the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Mar 2025
Northern North Atlantic climate variability controls on ocean carbon sinks in EC-Earth3-CC
Abstract. The northern North Atlantic is an important net sink of atmospheric CO2, though air-sea CO2 fluxes exhibit substantial variability across different timescales. The underlying drivers of this variability remain poorly understood across both temporal and regional scales. Here, we investigate interannual to decadal CO2 flux variability in the northern North Atlantic using historical simulations from the EC-Earth3-CC model. We assess the role of key dynamical and physical processes in shaping CO2 flux variability across five regions: the Nordic Seas, eastern Nordic Seas, the eastern and western subpolar North Atlantic, and the full North Atlantic. Our analysis reveals that physical parameters—including sea ice concentration (SIC), sea surface temperature (SST), sea surface salinity (SSS), and wind stress—along with dynamical processes related to ocean mixing and circulation, play a central role in regulating CO2 flux variability. Using regression analysis, we demonstrate that these drivers exert regionally and temporally varying influences, with our models achieving high R2 values indicating a strong degree of explanation for CO2 flux variability. The regression models capture interannual variability more effectively than decadal variability, highlighting the dominant role of short-term fluctuations in shaping CO2 flux dynamics. Overall, our results demonstrate that the predictors of CO2 flux variability are both spatially and temporally dependent. We find that CO2 flux variability cannot be fully explained by simple linear correlations with individual predictors but instead arises from complex interactions among multiple physical and dynamical processes. Notably, CO2 flux variability is particularly sensitive to changes in certain predictors, such as wind stress, consistent with expectations based on the gas transfer equation used to compute air-sea CO2 fluxes.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Biogeosciences. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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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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Preprint
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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1218', Anonymous Referee #1, 25 Apr 2025
-
AC1: 'Reply on RC1', Anna Pedersen, 23 May 2025
We thank R1 for the positive review of our manuscript. We are pleased to find that R1 finds the work interesting and useful. We find both the major and minor comments useful, and propose to incorporate them in the paper as described below:
L590-591: We acknowledge that this phrasing was unclear, and propose to rephrase to: “Based on the results of our regression model analysis we feel confident that we can use the regression models defined in this study to predict the CO2 flux variability in future EC-Earth3-CC scenario runs. Using the regression models for predicting the CO2 flux variability in the North Atlantic on scenario runs from EC-Earth3-CC and potentially other ESMs would be beneficial for the understanding of the future climate system”
L591-592: We agree that using the regression models set up for EC-Earth3-CC on other ESMs might be complex and propose to include a discussion emphasising the possible intermodel differences and outline future perspectives with this in mind. A collaborative study across a number of ESMs comparing the primary predictors on a regional basis and their sensitivities (regression coefficients) could lead to an interesting synthesis and new insights.
L556-557: R1 rightly addresses the issue of correlated predictors which we address methodologically by limiting the number of selected predictors in the regression modeles by requiring a certain model improvement for each predictor. This could be explored further but we propose to add instead a cautionary note on the possible issues and our way around it which we find robust. For the MLD and winds it is true that they cannot be expected to be independent, but MLD is as a proxy for the upper ocean state and dynamics, whereas wind speed is directly influencing the CO2 flux variability through the gas exchange equations.
L560-561: It definitely highlights the NSE region as an interesting region to study, and emphasises the need to investigate the dynamics of the smaller regions individually. Figure 2 shows the weighted mean CO2 flux variability - the NSE is showing the highest variability and mean flux in relation to it’s size, but the overall flux of the full North Atlantic is the greastest if you do not look at the weighted mean. The larger regions defined and regions influenced by sea ice in general show an interplay of processes where partly cancelating local anomalies also influence the average level of variability. A further stratification in sub regions is not considered constructive. We have balanced defining regions with (model dependent) dynamical characteristics and still geographically recognisable and partly established.
L569-586: Yes, we agree. We propose to include in the conclusion a statement on this finding along the lines suggested: “The main conclusion is that the CO2 flux variability cannot be attributed to simple linear relationships with individual predictors but instead emerges from complex interactions among multiple processes.”
We thank R1 for the rest of the minor comments, and if not commented above, they will be included in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-1218-AC1
-
AC1: 'Reply on RC1', Anna Pedersen, 23 May 2025
-
RC2: 'Comment on egusphere-2025-1218', Anonymous Referee #2, 25 Apr 2025
-
AC2: 'Reply on RC2', Anna Pedersen, 23 May 2025
We are pleased to read that R2 finds that ‘the manuscript has the potential to be an important contribution to the community’ and thank the reviewer for a thorough, constructive and generally positive review. We acknowledge the apparent need for a more clear motivation for our approach and scope of the paper as discussed by R2. By doing this it will also become clear why we have chosen the specific methods of analysis including working with indicators of ocean dynamics. We would like to note that this has not been raised as an issue with R1 which on the contrary nicely summarise and support our approach and storyline as follows: “The novelty of the current work is the establishment of which predictors that can explain the CO2 flux variability in five regions in the North Atlantic that are subject to quite different dynamic processes and atmospheric forcings.” and “The concept of the work is interesting and useful, especially because it allows connecting basic variables and processes that can be obtained from different ocean climate and biogeochemical models commonly used to study climate change.”
Still we see a need to better guide the readers as highlighted by R2. To address this we propose specifically to revise and clarify the introduction paragraph 1.1 (L104-122) clarifying the objective and logical progression of the sections. Furthermore, other revisions suggested below will serve to address this concern. The replies will be listed in order of the review.
Relation to MLD:
We argue that MLD represents the process of vertical mixing, which is not necessarily represented directly by other indices. We have chosen to use MLD to be able to describe and include dynamical processes indirectly affecting the CO2 flux variability such as ocean mixing. The different oceanic metrics and indicators will be partly correlated and interlinked through forcing and dynamics. See also comment to R1 on how we limit the predictors and regression models.Relation to ΔpCO2:
Our focus on physical parameters is addressed above. Still R2 is correct that repeating the arguments for not including any non-physical parameters will be useful when it comes to the discussion on ΔpCO2 as an otherwise central and obvious parameter in describing the CO2 flux variability. Rephrasing of section 1.1 L104-122 as proposed above is one step in clarifying this issue.Section 4.3:
The authors agree with this point to some degree, and suggest to rephrase the discussion point to focus on the possibility of using the regression model to predict the CO2 flux variability directly on scenario data from EC-Earth3-CC. However, we do believe that the regression models defined in this study could form a solid framework of explaining the CO2 flux variability in future scenarios from other ESMs or even uncopuled ocean-only simulations. They might not be applicable directly, but could work as a starting point for explaining the future CO2 flux variability and trends.Both reviewers have commented on the discussions in this section and we will rephrase section 4.3 to modify the discussion with an emphasis on both R1’s and R2’s perspective. We propose to refocus the discussion towards using the regression models to predict the CO2 flux in EC-Earth3-CC scenario runs, and to expand the discussion of using the regression models on other ESMs. The authors agree that the regression models defined in this study will not be directly applicable to other ESMs, to be elaborated in the revised discussion section. Furthermore, we will include the relevant references kindly highlighted by R2 as a discussion point on the ability to understand and predict interannual-to-decadal variations in ocean CO2 uptake.
We thank the reviewer for the minor comments and all of them will be incorporated in the revised manuscript as the authors believe they will improve the manuscript. Comments referring to major comment two has been addressed above. A few of the minor comments are commented below:
L130-152: We thank the reviewer for these clarifications and acknowledge that L133-136 was unclear. We propose to rephrase L133-136 to: “The configuration allows for simulations with emissions forcings, and the CO2 flux is calculated from and proportional to the difference in partial pressure of (ΔpCO2) between the atmosphere and the surface of the ocean (Döscher et al., 2022).” as this is the accurate description of the dataset used. We thank the reviewer for noticing the mistake.
L154-162: We thank R2 for the suggestion and will update Figure 1 with a subplot showing the gridded CO2 flux from Landschützer, so it is possible for the reader to visually compare the observational CO2 flux with the EC-Earth3-CC CO2 flux.
L208-210: We suggest to rephrase this sentence based on R2’s comments to clarify the meaning and to enhance the readability: “The reproducibility of simulated integrated fluxes (F) derived from other parameters (Eq. 1-3), particularly their variability across different timescales, provides a useful benchmark. It sets an upper limit on how much of the model variability we can expect to explain using physical quantities from archived monthly-averages data. ”
L221-224: The authors also agree with this point and suggest to rephrase as follows: “It is also expected that the CO2 flux variability is dependent on SST and SSS variability, however the effect of SST and SSS on the solubility constant (K0) is too small to be considered important in these calculations, and the SST and SSS components of Eq 1-3 is therefore not scaled.”
L248-253: We thank the reviewer for pointing this out and suggest to rephrase this section to: “These parameters include parameters already presented above (SST, SSS, SIC, ΔpCO2 and wind), however for the next part of the analysis we add mixed layer depth (MLD) and sea surface height (SSH). These parameters represent larger scale dynamics such as ocean circulation (SSH, gyre strength) and vertical mixing (MLD), which are candidates to be indirect processes controlling the CO2 flux variability in EC-Earth3-CC.”
Section 3.1: The authors agree with this clarification and has reworded the section using ‘compares spatially’ or ‘mirroring’ and not ‘correlates’. Furthermore, we thank the reviewer for suggesting a new title for the section, which will be added in the revised manuscript.
L566-570: The authors agree that the description does not reflect the figures well, and have reworded the paragraph in the revised manuscript, focussing on the counter-intuitive patterns of the MLD.
Citation: https://doi.org/10.5194/egusphere-2025-1218-AC2
-
AC2: 'Reply on RC2', Anna Pedersen, 23 May 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-1218', Anonymous Referee #1, 25 Apr 2025
The aim of the study is to understand the sensitivity and drivers of the atmosphere-ocean flux in an ESM system on different time scales The paper explores indices calculated from physical parameters and dynamical process in different Northern North Atlantic regions from the EC-Earth3-CC model to find controlling factors of atmosphere-ocean CO2 flux variability. This is done in terms of simple linear multivariable regression models across interannual and decadal time scales. The analyses finds that the chosen indices play a central role, but that they exert regionally and temporally varying influence in the areas of interest.
The main conclusion is that the CO2 flux variability cannot be attributed to simple linear relationships with individual predictors but instead emerges from complex interactions among multiple processes.
The novelty of the current work is the establishment of which predictors that can explain the CO2 fluxvariability in five regions in the North Atantic that are subject to quite different dynamic processes and atmospheric forcings.
The concept of the work is interesting and useful, especially because it allows connecting basic variables and processes that can be obtained from different ocean climate and biogeochemical models commonly used to study climate change.
Major comments:
L590-591: Here you say that the results of your regression model analysis allow you to consider whether you can predict CO2 flux variability in future scenarios. What do you mean by this?
L591-592: You also mention that these regression models applied on scenario runs from EC-Earth3-CC or other ESMs can be beneficial for the understanding of the future climate system.
I agree that using the regression models on future scenarios from EC-Earth3-CC can contribute to an increased understanding, but I would be careful by applying the same regression models on other ESMs as other ESMs may have 1) more realistic or less realistic indices (predictors) than EC-Earth3-CC (e.g. sea ice index), and 2) the relationship between them may be significanty different so that the regression models from EC-Earth3-CC may not hold in other models. The risk for this may be particularly high in future scenarios when models tend to deviate from each other in terms of circulation and trends. I therefore think that the method developed in this study is useful, but that the regression models should be developed separately for each model.
Minor comments:
L556-557: My impression here is that wind speed and MLD are considered as two independent predictors, but the MLD is also a function of wind, in addition to temperature and salinity.
L560-561: Is there a contradiction between NSE being the smallest region and exhibiting the highest variability and mean flux?
L569-586: I think this discussion of the counter-intuitive negative correlations is particularly interesting and underline the complex interplay between the different indices.
L67: What is meant by “deep decadal variability”?
L96: Also Orvik et al., 2022 could be added after Lozier et al., 2019.
L97: The last sentence in the paragraph is not very well connected to the ones before that.
L114: Change “maos” to “maps”.
L170: Remove “a” before “positive”.
L210: Remove “is”.
L305: Change “oir” to “our”.
L377: Hatun et al., 2005 should also be added.
L400: Change “stregthening” to “streingthening”.
Table 3: I guess only significant correlations are shown.
L477: Please add a “,” after “In the following”.
L552: Change “table” to “Table”.
L661: Change “amog” to “among”.
Citation: https://doi.org/10.5194/egusphere-2025-1218-RC1 -
AC1: 'Reply on RC1', Anna Pedersen, 23 May 2025
We thank R1 for the positive review of our manuscript. We are pleased to find that R1 finds the work interesting and useful. We find both the major and minor comments useful, and propose to incorporate them in the paper as described below:
L590-591: We acknowledge that this phrasing was unclear, and propose to rephrase to: “Based on the results of our regression model analysis we feel confident that we can use the regression models defined in this study to predict the CO2 flux variability in future EC-Earth3-CC scenario runs. Using the regression models for predicting the CO2 flux variability in the North Atlantic on scenario runs from EC-Earth3-CC and potentially other ESMs would be beneficial for the understanding of the future climate system”
L591-592: We agree that using the regression models set up for EC-Earth3-CC on other ESMs might be complex and propose to include a discussion emphasising the possible intermodel differences and outline future perspectives with this in mind. A collaborative study across a number of ESMs comparing the primary predictors on a regional basis and their sensitivities (regression coefficients) could lead to an interesting synthesis and new insights.
L556-557: R1 rightly addresses the issue of correlated predictors which we address methodologically by limiting the number of selected predictors in the regression modeles by requiring a certain model improvement for each predictor. This could be explored further but we propose to add instead a cautionary note on the possible issues and our way around it which we find robust. For the MLD and winds it is true that they cannot be expected to be independent, but MLD is as a proxy for the upper ocean state and dynamics, whereas wind speed is directly influencing the CO2 flux variability through the gas exchange equations.
L560-561: It definitely highlights the NSE region as an interesting region to study, and emphasises the need to investigate the dynamics of the smaller regions individually. Figure 2 shows the weighted mean CO2 flux variability - the NSE is showing the highest variability and mean flux in relation to it’s size, but the overall flux of the full North Atlantic is the greastest if you do not look at the weighted mean. The larger regions defined and regions influenced by sea ice in general show an interplay of processes where partly cancelating local anomalies also influence the average level of variability. A further stratification in sub regions is not considered constructive. We have balanced defining regions with (model dependent) dynamical characteristics and still geographically recognisable and partly established.
L569-586: Yes, we agree. We propose to include in the conclusion a statement on this finding along the lines suggested: “The main conclusion is that the CO2 flux variability cannot be attributed to simple linear relationships with individual predictors but instead emerges from complex interactions among multiple processes.”
We thank R1 for the rest of the minor comments, and if not commented above, they will be included in the revised manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-1218-AC1
-
AC1: 'Reply on RC1', Anna Pedersen, 23 May 2025
-
RC2: 'Comment on egusphere-2025-1218', Anonymous Referee #2, 25 Apr 2025
-
AC2: 'Reply on RC2', Anna Pedersen, 23 May 2025
We are pleased to read that R2 finds that ‘the manuscript has the potential to be an important contribution to the community’ and thank the reviewer for a thorough, constructive and generally positive review. We acknowledge the apparent need for a more clear motivation for our approach and scope of the paper as discussed by R2. By doing this it will also become clear why we have chosen the specific methods of analysis including working with indicators of ocean dynamics. We would like to note that this has not been raised as an issue with R1 which on the contrary nicely summarise and support our approach and storyline as follows: “The novelty of the current work is the establishment of which predictors that can explain the CO2 flux variability in five regions in the North Atlantic that are subject to quite different dynamic processes and atmospheric forcings.” and “The concept of the work is interesting and useful, especially because it allows connecting basic variables and processes that can be obtained from different ocean climate and biogeochemical models commonly used to study climate change.”
Still we see a need to better guide the readers as highlighted by R2. To address this we propose specifically to revise and clarify the introduction paragraph 1.1 (L104-122) clarifying the objective and logical progression of the sections. Furthermore, other revisions suggested below will serve to address this concern. The replies will be listed in order of the review.
Relation to MLD:
We argue that MLD represents the process of vertical mixing, which is not necessarily represented directly by other indices. We have chosen to use MLD to be able to describe and include dynamical processes indirectly affecting the CO2 flux variability such as ocean mixing. The different oceanic metrics and indicators will be partly correlated and interlinked through forcing and dynamics. See also comment to R1 on how we limit the predictors and regression models.Relation to ΔpCO2:
Our focus on physical parameters is addressed above. Still R2 is correct that repeating the arguments for not including any non-physical parameters will be useful when it comes to the discussion on ΔpCO2 as an otherwise central and obvious parameter in describing the CO2 flux variability. Rephrasing of section 1.1 L104-122 as proposed above is one step in clarifying this issue.Section 4.3:
The authors agree with this point to some degree, and suggest to rephrase the discussion point to focus on the possibility of using the regression model to predict the CO2 flux variability directly on scenario data from EC-Earth3-CC. However, we do believe that the regression models defined in this study could form a solid framework of explaining the CO2 flux variability in future scenarios from other ESMs or even uncopuled ocean-only simulations. They might not be applicable directly, but could work as a starting point for explaining the future CO2 flux variability and trends.Both reviewers have commented on the discussions in this section and we will rephrase section 4.3 to modify the discussion with an emphasis on both R1’s and R2’s perspective. We propose to refocus the discussion towards using the regression models to predict the CO2 flux in EC-Earth3-CC scenario runs, and to expand the discussion of using the regression models on other ESMs. The authors agree that the regression models defined in this study will not be directly applicable to other ESMs, to be elaborated in the revised discussion section. Furthermore, we will include the relevant references kindly highlighted by R2 as a discussion point on the ability to understand and predict interannual-to-decadal variations in ocean CO2 uptake.
We thank the reviewer for the minor comments and all of them will be incorporated in the revised manuscript as the authors believe they will improve the manuscript. Comments referring to major comment two has been addressed above. A few of the minor comments are commented below:
L130-152: We thank the reviewer for these clarifications and acknowledge that L133-136 was unclear. We propose to rephrase L133-136 to: “The configuration allows for simulations with emissions forcings, and the CO2 flux is calculated from and proportional to the difference in partial pressure of (ΔpCO2) between the atmosphere and the surface of the ocean (Döscher et al., 2022).” as this is the accurate description of the dataset used. We thank the reviewer for noticing the mistake.
L154-162: We thank R2 for the suggestion and will update Figure 1 with a subplot showing the gridded CO2 flux from Landschützer, so it is possible for the reader to visually compare the observational CO2 flux with the EC-Earth3-CC CO2 flux.
L208-210: We suggest to rephrase this sentence based on R2’s comments to clarify the meaning and to enhance the readability: “The reproducibility of simulated integrated fluxes (F) derived from other parameters (Eq. 1-3), particularly their variability across different timescales, provides a useful benchmark. It sets an upper limit on how much of the model variability we can expect to explain using physical quantities from archived monthly-averages data. ”
L221-224: The authors also agree with this point and suggest to rephrase as follows: “It is also expected that the CO2 flux variability is dependent on SST and SSS variability, however the effect of SST and SSS on the solubility constant (K0) is too small to be considered important in these calculations, and the SST and SSS components of Eq 1-3 is therefore not scaled.”
L248-253: We thank the reviewer for pointing this out and suggest to rephrase this section to: “These parameters include parameters already presented above (SST, SSS, SIC, ΔpCO2 and wind), however for the next part of the analysis we add mixed layer depth (MLD) and sea surface height (SSH). These parameters represent larger scale dynamics such as ocean circulation (SSH, gyre strength) and vertical mixing (MLD), which are candidates to be indirect processes controlling the CO2 flux variability in EC-Earth3-CC.”
Section 3.1: The authors agree with this clarification and has reworded the section using ‘compares spatially’ or ‘mirroring’ and not ‘correlates’. Furthermore, we thank the reviewer for suggesting a new title for the section, which will be added in the revised manuscript.
L566-570: The authors agree that the description does not reflect the figures well, and have reworded the paragraph in the revised manuscript, focussing on the counter-intuitive patterns of the MLD.
Citation: https://doi.org/10.5194/egusphere-2025-1218-AC2
-
AC2: 'Reply on RC2', Anna Pedersen, 23 May 2025
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Anna Pedersen
Carolin R. Löscher
Steffen M. Olsen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(7676 KB) - Metadata XML
The aim of the study is to understand the sensitivity and drivers of the atmosphere-ocean flux in an ESM system on different time scales The paper explores indices calculated from physical parameters and dynamical process in different Northern North Atlantic regions from the EC-Earth3-CC model to find controlling factors of atmosphere-ocean CO2 flux variability. This is done in terms of simple linear multivariable regression models across interannual and decadal time scales. The analyses finds that the chosen indices play a central role, but that they exert regionally and temporally varying influence in the areas of interest.
The main conclusion is that the CO2 flux variability cannot be attributed to simple linear relationships with individual predictors but instead emerges from complex interactions among multiple processes.
The novelty of the current work is the establishment of which predictors that can explain the CO2 fluxvariability in five regions in the North Atantic that are subject to quite different dynamic processes and atmospheric forcings.
The concept of the work is interesting and useful, especially because it allows connecting basic variables and processes that can be obtained from different ocean climate and biogeochemical models commonly used to study climate change.
Major comments:
L590-591: Here you say that the results of your regression model analysis allow you to consider whether you can predict CO2 flux variability in future scenarios. What do you mean by this?
L591-592: You also mention that these regression models applied on scenario runs from EC-Earth3-CC or other ESMs can be beneficial for the understanding of the future climate system.
I agree that using the regression models on future scenarios from EC-Earth3-CC can contribute to an increased understanding, but I would be careful by applying the same regression models on other ESMs as other ESMs may have 1) more realistic or less realistic indices (predictors) than EC-Earth3-CC (e.g. sea ice index), and 2) the relationship between them may be significanty different so that the regression models from EC-Earth3-CC may not hold in other models. The risk for this may be particularly high in future scenarios when models tend to deviate from each other in terms of circulation and trends. I therefore think that the method developed in this study is useful, but that the regression models should be developed separately for each model.
Minor comments:
L556-557: My impression here is that wind speed and MLD are considered as two independent predictors, but the MLD is also a function of wind, in addition to temperature and salinity.
L560-561: Is there a contradiction between NSE being the smallest region and exhibiting the highest variability and mean flux?
L569-586: I think this discussion of the counter-intuitive negative correlations is particularly interesting and underline the complex interplay between the different indices.
L67: What is meant by “deep decadal variability”?
L96: Also Orvik et al., 2022 could be added after Lozier et al., 2019.
L97: The last sentence in the paragraph is not very well connected to the ones before that.
L114: Change “maos” to “maps”.
L170: Remove “a” before “positive”.
L210: Remove “is”.
L305: Change “oir” to “our”.
L377: Hatun et al., 2005 should also be added.
L400: Change “stregthening” to “streingthening”.
Table 3: I guess only significant correlations are shown.
L477: Please add a “,” after “In the following”.
L552: Change “table” to “Table”.
L661: Change “amog” to “among”.