the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Curved atmospheric rivers and their moisture remnants: a new detection tool for Antarctica
Abstract. Atmospheric rivers (ARs) represent the main intrusions of moisture and heat into Antarctica, exerting a major influence on the continent’s surface mass balance. Yet, due to geometric and directional constraints, existing detection algorithms often fail to track their evolution inland after landfall or in regions where abrupt directional changes occur. We introduce DARK (Detecting ARs using their Kurvature), a new Antarctic AR detection framework designed to overcome these limitations. DARK applies a strict 98th-percentile threshold to total integrated vapor transport and computes AR length along the curved axis to evaluate the 2000-km AR criterion. This enables the continuous detection of ARs with complex geometries, including those that curve, overturn, or extend across the South Pole. An additional AR-children module identifies smaller but still intense moisture remnants that detach from parent ARs after landfall yet continue to transport vapor and heat inland. The resulting climatology shows that DARK ARs account for about 18 % of total Antarctic precipitation and are linked to roughly half of top 1 % daily precipitation anomalies, 60 % of top 1 % daily maximum temperature anomalies, and 80 % of compound warm-and-wet events. DARK provides a more detailed assessment of AR-related precipitation and temperature impacts in the South Pole region. Despite slightly higher occurrence, risk ratio analysis shows that DARK ARs more effectively capture the most intense events than earlier Antarctic schemes. Including AR-children further strengthens these associations, especially over Victoria Land, where they contribute to about one third of AR-related precipitation.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-6365', Anonymous Referee #1, 17 Feb 2026
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2026/egusphere-2025-6365/egusphere-2025-6365-RC1-supplement.pdfCitation: https://doi.org/
10.5194/egusphere-2025-6365-RC1 -
RC2: 'Comment on egusphere-2025-6365', Anonymous Referee #2, 27 Feb 2026
Review of Buffet et al.
Buffet et al. introduce a novel algorithm to detect ARs in Antarctica. The novelty of the algorithm lies in the ability to detect a wider range of geometric features, in addition to features that penetrate farther inland on the Antarctic Ice Sheet. They compare output from their algorithm to output from a common polar ARDT (Wille) and note that their algorithm identifies a higher number of impactful features over Antarctica. Overall, I think this is a very strong paper: well-written, with a clear story and an effective use of analysis to back up their main points. The algorithm’s development is well-justified, and the algorithm itself is well-presented and explained. I think this algorithm will be a good fit into the existing community of AR detection methods. I have one key concern I highlight below, but after considering this concern, and other comments, I’m happy to recommend publication!
General Comment:
My only concern with the manuscript is that, in placing their algorithm into context within the broader AR detection community, the authors only use one algorithm (Wille, albeit with different versions) for comparison. Given the robust selection of ARDTs available through the ARTMIP project (https://ncar.github.io/ARTMIP/intro.html), I’d be curious to see how this algorithm performs compared to other ARDTs. Of course, there may be limitations in the availability of algorithm output for ERA5 (especially polar algorithms). However, Guan and Waliser (2024), for example, recently updated their algorithm to be more regionally-refined. They tested this algorithm on ERA5 data and reported the potential for improved performance in polar regions. Since the authors specifically mention limitations related to older versions of Guan and Waliser, I encourage the authors to either a) add Guan and Waliser (2024) as an additional point of comparison (perhaps in the supplement) or b) provide some discussion about how global algorithms, such as Guan and Waliser, are similarly being adapted to perform better in polar regions.
Lines 30-44: I’m concerned that the Plain Language Summary seems a bit too technical in places. In its current form, it reads very closely to the abstract, so I suggest the authors find places where they can simplify or adjust the text to make it less technical. One possible avenue is to reduce the number of statistics discussed in the summary.
Lines 59-61: How is “skill” determined in this context? I gather that the authors are highlighting how the Guan and Waliser algorithm depicts fewer ARs. Do the authors then mean to say that the algorithm doesn’t adequately detect impactful features for precipitation and surface mass balance (in contrast to Wille)?
Lines 59-61: Guan and Waliser (2024) highlights an updated version of their algorithm, which is designed for better performance in polar regions:
Guan, B., Waliser, D.E. A regionally refined quarter-degree global atmospheric rivers database based on ERA5. Sci Data 11, 440 (2024). https://doi.org/10.1038/s41597-024-03258-4
Section 1: This is a strong introduction! The authors effectively introduce atmospheric rivers, explain how they impact Antarctica, highlight the need for adequate detection, show a demonstrable gap in current detection methods, and introduce their algorithm as means of filling that gap. Figure 1 provides a nice schematic introduction as well. My only suggestion is that it would be cool to also see an example of how DARK adequately detects a representative equatorward-curving feature.
Section 2.2: I appreciate the very detailed description of how the algorithm works. I don’t have any specific recommendations for the text itself, but I was curious if the authors have a schematic that visually depicts what’s being described. I realize that this methodology may not be easily distilled into a graphic, but a visual aid, if possible, could enhance this section.
Section 2.3: It’s cool how this functionality is included as an option with the algorithm. I have a few questions related to this add-on: 1) How much additional computational cost is required to enable this parent-child add-on feature? (And in general, what are estimates for computational cost for this method as a whole?) 2) Are there instances when this functionality wouldn’t be desired? I’d be curious to hear about example use cases for each “version” of the algorithm. This could provide useful guidance for future users.
Lines 213-216: Is there a citation (or a group of citations) that introduces the risk ratio?
Line 241: This may be a simple answer, but perhaps the authors could provide a sentence explaining why they adopted the log-normal approximation method.
Line 297: Very minor, but it looks like the section number (3.2) is repeated here.
Figure 5: I like the practice here, and in prior figures, where the authors add summary statistics to the figure labels. I suggest doing something similar for the overall frequency plot (Figure 2).
Line 416: Very minor, but there are double commas here.
Section 3.4: I find the use of case studies to be very helpful in a study such as this one. This section provides some visual intuition, and it also provides some physical understanding of the differences between algorithms.
Citation: https://doi.org/10.5194/egusphere-2025-6365-RC2
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