the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development, intercomparison and evaluation of an improved mechanism for the oxidation of dimethyl sulfide in the UKCA model
Ben A. Cala
Scott Archer-Nicholls
James Weber
Nathan Luke Abraham
Paul T. Griffiths
Lorrie Jacob
Y. Matthew Shin
Laura E. Revell
Matthew Woodhouse
Abstract. Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean. The oxidation of DMS has long been recognised as being important for global climate through the role DMS plays in setting the sulfate aerosol background in the troposphere. However, the mechanisms in which DMS is oxidised are very complex and have proved elusive to accurately determine in spite of decades of research. As a result the representation of DMS oxidation in global chemistry-climate models is often greatly simplified.
Recent field observations, laboratory and ab initio studies have prompted renewed efforts in understanding the DMS oxidation mechanism, with implications for constraining the uncertainty in the oxidation mechanism of DMS as incorporated in global chemistry-climate models. Here we build on recent evidence and develop a new DMS mechanism for inclusion in the UKCA chemistry-climate model. We compare our new mechanism (CS2-HPMTF) to a number of existing mechanisms used in UKCA (including the highly simplified 3 reactions, 2 species, ST mechanism used in CMIP6 studies) and to a range of recently developed mechanisms reported in the literature through a series of global and box model experiments. Global model runs with the new mechanism enable us to simulate the global distribution of hydroperoxyl methyl thioformate (HPMTF), which we calculate to have a burden of 2.6–26 Gg S (in good agreement with the literature range of 0.7–18 Gg S). We show that the sinks of HPMTF dominate uncertainty in the budget, not the rate of the isomerisation reaction forming it, and that based on the observed DMS/HPMTF ratio, rapid cloud uptake of HPMTF worsens the model-observation comparison. Our box model experiments highlight that there is significant variance in simulated secondary oxidation products from DMS across mechanisms used in the literature, with significant divergence in the sensitivity of these products to temperature exhibited; especially for methane sulfonic acid (MSA). Our global model studies show that our updated DMS scheme performs better than the current scheme used in UKCA when compared against a suite of surface and aircraft observations. However, sensitivity studies underscore the need for further laboratory and observational constraints.
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Ben A. Cala et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2023-42', Anonymous Referee #1, 17 Feb 2023
General Comments:
The authors develop a new DMS mechanism based on the literature before testing it with box model simulations and on a global scale. In the box model, they evaluate two versions of their new model against older models of low and medium complexity, examining both timeseries and sensitivity to temperature. They then adapt four recent literature mechanisms to the box model, and compare timeseries and temperature sensitivities. Next, they run their full new mechanism, the older medium complexity mechanism, and the simple mechanism in a global model. These are compared to each other and to assorted measurements. Finally, they run the global model with additional mechanisms exploring the addition of cloud loss, the addition of a faster HPMTF loss rate, and the addition of faster HPMTF production.
Key conclusions: The most novel aspect of this paper is the comparison between contemporary DMS oxidation mechanisms, which as the authors note has not been done since 2007. While the conclusions from this section could be stronger, these data would be of interest to the research community. Of secondary importance: the authors provide interesting discussion of some of the sensitivities and uncertainties in variations of their mechanism. They also demonstrate that their new mechanism represents an improvement over previous schemes for the UKCA model.
Major comments:
The paper is unnecessarily long and does not clearly frame the authors' conclusions. I would suggest halving the length and centering the work and analysis around the comparisons with literature mechanisms. This could primarily focus on the box model results while briefly touching on the global modeling, which seems to show similar sulfur burdens and sensitivities compared to the literature. As the manuscript currently stands, the authors carefully explain so many observations from their model perturbations that it is difficult for the reader to take home a clear message. Ideally, these observations should be condensed and focused towards a central idea.
The paper also insufficiently addresses the authors decision to leave out halogen and multiphase/aqueous chemistry. The authors state that “the contribution [of BrO and Cl] is either negligible or there is large uncertainty,” citing only two papers. While prior work is variable (see discussions in Fung et al., 2022 and Hoffmann et al., 2016), it largely suggests that DMS + halogen chemistry is an important sink on the order of 10% or more and its omission should at least be much more clearly explained. Considering that it could be reasonably represented with only one or two reactions, it is not clear why this is left out. Multiphase/aqueous chemistry, which has previously been shown to be important, is also omitted. Since this typically requires different treatment in the model, its omission is more understandable but should be more clearly discussed. Due to the omission of halogen and aqueous chemistry, discussions of the relative importance of different oxidants do not seem worthwhile.
Specific Comments:
Line 18: The authors imply mechanistic uncertainty is the main reason that mechanisms in global models are oversimplified. I believe this is more a result of attempts to keep the model efficient since sophisticated mechanisms for DMS have been around for at least twenty years.
Line 57: Consider additionally citing Fung et al., 2022.
Line 67: Reference S1.4.1.
Line 86: Cite other HPMTF yields (ex. Novak et al., 2021 à 46%, Fung et al., 2022 à 33%).
Line 89: Consider citing Ye et al., 2022. This paper includes a more up-to-date figure that demonstrates the uncertainty the isomerization rate constant.
Line 111 or thereabouts: Similar sensitivity studies have been done by Fung et al., 2022 and possibly others. How does this work differ or add to this literature?
Line 122: Consider specifying the NO/NO2 ratio. [NO] is of course quite important for MTMP fate.
Line 209: Why is MSA not considered here?
Line 297: Measurement of HPMTF + OH (Ye et al., 2022) should be mentioned in this section.
Line 331: It is clear that a steady state is not achieved for MSA and SO2 in the time shown in Figure 2.
Line 371: It is not clear to me that in-depth comparison of MSA for ST is relevant since MSA is barely treated by these schemes.
Line 468: This is surprising. Why does this significant loss not affect the diel profile?
Line 476: HPMTF may deserve more discussion here. A significant factor in the daytime decrease at the higher NOx level may be that production of HPMTF slows down due to competition from MTMP + NO. Is OH significantly different between the 10 ppt and 100 ppt NOx simulations?
Line 520: What fraction of H2SO4 in your model is produced by SO2 vs CH3SO3?
Line 567: The SO2 temperature sensitivity could be more clearly explained.
Line 578: Conclusions here are quite vague. The authors could for example more clearly discuss the agreements and disagreements of the different mechanisms and anticipate the impacts of changing global temperatures on DMS chemistry.
Line 593: ratios are > 100 ppt. As written, this statement does not really make sense.
Section 4.1.1 The authors note that the model is biased very high compared to measurements. It may be worth noting that this has been seen in other modeling studies as well.
Sections 4.1.1, 4.2.1, 4.2.3: What is the impact of comparing the model with measurements from different years? This seems particularly notable for SO2 which has major anthropogenic sources.
Line 662: It is clear that the lack of an HO2 pathway in the CS2 mechanism is an obvious flaw for modeling MTMP over the ocean. This analysis doesn’t emphasize this fact.
Figure 16 c. Due to anthropogenic sulfur and differences in modeled vs measured years, it isn’t clear how relevant this comparison is.
Figure 13.a.: Is the heterogeneity in MSA greater than for DMS? This seems interesting.
Line 809: See OH + HPMTF rate measured in Ye et al., 2022.
Line 841: If this is the case, why is HPMTF/DMS for CS2-HPMTF-CLD still so low in panel b?
Line 851: What fraction of SO2 is actually from DMS oxidation?
Line 903: See Line 86 comment.
Line 904 & 907: Fung et al., 2022 also reported that HPMTF burden is not sensitive to the isomerization rate constant and quite sensitive to cloud uptake.
Section 5.5 is separated from the data by ~20 pages so it doesn’t feel that relevant by the time you get to it. I think it is possible to draw larger conclusions from these data.
Technical Corrections:
Typos and run-on sentences found in lines 53, 208, 435, and 727. Typo in Table 1.
Citation: https://doi.org/10.5194/acp-2023-42-RC1 -
RC2: 'Comment on acp-2023-42', Anonymous Referee #2, 24 Mar 2023
Review of ”Development, intercomparison and evaluation of an improved mechanism for the oxidation of dimethyl sulfide in the UKCA model”
Dimethyl sulfide (DMS) is a sulfur containing volatile compound emitted from the ocean. It is oxidized in the atmosphere and form a range of compounds including sulfate. DMS is important in the global sulfur budget and the oxidation compounds contribute to the formation of efficient cloud condensation nuclei. During recent years, new knowledge on the oxidation mechanism of DMS has appeared and it is important that this knowledge is implemented in models. At the same time, models may help in elucidating where additional data or mechanistic insight is needed.
This manuscript describes an extensive effort to update the UKCA chemistry-climate model with a more detailed description of the atmospheric oxidation of DMS than in the current version.
While the authors should be complemented for their efforts and for implementing and comparing several chemical schemes, I fully second Reviewer 1 in that the manuscript is much too long. It is hard as a reader to get an overview of what the main findings of the work are. The comparison with the box-model of the different chemical mechanisms is very interesting and I think it could strengthen the paper if the authors based on this could come up with a list of concrete key problems to address in laboratory and field studies to help constrain models on DMS oxidation and fate of the oxidation products.
Below are some more detailed comments and suggestions. In particular, the last part of the paper on global model runs I find difficult to read and it has several sections, which do not really provide conclusions and could be shortened. Instead, the authors could expand on the observations and messages from the first part. Thus, my comments at this point are mainly to the first part of the manuscript.
Abstract: The abstract contains several abbreviations (ST, CMIP6,).
Line 31: “based on the observed DMS/HPMTF ratio” It should state where this ratio was observed.
Line 32: “with a significant divergence in the sensitivity of these products to temperature” – this should be reformulated – the products themselves are not sensitive to temperature – rates of formation or similar can be sensitive to temperature.
Line 56: “due to the uncertainty in DMS oxidation” – I suggest to say uncertainty about the kinetics and mechanism involved in the oxidation of DMS.
Line 61: were the initial conditions the same across the six different chemistry schemes? This should be stated.
Equations R1-R4 are not balanced chemically - should they not be?
I suggest to provide a table in the supporting material with the 19 reactions included in the CS2 model.
Page 3 In line 87 the authors mention kisom,1 – the rate constant of the first H-shift. Later it is referred to as kisom– should be consistent.
Page 4 line 12: sensitivity studies with a slower loss, a faster production …” – it should be explained what the authors compare to – faster than what?
Page 4 line 20: is there a reference for the values used in Table S1?
The reference Glasow and Crutzen 2004 is referenced multiple times but is missing in the reference list. I assume it is Glasow and Crutzen ACP 2004. Here the DMS emission rate in the remote marine boundary layer is given as only 2x 10^9 molecule cm-2 s-1 – why was a higher value used here (corresponding to Cape Grimm summer in Glasow and Crutzen 2004)?
Page 5: the first paragraph is difficult to read. For example, the last sentence: “the data from day 7 and 8 of the runs was averaged to enable the effects of changes in the temperature on species concentration simulated in the box model to be calculated” I do not understand the meaning of this sentence – why is it necessary to average from two days to see and effect of temperature? Can the first six days not be used?
Page 5 and 6 on the 3D simulations – there is information that is perhaps not so relevant here. I suggest to provide references and only focus on the DMS chemistry in this description – to shorten the text.
I suggest the authors add a short explanation what the nudging done is.
Figure 1 is quite central to follow the manuscript. I strongly suggest the authors add equation numbers to arrows corresponding to those in the tables. I suggest that the authors provide both the chemical formula and the abbreviation – e.g. for MSA the chemical formula is missing. For the reaction of MTMP with NO (2a) in the figure it also says MSP in Figure 1 on the right side of the arrow – I do not find that in Table 2?
Page 9
The authors should expand a bit on the reason why they do not consider oxidation by BrO and Cl.
Page 10: the authors note that the Henry’s law constant they use for MSA is two orders of magnitude higher than that used by Wollesen de Jonge et al. – what was the motivation for this. I might have overlooked it but did the authors do a sensitivity study to see the impact of varying this Henry’s law constant?
Tables 2 and 3: why are not all the chemical reactions chemically balanced ? They should be balanced so that it corresponds to the rate constants given.
Reaction 2c and others – please explain the factor in bold that is multiplied with the rate constant.
Page 12 line 82: I believe it should be reaction 2d and not 2c in parenthesis.
Table 3: for some of the rate constants it is stated that they are from this work – how were they obtained? This should be stated in the heading or with a reference to the section where it is explained.
3.1:
In the text it says “Comparing CS2-HPMTF and CS2-UPD-DMS we can see that this is due to reaction 7c” – please explain in more detail how this can be seen.
When talking about lifetimes please give an approximate value, e.g. line 46 page 14: SO2 is a relatively long-lived species – here the lifetime should be given.
Page 16 line 74: the temperature sensitivity between 270 and 290 K is attributed to difference in the rate constant of DMS oxidation through the OH-addition channel. Did the authors test this statement by running the models with the same rate constant for this reaction?
Can the authors show how the model develops and when steady state in the model is reached? For example plotting the DMS, MSA, SO2 and H2SO4 concentrations versus time in one panel and the temperature versus time in panel below on the same time scale – this would help the reader get an idea of how the model develops towards steady state.
Page 19: Perhaps I misunderstand something, but for DMSO the authors discuss deposition as a significant sink but deposition is not included in the box-model? (same on page 21 where deposition of side products.
Page 22: I miss a paragraph about what the main conclusions in 3.1.1 are.
Minor:
Page 2 line 51: something is wrong with the year in the reference 20114
Citation: https://doi.org/10.5194/acp-2023-42-RC2
Ben A. Cala et al.
Ben A. Cala et al.
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