This study explores Marine Cloud Brightening in five regions across the global ocean using the WRF-CMAQ model. Their results show that clouds are most susceptible to brightening in the North Pacific, South Pacific, and South Atlantic where marine stratocumulus dominate, this finding is consistent with previous studies. The authors look at MCB in the Western Pacific, a region they call Asia, and an equatorial region in the Philippine Sea they call Equa. In each region, the authors explored injecting Accumulation mode sea salt particles in four experiments which differed by the amount injected and whether the injection rate was sensitive to wind speed.

While the analysis is expansive, 11 main figures and 29 supplemental figures, their key points and a central storyline was very difficult to follow. I also see major issues in their methods which I will describe below. I think this paper will benefit from simplifying their paper goals, which I think is stated on line 345-347, “… when implementing geoengineering measures [MCB], it is essential to comprehensively consider the interactions between aerosols and clouds … in various regions”. The authors may choose to add aerosol direct effects to this main message, but I think the paper would benefit in focusing on why MCB works in different ways in different regions. To reach this goal with more clarity and ease for the reader, the results could be presented as the SW_TOT, SW_CLD, and SW_AER findings for each region and why the results differ regionally. Is it cloud cover?, cloud type?, saturation issues?. I think having four different injection methods (+ analysis of injection in sensitive areas, which was not described in the methods) makes this storyline difficult to follow. In summary, reducing some of the analysis, and certainly the number and references to supplemental material, will clarify this paper and provide focus which will enhance readability. The main points of the paper and which results are unique to this study, are currently difficult to decipher amongst the details.

For these reasons and the reasons below, I believe the paper needs to be re-worked before considered for publication.

Major Concerns:

1. There is only one simulation for each experiment and region. This makes the discussion of model uncertainty impossible and difficult to gauge whether the results would be consistent if other ensemble members were included. I recommend at least 3 ensemble members per region.

1. There is no discussion of cloud cover, cloud height, and other cloud characteristics in the “Base” simulation. In fact, there is no description of Base in the Methods. What is the (Base) climatology of relative variables to MCB in each of the regions?
2. Moreover, there is no description of “sensitive” regions in the Methods, nor how they were selected or why this analysis is essential to their paper. I suspect the sensitive regions are where there is cloud cover in the first set of simulations. But during the set of simulations to explore the “sensitive” regions, are you sure the clouds were still co-located with in the black box?
3. Why were wind-dependent injection scenarios considered? If this study wants to explore where injections are most efficient, I don’t think having wind-dependence is necessary. I would consider removing this analysis or include a justification.
4. Why were Accumulation mode particles used? Connolly et al 2014 and Wood 2021 demonstrate that the optimal distribution of sea salt aerosols for MCB is 30-100 nm, or 0.03-0.10 um – in the Aitken mode. I would like to see this analysis completed with Aitken mode particle to get a better sense of marine cloud brightening as opposed to marine sky brightening.
5. Where possible, can you also refer/cite tables and figures in the main text? It is difficult to read pages where the only tables or figures reference are in the supplemental. Can you combine some information into the main figures? – I would also recommend moving some of the results of supplemental analysis to the supplemental text. This may aid with the focus of the paper.

Minor Concerns:

Double check the verb tense that is used, the authors may find it easier to refer to their study and simulation results in the present tense.

The abstract could better capture what results were unique to this study and include a better justification for why this study is essential to the field.

Line 26 – “conducted experiments” sounds like field experiments, consider “designed computer simulations”

Line 32 – remove the word “still”

Line 34-36 – This is a well known characteristic of MCB and MSB, does your study offer any new findings?

Line 44 -47 – unclear

Line 47 – attention (not attentions)

Line 47 – I don’t think “radical” is the right word

Line 61 – more sunlight back “to space”

Line 65 – what is meant by “coarse part”?

Line 66 – clarify that these are aerosols that are not injected into clouds

Line 69 – New Paragraph after “sea-salt aerosols as MCB.”

Line 70 – non-polluting – I think terrestrial deposition of sea salt is pollution

Line 71 – You may wish to compare a global geoengineering intervention like SAI to MCB here.

Line 76 – “summarizes” – I would use the present tense to describe the results in this paper.

Line 83-86 – sentence is unclear

Line 100 – “considers” not “considered” – please correct all of these grammatical errors for next submission.

Line 141 – Did you consider calling the “Asia” region the “West Pacific (WP)”

Can you justify why you chose to look at the Asia and Equa regions? I’m not sure I understand why these were a focus of the study.

Line 163, not 1.5 times the wind speed, but raised to the 1.5 power.

Line 185 – “excluding the (direct effect) of the aerosols”

Line 196 – Indent new paragraph

Line 198 – reference table S2

Line 220 – “Base” is not defined. Please discuss the Base simulation and climatology of these regions in the Methods.

At the end of the Methods please include a paragraph that describes the analyses to follow – how will they be presented? Why simulation will be compared to one another? Are these results for regional averages/temporal averages, etc.

Line 236 – by discrepancy do you mean “variations in methods”?

Line 239 – relies -> rely

Line 241 – Can you reference Figure 2 as well as Table S2?

Line 246 – exceeded -> exceeds

Line 257-260 – sentences and results such of this would benefit with a reference to a figure/figure panel at the end of the sentence to aid the reader in deciphering what result is being examined.

Line 260-261 – unclear

Line 269 – higher (than what?)

Line 270 – Emcb decreased (where?)

Line 272 – reference figure 3.

Line 273 – were -> are

Line 288 – ignored -> ignore

Line 290 – grid (points)

Line 288 – 306 – please explain the sensitive regions and why this analysis is done in the Methods

Line 302-306 needs references.

Below line 306 – I stopped making editing requests. Please review and have others look at the paper for grammatical and clarity errors before resubmission.

Line 378-379 – Can the authors describe the variations in cloud types, cloud amounts (cover/fraction), and atmospheric conditions that make the SW_CLD response different in each of the 5 regions. I think this should be a main focus of the text.

Line 420- 447 – This could be supplemental text and figures

Line 448 – 455 – Please move this discussion to the beginning of the results or in the methods as you describe the 5 locations. I think it would be helpful to have low cloud cloud fraction as a main figure as this will highly correlate with SW_CLD and help explain the results and how they differ amongst regions. This section I would consider expanding and adding details such as how variable cloud cover and cloud type affect your results.

Section 3.5 – can these results be summarized in a main text table?

Figure 4 Caption – call it the y-axis not the vertical coordinate.

In general for Section 3.2, I don’t see how you can interpret the result of SW_CLD and SW_AER without discussion what the cloud cover is. If there are no clouds, of course SW_AER will dominate.

Throughout the manuscript, when making a comparison, please make sure to write out which two experiments are being compared to one another.

The use of significant digits seems arbitrary.

The discussion contains a lot of material that I’m not sure is relevant to the current study. Try to focus on results that are unique to this study.

Song and coauthors present an analysis of the effective radiative forcing (ERF) from sea salt aerosol injection (SSA) using the WRF regional circulation model. SSA are injected in five different regions using a range of emission rates and patterns. It is found that the indirect forcing is non-linear and saturates at high emission rates, after which the direct aerosol effect dominates the ERF. The efficacy of the cloud forcing is also found to be strongly regionally dependent. These are effects that have been shown in coarse resolution global circulation models (e.g., Alterskjaer and Kristjansson, 2013; Stjern et al., 2018; Rasch et al., 2024), but have not been tested for these regions using an RCM. However, the authors do not sufficiently articulate what new information is provided by their analysis. For example, the advantages of the higher RCM resolution versus GCMs should be discussed. i.e., which processes are expected to be better represented at these resolutions? 

Major comments

1. All multi-panel figures should be labelled (Fig. 5, 7, 8, 10, 11).

2. It makes sense to test NP, SP, and SA as they have extensive low cloud coverage, but as you show in Fig S13 the Equa and Asia regions have few clouds over most of the domains. What is the justification for testing these regions? Is the idea to investigate MSB in these regions? 

3. When describing the simulations, a range of geometric mean diameters is given (0.11 to 0.15 micron) - it would be useful to know the GMD values for each emission case, since going from 0.15 to 0.11 diameter would increase the number flux by >2x for the same mass flux.  I suggest computing the forcing efficiencies per number flux to check if this explains the discrepancy between the uniform emission and fixed wind adjusted cases.

4. Recommend adding a figure displaying climatological sea salt aerosol concentrations and cloud fields (fraction, CDNC, LWP) for each region in the main body. The distribution of clouds within the simulation regions is a major explanatory factor in the strength of the SW cloud forcing, which should be incorporated into the interpretation of the forcing differences.

5. Line 245-246/Line 299-300: I don't think it makes sense compare the global GHG radiative forcing to the regional average MCB forcing, since MCB can only be applied over part of the globe. You are also comparing MCB in its most effective season (summer) to annual mean GHG forcing. Both factors lead to overestimating the efficacy of MCB relative to GHG. It would be useful to also express the radiative forcing from the WRF simulations as a global annual mean forcing (i.e., ERF x [AREA_REG / AREA_GLOB] x [2 months / 12 months]) for a more apples-to-apples comparison of the forcing magnitude to the GHG effect.

6. Fig. 9: Why does NP see a much lower activation rate of CCN to CDNC compared to SP/SA? It also appears to have a stronger cloud forcing 2x10^-9 kg/m2/s (Fig. 7) despite having a weaker increase in cloud albedo. What is the explanation for this difference, given that the cloud fraction effect is small?

7. Section 3.5: You used two different decompositions to identify the albedo versus cloud fraction effect - the first gives a CF effect up to 23.8% but the second shows all CF effects are less than 10%. What is the cause of this discrepancy? Is there a reason you focus on the second decomposition rather than the first?

8. Add a supplementary table outlining the precise locations chosen for each "sensitive area" emission box. These simulations and the motivation for their inclusion should be described in the methods.

Minor comments:

1. Line 53: "geoengineering" -> "SRM"

2. Line 66: "pass" -> "path"

3. Line 83: "post-treatment" -> "post-injection" ?

4. Line 91: Expand WRF-CMAQ acronym when it is first used.

5. Please provide some more details on the aerosol model. How many modes does the model use? What is the geometric standard deviation of the accumulation mode? 

6. Line 163: "1.5 times the wind speed" -> "the wind speed to the power of 1.5"

7. Line 200: "It measured the efficiency" -> "this is a measure of the mass efficiency"

8. Line 200: "that was" -> "that is"

9. Line 246: "geoengineering" -> "greenhouse gas"

10. Line 312: "less injected" -> "lower mass injection"

11. Line 326-330/Fig 4: How does this mechanism explain the lower "fixed-wind-adjusted" efficiency in Equa, where there is practically zero SW_CLD effect regardless of emission rate or strategy?

12. Line 361: "west and northwest of the injection areas" -> "west and northwest of the injection by the prevailing winds"

13. Line 363: "would be" -> "will be"

14. Line 456-457: Recommend removing the first sentence here. The indirect effect is by definition the effect of aerosol on cloud microphysics and thus cloud radiative response, so this sentence is tautological. 

15. Line 515: remove "on the other hand"

16. Line 518: "depended" -> "depend"

17. Line 554-556: Note Wood, 2021 found that decreased activation due to competition may be overestimated in the Abdul-Razzak and Ghan activation parameterization used in many GCMs (e.g., by Alterskjaer et al., 2018; Rasch et al., 2024) relative to a parcel model.

18. Line 616: I don't see any basis for the statement that this study represents a lower limit on cooling, considering the large uncertainty in the cloud lifetime effect. Recommend removing this sentence.

19. Line 622: "with the Geoengineering Model Intercomparison Project (GeoMIP) under the same framework" -> "with the same framework under the Geoengineering Model Intercomparison Project (GeoMIP)"

20. Fig. 1: Which emission case is being plotted in the (a) panels?

21. Fig. 4 caption: "different ways" -> "different strategies"

22. Fig 6 caption: "slash style" -> "hatching"