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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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.
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.
This study examines how Antarctic coastal polynyas (areas of thin ice or open water) in the Ross...
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:
Specific comments
Technical corrections