For five marine cold-air outbreaks (MCAOs) that occurred during the ACTIVATE campaign, the authors generate Lagrangian trajectories and extract geostationary satellite retrievals along them, in particular liquid and ice water paths (LWP and IWP) and cloud droplet number concentration (Nd). Using stereotypical process signatures, the authors infer process occurrence during overcast-to-broken cloud transitions. Lastly, the authors examine cloud organizational metrics.

While the approach is highly innovative and the paper is well written, it rests on a series of assumptions. In my opinion, the authors should verify these assumptions. Given the scope of the proposed revisions, I recommend returning the manuscript for major revisions.

Major concerns

Satellite retrievals and derived products: The authors should explain SatCORPS retrievals performance under MCAO conditions where publications exist or else express the lack of such performance analysis. In addition, the authors should explain in more detail the pixel-based liquid-ice phase categorization. The latter issue is particularly relevant where condensate becomes increasingly mixed and retrievals may confuse condensate mass (e.g., in updrafts that are typically both high in LWP and IWP) and how it would affect the shown analysis. Furthermore, the authors should at least briefly demonstrate the veracity of subadiabaticity assumptions needed for Nd retrievals (e.g., via ACTIVATE dropsonde data), especially where clouds are increasingly convective natured. Lastly, the authors should clarify whether LWP includes cloud and rain condensate.

Process signatures: The authors show anticipated LWP-Nd process signatures in Fig. 3a. While the authors cite previous work, they should in more detail explain which synoptic conditions were previously targeted (e.g., is the cited work investigating subtropical Sc?) and weather any differences are expected under MCAO conditions. For example, currently entrainment is shown to have either no impacts on Nd when homogeneous or subtle impacts when heterogeneous; previous work (e.g., Tornow et al., 2022) has demonstrated strong cloud condensation nucleus (CCN) dilution effects as cleaner free-tropospheric (FT) air is entrained into the marine boundary layer. Furthermore, given the non-negligible role of secondary ice production in these cases – what type of process signature is expected?

Steady conditions: The authors show that horizontal winds remain approximately steady during daytime hours. Given the important role of large-scale vertical winds in shaping MCAO cloud evolution (e.g., Tornow et al., 2023), were vertical wind speeds along the trajectory truly steady? The authors should at least briefly explore this question for a single layer close to cloud tops (e.g., 700 hPa).

Test on Lagrangian simulations: In their discussion (l. 358-360) the authors suggest applying this framework to Lagrangian simulations. LES and SCM simulations now exist for four out of the five cases (https://github.com/NASA-GISS/LES-SCM). While LES simulations could serve as an additional proxy for field data. (e.g., to assess subadiabaticity assumptions), it also offers microphysical source terms that can directly connect to the process signatures. Lastly, observational constraints from MAC-LWP (Elsaesser et al., 2017, located at https://github.com/NASA-GISS/LES-SCM/tree/main/data_files) may help to further corroborate SatCORPS LWP retrievals.

Minor concerns

l. 164 It would be good to show the range of meteorological conditions across trajectories for each case as shading behind lines. It would also be good to show large-scale subsidence (see above major concerns).

ll. 173-174 Precipitation appears to set in at re << 15 um; please modify or else explain.

ll. 182-184 Nd appears to decrease once LWP ~ 100 g m^-2 is reached; could this be explained by early collision-coalescence or alternatively by FT CCN dilution (see above major concerns)?

ll. 192-193 Is this corroborated by ACTIVATE measurements?

ll. 193-195 Is a reduced LWP consistent with the existing literature?

ll. 201-205 Since most IWP retrievals are notoriously uncertain, it is important to explain any strengths and weakness of the SatCORPS retrieval (see above major concerns). Can we even trust the order of magnitude here?

Fig. 4 Along with the above concern, please add error bars to data points.

ll. 207-209 How many pixels are there in a 1x1 degree domain and is one ice pixel is sufficient to render the domain “mixed”?

ll. 214-216 Could rain also cause liquid depletion?

ll. 207-220 To what degree can this LWP-IWP evolution be affected by the binary condensate classes? I wonder if retrieval samples (e.g., spatially resolved IWP and LWP values in progressive domains) could be informative.

ll. 225-228 It is unclear how representative these 2DS samples are. Perhaps other metrics may be more informative (e.g., how many flight seconds of co-existing liquid and frozen particles from in-situ probes) or there is a way to quickly determine sample representation?

l. 232 Specific thresholds from the retrieval would be quite important here (see above major concerns).

ll. 232-234 CTTs from ACTIVATE’s HSRL seem to disagree here, showing 2022-01-29 at -10 degC (Fig. 5 in Tornow et al., 2025). Could this stem from surface contamination in optically thinner or broken clouds within GOES pixels?

ll. 247-256 A lot of the earlier findings (that were initially “intended to conceptually indicate the dominant characteristics of a given processes”) are relied on here without any uncertainty. This very much reads like a discussion, and I suggest moving it there.

ll. 252-253 Please check this sentence.

ll. 254-255 (and also l. 11 and l. 348) It is unclear what exactly the “spread” is. Is it a large range in albedo at any given cloud fraction?

ll. 257-266 Given the general importance of meteorological boundary conditions, I wonder if this paragraph should be moved to the beginning of Section 3?

ll. 271-275 Could the diurnal evolution of MBL aerosol upwind of cloud formation (e.g., Tornow et al., 2025b) explain some of this behavior?

ll. 289-290 I suggest also looking into changing subsidence patterns here (see earlier comment).

ll. 337-340 I would soften “evident” here, assuming that a combination of other processes (e.g., entrainment plus collision-coalescence) could also lead to a precipitation signature.

ll. 358-360 Output from observationally constrained Lagrangian simulations of four of these cases is now available (see major concerns). Application of the authors’ approach to simulations would make the paper (and the “line of evidence” for model evaluation) stronger by (1) bypassing potential satellite retrieval issues and (2) applying it to coherent output with known process rates in it. Please contact me (email: ft2544@columbia.edu) if needed.

References                                      

Elsaesser, G.S., C.W. O'Dell, M.D. Lebsock, R. Bennartz, and T.J. Greenwald, 2017: The Multi-Sensor Advanced Climatology of Liquid Water Path (MAC-LWP). J. Climate, 30, no. 24, 10193-10210, doi:10.1175/JCLI-D-16-0902.1.

Tornow, F., A.S. Ackerman, A.M. Fridlind, B. Cairns, E.C. Crosbie, S. Kirschler, R.H. Moore, D. Painemal, C.E. Robinson, C. Seethala, M.A. Shook, C. Voigt, E.L. Winstead, L.D. Ziemba, P. Zuidema, and A. Sorooshian, 2022: Dilution of boundary layer cloud condensation nucleus concentrations by free tropospheric entrainment during marine cold air outbreaks. Geophys. Res. Lett., 49, no. 11, e2022GL098444, doi:10.1029/2022GL098444.

Tornow, F., A.S. Ackerman, A.M. Fridlind, G. Tselioudis, B. Cairns, D. Painemal, and G. Elsaesser, 2023: On the impact of a dry intrusion driving cloud-regime transitions in a mid-latitude cold-air outbreak. J. Atmos. Sci., 80, no. 12, 2881-2896, doi:10.1175/JAS-D-23-0040.1.

Tornow, F., A. Fridlind, G. Tselioudis, B. Cairns, A. Ackerman, S. Chellappan, D. Painemal, P. Zuidema, C. Voigt, S. Kirschler, and A. Sorooshian, 2025: Measurement report: A survey of meteorological and cloud properties during ACTIVATE's postfrontal flights and their suitability for Lagrangian case studies. Atmos. Chem. Phys., 25, no. 9, 5053-5074, doi:10.5194/acp-25-5053-2025.

Tornow, F., E. Crosbie, A. Fridlind, A.S. Ackerman, L.D. Ziemba, G. Elsaesser, B. Cairns, D. Painemal, S. Chellappan, P. Zuidema, C. Voigt, S. Kirschler, and A. Sorooshian, 2025b: High accumulation mode aerosol concentration and moderate aerosol hygroscopicity limit impacts of recent particle formation on Northwest Atlantic post-frontal clouds. Geophys. Res. Lett., 52, no. 18, e2025GL116020, doi:10.1029/2025GL116020.

This study applies a ‘space–time exchange’ method to analyze satellite snapshots along the instantaneous trajectories of five marine cold-air outbreak (MCAO) events over the northwest Atlantic. The authors examine the evolution of geophysical variables (Nd, LWP, IWP, albedo) to infer cloud processes in stratiform and transition regimes. I found the approach effective for diagnosing dominant processes. However, as noted by another reviewer, assumptions and retrieval uncertainties deserve more careful treatment given the complexity of MCAO cloud–boundary layer interactions.

Major Comments

1. The manuscript uses a range of terms to describe cloud regimes and their transitions (e.g., cloud street, closed-cell, stratiform, overcast, convective, open-cell, closed-to-open transition). These are sometimes interchangeable but can imply different meanings. Please clarify definitions and adopt consistent terminology throughout.
2. Equation (1) is used for Nd estimation across both liquid and mixed-phase clouds. Is the expression valid for mixed-phase conditions without adjustment to constants like k or ρw? Please justify its application and discuss whether the apparent Nd decrease in mixed-phase regimes (Fig. 3b) reflects physical processes or retrieval artifacts. Similarly, retrieval uncertainties in LWP and especially IWP should be discussed.
3. Cloud-top temperature appears to increase after cloud breakup, is it unexpected? You state that only cloudy pixels are used for domain-mean values. Please confirm that clear-sky pixels were excluded from averaging. Mixing in SST could bias cloud-top temperatures higher in broken cloud regimes.
4. Could retrieval uncertainty and satellite viewing geometry (e.g., solar zenith and azimuth angles) contribute to the observed diurnal differences in Fig. 8? A brief discussion would strengthen this section.

Minor Comments

Line 90: Please clarify what is meant by "relatively liberal thresholds."

Section 2.4: In comparing Lagrangian and instantaneous trajectories, beyond wind, how invariant are other meteorological drivers of MCAO evolution (e.g., LHF, SHF, buoyancy, temperature)?

Lines 188–190: Could the decreased albedo also reflect the emergence of ice-phase clouds later in the trajectory?

Line 190: How is the location of cloud breakup (transition from filled to open symbols in Fig. 3) determined? Is a specific cloud fraction threshold applied?

Figure 6: Consider adding evolution direction arrows as in Fig. 4. Also, the use of filled/open symbols for trajectory stages might be confused with the closed/open-cell regimes in Figs. 3–4. 

Line 247: Based on Fig. 6 and your description, should this be “black group showing a steeper scaling”?

The authors have compiled a set of space-time-exchange ‘trajectories’, which are instantaneous snapshots of cold air outbreaks taken along a trajectory driven by 1000hPa winds observed at a single time step. This approach eliminates diurnal sampling problems due to daytime-only microphysical retrievals, allowing for a more complete picture of the time evolution of the cloud systems in CAOs. Results show that ice processes are major contributors to the scene albedo, and that these trajectories show evidence of vapor deposition and riming processes. There is also evidence that increased cloud drop concentration prolongs cloud lifetime while reducing peak LWP and possibly albedo.

This novel approach is noteworthy for its effectiveness and simplicity, and is appropriately applied in the CAO outbreak regime, where downstream evolution remains consistent for days, and cloud processes are less strongly influenced by the solar cycle. The work is well constructed and clearly written and presented. I only have a few minor points to raise, so I recommend only minor revisions.

Main points:

In the abstract, you mention that Elevated Nd suppresses peak LWP and delays cloud breakup. This is likely true, but you may want to qualify it a bit here, since you’re only working with five trajectories. It is difficult to make broad inferences with such a small sample size. This does motivate an enlarged study using this technique, which would be an excellent goal.

Although references to Turnow (2025) and Sorooshian (2023) are clearly made in the data section, I think it would help to give a little bit more attention to environmental factors such as surface winds, vertical winds within and above the PBL, and inversion strength. It would strengthen the paper to add a figure showing the breakdown of a few fundamental CCFs over time (maybe the Scott 2020 CCFs?) for each STE trajectory.

Should section 4.2 be in the results, and not the discussion? This is quite a bit of new work added, and it seems much more in-line with a core result than what is normally mentioned in a discussion section.

Introduction: Paragraph beginning on line 28: I can’t tell if you’re describing the Sc-Cu transition and closed-open transition as separate processes or the same process. It would be wise to make it clear that these two transitions are very different from one-another, then settle on a consistent set of terms that are used throughout.

Figure 7, caption: Do colors indicate max Nd or CTT?