the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Biogeochemistry of climate driven shifts in Southern Ocean primary producers
Abstract. As a net source of nutrients fuelling global primary production, changes in Southern Ocean productivity are expected to influence biological carbon storage across the global ocean. Following a high emissions, low mitigation pathway, primary productivity in the Southern Ocean is predicted to increase by up to 40 % over the 21st century. The ecophysiological response of marine phytoplankton experiencing climate change will be a key determinant in understanding the impact of Southern Ocean productivity shifts on the carbon cycle. Yet, phytoplankton ecophysiology is poorly represented in CMIP6 climate models, leading to substantial uncertainty in the representation of their role in carbon sequestration. Here we synthesise the existing spatial and temporal projections of Southern Ocean productivity from CMIP6 models, separated by phytoplankton class and identify key processes where greater observational data coverage can help to improve future model performance. We find bidirectional changes in iron and light limitation of phytoplankton, while the greatest changes in productivity occur in the coastal zone of the Southern Ocean. Different phytoplankton groups are responsible for driving productivity increases at different latitudes, yet we observe that models disagree on the ecological mechanism behind these productivity changes. We propose that an evidence-based sampling approach targeting climate-driven changes in ocean biogeochemistry and community assemblages in the regions of rapid projected productivity changes could help to resolve the empirical principles underlying phytoplankton community structure in the Southern Ocean.
- Preprint
(2083 KB) - Metadata XML
-
Supplement
(1309 KB) - BibTeX
- EndNote
Status: closed
-
RC1: 'Comment on bg-2023-10', Anonymous Referee #1, 17 Feb 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Ben Fisher, 12 Jul 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Ben Fisher, 12 Jul 2023
-
RC2: 'Comment on bg-2023-10', Anonymous Referee #2, 23 May 2023
Review of "Biogeochemistry of climate driven shifts in Southern Ocean primary producers" by Fisher et al.
Â
General comments:
This study provides a synthesis of previous works in regard to the Southern Ocean under climate change from the aspects of physics, biogeochemistry (including nutrients and ocean acidification), and phytoplankton. On top of the synthesis of previous works, this study is trying to investigate the underlying mechanisms that drive the change in phytoplankton productivity and analyse the potential phytoplankton structure change at different latitudes under the SSP5-8.5 scenario using CMIP6 models.
Â
The paper is trying to address the question of how future physical and biogeochemical changes will affect phytoplankton, which is a significant question and fits the scope of the journal. And I can see the authors went through tremendous effort to conduct a comprehensive literature review. However, the current formatting of the paper appears to lean more towards a review paper rather than a traditional research article. For the part of the CMIP6 ensemble analysis, despite the general research idea being interesting and valuable, the results are very little and not well presented (buried in the literature review), which makes the overall storyline of the model results unclear. And also, as listed in the method part in the supplementary (Table S1), the analysis of different variables is mostly based on the mean value of different model ensembles. Also mentioned in this paper and from previous studies, model projections, especially of phytoplankton, could be very different. I understand CMIP6 model output towards phytoplankton, especially towards detailed phytoplankton limitations and community structure, is very rare. However, comparing the mean values of different variables when the members of the model ensembles are significantly different is not very convincing.
Â
Overall, I think the current version of the paper requires major revisions and improvements before it can be considered for publication.
Â
Specific comments:
L24: unclear what an "evidence-based sampling approach" is.
L74: I don’t think temperature-driven zooplankton metabolism changes will modulate the zooplankton grazing pressure. Could you explain more or/and add a reference?
L90: sentence "As the main source … long term storage" is long and complex. Could you cut it short and make it more clear?
L97: Is "a targeted approach" the same as "an evidence-based sampling approach" in the abstract? If so, try to keep the same name and explain the approach more in detail.
L123: Figure 2a instead of 2d
Figure 2: "*Units in panels E and F" change to "*Units in panels D and F". Also, have you mentioned anywhere how exactly you calculated the limitation of irradiance and iron on phytoplankton? What you show in Figure 2 is for surface phytoplankton or averaged over the water column, and how?
L191–192: "Models do not show any increases in nitrate limitation over the remainder of the century." I wonder how you got this conclusion from figure 3 (or any other figure not listed).
Figure 3: CMIP6 models output both chlorophyll and phytoplankton biomass in carbon data. If you want to see how integrated phytoplankton change, I think phyC would be a more appropriate variable, as you don’t have to disentangle the varying chl:C ratio, which is also not included in every model.
L213: What is "rain rate"? Similar to export efficiency?
L274: Why do you discuss phytoplankton productivity (intpp) in carbon and biomass in chlorophyll, when you can directly compare carbon to carbon (phyc)?
L275: I don’t think phytoplankton productivity and biomass are two independent variables.
L296: It is figure 4 instead of figure 3.
Figure 4: The research area is not explicit in the caption.
L299–301: I like this part of the result comparing the different signals from GFDL and CESM.
Figure 5: The mean of total productivity (intpp) is calculated as the mean of 15 models (Table S1), while diatom productivity (intppdiat) is calculated as the mean of 6 models. intpp and intppdiat are calculated from very different model ensembles (intpp model ensemble does not even completely cover the intppdiat model ensemble). Therefore, directly compare intpp and intppdiat, and the calculation of non-diatom productivity as intpp-intppdiat, I think, is incorrect. I suggest here to use the models that have outputs of both intpp and intppdiat and then do the comparison and calculation.
L363–364: This part is shown in the second panel of Figure 5. Please label Figure 5 and indicate the figure once you talk about it.
L363–364: Productivity trends are based on the mean of a multi-model ensemble, while the iron variability is only from GFDL. If you want to use it this way, at least in the appendix, show that the GFDL productivity trend is not standing out from other model results.
L367–369: same here, please refer to the figure.
L421: Could you explain more why you exclude "some models"? What kind of difference in data structure are you referring to?
Â
Citation: https://doi.org/10.5194/bg-2023-10-RC2 -
AC2: 'Reply on RC2', Ben Fisher, 12 Jul 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Ben Fisher, 12 Jul 2023
Status: closed
-
RC1: 'Comment on bg-2023-10', Anonymous Referee #1, 17 Feb 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Ben Fisher, 12 Jul 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Ben Fisher, 12 Jul 2023
-
RC2: 'Comment on bg-2023-10', Anonymous Referee #2, 23 May 2023
Review of "Biogeochemistry of climate driven shifts in Southern Ocean primary producers" by Fisher et al.
Â
General comments:
This study provides a synthesis of previous works in regard to the Southern Ocean under climate change from the aspects of physics, biogeochemistry (including nutrients and ocean acidification), and phytoplankton. On top of the synthesis of previous works, this study is trying to investigate the underlying mechanisms that drive the change in phytoplankton productivity and analyse the potential phytoplankton structure change at different latitudes under the SSP5-8.5 scenario using CMIP6 models.
Â
The paper is trying to address the question of how future physical and biogeochemical changes will affect phytoplankton, which is a significant question and fits the scope of the journal. And I can see the authors went through tremendous effort to conduct a comprehensive literature review. However, the current formatting of the paper appears to lean more towards a review paper rather than a traditional research article. For the part of the CMIP6 ensemble analysis, despite the general research idea being interesting and valuable, the results are very little and not well presented (buried in the literature review), which makes the overall storyline of the model results unclear. And also, as listed in the method part in the supplementary (Table S1), the analysis of different variables is mostly based on the mean value of different model ensembles. Also mentioned in this paper and from previous studies, model projections, especially of phytoplankton, could be very different. I understand CMIP6 model output towards phytoplankton, especially towards detailed phytoplankton limitations and community structure, is very rare. However, comparing the mean values of different variables when the members of the model ensembles are significantly different is not very convincing.
Â
Overall, I think the current version of the paper requires major revisions and improvements before it can be considered for publication.
Â
Specific comments:
L24: unclear what an "evidence-based sampling approach" is.
L74: I don’t think temperature-driven zooplankton metabolism changes will modulate the zooplankton grazing pressure. Could you explain more or/and add a reference?
L90: sentence "As the main source … long term storage" is long and complex. Could you cut it short and make it more clear?
L97: Is "a targeted approach" the same as "an evidence-based sampling approach" in the abstract? If so, try to keep the same name and explain the approach more in detail.
L123: Figure 2a instead of 2d
Figure 2: "*Units in panels E and F" change to "*Units in panels D and F". Also, have you mentioned anywhere how exactly you calculated the limitation of irradiance and iron on phytoplankton? What you show in Figure 2 is for surface phytoplankton or averaged over the water column, and how?
L191–192: "Models do not show any increases in nitrate limitation over the remainder of the century." I wonder how you got this conclusion from figure 3 (or any other figure not listed).
Figure 3: CMIP6 models output both chlorophyll and phytoplankton biomass in carbon data. If you want to see how integrated phytoplankton change, I think phyC would be a more appropriate variable, as you don’t have to disentangle the varying chl:C ratio, which is also not included in every model.
L213: What is "rain rate"? Similar to export efficiency?
L274: Why do you discuss phytoplankton productivity (intpp) in carbon and biomass in chlorophyll, when you can directly compare carbon to carbon (phyc)?
L275: I don’t think phytoplankton productivity and biomass are two independent variables.
L296: It is figure 4 instead of figure 3.
Figure 4: The research area is not explicit in the caption.
L299–301: I like this part of the result comparing the different signals from GFDL and CESM.
Figure 5: The mean of total productivity (intpp) is calculated as the mean of 15 models (Table S1), while diatom productivity (intppdiat) is calculated as the mean of 6 models. intpp and intppdiat are calculated from very different model ensembles (intpp model ensemble does not even completely cover the intppdiat model ensemble). Therefore, directly compare intpp and intppdiat, and the calculation of non-diatom productivity as intpp-intppdiat, I think, is incorrect. I suggest here to use the models that have outputs of both intpp and intppdiat and then do the comparison and calculation.
L363–364: This part is shown in the second panel of Figure 5. Please label Figure 5 and indicate the figure once you talk about it.
L363–364: Productivity trends are based on the mean of a multi-model ensemble, while the iron variability is only from GFDL. If you want to use it this way, at least in the appendix, show that the GFDL productivity trend is not standing out from other model results.
L367–369: same here, please refer to the figure.
L421: Could you explain more why you exclude "some models"? What kind of difference in data structure are you referring to?
Â
Citation: https://doi.org/10.5194/bg-2023-10-RC2 -
AC2: 'Reply on RC2', Ben Fisher, 12 Jul 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2023-10/bg-2023-10-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Ben Fisher, 12 Jul 2023
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
874 | 576 | 44 | 1,494 | 98 | 45 | 41 |
- HTML: 874
- PDF: 576
- XML: 44
- Total: 1,494
- Supplement: 98
- BibTeX: 45
- EndNote: 41
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1