This is a fantastic paper.  The work that the authors have done has the power to make complex geoengineering simulations more accessible.  The development and exploration of the methodology are done quite well.  I do have some comments:

I don’t have a sense of pros and cons, i.e., when this simpler method would work versus when you need a more complex model like WACCM.  I don’t expect anything thorough, but if the authors could provide some opinions on this, it would be helpful.

There are situations where the authors claim to have explored a variety of scenarios and conclude that the method is robust to different scenarios.  This is only partially true.  You may get different answers if you use a different background scenario, for example one with strong mitigation or changes in tropospheric aerosols, as that will change the spatial patterns of forcing.  Some appropriate caveats would be useful.

Lines 152-153:  I think providing more details about the simulations here would be useful.  I got a little lost.  I suggest moving Table C1 into the main body of the text and expanding it so that it has more information about the specifications of each simulation.

Bullets on page 3:  This is essentially pattern scaling.  There’s a lot of literature you can lean on to show that this is a sensible thing to do.

Line 101:  What does “similar” mean?

Line 186:  I think you mean monotonically?

Section 2.2:  It would be useful if you said somewhere that the approximations you make are good enough for this purpose, as the point of a feedback algorithm is to correct for such uncertainties.  MacMartin et al. and Kravitz et al. both say this if you need citations.

Line 241:  Your errors seem kind of high.  Looking at Figure 2, an error of 0.4°C is a lot.  This likely means your controller isn’t tuned as well as it could be, which isn’t a big deal, but it would be worth saying so.

Figure 2:  I found the panels confusing, in that you’re mixing and matching units.

Line 257:  Per the above comment, maybe change “well” to “adequately”.

Line 292:  This is correct but also a strawman argument.  You didn’t try to restore the climate completely.

Figure 5:  I’m having trouble making sense of how important these results are.  I wonder if you could compute z-scores (or something like that) so I would know whether the CAM minus WACCM differences are large compared to the natural variability of WACCM.

I did not find the paragraph on lines 376, nor Appendix B, terribly convincing.  If you heat the stratosphere by 24°C, you are going to have substantial influences on ozone, and we know that ozone has an influence on surface climate.  I would be more comfortable if you simply said that this is what you did, its effects of ozone changes on climate are likely smaller than the effects of the stratospheric heating on climate, and this should be explored further.  That puts you on much safer ground.

Lines 400-402:  Kravitz et al. (2014) demonstrated that the controller is likely robust to these sorts of differences.  That gives some confidence that your controller indeed can handle this.

Lines 427ff:  See recent work from the Cornell group, specifically led by Farley or Brody.  They’re doing the initial steps of what you propose.

Code availability:  Journals tend to want a fixed repository (e.g., Zenodo) rather than a changeable repository (Github).

This manuscript by de Jong at al. is trying to do two things: first, it is trying to describe how to prescribe stratospheric aerosols forcing in a different model. Second, it’s trying to show the surface climate differences in two different CESM1 versions (CESM2-CAM6, CESM1 and CESM2-WACCM6) forced by the same aerosol fields (prescribed, in the case of CAM).

The main thing I take issue with, in the first part, is that essentially what they are proposing to do is not novel at all. Prescribing an aerosol field externally is something that has been done before multiple times not just for volcanism (the entirety of CMIP6 historical simulations used prescribed aerosols) but for SRM, too. Conveniently, the authors leave out all the references of this, trying to pass it off as “novel”. Personally, I deeply dislike this way of not acknowledging past work (or to avoid performing a literature search that would have made past works emerge) to hype up one’s own.

Here are three main examples:

• Tilmes et al. (2016) proposed a prescribed aerosol forcing file for models with no interactive sulfate cycle. Such forcing field was then used in Xia et al. (2017) and in one of the models for G6sulfur (CNRM) as described in Visioni et al. (2021).
• Still in G6sulfur, the model MIP prescribed their own aerosol field and scaled them up in their two fully-coupled versions, also as described in Visioni et al. (2021).
• Finally, Tilmes et al. (2024) proposed a new experiment for CCMI with a new aerosol field for SAI, also describing climatic differences in WACCM when fully interactive vs prescribed aerosols are used, with much more details provided on how to prescribe the aerosols fields in different models provided in Jörimann et al. (2025). Reading Jörimann et al. (2025) could also illuminate for the authors how hard it is to prescribe aerosol fields in other models’ version, as one needs to get optical properties that might be treated differently in different models with different ways to translate aerosol size distribution to forcing.

None of this is ever acknowledged in the (rather short and non comprehensive in general) cited literature, giving the reader the impression that this is the first time something like this has been attempted (cue the word “novel” used frequently in the text, and also in the Key Points).

So, what’s novel? The use of a scaled-up, mono-dimensional control algorithm (not that different from what was used in multiple G6 models, see above) used in multiple publications starting in MacMartin et al. (2014)? That’s hardly new – and the simplified controller used here hardly seem well tuned, considering the pretty large errors shown in Fig. 2!

The second part then offers a description of surface climate differences between CAM and WACCM. This part (which would also not be particularly fitting for GMD on its own) is awfully lacking as well.

First, I don’t see any kind of discussion of the biases between the two models’ versions without SAI, and I think that would be a fundamental starting point to understand the sources of differences to a forcing. Even very similar versions of CESM2-WACCM6 that differ by the inclusion or not of tropospheric chemistry can present relevant distinctions in some modeled trends (see i.e. Davis et al., 2023), let alone two basically different models. Aside from surface climate, most of the differences in the stratospheric response are also left very vague (for instance, Section 3.3 essentially doesn’t offer anything more than guesses (“These features occur in Control as well and are assumed to be model differences”) that don’t really provide much confidence to the reader that the authors know what they’re talking about. The entire paragraph from l. 376 to l. 384 is very high level, does not cite any of the relevant literature about the impacts of stratospheric ozone changes on surface climate (to begin with, but the list is very long, see Bednarz et al., 2022).

I do not wish to claim that novelty in itself makes a study worthy of being published, and actually I think quite the contrary: but a study that wishes to claim novelty as its main strength, fails to deliver and also ignores relevant literature that would help frame this work in the broader context (out of curiosity, the manuscript has 24 references, of which 7 are in the first paragraph to prove/disprove the permissibility of SRM research or to discuss climate change, one of them is a non-peer reviewed link to CarbonBrief, and one a never accepted preprint from 2023, casually including one of the authors of this piece) is setting itself up to fail for anyone who has any familiarity with the field.

This manuscript, in another venue but GMD, could provide some interesting analyses of what happens when you prescribe the same aerosol field in different model’s versions given much more in depth, careful analyses, exploring the sensitivities, performing further experiments (i.e. different scenarios, which are available in CESM2-WACCM6, could be compared with the same prescribed CAM6 results to understand if the differences observed are a function of magnitude, of the specifics of the AOD pattern, of the broader atmospheric response, of the underlying emission scenario; the ozone field could have actually been changed; CAM6 simulations with fixed SSTs could have been performed to understand the actual forcing response; different patterns of AOD from different injection locations could have been combined to understand the linearity of the response, etc. The authors do acknowledge that “It might be worthwhile to expand the method for additional use cases.” But, given the paucity of interesting results otherwise, I think they should be the ones, and not somebody else at a later date). In its current form, however, I cannot recommend publication in this venue.

References

 Bednarz, E. M., Visioni, D., Banerjee, A., Braesicke, P., Kravitz, B., & MacMartin, D. G. (2022). The overlooked role of the stratosphere under a solar constant reduction. Geophysical Research Letters, 49, e2022GL098773. https://doi.org/10.1029/2022GL098773

Davis, N. A., Visioni, D., Garcia, R. R., Kinnison, D. E., Marsh, D. R., Mills, M., et al. (2023). Climate, variability, and climate sensitivity of “Middle Atmosphere” chemistry configurations of the Community Earth System Model Version 2, Whole Atmosphere Community Climate Model Version 6 (CESM2(WACCM6)). Journal of Advances in Modeling Earth Systems, 15, e2022MS003579. https://doi.org/10.1029/2022MS003579

Jörimann, A., Sukhodolov, T., Luo, B., Chiodo, G., Mann, G., and Peter, T.: A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1), EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-145, 2025.

Tilmes, S., Mills, M. J., Niemeier, U., Schmidt, H., Robock, A., Kravitz, B., Lamarque, J.-F., Pitari, G., and English, J. M.: A new Geoengineering Model Intercomparison Project (GeoMIP) experiment designed for climate and chemistry models, Geosci. Model Dev., 8, 43–49, https://doi.org/10.5194/gmd-8-43-2015, 2015.

Tilmes, S., Bednarz, E. M., Jörimann, A., Visioni, D., Kinnison, D. E., Chiodo, G., and Plummer, D.: Stratospheric Aerosol Intervention Experiment for the Chemistry-Climate Model Intercomparison Project, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3586, 2024.

Visioni, D., MacMartin, D. G., Kravitz, B., Boucher, O., Jones, A., Lurton, T., Martine, M., Mills, M. J., Nabat, P., Niemeier, U., Séférian, R., and Tilmes, S.: Identifying the sources of uncertainty in climate model simulations of solar radiation modification with the G6sulfur and G6solar Geoengineering Model Intercomparison Project (GeoMIP) simulations, Atmos. Chem. Phys., 21, 10039–10063, https://doi.org/10.5194/acp-21-10039-2021, 2021.

Xia, L., Nowack, P. J., Tilmes, S., and Robock, A.: Impacts of stratospheric sulfate geoengineering on tropospheric ozone, Atmos. Chem. Phys., 17, 11913–11928, https://doi.org/10.5194/acp-17-11913-2017, 2017.