Gulf Stream and interior western boundary volume transport as key regions to constrain the future North Atlantic Carbon Uptake

Goris, Nadine; Johannsen, Klaus; Tjiputra, Jerry

doi:https://doi.org/10.5194/gmd-2022-152

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https://doi.org/10.5194/gmd-2022-152

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Submitted as: methods for assessment of models

07 Jun 2022

Submitted as: methods for assessment of models | 07 Jun 2022

Status: this preprint is currently under review for the journal GMD.

Gulf Stream and interior western boundary volume transport as key regions to constrain the future North Atlantic Carbon Uptake

Nadine Goris¹, Klaus Johannsen², and Jerry Tjiputra¹ Nadine Goris et al.

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¹NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
²NORCE Norwegian Research Centre, Bergen, Norway

Received: 02 Jun 2022 – Discussion started: 07 Jun 2022

Abstract. As one of the major carbon sinks in the global ocean, the North Atlantic is a key player in mediating the ongoing global warming. However, projections of the North Atlantic carbon sink in a high-CO₂ future are highly uncertain due to greatly varying model results. A previous study analysed an ensemble of 11 CMIP5-models and identified two indicators of contemporary model behavior that are highly correlated with a model´s projected future carbon-uptake in the North Atlantic: (i) the high latitude winter pCO₂^sea-anomaly, which is tightly linked to winter mixing and nutrient supply and (ii) the fraction of the anthropogenically altered carbon-inventory stored below 1000 m depth, indicating the efficiency of dissolved inorganic carbon transport into and within the deep ocean. Both relationships build so-called emergent constraints, where observed contemporary indicators can be used to improve future North Atlantic carbon sink estimates.

In this study, we apply a genetic algorithm to optimize these emergent relationships by constraining the spatial extent of the indicators, i.e. to identify key regions that maximise the cross-correlations between the indicators and the future carbon uptake. We pre-define the shape of the desired regions as (i) rectangles and ellipses of different sizes for the first surface-2D indicator and (ii) cuboids and ellipsoids of different volumes for the second water column-3D indicator. Independent on shape and size, the genetic algorithm persistently identifies the Gulf Stream region as optimal for the first indicator as well as the pathway of the broad interior southward volume transport for the second indicator. This is further confirmed with high correlations between the North Atlantic future carbon uptake and volume transport values extracted for the central latitudes and depths of these optimal regions. Though the importance of volume transport for the carbon uptake is well known, our results go beyond traditional knowledge and identify which depth-ranges and latitudes of this volume transport are consistently of importance across the multi-model ensemble. Our study shows that regional optimisations of emergent constraint can isolate key drivers responsible for multi-model spread and furthermore provide information on where observations are most crucial to constrain future projections. Moreover, a comparison of the model performance in the identified key regions and the large-scale North Atlantic indicates that models whose mean values are in good agreement with observations within one key area do not necessarily perform well when looking at another key area. This hampers the applicability of emergent constraints and highlights the need to additionally evaluate spatial model features.

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Nadine Goris et al.

Status: final response (author comments only)

CEC1:
'Comment on gmd-2022-152', Juan Antonio Añel, 16 Jun 2022

Dear authors,
After checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".

https://www.geoscientific-model-development.net/policies/code_and_data_policy.html
You have archived your code on GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other long-term archival and publishing alternatives, such as Zenodo. Therefore, please, publish your code in one of the appropriate repositories, and include the relevant primary input/output data. Similarly, we do not accept embargoes such as registration, previous contact with the authors or upon request statements. All the necessary input and configuration files must be made available with the paper through one of the repositories we accept.
For the storage of the observation files, we can not accept the servers noaa.gov and rapid.ac.uk. These are not trustable long-term archives that provide a DOI for the specific data files used in your work. Therefore, instead of pointing in a generic way to them, you must publish the specific files used in your work in one of the repositories listed in our policy.
Also, I have not seen a license listed in the GitHub repository of your code. If you do not include a license, the code continues to be your property and can not be used by others, despite any statement on being free to use. Therefore, when uploading the model's code to the new repository, you could want to choose a free software/open-source (FLOSS) license. We recommend the GPLv3. You only need to include the file 'https://www.gnu.org/licenses/gpl-3.0.txt' as LICENSE.txt with your code. Also, you can choose other options that Zenodo provides: GPLv2, Apache License, MIT License, etc.
Please reply as soon as possible to this comment with the link to the new repository (or repositories) for it so that it is available for peer-review, as it should be.

Be aware that failing to comply with this request could result in the rejection of your manuscript for publication.
Best regards,
Juan A. Añel

Geosci. Model Dev. Exec. Editor

Citation: https://doi.org/10.5194/gmd-2022-152-CEC1
- AC1: 'Reply on CEC1', Nadine Goris, 25 Aug 2022
  
  Dear Juan A. Añel,
  
  Thank you for your comments and our apologies for not carefully reading the “Code and data policy”.
  
  When it comes to the observation-based files, our generic pointing to the data was our mistake, as all these files are archived in trustable long-term archives that provide a DOI. Observation-based estimates for pCO₂^sea and C_ant are archived at NOAA’s OCADS (former CDIAC) National Centers for Environmental Information (NCEI) data archive and accessible at https://doi.org/10.7289/v5z899n6 (version 2.2, Landschützer et al., 2017) and https://doi.org/10.7289/v5kw5d97 (mapped, version GLODAPv2.2016, Lauvset et al., 2016), respectively. Observational estimates of the contemporary strength of northwards and southwards volume transports are based on data from the RAPID AMOC monitoring project, funded by the Natural Environment Research Council and archived at the NERC EDS British Oceanographic Data Centre NOC (Frajka-Williams et al., 2021, https://doi.org/10/gwqg).
  
  Additionally, we have now archived the code of the genetic algorithm including the relevant input and output data at zenodo. It is accessible at https://doi.org.10.5281/zenodo.6983146 (Johannsen, 2022a) for the 2D case and https://doi.org.10.5281/zenodo.6983169 (Johannsen, 2022b) for the 3D case.
  
  We will update the “Code and data availability”-section of our manuscript accordingly.
  
  All the best,
  
  Nadine Goris on behalf of the Authors
  
  References:
  
  Frajka-Williams, E., Moat, B., Smeed, D., Rayner, D., Johns, W., Baringer, M., Volkov, D., and Collins, J.: Atlantic meridional overturning circulation observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to 2020 (v2020.1), https://doi.org/10.5285/cc1e34b3-3385-662b-e053-6c86abc03444, 2021
  
  Johannsen, K.: klausjohannsen/Genetic-algorithm-for-regional- optimisation-of-Emergent-Constraints-in-the- surface-North-Atlantic:, https://doi.org/10.5281/zenodo.6983146, 2022a.
  
  Johannsen, K.: klausjohannsen/Genetic-algorithm-for-regional- optimisation-of-Emergent-Constraints-in-water- column-North-Atlantic:, https://doi.org/10.5281/zenodo.6983169, 2022b
  
  Landschützer, P., Gruber, N., and Bakker, D.: An updated observation-based global monthly gridded sea surface pCO₂ and air-sea CO₂ flux product from 1982 through 2015 and its monthly climatology (NCEI Accession 0160558). Version 2.2., https://www.nodc.noaa.gov/ocads/oceans/SPCO2_1982_2015_ETH_SOM_FFN.html, 2017.
  
  Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterstróm, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the 1◦ × 1◦ GLODAP version 2, Earth Syst. Sci. Data, 8, 325–340, https://doi.org/10.5194/essd-8-325-2016, 2016
  
  Citation: https://doi.org/10.5194/gmd-2022-152-AC1
- AC2: 'Second Reply on CEC1', Nadine Goris, 31 Aug 2022
  
  Dear Dr. Juan A. Añel,
  
  After carefully re-reading the GMD code and data policy, we realize that the data for pCO₂^sea, C_ant, and AMOC included in the "code and data availability"-section of our preprint do not belong to the 'data' category defined in the policy, i.e., they are clearly presented and referenced in the main text, and they are not required to reproduce the key findings presented in our paper. We sincerely apologize for this misunderstanding.
  
  Additionally, there has been a slip up in our previously archived version of the genetic algorithm and its related documentation, such that we have created new versions (Johannsen, 2022a, DOI: https://doi.org/10.5281/zenodo.7037947 and Johannsen, 2022b, DOI: https://doi.org/10.5281/zenodo.7037981).
  
  In our revised manuscript (if we are invited to resubmit), we will include the following statements under the "code and data availability"-section: "The code of the genetic algorithm including the relevant input and output data for our 2D North Atlantic case study is available through Johannsen (2022a) (DOI: https://doi.org/10.5281/zenodo.7037947). The genetic algorithm code and the relevant input and output data for our 3D North Atlantic case study is available through Johannsen (2022b) (DOI: https://doi.org/10.5281/zenodo.7037981)."
  
  Thank you for helping us to comply with the "Code and data policy", this is very much appreciated.
  
  All the best,
  
  Nadine Goris (on behalf of all co-authors)
  
  References:
  
  Klaus Johannsen (2022a). Genetic algorithm for regional optimisation of Emergent Constraints in the surface North Atlantic (v1.3). Zenodo. https://doi.org/10.5281/zenodo.7037947
  
  Klaus Johannsen (2022b). Genetic algorithm for regional optimisation of Emergent Constraints in water column North Atlantic (v1.3). Zenodo. https://doi.org/10.5281/zenodo.7037981
  
  Citation: https://doi.org/10.5194/gmd-2022-152-AC2
RC1:
'Comment on gmd-2022-152', Anonymous Referee #1, 13 Jul 2022

Review Goris et al., Gulf Stream and interior western boundary volume transport as key regions to constrain the future North Atlantic Carbon Uptake

This study aimed for regional optimization of the emergent constraints for projecting future North Atlantic carbon uptake. A previous study (Goris et al., 2018) identified two indicators, i.e., seasonal pCO₂^sea anomaly in middle-to-high latitude and fraction of anthropogenic carbon inventory below 1000m, for future carbon uptake projection in the North Atlantic. The authors apply a genetic algorithm to further find out which spatial area and depth ranges are crucial for the emergent relationships. This study is scientifically interesting to constrain the projections of the North Atlantic ocean carbon uptake, and also practically provide guidance for monitoring and observational strategies. However, this manuscript needs some further work and clarification to be published.

Major comments:

-Inconsistency of the season for pCO₂^sea anomaly: it is winter time pCO₂^sea in this study, but the cited paper (Goris et al., 2018) used summer time pCO₂^sea. The correlations should be reversed but are the same in both manuscripts.

-The constrained relationship of winter pCO₂^sea anomaly is relative small (r=0.79), maybe it is because the definition of winter months (November to April) in this study. The variations in different months should be quite different, especially in the transit seasons, i.e., spring and autumn. Definition of the focus season with less months, e.g. December to February, or January to March, might end up with clear relationship and higher correlation.

-This study presented several predictors including the two from Goris et al. (2018). As shown in Fig. 8, each predictor provides an different estimate of the constrained range of future North Atlantic carbon uptake. Which estimate is more plausible?

-How are the uncertainty range of the predictand C_ant*-uptake in Fig. 1, 8 and Table 1 calculated? I suppose they should be determined by the cross points of the linear regression line and the vertical lines of the observational uncertainty, but apparently it is not the case as shown in Fig. 1(c and e) and Fig. 8.

- It is not very clear how to interpretate Fig. 5 and Fig. 7. The results in the two figures seem contradict to each other, both upper ocean (Fig. 5) and deeper ocean (Fig. 7) have high correlations. How to combine the information? In addition, L467-469: “…the deep ocean southward volume transport between 700m-4700m at 26N.” Is this statement based on Fig. 7? This figure shows the 700m-5300m and 21N has reached the largest correlations.

Minor comments:

-The information of figures are incomplete. I would suggest the authors to ensure all the figures are more or less self-explainable.

Fig. 3: the titles of x-axis and y-axis are missing, the y-axis’ title is relative easy to guess, but the x-axis is not so straightforward. The readers need to check back and forth of the context to figure out that it should be number of iterations.

Fig. 4: the unit of the presented variable is missing on both plots and in the figure caption. Why are the color shadings much lighter in Fig. 4c-d than in the Fig. 4b, as they are presenting the same quantity? The same question is also for Fig. 6c-f.

-Fig. 8: as specific model like CESM1-BGC is mentioned to perform well in L413-414, and more information and comparison can be made if the authors present the models with colors as in Fig. 1.

-Abstract L3: “A previous study…” needs to add the reference paper citation so that the readers get the context. From reading the main text, I guess this study refers to Goris et al. (2018).

-Abstract L5: “…winter pCO₂^sea – anomaly…”, but the previous paper (Goris et al., 2018) suggested the pCO₂^sea anomaly in summer (May to October) NOT winter (November to April). As the winter and summer are taken actually half a year in this study, respectively, I guess the counterpart season should be with the same magnitude of correlation but a reversed sign. So I am quite confused that this study based on winter months and the previous study based on summer months get exactly the same correlations as shown in Fig. 1c.

-Some relevant details need to be described in this paper, so that the readers don’t need to refer to Goris et al. (2018) all the time. For instance: how is the pCO₂^sea anomaly defined, is it relative to the annual mean or long-term specific season mean? Which time periods are 1990s, 1997s, and 2090s?

-L350, 354, 363, Figs. S01, Figs. S03 and S04 are inconsistent with the figure numbering in the supplementary.

-L411: “…consistent which…” -> “…consistent with…”

-L487: “…averaged aver…” -> “…averaged over…”

Citation: https://doi.org/10.5194/gmd-2022-152-RC1
- AC3: 'Reply on RC1', Nadine Goris, 30 Sep 2022
  
  Please find the response to this review in the Supplement of "Reply on RC1".
  
  Citation: https://doi.org/10.5194/gmd-2022-152-AC3
- AC4: 'Reply on RC1', Nadine Goris, 30 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-152/gmd-2022-152-AC4-supplement.pdf
  
  Citation: https://doi.org/10.5194/gmd-2022-152-AC4
RC2:
'Comment on gmd-2022-152', Anonymous Referee #2, 21 Jul 2022
I’m surprised to see this paper in review for GMD as it does not obviously meet any of the journal’s manuscript types. It seems like the direct utility of this work is thinking about how to guide observational strategies to constrain N. Atlantic carbon uptake. This is hwoever a call for the editor.

Major comments.

I find the manuscript comes across a bit as a dump of all the work the authors have done in this area, and as such, I feel it would benefit from some curating. The manuscript seems to be doing all of the following:

1. Identifying specific regions where people should be making observations to constrain future N. Atlantic CO2 uptake (and in doing so they refine existing published emergent constraints slightly).

2. Exploring how a genetic algorithm can be used to select the optimum area of observational sampling to constrain models.

3. Expanding on the mechanisms behind the emergent constrains that the authors have previously put forward.

4. Better understand which key processes are leading to uncertainty in projections of future N. Atlantic CO2 uptake (which links quite closely to 3).

As it is written it is doing 1, suggesting that it is doing 2, and doing a bit of 3 and 4 around the edges. The editor will be able to provide guidance on which of these a GMD paper should be doing, but I would argue that 2, 3 or 4 done fully would make the most useful papers, while1 is useful for a very specific audience. As it stands 2, 3 and 4 are the less developed parts of this manuscript. Perhaps it is OK to do all of these things, but if that is what is done, a much clearer structure needs to be imposed on the manuscript and introduction of what is being done and why, so that the reader knows what information they should be getting from each section, and can efficiently take what they need from it. My preference would be to be clear about what the manuscript is trying to achieve and focus the manuscript on that, bringing in the other bits perhaps only as part of the discussion.

Fundamentally I can’t see any mistakes beyond that raised by the other reviewer. I would echo the other reviewer’s comments about it being difficult to interpret some of the figures, and would add that the manuscript would benefit from some careful editing for readability.

Minor comments:

Just a comment - I’m pleased to see the desire for mechanisms in emergent constraints!

The title does not make sense. “Gulf Stream and interior western boundary volume transport as key regions to constrain the future North Atlantic Carbon Uptake” Should it perhaps read “Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic Carbon Uptake”?

It seems to me that the ‘competition’ described in section 2 might benefit from a more detailed diagram than Fig 2.

Line 27 refer --> referred

Line 37 “Despite many progresses” --> “Despite much progress”

Line 37: ‘have not necessarily’ – be specific have they or haven’t they, or in what areas have they.

72: “could highly gain from” --> “could gain from”

Line 214: “we advice against” --> "we advise against"

Line 487 aver should be over
Citation: https://doi.org/10.5194/gmd-2022-152-RC2
- AC5: 'Reply on RC2', Nadine Goris, 30 Sep 2022
  
  The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2022-152/gmd-2022-152-AC5-supplement.pdf
  
  Citation: https://doi.org/10.5194/gmd-2022-152-AC5

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Short summary

Projections of the North Atlantic carbon sink in a high-CO₂ future are highly uncertain. A new study identifies key regions that dynamically explain the model spread of the future North Atlantic carbon uptake. The authors find that the upper and interior ocean volume transport at 26 N are key for a correct estimate of the North Atlantic carbon uptake. However, results indicate that models rarely perform well for both indicators, making it difficult to constrain the model uncertainty.


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