| 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.
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).