the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Oct 2025
Seasonal to long-term variability of natural and anthropogenic carbon concentrations and transports in the subpolar North Atlantic Ocean
Abstract. The Atlantic Meridional Overturning Circulation (AMOC) is integral to the climate system, transporting heat and anthropogenic carbon across the North Atlantic (NA) from subtropical to subpolar latitudes. This physical mechanism promotes the uptake and sequestration of atmospheric CO2 through surface cooling as warm water advances northward and consequently sinks through deep winter convection. Using ship-based observations, ocean reanalyses, neural networks, and a back-calculation approach, we present a 30-year monthly time series of contemporary carbon (natural, Cnat and anthropogenic, Cant) concentrations and transports at the A25-OVIDE hydrographic section in the subpolar NA Ocean, and assess their variability from seasonal to long-term scales. We divided the section into essential layers, including the upper branch of the AMOC (uMOC) and the mixed layer (ML). Our findings indicate that the full-section-averaged Cnat concentration shows no significant trend over the 30-year period. In contrast, the full-section-averaged Cant concentration increased by more than one third over the 30-year period, attributed to anthropogenic influences and atmospheric CO2 increase. Seasonal and interannual variability is more pronounced in the uMOC and in the ML, where deep convection and biological activity impact their concentration. The seasonal deepening of the ML in winter contributes two thirds and one half of its ML concentration for Cnat and Cant, respectively, the rest being attributed to biology and solubility. The Cant and Cnat transports are predominantly determined by the variability of volume transport, except for the decadal trend in Cant transport which is primarily influenced by changes in Cant concentration. The variability in tracer transport is the largest in the uMOC, which exhibits a seasonal peak-to-peak amplitude of approximately 25 % of the annual mean tracer transport. These results offer new insights to refine model representations and improve our understanding of the subpolar NA carbon dynamics.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-4425', Louise Delaigue, 10 Oct 2025
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RC2: 'Comment on egusphere-2025-4425', Anonymous Referee #2, 18 Feb 2026
Review comments to the manuscript EGUsphere-2025-4425, ‘Seasonal to long-term variability of natural and anthropogenic carbon concentrations and transports in the subpolar North Atlantic Ocean’, by Bajon et al.
General comments
The study describes a nearly 30-year monthly time series of natural (Cnat) and anthropogenic carbon (Cant) concentrations and transports in the subpolar North Atlantic at the A25 section, using a combination of ship-based observations, ocean reanalysis, neural networks, and a back-calculation approach. As pointed out by the authors the study is the first quantitative assessment of Cant seasonality in the SPNA, making it impossible to validate the findings. Nevertheless, the agreement in transports and changes from other studies is impressive and give confidence in the approach. It is a very interesting and mostly well written work, which deserves publication. I have some mostly minor comments that should be dealt with before publication.
One comment is on the Results section. This is fully packed with details and frankly took me some time to get through. This may in part be needed when there is so much information to juggle but this makes it less approachable for the reader. It is difficult to put the finger on specific parts, but I simply ask the authors to go through the Result sections and see if something could be described somewhat more accessible. The Discussion and Conclusions are all excellent.
Specific comments
L67: This is a minor remark, but it should read “GO-SHIP”. (See also other instances in the manuscript.)
L109 + 111: Since [Cant] means “concentrations of Cant” I am not sure this should be used here. “The inventory of …” would be “Cant” and not the concentration of Cant, right? Same for “the influence of …”. The same may be true for L274.
P5, Fig. 1 caption: The EGC is shown in the figure but not defined in the caption, and WBC is defined in the caption but not shown in the figure.
Also, a comma after “respectively” after “top panels”. (L4 of the caption)
L260-261: The transport numbers presented, are they shown in a table or figure? I was looking in table 3, but don’t fully recognise the values.
P14, Fig. 2: As far as I can find this figure is first referred to on page 18, and there in the figure caption of Figure 4. Since Figure 3 and 4 are referred to before, you should renumber this figure (Fig. 2 that is) for clarity.
L306-07: It is not clear to me how this is calculated (the seasonality by taking an annual value of [Cnat]). There is a reference to the Figure 3a, and the figure caption say something more (but not enough, to me), but this should also be added in the methods, as I see it. (I may of course have missed this information.)
L319-321: Since it has been stated that Cnat is much larger than Cant the difference in amplitudes is of course expected. The same goes for the opposite max/min of Cnat and Cant with deepened ML.
L403: Be clear in the phrasing; the net transport does not define a layer (as the sentence may be read as).
L411: These two maxima for the seasonal net signal are very small, and in addition there is another small maximum in June, and the value in December is as large as the maxima in March and September.
L450-451: The way I read Fig. 8c is that lMOC shows no trend between 1993 and 2008/09, after which it increases. Thus, saying it is an increase over the time series is misleading.
L554-555: Maybe more a technical comment but there must be some words missing in this sentence, between North Atlantic and Steinfeldt.
L558: Does this need to be true what you are suggesting? The deeper ML would lead to a minimum in [Cant], and wouldn’t this be true even if the [Cant] in the NAC was higher or unchanged? Possible I am misunderstanding something, but please have a look if this can be made clearer.
L590: The 36°N section is not defined in Figure S9. Yes, it is section A03 (right?), but please make this clear.
L613-14: From Fig. 3b I can’t see an order of magnitude difference, but then I read that you refer to the difference between ML and ML annual, but maybe this is not true? Please make this clearer. If you compare the variability between Cnat and Cant then yes.
L662: It may be suitable to mention the lack of observational evidence of this reduction until today. If there are indeed any documentations of such a change then refer to that.
Technical comments
Table captions: This is a very minor remark but there is an inconsistency in the placement of the table captions, where the ones in the main paper are below the tables and the ones in the Supplementary are found above. Typically, I think they should be on top, but this may of course differ among journals.
L182: A typo: remove “Irminger” (in the “East Greenland Current”.
L220: “RMSD” is used here for the first time (unless I missed an earlier one) but only defined in the paragraph after.
L265: Should this refer to Table 3, and not 4?
L305: You refer to Fig. 1b, but there is no such figure (only Fig. 1).
L325: Maybe use “hence” instead of “for”?
P20, Fig. 5: The numbers/details of the figure are difficult to read due to the small size/font. Maybe this is mostly in the printout, but please consider this when revising the manuscript.
L438: Do you mean “magnitude” here (instead of “amplitude”)?
L532: Should it be “average” instead of “averaged”?
Supplementary Information:
The table caption information should be made clearer for Tables S2-S5. Now it reads “Table 3 details” (for Table S2) or “Table 4 details” (for Tables S3-S5).
Fig. S3-S4: The figures are very small making it difficult to read numbers. Also, change “Iceland basin” to “Iceland Basin” in caption.
Fig. S9: As mentioned earlier please define that section 36°N refers to section A03.
Citation: https://doi.org/10.5194/egusphere-2025-4425-RC2
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- 1
The manuscript by Bajon et al. presents a robust assessment of the seasonal to long-term variability of natural and anthropogenic carbon concentrations and transports across the A25-OVIDE section in the subpolar North Atlantic. Combining ship-based observations, ocean reanalyses, neural networks, and a back-calculation approach, the authors build a 30-year monthly time series of [Cnat] and [Cant]. They show that [Cnat] remains stable, while [Cant] increases by over one third, reflecting rising atmospheric CO2 and circulation variability. The study highlights strong seasonal and interannual variability in the mixed layer and upper AMOC, with transport variability mainly driven by volume changes and long-term Cant trends by concentration changes. These findings provide valuable insight into North Atlantic carbon dynamics and offer a strong reference for model evaluation and regional carbon budget studies.
This is an excellent paper: very well written and structured, with a clear scientific contribution and a very complete methodology.
Specific comments
L20 surpassing 420 ppm, in 2024, 2025? Depending on the acceptance date of the paper it might be best to be precise
L20 I think natural reservoir can lead to confusion, maybe it’s best to say say carbon sink
L22 + L24 use the latest GCB citation
L25 defining DICtotal as Cnat + Cant is a bit reductive - or you need to say Cnat then includes everything else (i.e. preformed, the BCP and the carbonate pump)
L28 I think it’s an oversimplified definition of the ML, especially since this is core to the analysis of the paper. Something like « The oceanic mixed layer (ML) corresponds to the near-surface layer of the ocean where turbulent processes, primarily induced by wind forcing, buoyancy fluxes, and wave breaking, maintain quasi-homogeneous temperature and salinity profiles. It represents the portion of the ocean directly interacting with the atmosphere, while weak vertical gradients may still persist. The depth of the ML is generally defined from a threshold criterion based on potential density or temperature relative to surface values. » gives more ground to set the scene
L34 did you define NA in the intro?
Section 2.2: overall I am missing the reason why you want to use all of these different products in your comparison (I think that would be Table 1?)
L104 which version of GOBAI-O2 did you use? Latest version is 2.3 and I would recommend using 2.1 onwards
Figure 1: excellent figure - one point though, now that I see Cnat in this context I’m not sure nat is the best abbreviation as Cnat usually directly speaks to the BCP contribution rather than « all of the rest of DIC once we removed Cant »
L115 PyCO2SYS - which version?
Section 2.4: I am a bit skeptical about the use of multiple NNs here. I understand using ESPER NN T and S for preformed DIC (so only physical transport) but not to derive O2. Also, did you propagate uncertainty from the input data into ESPER and then into CANYON-B-CONTENT? I’m afraid the uncertainties might be very big. For transparency, I’d add some statistics for each step in this section. Also, using CANYON-B to retrieve macronutrients from GOBAI O2 is an excellent idea, but I believe the time component is not included in the CANYON-B algorithm. One would assume this is okay since the changes in the input parameters will have time varying changes but maybe having a check on that in the supplementary material would reassure the readers
Overall, I think you need a very robust uncertainty propagation section and 2.8 seems a bit weak in that regard (or maybe not detailed enough at this stage)
Section 2.8.1 did you take into account the recent paper by Bushinsky et al., 2025 that assesses float oxygen offsets of approximately -2.7 µmol kg-1 at depth lead to an overestimation of surface pCO2 by +3.2 µatm - this would matter in the use go GOBAI O2
ref: Bushinsky, S. M., Nachod, Z., Fassbender, A. J., Tamsitt, V., Takeshita, Y., & Williams, N. (2025). Offset Between Profiling Float and Shipboard Oxygen Observations at Depth Imparts Bias on Float pH and Derived pCO2. Global Biogeochemical Cycles, 39(5), e2024GB008185.https://doi.org/https://doi.org/10.1029/2024GB008185
Specific comments to Results:
The results are well presented and supported by figures. However, there is some redundancy between Sections 3.1 and 3.2 that could be reduced by emphasizing the new insights instead of re-describing patterns already visible in the figures.
Section 3.1.1: The seasonal analysis is convincing, but the interpretation of the [Cnat] seasonal amplitude could be more explicitly linked to mixed-layer depth dynamics (beyond the schematic explanation in Fig. 3b). It might be worth quantifying the relative contributions of MLD deepening vs. biological activity (e.g., from satellite chlorophyll or primary production climatologies).
Section 3.1.2: The interannual signal in [Cnat] (notably the 4–6 year periodicity) is intriguing. Could this be related to NAO variability? This connection would be worth testing or at least mentioning.
Fig. 5: It might be useful to report confidence intervals on the trends (e.g., shading or ± values in the legend) to facilitate comparison across layers.
Section 3.2 (Transports): The diapycnal vs. isopycnal decomposition is elegant. Still, the physical meaning of the “Test” estimator (Eq. 4) could be better integrated into the discussion — especially regarding how it relates to AMOC variability.
Also, the relative contribution of concentration vs. velocity variability to total transport (Fig. 8) could be more clearly quantified (percentages or variances explained).
Uncertainties: The RMSD and propagated uncertainties are well computed, but they are presented late. I would suggest summarizing the main numbers (e.g., 1–2 µmol kg-1 for [Cant]) in the Results rather than only in the Methods, to remind the reader of the confidence level.
Discussion
The discussion is rich but somewhat descriptive in parts. It could benefit from a sharper focus on mechanismsdriving the observed signals (ML deepening, AMOC variability, regional contrasts) and on how this study advances beyond previous works (Zunino et al., 2015; Pérez et al., 2013).
Section 4.1: The role of the mixed layer in modulating both [Cnat] and [Cant] is clearly established. It would be interesting to discuss whether the modeled MLD variability in the reanalyses (e.g. GLOSEA5 vs. ECCO) could bias the amplitude of the seasonal carbon signal.
Section 4.2: The link between AMOC strength and Cant transport trends is key. However, the text could more directly address whether the observed Cant increase is primarily due to atmospheric CO2 forcing or to changes in circulation pathways (uMOC thickening/thinning).
Section 4.3: The authors mention good agreement between reanalyses and observations, but this could be quantified (e.g. comparing GLOSEA5 and ECCO vs. bottle data).