A novel database of Antarctic meteorological extremes over key ice shelves during 1995&ndash;2023

Simpson, Ian; Hanna, Edward; Williams, Ryan S.; Luu, Linh; Orr, Andrew; Jones, Julie; Fettweis, Xavier; Alavez, Jose Abraham Torres; Christensen, Ole Bøssing; Gilbert, Ella; Gumber, Sid; Kittel, Christoph; Li, Sihan; Maure, Damien; Mottram, Ruth; Phillips, Tony; van de Berg, Willem Jan; Verro, Kristiina

doi:10.5194/egusphere-2026-2270

Preprints

https://doi.org/10.5194/egusphere-2026-2270

Preprints

01 Jun 2026

| 01 Jun 2026

A novel database of Antarctic meteorological extremes over key ice shelves during 1995–2023

Ian Simpson, Edward Hanna, Ryan S. Williams, Linh Luu, Andrew Orr, Julie Jones, Xavier Fettweis, Jose Abraham Torres Alavez, Ole Bøssing Christensen, Ella Gilbert, Sid Gumber, Christoph Kittel, Sihan Li, Damien Maure, Ruth Mottram, Tony Phillips, Willem Jan van de Berg, and Kristiina Verro

Abstract. The climate of Antarctica is showing increasing signs of being impacted by the warming trend in global temperatures, which has potential to result in accelerated break up of key ice shelves, which would contribute to global sea level rise. Here, we present a novel database of Antarctic extreme weather events over a selection of key ice shelves (Larsen, George VI, Wilkins, Abbot, Thwaites, Totten, Amery, Lazarev), using simulations from four regional climate models (RCMs: RACMO2, HCLIM, MetUM and MAR), driven by the ERA5 reanalysis, examining surface air temperature, precipitation, wind and surface pressure. In addition, we examine trends in the frequency of extreme events above or below specified thresholds (5th, 10th, 50th, 90th and 95th percentiles) and spatial atmospheric circulation and temperature anomaly patterns over Antarctica that are commonly associated with extreme events over key ice shelves. The RCM simulations have been compared with station observations close to the ice shelves, and we developed regressions to estimate simulated values during periods when only one or two of the RCMs were available.

Received: 20 Apr 2026 – Discussion started: 01 Jun 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of The Cryosphere. There are no other competing interests to declare.

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Status: final response (author comments only)

RC1: 'Comment on egusphere-2026-2270', Anonymous Referee #1, 29 Jun 2026

In this study, the reproducibility of basic meteorological parameters simulated by four regional climate models is evaluated through comparison with AWS observations over three ice shelves. In addition, using the outputs from these four models, the authors discuss trends in temperature and precipitation over eight ice shelves, as well as their relationships with large-scale atmospheric circulation modes such as ENSO and SAM. However, I have several concerns about this study, and I believe that substantial revisions are required. Therefore, I recommend that the manuscript be reconsidered for publication after major revision. My main reasons are as follows.

- Lines 156–159: One of the strengths of this study is the use of high-resolution regional climate models. However, it has also been reported that ERA5 can reproduce these variables to some extent (Yamada et al., 2026). Therefore, I encourage the authors to include ERA5 in the analysis and demonstrate how much added value these regional models provide relative to the reanalysis.
Reference: Yamada, K., et al. (2026). Interannual variations of precipitation events at Dome Fuji station, Antarctica. Journal of Geophysical Research: Atmospheres, 131, e2025JD045296.

- Lines 207–212: The definition of extreme events includes both one-day exceedances of the 2nd, 5th, 95th, and 98th percentiles and three consecutive days exceeding the 10th or 90th percentiles. However, the physical rationale for using these specific thresholds is not fully explained. The authors should clarify why these thresholds were selected and whether the main conclusions are sensitive to this choice.

- Lines 213–224: In this study, values for periods with missing model data are estimated using linear regression based on the available models. I assume that the regression coefficients were derived from the relationships among the models during the overlapping period, 2000–2020. If this understanding is correct, the relationships derived from 2000–2020 are applied to estimate values in the 2020s. However, because several extreme events occurred during the 2020s, these relationships may not necessarily hold for that period. At a minimum, the authors should compare the estimated values for the non-overlapping periods with available observations to evaluate the accuracy of this approach. In addition, the manuscript does not provide sufficient details on how the regression coefficients were calculated. Please explain the procedure more clearly.

- Lines 298–306: The authors appear to use the difference between the observed daily temperature and the observed 95th percentile temperature as the x-axis. However, it is unclear why this metric was chosen. I suggest that the authors explain the rationale for using this quantity in the Methods or Results section. If the objective is to evaluate model performance across different temperature conditions, would it not be more straightforward to use the observed daily temperature itself as the x-axis?

- Lines 307–311: Although the authors note that a similar warm bias has been reported in previous studies, the underlying cause of this warm bias is not discussed. It would be helpful for readers if the authors briefly addressed the possible reasons for this bias, or referred to previous studies that have investigated its origin.

- Lines 380–383: Similar findings have also been discussed in the following study. I recommend citing this reference.
References: Sato, K., and I. Simmonds, 2021: Antarctic skin temperature warming related to enhanced downward longwave radiation associated with increased atmospheric advection of moisture and temperature. Environ. Res. Lett., 16, 064059, https://doi.org/10.1088/1748-9326/ac0211.

- Lines 410–426, Tables 5–8: The relationships between ENSO, SAM, sea-ice extent, and the meteorological variables appear to vary considerably among seasons. Therefore, I suggest presenting seasonal correlation maps between ENSO, SAM, and sea-ice extent and each meteorological variable (temperature, precipitation, surface pressure, and wind speed) for four seasons. Such figures would provide a more comprehensive view of the seasonal dependence of these relationships than the current tables alone.

- Table 2: I suggest adding the model surface elevations at each observation site. Since the model elevations differ from the actual station elevations and elevation corrections were applied, including these values would help readers better assess the model performance.

- Table 2: It would also be useful to include the results for the model mean, as this is one of the primary products used throughout the study.

- Table 2: I also recommend including the corresponding ERA5 results. This would provide a useful benchmark and allow readers to evaluate the added value of the regional climate models over the driving reanalysis.

- Table 2: In addition to the correlation coefficients and RMSE, I suggest reporting the mean bias for each variable. While the temperature bias can be inferred from Figure 2, the biases in surface pressure and wind speed cannot be readily assessed from the current presentation. Including the mean bias would provide a more complete evaluation of model performance.

- Figure 2: The caption states that the Larsen Ice Shelf data cover the period 1985–2016. Does this refer to the observation period? What is the actual overlapping period used for the comparison between the observations and the model simulations? In addition, were the same time periods used for all four models at the Amery G3 and Fossil Bluff sites? For example, in the case of Fossil Bluff, RACMO2 extends to 2023, whereas the other models are only available until 2020. It is not clear whether all four models were compared over the same common period. If different periods were used, the comparison would not be directly comparable, and the analysis should instead be restricted to the common period.

- Figure 2: The caption refers to a "vertical line." Which line does this refer to? Is it the line at 0°C on the x-axis? If so, does every point to the right of this line represent a temperature exceeding the 95th percentile? Please clarify this in the figure or caption.

- Figure 4: I suggest adding the corresponding observational results to this figure. Including observations would allow readers to directly evaluate the realism of the model simulations.

- Figures 5 and 6: It would be helpful to provide the numerical values of the seasonal trends for each ice-shelf region to figures. This would allow readers to quantitatively compare the magnitude of the trends among regions and seasons.

Citation: https://doi.org/10.5194/egusphere-2026-2270-RC1
RC2:
'Comment on egusphere-2026-2270', Anonymous Referee #2, 13 Jul 2026
General comments
This study presents a novel dataset of extreme weather events over key Antarctic ice shelves based on a mini-ensemble of high resolution regional climate models. This combined PolarRES dataset has been previously used in an individual case study (Gilbert et al. 2025), but applying the PolarRES dataset to create a climatology of extreme events over select ice shelves represents an important and useful advancement. The authors present the statistics and trends from the PolarRES dataset in relation with large-scale modes of climate variability and sea-ice extent. The PolarRES dataset is then compared with previous case studies of previously documented Antarctic extreme events to demonstrate where those events fall within the distribution of the PolarRES climatology.
Unfortunately, I find the manuscript does not achieve in convincing the reader in the utility of the PolarRES dataset. The manuscript did not address why their methodology is an advancement in extreme event detection. The authors should demonstrate why their database in more useful than just using reanalysis data for a similar product. The high resolution of the models should answer this question, but this needs to be demonstrated in the manuscript.
Overall, I believe the PolarRES dataset will be a valuable tool for the Antarctic science community, which is why I feel the manuscript needs to be improved to better demonstrate its value. Thus my opinion this manuscript needs major revisions before it can be considered for publication. My specific and then minor comments are found below.
Specific comments
Introduction: Overall, the introduction lacks focus and organization. Each paragraph jumps to a different topic related to Antarctic weather extremes and melt, but with no real connection to the previous paragraph or a narrative throughline. Some topics seem randomly placed without a clear reason. For instance, in lines 59-65, the atmospheric river is introduced and then uses the Brunt ice shelf iceberg calving example to demonstrate their impacts even though Francis et al. (2022) describes a combination of factors that caused the calving. Then a few paragraphs later, the authors include a case of calving on the Amery ice shelf which was caused by similar circumstances as the Brunt ice shelf example, but is now being discussed under the context of ice shelf calving. Also, teleconnections are discussed in lines 51-58 and then again in line 74-81. I suggest a rewrite of the Introduction to create a clear narrative that connects the existence of extreme weather to a lack of observation and high resolution modeling. Try to condense the introduction and remove unnecessary material so that the reader can quickly get to the point of the study. Like there are only 5 lines (121-125) that actually address the project motivation (i.e. the creation of an extreme event database). In this regard, the authors should discuss Antarctic extremes in the context of how a lack of high resolution modeling (Gilbert et al. 2025), observations, and connecting the extreme events inhibits our ability to holistically understand their impacts.

Data: I understand the logic of not using ERA5 since its coarser resolution misses extreme weather activity in complex terrain, but comparing the PolarRES models with ERA5 would still be helpful to get a general understanding of their performance. Here the PolarRES models could be interpolated to the ERA5 resolution and then determine if there are any large systemic biases in temperature and wind. The other concern I have is the lack of AWS stations used for validation. Essentially, 5 out of the 8 ice shelves don’t have observations for comparison. If the authors use the 3-hourly AntAWS dataset, would they find more stations that could be used for validation? Since the data is being used to calculate daily means and not being analyzed at the hourly scale, is the downside from using 3-hourly data really that large? Having more observational and reanalysis data to compare against would give more confidence to the model extremes database.

Methods: I understand the need for using the linear regression to fill gaps in the model records, but is there a way to test the potential errors this might introduce? What about preforming a data deprivation experiment where data from a model over a set of year is removed and replaced with the linear regression method? This could be done over a period where all four models have data so that the estimated mean value from the four models can be compared with the actual four model mean.

Figure 2 and the corresponding analysis: If the x-axis is the difference between station daily temperature (please state daily mean if this is the case) and the 95^th percentile of that station’s daily mean temperature, then shouldn’t the y-axis be the difference the model daily mean temperature and the 95^th percentile of the corresponding station’s daily mean temperature? If the y-axis is comparing the model daily mean against the station daily mean, then the comparison does not make sense. Also, the description of the vertical line in the caption does not seem correct. Since the x-axis is just describing station data, 5% of the daily mean temperature from the station will cross the vertical line regardless of what the model simulated. Please check this figure and correct the analysis if changes are needed.

Case studies: One great justification of this research is to find extreme events that have not been previously discussed in the literature. As currently written, the case studies section mostly compares the PolarRES dataset against known extreme events while occasionally pointing out extremes identified in the database that were previously unknown. Also the analysis of the case studies focuses almost purely on warm extremes and not any potential cold or snowy extremes. I suggest a rewrite of the case studies section that quickly highlights the database picking up the well known extreme events, perhaps discussing ways the database expands our understanding of those events, and then giving examples of previously unknown events and discussing some of their impacts that may relate to cold extremes and precipitation. For instance, the authors mention an extreme event in June 2006 that I do not believe has been previously discussed. Here it would be nice to see some images of the snowfall anomalies that may have resulted from this extreme. Also the authors can take a previously documented extreme event and show how the high resolution of the PolarRES models reveals a greater detail about the temperature extremes over complex terrain than previously understood.

Discussion and Conclusion: I feel that the benefits of having an extreme event database are not fully discussed or realized in these sections. Here it would be interesting if the authors can state how their database can provide greater insight into already documented extremes or uncover previously undocumented extreme events. Finally, as the PolarRES dataset is a product that should benefit the Antarctic scientific community, the authors can do a better job “selling” their product in the Conclusion by mentioning examples of other disciplines that would be interested in this dataset like ecologists studying greening on the Antarctic Peninsula (Roland et al. 2024) or biologists studying microbe response to extreme weather (Barrett et al. 2024).

Minor comments
Line 21: Having two “which”s in the opening sentence is clunky. Would recommend phrasing the end of the sentence
Line 41: Please clarify here that the increased vulnerability to calving results from ocean waves adding stress to the ice shelf front when sea ice is absent.
Line 40-42: Recommend splitting this sentence into two; first focusing on the fohn wind that brings warm air over the ice shelf then discussing the sea ice disintegration and exposure to ocean swells when the strong wind reaches the ice shelf front.
Line 42: Massom et al. (2018) only discusses the ocean swell stress on ice shelves. You need citations for the fohn wind. For example, (Turton et al. 2020; Wille et al. 2022; Elvidge et al. 2020).
Line 44: Yes extreme precipitation events add to the surface mass balance and offset loses from melting, but does precipitation actually reduce surface meltwater ponding? van Wessem et al. (2023) only discusses melt pond formation and nothing about precipitation. You can look at (Maclennan et al. 2022; Turner et al. 2019; Wille et al. 2021; González-Herrero et al. 2023; Adusumilli et al. 2021) for papers about extreme precipitation.
Line 45-50: The authors should also mention that the mass balance has been slightly negative to neutral over the past five years or so due to an increase in extreme snowfall events. See (Kolbe et al. 2026; Wang et al. 2023, 2025)
Line 52: Add an oxford comma
Line 58: Specify that this is basal melt.
Line 59-60: Please add a citation for atmospheric rivers (Zhu and Newell 1998).
Line: 213-216: Please rephrase this sentence. Using the word “for” three times feels awkward.
Figure 3: Please clarify the months of the seasons in the caption.
Line 307-310: Check grammar. It feels like a run-on sentence.
Line 337: “over Amery”. Check grammar.
Table 3 and 4: Recommend including observational values where available. It would help the reader understand the model biases.
Figure 4: It would be helpful if there was a panel for the station observations as well. Then the simulated temperature spikes in the 2006 can be validated with observations.
Figure 5: Is the annual count just the sum of the four seasons? Or is it the number of days that exceed the annually defined 95^th quantile? Please ensure it is the former and specify in the caption.
Line 372-373: Please clarify if this statement refers to all ice shelves? Also calculating the trend for annual extremes and listing it in the upper right hand corners would be interesting.
Line 416: Double periods.
Line 451: The storm that triggered the final collapse of the Larsen A was identified as an atmospheric river which is worth mentioning here (Scambos et al. 2000; Wille et al. 2022)
Line 504-508: This event was extensively documented in Bozkurt et al. (2018) which discussed an atmospheric river and resulting foehn wind over the Antarctic Peninsula.
References
Adusumilli, S., M. A. Fish, H. A. Fricker, and B. Medley, 2021: Atmospheric River Precipitation Contributed to Rapid Increases in Surface Height of the West Antarctic Ice Sheet in 2019. Geophysical Research Letters, 48, e2020GL091076, https://doi.org/10.1029/2020GL091076.
Barrett, J. E., and Coauthors, 2024: Response of a Terrestrial Polar Ecosystem to the March 2022 Antarctic Weather Anomaly. Earth’s Future, 12, e2023EF004306, https://doi.org/10.1029/2023EF004306.
Bozkurt, D., R. Rondanelli, J. C. Marín, and R. Garreaud, 2018: Foehn Event Triggered by an Atmospheric River Underlies Record-Setting Temperature Along Continental Antarctica. Journal of Geophysical Research: Atmospheres, 123, 3871–3892, https://doi.org/10.1002/2017JD027796.
Elvidge, A. D., P. Kuipers Munneke, J. C. King, I. A. Renfrew, and E. Gilbert, 2020: Atmospheric Drivers of Melt on Larsen C Ice Shelf: Surface Energy Budget Regimes and the Impact of Foehn. Journal of Geophysical Research: Atmospheres, 125, e2020JD032463, https://doi.org/10.1029/2020JD032463.
Francis, D., R. Fonseca, K. S. Mattingly, O. J. Marsh, S. Lhermitte, and C. Cherif, 2022: Atmospheric Triggers of the Brunt Ice Shelf Calving in February 2021. Journal of Geophysical Research: Atmospheres, 127, e2021JD036424, https://doi.org/10.1029/2021JD036424.
Gilbert, E., D. Pishniak, J. A. Torres, A. Orr, M. Maclennan, N. Wever, and K. Verro, 2025: Extreme precipitation associated with atmospheric rivers over West Antarctic ice shelves: insights from kilometre-scale regional climate modelling. The Cryosphere, 19, 597–618, https://doi.org/10.5194/tc-19-597-2025.
González-Herrero, S., F. Vasallo, J. Bech, I. Gorodetskaya, B. Elvira, and A. Justel, 2023: Extreme precipitation records in Antarctica. International Journal of Climatology, 43, 3125–3138, https://doi.org/10.1002/joc.8020.
Kolbe, M., J. A. Torres Alavez, R. Mottram, M. Katurji, R. Bintanja, and E. C. van der Linden, 2026: Atmospheric rivers and winter sea ice drive recent reversal in Antarctic ice mass loss. Communications Earth & Environment, 7, 255, https://doi.org/10.1038/s43247-026-03242-3.
Maclennan, M. L., J. T. M. Lenaerts, C. Shields, and J. D. Wille, 2022: Contribution of Atmospheric Rivers to Antarctic Precipitation. Geophysical Research Letters, 49, e2022GL100585, https://doi.org/10.1029/2022GL100585.
Massom, R. A., T. A. Scambos, L. G. Bennetts, P. Reid, V. A. Squire, and S. E. Stammerjohn, 2018: Antarctic ice shelf disintegration triggered by sea ice loss and ocean swell. Nature, 558, 383–389, https://doi.org/10.1038/s41586-018-0212-1.
Roland, T. P., and Coauthors, 2024: Sustained greening of the Antarctic Peninsula observed from satellites. Nature Geoscience, 17, 1121–1126, https://doi.org/10.1038/s41561-024-01564-5.
Scambos, T. A., C. Hulbe, M. Fahnestock, and J. Bohlander, 2000: The link between climate warming and break-up of ice shelves in the Antarctic Peninsula. J. Glaciol., 46, 516–530, https://doi.org/10.3189/172756500781833043.
Turner, J., and Coauthors, 2019: The Dominant Role of Extreme Precipitation Events in Antarctic Snowfall Variability. Geophysical Research Letters, https://doi.org/10.1029/2018GL081517.
Turton, J. V., A. Kirchgaessner, A. N. Ross, J. C. King, and P. Kuipers Munneke, 2020: The influence of föhn winds on annual and seasonal surface melt on the Larsen C Ice Shelf, Antarctica. The Cryosphere Discussions, 2020, 1–25, https://doi.org/10.5194/tc-2020-72.
Wang, W., Y. Shen, Q. Chen, F. Wang, and Y. Yu, 2025: Spatiotemporal mass change rate analysis from 2002 to 2023 over the Antarctic Ice Sheet and four glacier basins in Wilkes-Queen Mary Land. Science China Earth Sciences, 68, 1086–1099, https://doi.org/10.1007/s11430-024-1517-1.
Wang, Y., Q. Wu, X. Zhang, and Z. Zhai, 2023: Record-breaking Antarctic snowfall in 2022 delays global sea level rise. Science Bulletin, https://doi.org/10.1016/j.scib.2023.08.055.
van Wessem, J. M., M. R. van den Broeke, B. Wouters, and S. Lhermitte, 2023: Variable temperature thresholds of melt pond formation on Antarctic ice shelves. Nature Climate Change, 13, 161–166, https://doi.org/10.1038/s41558-022-01577-1.
Wille, J. D., and Coauthors, 2021: Antarctic Atmospheric River Climatology and Precipitation Impacts. Journal of Geophysical Research: Atmospheres, 126, e2020JD033788, https://doi.org/https://doi.org/10.1029/2020JD033788.
——, and Coauthors, 2022: Intense atmospheric rivers can weaken ice shelf stability at the Antarctic Peninsula. Communications Earth & Environment, 3, 90, https://doi.org/10.1038/s43247-022-00422-9.
Zhu, Y., and R. E. Newell, 1998: A Proposed Algorithm for Moisture Fluxes from Atmospheric Rivers. Monthly Weather Review, 126, 725–735, https://doi.org/10.1175/1520-0493(1998)126%3C0725:APAFMF%3E2.0.CO;2.
Citation: https://doi.org/10.5194/egusphere-2026-2270-RC2
RC3:
'Comment on egusphere-2026-2270', Anonymous Referee #3, 17 Jul 2026
This study constructs a meteorological extreme event database covering eight major Antarctic ice shelves spanning 1995-2023 using four regional climate models, focusing on four core variables: surface air temperature, precipitation, surface pressure and wind speed. The authors validate model outputs against in-situ station observations and diagnose the driving mechanisms of extreme events via key climate modes including the SAM, ENSO and regional sea ice variability. Multiple well-documented ice shelf collapse and extreme heatwave cases are selected for targeted case analysis, rendering the topic highly relevant to the cutting-edge research on Antarctic ice shelf climate change.
Overall, the manuscript presents an interesting effort to build a database of Antarctic meteorological extremes over key ice shelves using a multi-model ensemble of Regional Climate Models (RCMs). While the objective is highly relevant to understanding ice shelf stability, there are fundamental methodological flaws regarding the statistical definitions and data gap-filling. These issues must be addressed to ensure the scientific validity and readability of the study. The manuscript reads more like a database documentation with preliminary descriptive statistics, while rigorous assessments of methodological robustness, inter-model consistency, and validation of detected extreme events remain insufficient. I recommend Major Revisions based on the following critical points.
Major Comments
The manuscript defines extreme events on its own (e.g., specific percentile thresholds within a 1-day or 3-day window) (L207-L212). However, most existing studies on extreme events have adopted definitions established by the Expert Team on Climate Change Detection and Indices (ETCCDI). Please explain why this study did not adopt or adjust the existing extreme index definitions, particularly regarding the selection of the base period for percentiles.

The methodology described for estimating missing model data during the periods 1995–2000 and 2020–2023 is inappropriate for an analysis of climate extremes (L213-L224). The authors state that they developed linear regressions ($y = ax + b$) to estimate the four-model mean using the available subset of RCMs to retain consistency with the 2001–2019 daily percentiles. However, linear regression is fundamentally designed to capture the central tendency and inherently minimizes variance. Extreme weather events, such as the 95th or 98th percentiles, exist at the absolute tails of the distribution and are characterized by high variance and non-linearity.

By applying linear regression to gap-fill daily extreme values, the methodology artificially suppresses this critical variance. Consequently, true extreme peaks will be mathematically flattened, inevitably leading to false negatives where severe events fail to breach the established percentile thresholds. Furthermore, extrapolating extreme values using a simple linear fit can produce physically unrealistic results.
The manuscript documents a prominent warm summer bias in HCLIM; at the Amery Ice Shelf in East Antarctica, the 98th percentile temperature simulated by HCLIM exceeds outputs from other models by more than 8 °C. The multi-model ensemble mean is simply calculated via unweighted arithmetic averaging without any weighting scheme to offset systematic model biases, which raises critical concerns regarding the reliability of derived extreme event statistics.

Daily climatological percentiles are computed based on the 2001-2019 reference period using the four-model ensemble mean. The reference period of only 19 years is relatively short, which may introduce substantial biases to percentile thresholds and subsequent extreme identification. In addition, the full study period (1995-2023) spans merely 29 years, an insufficient time length for robust long-term trend detection of extreme events. The discussion of trend results must be more cautious and well-qualified.

The abstract is overly general and fails to highlight core quantitative findings. A full rewrite of the abstract is required to incorporate key specific results from the analysis.

At least two to three well-documented historical Antarctic extreme events should be comprehensively examined to demonstrate that the proposed database can reliably capture observed extreme meteorological episodes.

The results section overemphasizes descriptive documentation of the database rather than highlighting novel scientific insights. The authors shall restructure this section to prioritize interpretative scientific discoveries.

Minor Comments
The introduction should more explicitly articulate the novelty and research gaps addressed by this work.

Subplots in Figure 2 adopt inconsistent axis limits, which hinders cross-panel comparison. The zero-reference lines (x=0, y=0) should be uniformly marked across all subplots for consistency.

Tick labels on the y-axis of Figure 3 are illegible. The vertical axis interval should be widened, and font sizes enlarged for readability.

Line traces in Figure 4 are indistinct, and axis labels use overly small fonts; graphical revisions are necessary to improve clarity.

The line charts used to represent the annual and seasonal counts of extreme events (Figures 5 and 6) are not an effective visualization choice. Figures 5, 6, 8, 10 and 11 contain identical legends across subplots. A single shared legend per figure is sufficient, and all axis text fonts should be enlarged.

Latitude and longitude annotations in Figures 7 and 9 are too small to distinguish and require resizing.

Statistical p-values should be italicized (e.g., p < 0.01) following standard journal formatting conventions.

The current correlation tables only present tabular values without visual spatial patterns of correlation coefficients. Supplementary spatial maps illustrating the geographical distribution of these correlations are recommended.

Manuscript formatting errors require correction: Line 113 contains a typo “Southern Annual Mode”, which should be revised to “Southern Annular Mode”.

A whitespace should be inserted between numerical values and their corresponding units throughout the text.

Standardize unit formatting uniformly across the full text, including mm day⁻¹ and m s⁻¹. (or mm/day and m/s)

Reference formatting is inconsistent; all citations should be unified following the journal’s official style guide.
Citation: https://doi.org/10.5194/egusphere-2026-2270-RC3

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Short summary

The warming trend in global temperatures has potential to result in accelerated break up of the Antarctic ice shelves, which would contribute to rising sea levels. Here we present a novel database of Antarctic extreme weather events over a selection of the Antarctic ice shelves. We examine air temperature, precipitation, wind and surface pressure. In addition, we examine trends in the frequency of extreme events and the links with weather patterns around Antarctica.


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