the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Importance of multiple sources of iron for the upper ocean biogeochemistry over the northern Indian Ocean
Priyanka Banerjee
Abstract. Although the northern Indian Ocean (IO) is globally one of the most productive regions and receives dissolved iron (DFe) from multiple sources, there is no comprehensive understanding of how these different sources of DFe can impact upper ocean biogeochemical dynamics. Using an Earth system model with an ocean biogeochemistry component this study shows that atmospheric deposition is the most important source of DFe to the upper 100 m of the northern IO, contributing more than 50 % of the annual DFe concentration. Sedimentary sources are locally important in the vicinity of the continental shelves and over the southern tropical IO, away from high atmospheric depositions. While atmospheric deposition contributes to more than 10 % (35 %) to 0–100 m (surface level) chlorophyll concentrations over large parts of the northern IO, sedimentary sources have similar contribution to chlorophyll concentrations over the southern tropical IO. Such increases in chlorophyll are primarily driven by an increase in diatom population over most of the northern IO. The regions that are susceptible to chlorophyll enhancement following external DFe additions are where low levels of background DFe and high background NO3:DFe values are observed. Analysis of DFe budget over selected biophysical regimes over the northern IO points to vertical mixing as most important for DFe supply, while the importance of advection (horizontal and vertical) varies seasonally. Apart from removal of surface DFe by phytoplankton uptake, subsurface balance between DFe scavenging and regeneration is crucial in replenishing DFe pool to be made available to surface layer by physical processes.
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Priyanka Banerjee
Status: closed
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RC1: 'Comment on bg-2022-224', Anh Pham, 01 Feb 2023
In this article, Dr. Banerjee performed a suite of computer simulations in a relatively complex ocean biogeochemistry model, which includes a state-of-the-art ocean iron (Fe) cycling scheme, to quantify for the relative roles of different sources of dissolved Fe (dFe) on controlling the dFe budget, primary productivity, phytoplankton composition, and nutrient limitation in the northern Indian Ocean (IO). By comparing results of different simulations in which a certain external source of dFe is removed with results of a simulation in which all dFe sources are considered, the author showed that atmospheric deposition is the most important source of dFe to the dFe budget and phytoplankton growth in the upper northern IO. Sedimentary dFe release plays a secondary role and is locally important near the continental shelves and in the southern tropical IO, while the impact of dFe fluxes from hydrothermal vents and river discharges on the upper northern IO biogeochemistry is negligible. More importantly, by analyzing the nutrient limitation status in the northern IO through these model simulations, the author suggested that phytoplankton growth is most sensitive to external sources of dFe in regions where the background dFe concentration is low and the nitrate-to-Fe ratio is high. In those regions, the increase in phytoplankton growth when additional source of dFe is considered is driven mostly by an increase in diatoms. Finally, by analyzing the dFe budget over five biophysical regimes in the northern IO (the western Arabian Sea, the northern Arabian Sea, the southern Bay of Bengal, the central Equatorial IO, and the central southern tropical IO), the author demonstrated that in the surface ocean, vertical mixing is the most important physical mechanism supplying dFe throughout the year. At the subsurface levels, the dFe budget is balanced through scavenging and remineralization processes.
In my opinion, these results while not surprising are still important for understanding ocean Fe cycling in an important ocean region where primary production is high, biogeochemical cycles of various chemical elements are linked, the ocean circulation is highly dynamic, and both biogeochemical and physical processes are sensitive to climate variabilities and global warming. I also find the analysis of the dFe budget over different biophysical regimes insightful. Besides, the manuscript is well-written and easy to follow.
As a reviewer of the previous version of this manuscript, I am also happy that my suggestions are taken into consideration and thoroughly addressed by the author in this version. I am happy to endorse its publication with a few questions/comments for clarification below:
Lines 127-129: How are iron and other tracers initialized in the model?
Lines 147-148: I am surprised that the Fe fluxes from river are quite small even though the author assumed a high constant concentration of dFe in rivers (10nM - line 166)
Lines 161-163: What are the constant low background fluxes here? What is its value?
Line 177: What is the value for the constant desorption rate?
Lines 259-260: Is this underestimation an implication that iron limitation here is not strong enough in the model?
Lines 831-834: I think Fe release from low-oxygen sediments is also vulnerable to global warming since the ocean oxygen level is a function of many biogeochemical and physical processes which are bound to change.
Anh Pham
Citation: https://doi.org/10.5194/bg-2022-224-RC1 -
AC1: 'Reply on RC1', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Priyanka Banerjee, 26 Mar 2023
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RC2: 'Comment on bg-2022-224', Nicola Wiseman, 05 Feb 2023
In this article, Dr. Banerjee utilized the CESM ocean and marine ecosystem model components to investigate the contributions of various iron sources to the Indian Ocean. This model is well suited for the study due to its complex iron cycle representation and robust ecosystem parameterization. The author specifically investigated the relative contributions of each soluble iron source to the total dissolved iron budget as well as biological productivity on a seasonal basis. The author concludes that atmospheric iron is the primary contributor to the dissolved iron budget and fuels productivity in much of the Indian Ocean, while sedimentary iron follows second, and has impactful contributions in continental shelf regions, as well as where dust deposition is at its minimum. This study clearly defines the role of each iron source to biological productivity and concludes by highlighting the uncertainty of atmospheric iron deposition in a changing climate.
Overall, the author performed a well through out series of experiments that clearly defines the interactions between iron supply and physical drivers in multiple regions of the Indian Ocean. I endorse this paper for publication with the following minor questions/comments for clarification below:
Lines 197-199: You mention that freshwater fluxes are calculated from monthly stream flow observations and CLM model. Do you mean from CLM5? What specific output from CLM5, if that is what you are referring to, are you using to derive freshwater fluxers?
Lines 293-295: What type of correlation coefficient are you utilizing here? It is the Pearson product-moment correlation coefficient or a rank correlation? How are you calculating this statistic?
Lines 378-379: Do you have maps showing the iron inputs for each field (atmospheric (with black carbon separated), sedimentary, river, vent)? While Fig. 3 shows the contribution to the total DFe averaged over the upper 100m, a supplementary figure with of each input would strengthen the conclusions made regarding the spatial distributions of the sources in the first paragraph of section 3.2.
Lines 528-534: Cellular Fe:C ratios are reported as Fe:C, not DFe:C. Diatom observations have also been expanded since de Baar et al., 2008 and can be greater than 2.00 x 10-4.
Suggested citations: Twining BS, Rauschenberg S, Morton PL, Vogt S (2015) Metal contents of phytoplankton and labile particulate material in the North Atlantic Ocean. Progress in oceanography, 137:261–283.
Twining BS, Antipova O, Chappell PD, Cohen NR, Jacquot JE, Mann EL, Marchetti A, Ohnemus DC, Rauschenberg S, Tagliabue A (2021) Taxonomic and nutrient controls on phytoplankton iron quotas in the ocean. Limnology and oceanography letters, 6(2):96–106.Citation: https://doi.org/10.5194/bg-2022-224-RC2 -
AC2: 'Reply on RC2', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC2-supplement.pdf
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AC2: 'Reply on RC2', Priyanka Banerjee, 26 Mar 2023
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RC3: 'Comment on bg-2022-224', Anonymous Referee #3, 20 Feb 2023
The author investigated the impact of different external iron sources into the northern Indian Ocean on phytoplankton growth using CESM. A control simulation was first presented considering four external iron sources: dust, sediments, hydrothermal vents and rivers. Then a series of sensitivity experiments were conducted with one of the four sources set to zero. The differences to the control simulation were used to illustrate contributions of single sources to surface DFe and chlorophyll distributions. At the end, mechanisms of DFe supply in defined biophysical regimes in this region were discussed.
The study area is important in the marine iron and carbon cycle due to high iron input and high biological productivity. The manuscript has a clear structure and the experiments were designed and conducted in a reasonable way. However, I have some major concerns that some details of the iron model are not clearly and concisely described and the discussion of model results not always supported by rigorous reasoning. Below are my general comments.General comments:
1. This is a modelling study on the iron cycle. A comprehensive understanding of the marine iron cycle and a precise and detailed description of the modelled iron cycle are required to analyse the model results and also to convince readers. The introduction of previous studies in ‘Introduction’ is not very precise. Here I give two examples:
L33-35: the author stated that several iron addition experiments demonstrated its significance in CO2 drawdown. In fact, iron addition experiments hardly demonstrate a significant effect on CO2 drawdown, since only one of the ship experiments detected a significant increase in carbon export and the others only observed chlorophyll increase induced by iron addition which is not necessarily relevant to CO2 drawdown. This is nicely summarised in Yoon et al. (2018). And the citations in L34-35 are for both natural and artificial fertilisation and do not fit the sentence.
L45-47: the author stated that hydrothermal vents can only impact productivity where these vents are located at shallow depths. This is not necessarily true. Considering mechanisms to stabilise iron released from hydrothermal vents, this iron could be transported far from vents and upwelled to the surface. And this is not something really new. Papers published 10 years ago already discussed different mechanisms preventing the precipitation of iron in near-vent fields (e.g. Sander and Koschinsky, 2011; Yücel et al. 2011).
Further in the ‘Data and model’ chapter, the description of the iron model (L169-183) does not have a clear structure and sometimes confusing. Readers need to know how many iron pools are considered in the model, which processes transfer iron between these pools and how these processes are described as equations. And the first two points are better shown in a scheme. If the code of the iron model was not changed for this study, previous model descriptions can be referred but a brief summary with the main features is still needed for understanding this manuscript without reading another one. If something was changed in the code for this study, please underline and explain these changes and give the equations. This part of model description is central for the manuscript and therefore I expected a much higher quality here.
2. Based on the arguments provided in the current version, I am not convinced that the control simulation is ‘good’ enough to serve as a reference for further sensitivity experiments. Figure 2 shows that the model overestimates surface DFe from Dec to May and in subsurface waters along the two transects, particularly the CLIVAR. In L299-350 the author mentioned several potential causes for overestimation in the subsurface waters: source strength, O2 and ligand concentration, biological uptake and scavenging. Although none of these seems to be able to explain the bias, the author claimed that the result of this simulation ‘gives confidence in using the model to study the iron cycle over the region’. In my opinion, there is still much work to do before coming to this conclusion:
1) The assumed source strength is of particular importance for this study, since the study aims to quantify contributions of different iron sources in regulating biology. All the sensitivity experiments were made based on this control run. If the control run shows a significant model-data mismatch and the assumed source strength probably causes this bias, more experiments need to done by changing strength of different sources or more analysis of model results, to exclude this possibility. Otherwise, how can the contributions of different sources be examined based on a ‘wrong’ assumption of source strength? So far a detailed analysis was presented in the manuscript for dust deposition, but not for the other sources. Just saying that it is difficult to exclude the effect of other sources does not sound convincing.
2) Biological uptake rather affects the loss of DFe in surface waters and the effect of scavenging onto organic particles is also stronger in the surface than in the subsurface waters due to the vertical gradient of particle concentration. Thus the subsurface bias (below 60m) might not be explained by these removal processes. A quantitative analysis of the two loss fluxes along the CLIVAR transect could help to conclude their role.
3) A clear increase of DFe in the subsurface waters which spreads from the near-coast region to the open ocean is likely caused by an additional input of iron below the surface in the coastal regions, e.g. sediment. I am wondering if the author checked the subsurface DFe along the CLIVAR transect in simulations without sediment (whether DFe is still elevated below 60m), and whether the sediment resuspension plays a role in this region. Another factor might be the dissolution of iron from the ‘soft’ dust. Even dust deposition could be underestimated, the slow release of iron from sinking dust particles is not taken up by phytoplankton (which is underestimated anyway) and could contribute to an increase of DFe below the surface waters where the biological uptake and concentration of organic particles become lower. These are just my hypotheses and this kind of open questions needs to be (quantitatively) analysed.
After the causes of the bias are found, it will be further checked if the causes strongly affect the analysis of source contribution or will affect DFe distribution in a systematical way that the relative differences between runs can still be assigned to different source strengths. Then the author can convince readers that this control run can be used as a reference for further sensitivity experiments. So I am not saying that this run can not be used or must be further tuned, but its validity needs to be better argued.
3. The explanation of the phytoplankton community shift in response to iron input is incomplete and not always true (L451-471). Generally, findings in model results should be explained based on model parameterisations. In the manuscript, the community shift is described, and then some possible reasons based on observations and lab experiments are mentioned. However, what explains a similar phenomenon in the reality or in lab is not necessarily the cause of the model behaviour. For example in L452-457, the author cited de Baar et al. (2005) to support the modelled the outcompete by diatoms and cited Sunda and Huntsmann (1995) to explain it with their large cell size and luxury uptake. These can not directly answer the question: what in the model causes the outcompete? Further, large diatoms should not outcompete small phytoplankton by iron uptake, since the surface:volume ratio matters, not the absolute cell surface, otherwise, they would not more suffer from iron limitation if iron is depleted. And I am wondering how this is taken into account in the model. A careful analysis of changes in growth rate and limitation factor of both species can easily reveal the model parameters determining the community shift. It has been already done in several studies. And here again, it is better to show model equations with parameters (in the main part or supplementary material) to make clear in which processes and parameters diatom and small phytoplankton differ in the model.
At this stage I don’t think giving more specific comments would help. I just like to encourage the author to improve the model description, do more detailed analysis of model results and support conclusions through better reasoning.References:
Sander, S., Koschinsky, A. Metal flux from hydrothermal vents increased by organic complexation. Nature Geosci 4, 145–150 (2011). https://doi.org/10.1038/ngeo1088
Yoon, J.-E., Yoo, K.-C., Macdonald, A. M., Yoon, H.-I., Park, K.-T., Yang, E. J., Kim, H.-C., Lee, J. I., Lee, M. K., Jung, J., Park, J., Lee, J., Kim, S., Kim, S.-S., Kim, K., and Kim, I.-N.: Reviews and syntheses: Ocean iron fertilization experiments – past, present, and future looking to a future Korean Iron Fertilization Experiment in the Southern Ocean (KIFES) project, Biogeosciences, 15, 5847–5889, https://doi.org/10.5194/bg-15-5847-2018, 2018.
Yücel, M., Gartman, A., Chan, C. et al. Hydrothermal vents as a kinetically stable source of iron-sulphide-bearing nanoparticles to the ocean. Nature Geosci 4, 367–371 (2011). https://doi.org/10.1038/ngeo1148
Citation: https://doi.org/10.5194/bg-2022-224-RC3 -
AC3: 'Reply on RC3', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Priyanka Banerjee, 26 Mar 2023
Status: closed
-
RC1: 'Comment on bg-2022-224', Anh Pham, 01 Feb 2023
In this article, Dr. Banerjee performed a suite of computer simulations in a relatively complex ocean biogeochemistry model, which includes a state-of-the-art ocean iron (Fe) cycling scheme, to quantify for the relative roles of different sources of dissolved Fe (dFe) on controlling the dFe budget, primary productivity, phytoplankton composition, and nutrient limitation in the northern Indian Ocean (IO). By comparing results of different simulations in which a certain external source of dFe is removed with results of a simulation in which all dFe sources are considered, the author showed that atmospheric deposition is the most important source of dFe to the dFe budget and phytoplankton growth in the upper northern IO. Sedimentary dFe release plays a secondary role and is locally important near the continental shelves and in the southern tropical IO, while the impact of dFe fluxes from hydrothermal vents and river discharges on the upper northern IO biogeochemistry is negligible. More importantly, by analyzing the nutrient limitation status in the northern IO through these model simulations, the author suggested that phytoplankton growth is most sensitive to external sources of dFe in regions where the background dFe concentration is low and the nitrate-to-Fe ratio is high. In those regions, the increase in phytoplankton growth when additional source of dFe is considered is driven mostly by an increase in diatoms. Finally, by analyzing the dFe budget over five biophysical regimes in the northern IO (the western Arabian Sea, the northern Arabian Sea, the southern Bay of Bengal, the central Equatorial IO, and the central southern tropical IO), the author demonstrated that in the surface ocean, vertical mixing is the most important physical mechanism supplying dFe throughout the year. At the subsurface levels, the dFe budget is balanced through scavenging and remineralization processes.
In my opinion, these results while not surprising are still important for understanding ocean Fe cycling in an important ocean region where primary production is high, biogeochemical cycles of various chemical elements are linked, the ocean circulation is highly dynamic, and both biogeochemical and physical processes are sensitive to climate variabilities and global warming. I also find the analysis of the dFe budget over different biophysical regimes insightful. Besides, the manuscript is well-written and easy to follow.
As a reviewer of the previous version of this manuscript, I am also happy that my suggestions are taken into consideration and thoroughly addressed by the author in this version. I am happy to endorse its publication with a few questions/comments for clarification below:
Lines 127-129: How are iron and other tracers initialized in the model?
Lines 147-148: I am surprised that the Fe fluxes from river are quite small even though the author assumed a high constant concentration of dFe in rivers (10nM - line 166)
Lines 161-163: What are the constant low background fluxes here? What is its value?
Line 177: What is the value for the constant desorption rate?
Lines 259-260: Is this underestimation an implication that iron limitation here is not strong enough in the model?
Lines 831-834: I think Fe release from low-oxygen sediments is also vulnerable to global warming since the ocean oxygen level is a function of many biogeochemical and physical processes which are bound to change.
Anh Pham
Citation: https://doi.org/10.5194/bg-2022-224-RC1 -
AC1: 'Reply on RC1', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Priyanka Banerjee, 26 Mar 2023
-
RC2: 'Comment on bg-2022-224', Nicola Wiseman, 05 Feb 2023
In this article, Dr. Banerjee utilized the CESM ocean and marine ecosystem model components to investigate the contributions of various iron sources to the Indian Ocean. This model is well suited for the study due to its complex iron cycle representation and robust ecosystem parameterization. The author specifically investigated the relative contributions of each soluble iron source to the total dissolved iron budget as well as biological productivity on a seasonal basis. The author concludes that atmospheric iron is the primary contributor to the dissolved iron budget and fuels productivity in much of the Indian Ocean, while sedimentary iron follows second, and has impactful contributions in continental shelf regions, as well as where dust deposition is at its minimum. This study clearly defines the role of each iron source to biological productivity and concludes by highlighting the uncertainty of atmospheric iron deposition in a changing climate.
Overall, the author performed a well through out series of experiments that clearly defines the interactions between iron supply and physical drivers in multiple regions of the Indian Ocean. I endorse this paper for publication with the following minor questions/comments for clarification below:
Lines 197-199: You mention that freshwater fluxes are calculated from monthly stream flow observations and CLM model. Do you mean from CLM5? What specific output from CLM5, if that is what you are referring to, are you using to derive freshwater fluxers?
Lines 293-295: What type of correlation coefficient are you utilizing here? It is the Pearson product-moment correlation coefficient or a rank correlation? How are you calculating this statistic?
Lines 378-379: Do you have maps showing the iron inputs for each field (atmospheric (with black carbon separated), sedimentary, river, vent)? While Fig. 3 shows the contribution to the total DFe averaged over the upper 100m, a supplementary figure with of each input would strengthen the conclusions made regarding the spatial distributions of the sources in the first paragraph of section 3.2.
Lines 528-534: Cellular Fe:C ratios are reported as Fe:C, not DFe:C. Diatom observations have also been expanded since de Baar et al., 2008 and can be greater than 2.00 x 10-4.
Suggested citations: Twining BS, Rauschenberg S, Morton PL, Vogt S (2015) Metal contents of phytoplankton and labile particulate material in the North Atlantic Ocean. Progress in oceanography, 137:261–283.
Twining BS, Antipova O, Chappell PD, Cohen NR, Jacquot JE, Mann EL, Marchetti A, Ohnemus DC, Rauschenberg S, Tagliabue A (2021) Taxonomic and nutrient controls on phytoplankton iron quotas in the ocean. Limnology and oceanography letters, 6(2):96–106.Citation: https://doi.org/10.5194/bg-2022-224-RC2 -
AC2: 'Reply on RC2', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Priyanka Banerjee, 26 Mar 2023
-
RC3: 'Comment on bg-2022-224', Anonymous Referee #3, 20 Feb 2023
The author investigated the impact of different external iron sources into the northern Indian Ocean on phytoplankton growth using CESM. A control simulation was first presented considering four external iron sources: dust, sediments, hydrothermal vents and rivers. Then a series of sensitivity experiments were conducted with one of the four sources set to zero. The differences to the control simulation were used to illustrate contributions of single sources to surface DFe and chlorophyll distributions. At the end, mechanisms of DFe supply in defined biophysical regimes in this region were discussed.
The study area is important in the marine iron and carbon cycle due to high iron input and high biological productivity. The manuscript has a clear structure and the experiments were designed and conducted in a reasonable way. However, I have some major concerns that some details of the iron model are not clearly and concisely described and the discussion of model results not always supported by rigorous reasoning. Below are my general comments.General comments:
1. This is a modelling study on the iron cycle. A comprehensive understanding of the marine iron cycle and a precise and detailed description of the modelled iron cycle are required to analyse the model results and also to convince readers. The introduction of previous studies in ‘Introduction’ is not very precise. Here I give two examples:
L33-35: the author stated that several iron addition experiments demonstrated its significance in CO2 drawdown. In fact, iron addition experiments hardly demonstrate a significant effect on CO2 drawdown, since only one of the ship experiments detected a significant increase in carbon export and the others only observed chlorophyll increase induced by iron addition which is not necessarily relevant to CO2 drawdown. This is nicely summarised in Yoon et al. (2018). And the citations in L34-35 are for both natural and artificial fertilisation and do not fit the sentence.
L45-47: the author stated that hydrothermal vents can only impact productivity where these vents are located at shallow depths. This is not necessarily true. Considering mechanisms to stabilise iron released from hydrothermal vents, this iron could be transported far from vents and upwelled to the surface. And this is not something really new. Papers published 10 years ago already discussed different mechanisms preventing the precipitation of iron in near-vent fields (e.g. Sander and Koschinsky, 2011; Yücel et al. 2011).
Further in the ‘Data and model’ chapter, the description of the iron model (L169-183) does not have a clear structure and sometimes confusing. Readers need to know how many iron pools are considered in the model, which processes transfer iron between these pools and how these processes are described as equations. And the first two points are better shown in a scheme. If the code of the iron model was not changed for this study, previous model descriptions can be referred but a brief summary with the main features is still needed for understanding this manuscript without reading another one. If something was changed in the code for this study, please underline and explain these changes and give the equations. This part of model description is central for the manuscript and therefore I expected a much higher quality here.
2. Based on the arguments provided in the current version, I am not convinced that the control simulation is ‘good’ enough to serve as a reference for further sensitivity experiments. Figure 2 shows that the model overestimates surface DFe from Dec to May and in subsurface waters along the two transects, particularly the CLIVAR. In L299-350 the author mentioned several potential causes for overestimation in the subsurface waters: source strength, O2 and ligand concentration, biological uptake and scavenging. Although none of these seems to be able to explain the bias, the author claimed that the result of this simulation ‘gives confidence in using the model to study the iron cycle over the region’. In my opinion, there is still much work to do before coming to this conclusion:
1) The assumed source strength is of particular importance for this study, since the study aims to quantify contributions of different iron sources in regulating biology. All the sensitivity experiments were made based on this control run. If the control run shows a significant model-data mismatch and the assumed source strength probably causes this bias, more experiments need to done by changing strength of different sources or more analysis of model results, to exclude this possibility. Otherwise, how can the contributions of different sources be examined based on a ‘wrong’ assumption of source strength? So far a detailed analysis was presented in the manuscript for dust deposition, but not for the other sources. Just saying that it is difficult to exclude the effect of other sources does not sound convincing.
2) Biological uptake rather affects the loss of DFe in surface waters and the effect of scavenging onto organic particles is also stronger in the surface than in the subsurface waters due to the vertical gradient of particle concentration. Thus the subsurface bias (below 60m) might not be explained by these removal processes. A quantitative analysis of the two loss fluxes along the CLIVAR transect could help to conclude their role.
3) A clear increase of DFe in the subsurface waters which spreads from the near-coast region to the open ocean is likely caused by an additional input of iron below the surface in the coastal regions, e.g. sediment. I am wondering if the author checked the subsurface DFe along the CLIVAR transect in simulations without sediment (whether DFe is still elevated below 60m), and whether the sediment resuspension plays a role in this region. Another factor might be the dissolution of iron from the ‘soft’ dust. Even dust deposition could be underestimated, the slow release of iron from sinking dust particles is not taken up by phytoplankton (which is underestimated anyway) and could contribute to an increase of DFe below the surface waters where the biological uptake and concentration of organic particles become lower. These are just my hypotheses and this kind of open questions needs to be (quantitatively) analysed.
After the causes of the bias are found, it will be further checked if the causes strongly affect the analysis of source contribution or will affect DFe distribution in a systematical way that the relative differences between runs can still be assigned to different source strengths. Then the author can convince readers that this control run can be used as a reference for further sensitivity experiments. So I am not saying that this run can not be used or must be further tuned, but its validity needs to be better argued.
3. The explanation of the phytoplankton community shift in response to iron input is incomplete and not always true (L451-471). Generally, findings in model results should be explained based on model parameterisations. In the manuscript, the community shift is described, and then some possible reasons based on observations and lab experiments are mentioned. However, what explains a similar phenomenon in the reality or in lab is not necessarily the cause of the model behaviour. For example in L452-457, the author cited de Baar et al. (2005) to support the modelled the outcompete by diatoms and cited Sunda and Huntsmann (1995) to explain it with their large cell size and luxury uptake. These can not directly answer the question: what in the model causes the outcompete? Further, large diatoms should not outcompete small phytoplankton by iron uptake, since the surface:volume ratio matters, not the absolute cell surface, otherwise, they would not more suffer from iron limitation if iron is depleted. And I am wondering how this is taken into account in the model. A careful analysis of changes in growth rate and limitation factor of both species can easily reveal the model parameters determining the community shift. It has been already done in several studies. And here again, it is better to show model equations with parameters (in the main part or supplementary material) to make clear in which processes and parameters diatom and small phytoplankton differ in the model.
At this stage I don’t think giving more specific comments would help. I just like to encourage the author to improve the model description, do more detailed analysis of model results and support conclusions through better reasoning.References:
Sander, S., Koschinsky, A. Metal flux from hydrothermal vents increased by organic complexation. Nature Geosci 4, 145–150 (2011). https://doi.org/10.1038/ngeo1088
Yoon, J.-E., Yoo, K.-C., Macdonald, A. M., Yoon, H.-I., Park, K.-T., Yang, E. J., Kim, H.-C., Lee, J. I., Lee, M. K., Jung, J., Park, J., Lee, J., Kim, S., Kim, S.-S., Kim, K., and Kim, I.-N.: Reviews and syntheses: Ocean iron fertilization experiments – past, present, and future looking to a future Korean Iron Fertilization Experiment in the Southern Ocean (KIFES) project, Biogeosciences, 15, 5847–5889, https://doi.org/10.5194/bg-15-5847-2018, 2018.
Yücel, M., Gartman, A., Chan, C. et al. Hydrothermal vents as a kinetically stable source of iron-sulphide-bearing nanoparticles to the ocean. Nature Geosci 4, 367–371 (2011). https://doi.org/10.1038/ngeo1148
Citation: https://doi.org/10.5194/bg-2022-224-RC3 -
AC3: 'Reply on RC3', Priyanka Banerjee, 26 Mar 2023
The comment was uploaded in the form of a supplement: https://bg.copernicus.org/preprints/bg-2022-224/bg-2022-224-AC3-supplement.pdf
-
AC3: 'Reply on RC3', Priyanka Banerjee, 26 Mar 2023
Priyanka Banerjee
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