General comments

This manuscript is a thorough evaluation of the EMAC v2.55 sulfur simulations and  makes a useful contribution by (i) closing a model‑internal sulfur budget (ii) documenting how the model compares with satellite data in 2019 to evaluate how it responds to volcanic emissions and (iii) evaluating against long‑term measurements, 2010–2019

The paper is well organized and generally clear. However, it is unnecessarily long, and the presentation of results is at times too detailed, making it difficult to extract the main messages and scientific significance. The description of the model setup partly repeats work published elsewhere, and it is not entirely clear what is new compared to earlier model versions (e.g., Jöckel et al., 2016). For the interactive gas–particle chemistry, more detail would be beneficial, as the current description is incomplete for interpreting SO₂ lifetime and deposition. I.e. the statement that “the simulation did not involve an interactive aerosol submodel” needs clarification. Does this mean that interactions with ammonia are excluded? If so, this should be explicitly stated, as ammonia strongly influences sulfur oxidation pathways, cloud pH, and the partitioning and deposition of sulfur.

Specific comments

• For the emissions it is important to notice that earlier global inventory for China have underestimated the reductions after around 2010. I am not sure if CMIP6 emissions have taken this into account? In Line 35 it is written that the emissions in China remain high, which is true, but the authors should also mention the substantial reductions in recent years.
• Why is the comparison made with EDGAR 5 instead of the more recent EDGAR 6.1 (2024)? Using EDGAR 6.1 would enable comparison over the full 2000–2019 period. It would strengthen the analysis to run short sensitivity tests (1–2 simulations) using alternative inventories (e.g., EDGAR 6.1) to quantify whether biases in CMIP6 emissions explain the overestimation. If such tests are beyond the study’s scope, this limitation should at least be discussed.
• Why are you not comparing to the more recent EDGAR6.1 (from 2024) instead of EDGAR5? Then you would be able to compare it with the whole 2000-2019 period. It would have been useful to run the model with EDGAR and not only compare the emissions inventory to evaluate more directly if it biases in the CMIP6 emissions that cause the overestimation. Not necessarily a full rerun of the model but quantify the bias by re‑running 1–2 short sensitivity test with different emissions. Though I understand if this is beyond the capacity of this work.
• Sulfur data from Africa (INDAAF; https://indaaf.obs-mip.fr) and from Canada (CAPMoN) could have been included to provide a more complete global picture. In the U.S., CASTNET is responsible for air and aerosol data, whereas wet deposition data originate from the National Atmospheric Deposition Program (NADP). It appears NADP data were used, but this is not stated explicitly. NADP also includes many more sites than those used here.
• The model response to volcanic emissions is evaluated only against TROPOMI satellite data. You write that “it is difficult to ascertain whether the differences originate near the surface or higher up in the atmosphere.” It would strengthen the analysis to use in-situ data from 2019, which are available for that year and would allow a more direct comparison.
• It is not written how trends were calculated, i.e. linear regression,  Sen’s Slope . Mann Kendall. This should be added to the methods
• For trend comparisons, it would be appropriate to refer to regional studies covering similar periods. Some suggestions:
  • In Europe recent work done by EMEP: https://doi.org/10.4209/aaqr.230237. Seems like you have  somewhat larger trend for SO2  (0.05 ug/m3/y)  compared to EMEP (0.034 ug/m3/y). The difference may stem from site selection, trend method, or emissions used.
  • Several studies in North America. Eg.: NADP data: https://doi.org/10.1016/j.atmosenv.2023.119783, CASTNet: https://doi.org/10.5194/acp-22-12749-2022 and from Canada by CAPMoN: https://doi.org/10.5194/acp-22-14631-2022
  • Japan: https://doi.org/10.1016/j.envpol.2021.117842
  • China: https://doi.org/10.3390/su17198815

Technical corrections/spelling errors

• Line 70: “caclulated” to calculated
• Line 80-81: “sun‑synchrinously” to sun‑
• Line 89: “isopren to isoprene.
• Line 146: “histrotical” to historical.
• Line 149: “soley” to solely.
• Line 230: “comparatative“ to comparative.
• Line 260: “correpsonding” to corresponding.
• Line 294: “drived“ to derived
• Line 328: “depostion“ to deposition
• Line 364: “heve” to have.
• Line 543; “cylce“ to cycle
• Line 604: “retrieveal” to retrieval.
• Line 658: “resepctively“ to respectively.
• Line 723: “anlyses” to analyses
• Line 724: “caclualted” to calculated
• Line 746 “undr” to under. Multiple instances of “acces” and “reslts” to be replaced with access and results.
• Table 2 caption: “aersol” to aerosol.
• Figure caption 12. EMEP should be replaced with EANET
• Standardize Tg(S)/yr vs Tg(S)/a
• There is a mix of units used for air concentration and fluxes (e.g., µg m⁻³ vs mmol m⁻² and kgS/ha). These should be standardized throughout the paper for clarity and comparability.

This manuscript comprehensively evaluates sulfur dioxide in the EMAC chemistry-climate model. This is done by first verifying that the sulfur cycle is closed, meaning mass is neither lost nor gained. The SO2 depositions and vertical column densities are then evaluated with a set of ground-based and satellite observations. The structure of the manuscript is generally very well laid out and the methods are explained thoroughly and well executed. However, there are some areas where more concrete explanations are needed.



P4, L90: The authors mention that the aerosol is not interactive in the simulations used for this study and refer to the aerosol radiative effects and heterogeneous chemistry. Is sulfate aerosol represented in some form in these simulations? If so, please state for clarity. If not, what does this imply for the sulfur cycle?



P16, Figure 5: Looking at this figure, the SO2 burden appears to decline exponentially in the TROPOMI retrieval, whereas all model simulations depict a more gradual, almost linear decay. The authors elaborate that the differences might stem from repetitive injections of SO2 into the atmosphere. However, the different changes in the decline rates suggest a difference in removal processes as well.

Fig 7, 9, 11: The near surface SO2 concentrations in EMAC seem to be more spread out, is there an underestimation of initial removal close to the source or could it be related to how SO2 is emitted in the model or could the distribution be “flatter” due to e.g. numerical diffusion?

Minor comments:

L6: close → closed

L6-8: This sentence is a bit long and confusing

L60: Jöckel et al. in parentheses

L89: isopren → isoprene

Table 4: Biomasse → Biomass

L204: citet → cited

L230: comparatative → comparative

L365: heve → have