the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
North Atlantic Ocean SST-gradient-driven variations in aerosol and cloud evolution along Lagrangian cold-air outbreak trajectories
Kevin J. Sanchez
Hongyu Liu
Matthew D. Brown
Ewan C. Crosbie
Francesca Gallo
Johnathan W. Hair
Chris A. Hostetler
Carolyn E. Jordan
Claire E. Robinson
Amy Jo Scarino
Taylor J. Shingler
Michael A. Shook
Kenneth L. Thornhill
Elizabeth B. Wiggins
Edward L. Winstead
Luke D. Ziemba
Georges Saliba
Savannah L. Lewis
Lynn M. Russell
Patricia K. Quinn
Timothy S. Bates
Jack Porter
Thomas G. Bell
Peter Gaube
Eric S. Saltzman
Michael J. Behrenfeld
Richard H. Moore
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- Final revised paper (published on 02 Mar 2022)
- Supplement to the final revised paper
- Preprint (discussion started on 11 Aug 2021)
- Supplement to the preprint
Interactive discussion
Status: closed
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RC1: 'Comment on acp-2021-540', Anonymous Referee #1, 22 Oct 2021
Review of North Atlantic Ocean–Atmosphere Driven Variations in Aerosol Evolution along Lagrangian Cold-Air Outbreak Trajectories by K. Sanchez et al.
This manuscript presents an interesting case study which provides new insights on aerosol-cloud interactions in cold-air outbreaks. The data presented in this manuscript and the derived conclusions may be useful both for the modelling and experimental aerosol-cloud interactions research communities. The manuscript is well organized and clearly written. I recommend publications after the following (minor) comments are addressed.
Further considerations on the data robustness and uncertainty would be necessary in the manuscript, to allow a more sound interpretation of the results. If we take Table 3, which is a keystone of the manuscript, as an example, and consider the data displayed in Figure 4, we can conclude that not all the numbers reported in the Table are characterized by the same level of robustness. Clearly AMS concentrations are derived from few and quite scattered data, which make them more uncertain than other data presented contextually. I understand the scientific value of these data and the technical efforts necessary to obtain them and believe they are worth of publication. Nevertheless, at least, in the Table it should be reported the number of observations (n1, n2) used to derive the delta values so that the reader can judge about the robustness of the provided information. In alternative, the authors might evidence (e.g., with a *) which of the delta values are based on poorer statistics than the others, based on appropriate criteria.
The results reported in Table 3 are likely depending on the selection of the representative 10-minute periods of measurements used to calculate the deltas. Figure 2 suggests that a different choice, even by few minutes, may result in significantly different results. The authors should explain better how they selected the reference periods and show that their choice does not affect the results in a significant way (i.e., they should present the sensitivity of the results to the selection of the reference periods).
Specific comments
L270. I would invite the authors to indicate in brackets at what time the transition occurred, to help the reader in interpreting Figure 2.
L313-314. Please refer to my comments on Fig. 1 and Fig. 3.
L388-390. A more robust statistic approach would make this assumption stronger. I invite the authors to apply a statistic test on the datasets to evidence which difference are statistically significant for a given confidence interval.
L402. The authors may want to double check this sentence: “… resulted in the decreased the overall rate in aerosol particle”.
Table 1. Please correct the units of measurement in the caption (superscripts are missing). I have not clear the concept of “updraft-weighted updraft velocity”, maybe some explanations are needed here..
Table 2. Please correct the units of measurement in the caption (superscripts are missing).
Figure 1. I believe that the manuscript would be more immediately comprehensible by a wider audience if more information were provided in Figure 1. I would invite the authors to mark the borders of the open cell, closed cell and clear sky regions object of their investigation on the satellite images of Figure 1. In alternative, they could provide the required information adding an extra Figure in the supporting information.
Figure 3. The Figure could be improve by showing which data points refer to which of the considered regimes: closed cell, open cell, clear sky… This could be done by adding a horizontal bar at the bottom of the plot, marking the respective regimes.
Figure 8. In panel d), it would be interesting to discriminate between statistically significant and not significant correlations (according to a chosen confidence interval). It can be done easily by using two different colours for significant and not significant R values data points.
According to the Journal guidelines, the Data availability statement should be separated from the Acknowledgement Section.
Citation: https://doi.org/10.5194/acp-2021-540-RC1 -
AC1: 'Reply on RC1', Kevin Sanchez, 29 Nov 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-540/acp-2021-540-AC1-supplement.pdf
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AC1: 'Reply on RC1', Kevin Sanchez, 29 Nov 2021
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RC2: 'Comment on acp-2021-540', Anonymous Referee #2, 25 Oct 2021
The manuscript presents a 3-days case study of a cold air outbreak. The study builds on combined observations from satellite, airborne and ship-based, as well as reanalysis data. The Lagrangian design of the observations provides insights into cloud morphology and its relations to particle concentrations and sea surface temperature. The sea surface temperature varies due to the Labrador current and controls the boundary stability, thus clouds. The study shows that although air masses origin for both trajectories had similar aerosol characteristics, the aerosol characteristics downwind were different, attributed to aerosol-clouds interactions along the trajectory. Linking these findings to the role SST in the two different trajectories is interesting. Although the study focuses on a single case study, the authors show that the observed pattern they identified occurs annually due to the location of ocean currents in the region, enhancing the relevance of the study.
The manuscript is clearly written and I would recommend publications after addressing the following issues:
Major comments:
1. In the introduction the authors nicely describe in details the theory of Sc-to-Cu transitions that was developed based on subtropical Stratocumulus cloud transitions. I am not sure the same arguments apply also for cold air outbreaks. I would like the authors to discuss and show how the decoupling and breakup processes in the stratocumulus regions are relevant to cold air outbreak as well (e.g., SST gradients, subsidence rates, humidity above the inversion layer and boundary layer deepening are fundamental ingredients of the Sc-to-Cu transitions. Are those important for cold air outbreak as well? Or that ocean fluxes due to the large air-sea temperature is the main cause of the transition? See, e.g., https://doi.org/10.1002/2017JD027031).
2. The authors emphasis the role of meteorology. As far as I understand, by meteorology the authors mean the SST (and subsequently stability) along the air mass trajectories. So why not calling it stability? Meteorology in that sense is somewhat miss leading, since meteorology is more than SST and stabilty. I would ask the authors to explain what they mean by meteorology. The explanation given in Line 161 for the different meteorological condition is not informative with respect to the role meteorology.
3. The case study presents a trajectory that passed through a cold air outbreak. But it is the different is SST between that trajtory and another one that makes it an interesting story. I think that emphaising this in the title, rather than the cold air outbreak, would be more infomative.
Specific comments:
Line 72: Not clear what the authors mean by “particle trajectory”.
Line 114: Instead of “break-up” I suggest writing “dissipation”, as the clouds are drying, as the authors write.
Line 243: Please elaborate on “cloud conditions”.
Line 271: https://doi.org/10.1002/2015JD023176 and https://doi.org/10.1073/pnas.261712099 showed case studies of continental air associated with overcast cloud regime over the north east Atlantic. Might be relevant.
Line 325: What do you mean by “differences in meteorology”?
Line 353-356: How decoupling can form along with a rapid increase in SST? Wouldn’t it enhance cumulus formation from the surface? Can you shows SST along the trajectory?
In addition, the authors relate closed-to-open cells transition to decoupling. The decoupling is part of the Stratocumulus break up to cumulus clouds, while closed to open cells transition process is more associated to drizzle formation (e.g., https://doi.org/10.5194/acp-6-2503-2006).
How is the decoupling measured?
Have you looked on the relationship between decoupling and warm advection? (e.g., https://doi.org/10.1029/2018GL078122 and DOI:10.1002/essoar.10507144.1)
Line 412: check for typos.
Line 499: Can entertainment of aerosol from the free troposphere explain aerosol properties (e.g. https://doi.org/10.1175/BAMS-D-13-00180.1)
Line 506: Do you have an estimate of the replenishing rate?
Figure 1: Can resolution be improved? Consider also enhancing the colors in the darker images to improve visibility.
Citation: https://doi.org/10.5194/acp-2021-540-RC2 -
AC2: 'Reply on RC2', Kevin Sanchez, 29 Nov 2021
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-540/acp-2021-540-AC2-supplement.pdf
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AC2: 'Reply on RC2', Kevin Sanchez, 29 Nov 2021