the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Apr 2024
Characterizing the marine iodine cycle and its relationship to ocean deoxygenation in an Earth System model
Abstract. Iodine abundance in marine carbonates (as an elemental ratio with calcium – I:Ca) is of broad interest as a proxy for local/regional ocean redox. This connection arises because the speciation of iodine in seawater—in terms of the balance between iodate (IO3-) and iodide (I-)—is sensitive to the prevalence of oxic vs. anoxic conditions. However, although I:Ca ratios are being increasingly commonly measured in ancient carbonate samples, a fully quantitative interpretation of this proxy is hindered by the scarcity of a mechanistic and quantitative framework for the marine iodine cycle and its sensitivity to the extent and intensity of ocean deoxygenation. Here we present and evaluate a representation of marine iodine cycling embedded in an Earth system model (‘cGENIE’) against both modern and paleo observations. In this, we account for IO3- uptake and reduction by primary producers, the occurrence of ambient IO3- reduction in the water column, plus the re-oxidation of I- to IO3-. We develop and test a variety of different mechanistic relationships between IO3- and I- against an updated compilation of observed dissolved IO3- and I- concentrations in the present-day ocean. In optimizing the parameters controlling previously proposed mechanisms behind marine iodine cycling, we find that we can obtain broad matches to observed iodine speciation gradients in zonal surface distribution, depth profiles, and oxygen deficient zones (ODZs). We also identify alternative, equally well performing mechanisms which assume a more explicit mechanistic link between iodine transformation and environment. This mechanistic ambiguity highlights the need for more process-based studies on modern marine iodine cycling. Finally, because our ultimate motivation is to further our ability to reconstruct ocean oxygenation in the geological past, we conducted ‘plausibility tests’ of our various different model schemes against available I:Ca measurements made on Cretaceous carbonates – a time of substantially depleted ocean oxygen availability compared to modern and hence a strong test of our model. Overall, the simultaneous broad match we can achieve between modelled iodine speciation and modern observations, and between forward-proxy modelled I:Ca and geological elemental ratios supports the application of our Earth system modelling in simulating the marine iodine cycle to help interpret and constrain the redox evolution of past oceans.
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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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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-677', Wanyi Lu, 23 Apr 2024
I come to review this manuscript from a paleo perspective. This study by Cheng et al. has provided fresh and deeper understandings on simulating the marine iodine cycle in the cGENIE model. I find the model ensembles are well designed from both modern and paleo angles. I agree with the authors that the simulations have overall good matches with modern observations and paleo data. The authors have also offered their detailed evaluations on model performance from three perspectives. Their explanations of model-data mismatches are reasonable and have pointed out some future research directions. I think the manuscript is well written and the main points are very clear.
I do have two questions regarding the paleo simulations. It has been speculated that the total iodine concentration in seawater in the geologic past may be different than modern oceans (Zhou et al., 2016 Paleo; Lu et al., 2018 Fig. S12). But the Cretaceous simulations seem to use modern total iodine value? If you used a higher total iodine in the pre-OAE simulations, I assume it will bring all the model-simulated I/Ca to higher values, thus presumably closer to observations?
L593-596: The conversion from seawater IO3- and Ca2+ to I/Ca may be more complex than the authors have suggested… For example, the substitution of IO3- into calcite may involve Na+, CO3-- ions (Podder et al., 2017 GCA); the seawater Ca2+ concentration in Cretaceous may be different than modern day, so whether Cretaceous Ca2+ is well-simulated needs to be considered. I understand this may be beyond the scope of this model-focused study, but I recommend the authors should at least acknowledge such complications.
Minor comments:
L65: strictly speaking, it should be “regional rather than in-situ redox conditions”
L78: I- re-oxidation
Fig. 6 caption: may add a short note to refer readers to see transect locations shown in Fig. 1
L373: strictly speaking, these papers studied both planktic and benthic forams
Citation: https://doi.org/10.5194/egusphere-2024-677-RC1 - AC1: 'Reply on RC1', Keyi Cheng, 05 Jun 2024
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RC2: 'Comment on egusphere-2024-677', Rosie Chance, 25 Apr 2024
To improve the utility of carbonate I/Ca ratios as a paleotracer requires acknowledgement and quantitative understanding of the fact that dissolved iodine speciation in the ocean is not simply and solely a product of redox conditions. This study addresses this issue by incorporating a range of iodine transformations in the cGENIE model. The representations of iodine cycling are well thought out and appropriate, and cGENIE is a suitable model for paleoreconstructions. The model development and evaluation appear to be well conducted, and the manuscript is well presented. As well as paleo-oceanographers, the iodine model described is also likely to be of interest for those working on present day iodine cycling, such as biogeochemists and air quality modellers.
I have the following major and minor comments on the manuscript:
Major comments:
- The model is limited in its ability to accurately model present day oxygen levels, and over-estimates levels in the north Pacific OMZ (L317, Fig 6; L513, L537-538). As iodine speciation in the model is a function of oxygen levels, this will affect model performance. Indeed, simulated present day iodine distributions are closer to observations when the model is forced to climatological oxygen values (L340). The accuracy of oxygen predictions for the geological past therefore requires scrutiny. Although the authors briefly allude to this issue (L538-543; L621), a more in depth and up front consideration is required. Is there any way the accuracy of paleo oxygen predictions made by cGENIE be assessed, and the uncertainty associated with this quantified? What future work is planned to reduce the uncertainty in predicted oxygen levels?
- Similarly, to make forward predictions of I/Ca ratios, modelled values of historic calcium concentrations are integral (L594). A brief description of how these are simulated, and discussion of the uncertainty in these values is required.
- Figure 7 and L590: State what type of carbonate archive the I/Ca values were measured in. Were these archives likely to have been subject to any diagenetic alterations that could change the I/Ca ratio from that incorporated at the time of calcite synthesis?
Minor comments:
- At a number of points, the paper states that biologically mediated iodate reduction is assimilatory (L14, Figure 1, L197, L410). However, it is not yet established whether this process is assimilatory or dissimilatory (e.g. Hepach et al., 2020), it may even be a mixture of processes. This should be made clear in the manuscript. Biologically mediated iodate reduction in the model is represented as an assimilatory process, which is reasonable given the current state of knowledge, but it should be made clear that this is an assumption in the model construction.
- In a few places throughout the manuscript (e.g. L77-79, L137-139, L422-424,) minor grammatical and/or wording improvements are needed to make the text more readily understandable.
- L87: This should be “deep” not “dissolved” chlorophyll maximum
- L139 and elsewhere: Check and correct the spelling of technical terms e.g. ‘respiration’ and ‘saturation’
- L149-151: Either add numbers 2-4 to the list of processes here, or remove the “(1)”.
- L155: It would be helpful to state here that these representations apply to water column reduction (i.e. process 1) and oxidation (i.e. process 2)
- L209 and SI Table 4: The machine learning model for sea surface iodide concentrations described Sherwen et al., 2019, was built using the data set in Chance et al., 2019, so it is not clear why this paper is referenced here and in SI Table 4? Were simulated iodide values from Sherwen et al., 2019, also used in the model evaluation?
- Figure 3 and SI Figure 1: The coloured dots in these figures are very difficult to see, can they be increased in size, and/or the quality of the figures improved?
- L179: The first sentence requires a reference, and/or more explanation, to make clear how this link between iodide and nitrification being considered here differs from the link with ammonium oxidation mentioned on L188. L432-441: Similarly, the reasoning for extending the proposed link between iodide oxidation and bacterial nitrification (L432-436) to a broader possible relationship with bacterial oxidising activity (L441) should be explained in a little more detail.
- L244: Why were only five different combinations of parameterisations tested, when nine combinations are possible? Please explain why the five tested combinations were selected.
- L289: Elevated observed iodide concentrations in surface waters at low latitudes are thought to be a function of biologically mediated reduction and strong vertical stratification (allowing the iodide to accumulate). This should be noted in the text, and the ability of the model to account for the impact of vertical mixing on iodine distribution discussed.
- Table 1. I think the ‘reminO3lifetime- parameters do not need to be given for simulation 2 (as in Table 3).
- Throughout – insert space between numbers and units
- L354: The assessment against I/Ca records has not yet been described at all, so perhaps should not be mentioned here. Consider including it within the main methods and results sections.
- L360: I think this should be -0.08 not -0.8?
- L451: Is this necessarily the case, if iodate reduction in the model is already a function of oxygen concentration?
- L478: This sentence implies that temperature is the main driver of primary production, which is misleading – although temperature has some effect on primary production rates, it is not the dominant controlling factor in the surface ocean. The relationship between iodide abundance and temperature reported in Chance et al. 2014, is instead thought to occur due to the relationship between temperature and vertical mixing. This sentence should be rephrased to reflect this more accurately
- L512: Does “data” here mean model output? Please clarify in the text
- L563: As noted above, I feel that description of the comparison with I/Ca records in this section might be better as part of the main method and results sections, with just the discussion of the findings in section 4.3.
- Figure 8. The caption here appears to incorrectly list more combinations of parameterisations than the three shown.
- L623: “DOC remineralisation” as an additional parametrisation variation has not been mentioned in the text before this point, either add an explanation or remove.
- Supplementary Information: A number of figures include “DOC remineralisation” as an additional parametrisation variation, but this is not explained anywhere in the text.
Citation: https://doi.org/10.5194/egusphere-2024-677-RC2 - AC2: 'Reply on RC2', Keyi Cheng, 05 Jun 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-677', Wanyi Lu, 23 Apr 2024
I come to review this manuscript from a paleo perspective. This study by Cheng et al. has provided fresh and deeper understandings on simulating the marine iodine cycle in the cGENIE model. I find the model ensembles are well designed from both modern and paleo angles. I agree with the authors that the simulations have overall good matches with modern observations and paleo data. The authors have also offered their detailed evaluations on model performance from three perspectives. Their explanations of model-data mismatches are reasonable and have pointed out some future research directions. I think the manuscript is well written and the main points are very clear.
I do have two questions regarding the paleo simulations. It has been speculated that the total iodine concentration in seawater in the geologic past may be different than modern oceans (Zhou et al., 2016 Paleo; Lu et al., 2018 Fig. S12). But the Cretaceous simulations seem to use modern total iodine value? If you used a higher total iodine in the pre-OAE simulations, I assume it will bring all the model-simulated I/Ca to higher values, thus presumably closer to observations?
L593-596: The conversion from seawater IO3- and Ca2+ to I/Ca may be more complex than the authors have suggested… For example, the substitution of IO3- into calcite may involve Na+, CO3-- ions (Podder et al., 2017 GCA); the seawater Ca2+ concentration in Cretaceous may be different than modern day, so whether Cretaceous Ca2+ is well-simulated needs to be considered. I understand this may be beyond the scope of this model-focused study, but I recommend the authors should at least acknowledge such complications.
Minor comments:
L65: strictly speaking, it should be “regional rather than in-situ redox conditions”
L78: I- re-oxidation
Fig. 6 caption: may add a short note to refer readers to see transect locations shown in Fig. 1
L373: strictly speaking, these papers studied both planktic and benthic forams
Citation: https://doi.org/10.5194/egusphere-2024-677-RC1 - AC1: 'Reply on RC1', Keyi Cheng, 05 Jun 2024
-
RC2: 'Comment on egusphere-2024-677', Rosie Chance, 25 Apr 2024
To improve the utility of carbonate I/Ca ratios as a paleotracer requires acknowledgement and quantitative understanding of the fact that dissolved iodine speciation in the ocean is not simply and solely a product of redox conditions. This study addresses this issue by incorporating a range of iodine transformations in the cGENIE model. The representations of iodine cycling are well thought out and appropriate, and cGENIE is a suitable model for paleoreconstructions. The model development and evaluation appear to be well conducted, and the manuscript is well presented. As well as paleo-oceanographers, the iodine model described is also likely to be of interest for those working on present day iodine cycling, such as biogeochemists and air quality modellers.
I have the following major and minor comments on the manuscript:
Major comments:
- The model is limited in its ability to accurately model present day oxygen levels, and over-estimates levels in the north Pacific OMZ (L317, Fig 6; L513, L537-538). As iodine speciation in the model is a function of oxygen levels, this will affect model performance. Indeed, simulated present day iodine distributions are closer to observations when the model is forced to climatological oxygen values (L340). The accuracy of oxygen predictions for the geological past therefore requires scrutiny. Although the authors briefly allude to this issue (L538-543; L621), a more in depth and up front consideration is required. Is there any way the accuracy of paleo oxygen predictions made by cGENIE be assessed, and the uncertainty associated with this quantified? What future work is planned to reduce the uncertainty in predicted oxygen levels?
- Similarly, to make forward predictions of I/Ca ratios, modelled values of historic calcium concentrations are integral (L594). A brief description of how these are simulated, and discussion of the uncertainty in these values is required.
- Figure 7 and L590: State what type of carbonate archive the I/Ca values were measured in. Were these archives likely to have been subject to any diagenetic alterations that could change the I/Ca ratio from that incorporated at the time of calcite synthesis?
Minor comments:
- At a number of points, the paper states that biologically mediated iodate reduction is assimilatory (L14, Figure 1, L197, L410). However, it is not yet established whether this process is assimilatory or dissimilatory (e.g. Hepach et al., 2020), it may even be a mixture of processes. This should be made clear in the manuscript. Biologically mediated iodate reduction in the model is represented as an assimilatory process, which is reasonable given the current state of knowledge, but it should be made clear that this is an assumption in the model construction.
- In a few places throughout the manuscript (e.g. L77-79, L137-139, L422-424,) minor grammatical and/or wording improvements are needed to make the text more readily understandable.
- L87: This should be “deep” not “dissolved” chlorophyll maximum
- L139 and elsewhere: Check and correct the spelling of technical terms e.g. ‘respiration’ and ‘saturation’
- L149-151: Either add numbers 2-4 to the list of processes here, or remove the “(1)”.
- L155: It would be helpful to state here that these representations apply to water column reduction (i.e. process 1) and oxidation (i.e. process 2)
- L209 and SI Table 4: The machine learning model for sea surface iodide concentrations described Sherwen et al., 2019, was built using the data set in Chance et al., 2019, so it is not clear why this paper is referenced here and in SI Table 4? Were simulated iodide values from Sherwen et al., 2019, also used in the model evaluation?
- Figure 3 and SI Figure 1: The coloured dots in these figures are very difficult to see, can they be increased in size, and/or the quality of the figures improved?
- L179: The first sentence requires a reference, and/or more explanation, to make clear how this link between iodide and nitrification being considered here differs from the link with ammonium oxidation mentioned on L188. L432-441: Similarly, the reasoning for extending the proposed link between iodide oxidation and bacterial nitrification (L432-436) to a broader possible relationship with bacterial oxidising activity (L441) should be explained in a little more detail.
- L244: Why were only five different combinations of parameterisations tested, when nine combinations are possible? Please explain why the five tested combinations were selected.
- L289: Elevated observed iodide concentrations in surface waters at low latitudes are thought to be a function of biologically mediated reduction and strong vertical stratification (allowing the iodide to accumulate). This should be noted in the text, and the ability of the model to account for the impact of vertical mixing on iodine distribution discussed.
- Table 1. I think the ‘reminO3lifetime- parameters do not need to be given for simulation 2 (as in Table 3).
- Throughout – insert space between numbers and units
- L354: The assessment against I/Ca records has not yet been described at all, so perhaps should not be mentioned here. Consider including it within the main methods and results sections.
- L360: I think this should be -0.08 not -0.8?
- L451: Is this necessarily the case, if iodate reduction in the model is already a function of oxygen concentration?
- L478: This sentence implies that temperature is the main driver of primary production, which is misleading – although temperature has some effect on primary production rates, it is not the dominant controlling factor in the surface ocean. The relationship between iodide abundance and temperature reported in Chance et al. 2014, is instead thought to occur due to the relationship between temperature and vertical mixing. This sentence should be rephrased to reflect this more accurately
- L512: Does “data” here mean model output? Please clarify in the text
- L563: As noted above, I feel that description of the comparison with I/Ca records in this section might be better as part of the main method and results sections, with just the discussion of the findings in section 4.3.
- Figure 8. The caption here appears to incorrectly list more combinations of parameterisations than the three shown.
- L623: “DOC remineralisation” as an additional parametrisation variation has not been mentioned in the text before this point, either add an explanation or remove.
- Supplementary Information: A number of figures include “DOC remineralisation” as an additional parametrisation variation, but this is not explained anywhere in the text.
Citation: https://doi.org/10.5194/egusphere-2024-677-RC2 - AC2: 'Reply on RC2', Keyi Cheng, 05 Jun 2024
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Andy Ridgwell
Dalton Hardisty
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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(3143 KB) - BibTeX
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- Final revised paper