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 B: Sea-air fluxes
Abstract. Dimethyl sulfide (DMS) significantly contributes to cloud condensation nuclei (CCN) formation in the marine environment. DMS is ventilated from the ocean to the atmosphere, and in most models, this flux is calculated using seawater DMS concentrations and a sea-air flux parameterization. Here, climatological seawater DMS concentrations from interpolation and parameterization techniques are passed through seven flux parametrizations to estimate the DMS flux. The seasonal means of calculated fluxes are compared to identify differences in absolute values and spatial distribution, which show large differences depending on the flux parameterization used. In situ flux observations were used to validate the estimated fluxes from all seven parameterizations. Even though we see a correlation between the estimated and observed values, all methods underestimate the fluxes in the higher range (>20 µmol m-2 d-1) and overestimate the fluxes in the lower range (< 20 µmol m-2 d-1). The estimated uncertainty in DMS fluxes is driven by the uncertainty in seawater DMS concentrations in some regions but by the choice of flux parameterization in others. We show that the resultant flux is hence highly sensitive to both and suggest that there needs to be an improvement in the estimation methods of global seawater DMS concentration and sea-air fluxes for accurately modeling the effect of DMS on the atmosphere.
- Preprint
(2438 KB) - Metadata XML
-
Supplement
(3219 KB) - BibTeX
- EndNote
Status: open (until 03 May 2024)
-
RC1: 'Comment on egusphere-2024-175', Nadja Steiner, 04 Apr 2024
reply
Review: Dimethyl sulfide (DMS) climatologies, fluxes, and trends - Part B: Sea air fluxes
By Sankirna D. Joge et al.
The paper evaluated DMS fluxes based on different DMS climatologies, windspeed fields and gas transfer velocity parameterisations. The paper further evaluates which of those influences is contributing most to the differences in total flux.
While the study is generally providing some useful insights, I would recommend some major revisions to the paper. In particular I am missing a proper discussion section, including comparisons to earlier comparisons among k-parameterisations and DMS fluxes. What is new/different here compared to earlier ones, are there new scientific insights/messages? Furthermore the scale differences (temporal and spatial) need to be better discussed with respect to the in situ vs calculated fluxes. It is questionable that those are comparable in this study.
The writing is generally ok, but some English language editing particularly with respect to missing articles is recommended. (I provided annotations in the pdf)
Detailed comments (smaller notes and language edits are only in the pdf annotations)
The introduction has generally rather old references and could do with some updates. Especially given the discussions following Quinn and Bates 2011, maybe some newer refs would help to emphasize DMS studies are still valuable.
L63: is Ca ignored here? Maybe see Steiner and Denman, 2008 (https://doi.org/10.1016/j.dsr.2008.02.010, their Fig 6) on difference in flux if the atmospheric concentration is ignored in higher emission regions, for example station papa (note dependence on boundary layer)
Section 2: Please clarify that you are not comparing flux calculations but parameterisations for the gas exchange velocity k (correct throughout). Also please add a k vs u figure and discuss the differences among parameterisations
Please be accurate with the wording. The word observed should be used for actual observations in the field, not what is seen in a parameterization-derived figure. Please correct throughout as it is confusing.
If talking about sensitivity, ensure the sensitivity has been defined and tested, if not use a different word. Similarly, if using the word significant, a significance test needs to be made. If not, please use another word. (locations indicated in annotated pdf).
L186 …seen in the LM86 parameterization, which consistently displays lower values than the N00b parametrization”. This is to be expected if linear versus quadratic windspeed dependence is applied – should be discussed in context of a k-u figure.
As a general rule, other than continents or large ocean basins, please identify specific locations in the map, e.g. Mauritius, Somalia... ( so the reader doesn’t have to take out an atlas to follow the discussion)
L258 …Overall, the choice of seawater DMS estimation method has larger influence on sea-air DMS flux than the
choice of flux parameterization (Bhatti et al., 2023).
Is this a result from Bahtti et al or from this study, if the latter clarify that this is also highlighted/shown in Bhatti et al. if it is not a result from this study it should be in the intro or discussion
A discussion session is missing. While some components of the result section can be moved into the discussion session, I am missing a comparison to earlier parameterisation intercomparisons, especially Tesdal et al 2015, https://doi.org/10.1071/EN14255 , but also e.g. Steiner et al. https://elischolar.library.yale.edu/journal_of_marine_research/170 their Fig 1. Also some discussion on why and where the parameterisations differ - link to k versus u figure. Improved discussion on spatial and temporal scales in context with the comparison to in situ observations
Results section and table 1: The text with the long lists of max and mins is rather tedious to read. Maybe remove a good part of it and add a max column into Table 1 or add a table with max/mean/mins in the Appendix, potentially divided into different ocean basins.
-
RC2: 'Comment on egusphere-2024-175', Anonymous Referee #2, 12 Apr 2024
reply
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-175/egusphere-2024-175-RC2-supplement.pdf
-
RC3: 'Comment on egusphere-2024-175', Anonymous Referee #3, 20 Apr 2024
reply
This study led by D. Joge offers a valuable comparison of DMS flux parameterizations. Here are some additional thoughts to complement the two existing reviews.
In the introduction, it would be beneficial to clarify the distinctive contribution of this analysis. The authors briefly reference a previous intercomparison (lines 31–37) before delving into the specifics of their current work (lines 38–45). Inserting an intermediate paragraph summarizing the key differences between this study and prior research, along with the main outcomes from the companion paper (part A), would enhance the paper’s coherence.
Figures:
While the current version of the manuscript includes compelling figures, a few more could enhance reader comprehension. Here are some suggestions:
- Section 2.1: Add figures to highlight the differences between the various parameterization methods (which may not be immediately clear from the equations alone). Potential figures could illustrate:
i) wind speed dependency of air-water gas transfer velocity for the different parameterizations, scaled to a Schmidt number at e.g., 20ºC;
ii) temperature dependency of air-water gas transfer velocity for the different parameterizations, scaled to different wind speeds (with one sub-figure per wind regime);
iii) temperature dependency of the Schmidt number for the different parameterizations. - Section 3: While Figure 3 is commendable, Figures S3 and S4 could be more informative. A ‘summary figure’ combining results from these different figures could be beneficial. For instance, consider a figure where each grid box indicates the dominant contributing to the total uncertainty (using distinct colors for k, DMS, and wind). Alternatively, create one global map per parameter (k, DMS, wind) displaying, for each grid box, the percentage contribution to the total uncertainty.
Additional comments:
Line 32: The statement “with the wind proven to be one of the most influencing factors” could be expanded upon. DMS flux measurements have revealed a decrease in gas transfer at medium to high wind speeds (> 10 m/s), attributed to wave-wind interactions and surfactant effects (Zavarsky et al., 2018), factors typically overlooked in traditional approaches (Bell et al., 2017). This discussion should be incorporated into the introduction.
Line 43: A closing parenthesis is missing after W20.
ReferencesBell, T. G., Landwehr, S., Miller, S. D., de Bruyn, W. J., Callaghan, A. H., Scanlon, B., Ward, B., Yang, M., and Saltzman, E. S.: Estimation of bubble-mediated air–sea gas exchange from concurrent DMS and CO2 transfer velocities at intermediate–high wind speeds, Atmospheric Chem. Phys., 17, 9019–9033, https://doi.org/10.5194/acp-17-9019-2017, 2017.
Zavarsky, A., Goddijn-Murphy, L., Steinhoff, T., and Marandino, C. A.: Bubble-Mediated Gas Transfer and Gas Transfer Suppression of DMS and CO2, J. Geophys. Res. Atmospheres, 123, 6624–6647, https://doi.org/10.1029/2017JD028071, 2018.
Citation: https://doi.org/10.5194/egusphere-2024-175-RC3 - Section 2.1: Add figures to highlight the differences between the various parameterization methods (which may not be immediately clear from the equations alone). Potential figures could illustrate:
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 255 | 55 | 16 | 326 | 21 | 6 | 8 |
- HTML: 255
- PDF: 55
- XML: 16
- Total: 326
- Supplement: 21
- BibTeX: 6
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1