General comments:

The authors evaluate the atmospheric conditions during anomalously extreme seasons in the Arctic. This is performed using a regional principal component (PC) analysis (PCA) from ERA5 data of the first two PCs of all seasons from 1979-2018. Furthermore, the PCA uses six key surface variables and divided spatially into 9 Arctic sub-regions. The sub-regions are subjectively chosen, but based on climatological sea ice conditions in either the Nordic Seas, Kara-Barents Seas, and the rest of the Arctic. Results identify 2-3 extreme seasons for each season (DJF, MAM, JJA, SON) in each sub-region. The PCA applied here provides a quantification of which variables contribute most to the extreme conditions of the respective season, and how consistent those conditions are during those particular seasons. The authors then choose two extreme seasons in the Kara-Barents sea during winter (DJF) to further investigate the synoptic weather conditions that were occurring and how they might have lead up to the resulting seasonal extremes. The chosen seasons are picked based on their orthogonal, yet extreme, projections onto the PCs.

This research nicely demonstrates how PCs can be used to identify seasonal anomalies and extremes in certain regions of the Arctic. It furthermore demonstrates how to use that information to provide an expectation of how an extreme season was characterized with regard to one of the six variables and how consistent those conditions were. It is certainly a nice way to be able to identify extreme seasons that might be worth analyzing in further detail at shorter time and space scales if desired. Overall, I think these results can make a contribution and be published once some remaining issues are addressed. In particular:

1) Picking the first two principal components is subjective and does not necessarily isolate most of the variance. It first needs to be established that the first two principal components are the only significant ones. I do not doubt that this is the case given that on line 282, it is stated that they usually explain 80-90% of the variance.  However, it should be shown that they are indeed statistically distinguishable from the others. North et al. (1982) provide a well-established method of statistically distinguishing the first few eigenvalues from the others.

2) Section 3 and generally throughout: The values of all correlations and their p-values that are described should be listed in a table.

3) Figures 5 and 6 are a very nice way to illustrate the seasonal anomalies and the variability that may have also been occurring within those seasons. Having never seen these diagrams before, it at first takes a little bit of time to understand. It would be very helpful if there were a schematic showing the "phase space" of the interpretation that illustrates what is said in words on lines 251-258 (i.e., regions on the graph where there would be anomalies that tend to be continuous, where there would be warm episodes alternating with weak cold episodes, where there would be several intense warm and cold episodes that nearly cancel, where they would be near the climatology, etc.).

4) The justification for choosing two winter cases is weak. Perhaps this is because the anomaly values are smaller in the summer. But is it not dM and the standardized anomalies that determine how extreme a season is with these methods? These are just as strong in the summer (Table 2). I can see that the results shown in Figure 5 are used to pick the cases, but again, this seems contrary to the main setup of this paper of using the PCs to determine the extremes. Also, why 2011/12 in the Kara Barents Sea when this categorizes as "anomalous" rather than "extreme" in these methods? Regardless, it is hard to justify the title "Identification, characteristics, and dynamics of Arctic extreme seasons" when only the dynamics of winter extreme seasons were discussed.

Specific comments:

1) Line 135: Why are only marine cold air outbreaks (CAOs) considered? There are also significant CAOs over land, described in Biernat et al. (2021).

2) Line 186: Choosing a dM threshold of 3 seems quite subjective. How is this threshold picked? If each principal component has a significant anomaly of two standard deviations, this could provide an expectation for what would be significant when considering the PCs in combination.

3) Line 219: Be more specific about "almost always." What percentage of the time is it true? Same thing for line 225... what percentage of the cases translates to `usually'?

4) Line 262: How close to the |P*| = P* line does a season need to be in order to be called "continuous?" For example, the 2016/2017 winter season was pretty close, but not exactly on it. On the other hand, there are very few cases of |T2m*| = T2m* being exactly equal in the summer while it is described as "continuous" on line 260.

5) Line 307: Would also be useful to point out that there is very little 2-m temperature variability over the Arctic sea ice in the summer. This could imply that temperature variability may not play a major role in sea ice loss, which has very large interannual variability in the summer.

6) The justification of how an extreme season is chosen on Lines 310-314 should be moved up to Section 2.3.

7) Line 315: Which season does Figure 2 show? This could also be referenced here along with Table 2.

8) Figures 9, 10, 14: Would be helpful to label the x-axis with the month/date instead of the day of the season, esp. to be consistent with the text.

9) Line 367: How are blocking, cyclone, and CAO frequencies computed exactly? Need references and a short description.

10) Line 389: "Several episodic precipitation events..." But wouldn't Fig. 5h suggest consistent precipitation events?

11) Line 431: Remove "it is obvious that"

12) Line 440: Please also label JJA 2016 in Figs. 6 and 8.

13) Lines 441-445: It is misleading to say that there were positive temperature anomalies over large parts of the Arctic in JJA 2016. This and the blocking was more centered over the Kara-Barents Sea region, while much of the central Arctic was not exceptionally warm and had frequent cyclones.

14) Section 5.3: If the blocking frequency was greatest over Scandinavia, why were the warmest temperature anomalies over the Kara-Barents (KB) region and not co-located with the blocking? Seems like there should have been northerly flow over much of the KB region from air flowing over sea ice. Is it surprising that the air mass was not modified by the time it reached KB?

15) Lines 120, 541: Is this approach really novel given that (Graf et al. 2017)  first introduced it in a similar application?

Technical corrections:

1) Table 1: 2 m temperature --> 2-m temperature

2) Table S1: Caption states standardized values are in brackets, but they are instead in paren-

theses.

3) Section 2 should be "Data and methods" given that there is more than one method used to

complete the analysis.

4) Figure 1 caption: State what the green and red boxes denote.

5) Line 135: There does not need to be a space between the number and the "%" symbol

6) Lines 140-141: What is the sign convention for the surface energy balance?

7) Line 183: There should be a period at the end of the equation.

8) Lines 352, 465: normal --> average

9) Line 393: This --> These

10) Line 424: Remove "of"

11) Line 453: Insert "of" after "Comparison"

12) Line 463: got --> became

References:

Biernat, K. A., L. F. Bosart, and D. Keyser, 2021: A climatological analysis of the linkages between

tropopause polar vortices, cold pools, and cold air outbreaks over the central and eastern united

states. Mon. Wea. Rev., 149 (1), 189-206.

Graf, M. A., H.Wernli, and M. Sprenger, 2017: Objective classification of extratropical cyclogenesis.

Quart. J. Roy. Meteor. Soc., 143 (703), 1047-1061.

North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation

of empirical orthogonal functions. Mon. Wea. Rev., 110 (7), 699-706.

The paper presents an analysis of variability in three Arctic regions using 6 metrics. An input of those metrics into the dominant modes of variability and links between those metric are discussed. Overall, I am impressed by the amount and quality of work done in this study.

Here is what I like about the paper:

• Fig 5 and 6, which show that while strong anomalies may be observed in one or two metrics, other metrics may remain close to their climatological values;
• assessment of the input of the six metrics into the main modes of variability and relationships between them;
• case studies (particularly fig. 10,11, 14) and the discussion around them. An attempt to establish a connection between the weather and seasonal anomalies is valuable;
• a wide range of metrics used in the study - not only t2m/SIC/P, but also energy fluxes, cyclone frequency, CAO and a blocking index.

However, there is a couple of major concerns that need to be addressed before the paper can be accepted for publication:

1. I am not convinced that the approach, introduced in the paper, is a good way to select extreme seasons. Despite the use of a multivariate approach, it often comes to just one metric showing a strong seasonal anomaly, which was enough to identify the season as extreme or anomalous. Thus, without applying this approach, one may simply go through all 6 metrics and select the most extreme season(s) in each of them. I don’t think I saw a proof that the seasons selected with the PCA analysis were more anomalous than those that showed a strong anomaly but were not picked up by the PCA approach. The latter may be even more anomalous than those, that were selected using the PCA.

On the other hand, there are seasons that were identified as anomalous though none of the variables showed a strong anomaly. Could it be proved that they are ‘true’ anomalous seasons and not artefacts of the method?

I am not asking for a change of the approach here, but I think more discussion around potential (dis)advantages of the proposed method is needed. In my opinion, this method identifiers the dominant modes of variability and allows for assessment of the contribution of each of 6 metrics into those modes and a link between them. Section 5 explores a few seasons when one of the first two modes of variability was among the strongest.

2. My other concern is the length of the manuscript. Considering the amount of work, it is hard to make it shorter, but I think the paper will benifit from it. Some plots (especially, Fig. 3) are too busy and are difficult to interpret. Section 3 and 4, while interesting, are hard to read, particularly when plots discussed in the text are a couple of pages away (which is inevitable). Please select the most robust and/or important relationships and focus on them. I understand that each plot provides a lot of information, but, unfortunately, human beings can only keep a few facts in mind at a time.

Other comments:

Abstract:   1.The abstract is a bit long, even if there is no word limit, a page-long abstract is not ideal.

2. I think it is worth mentioning that 2016/17 winter was mostly anomalous in terms of precipitation and maybe in some other variables, otherwise, until you read the paper, it remains unclear why it was anomalous.

Sect. 2.3: For the PCA analysis, was each metric was first averaged over the corresponding region? Meaning that the special structure of those anomalies was not accounted for.

Fig. 3: As I already mentioned above, it is a very busy plot, which is hard to read. The only thing that is obvious to me that in JJA the red/blue markers can be linked to positive/negative temperature anomalies. For DJF, what is obvious is a link between T2m and P anomalies and that the low right corner has predominantly negative Es anomalies. However, regional differences, discussed in the text, are very hard to see. If you decide to keep this plot, maybe splitting into different geographical locations or the sea ice concentrations helps.

l.230: Despite good clustering in Fig. 4, this plot is again very busy. Maybe you can show the average location for each of the nine sub-regions on top of the existing plot.

Fig.5, ‘the seasonal-mean absolute anomalies’: are these the seasonal-mean absolute daily anomalies, as in Fig. 4?

Fig.5,6: why Nordic seas are not shown?

l.285-287: The statement on correlation between T2m and P comes from the fact that the corresponding blue lines are close to each other?

Regarding the comment on the weather systems creating extremes in the high Arctic, I would like to agree, though none of the AR seasons across all regions in fig. 5 look particularly extreme. How about other regions that have stronger extreme seasons often just in one parameter - can they be explained by anomalous weather patterns?

l.303-307: Why the described connection between P and Rs over the sea, as well as between T2m and RL in KBM does not hold in the Kara-Barents Sea?

Section 4: The relationship between 6 metrics during cold and warm seasons, gained from the PCA analysis, is interesting. Could correlations found in this section be confirmed by using the raw data?

l. 322: “By design, extreme seasons have very large anomalies for at least one parameter… However, some anomalous seasons don’t show very strong anomalies in one particular parameter, which implies that for these seasons it is the combination of several parameters that makes them anomalous” I am not sure that the first sentence is true. Moderate anomalies in a few variables may also give an anomalous season and this is what happens in some cases.

l.367: I could not find a description of how cyclones, CAO and blocking events were defined.

l.372: Even during CAOs the temperature remained above the climatological mean, hence, I doubt that 38%-deficit in CAO can be responsible for the season being anomalous. During the first month (days1-27), there was no significant blocking events and CAOs, but T2m was well above average. To me it looks like there was a strong preconditioning. Furthermore, in the next case, shown in Fig. 10, there is a high number of CAOs but they have relatively small effect on T2m, especially during the first half of the season, brings the temperature down by only, perhaps, 2-3 deg.

l.465: A seasonal blocking anomaly over Scandinavia is probably not enough to support the statement that ‘Subsidence-induced warming [over Scandinavia] and long-range transport of warm air masses contributed to several warm episodes.’

l.498: why a persistent high does not cause subsidence warming? and why there are no blocking events during Jan 2013 at the time of a persistent high? I can also see a number of cyclones in Feb, despite the text says that Feb was also calm. I agree that probably the main reason for decreasing t2m and low P is that the High Arctic remained isolated from the lower latitudes, however, none of the metrics in this study reflect an exchange between latitudes. I am not suggesting adding such metric at this stage, but it might be something to add in the future.

l.529-534: the  paragraph first describes obvious seasonal differences (higher variability in winter due to stronger gradients) and then concludes ‘hence, it is reasonable to subdivide the Arctic into several regions considering these spatial differences to study anomalous Arctic winter seasons.’ But during summer the regions were also subdivided. I am not sure if this paragraph is needed at all.

l. 541: see my major comment on the PCA approach

Minor commnets:

l.61 ‘and of the feedback’: remove ‘of’

Table 1:  Es should be added

Table 2  is first mentioned in section 2.3 but is only shown in section 4. Replace ‘brackets’ with ‘parentheses’

l.160: it is not the entire ERA5 period, but the entire period covered by this paper

Please use either the Kara-Barents sea or the Kara and Barents seas

406: I’d replace ‘single’ with ‘individual’

432: on this date

Fig. 13 is mentioned earlier than fig. 12.

Fig. 10,11, 14: I suggest showing months and days of months’ along Axis X, instead of days of season, as specific dates are often mentioned in the text (e.g, 9 Jan or 17 Feb). Can SLP be added to fig. 10,11? In fig. 14 the legend mentions CAOs, but they are not shown - could they be added?

The authors have investigated seasonal extremes in the Arctic using PCA of six climate variables and analysis of some key dynamical elements – cyclones, blockings, and marine cold air outbreaks – to further investigate particular extreme seasons.  This is an interesting and valuable framework for understanding the various causes of seasonal extremes, and it is very well presented. I recommend the manuscript for publication with some minor adjustments. My principal concerns relate to the justification of the many choices which needed to be made in this analysis, these are detailed below.

Specific comments:

L13: “respectively” – this doesn’t quite follow when you say 2-3 extreme seasons for four seasons.

L15: I think a justification of why 2 winter seasons were chosen for the in-depth case studies is needed here

L117: It is very nice to have these questions in the Introduction to frame the paper, but as far as I could see the synoptic systems of interest are pre-defined in the study (cyclones, blockings, and marine CAOs), so perhaps this question should be reframed to reflect this.

L131: What was the method(s) of interpolation?

L155: What is the justification for choosing these regions?

L161: Are results sensitive to the choice of definition of ice, mixed, and sea? Why were these thresholds chosen?

L174: Why choose just the first 2 PCs? This seems arbitrary, although I see later you mention that these explain a very large part of the overall variance.

L178: Why are these rescaled by their respective SDs to give equal weight to each PC? Do you not wish to identify the extremeness of a season rather than the extremeness of a season with respect to these two PCs? (ref L114) If you don’t do this rescaling do you still identify the same seasons as being extreme seasons?

Fig 8 and elsewhere: why was 10^5 km^2 chosen as the size threshold for a region?

L393: grammar – “This periods typically are…”

L408: “exemplarily shows” -> “exemplifies”

L510: “as is the case”