Please find my review attached below. 

Review of Observing the role of wind-driven processes in the evolution of warm marine cloud properties

The authors have constructed an apparently Lagrangian study of changes in cloud droplet concentration and liquid water path stratified by wind speeds for the three hours separating the MODIS Terra and Aqua observations in the SE Atlantic Ocean. Results show that increased wind speed tends to lead to stronger surface fluxes of fine and coarse aerosol. These increased fluxes of coarse aerosol likely lead to more precipitation and reduced cloud droplet concentrations. Effects differ depending on whether scenes are already precipitating.

The mechanisms by which aerosol (and surface aerosol fluxes) can modify clouds is extremely important for 1) understanding recent global albedo trends concurrent with changing emission of air pollution, and 2) for estimating the possible power of marine cloud brightening climate interventions. The work is clearly written and appropriately succinct, though possibly a bit too succinct. The manuscript could be improved with a few clarifications and revisions detailed below, but is certainly strong, relevant, and well-constructed enough to be worthy of eventual publication.

Main points:

-Can you go into a little more detail concerning how you link the Aqua and Terra observations? Are you using trajectories to link each 1x1 degree box in Terra to their Aqua counterpart? What level winds are you using, and what assumptions are you making about these links?

-Data section: Are you doing any sort of filtration based on MODIS cloud cover or ERA5 meteorology? It is possible that differing cloud morphologies may have differing dominant processes, or that Nd and LWP anomalies could be associated with differing meteorology. It would be comforting to test for this, to see if these relationships persist when controlling for variables like cloud cover, EIS, SST, and humidity above the cloud.

-LWP and Nd have strong variability with the MODIS sensor view angle. Have you looked at these relationships while controlling for this view angle? I recommend sub-setting the data into high/low sensor zenith angle bins and checking for consistency, as the zenith angle biases can be extremely strong.

-Line 141 concerning regression to the mean: The patterns of increase/decrease seen in Figures 1a-b and 1d-e are almost certainly driven by regression to the mean, as stated. Anomalous initial values along trajectories have a strong tendency to regress to the mean as shown here:

Eastman, R., Wood, R., Bretherton, C.S., 2016. Time Scales of Clouds and Cloud-Controlling Variables in Subtropical Stratocumulus from a Lagrangian Perspective. https://doi.org/10.1175/JAS-D-16-0050.1

This figure shows this behavior clearly, with Low Nd and Low LWP adjusting positively as you move forward in time, and vice-versa for high values. That paper and others from that group explore how to deal with these tendencies, using a similar technique to the DoRs used here. It may be good to mention those papers to show how a similar technique has been successful in the past.

-In figure 2e-l there appears to be lots of noise on the left sides of the distributions. Is this signal believable? Larger bins or some sort of significance test may eliminate some of this distracting noise.

-I’m not sure I follow the reasoning on line 166 starting with ‘Consequently’. Can you elaborate on this and further tie this to the prior two sentences?

-Paragraph beginning on line 194: Can you add one additional paragraph explicitly stating how these numbers concerning the two pathways are determined. It wasn’t clear on first reading how this really worked in relation to the figures.

-Are the figures in the appendix using the data from Eastman et al. (2019)? If so, rain rate estimates in that dataset are constructed from AMSR/E and AMSR/2, with CloudSat only used to tune the relationships. If that is the case, I would change the labels to read ‘AMSR’ rain rates instead of CloudSat.

Minor points:

-Nd and r_e are used somewhat interchangeably in the paper. Since the figures only deal in Nd, it may be easier to interpret the work if you keep the discussion limited to one variable, but also thoroughly explain the relationship between Nd and r_e in the data section.

-Why restrict the work to just the SE Atlantic? Could there be regional differences if compared to Pacific Sc decks? Or more robust results?

-Line 192, 2 dashes: Instead of dashes, maybe label these two mechanisms as ‘pathways’ and reference Figure 3 here directly.

-Line 194: Contrary to which other results? This paper also shows that increased wind speed leads to decreased Nd and stronger rain rates:

Eastman, R., McCoy, I.L., Wood, R., 2022. Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus. Journal of Geophysical Research: Atmospheres 127, e2022JD036795. https://doi.org/10.1029/2022JD036795