Determining the key sources of uncertainty in dimethyl sulfide and methanethiol oxidation under tropical, temperate, and polar marine conditions

Jacob, Lorrie S. D.; Harvey, Benedict E. H.; Giorio, Chiara; Archibald, Alexander T.

doi:10.5194/egusphere-2025-5103

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https://doi.org/10.5194/egusphere-2025-5103

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23 Oct 2025

| 23 Oct 2025

Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Determining the key sources of uncertainty in dimethyl sulfide and methanethiol oxidation under tropical, temperate, and polar marine conditions

Lorrie S. D. Jacob, Benedict E. H. Harvey, Chiara Giorio, and Alexander T. Archibald

Abstract. This study quantifies how uncertainties in the gas-phase rate constants used in the oxidation mechanisms of dimethyl sulfide (DMS) and methanethiol (CH₃SH) (both major natural sources of sulfur to the atmosphere), affect products such as methanesulfonic acid and sulfuric acid, which influence cloud formation and climate.

We updated our previously reported DMS oxidation mechanism and extended it to include 9 halogen, 71 aqueous, and 4 CH₃SH reactions. This updated mechanism was then run in box models covering temperate, tropical, and polar marine conditions based on field campaigns.

Constrained Monte Carlo sampling was employed to propagate the uncertainties in the mechanism. Uncertainties in the concentrations of the products were time-dependent and ranged from 10–200 % for most species, with OCS, methanesulfonic acid, and sulfuric acid having the largest uncertainties.

Sensitivity analysis using the EASI RBD-FAST algorithm was performed to identify which reactions and processes were the largest sources of uncertainty for the modelled oxidation products. Individually, reactions involving the formation and loss of CH₃SO₂O₂ were major contributors to the uncertainties in gas-phase methanesulfonic acid and sulfuric acid. Reactions of species with OH and rate constants based on structure-activity relationships were commonly found to significantly contribute to uncertainty in most of the DMS oxidation products studied. Large uncertainties associated with OCS were attributed to the photolysis of hydroperoxymethyl thioformate, which has not yet been studied experimentally or theoretically. We suggest that future work on DMS oxidation should prioritise these processes to reduce the uncertainty in the climate impact of marine sulfur species.

Received: 15 Oct 2025 – Discussion started: 23 Oct 2025

Competing interests: Chiara Giorio is a member of the editorial board of Atmospheric Chemistry and Physics.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Lorrie S. D. Jacob, Benedict E. H. Harvey, Chiara Giorio, and Alexander T. Archibald

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RC1:
'Comment on egusphere-2025-5103', Anonymous Referee #1, 14 Nov 2025 reply
Overview:
This study by Jacob et al. presents a comprehensive analysis of uncertainties in the oxidation mechanisms of dimethyl sulfide (DMS) and methanethiol (CH₃SH). Building upon a DMS oxidation mechanism previously compiled by this team, this study implements that mechanism in a box model emulating three representative field campaigns to quantify the largest sources of uncertainty in the mechanism under various atmospheric conditions. Through a sensitivity analysis, they identify individual reactions and process that drive uncertainty in modelled reactants and oxidation products in each model case. Reactions involving CH₃SO₂O₂ formation and loss, HPMTF photolysis, BrO oxidation, and other processes were found to be major sources of uncertainty, which the authors suggest should be the focus of future studies. The box model setup and uncertainty sensitivity analysis both sound, and methods and results are described in a clear well-organized way. Given the considerable recent developments in DMS oxidation chemistry, including theoretical, lab, and field results, there is a clear need for a comprehensive and critical evaluation and synthesis of the literature which this study nicely contributes too. The mechanism and uncertainty analysis presented here, provide a potential common point of reference for future studies to compare against which should be quite a useful contribution to the literature. However, I have some comments related to the representativeness of the model conditions, and the evaluation criteria which should be addressed before publication.
General Comments:
I appreciate the effort to build the temperate, tropical, and polar marine box model conditions from well constrained field datasets. However, the specifics of the model configuration including oxidants and meteorology will directly feed through to the identified uncertainties in a way that deserves more consideration and comment. Specifically, I have questions regarding the representativeness of oxidant concentrations and the treatment of the aerosol thermodynamics and H₂SO₄ condensational sink which are detailed below.
Model representativeness: All three model cases have max BrO of at least 2 ppt, resulting in large contributions of BrO to DMS loss as shown in Fig 2. In contrast, daytime BrO measured in the marine boundary layer during the ATom campaign was typically 0.3 ppt (Veres et al., 2021). Similarly global chemical transport models have typically predicted much lower BrO and DMS loss fractions compared to the cases presented in this work (Wang et al., 2021, Jongebloed et al., 2025). Conversely DMS oxidation by NO₃ was essentially negligible in the cases modelled in this work, in contrast to the global models where global mean DMS loss rate from NO3 is ca. 10%. Similar comments apply to the temperature in the box model cases compared to global ranges given the steep sensitivity to temperature for many reactions in the DMS system. This point has no impact on the accuracy of the modelling or analysis for the cases considered but it does raise questions as to whether the identified uncertainties are generally representative of the regions where most DMS oxidation occurs. I would suggest some additional comment on this point and consideration of whether some model sensitivity studies would be informative (e.g. the Cape Grim model configuration but with BrO max <0.5 ppt)

Thermodynamics and H₂SO₄ condensational sink: Is the rapid morning decrease in LWC and therefore the H₂SO₄ condensational sink in the Cape Grim model physically reasonable? The thermodynamic model is clearly in a very unstable regime. Given the uncertainties in both meteorology and aerosol composition due to using reanalysis data and measurements from a different campaign it is hard to have confidence that this result is representative of the actual ambient conditions. That the calculated condensational sink is almost two orders of magnitude lower than the observed ATom range shown in Fig S12 makes this estimate seem suspect. The high values of H₂SO₄ in the model compared to observations as described at Line 311 would also seem to suggest an issue with the sink.

Section 5: It seems like the quoted uncertainties are in part dependent on assumed initial concentrations and the lifetime of the compound which does not directly represent the underlying uncertainty in the chemical mechanism. For example, OCS uncertainties are quoted at 100-340%, but this is only because OCS has a long lifetime and was not initialized in the model with an appropriate concentration (~500 ppt). If the initial OCS was increased in the model, then the quoted uncertainty in the concentration might only be ~10% but the underlying uncertainty in the net production rate would be unchanged. Similar concerns might apply for other longer-lived species like DMS and SO2 (e.g. you could retrieve a different % uncertainty if you used a different initial concentration in the box model, even if the oxidants, met, and mechanism were unchanged). The short-lived species are likely insensitive to this given the short lifetimes and relatively long (10 day) model spin up. As is, the quoted uncertainty for OCS seems to be incorrect, and DMS and SO₂could be susceptible to a bias which should be assessed.

Other Comments:
Are MSA mixing ratios (such as in Fig 5.) only for the gas phase? Since most of the MSA is in the condensed phase it is hard to assess differences between the model cases looking only at the gas phase.

Line 209 and elsewhere: I disagree slightly with framing the JPL uncertainties as more conservative than IUPAC. Absent some statistical analysis, it could well be that the IUPAC understates the true uncertainty and JPL more accurately captures it. I understand that this is tangential to the point of this paper. Some method of unifying the evaluations is needed and the approach taken here is as reasonable as any.

Line 232: Similarly, I would argue that uncertainties for rates from theory are often factors of 10 or larger. Some examples are found in the directly relevant DMS oxidation literature that is cited in this study: Both the isomerization rate and HPMTF + OH rate calculated by Wu et al. (2015) are more than 10x off from subsequent direct and indirect measurements respectively. Similarly, as described in Chen et al. (2023) their calculated k_eq for CH₃SOO was a factor of 34 lower than experimental results, requiring scaling of calculated rates in their analysis. To be clear, I am not suggesting the analysis should be changed, but I think it is worth reiterating in some points that results such as Fig 9 and Table 4 are semi-quantitative and depend on these assumptions about the uncertainty factor.

Line 309: I am not sure what is meant here, but 60 ppt of OCS is not consistent with typical marine boundary layer mixing ratios.

Fig 7. The structure in the dilution sink seems quite strange for the temperate and polar models. What is driving this?

References:
Chen, J., Lane, J.R., Bates, K.H., and Kjaergaard, H.G., Atmospheric Gas-Phase Formation of Methanesulfonic Acid, Environmental Science & Technology 2023 57 (50), 21168-21177, DOI: 10.1021/acs.est.3c07120
Jongebloed, U. A., Chalif, J. I., Tashmim, L., Porter, W. C., Bates, K. H., Chen, Q., Osterberg, E. C., Koffman, B. G., Cole-Dai, J., Winski, D. A., Ferris, D. G., Kreutz, K. J., Wake, C. P., and Alexander, B.: Dimethyl sulfide chemistry over the industrial era: comparison of key oxidation mechanisms and long-term observations, Atmos. Chem. Phys., 25, 4083–4106, https://doi.org/10.5194/acp-25-4083-2025, 2025.
Veres, P. R., Neuman, J. A., & Ryerson, T. B. (2021). ATom: L2 Measurements from NOAA ToF Chemical Ionization Mass Spectrometer, Version 2 (Version 2). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/1921 Date Accessed: 2025-11-14
Wang, X., Jacob, D. J., Downs, W., Zhai, S., Zhu, L., Shah, V., Holmes, C. D., Sherwen, T., Alexander, B., Evans, M. J., Eastham, S. D., Neuman, J. A., Veres, P. R., Koenig, T. K., Volkamer, R., Huey, L. G., Bannan, T. J., Percival, C. J., Lee, B. H., and Thornton, J. A.: Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants, Atmos. Chem. Phys., 21, 13973–13996, https://doi.org/10.5194/acp-21-13973-2021, 2021.
Wu, R., Wang, S., and Wang, L., New Mechanism for the Atmospheric Oxidation of Dimethyl Sulfide. The Importance of Intramolecular Hydrogen Shift in a CH3SCH2OO Radical, The Journal of Physical Chemistry A 2015 119 (1), 112-117 DOI: 10.1021/jp511616j

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Citation: https://doi.org/10.5194/egusphere-2025-5103-RC1
RC2:
'Comment on egusphere-2025-5103', Anonymous Referee #2, 14 Nov 2025 reply
This paper presents a thorough analysis of the sources and relative magnitudes of uncertainty in the DMS and methanethiol oxidation mechanisms. Such analysis is immensely useful for guiding future theoretical and experimental work, as it helps to clarify which reactions contribute the most to uncertainty in our understanding of this chemical system. The techniques used here will likely prove valuable in the study of other atmospheric chemical reaction systems. While I think the writing could be tightened in places to improve readability, I think this work represents a valuable contribution to the study of atmospheric sulfur chemistry and will fit in well in ACP. I have included a list of relatively minor comments below.
Major comments:
Some of the model description details are notably missing from the main text. The basic high-level details identifying the model are only included in the SI (Line 27). The authors describe the addition of aqueous chemistry (in the abstract and even the name of Section 2) as a substantial development, but fail to provide almost any description of it in the main text, and even fail to provide a main-text citation for the paper upon which most of the aqueous chemistry was based. (This is discussed further below).

The paper is a bit on the long side. To increase readability, the authors could consider shortening the article. If they chose to tackle this, I would consider moving Section 6.1 “Time dependence of the uncertainty” to the supporting information, as it seems the sensitivity index is only time-dependent in niche scenarios.

The authors present excellent sensitivity analysis, demonstrating which reactions contribute the most to uncertainty in species concentrations. However, the sensitivity indices as shown in Figures 8 and 9 all scale to 1, regardless of the absolute uncertainty in the concentration of a given compound. The absolute uncertainty can be quite variable, where for example HPMTF and SO₂ exhibit considerably less total uncertainty than OCS or MSA (Figure 5). Perhaps the authors could consider visualizing this alongside the data in Figures 8 and 9 (perhaps a smaller second-row bar chart showing the magnitude of variability for each product from the Monte Carlo analysis?), to help identify which reactions contribute the most to the largest uncertainties in the total reaction system.

ACP requires that authors include a statement of data availability, but none is presented here. I encourage the authors to at minimum publish their updated mechanism in a file format that can be easily used, and ideally include at least some of the code as was used to run to model.

Minor comments:
Line 23: Consider adding an additional more recent reference, as I believe there has been continued work in this space.
Line 71: Topic sentence could more clearly introduce paragraph. “One-at-a-time” strikes me as overly colloquial.
Line 81: EASI stands for something?
Line 92: Sentence introducing CH3SH is a little bit awkward. It could also probably be better motivated (CH3SH is an important source of SO2 and as such, uncertainties in its oxidation processes may contribute substantially to the uncertainty in SO2 production).
Line 98: A basic description of the box model is conspicuously missing! Further, this section title includes the word “aqueous” but these additions to the model are not discussed! This doesn’t need to be a long discussion but a couple sentences (e.g., “aqueous mechanisms is based on __ et al. Partitioning is based on __ etc., see SI for full description”) is important to include. Importantly, citations that are only found in the supplement don’t “count” from the perspective of appropriately giving other authors credit for their work in the form of citations that show up in google scholar etc. While it’s not necessary to include every citation in the main text, there are notable omissions here, for example: Hoffman et al. 2016 is not cited in the main text, despite forming the basis for the entire aqueous reaction scheme! Please put some time into making sure that key references are acknowledged in the main text.
Line 110: I’m slightly confused by this statement. When zooming in on the figure, the only product for which the updated mechanism clearly appears to have the lowest error is for TPA. In other cases, (for example OCS) the grey line or the yellow dot look slightly lower or the same. Perhaps it is just an issue with where the center of the point appears as plotted. Perhaps you can clarify in the caption or text which 6 products you’re referencing and check that calculation.
131: I find the framing of the modeling of field data a little unclear. Is this modeling supposed to represent real measurements taken at a time and place? Or do the field data merely represent average conditions from three different latitudes, useful for testing the model? I think it is the latter, but that could be made clearer.
Line 141: Some kind of annotation, such as a superscript, could be useful in more directly identifying which parameters in Table 1 are measured values from contemporary field campaigns, measurements taken from completely different field campaigns, or modeled/estimated values.
Line 143: Is it a good assumption that background air has no sulfur species in it?
Line 148: It’s not very clear what the 8 day period corresponds to. Are all the parameters the same for each day such that the 8 days represent a spin-up period? Or do these 8 days represent “real” days, following measurements from some of the days of the field campaigns? Are all the 24 hour results presented later just taken from the final day, or do they represent an average?
Line 148: Are emissions constant, or do these scale with factors such as wind speed?
Line 160: Scaling BrO and Cl to other oxidants seems a little arbitrary. Is this an approach that others have used?
Line 174: The implied complement to “reaction with BrO” would be “reaction with OH,” which makes up about half of reactivity. I think the way this sentence is written doesn’t quite do the importance of OH justice.
Line 176: The aqueous loss of DMS to O₃ is quite different than what was reported by Hoffman et al. 2016. Consider contextualizing this.
Line 210: It does not seem that the f²(298 K) notation is particularly common, but it is used (and discussed at length) in the NASA rate evaluation document. I would suggest adding a citation pointing this out for clarity. This is likewise the case the choice of g for the temperature dependence.
Line 226: The decision tree is quite useful for understanding your process. It would be useful to clarify that “Evaluated experimental data” refers to NASA/IUPAC evaluated experimental data, rather than your own evaluations.
Line 239: Thank you for including sources! It would be great to include all sources and not just the sources of those that are new to help readers more easily understand the source of the rates used.
Line 241: Poorly written wandering paragraph.
Line 269: Defining two parameters i and S_i sequentially is slightly confusing.
Line 270: Wouldn’t n be a more common variable here?
Line 289: I assume that no uncertainties in emissions are included. This should be specified.
Line 300: Not clear what “within” means here (±39%, 39% total uncertainty range?).
Line 303: This section is unclear, awkwardly written, and does not clearly communicate the accuracy of the simulations. Ideally, this section should briefly but systematically walk through a comparison of modeled concentrations with field measurements for a range of sulfur products. For example, Veres et al. 2020 measured HPMTF concentrations that seem to acceptably align with the box model results. Ending a sentence with “to them,” mixing units, the current focus on upper bounds rather than central estimates, and not clearly identifying concentrations for MSA and H₂SO₄ separately make things a bit confusing. Even if the match with observations is imperfect, I think the most important and novel part of this work is the sensitivity analysis, which is at least partially independent of the model fidelity.
Line 320: Some kind of section overview to guide the reader could be nice, as we currently jump right into a discussion of HPMTF without any rationale.
Line 322: Consider defining “Norrish Type I” for non-organic chemists.
Line 327: Specify that this is for midday if that is the case.
Line 328: In Figure 6, the “blue” of HPMTF appears gray-green and does not clearly stand out to me against the gray species labels. I would suggest re-coloring with a bluer blue. Consider italics to visually separate the species names. The acronym TPA is not particularly common and only defined later in the paper; consider introducing it here.
Line 339: Can you provide a little more information about how you ran the multiple linear regression model? Is this just [MSA] = a + b * [DMS] + c * [DMSO] + …?
Line 377: This paragraph is difficult to follow, but makes sense after some careful re-reading. I think it may be possible to make it shorter and clearer.
Line 408: I find the dark purple and dark blue difficult to distinguish on my screen and mixed them up multiple times while reading this paper. I would encourage the authors to select a lighter purple hue to give these colors better contrast.
Line 428: Briefly contextualize this change in the main text (“… based on analysis of measurements by…”).
Line 444: This sentence is probably unnecessary as it is well described in the methods.
Line 452 (and in Figure 9): Again clarify that “evaluated data” refers to data evaluated by NASA/IUPAC, not just the authors.
Line 491: “possible new products.” Who knows, perhaps we’ve got them all already? This statement (that analysis does not cover missing reactions) is important and maybe worth reiterating elsewhere in the paper.
Line 492: Slightly unconventional to put a table with new data in the conclusions but seems ok. Are there numerical criteria for what made it into this table? I think R_-HO is supposed to mean the RO₂ has lost an H and an O but it’s not very clear with the long dash. Perhaps R’O would be better?
Line 503: Kind of a meandering way to end a paper.

SI:
Line 27: This basic model info should be in the main text.
Line 64: The scaling performed here is unclear.
Line 77: Are both of the values (0.2 and 0.02) in this sentence supposed to match?
Line 90: I encourage the authors to include a reference for each reaction, not just a select few, to help with traceability.
Line 117: Maybe this is correct but it seems odd that no year is present for dataset citations.
Table S3: Are these loss rates rational? (Considering expected background levels of other OH-reactive species?)
Line 221: The discussion of a pooled RO₂ rate constant is unclear.

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Citation: https://doi.org/10.5194/egusphere-2025-5103-RC2

Lorrie S. D. Jacob, Benedict E. H. Harvey, Chiara Giorio, and Alexander T. Archibald

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Lorrie S. D. Jacob, Benedict E. H. Harvey, Chiara Giorio, and Alexander T. Archibald

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Short summary

We develop an approach to determine the uncertainty in reactions involved in the breakdown of dimethyl sulfide and methanethiol in the atmosphere, and run simulations spanning temperate, tropical, and polar conditions to quantify the impacts of this uncertainty on the concentration of these sulfur species and their products. We extend this analysis to identify key reactions that drive the uncertainty and highlight areas for further experimental and theoretical work.


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