.

L. 129: Why is the number 5000 selected here? A more detailed explanation is needed.

L. 178 “we choose the hidden layers of the network to be 64-128-128-64-32-16-16”: Please explain the meanings of each number, in particular for non-specialists in NN. 

L. 182, L. 184, and L. 185: Same as the comment on L. 178.

L. 188: Please explain in more detail about “LeakyReLU activation function.”

L. 193 “the optimal number of days to be used in the short-term module”: What do the authors mean by “optimal”? Please explain in more detail.

L. 215~216 “the total computational cost remains far lower than physical firn models”: Can the authors add quantitative information for this explanation? I think such information is useful for other emulator developers.

Technical corrections

L. 89: It is better to add something like “within DMIHH” at the end of this sentence.

L. 111: It is better to add the mathematical symbol “x” after “heat flux values.”

Table 1 caption: Please add “Autoreg” after “the autoregressive element.”

Figure 3 caption: It is better to explain the numbers in Gt listed in each panel.

L. 319: Suggest adding “surface” before “atmospheric variables.”

Overall, I think the authors have done a very good job. The manuscript is clearly written, the structure is logical, and the figures are generally of high quality. With some minor revisions, I believe the paper should be ready for publication. Please see the in-text comments for specific suggestions to improve clarity and to make certain figures more rigorous.

One point I would like to raise concerns the choice of using a single year (2016) as the test set. The manuscript does not provide a clear justification for this decision. Using only one test year raises the concern that the evaluation may depend on a particularly “lucky” year, or alternatively on a year with atypical behavior. In either case, it becomes difficult to convincingly demonstrate the model's generalisability.

As a reader without specific expertise in the Greenland Ice Sheet (GIS), I am unsure whether 2016 is representative of typical conditions or whether it may have experienced unusual or extreme events. From a scientific robustness perspective, it would strengthen the study to repeat the prediction-versus-observation scatter plots for one or two additional test years. This would allow the reader to assess whether, for example, the melt overestimation by the autoregressive and modular neural network models is a persistent feature or specific to 2016.

If the authors intentionally selected 2016, I would encourage them to provide a clear justification. For example, an appendix figure showing the distribution of SMB over the GIS compared to other years could help demonstrate whether 2016 is representative or exceptional.

A similar concern applies to Figure 5, which focuses on 21 July 2016. While this date is interesting, showing only a single summer day risks giving the impression of a carefully selected (“lucky”) example. I would encourage the authors to include additional dates in the appendix, ideally covering different seasons, for example, shoulder seasons or winter periods when little or no melt is expected. This would provide a more comprehensive picture of model behavior across varying surface mass balance regimes.

In addition, it could be helpful to show aggregated diagnostics, such as spatial plots of the mean SMB over several months (or seasonal averages) for the best-performing model. Such analyses would provide stronger evidence that the model captures robust patterns rather than performing well on isolated dates.

Overall, I consider this a valuable contribution, but addressing these points would further strengthen the manuscript.