the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 01 Jun 2026
A novel database of Antarctic meteorological extremes over key ice shelves during 1995–2023
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.
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.
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.- Preprint
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Status: open (until 30 Jul 2026)
- RC1: 'Comment on egusphere-2026-2270', Anonymous Referee #1, 29 Jun 2026 reply
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- 1
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.