the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2025
A new dataset of Southern Ocean sea-ice leads: First insights into regional lead patterns, seasonality and trends, 2003–2023
Abstract. Sea-ice leads play a key role in the climate system by facilitating heat and moisture exchanges between the ocean and atmosphere, as well as by providing essential habitats for marine life. This study presents a new dataset on monthly sea-ice leads in the Southern Ocean and a first comprehensive analysis of spatial patterns, seasonal variability, and long-term trends of wintertime (April to September) sea-ice leads over a 21-year period (2003–2023). Our findings reveal that leads are ubiquitous in the Southern Ocean and show distinct spatial patterns with maximum lead frequencies close to the coastline, over the shelf break and close to seafloor ridges and peaks. We see a strong seasonal variability in lead occurrence, with lead frequencies peaking in mid-winter. Weak but significant trends in lead frequencies can be inferred for the presented period for individual regions and months. Rather small changes in lead occurrence over the 21 years suggest rather stable wintertime sea-ice compactness despite the observed strong fluctuations and recent anomalies in sea-ice extent. This study provides the first results on the spatial and temporal dynamics of sea-ice leads in the Southern Ocean and can thereby contribute to an improved understanding of air-sea ice-ocean interactions in the climate system. It also underscores the need for further investigation into the individual contributions of atmospheric and oceanic drivers to sea-ice lead formation in the Antarctic.
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RC1: 'Comment on egusphere-2025-736', Anonymous Referee #1, 24 Mar 2025
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A review of “A new dataset of Southern Ocean sea-ice leads: First insights into regional lead patterns, seasonality and trends, 2003–2023” by Dubey and coauthors.
This is a well-written, detailed and generally well-supported characterization of leads in Antarctic sea ice, and is an aggregation and subsequent quantitative analysis of the underlying dataset of daily leads from Reiser et al, 2020. I find that the paper is well-structured, and that the content fits well within The Cryosphere (as opposed to ESSD). Furthermore, the content is of great interest to those of us trying to disentangle the rollercoaster ride of Antarctic sea ice over the past decade, and places many other works in new context.I have only one major comment, detailed below, regarding Section 3.6. All others can be regarded as quite minor, and I think the author team will deal with these without too much trouble.
Major:
The whole of Section 3.6 is a little perplexing. If my understanding is correct, based on the description presented, the following procedure is followed to determine regional trend magnitude/significance:
1) Each grid cell, in each month, has 21 data points (one point per month over the 21 year dataset)
2) In each grid cell, the linear trend is calculated from these 21 points.
3) For each grid cell, the trend is either determined to be “significant” or “insignificant” - based on what criteria? A “5% level” is mentioned but this isn’t really sufficient information to replicate your method. Is a linear regression calculated How many degrees of freedom used? Two tailed or one tailed? Etc.
4) Then these grid cells are aggregated into regions - but only significant (positive or negative) pixels are aggregated? It’s a little baffling why perfectly valid (but insignificant) pixels are left out of this calculation.
5) Then in Table 1, it’s not clear what a value of “-” means. Is this 0? Or are you taking 0 to also mean insignificant, and also reporting that as “-”? In that case, it’s very suspicious that a value of 0.1 just happens to coincide with your threshold of significance.
Basically I’m not convinced that leaving out insignificant pixels is the right thing to do, and not convinced that all values in Table 1 are significant trends.Minor, referenced by line number where possible:
Abstract/Intro:
7 - recommend change “can be inferred” to “are shown”
8 - lots of “rather” in this sentence
9 - I think Reiser et al 2020 could probably claim “first results on the spatial….” - reword to make the claim accurate.
18 - an inconsistency in the use of a hyphen between “sea” and “ice” where it precedes a noun. E.g. if you use “sea-ice leads”, as you do, then you should use “sea-ice drift” and “sea-ice deformation”. This is inconsistent in many places.
21 - regarding the citations for ice-albedo feedback, I recommend to cite an Antarctic-specific example - perhaps https://doi.org/10.1029/2005JC003447
32 - the use of “winter” is a little too unspecific in many places of this manuscript. April isn’t exactly a winter month. Also line 108, 123, etc...
35 - deduct isn’t quite the right word - is “deduce” better?
35 - this sentence should make clear that it’s an aggregation of the Reiser dataset, and also give the months in which the technique is valid.
Introduction in general - it’s not actually clear, especially for someone unfamiliar with the Reiser dataset, what you’re doing. I think the introduction would benefit from more description of the Reiser dataset. E.g. what instrument? Aqua only? Or Terra too? What are the drawbacks of just using the Reiser dataset (without the aggregation that you present here)?Data and methods:
49-50 - are these anomalies in space, time or both?
52 - this is the first mention of the April - Sept data validity - I think it needs to be done in the intro.
57 - please give some ideas of other datasets to combine.
61 - “annual” is only April - September, right?
67 - I think “averaging” is more correct than “integrating”
75 - there may be nothing that can be done within the style guide, but that subscript r is so small!
76 - You take anomalies from the long-term mean? Does this mean all months from April to September as your baseline? Wouldn’t it make more sense to just choose the same month as your baseline?
78 - sentence beginning “This approach” is hard to interpret. Do you mean “not to avoid…”?
89 - This section would benefit from first stating your motivation for looking at surface currents. E.g. “Surface currents are the main determinant of leads, so….”
94 - This CPE technique isn’t quite straightforward, and also no reference is provided.
96 - “study is” to “study are” (data is a plural)
97 - DBM is never used again, so don’t define the acronymResults
107 - “most dominant” is subjective - reword
116 - a publication showing the fast-ice long-term mean distribution would be important to reference here - https://doi.org/10.5194/tc-15-5061-2021
121 - MIZ is defined here but would be better to define 2 lines earlier.
124 - this sentence structure (sentence beginning “Areas, ..“) sounds unnatural. This structure is used in other places too (e.g., figure captions).
137 and Fig 4 - Somehow it’s a little strange to me for this figure to be presented as an anomaly. First of all, what’s it an anomaly from? I guess Fig 2a, right? Secondly, I kind of prefer to see this figure presented as a mean for each month, rather than anomaly from the mean. Without seeing the means, it occurred to me that the authors maybe already explored this and decided the anomalies are more meaningful or easy to interpret. That’s fine, if that’s the case, but for baseline characterisation I find that means can sometimes be more important. Just a thought. I have no problem with the maps in Fig 3, and somehow in my mind it’s clear that Fig 3 should be presented as anomalies, but somehow it doesn’t seem as natural for the baseline monthly figures in fig 4.
156 - unnatural sentence around “months, where”
Fig 5 caption - can you state the colours used for months in the caption? It’s a bit cramped and I think these would help.
178 - Fraser et al 2009 (https://doi.org/10.1109/TGRS.2009.2019726) showed that leads often coincide with an erroneous “cloud” determination from the MODIS cloud mask (see their Fig 6). Is this perhaps what you’re seeing here? Does the Reiser dataset rely on the MODIS cloud mask?
208 - a good idea to reference the map of long-term mean fast-ice persistence from Fraser et al., 2021 (https://doi.org/10.5194/tc-15-5061-2021)
210 - “inverted fast-ice frequencies” - I see what you’re saying but this might be overstating it. E.g. your plot in Fig 8b, right column shows the climatology of lead frequency increasing throughout the winter, but fast ice also increases throughout the winter in this region.
222 - missing an accent in “Adelie”. NB also typo in Fig 7, box 4 “Adleie”.
243 - I feel like Maud Rise is commonly-enough known to stand without a lat/lon, but Gunnerus Ridge probably isn’t.
253 - “These anomalies are superimposed…” not really - Fig 4 is *also* anomalies, not “general seasonal patterns”.Citation: https://doi.org/10.5194/egusphere-2025-736-RC1
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