the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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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Status: open (until 12 May 2024)
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RC1: 'Comment on egusphere-2024-173', Anonymous Referee #1, 28 Mar 2024
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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
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