Dynamical precursors to summer temperature extremes on the Antarctic Peninsula

Dow, William J.; Maycock, Amanda C.; Ross, Andrew N.; Williams, Ryan S.; Bracegirdle, Thomas J.

doi:10.5194/egusphere-2026-1179

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https://doi.org/10.5194/egusphere-2026-1179

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06 Mar 2026

| 06 Mar 2026

Dynamical precursors to summer temperature extremes on the Antarctic Peninsula

William J. Dow, Amanda C. Maycock, Andrew N. Ross, Ryan S. Williams, and Thomas J. Bracegirdle

Abstract. Extreme warm summer near-surface temperatures over the Antarctic Peninsula (AP) can lead to surface melting and the disintegration of ice shelves. While individual case studies have linked such events to anomalous large-scale circulation, a systematic assessment of the dynamical pathways leading to AP-wide extreme austral summer warm events remains limited. This study uses ERA5 reanalysis data to investigate the large-scale dynamical precursors associated with extreme warm days over the Antarctic Peninsula. We apply k-means clustering to mean sea level pressure anomalies during high temperature extremes and identify five distinct circulation patterns with different dominant zonal wavenumbers. We investigate the spatio-temporal evolution and persistence of near-surface wind, temperature and pressure for each cluster. Four clusters are associated with rapidly amplifying planetary-scale wave patterns, while a fifth resembles a negative Southern Annular Mode–like state with enhanced persistence prior to event onset. Despite these differing pathways, all regimes promote anomalous northerly flow toward the AP, driving strong meridional temperature advection and regional warming. We demonstrate that extreme Antarctic Peninsula warm events arise from distinct circulation pathways, reflecting diverse dynamical states likely influenced by hemispheric-scale teleconnections and planetary wave interactions.

Received: 04 Mar 2026 – Discussion started: 06 Mar 2026

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William J. Dow, Amanda C. Maycock, Andrew N. Ross, Ryan S. Williams, and Thomas J. Bracegirdle

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-1179', Anonymous Referee #1, 28 Mar 2026
General comments
This manuscript is very well written and is an interesting and comprehensive survey of the area. The reference list is very impressive, almost too impressive at 10 MS pages of references. I have only a short list of fairly minor comments.

Minor comments
Lines 128-130: Taking an average over the whole region obscures days of foehn warming in the eastern Peninsula, surely? Those conditions have been shown to be associated with extreme high temperatures in the east and with ice shelf collapse (Kyle paper). Are your results sensitive to how the spatial averages are calculated?

Lines 141-142: Reference for Lloyd’s algorithm?

Line 189: Fair enough I suppose, you’re away from the topography and the winds are strongest in the upper troposphere. But the vertical structure of temperature may well be important, and the warmest air is near the surface. Have you tested the sensitivity of your results to the pressure level chosen, or looked at vertically integrated heat flux?

Lines 207-210: Figure 1(a) suggests the foehn effect is important, and perhaps you’re picking it up anyway, even with a larger box-average. Interesting though that the percentage agreement with local extremes is lower in the east where the warming is largest, suggesting that local foehn influences are sometimes or often being missed.

Lines 256-257: Figure 3 is interesting, but the MSLP cluster patterns (3a) are hard to see in current format. Could you add contours, maybe have two rows of three to make the panels bigger? Panel (b) could be reduced in size.

Lines 271-277: Presumably the clusters with more members contain more of a mixture of patterns and hence the centroids are weaker on average. Is the variance of the MSLP fields related to cluster size, and does that inform the persistence and AP temperature anomaly statistics?

Lines 276-277: Cluster 2 has the largest number of events, just.

Lines 302-313: I see what you mean about evolution of the spatial patterns of MSLP, but what a confusing plot Fig 5c is. It takes some thinking about and I haven’t come up with a better way of showing it, though perhaps you could plot the spatial correlations between the observed MSLP anomaly pattern and the cluster centroid through time from day -5 to day +3.

Lines 376-380: Indeed, this sounds very reasonable.

Lines 394-397: In Fig. 8, there’s an impressive amount of consistency among the clusters in the heat flux patterns during days -2 to 0, suggesting that this is a dominant part of the story, as you say.

Lines 591-593: Repeated reference.
Citation: https://doi.org/10.5194/egusphere-2026-1179-RC1
RC2: 'Comment on egusphere-2026-1179', Anonymous Referee #2, 28 Apr 2026

This paper identifies different synoptic regimes associated with extreme high temperatures over the Antarctic Peninsula, using ERA5 data over 1979-2024. There are several significant issues in this manuscript regarding the methodology, and the figures and text contain several inconsistencies that should be addressed before publication can be considered.
On the clustering;
Days -3 to +1 are used, independently, to define the clusters. How are days before and after classified? This is never clarified in the text (though several figures show -5 to -3 and +1 to +3 lead analysis). If they are labeled into the clusters defined by the -3 to +1 days, since you’re only building clusters for the extreme warm events, these clusters are not representative of the full atmospheric space (note how for example there are no circulation patterns that are zonal or cold as identified in Gonzalez et al., 2018).
This is particularly concerning for identifying circulation patterns ahead of the warm extremes outside the +3 to -1 window, especially for the longer leads (-5 days) and the end of the events.
Illustratively, in Figure 6, clusters 2 and 5 at -3 to -5 days have, on average, no discernible circulation patterns associated with them (note the lack of significant SLP anomaly regions), which I would interpret as background variability ahead of the event comprising of a mix of different states. Or put another way, if the atmospheric circulation at -5 days was truly in one of these extreme warm patterns (as implied by figure 5), then at leads -5 days, you would have warm anomalies over the AP, yet for 4 out of 5 ‘clusters’, T anomalies at -5 day leads are close to climatology (as shown in Figure 5A).
Methodologically, this would seem to have a major impact on the interpretation of the analysis.
The other main issue with the methodology is that it is never stated how multiday events are assigned to a single cluster. In table 1, each of the 87 events is assigned to a single cluster. But if a multi-day event has single days in different clusters, how is that event assigned to a single cluster? Is it assigned according to the cluster on the peak of the event? Or the onset? How are days after +1 (for events lasting longer than 2 days) classified? Note my earlier comment that, in theory, you’re only using -3 to +1 days.
In addition, there are several inconsistencies in the text and figures as detailed below.
Other comments:
L69-83. It is said that the summer SAM has trended positive in austral summer, and resulted in more northerly flow over the AP. Is this still the case when analyzing 1979-2024? Since the authors find there is no trend in warm events over the AP (L131-L132), has a different circulation mode compensated for an expected SAM-driven increase in warm events?
Also, the description in this paragraph (that a positive trend in summer SAM results in more northerly flow, I.e., warmer AP) is inconsistent with the later discussion on SAM (L442-L445), where the authors state ‘…suggestive of a negative SAM-like state, which is known to weaken the circumpolar westerlies and the storm track equatorward, thereby promoting warm air intrusions into the AP’.
L90-L92 This is not what Nielsen et al found. They found the AP to be represented by three different clusters, what they call AP (northern peninsula), WA2 (southern peninsula), and SP (east coast of AP); see their Figure 3.
L130 ‘The seasonal DJF temperature trend over 1979-2024 is not significant across the AP region in ERA5 (Zhu et al., 2021)’ Typo? (how can a paper published in 2021 analyze 1979-2024 trends?)
L132 and Fig S2. I find Fig S2 very hard to interpret. Why not plot a timeseries of the number of events per year?
L133 How sensitive are results to allowing non-extreme days in the middle of the event? Since the average event is so short, do you even need to do this?
L170-L173 and figure S3. I am unclear as to why you select K=5 instead of K=4. The text says that two of the five clusters identified at k=5 merge into one at k=4, but comparing Fig 3A with S5, I disagree. It looks like K=4 clusters match clusters 1, 2, 4, and 5 for k=5, as follows (listing the K=5 clusters first, and k=4 clusters second: 1&4, 2&2, 4&3, 5&1). The only cluster at K=5 that doesn’t show in k=4 is #3, which is the more zonal-like mode with weak meridional circulation. Can the authors clarify the advantage of using k=4 over k=5?
Figure 1 How useful is this clustering mechanism for the ice shelves? Less than 50% of local warm extreme events are captured over the ice shelves along the AP east coast. Is the clustering algorithm missing out on local, east coast warm events?
L176-L189 This approach neglects the contribution of adiabatic warming and diabatic heating to temperature extremes, (e.g., Röthlisberger and Papritz, 2023); note also that latent heat release can be a major contributor to Antarctic heat extremes (Blanchard-Wrigglesworth et al., 2023). This limitation should at a minimum be acknowledged.
Figure 2. Label the SLP contours (impossible to tell what values are, since we are not told if the zero value is plotted, or if not, if authors plot at +/- 1 mb.
L253 Isn’t cluster 5 closer to a zonal wavenumber 4 pattern? The high and low SLP anomalies look to be 90 degrees longitude apart, not 120 degrees (which would be ZW3). Why aren’t all longitudes plotted?
Figure 3. Make panels in top row larger, they are hard to read, add contours, and show all longitudes? Consider also adding wind vectors.
Are the zonal wavenumber power spectrums calculated just using the anomalies over half the longitudes (as plotted in Fig 3A), or over all longitudes?
Figure 5 Show longer positive lags; cluster 1 is still p95 at day 3.
In panel B, why are growth rates for cluster 1 negative at all positive lags, if in panel A, cluster 1 day 3 is warmer than day 2? Also, in panel A, cluster 1 goes from 2K at day -1 to 5K at day 0, so a 3K/day dT/dt. But in panel B, the growth rate for cluster 1 peaks at 2K/day. Also, it is not defined if the growth rate for day ’t’ is T(t)-T(t-1), or T(t+1)-T(t), or something else.
Figure 6 show all SLP contours? Seems odd that in some panels, the higher amplitude SLP anomalies are not plotted. I would also plot a wider colorbar axis interval; the current intervals (-3 to 3C) implies that all the clusters are beyond the colorbar at the peak of the events (all clusters peak above 4C anomalies, table 1), making it impossible to differentiate between them over the AP.
Figure 7 The near-surface wind anomalies are inconsistent with the SLP anomalies for the clusters (as shown in Figure 1 and 6). For example, for cluster 2, at the -2 to zero lag, we are told there are anomalous southerly winds over the AP (which should be associated with cold temperatures), but from the SLP pattern shown in Figure 1 and Figure 6, one would expect NW winds over the AP, not southerly winds.
In cluster 2, there are strong anticylonic wind anomalies centered over the AP at -5 to -3 day leads (fig 7F), but no SLP anomalies associated with this wind pattern? (Fig 6B). How can that be?
Equally, in cluster 1, one would expect anticyclonic winds around the SLP high over the Bellingshausen sea, with southerly winds over the eastern Weddell (east of the high, see Fig 1), and yet, Fig 7 has strong northerly winds in the eastern Weddell. In short, the wind patterns in Figure 7 seem inconsistent with the SLP patterns.
References
Blanchard‐Wrigglesworth, E., Cox, T., Espinosa, Z.I. and Donohoe, A., 2023. The largest ever recorded heatwave—Characteristics and attribution of the Antarctic heatwave of March 2022. Geophysical Research Letters, 50(17), p.e2023GL104910.
Röthlisberger, M. and Papritz, L., 2023. Quantifying the physical processes leading to atmospheric hot extremes at a global scale. Nature Geoscience, 16(3), pp.210-216.

Citation: https://doi.org/10.5194/egusphere-2026-1179-RC2

William J. Dow, Amanda C. Maycock, Andrew N. Ross, Ryan S. Williams, and Thomas J. Bracegirdle

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William J. Dow, Amanda C. Maycock, Andrew N. Ross, Ryan S. Williams, and Thomas J. Bracegirdle

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Short summary

This study aims to advance understanding of the atmospheric drivers of extreme warm days across the Antarctic Peninsula during summer. We find that these days are linked to a range of large-scale atmospheric circulation patterns. Using a statistical method, we grouped similar events and compared their wind, temperature and pressure features, showing clear differences spatially and temporally. Understanding these dynamics improves understanding of the processes that drive these extreme events.


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