The Portugal-to-Greenland GO-SHIP A25 OVIDE hydrographic section (Fig. ), referred to here as A25, has been repeated biannually in summer since 2002 . This study uses data from nine A25 cruise repeats that span 2002–2018. To extend the reference dataset further back in time, data from the FOUREX 1997 cruise , which differs slightly from the A25 positions, have also been included. The T and S of the CTD sensors are collocated with nutrients (nitrate, phosphate, and silicate), [AT], pH and [O₂] from bottle samples. BGC data consistency was ensured by applying the GLODAP recommended adjustments to the measured values of [O₂], nitrate, phosphate, silicate, pH and [AT] (see “Recommended adjustment values” at https://glodapv2.geomar.de, last access: 17 October 2023) . We used these data to compute Cnat and Cant (Sect. ), which are used here in conjunction with absolute velocities to compute property transports (Sect. ) . Geostrophic velocities were derived by integrating geostrophic shears, calculated from T and S data obtained at hydrographic stations, using as reference simultaneous velocity measurements from a Ship-mounted Acoustic Doppler Current Profiler (S-ADCP). Ekman transport, estimated from NCEP data, was incorporated into the surface layer (0–30 m). An inverse model was applied to compute velocity corrections for each pair of hydrographic stations to ensure volume conservation (see ). The A25 velocities v used here are normal to the section and correspond to geostrophic velocities plus Ekman velocities .

The ocean reanalysis datasets used (Table ) are GLOSEA5 , ECCO , EN4 and CORA . Ocean reanalyses are data-driven products with varying levels of complexity, and each individual product has its own strengths and limitations. Using the mean concentration across products helps mitigate product-specific biases and errors, while the spread among them provides an estimate of the associated uncertainty. All reanalyses provide gridded T and S at monthly resolution. The velocity fields (v) of CORA and EN4 are geostrophic velocities derived from T, S, surface altimetry, while the Ekman velocities are derived from NCEP (Table ). Velocities for GLOSEA5 and ECCO are full general circulation model (GCM) dynamics . EN4 and CORA were interpolated to the positions of the A25 section. For ECCO and GLOSEA5 GCM, the nearest native grid points to the A25 section were used. The reader is referred to for a detailed discussion of seasonal to long-term volume transport variability at A25 from GLOSEA5, ECCO, EN4, and CORA. To complement the ocean reanalysis datasets, we also considered the use of the GOBAI-O₂ gridded product (version 1.1). GOBAI-O₂ provides O₂ monthly fields computed by applying NN to gridded monthly T and S fields derived from Argo . The original GOBAI-O₂ data (1°×1° resolution on 58 depth levels) were interpolated to the positions of the A25 section (Table ). The use of GOBAI-O₂ might introduce additional depth-dependent biases in O₂ associated to Argo-O₂ sensors . Our error estimation method, based on the comparison to the A25 ground truth (see Sect. ), ensures that any potential bias from the use of GOBAI-O₂ is reflected in the reported uncertainties.

To determine the Cant fraction from [DIC], we used the carbon-based BC φCT0 approach . This BC approach has been broadly applied to study the inventory of Cant, its storage rates, its variability and the influence of Cant on ocean acidification . The input variables for this approach include date, geographical location, T, S, O₂, macronutrients (NO3-, PO43- and Si(OH)₄), [AT] and [DIC]. To estimate the natural carbon concentration [Cnat], [Cant] (Sect. ) is subtracted from [DIC] ([Cnat] = [DIC] - [Cant]). For reference data, T, S, [O₂], macronutrients, [AT], and pH are measured at A25 bottles. [DIC] is derived from [AT] and pH from the in situ bottle measurements using the PyCO2SYS toolbox (version 1.8). This toolbox also provides the Revelle factor shown in this study. NNs are used to derive the necessary parameters for ocean reanalysis to obtain [Cant] (Sect. ).

Two different NNs were sequentially applied to ocean reanalysis data to estimate [DIC] and the evaluation of the error is detailed in Sect. . Each NN has limitations, and the use of two different NNs was intended to build on the strengths of each NN. First, we applied ESPER NN (, Eq. 8) to the T and S fields of the reanalysis (as well as date and position, i.e., longitude, latitude, and depth) as input to determine [O₂] and macronutrients. Second, we calculate [DIC] and [AT] using CANYON-B NN – CONTENT . CANYON-B uses the same input data as ESPER NN plus [O₂] (O₂ derived from ESPER_NN). The choice of ESPER NN and CANYON-B-CONTENT for the estimation of oxygen and macronutrients and carbon variables, respectively, is based on the performance of the corresponding NNs for each variable (i.e. lowest final error) . Using ESPER alone to calculate [DIC], we found that [DIC] values diverged from cruise-based estimates after 2010, whereas between CANYON-B-CONTENT and observations there was a better agreement (Fig. S1 in the Supplement). Instead of a time-dependent prediction of [DIC] as in CANYON-B , [DIC] is given for a reference year (2002) in ESPER. Within this NN, the anthropogenic component of DIC (Cant) is calculated as an exponential increase (see , their Eq. 1), assuming that Cant is in a transient steady state (that is, exponential increases in atmospheric anthropogenic CO₂ should result in the concentration of marine Cant that increases at rates proportional to the concentration of atmospheric anthropogenic CO₂). The use of ESPER NN to estimate [O₂] and macronutrients and CANYON-B-CONTENT to estimate [DIC] and [AT] is therefore a reliable compromise for applying NN to T and S fields in the subpolar gyre (Fig. S1). In the particular case of the GOBAI-O₂ dataset (Table ), we applied ESPER NN only to retrieve macronutrients, then CANYON-B-CONTENT to retrieve [DIC] and [AT] . It is important to note that the A25 2002–2014 data used in this study to obtain the reference estimates are among the source data used for the training phase of both NNs: GLODAPv2.2019 and GLODAPv2 . However, the reference estimates from the A25 data for 2016 and 2018 can be considered entirely independent of those obtained using the OR-NN-BC method.

From the monthly time series of concentration and transport, seasonal signal time series were obtained by applying a two-year high-pass filter over the time series. The new high-frequency time series were grouped by months and the mean for each month was calculated to derive the seasonal (also referred to as intraannual) signal.

Interannual time series were computed by subtracting the high-frequency time series (obtained using the high-pass filter) from the initial one. The Standard Deviation (SD) of this interannual time series will be used as a metric of interannual variability. In the following, the interannual signal will refer to this low-frequency signal. The standard error (SE) of the average over the reanalyses is defined as the SD between reanalyses divided by the square root of the number of reanalysis used to compute the mean.

Trends are calculated as the slope coefficient of a linear fit. The uncertainty in trends is estimated using the Moving Block Bootstrap method .

To better quantify the effects of seasonal changes in ML and uMOC thickness on the seasonality of [Cnat] and [Cant], we calculated an idealized seasonal anomaly for ML and uMOC using the annual mean vertical profile of the property ([Cannual]), rather than monthly varying concentrations, and the full variability of ML depth and uMOC thickness. This approach isolates the impact on ML concentrations of physically-driven seasonal changes in layer thickness from biologically-driven seasonal variations in concentration.

The error in the estimation of [Cnat] and [Cant] is the sum of the errors associated with the combined use of ocean reanalyses, NN, and the BC approach. The error associated with the use of NNs is evaluated at the hydrographic section A25, using estimates directly obtained from seawater samples as a reference, first at the sampling points (Sect. ) and then for integrated variables such as averaged concentrations (Sect. ) and transports (Sect. ) in predefined layers. The overall errors resulting from both the use of NN and reanalyses have been estimated altogether for average regional concentrations and transports and comparing them with hydrographic data. The resulting Root Mean Square Deviation (RMSD) give the final errors for integrated concentrations (Sect. ) and transports (Sect. ), where all errors have been taken into account. The error of the BC approach is fixed according to previous studies to 5.2 µmol kg⁻¹ .

Using error propagation to calculate an error on an integral quantity (e.g., average concentration per layer or property transport by uMOC) would require postulating the error correlation between different variables. This correlation is unknown. Our method of direct comparison with observational results avoids this issue, enabling us to compare final integrated values to reference hydrographic data.

Along the A25 OVIDE section, the Cnat fraction represents, on average, 98 % of the DIC, while the Cant fraction accounts for only the remaining 2 %. The [Cnat] distribution shows a vertical gradient of generally lower to higher concentrations from surface to depth (Fig. ), which is largely shaped by the biological carbon pump . In contrast, the [Cant] distribution shows an opposite vertical gradient, with the highest concentrations in the upper layers due to direct contact with the atmosphere (Figs. , ). [Cant] decreases with depth and also from East to West, as the cold subpolar waters contain less [Cant] than the warmer subtropical waters . In particular, the highest [Cant] signature is found in and east of the North Atlantic Current (NAC) (Fig. ) which transports Cant-loaded subtropical waters to higher latitudes. This current represents the primary source of Cant to the subpolar gyre .

The results discussed in this section are the averages over all reanalyses, unless otherwise indicated. We recall that typical errors of our method on integrated concentration range from 2.9 to 5.2 µmol kg⁻¹ for [Cnat] and from 1.4 to 2.0 µmol kg⁻¹ for [Cant] (Sect. ).

The time series of volume, Cnat and Cant transports across A25 for each of the ocean reanalysis are presented in Fig. , with independent cruise-based estimates shown in red.

The [Cant] derived from ocean reanalysis temperature and salinity, using NNs and the BC φCT0 approach, collectively referred to here as the OR-NN-BC method, shows good agreement with the 2002–2018 cruise-based estimates presented here using the same BC approach (RMSD of 5.1 µmol kg⁻¹ Table S1 and Fig. b), and with previous estimates at A25 reported by . The agreement between reanalysis products and bottle data was evaluated and summarized in Tables 4, S3, S5, and discussed in Sect. 2.8.2 and 2.8.3. The [Cant] increase rates obtained from our [Cant] time series are also in good agreement with the cruise-based rates. Here, we found [Cant] increase rates of 0.7 ± 0.03, 0.2 ± 0.006 and 0.3 ± 0.004 µmol kg⁻¹ yr⁻¹ for the uMOC, lMOC and the section average, respectively, during the entire period (1993–2022) (Fig. b). These results closely match the [Cant] increase rate estimates by of 0.6 ± 0.3, 0.2 ± 0.3 and 0.3 ± 0.2 µmol kg⁻¹ yr⁻¹ for the same layers, based on A25 cruise data spanning 1997–2010. The agreement between both estimates, considering the two distinct periods used for the calculations, highlights the linear nature of the [Cant] increase over time for all the layers under consideration. Looking at specific longitudinal regions of the A25 section, the [Cant] increase rate in the IS (0.36 ± 0.005 µmol kg⁻¹ yr⁻¹) is slightly higher than the increase rate for the section average (Fig. S3i, j), likely due to the transfer of [Cant] to intermediate depths in the IS by deep convection . This increase rate is comparable to that observed in the North East Atlantic Deep Water in the Labrador Sea (0.3 µmol kg⁻¹ yr⁻¹ for 1986–2016), but substantially lower than the 0.8 µmol kg⁻¹ yr⁻¹ rate found in Labrador Sea Water over the same period . This difference reflects the enhanced [Cant] storage capacity of Labrador Sea compared to that of the IS, which results from its direct exposure to frequent deep convection events that efficiently transport [Cant] from the surface to depths up to 2000 m .

A comparison of Cant transports with previous studies at A25 also reveals good consistency of the results. The cruise-based estimate of 0.092 ± 0.010 PgC yr⁻¹ by for June 2004 is similar to our estimate of 0.09 ± 0.02 PgC yr⁻¹ for the same period (Fig. c). The 2002–2010 Cant transport of 0.096 ± 0.011 PgC yr⁻¹ (254 ± 29 kmol s⁻¹) reported by is comparable to the 0.098 ± 0.02 PgC yr⁻¹ that we find for the same period. Our [Cnat] estimates are also in good agreement with the 2002–2018 cruise-based [Cnat] values (Table S3), showing no long-term trend. The Cnat transport estimates derived from ocean reanalysis (net, uMOC and lMOC) are generally consistent with the A25 cruise-based estimates presented in this study, lying within their uncertainty range (Fig. , Table S7), except in 2006, when the discrepancy between the two values exceeded the uncertainty (Fig. a, c). Taking into account what happens on an intraannual scale, the seasonality of ML [Cnat] and the seasonality of surface [Cnat] and [DIC] (Figs. a, S6) are consistent with seasonal amplitudes in the range of 42–60 µmol kg⁻¹ observed for surface [DIC] at high latitudes and ≈ 60 µmol kg⁻¹ for surface [DIC] in the SPNA . Finally, we note the lack of previous research on the seasonality of Cant and Cnat transports in the SPNA.

This study corroborates previous estimates , which relied on cruise observations, and provides a more detailed perspective on Cant and Cnat concentration and transport variability at seasonal to interannual time scales.

The deepening of the ML in winter favors the enrichment of [Cnat] within the ML through the entrainment of DIC-rich thermocline waters , increasing the seawater partial pressure of CO₂ (pCO₂) and thus leading to surface saturation or slight supersaturation. In this study, we quantify that this physical mechanism of the mixed-layer pump accounts for approximately two-thirds of the winter increase in ML [Cnat] (Fig. ). ML [Cnat] decreases in summer. During this period, changes in the depth of ML account for only one-third of the ML [Cnat] variability, with the remaining two-thirds largely favored by extensive biological carbon consumption resulting in surface undersaturation .

It is important to note that the OR-NN-BC method relies solely on T, S, date, and position as predictor variables to obtain [DIC] from which the [Cnat] and [Cant] components are derived (see Sect. ). Hence, by methodology, we expect that the surface [Cnat] and [Cant] correlate or anti-correlate to a certain extent with temperature (Figs. S7, S8). However, this does not imply that the temperature itself (i.e. solubility) is the sole or dominant driving mechanism of the observed seasonal surface [Cnat] and [Cant] variability. For [Cnat], the seasonal cycle, which anti-correlates with temperature (Fig. S7a, b), still remains when considering no seasonal temperature signal at all (Fig. S7c, d). For [Cant], however, there is a positive correlation with temperature (Fig. S8), and when the seasonal cycle of temperature is removed (Fig. S8c, d), the [Cant] seasonal signal largely disappears. This effect reflects seasonal changes in the ocean's CO₂ uptake capacity rather than temperature-driven solubility control: summer conditions show minimum Revelle factor values (Fig. S4), which indicates enhanced seawater buffer capacity, thereby favoring [Cant] uptake. Together, summer changes in both [Cnat] and [Cant] components are indicative of an enhanced uptake of atmospheric CO₂ in the SPNA .

At the intraaanual scale, Cnat and Cant transports are dominated by seasonal variations in volume transport, driven by uMOC volume changes in the EGC and velocity variations in the eastern boundary current . At A25, this combined effect of uMOC thickness variations and velocity changes on seasonal tracer transport variability differs from Cant transport variability at 26.5° N in the subtropical gyre, which was attributed primarily to velocity variability (Fig. S9). Less seasonal variation in Cant transport is observed in the SPNA at the A25 section (amplitude of 0.015 PgC yr⁻¹) (Fig. e–f)) than at 26.5° N (peak-to-trough amplitude of 0.08 PgC yr⁻¹) . However, the large seasonal variations for uMOC and lMOC represent more than 25 % of the annual average for volume and tracer transports at A25. Likewise, at 26.5° N between 2004 and 2012, the seasonal variability in the transport of uMOC Cant constituted 27 % of its mean value .

The long-term [Cant] increase at A25 is linear (Fig. ). This rise is primarily driven by the atmospheric increase in CO₂ and the increase in air-sea CO₂ fluxes in the NA , as suggested by the trend-less Cant transport at 26.5° N between 2004 and 2012 . The decadal rate of increase in [Cant] in the water column depends on the distance of the layer from the sea surface, with higher increase rates closer to the surface where atmospheric [Cant] enters the ocean via air-sea exchange (Fig. ). For example, a rate of increase of 6.0 µmol kg⁻¹ per decade was found for the uMOC compared to 2.4 µmol kg⁻¹ per decade for the lMOC. However, the relative increase for each layer is the same. The atmospheric CO₂ growth was about 61.35 ppm for 1993–2022 (1993: 357.21 ppm, 2022: 418.56 ppm) . This rise resulted in an almost doubling of [Cant] in ML (49.8 %). [Cant] variability is dominated by long-term changes in concentration, due to atmospheric CO₂ forcing, while [Cnat] variability is dominated by seasonal and interannual changes.

The interannual variability in ML [Cnat] and [Cant] is the strongest when particularly deep ML are found during deep convection periods, before 1995 (for [Cnat]) and between 2008–2016 (for both) (Figs. , S3). The interannual variability of seasonal deep convection events shapes the interannual [Cnat] and [Cant] in the ML. The signal is the largest in the IS and in agreement with previously reported events . In turn, the alternation of shallow and deep ML and the persistence of the signal at depth cause interannual variability in concentrations.

At 24.5° N, the rates of increase in [Cant] between 0.25 and 0.88 µmol kg⁻¹ yr⁻¹ were observed between 1992 and 2011 for deep waters and surface layers, respectively . This is consistent with our increase rate for 1993–2011 of 0.2 µmol kg⁻¹ yr⁻¹ for deep waters and more than 0.8 µmol kg⁻¹ yr⁻¹ in ML. Motivated by the study of on GLODAP data spanning over 1994 (1989–1999), 2004 (2000–2009) and 2014 (2010–2020) decades, we computed the accumulation rates obtained with our method for these three decades at A25 (the first decade for us is limited and starts in 1993). Despite using a different methodology, we observe the reduction in [Cant] accumulation rate in the North Atlantic at A25, with a lower [Cant] increase rate above 3000 m in the 2014 decade (3.4 µmol kg⁻¹ per decade) than in the 1994 and 2004 decades (4.0 µmol kg⁻¹ per decade) (Fig. ). This reduction is concomitant with the observation of deeper ML at the end of the 2011–2020 period (Fig. S2). The maximum [Cant] reduction is located on the Reykjanes Ridge (Fig. b), suggesting that the NAC brought less [Cant] during the 2014 decade. The early 1994 decade and the 2014 decade correspond to periods when the subpolar gyre was more intense and less affected by NAC waters . In addition, deeper ML (as in the 2014 decade) would lead to a minimum in [Cant], even if the [Cant] from the NAC was higher or unchanged.

Looking at volume transport, an interannual decrease is observed in the net and MOC transports until the early 2010s as in (Fig. a) and is consistent with the weakest state of the AMOC in recent decades before increasing since then (Fig. a).

For Cant transport, the decadal time scale that shows a linear trend is not driven by circulation but by the linear growth in [Cant], compared to Cnat, a result that refines previous results, obtained over a shorter time frame in the subtropical and subpolar gyres, which concluded that the variability of Cant transport is driven by circulation . Most of the net Cant transport variability comes from its diapycnal component, which is set by [Cant] growth (Figs. , ). There is a significant imbalance between the Cant transports of the uMOC and the lMOC due to the [Cant] strong vertical gradient so that the uMOC is the main driver of the North Atlantic northward transport of Cant . The increase in [Cant] results in almost a doubling of the northward Cant transport from 0.08 PgC yr⁻¹ in 1992 to 0.15 PgC yr⁻¹ in 2022. Over the shorter pre-2008 period, Cant transport exhibited a slower increase rate (0.013 PgC per decade compared to 0.038 PgC per decade after 2008), attributed to weakened MOC strength (uMOC decreased by 1.8 Sv per decade before 2008) that attenuated the increase in [Cant].

The variability of the Cnat net transport is driven by the variability of volume transport from intraannual to long-term scales, in agreement with , and that [Cnat] is mainly constant throughout the time range of this study. Relative changes in mean [Cnat] are small (Fig. ), so long-term Cnat transport changes are mainly determined by volume transport changes. Since Cnat transport strongly correlates with net volume transport in the subpolar gyre, having the latter well constrained and solved is crucial. The interannual net Cant transport variability is also circulation-driven because relative changes in [Cant] are not significant in successive years contrary to the relative changes in volume transport. The net volume transport of GLOSEA5 increases while ECCO one stays constant, which is reflected as well on net Cnat transports. The transport of uMOC Cnat also shows the periods of reduction and strengthening before and after 2008–2012, and is based on the diversity in the physics of ocean reanalysis. Due to the strong volume transport-driven variability of net Cnat transport, the spread of circulation in ECCO and GLOSEA5 has a greater influence on net Cnat decadal transport than on decadal net Cant decadal transport, being more influenced by [Cant] (see Sect. 2.8.3 and Tables 4, S5 for a comparison of ocean analysis products).

The Cant budget of an oceanic region is the result of the balance between lateral advection, air-sea fluxes, and storage. Net advective transport plays an important role in the NA budget, contributing to 65 ± 13 % of the NA Cant storage rate (reference to 2004, ). The observed increase in net northward Cant transport over the 30-year period is evident in both subtropical and subpolar regions of the NA, as documented through decadal GO-SHIP section repeats . At A03, Cant transport experienced an increase from 0.058 ± 0.036 to 0.104 ± 0.035 PgC yr⁻¹ between 2000–2009 and 2010–2019, while at 26.5° N Cant transport passed from 0.128 ± 0.032 to 0.222 ± 0.024 PgC yr⁻¹ (Fig. S9). During the same time periods (2000–2009 and 2010–2019), we found at A25 an increase in northward Cant transport from 0.09 ± 0.02 PgC yr⁻¹ to 0.11 ± 0.03 PgC yr⁻¹. At AR07, north of A25, similar values as at A25 are found: 0.088 ± 0.038 for 2000-2009 and 0.115 ± 0.042 PgC yr⁻¹ for 2010–2019 (Fig. S9). The 73 %–79 % increase in Cant transport in the subtropical region between 2000–2009 and 2010–2019 is greater than the 22 %–32 % increase in the subpolar region. However, stable Cant transport is found at 26.5° N over a shorter period (2004–2012) at high resolution , suggesting an analysis over a longer period to better identify long-term trends and a better consensus between distinct methods. The 2004–2012 average Cant transport convergence between A25 and 26.5° N is equal to 0.091 ± 0.033 PgC yr⁻¹: 0.191 ± 0.013 PgC yr⁻¹ at 26.5° N minus 0.10 ± 0.02 PgC yr⁻¹ at A25. Therefore, the northward transport of Cant at A25 is half the one observed at 26.5° N, similar to previous findings . Due to the northward increase in Cant transport at A25 concomitant with its constant supply at 26.5° N, the convergence is greater (reduced) at the beginning (end) of the time period. We found that the lateral Cant transport convergence between A25 and 26.5° N averages to 0.13 ± 0.07 PgC yr⁻¹ in 2004 and 0.10 ± 0.05 PgC yr⁻¹ in 2012. The documented increase in air-sea CO₂ fluxes between A25 and 26.5° N during this period may partially offset this reduced convergence; however, this compensation would imply a decrease in storage.

This study represents the first quantitative assessment of [Cant] seasonality in the SPNA, and no comparable studies exist in the literature to validate or challenge these findings. The seasonality of [Cant] may depend on the approach used to calculate it, and further investigations based on other [Cant] estimation approaches (TTD, TrOCA, CIPSLo, ΔC*) may be of interest. The abiotic model, used as an assumption in the BC approach to determine the saturation of [Cant] in the first 25 m based solely on the fraction of atmospheric CO₂, is a good approximation to constrain [Cant] seasonality at the sea surface. We obtained a seasonal variation of ML [Cant] of ±2.5–3.0 µmol kg⁻¹, that is 1.1 µmol kg⁻¹ higher (on average) compared to seasonal [Cant] in ML for an annual [Cant] profile (difference between ML and ML annual in Fig. d). The natural variability of [DIC] coming from NN is an order of magnitude higher (Fig. b, d). We found a ±12.5 µmol kg⁻¹ amplitude increase on average in ML [Cnat] compared to ML [Cnat] applied to annual [Cnat] profile (Fig. b). The natural variation of the CO₂ fraction in surface water, motivated by natural processes, and here incorporated in the OR-NN-BC method owing to NN, is then an order of magnitude higher than the variability caused by the increase in the anthropogenic fraction of CO₂ in the atmosphere.

Our results show that the seasonal variability of ML [Cnat] and Cant is generally well captured by surface Cnat and Cant concentrations (Fig. S10), as the difference between surface and ML mean concentrations remains nearly constant throughout most of the year (within -8.6 ± 4.9 µmol kg⁻¹ for [Cnat], and 1.8 ± 0.4 µmol kg⁻¹ for Cant). Surface concentrations therefore provide a reasonable proxy for seasonal variability within the ML, except in winter. In March, deviations in [Cnat] ([Cant]) of up to 16.8 (2.7) µmol kg⁻¹ are observed between surface and ML values. These differences result from the combined effect of a slight vertical decrease (increase) in [Cant] ([Cnat]) within the winter ML, and a deeper winter ML in the IS relative to the rest of the section (Fig. S3). The non-uniformity of ML [Cant] for winter deep ML has also been reported using a similar methodology with Argo-O₂ profilers in the IS . In our case, this effect is further amplified by the NN-generated [O₂], which exhibits a vertical gradient within the ML. This pattern likely reflects a methodological artifact rather than a physical signal, as Argo-O₂ observations generally show a vertically homogeneous [O₂] distribution within the winter ML in the IS . Consequently, the NN-induced [O₂] vertical gradient amplifies the vertical gradients in ML [Cant] and [Cnat], and such methodological limitation should be taken into account when applying the OR-NN-BC method.

As for the inter-product comparison, GLOSEA5 simulates deeper ML values in winter and shallower ML in summer than ECCO (Fig. S2), producing higher (lower) ML [Cnat] values in winter (summer), thereby amplifying the seasonal carbon signal. In contrast, the weaker ECCO ML depth seasonal amplitude dampens the seasonal variability of this property. The opposite behavior is observed for [Cant] owing to its opposed vertical gradient.

When looking at the interannual-to-long-term signal, this study suggests that the results in Cant increase rates depend strongly on the period chosen. Interannual variability is added to the linear trend, giving different results depending on the chosen period. Continuing to address [Cant] variability at higher resolution on multiple time scales is also of interest.

To summarize, and despite the methodological limitations discussed in this section, we are confident in our results regarding the main interannual variability signals and long-term trends in concentrations and transport, as well as for the mean seasonal cycle of [Cnat], which are supported by other independent studies. For [Cant], however, this study represents the first quantitative assessment of [Cant] seasonality in the SPNA, and no comparable studies exist in the literature to validate or challenge these findings. Each reanalysis shows good agreement with A25. However, year-round hydrographic data is not available for comparison with the seasonal transport estimates from the reanalysis. This would be all the more interesting in winter, when seasonal maxima for most of the concentrations and transports presented in this study are observed. In addition, the study of specific high-frequency processes will require additional data sources to further discuss the results and explore each of the reanalysis rather than an average signal. Finally, our results will depend even more on the reanalysis chosen if volume transport is predominant in the process, i.e. for [Cnat] transport (seasonal and interannual) and seasonal [Cant] transport.