Distinct atmospheric drivers of Ross Sea coastal polynya variability during winter

Burada, Girija Kalyani; Renwick, James A.; McDonald, Adrian J.

doi:10.5194/egusphere-2025-5369

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https://doi.org/10.5194/egusphere-2025-5369

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21 Jan 2026

| 21 Jan 2026

Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Distinct atmospheric drivers of Ross Sea coastal polynya variability during winter

Girija Kalyani Burada, James A. Renwick, and Adrian J. McDonald

Abstract. Coastal polynyas in the Ross Sea have well-documented links to atmospheric circulation, but the role of specific circulation patterns in driving extreme wind events and their differential impact on individual polynyas remains poorly explored. This study examines peak-winter (Aug–Oct) variability in the Ross Sea, Terra Nova Bay, and McMurdo Sound polynyas using EOF analysis of high-resolution passive microwave sea-ice concentration data. Patterns of variability are related to surface extreme winds and 500 hPa geopotential height anomalies from ERA5, allowing concurrent assessment of local forcing and hemispheric-scale circulation connections.

Results reveal that each polynya responds differently to shifts in large-scale atmospheric features. Variations in the position and intensity of the Amundsen Sea Low, and its influence on Ross Ice Shelf Air Stream winds, are associated with marked changes in polynya area. By combining high-resolution sea ice concentration records with targeted extreme-wind analysis, this work identifies previously unresolved, location-specific atmospheric controls on Ross Sea polynya variability.

Received: 30 Oct 2025 – Discussion started: 21 Jan 2026

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Girija Kalyani Burada, James A. Renwick, and Adrian J. McDonald

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RC1:
'Comment on egusphere-2025-5369', Anonymous Referee #1, 26 Feb 2026 reply
General comments
This paper examines the relationship between satellite-observed sea ice and large-scale wind patterns using an EOF analysis and ERA5. The paper is well placed within the broader field and the discussion of the role of short-term variability in the context of longer-term changes is sound. The insight that different polynyas respond differently to similar large-scale anomalies is valuable. The methodology is useful for understanding distinct modes of SIC variability in polynya regions and their association with large-scale wind patterns. I feel that quite a bit more work is needed to physically interpret the results robustly, i.e. link EOFs and MCA back to physical processes. See below:
Without e.g. composites (ideally lagged composites) of absolute conditions occurring during the high/low phases of different EOF modes it’s quite difficult to reach a physical interpretation. E.g. northerly wind anomalies in the Ross could be associated with either actual northerlies (potentially driving sea ice compaction) or weak southerlies (reduced divergence). For example, in the paper (e.g. L201) there is description of physical features such as jets in Figure 3Ad, but the Figure shows the association between winds and SIC, rather than physical jets. Similarly, it’s difficult to understand the interpretation of low-sea-ice conditions in mode 2 if thermodynamic factors play a role as no thermodynamic fields are shown.

Based on the methodology I would caution against the use of ‘extreme wind events’ in this paper. Papers describing extreme wind events usually focus on e.g. specific percentiles or thresholds. Here, the focus is on the daily maximum wind speed which may not be extreme. The name ‘ExWinds’ should be updated accordingly (maybe ‘MaxWinds’?).

Related to the above, what’s the justification for using daily maximum wind speeds (potentially more noisy) compared to daily mean?

Several figure improvements are needed:
All Figures’ DPI is quite low.

The BWCJ mechanism is central to the Mode 3 interpretation. Can you provide a suitable composite or at least local wind vectors as in Figure 3B over the BWCJ region?

Figure 3B does not have panel labels or a descriptive caption (are the wind vectors MCA spatial patterns?). Figure 4 caption should refer to panel labels.

It’s not clear whether Figure 3Ad,e,f is or should be different from the right panels of Figure 3B. Figure 3B Modes 1 and 2 look similar to Figure 3Ad,e but Mode 3 looks completely different to Figure 3Af?

Specific comments
L113 Square root of the cosine of latitude?

L191 ‘PCs associated 3’?

L212: No 3B(e) is marked on the figure. Also ‘Northwest-southeast pattern’ please clarify using e.g. northwesterly, southeasterly

L219: Stammerjohn et al. (2008) in the references gives a DOI: https://doi.org/10.1016/j.pocean.2008.02.002 which leads to a paper called “Statistics from Lagrangian observations”. I cannot find any paper entitled “Sea ice in the Southern Ocean: The Weddell Sea and Ross Sea”.

L210 This paragraph needs supporting evidence to interpret the Mode 2 pattern. I think it’s important to help readers understand how weak winds conditions can be associated with low sea ice states (it’s intuitive to understand how strong winds can be associated with low sea ice). I feel that composites of physical quantities are needed. Is it thermodynamic factors (e.g. increased shortwave absorption, low cloud cover) as you propose, or perhaps memory from previous high-wind states?

It may be worth looking at cutting down on acronyms a bit as there are many to keep track of – RS for Ross Sea is probably not needed. Similarly no need to define AP as an acronym.

L228 Barrier wind corner jet mechanism: it’s hard to discern this from the wind vectors (which are presumably anomalies anyway so not clear how the real wind field looks).

L248-249 Not sure what is meant by this localized analysis? Do you mean that the BWCJ doesn’t appear when you look at local wind vectors?

Figure 4: please explain what the hatching/stippling denotes. Also, because the regressions are computed from daily ASO data concatenated across years, the underlying time series are serially correlated and the nominal sample size will overstate the effective degrees of freedom. Please clarify how the p-values were calculated (e.g., using an effective sample size adjustment). If no autocorrelation treatment was applied, please revisit the significance masking accordingly.

L268–271: ‘weak ExWin anomalies’ indicates winds close to the seasonal mean, not necessarily weak absolute winds. I suggest avoiding interpreting near-zero anomalies as ‘subdued in speed’ unless you show composites of absolute ExWin. Unless you mean negative anomalies?

L283: Perhaps avoid using Cape Colbeck as it’s not a widely known geographic marker.

L287: The figures are not really sufficient to interpret the BWCJ

L300 This paragraph’s wording (‘dominated by a small set’) conflicts with the earlier acknowledgement that EOF1-3 leave 63% of SIC variance unexplained. I may have misunderstood so please qualify what is meant by ‘dominated’ or soften the claim.

L304: please explain somewhere the use of ‘accumulation’ in this paper and how you observe it?

I suggest moving discussion of limitations to the Discussion section and keeping the conclusion concise.

References section: I see many references listed which don’t appear in the text. I also see e.g. ‘n.d.’ for dated papers and typos e.g. “Academic Press.sss” and the Turner et al. citation.

Technical corrections
L29 double comma

L198/L199 ExWin? Or ExWinds? See other references to it as well.

L208 WinVec?

L246 periods

L311 erroneous full stop

Consistency of eg. Localised, localized…

L635 typo

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Citation: https://doi.org/10.5194/egusphere-2025-5369-RC1
CC1:
'Comment on egusphere-2025-5369', Lavkush Patel, 11 Mar 2026 reply
General Assessment:

The manuscript is well-organized, clearly written, and scientifically sound. The methodology is rigorous, combining long-term statistical analyses with process-aware interpretation. The use of high-resolution SIC data is particularly valuable, as it resolves frazil and young-floe bands critical for polynya dynamics. The study contributes significantly to understanding atmosphere–sea ice interactions in the Ross Sea, providing a valuable multi-decadal context.
Major Comments:

Scientific Contribution:

The manuscript effectively bridges the temporal-resolution divide in polynya studies, combining multi-decadal datasets with event-scale extreme wind analyses.

Linking upper-level circulation (H500 anomalies) to local extreme winds and SIC variability is a notable strength, allowing attribution of polynya responses to specific atmospheric regimes.

The EOF and MCA analyses are appropriate, and the interpretation is consistent with previous process studies.

Methodology:

Data preprocessing, anomaly calculation, and standardization are described clearly. Area-weighting by √cosφ is appropriate for high-latitude grids.

Use of daily maximum wind (ExWinds) is justified, but discussion of sensitivity to hourly vs. daily maxima could enhance robustness.

MCA is correctly applied; the squared covariance fraction (SCF) provides clear information about coupled variability.

Validation against Sentinel-1 SAR and ARTIST datasets is briefly mentioned. Including representative examples or quantitative metrics in supplementary materials would strengthen confidence in SIC retrievals.

Results:

The identification of three dominant EOF modes and corresponding MCA modes is convincing. EOF1 captures coherent variability across all polynyas, while EOF2 and EOF3 reveal dipole and east–west patterns.

The study successfully links MCA modes to physical processes: Mode 1 (RAS-driven offshore advection), Mode 2 (weak/anticyclonic winds affecting ice redistribution), and Mode 3 (BWCJ-induced lateral transport).

Temporal trends, particularly post-2016 SIC reduction in EOF1, are clearly documented and plausibly connected to large-scale Antarctic-wide sea-ice decline.

Discussion and Context:

The manuscript contextualizes findings with previous work on ASL modulation, katabatic flows, and oceanic preconditioning.

The discussion of dynamic vs. thermodynamic contributions is thoughtful and consistent with targeted field observations.

Limitations are acknowledged, particularly regarding temporal gaps in high-resolution datasets and event-scale variability.

Minor Comments / Suggestions:

Ensure all acronyms (ExWinds, RAS, BWCJ) are clearly defined on first use in the main text.

Figures: It would be helpful to provide a small schematic summarizing the circulation regimes associated with each MCA mode.

Consider discussing the potential implications for coupled climate model evaluation or future predictions of Ross Sea polynya activity.

Check minor typographical errors (e.g., “∼ 1500 W” likely should be “∼150° W”).

References to figures in supplementary materials (e.g., Figure S1, S2, S3) should explicitly note whether they are available online or included in the submission package.

Reply
Citation: https://doi.org/10.5194/egusphere-2025-5369-CC1

Girija Kalyani Burada, James A. Renwick, and Adrian J. McDonald

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Girija Kalyani Burada, James A. Renwick, and Adrian J. McDonald

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Short summary

This study examines how Antarctic coastal polynyas (areas of thin ice or open water) in the Ross Sea vary during peak sea-ice months. Satellite data reveal the main spatial patterns in sea-ice changes and their link to strong surface winds and large-scale circulation. Variations in the Amundsen Sea Low and the Ross Ice Shelf Air Stream influence polynya size, showing how large-scale weather patterns control local sea-ice processes in the Ross Sea region.


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