the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The sensitivity of Southern Ocean atmospheric dimethyl sulfide to modelled sources and emissions
Yusuf Bhatti
Laura Revell
Alex Schuddeboom
Adrian McDonald
Alex Archibald
Jonny Williams
Abhijith Venugopal
Catherine Hardacre
Erik Behrens
Abstract. The biogeochemical behaviour of the Southern Ocean is complex and dynamic. The processes that affect this behaviour are highly dependent on physical, chemical, and biological constraints, which are poorly constrained in Earth System Models. We assess how emissions of dimethyl sulfide (DMS), a precursor of sulfate aerosol, change over the Southern Ocean when the chlorophyll-a distribution, which influences oceanic DMS production, is altered. Using a nudged configuration of the atmosphere-only United Kingdom Earth System Model, UKESM1-AMIP, we performed nine 10-year simulations using forcings representative of the period 2009–2018. Four different seawater DMS data sets are tested as input for these simulations. Three different DMS sea-to-air flux parameterizations are also explored. Our goal is to evaluate the changes in oceanic DMS, sea-to-air fluxes of DMS, and atmospheric DMS through these different simulations during austral summer. The mean spread across all the simulations with different oceanic DMS datasets, but the same sea-to-air flux parameterizations, is 112 % (3.3 to 6.9 TgS Yr−1). The mean spread in simulations using the same oceanic DMS dataset, but differing sea-to-air flux parameterisations is 50–60 % (2.9 to 4.7 TgS Yr−1). The choice of DMS emission parameterisation has a larger influence on atmospheric DMS than the choice of oceanic DMS source. We also find that linear relationships between wind and DMS flux generally compare better to observations than quadratic relationships. Simulations that implement a quadratic emission rate show on average 35 % higher DMS mixing ratios than the linear emission rates. Simulations using seawater DMS derived from satellite chlorophyll-a data in combination with a recently-developed flux parameterisation for DMS show the closest agreement with atmospheric DMS observations and are recommended to be included in future simulations. This work recommends for Earth System Models to include a sea-to-air parameterization that is appropriate for DMS, and for oceanic DMS datasets to include inter-annual variability based on observed marine biogenic activity. Such improvements will provide a more accurate process-based representation of oceanic and atmospheric DMS, and therefore sulfate aerosol, in the Southern Ocean region.
Yusuf Bhatti et al.
Status: open (until 26 Jun 2023)
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RC1: 'Comment on egusphere-2023-868', Anonymous Referee #1, 01 Jun 2023
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General comments
The topic of the study is important and urgent (https://doi.org/10.1525/elementa.2022.00130) and the study is a step toward improving a component of Earth System models that is not well constrained. The study quantified the sensitivity of atmspheric DMS to oceanic DMS source and flux parameterization over the Southern Ocean. While these results are informative, there are a few issues that need to be addressed to support some of the conclusions made in the study. I provide these issues with suggestions on how they can be addressed below. I also provide specific comments below my general comments.
The choice of oceanic DMS parameterization: The authors chose Anderson et al. (2001) for deriving DMS from chlorophyll-a, while there are other algorithms to derive DMS from chlorophyll-a and other environmental variables developed since 2001.This choice needs to be explained/justified in the manuscript.
Investigation of inter-annual variability: The authors conclude that interannual variability is important (e.g. L18, L416), but this is not demonstrated well in the study. Figure A1 is the only result where the interannual variability can be discussed, bit it gives an impression that the interannual variability is not important (the year-to-year variation of the mean DMS concentration is subtle). To assess the impact of representing interannual variability in the DMS source, the authors could do an additional simulation in which MODIS-DMS does not change from year to year. By comparing this simulation with one of the MODIS simulations in Table 2, the impact can be quantified and its importance can be justified.
Investigation of spatio-temporal variability: The authors introduce the importance of spatio-temporal variability (L57) of DMS in the Southern Ocean, but the results are mostly discussed for spatially and temporally averaged values. Furthermore, some model results are averaged over the entire Southern Ocean, which are compared to the observations that are representative of specific regions/seasons (e.g. L312, L318, L320, L323, L352). While apple-to-apple comparison may not be possible, there are places for improvement. For example, the spatial maps of the simulated DMS flux and atmospheric DMS (such as done for oceanic DMS concentration as in Fig 2a-d) can be added to better compare with the observations from specific regions. These figures will also demonstrate the importance of spatial variability.
Writing style: Section 5 seems like a combination of discussion and conclusions, so I suggest to change the section title to “Discussion and conclusions”. However, I also noted in my specific comments that there are paragraphs in the Results section that I feel belong to Discussion.
Specific comments
L3: Suggest to delete or rewrite this sentence. The authors did not assess how DMS emissions change when the chlorophyll-a distribution is altered. This would require an investigation of the relationship between the DMS emissions and the chlorophyll-a distribution.
L9: “The mean spread” of which quantity?
L11: “The choice of” Is this conclusion correct? The previous sentences indicate that the choice of the oceanic DMS source (112%) has a larger influence on the atmospheric DMS than that of the flux parameterization (50-60%).
L12: Suggest to rewrite this sentence. It is not that linear relationships compare better to observations, but assuming/simulation with the linear dependence of the DMS flux on wind speed results in better representation of atmospheric DMS distribution.
L15: Where is the evidence for the closest agreement? This could be clarified in "Discussion and conclusions".
L34: Suggest to replace “mechanisms, with varying focus” by “approaches that are dependent”.
L40: Suggest to replace “approximate” by “prescribe”.
L48: Suggest to repalce “and has” by “and this emission has”.
L55: Older quadratic approaches are based on other gases? If so, this could be mentioned here to emphasize the issue.
L57: Should “observations” be “concentrations”? Observations are highly variable implies that some regions/seasons are observed more than the others. I am not sure if this is what the authors meant to state here.
L59: Suggest to replace “real-world” by “remotely-sensed”.
L61: Suggest to either remove “nudged to observation” or replace it by “nudged-to-observation".
L68: “the relative importance” to what?
L73: Suggest to move the description of MEDUSA later when the oceanic DMS dataset is introdued. The model used here is the atmopsheric-only configuration, so the oceanic component does not need to be introduced here.
L85: In addition to the details provided in this section, I suggest to add information on boundary conditions such as sea surface temperature data used for these simulations that affect both the atmospheric circulation and DMS emissions (Eq 3).
L86: Suggest to rewrite “biologically productive” to something else. “Biological” is a bit ambiguous, as it is broad. It could refer to microalgae to big animals that have different seasonality. For clarity, it may be better to refer to DMS instead of biology.
L88: This paragraph belongs somewhere else, as it is not a model description.
L110: "modified” implies that the parameterization was modified, but my impression is that the authors only modified the chlorophyll dataset. If so, please rewrite this sentence.
L115: Suggest to define the acronym MODIS when it appears for the first time in the text instead of here.
L117: How are DMS sources and emissions from sea ice covered areas treated? Small gap of 1% probably excludes ice-covered grid cells. DMS emissions from marginal ice zone can be important (https://doi.org/10.1525/elementa.2020.00113).
L148: It is unclear how applying W14 and B17 parameterizations leads to testing the lower limits.
L158: Related to my earlier comment on L85, was HadISST used also for the boundary conditions for the atmospheric circulation simulations?
L172: Suggest to replace “validate” by “evaluate”.
L201: Suggest to replace “includes chlorophyll-a” by “accounts for chlorophyll-a variations”.
L209: Suggest to move “respectively” to “used, respectively)”.
Fig 3: For readers not familiar with violin plots, the caption needs a brief description of what violin plots represent in general.
L249: Suggest to either delete “and would be expected to be highly biased” or clarify what it is meant by highly biased. Also for fair comparison, when was the TAN1802 data taken (before/during/after blooms)?
L265: Suggest to add a few words to briefly explain why higher correlations are expected at higher latitude.
L266: The fact that the correlation in the Southern Ocean is high (0.75) does not necessarily support the previous statement. It needs to show the latitudinal dependence (whether the correlations increase with latitude).
L270: Half sounds many, but in fact only two (out of four), I think? It might be good to also discuss the lack of DMS models in current ESMs. Also, this paragraph sounds more like discussion than results.
L273: Suggest to replace “realistic” by “readily available”.
L285: Is Figure 3 adequate to make the claim that MODIS-DMS is adequate?
Fig 5: Suggest to utilize color for distinguishing different DMS sources
L301-302: The two sentences say the same thing. Suggest to remove one. Also, this finding (the choice of DMS source is more important than the choice of flux parameterization) is contradictory to what is written in the abstract (L11).
L303: It is unclear what this sentence is referring to.
L313: Why not compare the model results averaged over the same region as Webb et al. (2019) instead of comparing with the Southern Ocean averages for apple-to-apple comparison?
L328: “is likely positively skewed” can be checked to see if the model agrees with this finding by looking at the spatial distribution.
L332: This paragraph seems more appropriate to be a part of Discussion and conclusions, instead of Results.
Sec 3.3: How does the distribution of atmospheric DMS differ from that of oceanic DMS emission?
Figure 6: Is this model result representative of the Southern Ocean or the three observational stations?
L365 and Fig 7: Suggest to delete the results of PMOA. As the authors stated (L378), the results of PMOA require further investigation that does not seem to fit into the scope of this study. Adding this result would require additional info on intro, methods, and discussion.
L374: Suggest to rewrite this sentence. It states about CMIP6 but the paper cited is from 2011, many years before CMIP6.
L385: Suggest to cite a paper that compares nudged vs non-nudged runs to support this argument.
L402: How is this different from L399?
L420: Why recommend Lana while there is an updated climatology?
Appendix A: Figures should be labeled as A1-4 instead of A-D1
Citation: https://doi.org/10.5194/egusphere-2023-868-RC1
Yusuf Bhatti et al.
Yusuf Bhatti et al.
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