Summary

This study systematically compares stratospheric aerosol intervention (SAI) simulations using two aerosol microphysical schemes – MAM4 (modal) and CARMA (sectional) – implemented within the same CESM2-WACCM6 framework, with all other model components identical. The experiments vary in injection material (SO2 vs. accumulation-mode H2SO4 aerosol), injection pattern (regional vs. point), and injection amount (5 vs. 25 TgS/yr). They show that the choice of microphysics alone can cause up to a twofold difference in simulated aerosol burden, and can even reverse the relative effectiveness of injection strategies (e.g., for SO₂, regional > point in MAM4 but the opposite in CARMA). These discrepancies arise because CARMA resolves a broader size distribution, leading to more nucleation of small particles and greater growth into coarse sizes, which enhances sedimentation and reduces total burden. They further show that these differences propagate to radiative forcing efficiency, stratospheric heating, and ozone responses, and that model divergence increases at higher injection rates.

Overall, this work provides a rigorous and timely evaluation of how aerosol microphysics shape SAI outcomes. As current SAI simulations (e.g., ARISE, GLENS) often rely on MAM schemes, this paper clearly points out that a better aerosol scheme might be needed when designing future community simulations. I recommend publication after minor revisions, as outlined below.

Comments

• The SAI-induced stratosphere heating could also influence water vapor transport across the tropopause, which in turn changes radiative forcing as H2O is a GHG. I wonder if MAM4 and CARMA show different signals in stratospheric water vapor, and whether that could contribute to the difference in RF per injection (besides effects from aerosol burden and effective radius).
• Since community datasets like GLENS and ARISE use MAM, this work has direct implications for how we interpret those results. From Fig. 5b, the injection amount needed to reach a given forcing appears underestimated by roughly a factor of two in MAM4. Do the authors have any recommendations or comments on how ARISE or similar datasets should be used or interpreted moving forward?
• The study is based on single runs. I think this is fine since the focus is on comparing MAM4 and CARMA, but ozone and transport results (Section 6) could be somewhat sensitive to internal variability. A brief comment on this limitation would be helpful.
• This paper involves multiple dimensions of comparison (SO2 vs. AM-H2SO4, regional vs. point, 5 vs. 25 TgS/yr), and at times these contrasts are discussed simultaneously, including some "difference in difference" discussions. Though the paper is generally very well written and I can infer meaning from context, many spots were confusing on the first read. I recommend adding clearer transitions when switching between comparison dimensions and explicitly stating “compared to what” (though it may add some redundancy, it’s helpful to improve clarity). Two examples below, but this recommendation applies to the manuscript as a whole:
  • Lines 132-133: “CARMA results in a larger … for point injections …”. Is it “larger” than regional injection, or MAM4? Can also add reference to fig 2b here.
  • Line 190-199: These two paragraphs are particularly confusing on first read, because all comparisons are mixed and it’s hard to track the logic. Maybe the authors could clearly state the structure, and use first paragraph to summarize results consistent across MAM4 vs. CARMA, and the second to highlight contrasting conclusions?

Technical corrections:

• Line 280-281: explicitly introduce “SAD” in line 280 before using it in line 281.
• Line 369: “TCO ozone” is repetitive
• Line 373-374: parenthesis not closed

There’s some very good work in this paper.  There have been other studies looking at modal vs sectional microphysical models, and the models show different answers, so it’s worth seeing what another model shows.

Most of my general comments stem from the fact that I was hoping to see direct comparison with other models, like the simulations from Laakso et al.  It’s a very important question to look at how different models differ in their aerosol microphysical uncertainties.  The questions answered in the present work seem to be somewhat niche by comparison.  I like the last science question posed in Section 1 (lines 64-65), but I don’t really feel like it was answered.

For instance, I struggled with the novelty of looking at point vs regional injections (see, for example, English et al., 2012 or Niemeier et al., 2013).  Figure 1 illustrates this point quite well, in that the aerosol burden increases where the aerosols are injected for both MAM and CARMA, which one could have hypothesized prior to doing any simulations.  CARMA shows a systematic low bias (or MAM shows a systematic high bias – it’s hard to say which), but you didn’t need to spread out the injection to learn that.  I think the purpose of looking at this particular aspect needs to be better justified, especially with regard to what fundamental uncertainties this study is aiming to solve.

Studying the microphysics of accumulation mode aerosols are fine from a purely scientific standpoint, although given the highly questionable feasibility of this method, I would hope for a better tie-in.  That is, how can we use accumulation mode injection to learn about microphysics and stratospheric processes more generally?  (See my point above about the science questions.)  If the only justification is that it’s a proposed type of aerosol, then I put it on par with studying diamond aerosol injection – interesting but ultimately just a modeling exercise.  Lines 180-189 provide a good illustration of what I’m talking about.  These lines basically said that some people proposed an idea, and that’s it.  You could have rephrased this to be more scientifically interesting – in the previous section, you found that the nucleation stage is critical, and AM-H2SO4 allows you to isolate nucleation from coagulation growth, allowing you to further narrow which processes contribute to uncertainties.

All of that said, the study does add to the knowledge base in general, and the study is done well.  I don’t see any faults in the analysis.

Specific comments:

Line 23:  I don’t understand what this means.  Just write it out please.

Line 60:  Why did you use a fixed QBO?

Lines 92ff:  The experimental setup seems strange.  Why do you need 30-year simulations with fixed SSTs?  I suppose there’s nothing wrong with doing extra, but it seems like overkill.

Lines 147-156:  Is there anything particularly surprising in these results?  Surely this parallels results that others have found.

Lines 175-178:  I wanted to see more about this.  This is the really interesting stuff.

Line 190:  Well, yes, because you put more injection in the tropics, so of course you’re going to see more aerosol there.

Lines 206-208:  This is written as though it’s surprising, but essentially these models are doing what they’re designed to do.

Lines 222-223:  I found this sentence really frustrating.  It’s written like a throwaway, but this is exactly the sort of thing that needs to be investigated further.