the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Feb 2026
Evaluation of climatic predictors of surface ponding on Antarctic ice shelves
Abstract. Ponding of surface meltwater on Antarctic Peninsula ice shelves has led to hydrofracture-driven calving and grounding line retreat, and other regions could become vulnerable to increased ponding as the climate continues to warm. Theory and qualitative observations suggest that ponding initiates when the meltwater-over-accumulation ratio (MOA) reaches 0.7. Here, we use present-day satellite-derived Antarctic meltwater products and RACMO climate model outputs to calibrate predictive thresholds of surface ponding based on air temperature, MOA, and a weighted combination of MOA ana grounding line proximity index (GLPI).
We tested three RACMO resolutions (27 km, 11 km, and 2 km) and three surface meltwater products. The meltwater product that is best aligned with MOA identifies ponding locations using aggregate meltwater depths at 27 km resolution. For this product, the calibrated MOA and GLPI threshold predicts present-day ponding with an F1 score over twice as high as the theoretical threshold of MOA ≥ 0.7 (F1 = 0.587 vs. 0.261). Under emissions pathway SSP1-2.6, the empirical threshold predicts 2.3 times more lake coverage by 2100 than the theoretical threshold, underscoring the importance of calibrating climatic ponding thresholds. If MOA-based thresholds are to be used in future ponding projections, we recommend they be applied at relatively coarse spatial scales, calibrated against present-day, depth-based meltwater products, and combined with grounding line proximal processes.
Competing interests: The contact author has declared that neither of the authors has any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(6398 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-839', Anonymous Referee #1, 06 Apr 2026
- AC1: 'Reply on RC1', Emily Glazer, 14 May 2026
-
RC2: 'Comment on egusphere-2026-839', Anonymous Referee #2, 20 Apr 2026
The manuscript evaluates the effectiveness of three metrics, i.e., temperature, MOA, and a newly proposed GLPI: Grounding Line Proximity Index, in predicting the distribution of surface meltwater ponding across Antarctic ice shelves. Given the implications posed by surface ponds, identifying accurate predictive ponding thresholds can be vital for forecasting future ice-sheet stability. The authors utilize several satellite-derived meltwater products and RACMO outputs at multiple resolutions to evaluate these thresholds. Their findings demonstrate that the widely used theoretical threshold of MOA ≥ 0.7 largely underpredicts present-day ponding. An empirical threshold combining MOA and GLPI provides a much more accurate prediction. When applied to future projections under the SSP1-2.6 emissions pathway, this empirical threshold predicts 2.3 times more lake coverage by 2100 than the theoretical threshold.
The study concludes that MOA-based thresholds are most effective when applied at coarse spatial scales, calibrated against depth-based meltwater observations, and augmented to account for grounding-line proximal processes like downslope winds. By quantifying the uncertainty introduced by threshold choice, this work underscores the necessity of using observation-constrained empirical models for reliable future projections of Antarctic surface hydrology.
The manuscript is generally well structured with clear text and formulation and adds significant value to the future understanding of the Antarctic surface hydrology. I recommend the manuscript for publication once the following (major) comments have been addressed.
GENERAL COMMENTS
1. Figures formatting and clarity: The current figures would benefit from enhanced visual clarity and appeal. I suggest re-aligning and enlarging the panels in most figures to ensure that the features discussed in the text are easily identifiable. To avoid potential confusion between the main text and appendices (e.g., Fig. A1 or B1), I recommend renaming the figure panels using lowercase letters (e.g., Fig. 1a, 1b, 1c as against Fig. 1A, 1B, 1C). Additionally, the text size of labels in Figs. 1, 2, 4, 5, 6, A1, A2, and A4 should be increased for better legibility. Finally, please include a colorbar for the F1 score values in Fig. 7 and Fig. A3 to assist in the interpretation of the results.
2. Could the authors provide more detail on how the MOA+GLPI thresholds, and their respective weights were evaluated to identify the optimal values discussed in the text? Wouldn’t it also be beneficial to include a plot of "F1 score vs. Weighted Sum MOA+GLPI" similar to the performance curves provided for temperature and MOA in Fig. 5?
3. The current discussion regarding downslope winds (Section 4.3) is primarily focused on foehn flows within the Antarctic Peninsula. It would be beneficial to expand this section to include the influence of both katabatic and foehn winds in other key regions, such as Dronning Maud Land, the Amery Ice Shelf, and the McMurdo Dry Valleys. See Saunderson et al., 2022; Mahagaonkar et al., 2025; Hofsteenge et al., 2022; etc to add relevant context.
4. While the use of GLPI is a valuable addition, it remains a proximity proxy rather than a physical parameter. Used to generalize physical processes and dynamics at the grounding zone using a simple 81km radius, this metric ignores the inherently directional nature of downslope winds, as melting typically occurs on the downwind side of mountain barriers. As a consequence, the index may over-predict meltwater on the upwind side of topographic features. I suggest the authors acknowledge this limitation and discuss how a directional component might refine the index's physical accuracy.
5. The application of present-day calibrated thresholds to projections out to 2100 assumes a stationary relationship between MOA and surface ponding. However, the study mentions that ice lens formation is expected to increase as the climate warms, which lowers firn permeability and can facilitate ponding at lower melt rates than a simple MOA threshold would predict. If these preconditioning processes become more prevalent, ponds could form at a significantly lower MOA (e.g., 0.1) by 2100 compared to the current empirical threshold of ~0.25. I suggest the authors clarify if their projections might therefore be conservative and recommend adding a discussion on the risks of applying static thresholds to an evolving firn environment.
SPECIFIC COMMENTS
L61/65: In Eq.2, where does the variable Tf appear? Please check and correct.
L86 and elsewhere: Is it necessary to describe what the figure shows in text? To make text more readable, I suggest just citing figures where they can be cited.
L133: How and why was 81 km radius used as a threshold? I notice some information in this regard in the discussion (L271-275) but might be relevant to add it here, in Methods.
L246: Recommend adding the F1 value after ‘nearly double that of the optimal temperature threshold’.
L247,248: Cite Fig. 6d (ii) and Fig. 6a (ii) in the text here instead.
“… Western Ross Ice Shelf (Fig. 6d (ii)) and many in Dronning Maud Land (Fig. 6a (ii))…”
L265: Delete ‘to’. “… and to have steeper surface slopes, …”
L264-268: Might be a good idea to break down this stretch into smaller sentences for the ease of the reader.
L279: Delete ‘upon’. “Incorporating GLPI improves upon the predictive performance of MOA alone in all five comparison scenarios”
L281, 292 and elsewhere(?): Should MOA + x (GLPI) y be MOA + x (GLPI) ≥ y ? Please check and correct.
L340: Since you are using directional inference ‘southwest’ in the text, would it be a good idea to have the North arrow on the image? Particularly because in Amery’s case here, North is not to the top, but to the right.
L384,385: Add relevant MOA threshold values in these sentences, easing the text for the reader.
L405: add reference after ‘… are a primary driver of surface melt near grounding lines’.
REFERENCES
1. Saunderson, D., Mackintosh, A., McCormack, F., Jones, R. S., & Picard, G. (2022). Surface melt on the Shackleton Ice Shelf, East Antarctica (2003–2021). The Cryosphere, 16(10), 4553-4569.
2. Mahagaonkar, A., Moholdt, G., Glaude, Q., & Schuler, T. V. (2025). Katabatic and foehn winds control the distribution of supraglacial lakes in Dronning Maud Land, Antarctica. Earth and Planetary Science Letters, 666, 119482.
3. Hofsteenge, M. G., Cullen, N. J., Reijmer, C. H., van den Broeke, M., Katurji, M., & Orwin, J. F. (2022). The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica. The Cryosphere, 16(12), 5041-5059.Citation: https://doi.org/10.5194/egusphere-2026-839-RC2 - AC2: 'Reply on RC2', Emily Glazer, 14 May 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-839', Anonymous Referee #1, 06 Apr 2026
Summary
Glazer and Tinto evaluate climate predictors for surface ponding on Antarctic ice shelves, testing the theoretical meltwater-over-accumulation (MOA) threshold of 0.7 against a calibrated threshold based on observational surface melt products, air temperature and RACMO across three spatial resolutions (2, 11 and 27 km). They further apply the best empirical MOA value and a grounding line proximity index against the theoretical one to project surface melt ponding until 2100 under three climate scenarios, demonstrating a substantial underestimation of surface ponding when relying solely on the theoretical threshold. Depending on the emission scenario, the theoretical threshold fails to capture a significant amount ponded pixels relative to the empirical threshold. This underscores the necessity of calibrating the MOA threshold to avoid systematic and potentially drastic underestimation of surface ponding on Antarctic ice shelves in future projections. The manuscript is well written and the findings are of considerable scientific significance. The following comments are intended to assist the authors in clarifying certain methodological choices and strengthening the overall presentation.
General Comments
Title: The title is currently quite broad. Consider refining it to explicitly reference MOA, grounding line proximity and air temperature thresholds, so that the scope and contribution of the study are immediately apparent to the reader.
Temporal aggregation of datasets and used variables: It is not entirely clear over which time spans the averages were calculated. For example, Morris & Vaughan report the −9°C isotherm based on the mean annual air temperature for the year 2000. It would be helpful to specify whether air temperature was calculated for each lake dataset over its respective observation period or over the full RACMO time span. The selection of air temperature over skin temperature would also benefit from explicit justification, and it may be worth considering whether a parallel analysis using skin temperature could be conducted to assess sensitivity to this choice.
Taking into account strong intra-annual variability of surface ponding on Antarctic ice shelves: As understood, lake ponding was aggregated based on the number of observed lakes on a pixel basis for the Dell and Tuckett datasets. However, e.g. RACMO covers the more recent year 2023, during which a pronounced peak in ponded surface meltwater occurred along the Antarctic Peninsula that is not captured by the observational products. Given that these datasets span different time periods, it would be important to clarify how this temporal inconsistency was accounted for, as it may substantially influence the results and how the strong intra- and inter-annual variability of surface ponding was accounted for.
Specific Comments
L22: It should be noted that not all parts of an ice shelf exert buttressing forces. See Fürst et al. 2016.
L28: Consider acknowledging the role of blue ice areas and rock outcrops in meltwater formation, given their characteristically lower albedo relative to surrounding snow and ice surfaces.
L37: The causal interpretation for Larsen B warrants more careful treatment. Leeson et al. 2020 raise the question of whether lake drainage constitutes a cause or a consequence of ice shelf collapse, which should be acknowledged here.
Figure 1: Please indicate the time period over which the average air temperature was calculated, and specify in the legend that 2 m air temperature is shown. It would also be helpful to clarify why the MOA color bar ends at 0.5 while the theoretical threshold is 0.7. Consider enlarging the maps for Dronning Maud Land, as the current size makes it difficult to resolve the gridded datasets.
L112: Please clarify which year's ice shelf shapefiles were used for clipping. E.g. whether they predate or postdate the collapsed ice shelves when clipping RACMO. Depending on the observed time span some very important pixels might be missing.
L118: A sensitivity analysis systematically testing a range of thresholds and their respective effects on the results would help quantify the uncertainty associated with what is acknowledged to be a somewhat subjective choice.
Figure 4: The maps for Dronning Maud Land should be enlarged, as spatial differences in the gridded datasets are currently very difficult to see.
Figure 5: Please indicate the time span over which the average air temperature shown was calculated.
Section 3.1. Average Air Temperature
L194: The characterisation "vastly underpredicts" may require revision in light of Table 2, which shows that the F1 score for the Tuckett-2k dataset is second highest at F1-score of 0.454, corresponding to an optimal temperature threshold of −9.2°C.
L197: The finding that no single temperature threshold constitutes an effective predictor merits further discussion. It would be worthwhile to examine whether skin temperature, maximum temperatures, or mean temperatures over the summer months might offer greater predictive utility.
Discussion: A notable gap in the discussion is the absence of a section addressing the need for region-specific thresholds. A more physically motivated distinction between the Antarctic Peninsula and the East Antarctic Ice Sheet would likely yield more robust and actionable recommendations than the current AP versus non-AP split.
Section 4.1: It should be noted in the discussion that the Dell et al. 2024 dataset includes areas of slush in addition to open water. Could this be a reason why in Figure 5 the Dell dataset is slightly different from the other datasets? Furthermore, the implications of the differing temporal coverages of the respective datasets for the comparability of results should be addressed.
L375: I am not a modelling expert but I wonder if you can actually do a proper comparison over different spatial scales when not all gridded datasets are based on the same RACMO version.
L414: The discussion would benefit from incorporating recent findings on the role of foehn winds in Dronning Maud Land from Mahagaonkar et al. 2025, rather than restricting this aspect of the discussion to the Antarctic Peninsula.
Conclusion: The manuscript presents scientifically valuable findings that merit a more confident presentation in the conclusion. In particular, the key result from the future projections section, namely that substantially greater ponding is projected under the empirical threshold relative to the theoretical one, is currently underrepresented and should be stated more prominently. I would suggest to provide explicit guidance on region-specific thresholds for the Antarctic Peninsula and EAIS/non-AP regions, and to state the optimum threshold values directly so that readers can readily reference them.
L585: The data aggregation procedure should be described in greater methodological detail. Downscaling from 30 m to 27 km resolution can introduce substantial errors depending on the approach taken, and a transparent account of the methodology is essential for reproducibility.
References mentioned:
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Tavard, L., Rankl, M., Braun, M., and Gagliardini, O.: The safety band of Antarctic ice shelves, Nature Climate Change, 6, 479–482, 2016.
Leeson, A. A., Forster, E., Rice, A., Gourmelen, N., and Van Wessem, J. M.: Evolution of Supraglacial Lakes on the Larsen B Ice Shelf in the Decades Before it Collapsed, Geophysical Research Letters, 47, e2019GL085591, https://doi.org/10.1029/2019GL085591, 2020.
Mahagaonkar, A., Moholdt, G., Glaude, Q., and Schuler, T. V.: Katabatic and foehn winds control the distribution of supraglacial lakes in Dronning Maud Land, Antarctica, Earth and Planetary Science Letters, 666, 119482, https://doi.org/10.1016/j.epsl.2025.119482, 2025.
Morris, E. M. and Vaughan, D. G.: Spatial and Temporal Variation of Surface Temperature on the Antarctic Peninsula And The Limit of Viability of Ice Shelves, in: Antarctic Peninsula Climate Variability: Historical and Paleoenvironmental Perspectives, American Geophysical Union (AGU), 61–68, https://doi.org/10.1029/AR079p0061, 2003.
Citation: https://doi.org/10.5194/egusphere-2026-839-RC1 - AC1: 'Reply on RC1', Emily Glazer, 14 May 2026
-
RC2: 'Comment on egusphere-2026-839', Anonymous Referee #2, 20 Apr 2026
The manuscript evaluates the effectiveness of three metrics, i.e., temperature, MOA, and a newly proposed GLPI: Grounding Line Proximity Index, in predicting the distribution of surface meltwater ponding across Antarctic ice shelves. Given the implications posed by surface ponds, identifying accurate predictive ponding thresholds can be vital for forecasting future ice-sheet stability. The authors utilize several satellite-derived meltwater products and RACMO outputs at multiple resolutions to evaluate these thresholds. Their findings demonstrate that the widely used theoretical threshold of MOA ≥ 0.7 largely underpredicts present-day ponding. An empirical threshold combining MOA and GLPI provides a much more accurate prediction. When applied to future projections under the SSP1-2.6 emissions pathway, this empirical threshold predicts 2.3 times more lake coverage by 2100 than the theoretical threshold.
The study concludes that MOA-based thresholds are most effective when applied at coarse spatial scales, calibrated against depth-based meltwater observations, and augmented to account for grounding-line proximal processes like downslope winds. By quantifying the uncertainty introduced by threshold choice, this work underscores the necessity of using observation-constrained empirical models for reliable future projections of Antarctic surface hydrology.
The manuscript is generally well structured with clear text and formulation and adds significant value to the future understanding of the Antarctic surface hydrology. I recommend the manuscript for publication once the following (major) comments have been addressed.
GENERAL COMMENTS
1. Figures formatting and clarity: The current figures would benefit from enhanced visual clarity and appeal. I suggest re-aligning and enlarging the panels in most figures to ensure that the features discussed in the text are easily identifiable. To avoid potential confusion between the main text and appendices (e.g., Fig. A1 or B1), I recommend renaming the figure panels using lowercase letters (e.g., Fig. 1a, 1b, 1c as against Fig. 1A, 1B, 1C). Additionally, the text size of labels in Figs. 1, 2, 4, 5, 6, A1, A2, and A4 should be increased for better legibility. Finally, please include a colorbar for the F1 score values in Fig. 7 and Fig. A3 to assist in the interpretation of the results.
2. Could the authors provide more detail on how the MOA+GLPI thresholds, and their respective weights were evaluated to identify the optimal values discussed in the text? Wouldn’t it also be beneficial to include a plot of "F1 score vs. Weighted Sum MOA+GLPI" similar to the performance curves provided for temperature and MOA in Fig. 5?
3. The current discussion regarding downslope winds (Section 4.3) is primarily focused on foehn flows within the Antarctic Peninsula. It would be beneficial to expand this section to include the influence of both katabatic and foehn winds in other key regions, such as Dronning Maud Land, the Amery Ice Shelf, and the McMurdo Dry Valleys. See Saunderson et al., 2022; Mahagaonkar et al., 2025; Hofsteenge et al., 2022; etc to add relevant context.
4. While the use of GLPI is a valuable addition, it remains a proximity proxy rather than a physical parameter. Used to generalize physical processes and dynamics at the grounding zone using a simple 81km radius, this metric ignores the inherently directional nature of downslope winds, as melting typically occurs on the downwind side of mountain barriers. As a consequence, the index may over-predict meltwater on the upwind side of topographic features. I suggest the authors acknowledge this limitation and discuss how a directional component might refine the index's physical accuracy.
5. The application of present-day calibrated thresholds to projections out to 2100 assumes a stationary relationship between MOA and surface ponding. However, the study mentions that ice lens formation is expected to increase as the climate warms, which lowers firn permeability and can facilitate ponding at lower melt rates than a simple MOA threshold would predict. If these preconditioning processes become more prevalent, ponds could form at a significantly lower MOA (e.g., 0.1) by 2100 compared to the current empirical threshold of ~0.25. I suggest the authors clarify if their projections might therefore be conservative and recommend adding a discussion on the risks of applying static thresholds to an evolving firn environment.
SPECIFIC COMMENTS
L61/65: In Eq.2, where does the variable Tf appear? Please check and correct.
L86 and elsewhere: Is it necessary to describe what the figure shows in text? To make text more readable, I suggest just citing figures where they can be cited.
L133: How and why was 81 km radius used as a threshold? I notice some information in this regard in the discussion (L271-275) but might be relevant to add it here, in Methods.
L246: Recommend adding the F1 value after ‘nearly double that of the optimal temperature threshold’.
L247,248: Cite Fig. 6d (ii) and Fig. 6a (ii) in the text here instead.
“… Western Ross Ice Shelf (Fig. 6d (ii)) and many in Dronning Maud Land (Fig. 6a (ii))…”
L265: Delete ‘to’. “… and to have steeper surface slopes, …”
L264-268: Might be a good idea to break down this stretch into smaller sentences for the ease of the reader.
L279: Delete ‘upon’. “Incorporating GLPI improves upon the predictive performance of MOA alone in all five comparison scenarios”
L281, 292 and elsewhere(?): Should MOA + x (GLPI) y be MOA + x (GLPI) ≥ y ? Please check and correct.
L340: Since you are using directional inference ‘southwest’ in the text, would it be a good idea to have the North arrow on the image? Particularly because in Amery’s case here, North is not to the top, but to the right.
L384,385: Add relevant MOA threshold values in these sentences, easing the text for the reader.
L405: add reference after ‘… are a primary driver of surface melt near grounding lines’.
REFERENCES
1. Saunderson, D., Mackintosh, A., McCormack, F., Jones, R. S., & Picard, G. (2022). Surface melt on the Shackleton Ice Shelf, East Antarctica (2003–2021). The Cryosphere, 16(10), 4553-4569.
2. Mahagaonkar, A., Moholdt, G., Glaude, Q., & Schuler, T. V. (2025). Katabatic and foehn winds control the distribution of supraglacial lakes in Dronning Maud Land, Antarctica. Earth and Planetary Science Letters, 666, 119482.
3. Hofsteenge, M. G., Cullen, N. J., Reijmer, C. H., van den Broeke, M., Katurji, M., & Orwin, J. F. (2022). The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica. The Cryosphere, 16(12), 5041-5059.Citation: https://doi.org/10.5194/egusphere-2026-839-RC2 - AC2: 'Reply on RC2', Emily Glazer, 14 May 2026
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Emily C. Glazer
Kirsty J. Tinto
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6398 KB) - Metadata XML
Summary
Glazer and Tinto evaluate climate predictors for surface ponding on Antarctic ice shelves, testing the theoretical meltwater-over-accumulation (MOA) threshold of 0.7 against a calibrated threshold based on observational surface melt products, air temperature and RACMO across three spatial resolutions (2, 11 and 27 km). They further apply the best empirical MOA value and a grounding line proximity index against the theoretical one to project surface melt ponding until 2100 under three climate scenarios, demonstrating a substantial underestimation of surface ponding when relying solely on the theoretical threshold. Depending on the emission scenario, the theoretical threshold fails to capture a significant amount ponded pixels relative to the empirical threshold. This underscores the necessity of calibrating the MOA threshold to avoid systematic and potentially drastic underestimation of surface ponding on Antarctic ice shelves in future projections. The manuscript is well written and the findings are of considerable scientific significance. The following comments are intended to assist the authors in clarifying certain methodological choices and strengthening the overall presentation.
General Comments
Title: The title is currently quite broad. Consider refining it to explicitly reference MOA, grounding line proximity and air temperature thresholds, so that the scope and contribution of the study are immediately apparent to the reader.
Temporal aggregation of datasets and used variables: It is not entirely clear over which time spans the averages were calculated. For example, Morris & Vaughan report the −9°C isotherm based on the mean annual air temperature for the year 2000. It would be helpful to specify whether air temperature was calculated for each lake dataset over its respective observation period or over the full RACMO time span. The selection of air temperature over skin temperature would also benefit from explicit justification, and it may be worth considering whether a parallel analysis using skin temperature could be conducted to assess sensitivity to this choice.
Taking into account strong intra-annual variability of surface ponding on Antarctic ice shelves: As understood, lake ponding was aggregated based on the number of observed lakes on a pixel basis for the Dell and Tuckett datasets. However, e.g. RACMO covers the more recent year 2023, during which a pronounced peak in ponded surface meltwater occurred along the Antarctic Peninsula that is not captured by the observational products. Given that these datasets span different time periods, it would be important to clarify how this temporal inconsistency was accounted for, as it may substantially influence the results and how the strong intra- and inter-annual variability of surface ponding was accounted for.
Specific Comments
L22: It should be noted that not all parts of an ice shelf exert buttressing forces. See Fürst et al. 2016.
L28: Consider acknowledging the role of blue ice areas and rock outcrops in meltwater formation, given their characteristically lower albedo relative to surrounding snow and ice surfaces.
L37: The causal interpretation for Larsen B warrants more careful treatment. Leeson et al. 2020 raise the question of whether lake drainage constitutes a cause or a consequence of ice shelf collapse, which should be acknowledged here.
Figure 1: Please indicate the time period over which the average air temperature was calculated, and specify in the legend that 2 m air temperature is shown. It would also be helpful to clarify why the MOA color bar ends at 0.5 while the theoretical threshold is 0.7. Consider enlarging the maps for Dronning Maud Land, as the current size makes it difficult to resolve the gridded datasets.
L112: Please clarify which year's ice shelf shapefiles were used for clipping. E.g. whether they predate or postdate the collapsed ice shelves when clipping RACMO. Depending on the observed time span some very important pixels might be missing.
L118: A sensitivity analysis systematically testing a range of thresholds and their respective effects on the results would help quantify the uncertainty associated with what is acknowledged to be a somewhat subjective choice.
Figure 4: The maps for Dronning Maud Land should be enlarged, as spatial differences in the gridded datasets are currently very difficult to see.
Figure 5: Please indicate the time span over which the average air temperature shown was calculated.
Section 3.1. Average Air Temperature
L194: The characterisation "vastly underpredicts" may require revision in light of Table 2, which shows that the F1 score for the Tuckett-2k dataset is second highest at F1-score of 0.454, corresponding to an optimal temperature threshold of −9.2°C.
L197: The finding that no single temperature threshold constitutes an effective predictor merits further discussion. It would be worthwhile to examine whether skin temperature, maximum temperatures, or mean temperatures over the summer months might offer greater predictive utility.
Discussion: A notable gap in the discussion is the absence of a section addressing the need for region-specific thresholds. A more physically motivated distinction between the Antarctic Peninsula and the East Antarctic Ice Sheet would likely yield more robust and actionable recommendations than the current AP versus non-AP split.
Section 4.1: It should be noted in the discussion that the Dell et al. 2024 dataset includes areas of slush in addition to open water. Could this be a reason why in Figure 5 the Dell dataset is slightly different from the other datasets? Furthermore, the implications of the differing temporal coverages of the respective datasets for the comparability of results should be addressed.
L375: I am not a modelling expert but I wonder if you can actually do a proper comparison over different spatial scales when not all gridded datasets are based on the same RACMO version.
L414: The discussion would benefit from incorporating recent findings on the role of foehn winds in Dronning Maud Land from Mahagaonkar et al. 2025, rather than restricting this aspect of the discussion to the Antarctic Peninsula.
Conclusion: The manuscript presents scientifically valuable findings that merit a more confident presentation in the conclusion. In particular, the key result from the future projections section, namely that substantially greater ponding is projected under the empirical threshold relative to the theoretical one, is currently underrepresented and should be stated more prominently. I would suggest to provide explicit guidance on region-specific thresholds for the Antarctic Peninsula and EAIS/non-AP regions, and to state the optimum threshold values directly so that readers can readily reference them.
L585: The data aggregation procedure should be described in greater methodological detail. Downscaling from 30 m to 27 km resolution can introduce substantial errors depending on the approach taken, and a transparent account of the methodology is essential for reproducibility.
References mentioned:
Fürst, J. J., Durand, G., Gillet-Chaulet, F., Tavard, L., Rankl, M., Braun, M., and Gagliardini, O.: The safety band of Antarctic ice shelves, Nature Climate Change, 6, 479–482, 2016.
Leeson, A. A., Forster, E., Rice, A., Gourmelen, N., and Van Wessem, J. M.: Evolution of Supraglacial Lakes on the Larsen B Ice Shelf in the Decades Before it Collapsed, Geophysical Research Letters, 47, e2019GL085591, https://doi.org/10.1029/2019GL085591, 2020.
Mahagaonkar, A., Moholdt, G., Glaude, Q., and Schuler, T. V.: Katabatic and foehn winds control the distribution of supraglacial lakes in Dronning Maud Land, Antarctica, Earth and Planetary Science Letters, 666, 119482, https://doi.org/10.1016/j.epsl.2025.119482, 2025.
Morris, E. M. and Vaughan, D. G.: Spatial and Temporal Variation of Surface Temperature on the Antarctic Peninsula And The Limit of Viability of Ice Shelves, in: Antarctic Peninsula Climate Variability: Historical and Paleoenvironmental Perspectives, American Geophysical Union (AGU), 61–68, https://doi.org/10.1029/AR079p0061, 2003.