the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Feb 2024
Dimethyl sulfide (DMS) climatologies, fluxes and trends – Part A: Differences between seawater DMS estimations
Abstract. Dimethyl sulfide (DMS) is a naturally emitted trace gas that can affect the Earth's radiative budget by changing cloud albedo. Most models depend on regional or global distributions of seawater DMS concentrations and sea-air flux parameterizations to estimate its emissions. In this study, we analyze the differences between three estimations of seawater DMS, one of which is an observation-based interpolation method (Hulswar et al., 2022 (hereafter referred to as H22)) and two are proxy-based parameterization methods (Galí et al., 2018a (G18); Wang et al., 2020 (W20)). The interpolation-based method depends on the distribution of observations and the methods used to fill data between observations, while the parameterization-based methods rely on establishing a relationship between DMS and environmental parameters such as chlorophyll a, mixed layer depth, nutrients, sea surface temperature, etc., which can then be used to predict DMS concentrations. On average, the interpolation-based methods show higher DMS values compared to the parameterization-based methods. Even though the interpolation method shows higher values than the parameterization-based methods, it fails to capture mesoscale variability. The regression-based parameterization method (G18) shows the lowest values compared to other estimations, especially in the Southern Ocean, which is the high DMS region in Austral summer. The parameterization-based methods suggest significant positive long-term trends in seawater DMS (6.94 ±1.44 % decade-1 for G18 and 3.53 ±0.53 % decade-1 for W20). Since large differences, often more than 100 %, are observed between the different estimations of seawater DMS, the derived sea-air fluxes and hence the impact of DMS on the radiative budget are very sensitive to the estimate used.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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RC1: 'Comment on egusphere-2024-173', Anonymous Referee #1, 28 Mar 2024
Review for Sankirna et al. (2024) ‘Dimethyl sulfide (DMS) climatologies, fluxes and trends - Part A: Differences between seawater DMS estimations’
Sankirna et al. (2024) have presented an important evaluation of three of the most recent dimethyl sulfide (DMS) climatologies, plus a few online model parameterisations, providing a timely guide to modellers who may be deciding on how DMS should be represented in their systems. This paper also provides an important benchmarking of the updated DMS climatology interpolated from observations (Hulswar et al. 2022) which may be expected to replace the most commonly used Lana et al. (2011) climatology. While this paper is not long, and does not provide analysis beyond statistical comparison, it addresses an important question clearly. I have only minor comments on this manuscript and would recommend its publication after they have been addressed.
Comments:
Line 13: ‘Most models’ – I think you should be more specific here – you are talking about atmospheric models that represent aerosol processes.
Line 26: ‘.. the impact of DMS on the radiative budget are very sensitive to the estimate used’ – I’d perhaps remove the word ‘very’, as even if it’s a 100% increase, if its 1nm to 2nm I don’t think that would have a ‘very’ large impact on the radiative balance… Until it has been shown what the impact on the radiative balance is, I’d temper this argument.
Line 35: Sentence begging with ‘Thus ..’ - this sentence is a bit long and a little bit confusing. I think you need to make more clear the feedback that you are alluding to.
Line 37: I think you need to make mention of the comparatively large amount of literature indicating that the CLAW hypothesis likely is not plausible in the complexities of the real world (eg. Quinn & Bates, 2011), but you can at the same time quantify its importance to the global energy balance (eg. Fiddes et al. 2018).
Line 74: Can you provide a reference here: ‘A recent study…’
Methods section: I was a little bit confused here, you are using three data sets that are publicly available, but your writing makes it sound like you have re-run some of this analysis? Perhaps you can revise your writing a little in this section to make clear that you are describing the data sets and not your own methods.
Line 100: Can you clarify that the input parameters you are discussing are those that went into the G18/W20 parameterisations?
Line 103: Were G18 & W20 data sets available over the exact same time periods (1998-2010)? Can you explain a little bit here why this time period? (I think you do later, but would be good to have it upfront).
Line 130: In light of your results here, could you comment on how effective using chlorophyll as a proxy is?
Line 142: I wonder how many observations the H22 data set has in these regions? How would that impact the results?
Line 209-214: Can you speculate on why these differences exist across methods? Is it due to poor data availability to train on? Or the quality of the inputs?
Line 221: I think ‘with’ should be ‘while
Section 3.3 Long term trend: I think this section needs to have an acknowledgement that 12 years is in fact not a long term trend, certainly not enough to understand the full variability of a system with respect to important climatic and oceanographic events (eg. ENSO). I think it’s still a valuable contribution and the trends to appear quite large, but I think it just needs to be recognised that this is still really quite a short period! (and starts with one of the strongest El Nino’s recorded – I don’t know how ENSO might affect DMS, but I would be surprised if it didn’t!).
Line 230: ‘We used monthly …’ – I’m not sure why this is here, as you don’t mention any results from these data sets?
Line 247: What do you mean by ‘predictors obtained from CMIP5 and CMIP6 reconstructed models’? I’m not confident that ‘this issue can be resolved’ using climate model output – I think there are lot of issues in the CMIP6 models still around these processes, so I wouldn’t really trust what they suggest (as you just said – CMIP5 and CMIP6 suggest opposite trends for DMS, so there is large uncertainty there still!).
Line 255: this paragraph and those below is pretty dense – could you perhaps use dot points to describe each model so it flows a bit more clearly? Also – perhaps this would be better in a methods section? Also, can you describe the time period you are using here? And where did you get this data from.
Line 285: I would really love to see this geographic breakdown and perhaps a similar analysis to what you did with the other three data sets (I know that this has been done to a degree already eg. Bock et al. 2021, but it would be nice to have it all in the same place & in comparison to H22).
Citation: https://doi.org/10.5194/egusphere-2024-173-RC1 - AC2: 'Reply on RC1', Anoop Mahajan, 18 Jun 2024
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RC2: 'Comment on egusphere-2024-173', Anonymous Referee #2, 10 Jun 2024
Joge et al examine the regional and temporal variability in DMS by comparing three climatologies, one of which was generated by interpolating observational data and two of which are parameterisation based. In addition, they generate the trend in DMS for a fourteen-year period using the two parameterisation-based climatologies.
Scientific significance: 3
The science question is important, as improved estimates of the oceans DMS source are required; however, the paper is somewhat brief and perfunctory, and not very “stretchy”, as it just compares the outputs of the three different climatologies without testing sensitivities and only providing limited interpretation. In addition, the Conclusions could have been more substantial; for example, Figures 1 and 2 highlight that agreement between climatologies was poorest during summer in the southern hemisphere where marine DMS emissions will arguably have greatest impact on aerosol chemistry. This point could then have generated a recommendation in the Conclusion and abstract.
Scientific quality: 2
Scientific methods and assumptions clearly outlined and description of experiments and calculations are sufficient to enable reproduction
Presentation quality: 1
Presentation, referencing and language are all fine apart from a few typos
Specific comments
Title - is a little misleading. “Fluxes” are mentioned in the Introduction but there is no generation or presentation of fluxes in the analysis.
Line 65 notes “are usually to the order of 0.25º×0.25º and hence can include mesoscale dynamic changes” yet Lines 97-99 identify that environmental parameters are at coarser resolution with SST, salinity & nutrients at 1º×1º and MLD at 0.5°×0.5º. Consequently, the DMS climatologies are generated at 1 degree resolution (Line 102) but how significant is this lower resolution in terms of the generated DMS and the differences between climatology outputs? Could these parameters be scaled to higher resolution? Re-gridding is noted as a possible reason in Line 130 but not discussed and the reader is instead referred to G16.
Lines 129-144 My interpretation of Figure 1b is that there is generally poor agreement between the climatologies in the southern hemisphere and perhaps this point should be made clearer. The text says that “a band of elevated DMS is seen in the South Atlantic and Indian Oceans centered around the 45° S latitude as the satellite data of chlorophyll may be biased towards colored dissolved organic matter (CDOM) and detritus on the Argentinian basin.” and this comment appears to be directed at G18. Why does the potential bias of CDOM & detritus on the Argentine basin only influence the G18 climatology, when satellite chlorophyll data were used in all climatologies? The band of elevated DMS at 45S is restricted meridionally in G18, whereas H22 and W20 show broader meridional spread – does this reflect interpolation in G18, or something else? Further analysis of this discrepancy would be useful.
Fig 2A. It would be interesting to see this plotted as proportional rather than absolute difference in DMS.
Results section is quite detailed in description of regional differences between climatologies, but doesn’t highlight the fundamental point that spatial disagreement is poorest in the southern hemisphere summer where DMS is arguably having a greater impact on aerosol formation than in the Northern hemisphere.
Its not clear in the Methods section how the limited availability of southern ocean environmental data is accounted for. For example, satellite data does not generate robust PAR data where sea ice is present, and the general availability of satellite data is restricted south of 50S in early spring and late autumn which may bias DMS climatologies that are reliant on satellite-derived environmental data. Again, this limitation could be discussed.
I was disappointed that there wasn’t further analysis of the sensitivity of, and so error derived from, factors such as interpolation (for example, by testing different interpolation approaches), and spatial resolution (comparing climatologies developed using different spatial scales, and so potentially accommodating for mesoscale eddies).
Line 229. Why is the trend not examined in H22?
Section 3.4 primarily describes (and repeats) Table 1, and so could be reduced
The Conclusions section is largely Discussion, and so should be divided into two sections.
technical corrections
Line 84-85 Explain “the interconnected input, hidden and output layers”
Line 146 Where is the Corne Sea?
Line 210 These are not “decreases” but instead are underestimates
Line 224 “shows” should be “showing”
Line 255 “parametrization”
Line 291 missing word
Line 295 “W20 estimates ~3.4 % higher weighted global mean DMS”
Line 318 “there is an increase….”
Citation: https://doi.org/10.5194/egusphere-2024-173-RC2 - AC1: 'Reply on RC2', Anoop Mahajan, 18 Jun 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-173', Anonymous Referee #1, 28 Mar 2024
Review for Sankirna et al. (2024) ‘Dimethyl sulfide (DMS) climatologies, fluxes and trends - Part A: Differences between seawater DMS estimations’
Sankirna et al. (2024) have presented an important evaluation of three of the most recent dimethyl sulfide (DMS) climatologies, plus a few online model parameterisations, providing a timely guide to modellers who may be deciding on how DMS should be represented in their systems. This paper also provides an important benchmarking of the updated DMS climatology interpolated from observations (Hulswar et al. 2022) which may be expected to replace the most commonly used Lana et al. (2011) climatology. While this paper is not long, and does not provide analysis beyond statistical comparison, it addresses an important question clearly. I have only minor comments on this manuscript and would recommend its publication after they have been addressed.
Comments:
Line 13: ‘Most models’ – I think you should be more specific here – you are talking about atmospheric models that represent aerosol processes.
Line 26: ‘.. the impact of DMS on the radiative budget are very sensitive to the estimate used’ – I’d perhaps remove the word ‘very’, as even if it’s a 100% increase, if its 1nm to 2nm I don’t think that would have a ‘very’ large impact on the radiative balance… Until it has been shown what the impact on the radiative balance is, I’d temper this argument.
Line 35: Sentence begging with ‘Thus ..’ - this sentence is a bit long and a little bit confusing. I think you need to make more clear the feedback that you are alluding to.
Line 37: I think you need to make mention of the comparatively large amount of literature indicating that the CLAW hypothesis likely is not plausible in the complexities of the real world (eg. Quinn & Bates, 2011), but you can at the same time quantify its importance to the global energy balance (eg. Fiddes et al. 2018).
Line 74: Can you provide a reference here: ‘A recent study…’
Methods section: I was a little bit confused here, you are using three data sets that are publicly available, but your writing makes it sound like you have re-run some of this analysis? Perhaps you can revise your writing a little in this section to make clear that you are describing the data sets and not your own methods.
Line 100: Can you clarify that the input parameters you are discussing are those that went into the G18/W20 parameterisations?
Line 103: Were G18 & W20 data sets available over the exact same time periods (1998-2010)? Can you explain a little bit here why this time period? (I think you do later, but would be good to have it upfront).
Line 130: In light of your results here, could you comment on how effective using chlorophyll as a proxy is?
Line 142: I wonder how many observations the H22 data set has in these regions? How would that impact the results?
Line 209-214: Can you speculate on why these differences exist across methods? Is it due to poor data availability to train on? Or the quality of the inputs?
Line 221: I think ‘with’ should be ‘while
Section 3.3 Long term trend: I think this section needs to have an acknowledgement that 12 years is in fact not a long term trend, certainly not enough to understand the full variability of a system with respect to important climatic and oceanographic events (eg. ENSO). I think it’s still a valuable contribution and the trends to appear quite large, but I think it just needs to be recognised that this is still really quite a short period! (and starts with one of the strongest El Nino’s recorded – I don’t know how ENSO might affect DMS, but I would be surprised if it didn’t!).
Line 230: ‘We used monthly …’ – I’m not sure why this is here, as you don’t mention any results from these data sets?
Line 247: What do you mean by ‘predictors obtained from CMIP5 and CMIP6 reconstructed models’? I’m not confident that ‘this issue can be resolved’ using climate model output – I think there are lot of issues in the CMIP6 models still around these processes, so I wouldn’t really trust what they suggest (as you just said – CMIP5 and CMIP6 suggest opposite trends for DMS, so there is large uncertainty there still!).
Line 255: this paragraph and those below is pretty dense – could you perhaps use dot points to describe each model so it flows a bit more clearly? Also – perhaps this would be better in a methods section? Also, can you describe the time period you are using here? And where did you get this data from.
Line 285: I would really love to see this geographic breakdown and perhaps a similar analysis to what you did with the other three data sets (I know that this has been done to a degree already eg. Bock et al. 2021, but it would be nice to have it all in the same place & in comparison to H22).
Citation: https://doi.org/10.5194/egusphere-2024-173-RC1 - AC2: 'Reply on RC1', Anoop Mahajan, 18 Jun 2024
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RC2: 'Comment on egusphere-2024-173', Anonymous Referee #2, 10 Jun 2024
Joge et al examine the regional and temporal variability in DMS by comparing three climatologies, one of which was generated by interpolating observational data and two of which are parameterisation based. In addition, they generate the trend in DMS for a fourteen-year period using the two parameterisation-based climatologies.
Scientific significance: 3
The science question is important, as improved estimates of the oceans DMS source are required; however, the paper is somewhat brief and perfunctory, and not very “stretchy”, as it just compares the outputs of the three different climatologies without testing sensitivities and only providing limited interpretation. In addition, the Conclusions could have been more substantial; for example, Figures 1 and 2 highlight that agreement between climatologies was poorest during summer in the southern hemisphere where marine DMS emissions will arguably have greatest impact on aerosol chemistry. This point could then have generated a recommendation in the Conclusion and abstract.
Scientific quality: 2
Scientific methods and assumptions clearly outlined and description of experiments and calculations are sufficient to enable reproduction
Presentation quality: 1
Presentation, referencing and language are all fine apart from a few typos
Specific comments
Title - is a little misleading. “Fluxes” are mentioned in the Introduction but there is no generation or presentation of fluxes in the analysis.
Line 65 notes “are usually to the order of 0.25º×0.25º and hence can include mesoscale dynamic changes” yet Lines 97-99 identify that environmental parameters are at coarser resolution with SST, salinity & nutrients at 1º×1º and MLD at 0.5°×0.5º. Consequently, the DMS climatologies are generated at 1 degree resolution (Line 102) but how significant is this lower resolution in terms of the generated DMS and the differences between climatology outputs? Could these parameters be scaled to higher resolution? Re-gridding is noted as a possible reason in Line 130 but not discussed and the reader is instead referred to G16.
Lines 129-144 My interpretation of Figure 1b is that there is generally poor agreement between the climatologies in the southern hemisphere and perhaps this point should be made clearer. The text says that “a band of elevated DMS is seen in the South Atlantic and Indian Oceans centered around the 45° S latitude as the satellite data of chlorophyll may be biased towards colored dissolved organic matter (CDOM) and detritus on the Argentinian basin.” and this comment appears to be directed at G18. Why does the potential bias of CDOM & detritus on the Argentine basin only influence the G18 climatology, when satellite chlorophyll data were used in all climatologies? The band of elevated DMS at 45S is restricted meridionally in G18, whereas H22 and W20 show broader meridional spread – does this reflect interpolation in G18, or something else? Further analysis of this discrepancy would be useful.
Fig 2A. It would be interesting to see this plotted as proportional rather than absolute difference in DMS.
Results section is quite detailed in description of regional differences between climatologies, but doesn’t highlight the fundamental point that spatial disagreement is poorest in the southern hemisphere summer where DMS is arguably having a greater impact on aerosol formation than in the Northern hemisphere.
Its not clear in the Methods section how the limited availability of southern ocean environmental data is accounted for. For example, satellite data does not generate robust PAR data where sea ice is present, and the general availability of satellite data is restricted south of 50S in early spring and late autumn which may bias DMS climatologies that are reliant on satellite-derived environmental data. Again, this limitation could be discussed.
I was disappointed that there wasn’t further analysis of the sensitivity of, and so error derived from, factors such as interpolation (for example, by testing different interpolation approaches), and spatial resolution (comparing climatologies developed using different spatial scales, and so potentially accommodating for mesoscale eddies).
Line 229. Why is the trend not examined in H22?
Section 3.4 primarily describes (and repeats) Table 1, and so could be reduced
The Conclusions section is largely Discussion, and so should be divided into two sections.
technical corrections
Line 84-85 Explain “the interconnected input, hidden and output layers”
Line 146 Where is the Corne Sea?
Line 210 These are not “decreases” but instead are underestimates
Line 224 “shows” should be “showing”
Line 255 “parametrization”
Line 291 missing word
Line 295 “W20 estimates ~3.4 % higher weighted global mean DMS”
Line 318 “there is an increase….”
Citation: https://doi.org/10.5194/egusphere-2024-173-RC2 - AC1: 'Reply on RC2', Anoop Mahajan, 18 Jun 2024
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Sankirna D. Joge
Anoop Sharad Mahajan
Shrivardhan Hulswar
Christa Marandino
Martí Galí
Thomas Bell
Rafel Simo
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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