the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Apr 2026
DMS, MeSH and nanoparticles in semi-controlled deck-borne experiments using Antarctic seawater: on the effect of UV light
Abstract. Sulfur-containing volatile organic compounds (VOCs) such as dimethyl sulfide (DMS) and methanethiol (MeSH) are are of particular interest among oceanic VOCs emitted by the ocean, both for their central role in the marine sulfur cycle and as potential precursors to secondary aerosol formation. However, the quantification of DMS and MeSH emissions as a function of biological components of the ocean under variable environmental factors are still too scarce for reliable future predictions. In this study we report on measurements of DMS, MeSH and nanoparticle concentrations in the headspace of two on-deck Air-Sea Interface Tanks (ASITs). The cover of one ASIT prevented the transmission of UV light below 380 nm in wavelength and we report on the effect of UV light on fluxes and concentrations. These measurements were carried out near the Antarctic Peninsula during the POLAR-CHANGE campaign in summer 2023. Air-sea fluxes inside the ASITs were always positive, i.e. degassing from seawater to air, with DMS and MeSH fluxes averaging 3.03 pmol·m⁻²·s⁻¹ (FASIT-DMS) and 0.64 pmol·m⁻²·s⁻¹ (FASIT-MeSH), respectively. DMS emission did not vary significantly between day and night, but the ratio FASIT-MeSH/(FASIT-DMS + FASIT-MeSH) showed a clear maximum at night and a decrease over daytime. Calculated aqueous DMS concentrations showed maxima in the open Southern Ocean north of the Antarctic Peninsula (2.5–3 nM), minima in the Marginal Ice Zone (MIZ) in the Weddell Sea (1 nM) and moderate values along the western coast of the peninsula (around 1.5–2 nM). Cryptophytes, nanophytoplankton, and bacterial concentrations showed positive correlations with calculated aqueous DMS and MeSH concentrations during two experiments when seawater was held in the ASITs for two days. Removal of UV light increased DMS fluxes by 24 % and MeSH fluxes by 58 %. New particle formation occurred only in the absence of UV-light. Interestingly, the highest impact of UV removal, especially on increased MeSH emission, was seen during the night suggesting a lag period between the exposure and the physiological response of the cells. UV light caused slight phytoplankton light stress at noon, which negatively affected the short-term growth of nanophytoplankton in the ASIT, especially in open Southern Ocean waters.
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Notice on discussion status
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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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-1720', Anonymous Referee #1, 13 May 2026
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AC1: 'Reply on RC1', Guillaume Chamba, 29 Jun 2026
I would like to express my sincere thanks to the reviewers of this article for their careful reading, the improvements and corrections they have made, and their highly pertinent questions, including those regarding the limitations of this study and, in particular, the difficulty of linking atmospheric measurements to marine biology, which lies at the heart of the approach presented. A response to each of these questions is provided in the document below, and I would like to assure the reviewers that their suggestions will be taken into account in the next version of the article, particularly regarding the statistical analysis of the results presented, the discussion of these results and the changes in the figures.
-
AC1: 'Reply on RC1', Guillaume Chamba, 29 Jun 2026
-
RC2: 'Comment on egusphere-2026-1720', Anonymous Referee #2, 19 May 2026
Antarctic seawater: on the effect of UV light”, presents some interesting and novel results applying a new technique to study this topic in the Southern Ocean. Overall, the article is interesting, and the authors present some intriguing results in an article that read well and it is easy to follow. However, without a more rigorous statistical treatment of the data it is very hard to assess if the differences presented by the authors are significant or not, which hinders the ability of the article to convey a more compelling argument.
For example, the authors report a very interesting result on the influence of UV light absence on particle formation, but in the article, there is no statistical test to compare the results (a Wilcoxon pairwise test will suffice). Besides in figures 9B and 9C have different Y axis so it is very hard to assess if the differences are significant or not, which clearly limits the ability of the manuscript to convey a strong message with significant results. Therefore, my main concern is with the statistical analysis, or the lack of it for most of the article. My general suggestion for the authors is to include a statistical analysis section in the methods where they explain in detail how they tested for the significance of the differences they mention throughout the article. Following the example in lines 369-371, when the authors compare DMS fluxes, will greatly improve the quality of the article and will give more confidence in the findings and conclusions of the present work.
In fact, in some parts of the manuscript (lines 538-540) the authors refer to cryptophytes abundance as closely related to DMS and MeSH sweater concentrations, which does not correspond with the results the authors present in figure S8A where there is no significant relationship between cryptophytes and DMS/MeSH sweater concentrations. Results in figure 11 look like they are not significant and therefore should not be considered as such, that is why the authors should show the results from some statistical test that supports the differences found by them because as the article is it looks like there are no differences. Therefore, a general review of the statistics and results interpretation in light of the new statistical analysis is needed to improve the quality of the article. There are more examples in the pdf enclosed including the statistical problems of using a linear regression forced through the origin (figure 13) which are not addressed in the text.
Specific comments
Role of phytoplankton in dissolved gases concentrations
Diatoms in Antarctica are also capable of producing DMSP (see details in pdf enclosed) and since they may represent a large fraction of the phytoplankton community in some parts of the study area, their contribution towards the DMS and MeSH concentrations may be not negligible. I do not know if the authors have some data/samples to study microphytoplankton, including diatoms, but so far, they have present none. If that is the case it is impossible to include the information now, but at least the authors should discuss this situation in the introduction, methods and discussion sections so they fully address the potential limitations and problems derived for not considering this large group within the phytoplankton community.
Finally, all the relationships between microbial abundances and the sweater concentrations of DMS and MeSH are related to the experiments where the DMS and MeSH seawater concentrations were not measured directly, but back calculated from atmospheric measurements where mixing ratios and the fast transformation of the DMS into SO2/H2SO4/MSA may likely affect the back calculation of seawater concentrations. This calls for some nuance and care when analyzing the patterns obverse, specially when the authors do not report such patters for the continuous flow observations, which is always difficult observed since both (sea and air) are two moving fluids.
More specific comments are available in the pdf enclosed.
-
AC2: 'Reply on RC2', Guillaume Chamba, 29 Jun 2026
I would like to express my sincere thanks to the reviewers of this article for their careful reading, the improvements and corrections they have made, and their highly pertinent questions, including those regarding the limitations of this study and, in particular, the difficulty of linking atmospheric measurements to marine biology, which lies at the heart of the approach presented. A response to each of these questions is provided in the document below, and I would like to assure the reviewers that their suggestions will be taken into account in the next version of the article, particularly regarding the statistical analysis of the results presented, the discussion of these results and the changes in the figures.
-
AC2: 'Reply on RC2', Guillaume Chamba, 29 Jun 2026
Peer review completion
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2026-1720', Anonymous Referee #1, 13 May 2026
The manuscript „DMS, MeSH and nanoparticles in semi-controlled deck-borne experiments using Antarctic seawater: on the effect of UV light” from Chamba et al., reports on fluxes of MeSH and DMS during on-deck incubations in so called Air-sea Interaction Tanks (ASITs) and their relation to particle formation. The authors found differences in fluxes using Antarctic waters dependent on UV light availability and furthermore could show that significant new particle formation was only visible when no UV light was available. Seawater concentrations of MeSH and DMS have been calculated from air mixing ratios and are related to biological parameters in order to explain the variability of those gases. The data shows that nanophytoplankton cell counts could better explain DMS variabilities compared to the widely used proxy chl-a.
The presented work shows a clear research idea in order to improve our understanding on the influence of biologically produced trace gases and their air-sea exchange on particle formation in the atmosphere. The manuscript is well structured and most of the time well presented. However, before publication, I would like to address following points.
General comments:
Calculation from atmospheric mixing ratios to oceanic water concentrations.
Could the authors elaborate a bit on the factor 2.6 (deviation from equilibrium) used in eq(3). They refer to data from Rocco et al. (2025a), but it is still unclear where the factor 2.6 is derived from. I understand that the authors derived the factor for MeSH from data shown in Fig.S7 to match the calculated water concentrations from the ASIT with the seawater measurements at the UW outlet from Wohl & Williams et al., (2026). Given that this factor accounts for the deviation from equilibrium I was wondering why MeSH has an almost 3 times stronger factor than DMS?
Links between dissolved gases and microorganisms during experiments.
The authors try very well to explain the links between dissolved gases and microorganisms. However, there are no direct measurement of dissolved trace gases within the ASITs during the experiments. DMS and MeSH are solely calculated based on measured atmospheric mixing ratios. Calculated fluxes (and finally water concentrations) are always in misbalance between water and atmosphere. This is partly accounted for in the calculation of dissolved aqueous concentrations, however, fast changing atmospheric mixing ratios throughout an experiment could overestimate the calculated variability in the aqueous phase. This can particularly be seen in Fig.S7. On Feb 25th DMS air mixing ratios significantly decrease (Fig.3), so do the calculated DMS water concentrations. However, direct measurements from Wohl & Williams et al. (2026) at the underway outlet at the same time show significantly higher concentrations. A similar offset (but vice versa) is visible on March 7th. Furthermore, calculated DMS and MeSH concentrations at the start of ExpB are significantly higher compared to measured water values at the same time. If this discrepancy is potentially induced only through calculation from air-side to water-side values it does not reflect the actual trace gas variability in the water phase of ExpB. Therefore, drawing conclusions from correlating measured biological parameters with calculated trace gas concentrations should be undertaken with care. Moreover, the authors state that they found no strong correlation between aqueous DMS and MeSH and biological parameters during continuous flow which already points to my previous point (when having two continuous flows (water and air) instead of just one).
Specific comments:
Figure 1: It is not clear to me what the points of the cruise track exactly mean. As the distance between the points changes from time to time, I was thinking that each point might be a discrete (or averaged continuous) measurement point. But as no data is shown before Feb 22nd and after March 11th I might be wrong. Please clarify in the figure caption.
l.125. Which kind of seawater pump was used for the water supply? A centrifugal pump e.g. might damage phytoplankton and causes cell lysis which would significantly influence e.g. DMS and MeSH concentrations and subsequent fluxes when monitored in static flow mode in the experiments (e.g. dissolved DMSP conversion to DMS/MeSH). At least the community in he ASITs would not be “natural” anymore.
l.258ff.: All mentioned compounds which show no enrichment in the ASIT headspace are (much) less soluble in seawater compared to MeSH and DMS. Therefore, proposed cold seawater and absence of wind friction should have a stronger influence (dampening) on the gas exchange on MeSH and DMS than on the other gases. Very low oceanic concentrations might be the dominant factor, however, is there any possibility to prove such low concentration with the help of data availability from Wohl & Williams (2026)? With the biological data provided in the manuscript together with a concentration range of seawater DMS of 1-3nM, isoprene (direct product by phytoplankton) should be supersaturated in the water phase which would lead to elevated atmospheric values (at least during the experiments).
Figure 3: Did the authors also measure DMS and MeSH in the ambient line as they did for ozone? As there appears to be a (known) loss of ozone in the long sampling lines (and in the ASIT itself) it would be helpful to rule that out for the other gases. If there are losses as well e.g. in the ASIT this could potentially explain why the authors did not see an increase in atm mixing rations of isoprene, BTEX, etc. Flux from the water to the air and loss in the ASIT itself could cancel each other out. Furthermore, information of kind of particle filters is missing in the manuscript. Might those filters also influence ozone levels downstream if particles (already loaded on filters after some time) react with ozone?
Figure 5: For the fluxes in the noUV Exp A (green crosses) there seems to be a more exponential relation (than linear) which is not visible in the Control ASIT. Did the authors check if this might be a significant difference? This feature is not visible in the relation between atm mixing ratios. Wouldn’t this imply that under no UV conditions atmospheric MeSH reacts faster than DMS whereas with UV the loss rates in the atmosphere are similar?
Figure 7, Figure S3: Diel variations. Did the authors try to normalize the atmospheric mixing ratios (e.g. to the maximum or mean value per day) when checking diel variations? As discussed earlier in the manuscript the absolute concentrations changed significantly dependent on regions (e.g. coastal vs open ocean). This would probably reduce the standard deviation and a potential dial cycle might be more pronounced (visually but also statistically). This technique could also be applied to the radiation data as of now it seems that data is skewed to stronger irradiations in early afternoon.
l.398ff/Figure 9: Do the authors have any explanation why the particle numbers in the noUV-ASIT are lower during each experiment compared to the particle data of the respective region (coastal vs ExpA and open ocean vs ExpB)?
ll.408ff.: It is obvious that chl-a values at the beginning of ExpA are significantly lower (0.5µg/L) than following concentrations (2.0 µg/L) throughout the experiment. Has this sample also been taken from the tap of one of the ASITs or directly at the UW pump outlet? If so, could the location of sampling bias the data? Nanoplankton 15-20µm data show a similar trend (also for ExpB). Furthermore, are chl-a datapoints hidden behind the legend of ExpA or is there no data available for 6am and 12pm?
Fv/Fm ratio: Could there also be mechanistic stress (e.g. pumping shear stress) showing up in the Fv/Fm ratio? It is quite obvious that Fv/Fm is much lower in the beginning of the experiments compared to later stages. According to Fig. 11 UW Fv/Fm ratios are generally lower compared to ASIT ratios. Do the authors also have Fv/Fm data from CTD measurements? This could give valuable insight if microbial community in ASIT is either just less stressed compared to being pumped through hoses, or if microbes are in general less stressed compared to the “real ocean”. If light stress would be the only issue for Fv/Fm ratios, the difference between UW and ASITs should be vice versa due to the different water depths (UW: 4m, ASIT: ~1m).
l.456ff. Aqueous-phase reactivity of DMS. I like the discussion about the potential loss to explain differences in DMS water concentrations in between both ASITs. How about DMS to DMSO oxidation as a potential loss mechanism? Additionally, I was wondering if the authors also considered a potential stronger DMS production in the noUV ASIT compared to the control ASIT. As the authors already pointed out, the difference in between both treatments is strongest during the two experiments. Is there anything known about phytoplankton types around Antarctica with inhibited DMSP production under UV stress?
Citation: https://doi.org/10.5194/egusphere-2026-1720-RC1 -
AC1: 'Reply on RC1', Guillaume Chamba, 29 Jun 2026
I would like to express my sincere thanks to the reviewers of this article for their careful reading, the improvements and corrections they have made, and their highly pertinent questions, including those regarding the limitations of this study and, in particular, the difficulty of linking atmospheric measurements to marine biology, which lies at the heart of the approach presented. A response to each of these questions is provided in the document below, and I would like to assure the reviewers that their suggestions will be taken into account in the next version of the article, particularly regarding the statistical analysis of the results presented, the discussion of these results and the changes in the figures.
-
AC1: 'Reply on RC1', Guillaume Chamba, 29 Jun 2026
-
RC2: 'Comment on egusphere-2026-1720', Anonymous Referee #2, 19 May 2026
Antarctic seawater: on the effect of UV light”, presents some interesting and novel results applying a new technique to study this topic in the Southern Ocean. Overall, the article is interesting, and the authors present some intriguing results in an article that read well and it is easy to follow. However, without a more rigorous statistical treatment of the data it is very hard to assess if the differences presented by the authors are significant or not, which hinders the ability of the article to convey a more compelling argument.
For example, the authors report a very interesting result on the influence of UV light absence on particle formation, but in the article, there is no statistical test to compare the results (a Wilcoxon pairwise test will suffice). Besides in figures 9B and 9C have different Y axis so it is very hard to assess if the differences are significant or not, which clearly limits the ability of the manuscript to convey a strong message with significant results. Therefore, my main concern is with the statistical analysis, or the lack of it for most of the article. My general suggestion for the authors is to include a statistical analysis section in the methods where they explain in detail how they tested for the significance of the differences they mention throughout the article. Following the example in lines 369-371, when the authors compare DMS fluxes, will greatly improve the quality of the article and will give more confidence in the findings and conclusions of the present work.
In fact, in some parts of the manuscript (lines 538-540) the authors refer to cryptophytes abundance as closely related to DMS and MeSH sweater concentrations, which does not correspond with the results the authors present in figure S8A where there is no significant relationship between cryptophytes and DMS/MeSH sweater concentrations. Results in figure 11 look like they are not significant and therefore should not be considered as such, that is why the authors should show the results from some statistical test that supports the differences found by them because as the article is it looks like there are no differences. Therefore, a general review of the statistics and results interpretation in light of the new statistical analysis is needed to improve the quality of the article. There are more examples in the pdf enclosed including the statistical problems of using a linear regression forced through the origin (figure 13) which are not addressed in the text.
Specific comments
Role of phytoplankton in dissolved gases concentrations
Diatoms in Antarctica are also capable of producing DMSP (see details in pdf enclosed) and since they may represent a large fraction of the phytoplankton community in some parts of the study area, their contribution towards the DMS and MeSH concentrations may be not negligible. I do not know if the authors have some data/samples to study microphytoplankton, including diatoms, but so far, they have present none. If that is the case it is impossible to include the information now, but at least the authors should discuss this situation in the introduction, methods and discussion sections so they fully address the potential limitations and problems derived for not considering this large group within the phytoplankton community.
Finally, all the relationships between microbial abundances and the sweater concentrations of DMS and MeSH are related to the experiments where the DMS and MeSH seawater concentrations were not measured directly, but back calculated from atmospheric measurements where mixing ratios and the fast transformation of the DMS into SO2/H2SO4/MSA may likely affect the back calculation of seawater concentrations. This calls for some nuance and care when analyzing the patterns obverse, specially when the authors do not report such patters for the continuous flow observations, which is always difficult observed since both (sea and air) are two moving fluids.
More specific comments are available in the pdf enclosed.
-
AC2: 'Reply on RC2', Guillaume Chamba, 29 Jun 2026
I would like to express my sincere thanks to the reviewers of this article for their careful reading, the improvements and corrections they have made, and their highly pertinent questions, including those regarding the limitations of this study and, in particular, the difficulty of linking atmospheric measurements to marine biology, which lies at the heart of the approach presented. A response to each of these questions is provided in the document below, and I would like to assure the reviewers that their suggestions will be taken into account in the next version of the article, particularly regarding the statistical analysis of the results presented, the discussion of these results and the changes in the figures.
-
AC2: 'Reply on RC2', Guillaume Chamba, 29 Jun 2026
Peer review completion
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- 1
Cited
Guillaume Chamba
Karine Sellegri
Valérie Gros
Clémence Rose
Leah Williams
Elisa Berdalet
Dolors Vaqué
Magda Vila
Queralt Güell-Bujons
Elisabet-Laia Sà
Yaiza M. Castillo
Arianna Rocchi
Aurélie Colomb
Jean-Marc Pichon
Mickaël Ribeiro
Charel Wohl
Manuel Dall'Osto
Rafel Simó
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3681 KB) - Metadata XML
-
Supplement
(2234 KB) - BibTeX
- EndNote
- Final revised paper
The manuscript „DMS, MeSH and nanoparticles in semi-controlled deck-borne experiments using Antarctic seawater: on the effect of UV light” from Chamba et al., reports on fluxes of MeSH and DMS during on-deck incubations in so called Air-sea Interaction Tanks (ASITs) and their relation to particle formation. The authors found differences in fluxes using Antarctic waters dependent on UV light availability and furthermore could show that significant new particle formation was only visible when no UV light was available. Seawater concentrations of MeSH and DMS have been calculated from air mixing ratios and are related to biological parameters in order to explain the variability of those gases. The data shows that nanophytoplankton cell counts could better explain DMS variabilities compared to the widely used proxy chl-a.
The presented work shows a clear research idea in order to improve our understanding on the influence of biologically produced trace gases and their air-sea exchange on particle formation in the atmosphere. The manuscript is well structured and most of the time well presented. However, before publication, I would like to address following points.
General comments:
Calculation from atmospheric mixing ratios to oceanic water concentrations.
Could the authors elaborate a bit on the factor 2.6 (deviation from equilibrium) used in eq(3). They refer to data from Rocco et al. (2025a), but it is still unclear where the factor 2.6 is derived from. I understand that the authors derived the factor for MeSH from data shown in Fig.S7 to match the calculated water concentrations from the ASIT with the seawater measurements at the UW outlet from Wohl & Williams et al., (2026). Given that this factor accounts for the deviation from equilibrium I was wondering why MeSH has an almost 3 times stronger factor than DMS?
Links between dissolved gases and microorganisms during experiments.
The authors try very well to explain the links between dissolved gases and microorganisms. However, there are no direct measurement of dissolved trace gases within the ASITs during the experiments. DMS and MeSH are solely calculated based on measured atmospheric mixing ratios. Calculated fluxes (and finally water concentrations) are always in misbalance between water and atmosphere. This is partly accounted for in the calculation of dissolved aqueous concentrations, however, fast changing atmospheric mixing ratios throughout an experiment could overestimate the calculated variability in the aqueous phase. This can particularly be seen in Fig.S7. On Feb 25th DMS air mixing ratios significantly decrease (Fig.3), so do the calculated DMS water concentrations. However, direct measurements from Wohl & Williams et al. (2026) at the underway outlet at the same time show significantly higher concentrations. A similar offset (but vice versa) is visible on March 7th. Furthermore, calculated DMS and MeSH concentrations at the start of ExpB are significantly higher compared to measured water values at the same time. If this discrepancy is potentially induced only through calculation from air-side to water-side values it does not reflect the actual trace gas variability in the water phase of ExpB. Therefore, drawing conclusions from correlating measured biological parameters with calculated trace gas concentrations should be undertaken with care. Moreover, the authors state that they found no strong correlation between aqueous DMS and MeSH and biological parameters during continuous flow which already points to my previous point (when having two continuous flows (water and air) instead of just one).
Specific comments:
Figure 1: It is not clear to me what the points of the cruise track exactly mean. As the distance between the points changes from time to time, I was thinking that each point might be a discrete (or averaged continuous) measurement point. But as no data is shown before Feb 22nd and after March 11th I might be wrong. Please clarify in the figure caption.
l.125. Which kind of seawater pump was used for the water supply? A centrifugal pump e.g. might damage phytoplankton and causes cell lysis which would significantly influence e.g. DMS and MeSH concentrations and subsequent fluxes when monitored in static flow mode in the experiments (e.g. dissolved DMSP conversion to DMS/MeSH). At least the community in he ASITs would not be “natural” anymore.
l.258ff.: All mentioned compounds which show no enrichment in the ASIT headspace are (much) less soluble in seawater compared to MeSH and DMS. Therefore, proposed cold seawater and absence of wind friction should have a stronger influence (dampening) on the gas exchange on MeSH and DMS than on the other gases. Very low oceanic concentrations might be the dominant factor, however, is there any possibility to prove such low concentration with the help of data availability from Wohl & Williams (2026)? With the biological data provided in the manuscript together with a concentration range of seawater DMS of 1-3nM, isoprene (direct product by phytoplankton) should be supersaturated in the water phase which would lead to elevated atmospheric values (at least during the experiments).
Figure 3: Did the authors also measure DMS and MeSH in the ambient line as they did for ozone? As there appears to be a (known) loss of ozone in the long sampling lines (and in the ASIT itself) it would be helpful to rule that out for the other gases. If there are losses as well e.g. in the ASIT this could potentially explain why the authors did not see an increase in atm mixing rations of isoprene, BTEX, etc. Flux from the water to the air and loss in the ASIT itself could cancel each other out. Furthermore, information of kind of particle filters is missing in the manuscript. Might those filters also influence ozone levels downstream if particles (already loaded on filters after some time) react with ozone?
Figure 5: For the fluxes in the noUV Exp A (green crosses) there seems to be a more exponential relation (than linear) which is not visible in the Control ASIT. Did the authors check if this might be a significant difference? This feature is not visible in the relation between atm mixing ratios. Wouldn’t this imply that under no UV conditions atmospheric MeSH reacts faster than DMS whereas with UV the loss rates in the atmosphere are similar?
Figure 7, Figure S3: Diel variations. Did the authors try to normalize the atmospheric mixing ratios (e.g. to the maximum or mean value per day) when checking diel variations? As discussed earlier in the manuscript the absolute concentrations changed significantly dependent on regions (e.g. coastal vs open ocean). This would probably reduce the standard deviation and a potential dial cycle might be more pronounced (visually but also statistically). This technique could also be applied to the radiation data as of now it seems that data is skewed to stronger irradiations in early afternoon.
l.398ff/Figure 9: Do the authors have any explanation why the particle numbers in the noUV-ASIT are lower during each experiment compared to the particle data of the respective region (coastal vs ExpA and open ocean vs ExpB)?
ll.408ff.: It is obvious that chl-a values at the beginning of ExpA are significantly lower (0.5µg/L) than following concentrations (2.0 µg/L) throughout the experiment. Has this sample also been taken from the tap of one of the ASITs or directly at the UW pump outlet? If so, could the location of sampling bias the data? Nanoplankton 15-20µm data show a similar trend (also for ExpB). Furthermore, are chl-a datapoints hidden behind the legend of ExpA or is there no data available for 6am and 12pm?
Fv/Fm ratio: Could there also be mechanistic stress (e.g. pumping shear stress) showing up in the Fv/Fm ratio? It is quite obvious that Fv/Fm is much lower in the beginning of the experiments compared to later stages. According to Fig. 11 UW Fv/Fm ratios are generally lower compared to ASIT ratios. Do the authors also have Fv/Fm data from CTD measurements? This could give valuable insight if microbial community in ASIT is either just less stressed compared to being pumped through hoses, or if microbes are in general less stressed compared to the “real ocean”. If light stress would be the only issue for Fv/Fm ratios, the difference between UW and ASITs should be vice versa due to the different water depths (UW: 4m, ASIT: ~1m).
l.456ff. Aqueous-phase reactivity of DMS. I like the discussion about the potential loss to explain differences in DMS water concentrations in between both ASITs. How about DMS to DMSO oxidation as a potential loss mechanism? Additionally, I was wondering if the authors also considered a potential stronger DMS production in the noUV ASIT compared to the control ASIT. As the authors already pointed out, the difference in between both treatments is strongest during the two experiments. Is there anything known about phytoplankton types around Antarctica with inhibited DMSP production under UV stress?