Review for „Imprints of Sea Ice, Wind Patterns, and Atmospheric Systems on Summer Water Isotope Signatures at Hercules Névé, East Antarctica “ by Kim et al. submitted to The Cryosphere

This relatively short, well-written study presents stable water isotope data from the top 26 m of the 80 m ice core drilled approximately 80 km from the coast at Hercules Névé, Victoria Land, East Antarctica. The study analyses the stable water isotope record of the top 15m of this ice core, which spans the period from 1979 to 2015, and investigates potential climatic drivers for the observed isotope variability by performing (spatial) correlation analysis between the stable water isotope record and ERA5 reanalysis data. The study further examines the imprint of two atmospheric circulation patterns on the isotope record, specifically the Amundsen Sea Low (ASL) and Zonal Wave-3 (ZW3) and finds significant correlations between the austral summer isotope signal and the indices. The study concludes with the rather generic assessment that climate variables such as temperature, precipitation, wind patterns and sea ice extent influence the water isotope variations in the ice core.

Although the manuscript is mostly clearly written and the presented data is a valuable addition to the growing number of Antarctic ice cores and, as such, suitable for publication in The Cryosphere, and of interest to The Cryosphere reader community, the findings are rather descriptive, and the authors do not fully utilise the potential of the presented dataset. A couple of interesting research questions are raised in the text, yet they are not investigated in depth and the presented content does not advance beyond spatial correlation analysis and generic statements about the (complex and intertwined) influence of climate variables on the isotope record variability. The manuscript would benefit from a clear research question or hypothesis that is being tested, which would give the study a better structure and increase the reader's interest. I suggest the authors consider restructuring the manuscript around a research question such as whether the Hercules Névé stable water isotope record can be used to learn about sea ice/polynya activity in the past (Section 4.1) or which climatic variables dominate the stable water isotope record variability on different timescales (is this ice core site suitable to study seasonal climate variability (L. 47-51, L.110)?).

General comments:

Please consider including (some) of the following analysis to support your statements and make more nuanced and less generic statements:

• Trend analysis: Although the time period investigated is short on climate time scales, consider including a trend analysis, potentially also including the full dataset going back to 1949, and compare to potential trends in ERA5 reanalysis.
• Principal component analysis: As is stated in L. 316 correlation is not equal to causality, yet only correlation analyses are presented. Consider extending the analysis of identifying drivers of the isotope signal by investigating principal component analysis to be able to make statements about the importance of drivers for specific timescales of climate variability. (Dixon et al., 2012; Noone, 2004)
• Spectral analysis of the ice core timeseries (how much of the variability is noise and how much is signal): see the extensive literature by Thom Laepple’s group on complications of extracting (sub)-annual climate information from a single ice core, specifically in low accumulation sites such as the Antarctic Ice Sheet (Casado et al., 2020; Laepple et al., 2018; Münch et al., 2016; Münch and Laepple, 2018). The Hercules Névé site is coastal and as such has a higher annual layer thickness, yet the signal-to-noise ratio is worth investigating before correlation analysis is performed especially if sub-annual data is analysed.
• Back trajectory analysis: To support the spatial correlation maps and claims about moisture origin influences, it would be beneficial to show some back trajectory analysis using HYSPLIT or similar. See e.g: (Dixon et al., 2012; Neff and Bertler, 2015; Thomas and Bracegirdle, 2009)
• Sensitivity analysis of the age model: Do you have access to the impurity dataset of the ice core to support the age model (L. 173)? Relying solely on the water isotope signal itself for layer counting and then correlating the water isotope data with climate variable timeseries bears the potential for a circular argument. Further, it is unclear how the sub-annual age model was developed (see specific comments).
• Wind analysis: The manuscript investigates the influence of wind variability with regards to katabatic winds, ASL and ZW3 (L. 75 and many more). It would be beneficial to see an analysis of the wind conditions of the Hercules Névé site, both from the local AWS and the reanalysis product to better understand how important and variable the wind is for the site. This is also important to understand the importance of wind on the stratigraphy of the ice core. Please include a wind rose in Fig. 1 and extend the AWS analysis (Section 2.2.2) with findings from the wind data (extend Fig S1).
• Section 4.1: The fact that the ice core isotope records (d18O and d-excess) seem to be strongly influenced by polynya activity is very exciting and should be the focus of the study, in my opinion. It would require more supporting literature, sea ice concentration maps, and back-trajectory analysis, but this could serve as a very interesting central research question. In any case, the manuscript would benefit from a map where sea ice extent in summer and winter (and polynya locations) is shown.

Specific comments:

• Age model (L. 186): - Please specify how the age-depth tie points were chosen. Also specify how the age was determined between age-depth tie points (interpolation? Weighted?). Since it is identified that accumulation is biased to the austral summer months (L.232, L.104), how can you justify linear interpolation between tie points? And following from this, how robust are your correlation results concerning the chosen tie points? (Would it change if only min, only max or min and max values were used as tie points?) see, e.g. (Gautier et al., 2016; Parrenin et al., 2024; Thomas et al., 2024)
  • Also, delete L. 185 “To achieve precise age dating, we utilised ERA5 isotope data from the polar regions over the 1979–2015 period"which is unclear. You used ERA5 isotope data?
    
    
    
    
• Isotope variability and correlation (Section 3.2, 4.1, 4.2): It is often unclear what resolution was the basis for the correlation analyses between reanalysis and isotope record. Was the isotope maximum used as DFJ value, or annual means, or 3-month means (how were the 3 month period defined in this case) please specify in the relevant sections (3.2, 4.1, 4.2).

• Spatial correlation maps (Fig 3): It is unclear to me why the authors perform a spatial correlation analysis with one ice core and all of the AIS region when you are specifically looking for large-scale meteorological patterns with the indices later. Usually, stacks of several ice cores are used for such analysis to reduce the noise terms and access only the common spatially representative signal (Casado et al., 2023). Without a signal-to-noise ratio analysis first, this spatial correlation analysis seems excessive and not insightful and I would recommend zooming into the region where you expect influential regions. Also, in the discussion section, the authors do not discuss spatial patterns of the correlation results, but rather focus on the strength of the correlation at the site.

• Climate indices and isotope record (Section 4.2 and 4.3): please consider plotting a timeline together with the indices that you are investigating, so the reader can better understand the conclusions you draw and elaborate on which timescales these indices influence the isotope records. See for example: (Servettaz et al., 2020a)

• According to Casado et al., (2018) others, it is not only the precipitation input that builds the water isotope record, but post-depositional processes (such as sublimation, stratigraphic noise, metamorphism, diffusion, etc) can also affect the isotope record variability. Sublimation is considered a potential driver that affects the stable water isotopic composition. However, it is influential primarily in summer(Dietrich et al., 2023; Ollivier et al., 2024; Wahl et al., 2022). In lines 231-235, however, the authors make a claim that post-depositional processes are “relatively reduced in summer” without further elaborating on their claim. Please elaborate on whether and how post-depositional processes might influence the climate signal at Hercules Névé, and on what timescales or for what reasons it might be ok to disregard post-depositional effects in this study.

• L. 12: The abstract states that the aim of the study was to evaluate the influence of climate variables on the variability of the ice core isotopes, yet the manuscript does not identify drivers for different timescales. Please rephrase or restructure.

• L. 45: Based on the annual layer counting, could you include information on annual layer thicknesses and accumulation variability that link with the climate information you analyse as well? This would be supported by the fact that accumulation seems to be much better correlated with isotopes compared to temperature as you state in Section 3.2.

• L. 75: Are katabatic winds only a summer phenomenon? The paragraph focuses on the austral summer months, yet sea ice expansion and katabatic winds are mentioned here which is confusing. Please clarify if these are general statements or summer-specific, and otherwise restructure.

• L. 81: Here and elsewhere: It is unclear what part of the isotope record is chosen to represent the “DFJ” austral summer months. Only the maximum? A three-months window? How are the monthly isotope values age-referenced?

• L. 87: Here and elsewhere the authors refer to “seasonal” climate and isotope variability, yet I think they only want to refer to summer information. Using the word “seasonal” makes the reader expect information on other seasons as well, possibly even the identification of seasonal variability but this is not given in the manuscript. Please add a definition or rephrase.

• Section 2.2.1: Please specify the CRDS sampling analysis protocol or cite a study that gives details on the protocol that was used.

• L. 150: If the AWS measured for 1 year why are the authors only evaluating the temperature record. Please include at least wind analysis (see comment above) as this is important for the presented discussion.

• L. 154: The authors claim that ERA5 “effectively” captures temperature and wind yet this statement is not supported by any statistics. Please add so that the reader can understand this statement.

• L. 153, FigS1: From the analysis (Fig: S1) it actually looks like ERA5 overestimates warm temperatures generally but specifically in the summer months, yet this is the period you are focusing on. How does that influence your analysis using ERA5. Please discuss.

• L. 202 and Section 4.2: It is unclear how you define and test for the ASL strength. Which definition for the ASL are you using? Please give a definition and a brief introduction to the interplay between ASL and Southern Annular Mode (SAM) which is generally known to influence the Antarctic climate. (Servettaz et al., 2020b)

• L. 235: This statement seems misleading. Over 50% of the accumulation falls outside the summer months, which, as a priori assumption, will greatly influence the isotopic composition on an annual resolution. You could elaborate on this by presenting annual vs summer statistics. Please give details and rephrase.

• L. 248: Why are you expecting reduced post-depositional processes in warmer conditions? Both diffusion and sublimation influences are stronger in warmer conditions. See (Johnsen et al., 2000; Ollivier et al., 2024)

• L. 251: Calling a correlation of r=0.32 robust is a stretch. Please rephrase.

• L. 255: Or atmospheric rivers. Please discuss. (Wille et al., 2025)

• Correlation with temperature: From your analysis, it seems that the parameter to test is the precipitation-weighted temperature correlation, which is expected to perform better than temperature alone, especially in ice cores with seasonally biased accumulation. Please see (Persson et al., 2011) and include in analysis.

• L. 342: Please be more specific in your interpretations. The d18O variability is influenced by ZW3 and ASL on what timescales and what are you basing this on? How does SAM influence the indices you are investigating and could this be a common large atmospheric mechanism (L. 345).

• L. 382: Please delete the last sentence as this manuscript is not actually using stable water isotopes to draw conclusions about the past climate state, but in reverse, climate variables are used to try to explain the isotope record. The conclusions drawn from the Hercules Névé ice core at this point are not clear enough to interpret the stable water isotope record unambiguously back in time.

Technical corrections:

• L. 57: This is formulated in a confusing way. The reason for isotopic depletion during expanded sea ice conditions is the increased distillation pathway, not the colder regions (northward expanse would result in more evaporation in warmer regions, except if the authors wanted to highlight increased moisture recycling but this would need to be stated specifically here). Please see (Noone, 2004) and rephrase.
• L. 65: Please add information on what timescales the ZW3 index is “critical”
• L. 67: Check citation brackets
• L. 75: ice sheet not ice sheets
• L. 105-107: The reason for intensive sublimation during katabatic winds is that these air masses are predominantly very dry and can thus take up a lot of water but their influence on the local climate and temperature is not always one-directional. See e.g. (Davrinche et al., 2024; Vihma et al., 2011). Please rephrase or cite relevant literature.
• L. 116: When was the ice core drilled?
• L. 118: This sounds like not all parts of the ice core were frozen? Please rephrase
• L. 130: How long between cutting, melting and subsequent analysis? Add time.
• L. 138: Are these the manufacturer’s numbers for accuracy? Please add a value that is representative for the measurement protocol and the laboratory at which the samples were measured? Usually this is done by referencing a control lab standard. See (Sodemann et al., 2023)
• L. 156: Are you using new “data” to calculate the ZW3 index? If I understood correctly you are using the newly developed index by Goyal 2023 et al.. Which data is the basis for the ZW3 index calculations you are using? Please specify.
• L. 173: Do you have access to “chemical variations” for the age model?
• L. 185: The shaded area is not green in my pdf version.
• L. 198: Please include the results of the “multiple linear regression” for the ZW3 index in the results section.
• L. 201-203: These lines should go in the introduction.
• L. 206ff: Since the averaging periods are not all the same this comparison is not very insightful. I suggest to keep them in the supplementary Table S1 and possibly compare only the overlapping periods in the main text.
• L. 215-220: Are lines 219 and 220 a repetition of 215 and 216? If not please rephrase so it’s less confusing.
• L. 223: include one decimal place in the slope values
• L. 253: delete double “that”
• L. 278: This is formulated in a confusing way. As sea-ice concentration increases, the moisture sources are shifted northward and evaporation from local oceanic sites is limited. Please rephrase
• Fig. 1: As the bathymetry is not important for this study it might be more useful to show elevation starting from sea level. Please also add wind rose with AWS data to this figure.
• Fig. 2: The shaded are is not green in my pdf but grey. Consider adding the indices to this plot or make a new plot including temperature (and precipitation) and indices.

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The paper by Kim et al. investigates the summer (DJF) isotopic characteristics of an ice core from Hercules Névé, East Antarctica, using δ¹⁸O, δ²H, and deuterium excess (d-excess) measurements. The authors explore the relationships between these isotopes and large-scale atmospheric and oceanic drivers—including the Amundsen Sea Low (ASL), Zonal Wave-3 (ZW3), sea ice concentration (SIC), sea surface temperature (SST), and ERA5 reanalysis fields (e.g., wind and temperature)—over the period 1979–2015. The study finds that higher temperatures and precipitation during summer are associated with isotopic enrichment, while d-excess shows a positive correlation with SIC and a negative correlation with SST. The authors interpret these relationships in terms of regional moisture source variability and synoptic-scale atmospheric transport.

This study uses relevant literature and includes appropriate data sets. Some areas of improvements are suggested below. Major revisions are recommended.

Major revisions:

1. This study would benefit from expanding beyond summer to include annual and seasonal composites, especially if the goal is to understand long-range transport.

2. The study dismisses post-depositional processes without sufficient literature support. This may be true, but better justification is necessary. 

3. Language describing the 'cause and effect' between large scale patterns with each other, the meteorological fields and the ice core d18O/dxs records are often overstated. Most of these are at best associations, or the influences require better evidence-based justifications.

4. A more rigorous treatment of atmospheric dynamics (e.g., via 500 hPa height fields and moisture source diagnostics), incorporation of modern statistical tools (e.g., York regression, multi-linear feature analysis), and a clearer, more defensible linkage between the isotopic signal and proposed synoptic drivers are recommended.

Minor revisions and expansion on these major recommendations are below.

Line 57 - expanded sea ice does not shift moisture sources to colder regions necessarily, but rather lower latitudes. Expanded sea ice induces more along path depletion.

ZW3 - throughout this paper ZW3 is discussed as 'causing' or 'affecting' things. However, it is statistical representation of things such as the ASL, which itself is result of sea level pressure averaging. This language should be scaled back.

lines 74-75 - katabatic winds happen all year long, but more in the winter.

The last paragraph starting at line 71 has an unsatisfying defense for only studying DJF. It is possible to assess the impact of summer months. There must be better reasons for looking at only summer months. Also the latter half of February is sometimes consider autumn in parts of the Antarctic. In general, it seems as if this whole study could be expanded to the annual cycle, with seasonal composites. The seasons should be well-defined either by temperature and/or isotopes (d18O).

Overall, the readers need more environmental context for this ice core location beyond AWS temperatures for one year. E.g., multi-year accumulation rates, temperatures, wind speeds and directions from ERA5 provided the context in S1.

lines 113-114. The link between high accumulation and drilling season is not clearly established'drilling during austral-summer season' does not track with high accumulation rate. The region is one of complexity wrt to when and where accumulation comes from. The high accumulation rate makes this an ideal location to do seasonal and possibly subseasonal isotope studies. Please provide numbers of previous results here on accumulation rate - this will make the 5 cm section number on line 120 more immediately meaningful.

From the information around line 185 it seems like 5 cm of snow/ice represents about ~1/8 year, or a little more than one month (not accounting for compression properly here). Do I understand the data correctly? It would be good to put this in context for the reader.

line 135. This may be relatively 'warm' polar accumulation, but it is best to use the logarithmic definition of dexc.

Figure S1 could also include local winds (wind rose) from observations and ERA5, and some histograms of the values. S1A is not that meaningful without either a residual subplot or some mean values. The scatterplot of S1B helps here, but is hides information about when and how the differences occur over this year. It also that the authors used an OLS regression for S1B. They should consider using a York regression that will minimize errors in both variables. Otherwise, the slope will be too shallow. This is still a common oversight in observational statistics.

Trappitsch, R., Boehnke, P., Stephan, T., Telus, M., Savina, M. R., Pardo, O., Davis, A. M., Dauphas, N., Pellin, M. J., and Huss, G. R.: New Constraints on the Abundance of 60Fe in the Early Solar System, Astrophys. J., 857, L15, https://doi.org/10.3847/2041-8213/aabba9, 2018.

line 232 - Why would post-depositional processes be reduced in the summer? Town et al. (2008) show that warmer temperatures would increase post-depositional processes. Is there a trade-off on higher summertime accumulation rate? This puts more importance on showing the seasonal cycle of the accumulation rate for this site to make your point here. In any case, some reference and better reasoning is necessary to back up this claim here. There are some Antarctic references available in this regard.

Town, M. S., Waddington, E. D., Walden, V. P., and Warren, S. G.: Temperatures, heating rates and vapour pressures in near-surface snow at the South Pole, J. Glaciol., 54, 487–498, https://doi.org/10.3189/002214308785837075, 2008

Casado, M., Landais, A., Picard, G., Arnaud, L., Dreossi, G., Stenni, B., and Prié, F.: Water Isotopic Signature of Surface Snow Metamorphism in Antarctica, Geophys. Res. Lett., 48, e2021GL093382, https://doi.org/10.1029/2021GL093382, 2021.

Section 3.2

What is the seasonal cycle of surface pressure in this region according to ERA5?

line 240-242 - What analysis package is used here in section 3.2 (figures 3/4)? Is this EOF/PCA? How are summer and winter defined in the ice core(s) and indexed to any meteorology time series here (this is a tricky process, especially in the presence of post-dep processes which may or may not be a factor. A broad literature base exists for this problem alone.)

Section 4.1. The dxs results should be presented in the results section first. Their implications go in the discussion.

dxs can change after deposition, even if d18O does not (Town et al. 2024; https://doi.org/10.5194/tc-18-3653-2024)

line 280 - this claim about polynyas-derived (I would rather say polynya-influenced) air masses dominating the regional isotope signature is not supported by evidence. It is a fine hypothesis to pursue, but requires either evidence from this paper or direct references. It seems that some of this discussion about dxs maybe should be in the background? In any case, this work ignores the growing literature base on antarctic atmospheric rivers (of low-latitude origin), which I think is a dominate influence on isotopic content of moisture laden air coming to the Antarctic. Most Antarctic-bound atmospheric rivers do not become so as a result of polynyas - although I do not disagree that polynyas are a strong local source of water vapor to passing air masses.

In this range of temperatures, Pfahl and Sodemann do not make any strong claim about the relationship between dxs and SST. (is there some more background research besides the old classics that can be provided here?).

In my opinion the text does not provide enough context or assistance in interpreting Figures 3 and 4 with respect to the claims made. Showing the 'significant' correlation spatial patterns across the Antarctic region undercuts any potential significance in valid correlations close to the ice core site in the Ross Sea region.

Figure 5. This is an intriguing plot. Did the authors also look at 500 mb heights in addition to MSLP? That may provide a better indication of synoptic activity than MSLP. What about a precipitation or moisture-weight temperature feidl for figure 5b? This may result in a field that is more directly related to d18O.

In a similar way, the authors may consider a 'figure 6' that curates similar fields for dxs but uses maybe a combination of SST, RH, and wind speed for the new figure 6b.

Related to the concept of curating feature variables for spatial correlation analysis for Figure 5 (and a possible Figure 6), the authors may consider employing some of the more modern tool boxes for multiple linear regressions in this analysis. They may find (without giving too many variables or variable combinations chances) some efficient success in explaining spatial variance in several fields relevant to d18O (e.g., T, moisture content of air, ), dxs (local winds, RH, SST, sea ice concentration), and spatial pressure fields that represent synoptic activity.

line 337-338. ZW3 does not interact with the ASL. Part of ZW3 is a spatially broader climatological representation of the fact that there is a climatological low in the Amundsen Sea region.