An Overview of the Vertical Structure of the Atmospheric Boundary Layer in the Central Arctic during MOSAiC

Jozef, Gina C.; Cassano, John J.; Dahlke, Sandro; Dice, Mckenzie; Cox, Christopher J.; de Boer, Gijs

doi:https://doi.org/10.5194/egusphere-2023-780

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https://doi.org/10.5194/egusphere-2023-780

Preprints

26 Apr 2023

| 26 Apr 2023

An Overview of the Vertical Structure of the Atmospheric Boundary Layer in the Central Arctic during MOSAiC

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

Abstract. Observations collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) provide an annual cycle of the vertical thermodynamic and kinematic structure of the atmospheric boundary layer (ABL) in the central Arctic. A self-organizing map (SOM) analysis conducted using radiosonde observations shows a range in the Arctic ABL vertical structure from very shallow and stable, with a strong surface-based virtual potential temperature (θ_v) inversion, to deep and near-neutral, with a weak elevated θ_v inversion. Profile observations from the DataHawk2 uncrewed aircraft system between 23 March and 26 July 2020 largely sampled the same profile structures, which can be further analyzed to provide unique insight into the turbulent characteristics of the ABL. The patterns identified by the SOM allowed for the derivation of criteria to categorize stability within and just above the ABL, which reveals that the Arctic ABL is stable and near-neutral with similar frequencies. In conjunction with observations from additional measurement platforms, including a 10 m meteorological tower, ceilometer, and microwave radiometer, the radiosonde observations provide insight into the relationships between atmospheric stability and a variety of atmospheric thermodynamic and kinematic features. The average ABL height was found to be 150 m, and ABL height increases with decreasing stability. A low-level jet was observed in 76 % of the radiosondes, with an average height of 401 m and an average speed of 11.5 m s⁻¹. At least one temperature inversion below 5 km was observed in 99.7 % of the radiosondes, with an average base height of 260 m and an average intensity of 4.8 °C. The only cases without a temperature inversion were those with weak stability aloft. Clouds were observed within the 30 minutes preceding radiosonde launch 64 % of the time. These were typically low clouds, and high clouds largely coincide with a stable ABL. The amount of atmospheric moisture present increases with decreasing stability.

Received: 19 Apr 2023 – Discussion started: 26 Apr 2023

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30 Jan 2024

An overview of the vertical structure of the atmospheric boundary layer in the central Arctic during MOSAiC

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

Atmos. Chem. Phys., 24, 1429–1450, https://doi.org/10.5194/acp-24-1429-2024,https://doi.org/10.5194/acp-24-1429-2024, 2024

Short summary

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2023-780', Günther Heinemann, 13 May 2023

The fraction of LLJ profiles of 76% is very high and exceeds the values found by previous studies for sea ice in polar regions. Lopez-Garcia et al. (2022) found about 50% of the cases using the same radiosonde data set, but only 6-hourly ascents. Tuononen et al. (2015) found about 20% as a model-based climatology for the inner Arctic.
The method of LLJ detection needs more explanation. How do you treat multiple maxima? Do you just search for the next minimum about the LLJ height or any minimum below 1500m? Do you apply a low-pass filter on the radiosonde data to remove turbulent bursts (which was the motivation of Tuononen et al. (2015) to use a 25% criterion)? Have you made any consistency checks, if you have jumps in LLJ height between consecutive profiles? This should be tested for periods with 3-hourly radiosonde profiles.
It should be proved that turbulent bursts do not influence the results. The evaluation should be repeated using the 25% criterion and/or a filtering. The differences particularly to the results of Tuononen et al. (2015) should be discussed.

Citation: https://doi.org/10.5194/egusphere-2023-780-CC1
- AC1: 'Reply on CC1', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to CC1.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC1
CC2:
'Comment on egusphere-2023-780', Günther Heinemann, 19 May 2023

Further comments on radiosonde wind:
The authors used level-2 data, which are almost identical to the level-3 data. Both data sets already have a low-pass filter for the wind in order to remove the pendulum motion of the sonde (frequency 15s corresponding to 75m in the vertical). Level-3 data yield error estimates, which is an additional information compared to level-2 data. While the errors for temperature are quite small (mean error about 0.2K), the wind speed shows pretty large errors of about 4m/s in the monthly mean, 5-7 m/s for the maxima and 2.5-3.0 m/s as minimum values. These errors apply to the 5m-resolution data. They can be reduced by vertical averaging, which reduces the uncorrelated error. However, only the total error (sum of uncorrelated and correlated error) is specified by GRUAN in the level-3 data.
Given these large wind speed errors, the criterion of LLJ detection (2m/s anomaly) lies inside the error of the data. Thus the criterion should be stronger (e.g. 4m/s or adapted to the error) and profiles with very large errors should be discarded.

Citation: https://doi.org/10.5194/egusphere-2023-780-CC2
- AC2: 'Reply on CC2', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to CC2.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC2
RC1:
'Comment on egusphere-2023-780', Anonymous Referee #1, 19 Jun 2023

This paper investigates in some detail the vertical structure of the lower atmosphere in the Arctic using MOASiC data. While the attempt as such is commendable and the methods are interesting, the paper does not harvest. Nothing new or unexpected comes to light; instead most of the conclusions either follows logically at a textbook level from cited criteria, or worse, in some cases are side effects of the sampling. The authors seem unaware of a lot of material already published on this topic, but are hung up on a few widely spread misconceptions that they belive they have proven but not, and also seem to lack insight into turbulent flows and boundary-layer dynamics.
These concerns are grave enough that I must recommend rejection at this point, while hoping the authors come back and do a better job of this topic because it is important.
Major concerns:
Off the bat, the authors erroneously claim that the Arctic ABL is almost always stably stratified and then does whatever they can to make that the truth, although their own results actually shows this to (still!) not be the case. The most common stability in the Arctic ABL is in fact near neutral; after all, what is the stability difference near the surface between “near-neutral with stability aloft” and “shallow and well-mixed”? It is not the stability but the depth!
These authors need to read up more literature on the vertical structure of the Arctic ABL, perhaps beginning with the Tjernstrom and Graversen paper (2009 in QJRMS), performing a similar analysis on the SHEBA data, however, without the benefit of SOM. Yes, SHEBA was a while ago, but all things old are not useless.
In the end, the most common of the 12 stability classes used in this paper comes out to be NN in all seasons except summer (Figure 6) and adding in the well-mixed shallow cases, that all has a median Rib close to zero (the very definition of near neutral), it is clear that the initial statement and the conclusions by these authors in the text is just plain wrong (cf. e.g. lines 39, 363 and 693 in the manuscript)!
The SOM approach is interesting but poorly both explained and executed. Why so very many nodes? Is there no way to objectively determine the optimal number of nodes, e.g. by minimizing the inter-node and maximizing the intra-node variances? In fact, by reducing everything to 12 classes in Table 2, the authors themselves pretty much abandons the SOM, and does something else that does not require it; the SOM part becomes underutilized and, in the end, doesn’t add much to the final results, mostly based on the 12 classes and not on the 30 nodes. I would also like to know much more about how the SOM analysis handles structure versus geometry. Will, for example, profiles profiles with similar structure but very different geometry end up in the same node? Consider, for example, two inversion capped but well-mixed ABLs, one with an inversion base at 200 m and the other at 1 km, end up in the same node? A lot is confusing in the way the SOMs are described, for example there seems to be way more than a 100% of soundings, adding the numbers in each node for a total. BTW, many of the numbers printed in the large node boxes in the figures are so small I neede a magnifying glass to read them. If you insist that the SOM analysis is fruitful, and I'm not saying it isn't, then do it properly and explain it well; that could very well be a useful paper all by itself!
Then make a separate paper out of the 12 stability classes, but do a proper analysis and try and find something new. Work more with the crieria; ask yourself, for example, what would be necessary to characterize a decoupled stratocumulus, with a high inversion and most of the turbulence is generated in the cloud layer, or an inversion-and-stratocumulus-capped relatively deep but coupled ABL where the buoyancy from the cloud top generates a much deeper total ABL than motivated by the surface fluxes (e.g. Brooks et al. 2018 in JGR). There is a lot to be gained here but it does need an insightful analysis. A very stable boundary layer is, as we all know, shallow with large stability, large wind shear and Rib but low u*; everything in Figure 7 is just a confirmation of textbook ABL meteorology. If you select your criteria this way, cases will have all these other characteristics automatically; no use even looking at the result and the question has become the answer, while no one learned anything new.
In Figure 8, nothing except panel d, is significantly different from anything else and most of the conclusions are hand waiving. WS & NN are the deepest and hence expected to have the largest jet wind speeds simply by their distance from the surface; only surface friction can reduce wind speed; ABL turbulence just mixes things around. Temperature inversions (Figure 9) are common – everywhere! You find them also in the mid-latitude convective ABL. What I think is special for the Arctic is that: i) the ABL is so often quite shallow; ii) when not shallow, it is often capped by stratocumulus providing the extra energy explaining the depth, and iii) when it is stable (which is mostly in winter), it can be so very stronly stable for a long time, since there is no diurnal cycle that resets the stability once per day. If you base a category on having weak stability aloft, is it surprising that this class stands out when you analyze that inversion, and what does a median inversion-base height at 2 km has to do with the ABL? And how can the WS alone class have a larger “inversion intensity” than the WS-MSA and WS-SSA classes; ins’t that counterintuitive? Going on, the WS ABL is formed by surface cooling, so it stands to reason that it has no clouds or at least a high cloud base; clouds higher than a few km in winter has very little effect on the surface energy budget. The lowest clouds seem to be found in well mixed or near neutral cases; one may wonder if that is because these low clouds force that particular stability? Or is it the other way around?
Finally, drop the AUV data! For two reasons: i) I can’t see that it adds anything useful, and ii) the risk that it contaminates the statistics; radiosounding where done every day regardless of weather but UAVs flew only occasionally during a part of the year.
I could go on for many more pages, and list many detailed complaints and things I don't understand, and I would have if I could find it in me to recommend - at least - major revision. It makes me somewhat sad to have to be so negative; obviously this looks like a failure in supervision. But I wouldn’t be doing my part in upholding the quality of the journal if I let this through – I'm so sorry.

Citation: https://doi.org/10.5194/egusphere-2023-780-RC1
- AC3: 'Reply on RC1', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to RC1.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC3
RC2:
'Comment on egusphere-2023-780', Anonymous Referee #2, 20 Jun 2023

This manuscript describes a statistical analysis for a year-round period of Arctic boundary layer observations based largely on radiosondes during the MOSAiC project accompanied by data observed by the DataHawk2 unmanned vehicle. As a central tool, a "self-organizing map" approach was applied to the data. There is no doubt that such a statistical analysis is extremely helpful in addition to all the case studies that have been and are being evaluated. Therefore, the enormous amount of work is greatly appreciated, and after major improvements to the manuscript, I also support the publication of this analysis. However, the manuscript needs a thorough revision that goes far beyond classical "major revisions". I will try to justify this in detail in the attached review.

Citation: https://doi.org/10.5194/egusphere-2023-780-RC2
- AC4: 'Reply on RC2', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to RC2.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC4

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2023-780', Günther Heinemann, 13 May 2023

The fraction of LLJ profiles of 76% is very high and exceeds the values found by previous studies for sea ice in polar regions. Lopez-Garcia et al. (2022) found about 50% of the cases using the same radiosonde data set, but only 6-hourly ascents. Tuononen et al. (2015) found about 20% as a model-based climatology for the inner Arctic.
The method of LLJ detection needs more explanation. How do you treat multiple maxima? Do you just search for the next minimum about the LLJ height or any minimum below 1500m? Do you apply a low-pass filter on the radiosonde data to remove turbulent bursts (which was the motivation of Tuononen et al. (2015) to use a 25% criterion)? Have you made any consistency checks, if you have jumps in LLJ height between consecutive profiles? This should be tested for periods with 3-hourly radiosonde profiles.
It should be proved that turbulent bursts do not influence the results. The evaluation should be repeated using the 25% criterion and/or a filtering. The differences particularly to the results of Tuononen et al. (2015) should be discussed.

Citation: https://doi.org/10.5194/egusphere-2023-780-CC1
- AC1: 'Reply on CC1', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to CC1.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC1
CC2:
'Comment on egusphere-2023-780', Günther Heinemann, 19 May 2023

Further comments on radiosonde wind:
The authors used level-2 data, which are almost identical to the level-3 data. Both data sets already have a low-pass filter for the wind in order to remove the pendulum motion of the sonde (frequency 15s corresponding to 75m in the vertical). Level-3 data yield error estimates, which is an additional information compared to level-2 data. While the errors for temperature are quite small (mean error about 0.2K), the wind speed shows pretty large errors of about 4m/s in the monthly mean, 5-7 m/s for the maxima and 2.5-3.0 m/s as minimum values. These errors apply to the 5m-resolution data. They can be reduced by vertical averaging, which reduces the uncorrelated error. However, only the total error (sum of uncorrelated and correlated error) is specified by GRUAN in the level-3 data.
Given these large wind speed errors, the criterion of LLJ detection (2m/s anomaly) lies inside the error of the data. Thus the criterion should be stronger (e.g. 4m/s or adapted to the error) and profiles with very large errors should be discarded.

Citation: https://doi.org/10.5194/egusphere-2023-780-CC2
- AC2: 'Reply on CC2', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to CC2.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC2
RC1:
'Comment on egusphere-2023-780', Anonymous Referee #1, 19 Jun 2023

This paper investigates in some detail the vertical structure of the lower atmosphere in the Arctic using MOASiC data. While the attempt as such is commendable and the methods are interesting, the paper does not harvest. Nothing new or unexpected comes to light; instead most of the conclusions either follows logically at a textbook level from cited criteria, or worse, in some cases are side effects of the sampling. The authors seem unaware of a lot of material already published on this topic, but are hung up on a few widely spread misconceptions that they belive they have proven but not, and also seem to lack insight into turbulent flows and boundary-layer dynamics.
These concerns are grave enough that I must recommend rejection at this point, while hoping the authors come back and do a better job of this topic because it is important.
Major concerns:
Off the bat, the authors erroneously claim that the Arctic ABL is almost always stably stratified and then does whatever they can to make that the truth, although their own results actually shows this to (still!) not be the case. The most common stability in the Arctic ABL is in fact near neutral; after all, what is the stability difference near the surface between “near-neutral with stability aloft” and “shallow and well-mixed”? It is not the stability but the depth!
These authors need to read up more literature on the vertical structure of the Arctic ABL, perhaps beginning with the Tjernstrom and Graversen paper (2009 in QJRMS), performing a similar analysis on the SHEBA data, however, without the benefit of SOM. Yes, SHEBA was a while ago, but all things old are not useless.
In the end, the most common of the 12 stability classes used in this paper comes out to be NN in all seasons except summer (Figure 6) and adding in the well-mixed shallow cases, that all has a median Rib close to zero (the very definition of near neutral), it is clear that the initial statement and the conclusions by these authors in the text is just plain wrong (cf. e.g. lines 39, 363 and 693 in the manuscript)!
The SOM approach is interesting but poorly both explained and executed. Why so very many nodes? Is there no way to objectively determine the optimal number of nodes, e.g. by minimizing the inter-node and maximizing the intra-node variances? In fact, by reducing everything to 12 classes in Table 2, the authors themselves pretty much abandons the SOM, and does something else that does not require it; the SOM part becomes underutilized and, in the end, doesn’t add much to the final results, mostly based on the 12 classes and not on the 30 nodes. I would also like to know much more about how the SOM analysis handles structure versus geometry. Will, for example, profiles profiles with similar structure but very different geometry end up in the same node? Consider, for example, two inversion capped but well-mixed ABLs, one with an inversion base at 200 m and the other at 1 km, end up in the same node? A lot is confusing in the way the SOMs are described, for example there seems to be way more than a 100% of soundings, adding the numbers in each node for a total. BTW, many of the numbers printed in the large node boxes in the figures are so small I neede a magnifying glass to read them. If you insist that the SOM analysis is fruitful, and I'm not saying it isn't, then do it properly and explain it well; that could very well be a useful paper all by itself!
Then make a separate paper out of the 12 stability classes, but do a proper analysis and try and find something new. Work more with the crieria; ask yourself, for example, what would be necessary to characterize a decoupled stratocumulus, with a high inversion and most of the turbulence is generated in the cloud layer, or an inversion-and-stratocumulus-capped relatively deep but coupled ABL where the buoyancy from the cloud top generates a much deeper total ABL than motivated by the surface fluxes (e.g. Brooks et al. 2018 in JGR). There is a lot to be gained here but it does need an insightful analysis. A very stable boundary layer is, as we all know, shallow with large stability, large wind shear and Rib but low u*; everything in Figure 7 is just a confirmation of textbook ABL meteorology. If you select your criteria this way, cases will have all these other characteristics automatically; no use even looking at the result and the question has become the answer, while no one learned anything new.
In Figure 8, nothing except panel d, is significantly different from anything else and most of the conclusions are hand waiving. WS & NN are the deepest and hence expected to have the largest jet wind speeds simply by their distance from the surface; only surface friction can reduce wind speed; ABL turbulence just mixes things around. Temperature inversions (Figure 9) are common – everywhere! You find them also in the mid-latitude convective ABL. What I think is special for the Arctic is that: i) the ABL is so often quite shallow; ii) when not shallow, it is often capped by stratocumulus providing the extra energy explaining the depth, and iii) when it is stable (which is mostly in winter), it can be so very stronly stable for a long time, since there is no diurnal cycle that resets the stability once per day. If you base a category on having weak stability aloft, is it surprising that this class stands out when you analyze that inversion, and what does a median inversion-base height at 2 km has to do with the ABL? And how can the WS alone class have a larger “inversion intensity” than the WS-MSA and WS-SSA classes; ins’t that counterintuitive? Going on, the WS ABL is formed by surface cooling, so it stands to reason that it has no clouds or at least a high cloud base; clouds higher than a few km in winter has very little effect on the surface energy budget. The lowest clouds seem to be found in well mixed or near neutral cases; one may wonder if that is because these low clouds force that particular stability? Or is it the other way around?
Finally, drop the AUV data! For two reasons: i) I can’t see that it adds anything useful, and ii) the risk that it contaminates the statistics; radiosounding where done every day regardless of weather but UAVs flew only occasionally during a part of the year.
I could go on for many more pages, and list many detailed complaints and things I don't understand, and I would have if I could find it in me to recommend - at least - major revision. It makes me somewhat sad to have to be so negative; obviously this looks like a failure in supervision. But I wouldn’t be doing my part in upholding the quality of the journal if I let this through – I'm so sorry.

Citation: https://doi.org/10.5194/egusphere-2023-780-RC1
- AC3: 'Reply on RC1', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to RC1.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC3
RC2:
'Comment on egusphere-2023-780', Anonymous Referee #2, 20 Jun 2023

This manuscript describes a statistical analysis for a year-round period of Arctic boundary layer observations based largely on radiosondes during the MOSAiC project accompanied by data observed by the DataHawk2 unmanned vehicle. As a central tool, a "self-organizing map" approach was applied to the data. There is no doubt that such a statistical analysis is extremely helpful in addition to all the case studies that have been and are being evaluated. Therefore, the enormous amount of work is greatly appreciated, and after major improvements to the manuscript, I also support the publication of this analysis. However, the manuscript needs a thorough revision that goes far beyond classical "major revisions". I will try to justify this in detail in the attached review.

Citation: https://doi.org/10.5194/egusphere-2023-780-RC2
- AC4: 'Reply on RC2', Gina Jozef, 29 Jul 2023
  
  Please see attachment for the author comments, responding to RC2.
  
  Citation: https://doi.org/10.5194/egusphere-2023-780-AC4

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Gina Jozef on behalf of the Authors (29 Jul 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (14 Aug 2023) by Birgit Wehner

RR by Anonymous Referee #2 (17 Aug 2023)

Suggestions for revision or reasons for rejection

Comments about the revised manuscript

An Overview of the Vertical Structure of the Atmospheric Boundary Layer in the Central Arctic during MOSAiC

By
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox and Gijs de Boer

First of all, I must say that my comments on the first version of the manuscript were addressed quite thoroughly and the revised version is much clearer and easier to read. A lot of effort has been put into the revision, but it has benefited the quality of the manuscript and is therefore worth it from my point of view. Besides a few minor points - mainly of a technical nature - I still have some concerns about the LLJ analysis which I will describe in detail below and which, in my view, need further discussion.

As already written in my first review: I am not a native speaker but for me some sentences and phrases still sound a bit bumpy but that is certainly a matter of taste should be judged by the native speaker.

I refer to the lines in the pdf of the revised manuscript version without track changes

General note on wording: in some places the manuscript still contains some very imprecise wording that should be better avoided, just a few examples:

i) A LLJ cannot be fast or slow - only the wind speed (in its core) can be fast (after line 505). In the same paragraph, sometime you just write speed, then LLJ speed - be consistent, in particular when comparing typical wind speeds of the LLJ you could introduce a parameter such as ULLJ or so –

or

ii) If you mean the “wind speed” than do not just write “speed” (see line 22)

Line 46: what do you mean with flux perturbations? Please define!

Line 58: the argument about ozone destruction is probably not wrong but a little out of the context – I would skip it

Line 61: I think decoupling is frequently observed but “typically” is probably going too far as there are have been also coupled ABLs observed

Line 86: If I interpret Egerer et al 2023 correctly, they argued that turbulence below the LLJ can be created by shear only during the collapsing phase of the LLJ and decoupling is one of the prerequisites of a LLJ (turbulent exchange coefficient tends to zero).

Line 109ff: why are the conclusions of the two references so different? (Although I think there should be better references than a textbook contribution). Are they both observations at the same time of year? I think if you are going to make such a strong comment, you should elaborate on this a bit more.

Line 132ff: It is hard for me to follow your argumentation: If you want to test if the results of MOSAiC agree with previous observations you should use the same method (SOM) with older data sets – right? There is probably nothing “wrong” with the past observations and only the conclusions drawn from the observations can agree or not. So, I am a little bit confused about your argumentation here – maybe it is only the wording.

Line 156: such a reference should be placed in the bibliography and not inside the main text, maybe a matter of taste.

Line 159: additional averaging can reduce noise but not uncertainties – please consider rephrasing and I think you should change “winds” to “wind speeds” or similar. I still have mixed feelings about this issue. All the problems related to wind speed determination, especially in the lowest 100 m or so, cannot be solved simply by vertical averaging.

What exactly convinces you that wind measurements above 35 m are trustworthy? I have my doubts and have heard several comments that radiosondes should not be trusted too much for wind measurements below 100 meters. I would at least make a clear comment that this problem exists and one should be somewhat careful with the interpretation.

Line 168ff: The explanation of the bulk velocity is a bit misleading and needs a more precise scientific description including the equation to make sure that we all mean the same thing. You have the information from the ultrasonic anemometer to calculate u* based on co-variances how it is developed from theory so what’s the problem with this calculation and why do you use a bulk estimate and how does the latent heat flux contribute to the friction velocity? Maybe I have overseen it in Andreas et al., 2010b but he uses in Eq. 2.1a the drag coefficient and a mean wind velocity to estimate the friction velocity. For me it is not clear what you are exactly doing here and why? The argument that another method is frequently used by modelers is not really convincing.

Line 170: maybe you could combine bot Cox citations (Cox, et al., 2023 a,b) or so ?!

Line 176: I think the ceilometer derives cloud base height from backscatter – right? From your wording it sounds like it measures both quantities (backscatter and cloud base height).

Line 185: please include in the table caption that these numbers are provided by the manufacturer (which might differ from the reality…) to make this clear.

Line 338: “We make this distinction because we there are different processes that would lead to a shallow versus deep well-mixed layer.” - The sentence does not make sense, maybe just delete “we” ?

Figure 2: Still, the labels and numbers are partly quite small; in particular the numbers for the altitude in Fig 2 should be enlarged; same for the labels for the vertical lines in the subplot key of Fig 2. However, it is really appreciated that a subplot key is included to explain the complex figures and the figure quality has improved a lot.

And the same for Fig 3: the upper line in the subplot describing the “Annual” is hardly readable but for the four seasonal cases it is still very tiny.

Line 536: It is obvious what you want to express here but your wording could be more precise: What cloud property can lead to de-coupling of the sub-cloud layer (better than “below-cloud” layer) from the atmosphere above? This statement is quite vague…
I'm still having some trouble following this line of reasoning: What kind of relationships between mechanical mixing and atmospheric stability have you uncovered? It is probably true that around a LLJ - if the temperature gradient is not too strong - there is shear-induced turbulent mixing, but you have not measured it, so all very speculative. I am not convinced that friction velocity is generally an appropriate measure of potential mixing around the LLJ, or have I misunderstood the reasoning?

About the LLJ discussion:

In the revised version, you explain the two different ways an LLJ can form, but their characteristics and causes are quite different, and you have no way to analyze exactly what kind of LLJ you observed. Also, for the SOM analysis it is not possible to distinguish between the two LLJ variants although they have very different characteristics or are actually two completely different phenomena. On the other hand, this could be important for the interpretation of a possible correlation between the friction velocity and the properties of the LLJ.

However, from my point of view the friction velocity and the LLJ have not necessarily to do with each other - especially not for the LLJ which can be described by an inertial oscillation. The friction velocity only describes the momentum transfer from the surface to the lower atmosphere, and probably a low friction velocity is a prerequisite for the occurrence of one LLJ variant to minimize the turbulent vertical energy exchange (for the "inertial oscillation LLJ"). Therefore, I also have some concerns about the described situation and interpretation with stronger LLJs (faster wind speeds within the LLJ combined with higher friction speed) in a near neutral ABL, as you mention in line 603.

If a LLJ is caused by baroclinicity and is directly above the ABL according to your reasoning, how can this LLJ be coupled to the surface since most ABLs are covered by at least a weak inversion? By the way, to which section in Brümmer and Thiemann are you referring exactly?

The detailed description of the two LLJ types helps in the text, but what consequences it has for the analysis is just not clear to me yet. I think that this point of the manuscript needs some deeper discussion and especially the differences of the two LLJ types and in particular their consequence for the analysis needs to be better discussed.

Line 612: “These results suggest…“ is this a surprising result? By the way, the argumentation is somewhat misleading: in a stable environment you need more wind shear to develop turbulence and finally mix the ABL – right? The amount of turbulence to mix the ABL is the same – independent of stability. It is a classical Richardson problem.

Line 652: It sounds to me like you consider a "storm" and an LLJ to be equivalent? Maybe it is more about the wording but please clarify!

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RR by Anonymous Referee #1 (18 Sep 2023)

Suggestions for revision or reasons for rejection

This is a revised version of a paper dealing with an extensive analysis of the vertical structure of the Arctic lower troposphere, and especially the boundary layer (BL), from the MOSAiC year-long field campaign. Let me start by saying that the revised version is a clear improvement to the original manuscript and there is a lot of interesting new information.

That being said it is still not a great paper and this is sad because it could be a great paper. So, at this juncture the choice is whether to accept an extensive paper that has several flaws, in which case some revisions are still necessary, or if yet another major revision is required to make this the really great paper it could become. I will recommend major revision, because I want to maintain a high standard, especially when it is possible, but ultimately this is an editorial decision.

Major concerns
The first thing that becomes obvious is that the extensive scope of the paper is a problem. For one it is quite long; close to 40 pages including figures and references (although the review manuscript is longer than necessary depending on how the figures are set). There are three reasons for this: 1) A quite long introduction and methods section; 2) that the manuscript is actually two studies merged together, and; 3) the inclusion of some other related data to the analysis of vertical structure (low-level jets (LLJs) & clouds) at the end.

The introduction (~3 pages) reviews almost anything that one could think of being done previously on the topic and is written quite long. It also cover topics that are not at the center of this paper, such as decoupling, and I’m, sure it could be shortened by 30%. There is a 12-line paragraph on page 2 that deals with decoupling, which is important – indeed suggested even to be “typical” (I would have settled for common)"- however, the methods used in this paper makes it impossible to separate out decoupled BL clouds in this study. So even if it is indicated to be important in the introduction, there is no feed-back to this from the results in the study, nor are there any comments on this. By using a bulk-Richardson approach to determine the BL depth only the lower coupled layer will be detected. When this is later used as a criterium in the profile analysis, that seals the deal, although several of the upper-left SOM nodes clearly indicate what this reviewer interprets as a clear case of decoupling; a layer of high static stability – presumably a capping inversion – much higher than the indicated BL depth. At the very least this should be discussed. Also, while focusing on thermal stability, very little is discussed about the moisture structure. For example, what about moisture inversions; another feature where the Arctic seems to be special.

This brings me to the duality of the study; the SOM part and the criterium-based analysis. It seems to this reviewer that the former is not at all necessary for latter and that instead this mix causes problems with the narrative of the paper. That doesn’t at all mean the SOM analysis is pointless; in fact, I think that the SOM analysis, with a more in-depth discussion of the results, would make a very nice paper all by itself. The text argues that the SOM analysis is a prerequisite for the formulation of the criteria later used, but I see very little of that.

This also leads to another peculiarity. First in the methods section (section 2.4), where the SOM analysis is referred to in a “hand-waiving” manner long before any of the SOM results are even presented, let alone discussed. Second, in Section 3.1 where the SOM results are now described already having defined the SOM nodes in terms if the stability classes, that here have not yet been presented and discussed. Either the stability classes a re dependent on the SOM analysis, and then you need to present those, followed by how they inform the stability classes and then discuss those. Or you define the stability classes first, then do the SOM analysis and then identify where they fit. Now you’re trying to do both and the result is confusing.

In the end, very little quantitative information from the SOM makes it into the criterium selection. If the authors actually did use quantitative results from the SOM directly informing the criterium selection in the vertical-structure statistics analysis, that needs to be much discussed in detail and how this is handled in the narrative needs to be clearer. That this doesn’t seem to be the case is borne out by the results. There are very few WS cases; for some seasons there are only a hand-full or less and even annually there’s one class with only 9 cases. Is there a point in having a specific criteria that hardly ever happens? To me this seems to be proof that the criteria was not determined objectively.

At the end, the inclusion of the LLJ & clouds into the analysis is also not uninteresting, but somewhat superficial and doesn’t add much to the results in general. Almost a page of the introduction (page 3) discusses LLJs and how they can form and LLJs are also taking up almost half a page in the methods section. Also, the cloud information if very superficial; how much clouds (a few octas, scattered or overcast) and how are multi-layer clouds treated? Together the LLJ and cloud section adds 3 pages and two complex figures to an already long paper without adding too much new information on the physics, aside from the frequencies of occurrence.

In summary, the content of this paper holds great potential. The novelty of the SOM analysis is however underutilized and the SOM results could have been discussed in much more detail. The criterium-based vertical structure analysis, which I think is the core of this paper, does not really build on the SOM analysis, although some of the results refer back to it. Criterium-based studies are not really very novel but is here more detailed and extensive. But having both in the same paper in this way is at best confusing and is causing the paper to be very long. The addition of the LLJ and cloud analysis at the end makes it even longer without adding very much; both these aspects deserve better. So even if I’m typically not a fan of 2-part-papers, this is a case where I would advocate that method: Part I with the SOM analysis and a Part II with the vertical structure analysis, extended with the moisture profiles and a deeper analysis of the LLJs and cloud information.

And possibly as a side issue, and not being a native English speaker, the wind speed can be “large” or “small”. The wind may be “strong” or “weak”. Bu neither are “fast” or “slow”.

Detailed issues:
Lines 45-47: If this is an explanation of the so-called lase-rate feedback, it needs another attempt. The surface heat fluxes have very little to do with Arctic warming; that is determined at the top of the atmosphere. The key issue here is the almost-total absence of deep convection.
Lines 56-60: This was a really looong sentence; I’m sure it can be broken up in two or three.
Line 61: To me, “typical” means “very often”, almost always. Studies indicate maybe 30% of the time, so I would say “often” instead.
Lines 66-67: And how would this work? To me its not evident so a little more here would be nice
Lines 68-71: Maye also a bit long; shorten or divide if you can.
Lines 92-93: The very few direct studies of turbulence on top of a low-level jet that this reviewer has seen doesn’t seem to indicate very much mixing.
Line 97: Does “current paper” mean this manuscript? That begs the question, similar how?
Line 94: Is the fact that models have a lower frequency surprise? Is there an explanation for this; comment please!
Lines 133-134: Strange mix of “firstly” and “additionally”. If you use “first” there has to be a “second”. If you use “additional”, “first” is not necessary
Lines 144-145: Change “with the result being being” to “resulting in”.
Line 151: Discussion about Level 2 and 3 sounding data is meaningless without a description.
Lines 157-161: This discussion is meaningless without more information. What data sheet is that?
Figure 1: The makers are not seen with this resolution, so maybe use a line instead. Also, some of the yellow parts of the legs fade away into the white paper.
Lines 168-170: This was a weird explanation of the “friction velocity”. It is not a “theoretical wind speed”; it is a velocity scale derived directly from the momentum flux. It does not express the “magnitude of turbulence”; it is perfectly possible to have high turbulence and friction velocity, for example of turbulence is dominated by buoyancy.
Lines 172-175: Using the eddy-covariance momentum flux gives exactly the momentum flux and nothing else; this has nothing to do with latent heat flux! There may be other reasons for using the bulk flux formulas, but this is not one of them!
Lines 211-212: Would be useful to know how much data was lost when retaining 1377 soundings. Does this number include any inetsive period with more than 6-hourly? 1377 divided by 4 gives a little over 344; very close to the 352 of a full year.
Lines 213-215: First, a bit more detail would be appreciated; the term “bulk” here can refer either to a finite difference across two layers (in contrast o a real derivative) or between a layer and the surface. Second, if the latter (which I tend to believe) this means, as far as I can see, that the BL top detected will be that of the lower surface-based layer and will not capture a decoupled but turbulent layer aloft, unless the decoupled layer is less than 20 meters on top. You need to be clear about this. The text in lines 218-220 is not enough, since this is such a central issue.
Line 222: What do you mean by “below”? The wind at the surface below is zero, right?
Lines 231-234: This doesn’t make sense to me. If you include cases where the LLJ is less than 25% larger than then the next minimum, how does that make you include high-wind environments? Or should it be “low” on line 234.
Line 254: Is this correct? The figure seems to indicate the SOM is applied to the gradient of v (see Lines 284-285).
Line 277: “in the 1377”
Line 285: Subtracting the value at 1 km does not make the result an “anomaly”.
Line 287: What do you mean by “distinguished”? Maybe the wrong word?
Line 289-230: Wouldn’t it be easier to calculate the specific humidity, then v and if necessary linearly interpolate that directly, rather than calculating the pressure separately with the hypsometric equation?
Line 300: Again, subtracting the value at 1 km from a profile doesn’t make the result an “anomaly”. Just a difference.
Section 2.3 starts out by discussing relationships with a SOM analysis from which no results have yet been presented and discussed.
Lines 326-335: Maybe just language, but in the definition of the criteria, how come “mixed” is used for something more stable than “weakly stable”? In my book “mixed” is a synonym to “near neutral”.
Line 338: “… we there …” – drop “we”.
Lines 364-365: I don’t understand “… was never observed in an individual MOASiC profile …”.. There seems to be several NN-like profiles in the upper left of the SOM.
Line 394-396: Isn’t the number of SOM-patterns representing a certain stability a bit beside the point, as they are not equally populated?
Line 458: “Drop “in descending order” – pretty obvious if you read.
Line 475: What is df?
Figure 5 and corresponding discussion: Well, now when you look at the results, compared to the other classes, there are very few WS cases, correct? For some seasons there are only a hand-full or less and even annually there’s one class with 9 cases. Does that tell you anything about the validity of the stability criteria? Is there a point in having a criterion that is so specific that it hardly ever happens?
Line 577: in a coupled system, the cloud is mixed all the way to the cloud top; not the cloud base.
Line 582-584: This statement is so weak that it is entirely useless. Essentially all boundary layers are capped by an inversion, except for the stable boundary layer which is inside an inversion. Hence, there is always a stable layer in the lower troposphere regardless of what the boundary layer looks like. The distinction here is not the stable layer; it is the height. In the tropics you may have to go to several km to find the capping inversion, over the extratropical land maybe 1-3 km on a sunny summer day. The stability of the boundary layer is determined by the stability in the boundary layer. And that is not dominated by stable conditions. How hard is that to say?
Lines 616-628: This paragraph is very speculative, to the point that I think it should be dropped. First, the discussion about how a LLJ is formed in relation to the LLJ core height and BL depth is very hand-waiving to say the least. Second, it is also dependent on the definition of the BL-height which here is not the same as the capping inversion depth. In all cases where there is a decoupling, one may find the LLJ at the top of the turbulent layer associated with the decoupled cloud and that would not necessarily be a sign of baroclinicity.
Line 638: If you mention the ASR, you will have to tell the reader what that is and why its resolution is lower.

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ED: Reconsider after major revisions (26 Sep 2023) by Birgit Wehner

AR by Gina Jozef on behalf of the Authors (03 Nov 2023) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (01 Dec 2023) by Birgit Wehner

RR by Anonymous Referee #2 (07 Dec 2023)

ED: Publish subject to minor revisions (review by editor) (07 Dec 2023) by Birgit Wehner

AR by Gina Jozef on behalf of the Authors (07 Dec 2023) Author's response Author's tracked changes Manuscript

ED: Publish as is (09 Dec 2023) by Birgit Wehner

AR by Gina Jozef on behalf of the Authors (11 Dec 2023) Manuscript

Journal article(s) based on this preprint

30 Jan 2024

An overview of the vertical structure of the atmospheric boundary layer in the central Arctic during MOSAiC

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

Atmos. Chem. Phys., 24, 1429–1450, https://doi.org/10.5194/acp-24-1429-2024,https://doi.org/10.5194/acp-24-1429-2024, 2024

Short summary

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

Supplement

https://doi.org/10.5194/egusphere-2023-780-supplement

Data sets

Initial radiosonde data from 2019-10 to 2020-09 during project MOSAiC, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven M. Maturilli, D. J. Holdridge, S. Dahlke, J. Graeser, A. Sommerfeld, R. Jaiser, H. Deckelmann, and A. Schulz https://doi.org/10.1594/PANGAEA.928656

DataHawk2 Uncrewed Aircraft System data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign, B1 level G. Jozef, G. de Boer, J. Cassano, R. Calmer, J. Hamilton, D. Lawrence, S. Borenstein, A. Doddi, J. Schmale, A. Preußer, and B. Argrow https://doi.org/10.18739/A2KH0F08V

Met City meteorological and surface flux measurements (Level 3, final), Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), central Arctic, October 2019 – September 2020 C. J. Cox, M. Gallagher, M. D. Shupe, P. O. G. Persson, A. Grachev, A. Solomon, T. Ayers, D. Costa, J. Hutchings, J. Leach, S. Morris, J. Osbern, S. Pezoa, and T. Uttal https://doi.org/10.18739/A2PV6B83F

Ceilometer (CEIL). 2019-10-11 to 2020-10-01, ARM Mobile Facility (MOS) MOSAIC (Drifting Obs - Study of Arctic Climate); AMF2 (M1) Atmospheric Radiation Measurement (ARM) user facility. Compiled by V. Morris, D. Zhang, and B. Ermold http://dx.doi.org/10.5439/1181954

MWR Retrievals (MWRRET1LILJCLOU). 2019-10-11 to 2020-10-01, ARM Mobile Facility (MOS) MOSAIC (Drifting Obs - Study of Arctic Climate); AMF2 (M1) Atmospheric Radiation Measurement (ARM) user facility. Compiled by D. Zhang http://dx.doi.org/10.5439/1027369

Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer

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Observations collected during MOSAiC were used to identify the range in vertical structure and stability of the central Arctic lower atmosphere, through a self-organizing map analysis. Statistics on atmospheric boundary layer height and stability, low-level jet and temperature inversion characteristics, clouds and atmospheric moisture depending on season and stability are provided in this paper to give an overview of the thermodynamic and kinematic features of the central Arctic atmosphere.


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