the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Jan 2021
08 Jan 2021
Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models
- 1CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
- 2Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
- 3Met Office Hadley Center, Exeter, UK
- 4Norwegian Meteorological Institute, Oslo, Norway
- 5NORCE Climate and Bjerknes Centre for Climate Research, Bergen, Norway
- 6School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
- 7Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway
- 8National Oceanography Centre, European Way, Southampton, SO14 3ZH, United Kingdom
- 1CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
- 2Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
- 3Met Office Hadley Center, Exeter, UK
- 4Norwegian Meteorological Institute, Oslo, Norway
- 5NORCE Climate and Bjerknes Centre for Climate Research, Bergen, Norway
- 6School of Environmental Sciences, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK
- 7Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway
- 8National Oceanography Centre, European Way, Southampton, SO14 3ZH, United Kingdom
Abstract. Characteristics and trends of surface ocean dimethylsulfide (DMS) concentrations and fluxes into the atmosphere of four Earth System Models (ESMs: CNRM-ESM2-1, MIROC-ES2L, NorESM2-LM and UKESM1-0-LL) are analysed over the recent past (1980–2009) and into the future, using Coupled Model Intercomparison Project 6 (CMIP6) simulations. The DMS concentrations in historical simulations systematically underestimate the most widely-used observed climatology, but compare more favourably against two recent observation based datasets. The models better reproduce observations in mid to high latitudes, as well as in polar and westerlies marine biomes. The resulting multi-model estimate of contemporary global ocean DMS emissions is of 16–24 Tg S year−1, which is narrower than the observational-derived range of 16 to 28 Tg S year−1. The four models disagree on the sign of the trend of the global DMS flux from 1980 onwards, with two models showing an increase and two models a decrease. At the global scale, these trends are dominated by changes in surface DMS concentrations in all models, irrespective of the air-sea flux parameterisation used. In turn, three models consistently show that changes in DMS concentrations are correlated with changes in marine productivity, however the latter is poorly constrained in the current generation of ESMs, thus limiting the predictive ability of this relationship. In contrast, a consensus is found among all models over polar latitudes where an increasing trend is predominantly driven by the retreating sea-ice extent. However, the magnitude of this trend between models differs by a factor of three, from 2.9 to 9.2 Gg S decade−1 over the period 1980–2014, which is at the low end of a recent satellite-derived analysis. Similar increasing trends are found in climate projections over the 21st century.
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Josué Bock et al.
Status: final response (author comments only)
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RC1: 'Comment on bg-2020-463', Martí Galí, 29 Jan 2021
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2020-463/bg-2020-463-RC1-supplement.pdf
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AC1: 'Reply on RC1', Josué Bock, 19 Apr 2021
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2020-463/bg-2020-463-AC1-supplement.pdf
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AC1: 'Reply on RC1', Josué Bock, 19 Apr 2021
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RC2: 'Comment on bg-2020-463', Anonymous Referee #2, 24 Feb 2021
General comments:
This paper presents an evaluation of ocean DMS in CMIP6 models over the historical period, and discusses their projected changes by the late 21st century under SSP585. To my knowledge, no previous work on DMS has been done using CMIP6 models. Therefore, this paper provides useful insights into the current state of DMS represented in the latest generation of ESMs. I recommend publication after major revisions, addressing my general and specific comments below.The historical evaluation is very extensive, but maybe a bit too extensive to be included in the main text. I do not suggest to delete anything, but I do suggest to move some content into Supplementary Information (SI). One suggestion is to move Section 3.1.2 and associated figure/table (Figure 5 & Table 5) into SI. I particularly pick on this section because: (1) this section compares the models with L11 only, which is now considered to be outdated (i.e. G18 and W20 are better replacements); and (2) this section compares over small biogeographical regions, in which global models are not necessarily expected to perform well. I think Figure 3 is just sufficient for regional evaluation of these coarse-resolution models.
In addition to the environmental variables considered in the paper, I suggest to consider three additional variables for analysis: pH, MLD, and SST. Projected changes in these variables might play a substantial or additive role, as they influence directly or indirectly the DMS concentration and flux, as parameterised in the models. The Arctic might have experienced greater changes in these variables, so it is worthwhile checking these variables.
Specific comments:
L48: replace “last” with “latest”.L49: I’m not sure if “unprecedented” is an appropriate term here, considering that: (1) Tesdal et al. (2016) have incorporated more products (measurement/empirical/prognostic approaches) in their assessment; and (2) there are only 4 ESMs in CMIP6 that simulated ocean DMS. Has this number increased/decreased from CMIP5?
Sec.2.1.1: Given the dependence of DMSP (DMS) production rate on phytoplankton species, I suggest to list the types of phytoplankton and cellular quota (sulfur to carbon/CHL ratios) specified in all of the 4 models.
L98&L101: I’m confused about pH dependency. In L98, it says DMS release is computed as a function of pH. In L101, it says it has not been activated in CMIP6 runs. So which one is used for this paper? If it has not been activated, the word “pH” should be removed from L98.
L142: Is there plan to publish the DMS data for MIROC-ES2L on ESGF nodes in the near future?
L158: What about mixed layer depth (MLD)? Given its direct effect on DMS in diagnostic models, I think MLD should be assessed in addition to Chl.
L159: In addition to these, I suggest to show pH of the models whose DMS depends on pH. The parameterisation of Six et al. (2016) has quite strong pH effect, so this might play an important role in some regions like the Arctic. Figures can go into SI.
L163: I am not sure if I get this correct. Is MMM calculated by averaging the ensemble means of the 4 models? Or is it calculated by averaging the ensembles of the 4 models (11 + 10 + 3 + 16 for historical)? I think it is the former, but it is not clear from this sentence.
Figure 2: For readability, indicate in the caption whether these differences represent model-minus-obs or obs-minus-model.
L365: why is it “striking” that models do well in these regions?
L392: Instead of text, it might be helpful to visualise the different wind-speed-based paramterisations used by these models. Consider creating a simple plot like Figure 2 of Ho et al. (2006) (but do this for Schmidt number for DMS).
Ho et al. (2006): Measurements of air-sea gas exchange at high wind speeds in the Southern Ocean: Implications for global parameterizations, GRL, 10.1029/2006GL026817
L410: In addition to wind, would temperature bias play a role in modifying flux via solubility/diffusivity? SST figures could be added to SI.
Figures 6&8: I suggest to add a subplot showing the results of Wang et al. (2020), which I assume are better obs-based products than L11/CAMS19? Without them, it just gives an impression that model are performing badly compared to the obs (L11/CAMS19). I think they compare better with Wang et al. (2020), and this point should be made clear in these figures.
L465: The 4 CMIP6 models differ in the flux parameterisation, so the finding here does not confirm the conclusion of Tesdal et al. (2016) that global emission is roughly linearly dependent upon global mean concentration for “a given flux parameterisation”.
L546: I recommend two papers from Wang et al. (2018), which incorporates perhaps more DMS producers than the 4 CMIP6 models, including Phaeocystis.
Wang et al. (2018): Impacts of Shifts in Phytoplankton Community on Clouds and Climate via the Sulfur Cycle, Global Biogeochemical Cycles, 10.1029/2017GB005862
Wang et al. (2018): Influence of dimethyl sulfide on the carbon cycle and biological production, Biogeochemistry, 10.1007/s10533-018-0430-5
Figures 13,14,15: For understanding what each colour represents easily, could you plot a legend in one of the subplots? I know the colours are described in figure caption, but it is easier with a legend.
L601: I think this paragraph deserves a bit more discussion. The strong relationship between DMS and Chl/NPP is probably true for a given phytoplankton species (and therefore, this replationship holds for in situ observations of a particular phytoplankton bloom or relatively simple-complexity phytoplankton models). However, should this really be the case at global scale where different phytoplankton species dominate in different regions and phytoplankton have a wide range of DMS production rates (i.e. cellular quota; Stefels et al. 2007)? I understand that this point leads to the conclusion in the subsequent paragraph, but I think the reality of the DMS-Chl/NPP relationship is highly variable regionally due to the diversity of phytoplankton species, which should be acknowledged.
L622: Briefly state what the conclusions are.
L650: I don’t really understand the latter part of this sentence: “the specific role … are clearly visible.” I think this latter part can be deleted, and combine the earlier part with the previous sentence, i.e. “Comparing the time series … variables, especially when considering … (dashed lines).”
Figure 15: DMS emissions at 100 % are indicated only for two models?
Section 5: I think this section should be named as “Discussion and Conclusions”, as it is quite extensive for just Conclusions.
L694: I understand L11 has sampling biases. However, should W20 have similar sampling biases because it also relies on the same dataset (well, twice more) for both training and evaluation (L196-204)? So unless W20 accounts for a preferential sampling of DMS-productive conditions incorporated into the dataset (L189), how can we conclude that W20 does not suffer from similar sampling biases as in L11?
L719: Briefly state what the conclusions are.
L725: I don’t think the word “overcome” is appropriate here. Overcome suggests one effect counteracts and defeats another effect. The trend of DMS concentration is neutral (neither increasing/decreasing; Figure 14 bottom panel), so it’s just that the positive trend of ice-free extent drives the trend of DMS emission.
L750: Data availability for the CMIP6 models should also be mentioned here.
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AC2: 'Reply on RC2', Josué Bock, 19 Apr 2021
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2020-463/bg-2020-463-AC2-supplement.pdf
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AC2: 'Reply on RC2', Josué Bock, 19 Apr 2021
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RC3: 'Comment on bg-2020-463', Nadja Steiner, 25 Feb 2021
Review Bock et al.:Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models
The paper by Bock et al evaluates the ocean dimethylsulfide concentrations and emissions in CMIP6 models.
Only two of the DMS models are actually prognostic DMS models, while the other two use different diagnostic algorithms. Hence the comparison is difficult to evaluate. Quite a bit of work has been done comparing the various algorithms estimating DMS based on Chl, light MLD etc. The way the simulated DMS is used (or not used) in the models also varies significantly - One model calculates DMS prognostically but does not use it, one calculates DMS prognostically and uses it in the atmosphere chemistry module for conversion and presumably aerosol formation, one model calculates DMS diagnostically and uses it directly in the aerosol module and the last one calculates DMS diagnostically and uses it in the atmosphere chemistry and aerosol formation module.
Essentially, the authors ran into the not unfamiliar problem to try compare multiple models which are not only vastly different with respect to the parameterizations of the variable in question, but also with respect to several other components such as gas exchange velocity, atmospheric feedbacks etc. In addition the climatologies used for evaluation have their own issues and potential errors. This makes it extremely difficult to understand respective differences among the output.
However, I feel that despite these difficulties the authors did an excellent job in comparing the models, and identifying and describing the cause of differences. The evaluations are well linked and brought into context with earlier estimates and evaluations which helps to assess advancement from those and the uncertainties and concerns are well stated. This does provide scientific value despite the variety of parameterisations in the models.
The manuscript is well written and the evaluation procedures are sound. In fact the manuscript provides an excellent template for future analysis in (hopefully) more coordinated DMS model intercomparisons (Maybe consider adding a note with such a recommendation in the paper).
Hence, I recommend publication of the manuscript with minor changes.
What I am missing is a brief note on the potential impact of DMS emissions in the atmosphere, i.e a note indicating that areas with highest emissions are not necessarily those where the emissions have the highest impact, particularly with respect to the Arctic (see notes below).
Since the authors do include a focus section on the Arctic, I would also recommend to include a sentence or two on the missing ice algae component (see note below).
There are a few spelling mistakes which I noted ( if I caught them)
Detailed comments:
l23 insert space after DMS) , rm "as" after considered
l26 "sulfate aerosols formed DMS" - from DMS?
l27 could mention the Arctic here, too (Abbatt citation?)
l60 measurements
l107 "the" marine DMS cycle
l135 adjusted to compensate... for what?
l205 unclear what "these " refers to in "to compare the skills of these methods" which makes the following sentence confusing. Please clarify what is compared to what etc.
l206 The yearly mean of this climatology - what does "this climatology" refer to? ANN?
L220 also issues with CDOM in coastal areas (see Hayashida et al. 2020)
L344 higher than
L363 The is paragraph seams a bit too generalized. E.g. it might be relevant to note that some of the regions indicates as poorly represented show hardly any variation and generally a much smaller range than the regions which have a clearer signal in both model and obs. I notice that the lower seasonality is discussed at the end of the section, but would help to briefly mention at time of the figure discussion.
L417 mirror - mirrors
L506 a weakly
L517 To help understanding => To help understand
L526 The coastal biome
L527 I am a bit concerned with the statement: "improving the models in the low latitudes regions is needed to gain confidence in the predicted global trends of DMS" => While this may be true, the question is if the global emission is the relevant one or if the emission is more important to improve in regions where DMS emissions have significant impact (as in the clean polar atmosphere) eventhough it might be a smaller contribution to the global mean (e.g, Abbatt et al. 2019,https://doi.org/10.5194/acp-19-2527-2019, and references therein)- maybe something to pick up in the discussion???
L615 suggest including reference to Hayashida et al 2020 (10.1029/2019GB006456) DMS model for the Arctic (also provides detailed comparison with G19), including a note on the ice algae contribution which is not represented in the described ESMs (see note below)
Also suggest a note here on the impact of DMS in an otherwise clean atmosphere (Arctic spring summer, see note above)
L634/635 "This means that the models consistently predict lower DMS concentration below the sea-ice, in line with reduced photosynthetically available radiation." suggest adding a note on ice algae DMS production here
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AC3: 'Reply on RC3', Josué Bock, 19 Apr 2021
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2020-463/bg-2020-463-AC3-supplement.pdf
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AC3: 'Reply on RC3', Josué Bock, 19 Apr 2021
Josué Bock et al.
Josué Bock et al.
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