the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An Overview of the Vertical Structure of the Atmospheric Boundary Layer in the Central Arctic during MOSAiC
John J. Cassano
Sandro Dahlke
Mckenzie Dice
Christopher J. Cox
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 (θv) inversion, to deep and near-neutral, with a weak elevated θv 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.
- Preprint
(3574 KB) - Metadata XML
-
Supplement
(1610 KB) - BibTeX
- EndNote
Gina C. Jozef et al.
Status: final response (author comments only)
-
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
-
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
-
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
-
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.
- AC4: 'Reply on RC2', Gina Jozef, 29 Jul 2023
Gina C. Jozef et al.
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 et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 353 | 137 | 23 | 513 | 54 | 5 | 5 |
- HTML: 353
- PDF: 137
- XML: 23
- Total: 513
- Supplement: 54
- BibTeX: 5
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1