Large contribution of the sea-ice zone to Southern Ocean carbon export revealed by BGC-Argo floats

Large contribution of the sea-ice zone to Southern Ocean carbon export revealed by BGC-Argo floats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Large contribution of the sea-ice zone to Southern Ocean carbon export revealed by BGC-Argo floats Guillaume Liniger, Sébastien Moreau, Delphine Lannuzel, Peter Strutton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3937570/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2025 Read the published version in Global Biogeochemical Cycles → Version 2 posted You are reading this latest preprint version Show more versions Abstract The Southern Ocean (south of 30°S) contributes significantly to global ocean carbon uptake through the solubility pump and phytoplankton productivity. Many studies have estimated carbon export to the deep ocean, but very few have attempted a basin-scale perspective. In this study, we use an extensive array of BGC-Argo floats to improve previous estimates of carbon export across basins and frontal zones, with a focus on the sea-ice zone (SIZ). We find that the SIZ contributes 33% of the 5.08 PgC y − 1 total Southern Ocean carbon export. We also show that subsurface carbon respiration, not flux out of the surface ocean, contributes most strongly to the temporal and spatial variability of carbon export. Our work highlights the importance of closely monitoring the SIZ to accurately quantify the total Southern Ocean carbon sink, especially as the SIZ is prone to strong interannual variability. Tightening these estimates and their drivers ultimately impacts our understanding of climate variability at the global ocean scale. Oceanography BGC-Argo floats Carbon export Southern Ocean Sea-ice zone Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Key Points We used an extensive fleet of circumpolar BGC-Argo floats to investigate carbon export in the Southern Ocean Using oxygen drawdown and sinking particulate organic carbon, we estimated a total basin-integrated carbon export of 5.08 PgC y -1 The sea-ice zone is responsible for 33% of the Southern Ocean carbon export Plain Language summary Phytoplankton take up atmospheric carbon dioxide through photosynthesis and help sequester carbon to the deep ocean. Using robotic floats equipped with biological and physical sensors, we investigate the carbon export from the surface to the deep ocean across the Southern Ocean. We highlight that the Southern Ocean exports 5.08 billion tonnes of carbon per year, which is about a third of the mean global ocean carbon export. The sea-ice zone, an area mostly overlooked due to a historical lack of data, is responsible for ~33% of the Southern Ocean carbon export. Our results suggest that the Southern Ocean, and particularly the sea-ice zone, plays a significant role in the carbon cycle. 1. Introduction The Southern Ocean (SO) represents a significant sink for atmospheric CO 2 (Gruber et al., 2009 ) contributing ~ 20–40% of the total ocean CO 2 uptake (DeVries et al., 2012 ; Takahashi et al., 2002 ). The particulate organic carbon (POC) exported to the deep ocean of the SO constitutes about 30% of the ocean global carbon export (Schlitzer, 2002 ). Sinking POC particles are subject to modification by biogeochemical processes through the water column. Respiration by heterotrophs organisms consumes oxygen to degrade organic matter. This can be done by zooplankton and bacteria, with the latter being able to return particulate organic matter to inorganic nutrients (remineralization). Respiration is the dominant process consuming most of the POC in the water column, and only 1–3% of the particles that exit the upper 100 m reach 1,000 m (Cavan et al., 2017 ; De La Rocha & Passow, 2007 ; Passow & Carlson, 2012 ; Steinberg et al., 2008 ). The SO biological carbon pump is biogeochemically significant but exhibits strong spatial and temporal variability. Primary production in the sea-ice zone (SIZ) is strongly light-limited due to both seasonal solar patterns and the sea-ice cycle. Nutrient limitation also plays an important role, and the essential trace metal iron is supplied from sea ice (Lannuzel et al., 2016 ), coastal features (Gerringa et al., 2012 ; Sherrell et al., 2015 ; St-Laurent et al., 2019 ), deep mixing (Tagliabue et al., 2014 ), upwelling (Moreau et al., 2023 ) or dust transported via the atmosphere (Tang et al., 2021 ). Nutrients, including iron, and mixed layer depth become more influential northward as nutrient-rich coastal sources (Boyd et al., 2007 ; Moore et al., 2013 ). The distributions of grazers, silicate, phosphate and nitrate around the Antarctic Circumpolar Current also directly impact the density and type of phytoplankton in surface waters (Assmy et al., 2013 ; Smetacek et al., 2012 ). Considering the biogeochemical and physical differences between zones in the SO, it is important to understand if and how the processes influencing carbon export vary spatially, as this ultimately impacts POC and associated carbon export. Studies have estimated carbon export or transfer efficiency from several techniques including satellites (Arteaga et al., 2018 ), moorings (Lourey & Trull, 2001 ; Manno et al., 2022 ), modelling (Karakuş et al., 2021 ; Lancelot et al., 2000 ), ship-based observations (Buesseler et al., 2005 ; DeJong et al., 2017 ; Ducklow et al., 2008 ; Pilskaln et al., 2004 ; Planchon et al., 2013 ; Puigcorbé et al., 2017 ; Ratnarajah et al., 2022 ; Smetacek et al., 2012 ), and Biogeochemical (BGC) Argo floats (Davies et al., 2019 ; Hennon et al., 2016 ; Munro et al., 2015 ). Some studies also combined these tools (Dall’Olmo et al., 2016 ; Fan et al., 2020 ; Nowicki et al., 2022 ; Schlitzer, 2002 ). BGC-Argo floats are autonomous instruments platforms equipped with sensors to measure physical and biogeochemical variables, including temperature, salinity, dissolved nitrate, downwelling irradiance, dissolved oxygen (D oxy ), chlorophyll- a (chl- a ) fluorescence, pH and particle backscattering (bbp; Johnson et al., 2017a ). BGC-Argo floats allow the study of biogeochemical and physical processes in the upper 2000 m. They are usually designed to sample on a 10-day cycle frequency. This temporal resolution may not be sufficient to capture events on a shorter time scales, such as rapid bloom pulses (Williams et al., 2018 ) or mixed layer pump events (Xing et al., 2020 ), leading to underestimation of carbon export. However, they are well suited to capturing seasonal carbon export (Lacour et al., 2023 ) and may also occasionally capture fast sinking eddy-driven carbon export (Llort et al., 2018 ). Previous work used BGC-Argo data to report biological activity and carbon export, but these results are usually regional in scope, over short time scale and north of 60°S (Bishop et al., 2004 ; Davies et al., 2019 ; Le Moigne et al., 2016 ; Xing et al., 2020 ). In the SO, only a few studies have focused on the SIZ (Briggs et al., 2018 ; Moreau et al., 2020 ), despite the susceptibility of this area to extreme environmental changes (Turner et al., 2022 ) which could alter phytoplankton productivity and subsequent carbon export (Kaufman et al., 2017 ). The continuous expansion of BGC-Argo deployments and growing data availability allow the study of oceanographic processes over large spatial scales, and with increasingly detailed temporal resolution. These advances have potential to better understand areas like the SIZ, where historical data coverage is poor. In this context, our study makes use of the extensive array of floats to give a new estimate of carbon export across basins and frontal zones, with a focus on the SIZ. We hypothesize that the SIZ contributes more to the total SO carbon export than previously reported. 2. Material and Methods 2.1 BGC-Argo floats and fronts We selected floats from the SOCCOM, SOCLIM and remOcean programs south of 30°S equipped with bio-optical sensors for particulate backscatter at 700nm (bbp 700 ). Data were downloaded on 08/06/22 and consisted of 25,953 profiles from 212 floats (191 SOCCOM, 8 SOCLIM, 13 remOcean) spanning March 2014 to June 2022, in the open ocean and under the sea ice (Fig. 1). We also used D oxy data from the downloaded floats when available (only four floats lacked D oxy , less than 2% of the total dataset). The temporal sampling resolution of SOCCOM floats is usually 10 days with a vertical resolution decreasing with depth, from 5 m in the first 100 m to 50 m from 400 m to 2000 m. SOCLIM and remOcean floats provide higher resolution data, with temporal sampling every 1–7 days at 1 m to 10 m resolution in the first 1,000 m. Each profile was assigned to a zone defined by fronts or sea-ice extent. Fronts for the polar Antarctic zone (PAZ), the subantarctic zone (SAZ) and subtropical zone (STZ) were defined from a recomputed Argo temperature and salinity 2004–2014 climatology (Bushinsky et al., 2017 ; Roemmich & Gilson, 2009 ). The STZ and SAZ are separated by the subtropical front, SAZ and PAZ are separated by the polar front, and PAZ and SIZ are separated by the seasonal ice front. For the SIZ, we acquired daily Ocean and Sea Ice Satellite Application Facility (OSI SAF) 25km resolution sea-ice concentration data from the Copernicus website https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration?tab=form that we used to compute the yearly sea-ice extent from 2014 to 2022. The extent of the SIZ varies widely compared to the STZ, SAZ and PAZ. This area is important to accurately quantify as sea ice drives seasonal stratification and iron fertilisation of this part of the SO and is prone to rapid and dramatic changes now and in the future (Eayrs et al., 2021; Hobbs et al., 2024). We therefore used the maximum sea-ice extent to define the SIZ front in any given year. This delineation allows the capture of floats involved in ocean - sea-ice interactions. Other fronts are mostly linked to local topography (Moore et al., 1999 ), and the use of a large float array reduces potential problems that could be linked to the moving fronts position. Fixed fronts position has been commonly used in the recent studies investigating large scale biogeochemistry in the SO (Arteaga et al., 2019 , 2020 ; Johnson et al., 2017b ; Llort et al., 2018 ; Su et al., 2021 , 2022 ). 2.2 BGC-Argo data quality control We used “adjusted” variables following the initial quality control performed on floats (Bittig et al., 2019 ; Schmechtig et al., 2018 ). For all variables, we removed all bad or questionable data (QC flag 0, 3, 4, 6, 7, 9). For bbp 700 , we removed data considered outliers as in Bisson et al. ( 2019 , 2021 ). All profiles were then interpolated on a common depth grid at 1m interval. Both bbp 700 and D oxy profiles were smoothed using a 7-points moving median for SOCCOM floats (Arteaga et al., 2019 ) and 9-points moving median for SOCLIM and remOcean (because of their higher resolution compared to SOCCOM). This smoothing helps to reduce the backscattering contribution of bubbles and white caps (Stramski et al., 2004 ), which tend to increase bbp and potentially lead to an overestimation of variables derived from it. Finally, profiles lacking temperature, salinity (and therefore mixed layer depth, MLD), time or location were removed from the analysis. After QC, our dataset consists of 18,434 valid profiles. In this study, we consider the floats to be quasi-lagrangian, but they can in reality cross regional boundaries when they are drifting, potentially introducing bias in calculation for each zone defined by a fixed area. Water masses may also differ between profiles (10-day sample period on average) as floats are drifting. Hence, we first made sure that pairs of consecutive profiles were located in the same area and month. If not, the profiles were discarded from the analysis. After that, we investigated the physical properties of the water column between every pair of valid profiles. We followed Johnson et al. ( 2017b ) who suggested removing pairs of profiles that observed: (i) a change of salinity by more than 0.05 psu at 500 m, (ii) a change of latitude by more than 5 degrees, or (iii) a change of longitude by more than 8.8 degrees. We however strengthened these criteria by also discarding pairs of profiles showing changes in salinity of more than 0.05 psu at 300 m. By doing so, we ensure that the pairs of profiles used to derive carbon export come from the same water masses, strongly decreasing bias that could be due to lateral and vertical advection. After carefully investigating our database, 4,815 pairs of profiles breaching at least one of these criteria were removed from the analysis, bringing our final pool of profiles down to 13,619. 2.3 Selecting export and horizon depths Most primary productivity is constrained in the upper layer of the ocean, from the surface to an export depth (E d ), usually defined as the euphotic zone depth (Z eu ), MLD or productivity layer depth (Z p ), depending on studies. Below E d , surface gas exchanges can be neglected because water masses are too deep to be in contact with the atmosphere and photosynthesis is limited by low light availability is low. Previous studies on carbon export have used several ranges of integration for their calculations. However, the magnitude of estimated carbon export is extremely sensitive to the chosen export and horizon depths (Palevsky & Doney, 2018 ). For this reason, we first conducted a sensitivity analysis for each zone, combining different export depths (100 m, Z eu , MLD and Z p ) and nine different horizon depths (E d + Z i , where Z i = 100, 200, 300, 400, 500, 600, 700, 800 and 900 m). Z p is defined as the deepest of either the MLD or Z eu . The MLD is defined as the depth where the density exceeded the density at 10m by 0.03 kg m -3 (de Boyer Montégut et al., 2004 ). We calculated Z eu (from the BGC-Argo chl- a fluorescence data) using two methods described in Dierssen et al. ( 2000 ) and Morel and Maritorena ( 2001 ). Both gave very similar seasonal variability except in the STZ where Z eu from Dierssen et al. ( 2000 ) was deeper in summer (Supplementary Figure S1). The difference could be due to the method being primarily developed for Antarctic coastal waters. We chose Z eu from Morel and Maritorena ( 2001 ) to compare with our MLD and to compute Z p . This comparison exercise led us to make later calculations of integrated carbon export from Z p to [Z p + 500 m]. Details can be found in Supplementary Figures S2 and S3, and associated text Tx1. 2.4 Carbon export and respiration calculation We derived POC using: \(\text{POC = 3.12 x }{\text{10}}^{\text{4}}\text{ x }{\text{bbp}}_{\text{700}}\text{+ 3.0}\) (Eq. 1, Haëntjens et al., 2017 ) The integrated POC (iPOC) for a single profile at a time t was defined as: $${\text{iPOC}}_{\text{t}\text{ }}\text{= }{\int }_{\text{ }\text{Ed}}^{\text{ }\text{Ed+Zi}}\text{POC d}\text{z}\text{ }\text{ }\text{(eq2, mmolC }{\text{m}}^{\text{-2}}\text{)}$$ With E d being the export depth and [E d + Z i ] the horizon depth. The rate of change was calculated as the difference in iPOC between two profiles divided by the time elapsed (Fig. 2a): $$\text{∆POC}\text{ }\text{= }{\text{(iPOC}}_{\text{t+dt}}-\text{ }{\text{iPOC}}_{\text{t}}\text{) / dt (eq3, }\text{mmolC}\text{ }{\text{m}}^{\text{-2}}{\text{ }\text{d}}^{\text{-1}}\text{)}$$ where dt is usually 10 days between BGC-Argo profiles. We quantified the heterotrophic respiration rate of change from D oxy using the same method as for POC. We first converted D oxy from µmol kg -1 to mmol m -3 by multiplying by 1.025 (an average seawater density in kg l -1 ). Second, we integrated D oxy from E d to [E d + Z i ] to obtain iD oxy in units of mmol m -2 . $${\text{iD}}_{\text{oxy(t) }}\text{= 1.025 x }{\int }_{\text{ }\text{Ed}}^{\text{ }\text{Ed+Zi}}{\text{D}}_{\text{oxy }}\text{ d}\text{z}\text{ (eq4, mmol}{\text{O}}_{\text{2}}\text{ }{\text{m}}^{\text{-2}}\text{)}$$ The rate of change in iD oxy was calculated as the difference between two profiles divided by the time elapsed (Fig. 2b): $$\text{∆}{\text{D}}_{\text{oxy}}\text{ }\text{= }{\text{(iD}}_{\text{oxy(}\text{t+dt}\text{)}}- {\text{iD}}_{\text{oxy(}\text{t}\text{)}}\text{) / dt (eq5, mmol}{\text{O}}_{\text{2}}\text{ }{\text{m}}^{\text{-2}} {\text{d}}^{\text{-1}}\text{)}$$ Finally, we multiplied the results from Eq. 5 by -0.69 to obtain a carbon equivalent of iD oxy rate of change (R, in mmolC m -2 d -1 ), where 0.69 is the C:O 2 quotient for organic matter respiration (Anderson & Sarmiento, 1994 ). Negative \(\text{∆}\) R were discarded similar to the way remineralization data were treated by Su et al. ( 2022 ) and Arteaga et al. ( 2019 ). This approach fits our focus on the respiration activity. When deriving R, we assume that floats have an even probability of travelling through water masses that are characterized by both positive and negative oxygen balance, cancelling each other out when using a large array of floats. Therefore, we inferred R to heterotrophic activity. We caution that this approach only works when using a large array of BGC-Argo floats with extensive spatial and temporal coverage. We discuss this in section 4.4 . The total carbon rate of change from both R and ∆POC is hereafter called ∆TC (∆TC = R + ∆POC), and its annual integral is called TC. That is, for any given time period, ∆TC sums the new particles appearing in the layer and the particles disappearing via respiration, meaning it provides an estimate of the total change in biomass, or export into the layer in the absence of respiration. We refer to ∆TC and TC also as carbon export. The performance of our method is compared to previous studies in the results and discussion section. 2.5 Water column carbon variability We also calculated integrated POC and R for five successive 150 m layers L i at depths D Li in the water column: $${\text{Variable}}_{\text{Li+1}}\text{= }{\int }_{ {\text{D}}_{\text{Li}}}^{{\text{ }\text{D}}_{\text{Li}}\text{+150m}}\text{Variable d}\text{z}\text{ (eq6)}$$ With D Li = Z p for i = 0, then D Li = D L1 for i = 1, D Li = D L2 for i = 2 and so on until L 5 . That is, for each profile, the calculation for the first layer was made from Z p to [Z p +150 m] (called depth D Li ), and then from this depth D Li + 150m, and so on until the 5th layer (Supplementary Figure S4). The rate of change between a profile t+dt and profile t was calculated following Eq. 3 and Eq. 5 for POC and R respectively. The average five D Li from all floats used in this study are D L1 = 258 m (± 67 m), D L2 = 404 m (± 67 m), D L3 = 556 m (± 67 m), D L4 = 707 m (± 67 m) and D L5 = 858 m (± 67 m). While the sensitivity analysis allows us to carefully investigate the role of export and horizon depths in carbon export estimates, this analysis lets us examine changes in carbon from a profile t + dt to t at consecutive depths range through the mesopelagic zone. We chose a consistent iteration of + 150m for the depth integration that would capture changes across reasonable depth intervals down to almost 1,000 m (compared to smaller increments), as well as changing the horizon depth threshold between layers would create inconsistent comparisons. 3. Results 3.1 Spatial and temporal distribution of surface particulate organic carbon Figure 1 shows the spatial distribution of float profiles for all zones. From a visual inspection, the SIZ, PAZ and SAZ have a relatively even circumpolar spread of profiles. In the STZ, there are more profiles in the Indian and Atlantic sectors compared to the Pacific. Most of the profiles are in the SIZ (5,322; 39%), while the PAZ, SAZ and STZ account for 4,810 (35%), 1,760 (13%) and 1,727 (13%) profiles respectively. Surface integrated values show clear seasonality in POC (Fig. 3), with a latitudinal gradient in maximum values. The highest POC concentrations in the productivity layer are reached in late winter and early spring in the STZ and SAZ, respectively. Further south, POC peaks in the PAZ and SIZ in December and March respectively. The highest POC is observed in the PAZ during summer (5.45 gC m -2 ). The climatological section plots (gridded average monthly profiles in each zone, Fig. 4) highlight the vertical and seasonal changes in POC. The production period at the ocean surface lengthens towards the north, and the exported productivity is visible at depth. 3.2 Water column changes in POC and respiration Similar to surface POC patterns, ∆POC and R follow a latitudinal gradient. The maximum positive ∆POC is reached in October for STZ and SAZ, and in November and December for PAZ and SIZ, respectively (Fig. 5a). For all zones, the changes from positive to negative ∆POC after the peak coincide with a change from negative to positive R (Fig. 5b). The relationship between the two parameters is further highlighted in Fig. 5c, where ∆POC is significantly correlated with ∆D oxy (p-value < 0.05) for all zones. Figure 5d shows ∆TC (∆POC + R) after the negative R values from Fig. 5b were removed. The flat parts of the curves in Fig. 5d, mostly September to January, represent only the positive ∆POC signal (new particles appearing subsurface), while the sharp increase and decrease represent mainly R. R makes a greater contribution to carbon changes than ∆POC from Z p to [Z p + 500 m]. The lowest value of TC (annual integral of ∆TC, Fig. 6a) is 1.48 molC m -2 y -1 in the STZ, six times lower than the maximum TC in the PAZ (9.99 molC m -2 y -1 , Fig. 6a). The SIZ and SAZ have lower TC than the PAZ (5.72 and 3.64 molC m -2 y -1 respectively, Fig. 6a). When expressed over their entire geographical coverage, the STZ remains the smallest contributor to TC with 0.38 PgC y -1 . The contribution increases in the SAZ and PAZ, with values of 0.57 to 2.45 PgC y -1 , then decreases again in the SIZ with 1.68 PgC y -1 . The TC calculated for the entire SO is 5.08 PgC y -1 (Fig. 6b). The rate of change in carbon through the water column for the four zones is shown in Fig. 7. Because very little contribution of ∆POC to ∆TC is observed (Fig. 5; Supplementary Figure S6), we focus on heterotrophic respiration. In the SIZ, respiration is strongest just below the productivity layer, reaching 26 mmolC m − 2 d − 1 (Fig. 8). The PAZ shows a consistent decrease in R throughout the mesopelagic layer. Conversely, the SAZ displays a relatively constant R through the whole water column, with average respiration rates of 4.58 ± 0.85 mmolC m − 2 d − 1 . The minimum is found in L 2 (3.51 mmolC m − 2 d − 1 ) and the maximum in L 4 (5.83 mmolC m − 2 d − 1 ). Finally, the STZ shows the lowest R value, with an average of 0.59 ± 0.8 molC m − 2 d − 1 . 4. Discussion 4.1 Contrast in biological seasonality between zones The time series highlights the seasonality in integrated POC production. The timing and magnitude increase southward, except for the SIZ which shows the lowest POC production in surface waters (Fig. 3). In the SIZ, the phytoplankton bloom is strongly influenced by the sea-ice seasonal cycle (Stammerjohn et al., 2012 ). When retreating in spring, sea ice relieves phytoplankton from light limitation and enhances stratification (Taylor et al., 2013 ; Vaillancourt et al., 2003 ). This creates a favourable environment for phytoplankton productivity compared to the other regions. The PAZ is not ice-covered, so POC increases earlier than in the SIZ. The sea-ice retreat at the SIZ-PAZ boundaries triggers ice edge blooms (Lancelot et al., 1993 ; Smith & Nelson, 1985 ). Ship-based studies have reported enhanced levels of chl- a just north of the ice edge in early December, where large diatoms are abundant in the southern PAZ, while small pennate diatoms and Phaeocystis dominate in the SIZ (Kauko et al., 2022; Landry et al., 2002 ). Although the SIZ shows higher values of POC (Fig. 4d), the shallower productivity layer depths translate into lower integrated values, explaining the higher surface POC in the PAZ compared to the SIZ (Figs. 3 and 4). Ultimately, the higher light availability in the PAZ during early spring causes stronger and longer primary production, compared to the SIZ. The lack of data in polynyas and coastal areas could also explain the average lower surface POC in the SIZ compared to the other regions which have more observational coverage. Despite the good circumpolar distribution of BGC-Argo profiles (Fig. 1), data are lacking near the Antarctic continent, where the highest phytoplankton production is usually found, particularly in coastal polynyas (Arrigo & Dijken, 2003 ; Liniger et al., 2020 ; Moreau et al., 2019 ). This is a known limitation of Argo floats. Sampling under ice has dramatically improved the breadth of data available for the Southern Ocean community in recent years, but sampling in depths shallower than 1,000 m remains challenging. In the SAZ, average and maximum POC is higher than in the SIZ, but lower than in the PAZ. Surface POC remains higher for longer, and Z p is deeper in the SAZ (throughout the year but most notably in winter, Fig. 4). This translates to higher integrated POC in the SAZ than in the SIZ, with a significant peak in late winter (Fig. 3). The marked seasonal succession in POC maxima at the ocean surface from north to south (STZ to SIZ, Fig. 3) translates into a similar seasonal succession for ∆POC from the export to the horizon depth (Fig. 5a; Supplementary Figure S5a). Our results show that temporal variability in POC represents a small proportion of the observed variability of TC (Fig. 5; Supplementary Figure S5), compared to respiration. However, respiration varies between zones and depths layers, as described below. 4.2 Heterotrophic respiration in the water column The highest respiration is observed in the SIZ, directly under the surface (Fig. 7). The SIZ and STZ, which are most different in terms of water masses, show very little changes in carbon export regardless of the chosen depth horizons (Supplementary Figure S2). This suggests that most of the respiration in the SIZ and STZ occurs below the productivity layer. In the SIZ, this is likely a direct response to the increased phytoplankton productivity when sea ice retreats (Fig. 3). Strong respiration was also reported during sea-ice covered periods over the top 300 m from mid-June to December for a small number of floats (Briggs et al., 2018 ). Our climatological estimations of integrated D oxy (surface to Z p ) show similar trends, where D oxy sharply decreases from August until December (Supplementary Figure S7). For comparison, strong remineralization was found in the top 250 m in the Weddell Sea (Usbeck et al., 2002 ). The observed surface POC in the STZ is more consistent than in the SIZ throughout the year and the highest respiration rate is also found directly under the productivity layer (Fig. 7). Several studies reported that in warm water conditions, respiration is enhanced (Cavan et al., 2019 ; Wohlers et al., 2009 ), and can respond twice as fast to increasing ocean temperature compared to photosynthesis (Boscolo-Galazzo et al., 2018 ). Combined with weaker seasonal variability, this explains the relatively high respiration values subsurface in the STZ, although still lower than in the SIZ. The vertical distribution of carbon export (TC) stands out in the PAZ and SAZ. In the PAZ, values converge at + 500 m on average from Z p (Supplementary Figure S2c), while the estimates increase with depth in the SAZ. This means that from + 500 m, the remineralization from heterotrophs in the PAZ decreases, while it remains relatively constant with depth in the SAZ. A similar respiration rate at depth (Fig. 7, SAZ) can imply a constant transfer of POC through the water column, potentially via vertical export of zooplankton fecal pellets (Cavan et al., 2015 ; Le Moigne, 2019 ). Zooplankton usually migrate to the surface and feed at night to avoid predation, and defecate at deeper depths during the day (Steinberg & Landry, 2017 ), resulting in carbon transfer deeper in the water column and therefore deeper respiration. This transport of organic carbon by mesozooplankton usually represents less than 40% of the total POC flux (Turner, 2015 ). In addition, fecal pellets can be highly resistant to degradation (Riou et al., 2018 ; Tamburini et al., 2006 ), leading to high carbon flux and transfer in the water column. In this scenario, in the SAZ, the zooplankton migration and fecal pellets signals seem to dominate a more evenly distributed respiration signal throughout the mesopelagic layer (Fig. 7). 4.3 Perspective from previous studies Our estimates of total carbon export are higher than previously reported annual net community production (ANCP; Fig. 6b). Our values are close to those previously reported in the SAZ and STZ, but greater in areas of higher biological productivity (Fig. 6a). Many approaches have been used to estimate carbon export and ANCP. Some studies looked at nutrient drawdown in the upper layer (Arteaga et al., 2019 ; Johnson et al., 2017b ; Munro et al., 2015 ), but this technique does not account for nutrient replenishment from below (vertical mixing or advection) or heterotrophic activity within the mixed layer. Others quantified export below their defined productivity layer from sediment traps (Lourey & Trull, 2001 ; Pilskaln et al., 2004 ) which does not account for respiration. Both methods are likely to be prone to underestimation, as respiration can represent up to 90% of the export production in the mesopelagic zone (Jacquet et al., 2011 ). Thorium isotopes have also been used (Le Moigne et al., 2016 ; Planchon et al., 2013 ; Puigcorbé et al., 2017 ; Smetacek et al., 2012 ), accounting for all the POC exported from the surface waters. When we performed our calculations over 100–500 m depth range like in Arteaga et al. ( 2019 ), we obtained very similar results (Supplementary Figure S8), because both methods rely on oxygen drawdown, although applied differently. Our estimates are 1.04, 1.89, 1.42 and 1.11 molC m -2 y -1 for SIZ, PAZ, SAZ and STZ respectively, compared to 0.9, 1.80, 1.80 and 1.25 molC m -2 y -1 for their study. Note that we binned their results in similar zones for comparison. This demonstrates the rigor of this new method to derive respiration from BGC-Argo floats. Our basin integrated calculation estimated a SO carbon export of 5.08 PgC y -1 , which is higher than previously reported values. However, most studies did not consider the SO to extend as far north as 30°S, or include the SIZ, or both. For example, the latest budget of 3.89 PgC y -1 calculated by Su et al. ( 2022 ) using BGC-Argo floats observations did not consider the SIZ. If we remove the SIZ, our estimation falls to 3.4 PgC y -1 , slightly smaller, but still with some differences in zone definition and method used. Furthermore, the studies compared in Fig. 6b mostly defined the SO south of 40 or 50°S, discarding estimations from the STZ and some of the SAZ. Aside from the definition of regions, we argue that a basin scale calculation using all available floats allows for a better estimation of carbon export compared to studies relying on extrapolation of fewer data from restricted locations. In particular, our work provides a new and extended circumpolar SIZ estimate, an area which has been largely unstudied in the past because of the paucity of observational data under ice. 4.4 Uncertainties and caveats of carbon estimations The primary goal of our study was to quantify the importance of the SIZ in the overall circumpolar SO carbon export. However, sea ice prevents floats from surfacing and transmitting their data in real time (Hague & Vichi, 2021 ; Riser et al., 2018 ). Therefore, distances between consecutive under ice profiles may bias our SIZ carbon export estimates compared to PAZ, SAZ and STZ - that is, if the observed changes in POC or oxygen are due to spatial and not temporal variability. In the SIZ, 99% consecutives pairs of profiles under sea ice (Fig. 8c) are within 50 km from each other, while the range expands to 140 km in open waters (80% below 50 km, Fig. 8b). This confirms that the pairs of profiles used to derive our metrics of interest under sea ice are likely representative of processes within the same water masses. The decorrelation scale, defined as the measuring distance and time after which the correlation between two consecutive profiles stopped being significant at the 0.01 level, was also investigated. The average decorrelation scale in space and time was greater than the mean space and time interval between profiles (Supplementary Figure S9). These additional analyses suggest that all consecutive pairs of profiles used to derive R and ∆POC likely capture the same water masses. Sinking rate of POC depends on when or where iron is available in the Southern Ocean (Obernosterer et al., 2008 ), The SOIREE experiment reported sinking rates reaching 1.6 m d -1 , 2.5 m d -1 and up to 4 m d -1 in the SIZ following iron addition conditions (Boyd et al., 2000 ; Maldonado et al., 2001 ; Waite & Nodder, 2001 ). South of Tasmania, Cassar et al. ( 2015 ) described a sinking rate of 6 m d -1 on average, reaching a maximum of 19.5 m d -1 . Fecal pellet sinking rates were also shown to be high in the SO, ranging from 82 m d -1 to 437 m d -1 north of the Antarctic Peninsula in the top 400 m (Liszka et al., 2019 ). This wide range of sinking rates observed during ship-based campaigns imply that, over the 10 days sampling frequency by the BGC-Argo floats, large sinking events (and the associated respiration) could be missed. To try to resolve this, we compared our carbon estimates (from the entire dataset) to estimates derived from SOCCOM floats only (10 days sampling interval), and SOCLIM and remOcean only (1 to 7 days sampling interval). Using SOCLIM and remOcean floats reduced our sample size by 75%, with very few SOCLIM/remOcean profiles in the SIZ and STZ compared to PAZ and SAZ (Supplementary Figure S10). Despite being higher, estimates of carbon export from only SOCLIM and remOcean floats, both in molC m -2 y -1 and PgC y -1 , were not statistically different (Kruskall-Wallis; p-value > 0.1) from the whole array of BGC-Argo floats nor from the SOCCOM floats array in the PAZ (13.02 vs 9.99 vs 10.77 molC m -2 y -1 ; 3.19 vs 2.45 vs 2.64 PgC y -1 ; Supplementary Table T1). However, in the other regions, the carbon export estimates from SOCLIM and remOcean were significantly higher than those from the all-floats and SOCCOM floats only arrays. So, when more profiles are used, the carbon export estimates between the three groups of floats are closer, as seen in the PAZ. Estimates from the SOCLIM and remOcean profiles in the SIZ, SAZ and STZ only have very few profiles that are spatially restricted to a small region in the Indian sector, compared to the PAZ profiles which show a better spatial coverage in the eastern SO. Therefore, we place higher confidence in our calculations using all floats and profiles, despite the lower sampling frequency of 10 days for the majority of them. This point of view is corroborated by Llort et al. ( 2018 ), who showed that SOCCOM floats can be used to capture fast sinking eddy-related carbon export episodes. Another avenue to address the sampling frequency would be to merge profiles from all floats for a given province and derive parameters from the closest profiles in time. Opting for this method certainly allows for a greater sampling frequency compared to a usual 10-day interval, from the same day to 2 days in most cases (Supplementary Figure S11a-b-c-d). However, this method implies that every calculation made from the closest time t 1 to time t + 1 can be performed on profiles from different floats that are in different areas, and possibly different water masses, within the same zone. With this approach, about 90% of all consecutive profiles used to derive ∆POC and R are from different floats for the 4 zones (Supplementary Figure S11e), with significant spatial gaps (Supplementary Figure S12). Therefore, we believe that comparing consecutive profiles from a same float, and then averaging per month and zones, is the most suitable method for this study. Although we constrained physical fluxes using a strong salinity change criteria threshold, we recognize that, in reality, the hydrodynamics likely modify the local oxygen balance, therefore adding uncertainties. The method proposed by Arteaga et al. ( 2019 ) captured the spatial pattern of carbon flux in CM2-1 models, but underestimated the magnitude compared to the models. They suggested that this could be due to a mismatch between floats position and the lack of detritus flux in the gridded cell model. This implies that their observation-based estimates might be more representative of the ocean processes than the models, with respect to the natural spatial variability of carbon export in the SO. We also find a good relationship between positive POC and oxygen changes in the mesopelagic layer (negative R in Fig. 5b). Because we previously addressed the physical fluxes contribution, we believe this signal may reflect another process. As phytodetritus sink below the productive layer, they often retain somewhat elevated chl-a levels (Moreau et al., 2020 ). We therefore hypothesise that sinking phytodetritus might still be producing low levels of O 2 while sinking, which would explain the positive O 2 signal observed in the mesopelagic layer. Negative R estimates (positive ∆Oxygen in Fig. 5c) may therefore not be linked to abiotic factors, which are generally well constrained by criteria imposed on BGC-Argo profile pairs. Evidence of phytodetritus in the mesopelagic layer was also reported in the SO near the Crozet plateau (Hughes et al., 2007 ). Our respiration rates might be underestimated during the productive season when the effects on small but positive sub-surface biological O 2 production cannot be disentangled from external oxygen inputs and lead to negative R. This caveat requires new approaches. Nevertheless, lateral input processes have been shown to be significantly smaller than ANCP itself ( Arteaga et al., 2019 ; MacCready & Quay, 2001 ; Munro et al., 2015 ), or accounting for a very small fraction of change in D oxy (3% for advection in the SIZ, Briggs et al., 2018 ). Considering the floats as quasi-lagrangian, looking at large spatial and temporal scales (monthly variability) and averaging from an extended fleet could mean that smaller scale positive and negative changes in oxygen balance each other (Hennon et al., 2016 ; Martz et al., 2008 ; Najjar & Keeling, 1997 ). Another factor to consider is the calcium carbonate signal in the Great Calcite Belt (GCB), extending from 30° to 60°S. Balch et al. ( 2016 ) showed that bbp is high in this area due high particulate inorganic carbon (PIC) from calcification. Using our method, it is not possible to distinguish PIC from POC, so the presence of PIC likely causes some overestimation of POC. Methods have been developed to detect coccolithophore blooms using BGC-Argo floats (Terrats et al., 2020 ) based on bbp and chl- a . However, we argue this would likely have little effect on our carbon export estimation as (i) PIC was shown to have very little contribution to annual net community production compared to POC (Haskell II et al., 2020 ), (ii) high calcium carbonate production does not increase POC export (Balch et al., 2016 ), (iii), most of the calcium carbonate is remineralized in the photic zone, therefore having little effect on export (Ziveri et al., 2023 ) and (iv) POC contributes very little to the total carbon export compared to respiration (Fig. 5d). 5. Conclusions In this study, we calculated circumpolar SO carbon biomass, its production, export and respiration in the mesopelagic zone. Our results provide insights into the relative contribution of phytoplankton productivity, downward vertical particle export and heterotrophic respiration to the variability of the SO carbon inventory. We found that respiration (R) represents a larger part of the total carbon variability compared to the temporal variability of sinking particles (∆POC). The SIZ contributes more (33%) to SO carbon export than previously reported, increasing the total SO estimate to 5.08 PgC y − 1 based on observations from 2014 to 2022. Our work demonstrates the importance of closely monitoring the SIZ. Not only does it represent a significant proportion of SO carbon export, but it is also an area prone to high variability such as the 2023 low in winter ice extent. Sustained monitoring of the SIZ is essential for accurate quantification of the SO carbon sink, as this could ultimately impact our understanding of climate variability at the global ocean scale. Declarations Acknowledgments Guillaume Liniger, Peter Strutton and Delphine Lannuzel thank the University of Tasmania, the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023), and the Australian Centre for Excellence in Antarctic Sciences (ACEAS; SR200100008) for their support. Sebastien Moreau received funding from the Research Council of Norway (RCN) for the project “I-CRYME: Impact of CRYosphere Melting on Southern Ocean Ecosystems and biogeochemical cycles” (grant number 335512) and for the Norwegian Centre of Excellence “iC3: Center for ice, Cryosphere, Carbon and Climate” (grand number 332635). Delphine Lannuzel is funded by the Australian Research Council through a Future Fellowship (FT190100688). We also would like to extend our thanks to the BGC-Argo program and the several Southern Ocean projects to make the data available and free of access. Data were collected and made freely available by the Southern Ocean Carbon and Climate Observations and Modelling (SOCCOM) Project funded by the National Science Foundation, Division of Polar Programs (NSF PLR – 1425989), supplemented by NOAA and NASA. We thank all the people involved in the conception, deployment, and quality control of BGC-Argo float. We finally extend our gratitude to the reviewers for their help in improving the quality of the manuscript. Author contributions The study was primarily developed by G.L under the supervision of S.M, D.L and P.G.S. who were involved in conducting the analyses. All authors provided ideas in Figures conception, analyses, and feedback in the manuscript development. Competing interest statement We declare having no competing interest of any kind with this research and the publication of this manuscript. 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Nature Communications , 14 (1), 805. https://doi.org/10.1038/s41467-023-36177-w Additional Declarations The authors declare no competing interests. Supplementary Files SuppFigureS1.png Figure S1. Comparison of seasonal mixed layer depth (MLD) and euphotic zone depth (Z eu ) estimates for (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ. The two methods for Z eu calculations are from Dierssen et al. (2000) and Morel and Maritorena (2001) in red and blue respectively. SuppFigureS2.png Figure S2. Carbon export (TC) in molC m -2 y -1 across four different export depths (a) 100m, (b) Z eu , (c) Zp and (d) MLD and nine horizon depths. Averages (and associated standard deviation) for each method in each zone are presented in supplementary Figure S3. SuppFigureS3.png Figure S3. Average carbon export across all tested horizon depths for the four export depths in (a) molC m -2 y -1 and (b) PgC y -1 . The bars are the standard deviation across all horizon depths shown in Figure S2. SuppFigureS4.png Figure S4. Visualization of the layer respiration calculation described in section 2.5. SuppFigureS5.png Figure S5. Same figure as Figure 5 but using Z p to 1000m as the integration range instead of Z p to [Z p +500m]. Data are in mmol m -2 d -1 . SuppFigureS6.png Figure S6. Same figure as Figure 8 but using the positive change R from Zp to [Z p +500 m] in Figure 5b instead of Z p to 1000 m in Figure S5b. The color bars represent each averaged depth interval. SuppFigureS7.png Figure S7. Climatological Doxy and SIC time series. Rho and p-value represent the correlation between Doxy and SIC. SuppFigureS8.png Figure S8. Carbon export in the 100-500m depth range in the four zones of interest. Arteaga et al. (2019) data were average per zone to facilitate the comparison. SuppFigureS9.png Figure S9. Difference in mean decorrelation scale (filled squares) and mean space/time intervals (filled triangle) between all pairs of profiles used in our study. SuppFigureS10.png Figure S10. Distribution of SOCLIM and remOcean float used in this study. The climatological fronts are represented by the blue line (sea ice front), the red line (polar front) and the magenta line (subtropical front). Most of the data points are located in the polar antarctic zone, between the sea ice and polar fronts. SuppFigureS11.png Figure S11. Consecutive pairs of profile count plotted against the sampling frequency for the (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ using the “closest time between profiles method”. (e) Percentage of consecutive profiles coming from different floats if using the “closest time between profiles method”. “0” on the x-axis means consecutive pairs of profiles are from the same day. SuppFigureS12.png Figure S12. Consecutive pairs of profile count plotted against the distance between pairs of profiles for the (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ using the “closest time between profiles” method. (e) Example of two consecutive profiles that would be used using the “closest time between profiles” method. Time in subpanel (e) is dd-mm-yyyy. Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2025 Read the published version in Global Biogeochemical Cycles → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3937570","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271923259,"identity":"dea2b77e-7fa7-499f-b7a9-bdf9ede15330","order_by":0,"name":"Guillaume Liniger","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACPmYGhgNAWo5BAkgmEKOFDarFmAQtUDqxQYJYh7Gx8x48+KXiXvra2c3PPjxgsMmXdyDoML6EwzJninO33TlmPCOBIc1y4wGCWngMDku2JeRuu5FgDPTLYQPDBqK0/EtIN7uR/pl4LQc/NiQkmN3IgdgiT0AHxBaGYwmG2+6cKWZIMEgzMCCkhZ//jPHHHzUJ8ma32zcz/qiwMZAn5DAQYOaBM4FWGBwgQgvjD2QeUbaMglEwCkbBiAIAjCg8npGaYG8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5380-5425","institution":"Institute for Marine and Antarctic Studies","correspondingAuthor":true,"prefix":"","firstName":"Guillaume","middleName":"","lastName":"Liniger","suffix":""},{"id":271923260,"identity":"2f265a1f-0320-4dfe-a6b1-ffa53cf93278","order_by":1,"name":"Sébastien Moreau","email":"","orcid":"https://orcid.org/0000-0001-9446-812X","institution":"Norwegian Polar Institute","correspondingAuthor":false,"prefix":"","firstName":"Sébastien","middleName":"","lastName":"Moreau","suffix":""},{"id":271923261,"identity":"04d31ef0-7933-4e90-b96d-04d38a3c6525","order_by":2,"name":"Delphine Lannuzel","email":"","orcid":"https://orcid.org/0000-0001-6154-1837","institution":"Institute for Marine and Antarctic Studies","correspondingAuthor":false,"prefix":"","firstName":"Delphine","middleName":"","lastName":"Lannuzel","suffix":""},{"id":271923262,"identity":"1e9fb5d3-b877-437f-a421-57412c036c0e","order_by":3,"name":"Peter Strutton","email":"","orcid":"https://orcid.org/0000-0002-2395-9471","institution":"Institute for Marine and Antarctic Studies","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Strutton","suffix":""}],"badges":[],"createdAt":"2024-02-07 17:19:29","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3937570/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3937570/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1029/2024GB008193","type":"published","date":"2025-04-28T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54033305,"identity":"dfea9f20-8273-447d-8ba9-a281c608887a","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1356453,"visible":true,"origin":"","legend":"\u003cp\u003eFronts and BGC-Argo profile locations. From north to south: subtropical zone (STZ, orange), subantarctic zone (SAZ, red), polar Antarctic zone (PAZ, green) and sea-ice zone (SIZ, blue). The black lines represent the climatology of the fronts (Bushinsky et al., 2017). The STZ and SAZ are separated by the subtropical front, SAZ and PAZ are separated by the polar front, and PAZ and SIZ are separated by the seasonal ice front.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/f6d9ba97f96721d248183e51.png"},{"id":54033303,"identity":"28356b51-94c8-44fe-b6f6-d569548a048b","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112305,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual schematic of POC and Dissolved oxygen (D\u003csub\u003eoxy\u003c/sub\u003e) calculation. Averaged values in POC and D\u003csub\u003eoxy\u003c/sub\u003e were calculated from the export depth to the horizon depth for both profiles and multiplied by the difference in depth (horizon - export). Changes in integrated POC and D\u003csub\u003eoxy\u003c/sub\u003e \u003csub\u003e\u0026nbsp;\u003c/sub\u003ewere calculated by doing t\u003csub\u003e+1\u003c/sub\u003e - t\u003csub\u003e1\u003c/sub\u003e.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/f8b64911d8781729b7abc984.png"},{"id":54033306,"identity":"556ea5be-1fe8-49df-8e96-e17e5bb07698","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":581103,"visible":true,"origin":"","legend":"\u003cp\u003eSurface integrated monthly time series of POC in the four zones.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/c58cbc5658fa65fcc142e8cc.png"},{"id":54033297,"identity":"ebac79bb-4421-4f61-884f-ccdebcaa9112","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":899066,"visible":true,"origin":"","legend":"\u003cp\u003eClimatological section plots of POC for (a) STZ, (b) SAZ, (c) PAZ and (d) SIZ. The blue and red lines are the climatological euphotic depth Zeu and MLD respectively. The white filled circles are climatological productivity layer depth used for our calculation (deepest of Zeu or MLD).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/d083a4c4af7dead332ffea58.png"},{"id":54033298,"identity":"1a7b1fbf-7661-42c0-9e24-18cea6fb8cf0","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":906694,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly changes in (a) ∆POC and (b) R, from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+500m]. (c) Linear relationship between ∆Doxy and ∆POC from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+500m]. (d) Monthly distribution of ∆TC from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+500m]. Data are in mmol m\u003csup\u003e-2\u003c/sup\u003e d\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/35ce47af5412338fa9d17dfd.png"},{"id":54033299,"identity":"cc0483c6-4a7e-4cd5-afa1-7b01fa589c99","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":799364,"visible":true,"origin":"","legend":"\u003cp\u003e(a)\u003cstrong\u003e \u003c/strong\u003eAnnual carbon export from ∆TC for the four zones. (b) Annual total area integrated carbon export from TC for the four zones. Results from comparative studies are all averaged and classified by zones to facilitate the comparison and include Arteaga et al. (2019), Johnson et al. (2017a), MacCready and Quay (2001), Munro et al (2015), Lourey and Trull (2001), Hennon et al. (2016), McNeil and Tilbrook (2009), Shadwick and al. (2015), Bender and Jönsson (2016), Riser and Johnson (2008), Martz et al. (2008), Schlitzer (2002), Su et al. (2022), Louanchi and Najjar. (2000), Dunne et al. (2007), Lee (2001), Arrigo et al. (2008), Moore and Abbot (2000), Li et al. (2021), Chang et al (2014), and Pan et al (2023). Compared studies include BGC-Argo floats, satellite, model and ship-based estimations.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/347c3dec8c077ed1535acfe2.png"},{"id":54033301,"identity":"dea0dbf3-8367-4d75-a493-5dd3de8343d4","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":302818,"visible":true,"origin":"","legend":"\u003cp\u003eRespiration rate (R) in successive depth intervals for the four zones. The rate was calculated from the period of positive changes in R in supplementary Figure S5b (Zp to 1000m). Similar results are obtained when looking at the period of positive changes in R in figure 5b (Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+500m]; Supplementary Figure S6). The color bars represent each averaged depth interval.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/b9b13720e7642d2901f81506.png"},{"id":54033348,"identity":"6843254b-cc42-405b-ab68-e9028221d39a","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":441733,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of consecutive profiles against the distance between consecutive profiles for (a) all the Southern Ocean, (b) ice free condition in the sea-ice zone and (c) under ice condition. Numbers om the x-axis are the bin mean (e.g 5 for the bin 0-10 km).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/3a0e20cd02e2aef03bc162ba.png"},{"id":81646594,"identity":"517b43b5-b62b-43f8-9e0b-7149d52ea12c","added_by":"auto","created_at":"2025-04-29 14:49:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5778470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/3dd0edec-db2a-4320-bd94-d3b9d510969d.pdf"},{"id":54033304,"identity":"9de94a21-57e4-453a-8af9-2d02332e81a8","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":538740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eComparison of seasonal mixed layer depth (MLD) and euphotic zone depth (Z\u003csub\u003eeu\u003c/sub\u003e) estimates for (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ. The two methods for Z\u003csub\u003eeu\u003c/sub\u003e calculations are from Dierssen et al. (2000) and Morel and Maritorena (2001) in red and blue respectively.\u003c/p\u003e","description":"","filename":"SuppFigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/85692dab3d98ee44b42988e7.png"},{"id":54033308,"identity":"25f004db-924b-4775-b81c-aa1421a5045b","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1118409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003eCarbon export (TC) in molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e across four different export depths (a) 100m, (b) Z\u003csub\u003eeu\u003c/sub\u003e, (c) Zp and (d) MLD and nine horizon depths. Averages (and associated standard deviation) for each method in each zone are presented in supplementary Figure S3.\u003c/p\u003e","description":"","filename":"SuppFigureS2.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/77b9a7a51cab6c5769fcb866.png"},{"id":54033295,"identity":"a7f17994-c4c3-452d-9b63-5d9a2dc7e270","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":516772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. \u003c/strong\u003eAverage carbon export across all tested horizon depths for the four export depths in (a) molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e and (b) PgC y\u003csup\u003e-1\u003c/sup\u003e. The bars are the standard deviation across all horizon depths shown in Figure S2.\u003c/p\u003e","description":"","filename":"SuppFigureS3.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/8188a0246913b56c5fcc2623.png"},{"id":54033302,"identity":"3d89909d-bf84-4023-bdcd-480ba46f83df","added_by":"auto","created_at":"2024-04-03 16:31:57","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":102750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003e Visualization of the layer respiration calculation described in section 2.5.\u003c/p\u003e","description":"","filename":"SuppFigureS4.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/0876b2380019c009d00a37a4.png"},{"id":54033296,"identity":"0082531c-7408-4399-a0a5-82466757bdff","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":977699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5. \u003c/strong\u003eSame figure as Figure 5 but using \u0026nbsp;Z\u003csub\u003ep\u003c/sub\u003e to 1000m as the integration range instead of Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+500m]. Data are in mmol m\u003csup\u003e-2\u003c/sup\u003e d\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"SuppFigureS5.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/87266dbe20e494018402ad9c.png"},{"id":54033347,"identity":"193e2379-6d3c-4fc6-91e6-1e865c13e7ae","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":302557,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6. \u003c/strong\u003eSame figure as Figure 8 but using the positive change R from Zp to [Z\u003csub\u003ep\u003c/sub\u003e+500 m] in Figure 5b instead of Z\u003csub\u003ep\u003c/sub\u003e to 1000 m in Figure S5b. The color bars represent each averaged depth interval.\u003c/p\u003e","description":"","filename":"SuppFigureS6.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/f84ed4e3e71177a64bafe59e.png"},{"id":54033346,"identity":"5a82c049-3249-42cf-9cf8-07c61e70ef9c","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":374618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S7. \u003c/strong\u003eClimatological Doxy and SIC time series. Rho and p-value represent the correlation between Doxy and SIC.\u003c/p\u003e","description":"","filename":"SuppFigureS7.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/fc0693618a9bead3917490e6.png"},{"id":54033345,"identity":"6b2c590c-0448-4fb5-a3e9-5a0ca116dc02","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":286054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S8.\u003c/strong\u003e Carbon export in the 100-500m depth range in the four zones of interest. Arteaga et al. (2019) data were average per zone to facilitate the comparison.\u003c/p\u003e","description":"","filename":"SuppFigureS8.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/213b5c56ab65fc4c4e8bfb4d.png"},{"id":54033307,"identity":"84fa921f-dd86-4af3-953d-1b78c9b9e450","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":472270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S9.\u003c/strong\u003e Difference in mean decorrelation scale (filled squares) and mean space/time intervals \u0026nbsp;(filled triangle) between all pairs of profiles used in our study.\u003c/p\u003e","description":"","filename":"SuppFigureS9.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/fea81c157a10e3f6c13e8728.png"},{"id":54033309,"identity":"b86ae5da-0bc1-417b-9008-1a58bddbf93c","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":735912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S10. \u003c/strong\u003eDistribution of SOCLIM and remOcean float used in this study. The climatological fronts are represented by the blue line (sea ice front), the red line (polar front) and the magenta line (subtropical front). Most of the data points are located in the polar antarctic zone, between the sea ice and polar fronts.\u003c/p\u003e","description":"","filename":"SuppFigureS10.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/0039162b31f413953ea3b122.png"},{"id":54033310,"identity":"d5793aaa-7170-415e-900d-e852ee81bb17","added_by":"auto","created_at":"2024-04-03 16:31:58","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":571663,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S11. \u003c/strong\u003eConsecutive pairs of profile count plotted against the sampling frequency for the (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ using the “closest time between profiles method”. (e) Percentage of consecutive profiles coming from different floats if using the “closest time between profiles method”. “0” on the x-axis means consecutive pairs of profiles are from the same day.\u003c/p\u003e","description":"","filename":"SuppFigureS11.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/d6518595ea9d3c82d31492e7.png"},{"id":54033300,"identity":"f06da21c-130e-4df7-b06c-453a1859a4cc","added_by":"auto","created_at":"2024-04-03 16:31:56","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":675981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S12\u003c/strong\u003e. Consecutive pairs of profile count plotted against the distance between pairs of profiles for the (a) SIZ, (b) PAZ, (c) SAZ and (d) STZ using the “closest time between profiles” method. (e) Example of two consecutive profiles that would be used using the “closest time between profiles” method. Time in subpanel (e) is dd-mm-yyyy.\u003c/p\u003e","description":"","filename":"SuppFigureS12.png","url":"https://assets-eu.researchsquare.com/files/rs-3937570/v2/8a6c4b24c9a97f130351f4be.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Large contribution of the sea-ice zone to Southern Ocean carbon export revealed by BGC-Argo floats","fulltext":[{"header":"Key Points","content":"\u003col\u003e\n \u003cli\u003eWe used an extensive fleet of circumpolar BGC-Argo floats to investigate carbon export in the Southern Ocean\u003c/li\u003e\n \u003cli\u003eUsing oxygen drawdown and sinking particulate organic carbon, we estimated a total basin-integrated carbon export of 5.08 PgC y\u003csup\u003e-1\u003c/sup\u003e\u003c/li\u003e\n \u003cli\u003eThe sea-ice zone is responsible for 33% of the Southern Ocean carbon export\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Plain Language summary","content":"\u003cp\u003ePhytoplankton take up atmospheric carbon dioxide through photosynthesis and help sequester carbon to the deep ocean. Using robotic floats equipped with biological and physical sensors, we investigate the carbon export from the surface to the deep ocean across the Southern Ocean. We highlight that the Southern Ocean exports 5.08 billion tonnes of carbon per year, which is about a third of the mean global ocean carbon export. The sea-ice zone, an area mostly overlooked due to a historical lack of data, is responsible for ~33% of the Southern Ocean carbon export. Our results suggest that the Southern Ocean, and particularly the sea-ice zone, plays a significant role in the carbon cycle.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe Southern Ocean (SO) represents a significant sink for atmospheric CO\u003csub\u003e2\u003c/sub\u003e (Gruber et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) contributing\u0026thinsp;~\u0026thinsp;20\u0026ndash;40% of the total ocean CO\u003csub\u003e2\u003c/sub\u003e uptake (DeVries et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Takahashi et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The particulate organic carbon (POC) exported to the deep ocean of the SO constitutes about 30% of the ocean global carbon export (Schlitzer, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Sinking POC particles are subject to modification by biogeochemical processes through the water column. Respiration by heterotrophs organisms consumes oxygen to degrade organic matter. This can be done by zooplankton and bacteria, with the latter being able to return particulate organic matter to inorganic nutrients (remineralization). Respiration is the dominant process consuming most of the POC in the water column, and only 1\u0026ndash;3% of the particles that exit the upper 100 m reach 1,000 m (Cavan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; De La Rocha \u0026amp; Passow, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Passow \u0026amp; Carlson, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Steinberg et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SO biological carbon pump is biogeochemically significant but exhibits strong spatial and temporal variability. Primary production in the sea-ice zone (SIZ) is strongly light-limited due to both seasonal solar patterns and the sea-ice cycle. Nutrient limitation also plays an important role, and the essential trace metal iron is supplied from sea ice (Lannuzel et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), coastal features (Gerringa et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sherrell et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; St-Laurent et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), deep mixing (Tagliabue et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), upwelling (Moreau et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) or dust transported via the atmosphere (Tang et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nutrients, including iron, and mixed layer depth become more influential northward as nutrient-rich coastal sources (Boyd et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Moore et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The distributions of grazers, silicate, phosphate and nitrate around the Antarctic Circumpolar Current also directly impact the density and type of phytoplankton in surface waters (Assmy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Smetacek et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Considering the biogeochemical and physical differences between zones in the SO, it is important to understand if and how the processes influencing carbon export vary spatially, as this ultimately impacts POC and associated carbon export. Studies have estimated carbon export or transfer efficiency from several techniques including satellites (Arteaga et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), moorings (Lourey \u0026amp; Trull, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Manno et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), modelling (Karakuş et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lancelot et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), ship-based observations (Buesseler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; DeJong et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ducklow et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pilskaln et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Planchon et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Puigcorb\u0026eacute; et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ratnarajah et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Smetacek et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Biogeochemical (BGC) Argo floats (Davies et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hennon et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Munro et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Some studies also combined these tools (Dall\u0026rsquo;Olmo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nowicki et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Schlitzer, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBGC-Argo floats are autonomous instruments platforms equipped with sensors to measure physical and biogeochemical variables, including temperature, salinity, dissolved nitrate, downwelling irradiance, dissolved oxygen (D\u003csub\u003eoxy\u003c/sub\u003e), chlorophyll-\u003cem\u003ea\u003c/em\u003e (chl-\u003cem\u003ea\u003c/em\u003e) fluorescence, pH and particle backscattering (bbp; Johnson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e). BGC-Argo floats allow the study of biogeochemical and physical processes in the upper 2000 m. They are usually designed to sample on a 10-day cycle frequency. This temporal resolution may not be sufficient to capture events on a shorter time scales, such as rapid bloom pulses (Williams et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) or mixed layer pump events (Xing et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), leading to underestimation of carbon export. However, they are well suited to capturing seasonal carbon export (Lacour et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and may also occasionally capture fast sinking eddy-driven carbon export (Llort et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious work used BGC-Argo data to report biological activity and carbon export, but these results are usually regional in scope, over short time scale and north of 60\u0026deg;S (Bishop et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Davies et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Le Moigne et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xing et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the SO, only a few studies have focused on the SIZ (Briggs et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Moreau et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), despite the susceptibility of this area to extreme environmental changes (Turner et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) which could alter phytoplankton productivity and subsequent carbon export (Kaufman et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe continuous expansion of BGC-Argo deployments and growing data availability allow the study of oceanographic processes over large spatial scales, and with increasingly detailed temporal resolution. These advances have potential to better understand areas like the SIZ, where historical data coverage is poor. In this context, our study makes use of the extensive array of floats to give a new estimate of carbon export across basins and frontal zones, with a focus on the SIZ. We hypothesize that the SIZ contributes more to the total SO carbon export than previously reported.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 BGC-Argo floats and fronts\u003c/h2\u003e \u003cp\u003eWe selected floats from the SOCCOM, SOCLIM and remOcean programs south of 30\u0026deg;S equipped with bio-optical sensors for particulate backscatter at 700nm (bbp\u003csub\u003e700\u003c/sub\u003e). Data were downloaded on 08/06/22 and consisted of 25,953 profiles from 212 floats (191 SOCCOM, 8 SOCLIM, 13 remOcean) spanning March 2014 to June 2022, in the open ocean and under the sea ice (Fig.\u0026nbsp;1). We also used D\u003csub\u003eoxy\u003c/sub\u003e data from the downloaded floats when available (only four floats lacked D\u003csub\u003eoxy\u003c/sub\u003e, less than 2% of the total dataset). The temporal sampling resolution of SOCCOM floats is usually 10 days with a vertical resolution decreasing with depth, from 5 m in the first 100 m to 50 m from 400 m to 2000 m. SOCLIM and remOcean floats provide higher resolution data, with temporal sampling every 1\u0026ndash;7 days at 1 m to 10 m resolution in the first 1,000 m.\u003c/p\u003e \u003cp\u003eEach profile was assigned to a zone defined by fronts or sea-ice extent. Fronts for the polar Antarctic zone (PAZ), the subantarctic zone (SAZ) and subtropical zone (STZ) were defined from a recomputed Argo temperature and salinity 2004\u0026ndash;2014 climatology (Bushinsky et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roemmich \u0026amp; Gilson, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The STZ and SAZ are separated by the subtropical front, SAZ and PAZ are separated by the polar front, and PAZ and SIZ are separated by the seasonal ice front. For the SIZ, we acquired daily Ocean and Sea Ice Satellite Application Facility (OSI SAF) 25km resolution sea-ice concentration data from the Copernicus website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration?tab=form\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration?tab=form\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e that we used to compute the yearly sea-ice extent from 2014 to 2022.\u003c/p\u003e \u003cp\u003eThe extent of the SIZ varies widely compared to the STZ, SAZ and PAZ. This area is important to accurately quantify as sea ice drives seasonal stratification and iron fertilisation of this part of the SO and is prone to rapid and dramatic changes now and in the future (Eayrs et al., 2021; Hobbs et al., 2024). We therefore used the maximum sea-ice extent to define the SIZ front in any given year. This delineation allows the capture of floats involved in ocean - sea-ice interactions. Other fronts are mostly linked to local topography (Moore et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), and the use of a large float array reduces potential problems that could be linked to the moving fronts position. Fixed fronts position has been commonly used in the recent studies investigating large scale biogeochemistry in the SO (Arteaga et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Llort et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 BGC-Argo data quality control\u003c/h2\u003e \u003cp\u003eWe used \u0026ldquo;adjusted\u0026rdquo; variables following the initial quality control performed on floats (Bittig et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schmechtig et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For all variables, we removed all bad or questionable data (QC flag 0, 3, 4, 6, 7, 9). For bbp\u003csub\u003e700\u003c/sub\u003e, we removed data considered outliers as in Bisson et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). All profiles were then interpolated on a common depth grid at 1m interval. Both bbp\u003csub\u003e700\u003c/sub\u003e and D\u003csub\u003eoxy\u003c/sub\u003e profiles were smoothed using a 7-points moving median for SOCCOM floats (Arteaga et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and 9-points moving median for SOCLIM and remOcean (because of their higher resolution compared to SOCCOM). This smoothing helps to reduce the backscattering contribution of bubbles and white caps (Stramski et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which tend to increase bbp and potentially lead to an overestimation of variables derived from it. Finally, profiles lacking temperature, salinity (and therefore mixed layer depth, MLD), time or location were removed from the analysis. After QC, our dataset consists of 18,434 valid profiles.\u003c/p\u003e \u003cp\u003eIn this study, we consider the floats to be quasi-lagrangian, but they can in reality cross regional boundaries when they are drifting, potentially introducing bias in calculation for each zone defined by a fixed area. Water masses may also differ between profiles (10-day sample period on average) as floats are drifting. Hence, we first made sure that pairs of consecutive profiles were located in the same area and month. If not, the profiles were discarded from the analysis. After that, we investigated the physical properties of the water column between every pair of valid profiles. We followed Johnson et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e) who suggested removing pairs of profiles that observed: (i) a change of salinity by more than 0.05 psu at 500 m, (ii) a change of latitude by more than 5 degrees, or (iii) a change of longitude by more than 8.8 degrees. We however strengthened these criteria by also discarding pairs of profiles showing changes in salinity of more than 0.05 psu at 300 m. By doing so, we ensure that the pairs of profiles used to derive carbon export come from the same water masses, strongly decreasing bias that could be due to lateral and vertical advection. After carefully investigating our database, 4,815 pairs of profiles breaching at least one of these criteria were removed from the analysis, bringing our final pool of profiles down to 13,619.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selecting export and horizon depths\u003c/h2\u003e \u003cp\u003eMost primary productivity is constrained in the upper layer of the ocean, from the surface to an export depth (E\u003csub\u003ed\u003c/sub\u003e), usually defined as the euphotic zone depth (Z\u003csub\u003eeu\u003c/sub\u003e), MLD or productivity layer depth (Z\u003csub\u003ep\u003c/sub\u003e), depending on studies. Below E\u003csub\u003ed\u003c/sub\u003e, surface gas exchanges can be neglected because water masses are too deep to be in contact with the atmosphere and photosynthesis is limited by low light availability is low. Previous studies on carbon export have used several ranges of integration for their calculations. However, the magnitude of estimated carbon export is extremely sensitive to the chosen export and horizon depths (Palevsky \u0026amp; Doney, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For this reason, we first conducted a sensitivity analysis for each zone, combining different export depths (100 m, Z\u003csub\u003eeu\u003c/sub\u003e, MLD and Z\u003csub\u003ep\u003c/sub\u003e) and nine different horizon depths (E\u003csub\u003ed\u003c/sub\u003e + Z\u003csub\u003ei\u003c/sub\u003e, where Z\u003csub\u003ei\u003c/sub\u003e = 100, 200, 300, 400, 500, 600, 700, 800 and 900 m). Z\u003csub\u003ep\u003c/sub\u003e is defined as the deepest of either the MLD or Z\u003csub\u003eeu\u003c/sub\u003e. The MLD is defined as the depth where the density exceeded the density at 10m by 0.03 kg m\u003csup\u003e-3\u003c/sup\u003e (de Boyer Mont\u0026eacute;gut et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe calculated Z\u003csub\u003eeu\u003c/sub\u003e (from the BGC-Argo chl-\u003cem\u003ea\u003c/em\u003e fluorescence data) using two methods described in Dierssen et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Morel and Maritorena (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Both gave very similar seasonal variability except in the STZ where Z\u003csub\u003eeu\u003c/sub\u003e from Dierssen et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) was deeper in summer (Supplementary Figure S1). The difference could be due to the method being primarily developed for Antarctic coastal waters. We chose Z\u003csub\u003eeu\u003c/sub\u003e from Morel and Maritorena (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) to compare with our MLD and to compute Z\u003csub\u003ep\u003c/sub\u003e. This comparison exercise led us to make later calculations of integrated carbon export from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e + 500 m]. Details can be found in Supplementary Figures S2 and S3, and associated text Tx1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Carbon export and respiration calculation\u003c/h2\u003e \u003cp\u003eWe derived POC using:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\text{POC = 3.12 x }{\\text{10}}^{\\text{4}}\\text{ x }{\\text{bbp}}_{\\text{700}}\\text{+ 3.0}\\)\u003c/span\u003e \u003c/span\u003e (Eq.\u0026nbsp;1, Ha\u0026euml;ntjens et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe integrated POC (iPOC) for a single profile at a time \u003cem\u003et\u003c/em\u003e was defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{iPOC}}_{\\text{t}\\text{ }}\\text{= }{\\int }_{\\text{ }\\text{Ed}}^{\\text{ }\\text{Ed+Zi}}\\text{POC d}\\text{z}\\text{ }\\text{ }\\text{(eq2, mmolC }{\\text{m}}^{\\text{-2}}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWith E\u003csub\u003ed\u003c/sub\u003e being the export depth and [E\u003csub\u003ed\u003c/sub\u003e + Z\u003csub\u003ei\u003c/sub\u003e] the horizon depth. The rate of change was calculated as the difference in iPOC between two profiles divided by the time elapsed (Fig.\u0026nbsp;2a):\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{∆POC}\\text{ }\\text{= }{\\text{(iPOC}}_{\\text{t+dt}}-\\text{ }{\\text{iPOC}}_{\\text{t}}\\text{) / dt (eq3, }\\text{mmolC}\\text{ }{\\text{m}}^{\\text{-2}}{\\text{ }\\text{d}}^{\\text{-1}}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere dt is usually 10 days between BGC-Argo profiles.\u003c/p\u003e \u003cp\u003eWe quantified the heterotrophic respiration rate of change from D\u003csub\u003eoxy\u003c/sub\u003e using the same method as for POC. We first converted D\u003csub\u003eoxy\u003c/sub\u003e from \u0026micro;mol kg\u003csup\u003e-1\u003c/sup\u003e to mmol m\u003csup\u003e-3\u003c/sup\u003e by multiplying by 1.025 (an average seawater density in kg l\u003csup\u003e-1\u003c/sup\u003e). Second, we integrated D\u003csub\u003eoxy\u003c/sub\u003e from E\u003csub\u003ed\u003c/sub\u003e to [E\u003csub\u003ed\u003c/sub\u003e + Z\u003csub\u003ei\u003c/sub\u003e] to obtain iD\u003csub\u003eoxy\u003c/sub\u003e in units of mmol m\u003csup\u003e-2\u003c/sup\u003e.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${\\text{iD}}_{\\text{oxy(t) }}\\text{= 1.025 x }{\\int }_{\\text{ }\\text{Ed}}^{\\text{ }\\text{Ed+Zi}}{\\text{D}}_{\\text{oxy }}\\text{ d}\\text{z}\\text{ (eq4, mmol}{\\text{O}}_{\\text{2}}\\text{ }{\\text{m}}^{\\text{-2}}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe rate of change in iD\u003csub\u003eoxy\u003c/sub\u003e was calculated as the difference between two profiles divided by the time elapsed (Fig.\u0026nbsp;2b):\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\text{∆}{\\text{D}}_{\\text{oxy}}\\text{ }\\text{= }{\\text{(iD}}_{\\text{oxy(}\\text{t+dt}\\text{)}}- {\\text{iD}}_{\\text{oxy(}\\text{t}\\text{)}}\\text{) / dt (eq5, mmol}{\\text{O}}_{\\text{2}}\\text{ }{\\text{m}}^{\\text{-2}} {\\text{d}}^{\\text{-1}}\\text{)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFinally, we multiplied the results from Eq.\u0026nbsp;5 by -0.69 to obtain a carbon equivalent of iD\u003csub\u003eoxy\u003c/sub\u003e rate of change (R, in mmolC m\u003csup\u003e-2\u003c/sup\u003e d\u003csup\u003e-1\u003c/sup\u003e), where 0.69 is the C:O\u003csub\u003e2\u003c/sub\u003e quotient for organic matter respiration (Anderson \u0026amp; Sarmiento, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Negative \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{∆}\\)\u003c/span\u003e\u003c/span\u003eR were discarded similar to the way remineralization data were treated by Su et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Arteaga et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This approach fits our focus on the respiration activity. When deriving R, we assume that floats have an even probability of travelling through water masses that are characterized by both positive and negative oxygen balance, cancelling each other out when using a large array of floats. Therefore, we inferred R to heterotrophic activity. We caution that this approach only works when using a large array of BGC-Argo floats with extensive spatial and temporal coverage. We discuss this in section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4.4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe total carbon rate of change from both R and ∆POC is hereafter called ∆TC (∆TC\u0026thinsp;=\u0026thinsp;R + ∆POC), and its annual integral is called TC. That is, for any given time period, ∆TC sums the new particles appearing in the layer and the particles disappearing via respiration, meaning it provides an estimate of the total change in biomass, or export into the layer in the absence of respiration. We refer to ∆TC and TC also as carbon export. The performance of our method is compared to previous studies in the results and \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003ediscussion\u003c/span\u003e section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Water column carbon variability\u003c/h2\u003e \u003cp\u003eWe also calculated integrated POC and R for five successive 150 m layers L\u003csub\u003ei\u003c/sub\u003e at depths D\u003csub\u003eLi\u003c/sub\u003e in the water column:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${\\text{Variable}}_{\\text{Li+1}}\\text{= }{\\int }_{ {\\text{D}}_{\\text{Li}}}^{{\\text{ }\\text{D}}_{\\text{Li}}\\text{+150m}}\\text{Variable d}\\text{z}\\text{ (eq6)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWith D\u003csub\u003eLi\u003c/sub\u003e = Z\u003csub\u003ep\u003c/sub\u003e for i\u0026thinsp;=\u0026thinsp;0, then D\u003csub\u003eLi\u003c/sub\u003e = D\u003csub\u003eL1\u003c/sub\u003e for i\u0026thinsp;=\u0026thinsp;1, D\u003csub\u003eLi\u003c/sub\u003e = D\u003csub\u003eL2\u003c/sub\u003e for i\u0026thinsp;=\u0026thinsp;2 and so on until L\u003csub\u003e5\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThat is, for each profile, the calculation for the first layer was made from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e+150 m] (called depth D\u003csub\u003eLi\u003c/sub\u003e), and then from this depth D\u003csub\u003eLi\u003c/sub\u003e + 150m, and so on until the 5th layer (Supplementary Figure S4). The rate of change between a profile\u003csub\u003et+dt\u003c/sub\u003e and profile\u003csub\u003et\u003c/sub\u003e was calculated following Eq.\u0026nbsp;3 and Eq.\u0026nbsp;5 for POC and R respectively. The average five D\u003csub\u003eLi\u003c/sub\u003e from all floats used in this study are D\u003csub\u003eL1\u003c/sub\u003e = 258 m (\u0026plusmn;\u0026thinsp;67 m), D\u003csub\u003eL2\u003c/sub\u003e= 404 m (\u0026plusmn;\u0026thinsp;67 m), D\u003csub\u003eL3\u003c/sub\u003e = 556 m (\u0026plusmn;\u0026thinsp;67 m), D\u003csub\u003eL4\u003c/sub\u003e = 707 m (\u0026plusmn;\u0026thinsp;67 m) and D\u003csub\u003eL5\u003c/sub\u003e = 858 m (\u0026plusmn;\u0026thinsp;67 m).\u003c/p\u003e \u003cp\u003eWhile the sensitivity analysis allows us to carefully investigate the role of export and horizon depths in carbon export estimates, this analysis lets us examine changes in carbon from a profile \u003cem\u003et\u0026thinsp;+\u0026thinsp;dt\u003c/em\u003e to \u003cem\u003et\u003c/em\u003e at consecutive depths range through the mesopelagic zone. We chose a consistent iteration of +\u0026thinsp;150m for the depth integration that would capture changes across reasonable depth intervals down to almost 1,000 m (compared to smaller increments), as well as changing the horizon depth threshold between layers would create inconsistent comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial and temporal distribution of surface particulate organic carbon\u003c/h2\u003e \u003cp\u003eFigure 1 shows the spatial distribution of float profiles for all zones. From a visual inspection, the SIZ, PAZ and SAZ have a relatively even circumpolar spread of profiles. In the STZ, there are more profiles in the Indian and Atlantic sectors compared to the Pacific. Most of the profiles are in the SIZ (5,322; 39%), while the PAZ, SAZ and STZ account for 4,810 (35%), 1,760 (13%) and 1,727 (13%) profiles respectively.\u003c/p\u003e \u003cp\u003eSurface integrated values show clear seasonality in POC (Fig.\u0026nbsp;3), with a latitudinal gradient in maximum values. The highest POC concentrations in the productivity layer are reached in late winter and early spring in the STZ and SAZ, respectively. Further south, POC peaks in the PAZ and SIZ in December and March respectively. The highest POC is observed in the PAZ during summer (5.45 gC m\u003csup\u003e-2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe climatological section plots (gridded average monthly profiles in each zone, Fig.\u0026nbsp;4) highlight the vertical and seasonal changes in POC. The production period at the ocean surface lengthens towards the north, and the exported productivity is visible at depth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Water column changes in POC and respiration\u003c/h2\u003e \u003cp\u003eSimilar to surface POC patterns, ∆POC and R follow a latitudinal gradient. The maximum positive ∆POC is reached in October for STZ and SAZ, and in November and December for PAZ and SIZ, respectively (Fig.\u0026nbsp;5a). For all zones, the changes from positive to negative ∆POC after the peak coincide with a change from negative to positive R (Fig.\u0026nbsp;5b). The relationship between the two parameters is further highlighted in Fig.\u0026nbsp;5c, where ∆POC is significantly correlated with ∆D\u003csub\u003eoxy\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for all zones. Figure\u0026nbsp;5d shows ∆TC (∆POC\u0026thinsp;+\u0026thinsp;R) after the negative R values from Fig.\u0026nbsp;5b were removed. The flat parts of the curves in Fig.\u0026nbsp;5d, mostly September to January, represent only the positive ∆POC signal (new particles appearing subsurface), while the sharp increase and decrease represent mainly R. R makes a greater contribution to carbon changes than ∆POC from Z\u003csub\u003ep\u003c/sub\u003e to [Z\u003csub\u003ep\u003c/sub\u003e + 500 m].\u003c/p\u003e \u003cp\u003eThe lowest value of TC (annual integral of ∆TC, Fig.\u0026nbsp;6a) is 1.48 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e in the STZ, six times lower than the maximum TC in the PAZ (9.99 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e, Fig.\u0026nbsp;6a). The SIZ and SAZ have lower TC than the PAZ (5.72 and 3.64 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e respectively, Fig.\u0026nbsp;6a). When expressed over their entire geographical coverage, the STZ remains the smallest contributor to TC with 0.38 PgC y\u003csup\u003e-1\u003c/sup\u003e. The contribution increases in the SAZ and PAZ, with values of 0.57 to 2.45 PgC y\u003csup\u003e-1\u003c/sup\u003e, then decreases again in the SIZ with 1.68 PgC y\u003csup\u003e-1\u003c/sup\u003e. The TC calculated for the entire SO is 5.08 PgC y\u003csup\u003e-1\u003c/sup\u003e (Fig.\u0026nbsp;6b).\u003c/p\u003e \u003cp\u003eThe rate of change in carbon through the water column for the four zones is shown in Fig.\u0026nbsp;7. Because very little contribution of ∆POC to ∆TC is observed (Fig.\u0026nbsp;5; Supplementary Figure S6), we focus on heterotrophic respiration. In the SIZ, respiration is strongest just below the productivity layer, reaching 26 mmolC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;8). The PAZ shows a consistent decrease in R throughout the mesopelagic layer. Conversely, the SAZ displays a relatively constant R through the whole water column, with average respiration rates of 4.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85 mmolC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The minimum is found in L\u003csub\u003e2\u003c/sub\u003e (3.51 mmolC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and the maximum in L\u003csub\u003e4\u003c/sub\u003e (5.83 mmolC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Finally, the STZ shows the lowest R value, with an average of 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 molC m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Contrast in biological seasonality between zones\u003c/h2\u003e \u003cp\u003eThe time series highlights the seasonality in integrated POC production. The timing and magnitude increase southward, except for the SIZ which shows the lowest POC production in surface waters (Fig.\u0026nbsp;3). In the SIZ, the phytoplankton bloom is strongly influenced by the sea-ice seasonal cycle (Stammerjohn et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). When retreating in spring, sea ice relieves phytoplankton from light limitation and enhances stratification (Taylor et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vaillancourt et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This creates a favourable environment for phytoplankton productivity compared to the other regions. The PAZ is not ice-covered, so POC increases earlier than in the SIZ. The sea-ice retreat at the SIZ-PAZ boundaries triggers ice edge blooms (Lancelot et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Smith \u0026amp; Nelson, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Ship-based studies have reported enhanced levels of chl-\u003cem\u003ea\u003c/em\u003e just north of the ice edge in early December, where large diatoms are abundant in the southern PAZ, while small pennate diatoms and \u003cem\u003ePhaeocystis\u003c/em\u003e dominate in the SIZ (Kauko et al., 2022; Landry et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Although the SIZ shows higher values of POC (Fig.\u0026nbsp;4d), the shallower productivity layer depths translate into lower integrated values, explaining the higher surface POC in the PAZ compared to the SIZ (Figs.\u0026nbsp;3 and 4). Ultimately, the higher light availability in the PAZ during early spring causes stronger and longer primary production, compared to the SIZ. The lack of data in polynyas and coastal areas could also explain the average lower surface POC in the SIZ compared to the other regions which have more observational coverage. Despite the good circumpolar distribution of BGC-Argo profiles (Fig.\u0026nbsp;1), data are lacking near the Antarctic continent, where the highest phytoplankton production is usually found, particularly in coastal polynyas (Arrigo \u0026amp; Dijken, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Liniger et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moreau et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is a known limitation of Argo floats. Sampling under ice has dramatically improved the breadth of data available for the Southern Ocean community in recent years, but sampling in depths shallower than 1,000 m remains challenging.\u003c/p\u003e \u003cp\u003eIn the SAZ, average and maximum POC is higher than in the SIZ, but lower than in the PAZ. Surface POC remains higher for longer, and Z\u003csub\u003ep\u003c/sub\u003e is deeper in the SAZ (throughout the year but most notably in winter, Fig.\u0026nbsp;4). This translates to higher integrated POC in the SAZ than in the SIZ, with a significant peak in late winter (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe marked seasonal succession in POC maxima at the ocean surface from north to south (STZ to SIZ, Fig.\u0026nbsp;3) translates into a similar seasonal succession for ∆POC from the export to the horizon depth (Fig.\u0026nbsp;5a; Supplementary Figure S5a). Our results show that temporal variability in POC represents a small proportion of the observed variability of TC (Fig.\u0026nbsp;5; Supplementary Figure S5), compared to respiration. However, respiration varies between zones and depths layers, as described below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Heterotrophic respiration in the water column\u003c/h2\u003e \u003cp\u003eThe highest respiration is observed in the SIZ, directly under the surface (Fig.\u0026nbsp;7). The SIZ and STZ, which are most different in terms of water masses, show very little changes in carbon export regardless of the chosen depth horizons (Supplementary Figure S2). This suggests that most of the respiration in the SIZ and STZ occurs below the productivity layer. In the SIZ, this is likely a direct response to the increased phytoplankton productivity when sea ice retreats (Fig.\u0026nbsp;3). Strong respiration was also reported during sea-ice covered periods over the top 300 m from mid-June to December for a small number of floats (Briggs et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our climatological estimations of integrated D\u003csub\u003eoxy\u003c/sub\u003e (surface to Z\u003csub\u003ep\u003c/sub\u003e) show similar trends, where D\u003csub\u003eoxy\u003c/sub\u003e sharply decreases from August until December (Supplementary Figure S7). For comparison, strong remineralization was found in the top 250 m in the Weddell Sea (Usbeck et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The observed surface POC in the STZ is more consistent than in the SIZ throughout the year and the highest respiration rate is also found directly under the productivity layer (Fig.\u0026nbsp;7). Several studies reported that in warm water conditions, respiration is enhanced (Cavan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wohlers et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and can respond twice as fast to increasing ocean temperature compared to photosynthesis (Boscolo-Galazzo et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Combined with weaker seasonal variability, this explains the relatively high respiration values subsurface in the STZ, although still lower than in the SIZ.\u003c/p\u003e \u003cp\u003eThe vertical distribution of carbon export (TC) stands out in the PAZ and SAZ. In the PAZ, values converge at +\u0026thinsp;500 m on average from Z\u003csub\u003ep\u003c/sub\u003e (Supplementary Figure S2c), while the estimates increase with depth in the SAZ. This means that from +\u0026thinsp;500 m, the remineralization from heterotrophs in the PAZ decreases, while it remains relatively constant with depth in the SAZ. A similar respiration rate at depth (Fig.\u0026nbsp;7, SAZ) can imply a constant transfer of POC through the water column, potentially via vertical export of zooplankton fecal pellets (Cavan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Le Moigne, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Zooplankton usually migrate to the surface and feed at night to avoid predation, and defecate at deeper depths during the day (Steinberg \u0026amp; Landry, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), resulting in carbon transfer deeper in the water column and therefore deeper respiration. This transport of organic carbon by mesozooplankton usually represents less than 40% of the total POC flux (Turner, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, fecal pellets can be highly resistant to degradation (Riou et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tamburini et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), leading to high carbon flux and transfer in the water column. In this scenario, in the SAZ, the zooplankton migration and fecal pellets signals seem to dominate a more evenly distributed respiration signal throughout the mesopelagic layer (Fig.\u0026nbsp;7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Perspective from previous studies\u003c/h2\u003e \u003cp\u003eOur estimates of total carbon export are higher than previously reported annual net community production (ANCP; Fig.\u0026nbsp;6b). Our values are close to those previously reported in the SAZ and STZ, but greater in areas of higher biological productivity (Fig.\u0026nbsp;6a). Many approaches have been used to estimate carbon export and ANCP. Some studies looked at nutrient drawdown in the upper layer (Arteaga et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Johnson et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Munro et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), but this technique does not account for nutrient replenishment from below (vertical mixing or advection) or heterotrophic activity within the mixed layer. Others quantified export below their defined productivity layer from sediment traps (Lourey \u0026amp; Trull, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Pilskaln et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) which does not account for respiration. Both methods are likely to be prone to underestimation, as respiration can represent up to 90% of the export production in the mesopelagic zone (Jacquet et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thorium isotopes have also been used (Le Moigne et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Planchon et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Puigcorb\u0026eacute; et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Smetacek et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), accounting for all the POC exported from the surface waters.\u003c/p\u003e \u003cp\u003eWhen we performed our calculations over 100\u0026ndash;500 m depth range like in Arteaga et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we obtained very similar results (Supplementary Figure S8), because both methods rely on oxygen drawdown, although applied differently. Our estimates are 1.04, 1.89, 1.42 and 1.11 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e for SIZ, PAZ, SAZ and STZ respectively, compared to 0.9, 1.80, 1.80 and 1.25 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e for their study. Note that we binned their results in similar zones for comparison. This demonstrates the rigor of this new method to derive respiration from BGC-Argo floats.\u003c/p\u003e \u003cp\u003eOur basin integrated calculation estimated a SO carbon export of 5.08 PgC y\u003csup\u003e-1\u003c/sup\u003e, which is higher than previously reported values. However, most studies did not consider the SO to extend as far north as 30\u0026deg;S, or include the SIZ, or both. For example, the latest budget of 3.89 PgC y\u003csup\u003e-1\u003c/sup\u003e calculated by Su et al. (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) using BGC-Argo floats observations did not consider the SIZ. If we remove the SIZ, our estimation falls to 3.4 PgC y\u003csup\u003e-1\u003c/sup\u003e, slightly smaller, but still with some differences in zone definition and method used. Furthermore, the studies compared in Fig.\u0026nbsp;6b mostly defined the SO south of 40 or 50\u0026deg;S, discarding estimations from the STZ and some of the SAZ. Aside from the definition of regions, we argue that a basin scale calculation using all available floats allows for a better estimation of carbon export compared to studies relying on extrapolation of fewer data from restricted locations. In particular, our work provides a new and extended circumpolar SIZ estimate, an area which has been largely unstudied in the past because of the paucity of observational data under ice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Uncertainties and caveats of carbon estimations\u003c/h2\u003e \u003cp\u003eThe primary goal of our study was to quantify the importance of the SIZ in the overall circumpolar SO carbon export. However, sea ice prevents floats from surfacing and transmitting their data in real time (Hague \u0026amp; Vichi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Riser et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, distances between consecutive under ice profiles may bias our SIZ carbon export estimates compared to PAZ, SAZ and STZ - that is, if the observed changes in POC or oxygen are due to spatial and not temporal variability. In the SIZ, 99% consecutives pairs of profiles under sea ice (Fig.\u0026nbsp;8c) are within 50 km from each other, while the range expands to 140 km in open waters (80% below 50 km, Fig.\u0026nbsp;8b). This confirms that the pairs of profiles used to derive our metrics of interest under sea ice are likely representative of processes within the same water masses. The decorrelation scale, defined as the measuring distance and time after which the correlation between two consecutive profiles stopped being significant at the 0.01 level, was also investigated. The average decorrelation scale in space and time was greater than the mean space and time interval between profiles (Supplementary Figure S9). These additional analyses suggest that all consecutive pairs of profiles used to derive R and ∆POC likely capture the same water masses.\u003c/p\u003e \u003cp\u003eSinking rate of POC depends on when or where iron is available in the Southern Ocean (Obernosterer et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), The SOIREE experiment reported sinking rates reaching 1.6 m d\u003csup\u003e-1\u003c/sup\u003e, 2.5 m d\u003csup\u003e-1\u003c/sup\u003e and up to 4 m d\u003csup\u003e-1\u003c/sup\u003e in the SIZ following iron addition conditions (Boyd et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Maldonado et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Waite \u0026amp; Nodder, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). South of Tasmania, Cassar et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) described a sinking rate of 6 m d\u003csup\u003e-1\u003c/sup\u003e on average, reaching a maximum of 19.5 m d\u003csup\u003e-1\u003c/sup\u003e. Fecal pellet sinking rates were also shown to be high in the SO, ranging from 82 m d\u003csup\u003e-1\u003c/sup\u003e to 437 m d\u003csup\u003e-1\u003c/sup\u003e north of the Antarctic Peninsula in the top 400 m (Liszka et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This wide range of sinking rates observed during ship-based campaigns imply that, over the 10 days sampling frequency by the BGC-Argo floats, large sinking events (and the associated respiration) could be missed.\u003c/p\u003e \u003cp\u003eTo try to resolve this, we compared our carbon estimates (from the entire dataset) to estimates derived from SOCCOM floats only (10 days sampling interval), and SOCLIM and remOcean only (1 to 7 days sampling interval). Using SOCLIM and remOcean floats reduced our sample size by 75%, with very few SOCLIM/remOcean profiles in the SIZ and STZ compared to PAZ and SAZ (Supplementary Figure S10). Despite being higher, estimates of carbon export from only SOCLIM and remOcean floats, both in molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e and PgC y\u003csup\u003e-1\u003c/sup\u003e, were not statistically different (Kruskall-Wallis; p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.1) from the whole array of BGC-Argo floats nor from the SOCCOM floats array in the PAZ (13.02 vs 9.99 vs 10.77 molC m\u003csup\u003e-2\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e; 3.19 vs 2.45 vs 2.64 PgC y\u003csup\u003e-1\u003c/sup\u003e; Supplementary Table T1). However, in the other regions, the carbon export estimates from SOCLIM and remOcean were significantly higher than those from the all-floats and SOCCOM floats only arrays. So, when more profiles are used, the carbon export estimates between the three groups of floats are closer, as seen in the PAZ. Estimates from the SOCLIM and remOcean profiles in the SIZ, SAZ and STZ only have very few profiles that are spatially restricted to a small region in the Indian sector, compared to the PAZ profiles which show a better spatial coverage in the eastern SO. Therefore, we place higher confidence in our calculations using all floats and profiles, despite the lower sampling frequency of 10 days for the majority of them. This point of view is corroborated by Llort et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who showed that SOCCOM floats can be used to capture fast sinking eddy-related carbon export episodes.\u003c/p\u003e \u003cp\u003eAnother avenue to address the sampling frequency would be to merge profiles from all floats for a given province and derive parameters from the closest profiles in time. Opting for this method certainly allows for a greater sampling frequency compared to a usual 10-day interval, from the same day to 2 days in most cases (Supplementary Figure S11a-b-c-d). However, this method implies that every calculation made from the closest time \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e to time \u003cem\u003et\u003c/em\u003e\u003csub\u003e\u003cem\u003e+\u0026thinsp;1\u003c/em\u003e\u003c/sub\u003e can be performed on profiles from different floats that are in different areas, and possibly different water masses, within the same zone. With this approach, about 90% of all consecutive profiles used to derive ∆POC and R are from different floats for the 4 zones (Supplementary Figure S11e), with significant spatial gaps (Supplementary Figure S12). Therefore, we believe that comparing consecutive profiles from a same float, and then averaging per month and zones, is the most suitable method for this study.\u003c/p\u003e \u003cp\u003eAlthough we constrained physical fluxes using a strong salinity change criteria threshold, we recognize that, in reality, the hydrodynamics likely modify the local oxygen balance, therefore adding uncertainties. The method proposed by Arteaga et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) captured the spatial pattern of carbon flux in CM2-1 models, but underestimated the magnitude compared to the models. They suggested that this could be due to a mismatch between floats position and the lack of detritus flux in the gridded cell model. This implies that their observation-based estimates might be more representative of the ocean processes than the models, with respect to the natural spatial variability of carbon export in the SO. We also find a good relationship between positive POC and oxygen changes in the mesopelagic layer (negative R in Fig.\u0026nbsp;5b). Because we previously addressed the physical fluxes contribution, we believe this signal may reflect another process. As phytodetritus sink below the productive layer, they often retain somewhat elevated chl-a levels (Moreau et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We therefore hypothesise that sinking phytodetritus might still be producing low levels of O\u003csub\u003e2\u003c/sub\u003e while sinking, which would explain the positive O\u003csub\u003e2\u003c/sub\u003e signal observed in the mesopelagic layer. Negative R estimates (positive ∆Oxygen in Fig.\u0026nbsp;5c) may therefore not be linked to abiotic factors, which are generally well constrained by criteria imposed on BGC-Argo profile pairs. Evidence of phytodetritus in the mesopelagic layer was also reported in the SO near the Crozet plateau (Hughes et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Our respiration rates might be underestimated during the productive season when the effects on small but positive sub-surface biological O\u003csub\u003e2\u003c/sub\u003e production cannot be disentangled from external oxygen inputs and lead to negative R. This caveat requires new approaches.\u003c/p\u003e \u003cp\u003eNevertheless, lateral input processes have been shown to be significantly smaller than ANCP itself ( Arteaga et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; MacCready \u0026amp; Quay, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Munro et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), or accounting for a very small fraction of change in D\u003csub\u003eoxy\u003c/sub\u003e (3% for advection in the SIZ, Briggs et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Considering the floats as quasi-lagrangian, looking at large spatial and temporal scales (monthly variability) and averaging from an extended fleet could mean that smaller scale positive and negative changes in oxygen balance each other (Hennon et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Martz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Najjar \u0026amp; Keeling, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother factor to consider is the calcium carbonate signal in the Great Calcite Belt (GCB), extending from 30\u0026deg; to 60\u0026deg;S. Balch et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) showed that bbp is high in this area due high particulate inorganic carbon (PIC) from calcification. Using our method, it is not possible to distinguish PIC from POC, so the presence of PIC likely causes some overestimation of POC. Methods have been developed to detect coccolithophore blooms using BGC-Argo floats (Terrats et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) based on bbp and chl-\u003cem\u003ea\u003c/em\u003e. However, we argue this would likely have little effect on our carbon export estimation as (i) PIC was shown to have very little contribution to annual net community production compared to POC (Haskell II et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (ii) high calcium carbonate production does not increase POC export (Balch et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), (iii), most of the calcium carbonate is remineralized in the photic zone, therefore having little effect on export (Ziveri et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and (iv) POC contributes very little to the total carbon export compared to respiration (Fig.\u0026nbsp;5d).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, we calculated circumpolar SO carbon biomass, its production, export and respiration in the mesopelagic zone. Our results provide insights into the relative contribution of phytoplankton productivity, downward vertical particle export and heterotrophic respiration to the variability of the SO carbon inventory. We found that respiration (R) represents a larger part of the total carbon variability compared to the temporal variability of sinking particles (∆POC). The SIZ contributes more (33%) to SO carbon export than previously reported, increasing the total SO estimate to 5.08 PgC y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e based on observations from 2014 to 2022. Our work demonstrates the importance of closely monitoring the SIZ. Not only does it represent a significant proportion of SO carbon export, but it is also an area prone to high variability such as the 2023 low in winter ice extent. Sustained monitoring of the SIZ is essential for accurate quantification of the SO carbon sink, as this could ultimately impact our understanding of climate variability at the global ocean scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGuillaume Liniger, Peter Strutton and Delphine Lannuzel thank the University of Tasmania, the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023), and the Australian Centre for Excellence in Antarctic Sciences (ACEAS; SR200100008) for their support. Sebastien Moreau received funding from the Research Council of Norway (RCN) for the project \u0026ldquo;I-CRYME: Impact of CRYosphere Melting on Southern Ocean Ecosystems and biogeochemical cycles\u0026rdquo; (grant number 335512) and for the Norwegian Centre of Excellence \u0026ldquo;iC3: Center for ice, Cryosphere, Carbon and Climate\u0026rdquo; (grand number 332635).\u0026nbsp;Delphine Lannuzel is funded by the Australian Research Council through a Future Fellowship (FT190100688).\u0026nbsp; We also would like to extend our thanks to the BGC-Argo program and the several Southern Ocean projects to make the data available and free of access. Data were collected and made freely available by the Southern Ocean Carbon and Climate Observations and Modelling (SOCCOM) Project funded by the National Science Foundation, Division of Polar Programs (NSF PLR \u0026ndash; 1425989), supplemented by NOAA and NASA. We thank all the people involved in the conception, deployment, and quality control of BGC-Argo float. We finally extend our gratitude to the reviewers for their help in improving the quality of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was primarily developed by G.L under the supervision of S.M, D.L and P.G.S. who were involved in conducting the analyses. All authors provided ideas in Figures conception, analyses, and feedback in the manuscript development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare having no competing interest of any kind with this research and the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Research statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSOCCOM data were obtained from\u0026nbsp;\u003ca href=\"http://soccom.ucsd.edu/floats/SOCCOM_data_ref.html\"\u003ehttp://soccom.ucsd.edu/floats/SOCCOM_data_ref.html\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOCLIM and remOcean argo data were downloaded from\u0026nbsp;\u003ca href=\"https://maps.biogeochemical-argo.com/bgcargo/\"\u003ehttps://maps.biogeochemical-argo.com/bgcargo/\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data can be accessed at\u0026nbsp;\u003ca href=\"https://www.seanoe.org/data/00311/42182/\"\u003ehttps://www.seanoe.org/data/00311/42182/\u003c/a\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSea ice concentration data ca be found at\u0026nbsp;\u003ca href=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration?tab=form\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-ice-concentration?tab=form\u003c/a\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAnderson, L. 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Pelagic calcium carbonate production and shallow dissolution in the North Pacific Ocean. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 805. https://doi.org/10.1038/s41467-023-36177-w\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Insititute for Marine and Antarctic Studies","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"BGC-Argo floats, Carbon export, Southern Ocean, Sea-ice zone","lastPublishedDoi":"10.21203/rs.3.rs-3937570/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3937570/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Southern Ocean (south of 30\u0026deg;S) contributes significantly to global ocean carbon uptake through the solubility pump and phytoplankton productivity. Many studies have estimated carbon export to the deep ocean, but very few have attempted a basin-scale perspective. In this study, we use an extensive array of BGC-Argo floats to improve previous estimates of carbon export across basins and frontal zones, with a focus on the sea-ice zone (SIZ). We find that the SIZ contributes 33% of the 5.08 PgC y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e total Southern Ocean carbon export. We also show that subsurface carbon respiration, not flux out of the surface ocean, contributes most strongly to the temporal and spatial variability of carbon export. Our work highlights the importance of closely monitoring the SIZ to accurately quantify the total Southern Ocean carbon sink, especially as the SIZ is prone to strong interannual variability. Tightening these estimates and their drivers ultimately impacts our understanding of climate variability at the global ocean scale.\u003c/p\u003e","manuscriptTitle":"Large contribution of the sea-ice zone to Southern Ocean carbon export revealed by BGC-Argo floats","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-04-03 16:31:45","doi":"10.21203/rs.3.rs-3937570/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-02-09 06:46:32","doi":"10.21203/rs.3.rs-3937570/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fec2a895-bc0d-4a11-9197-321b699ec938","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28672885,"name":"Oceanography"}],"tags":[],"updatedAt":"2025-04-29T14:49:02+00:00","versionOfRecord":{"articleIdentity":"rs-3937570","link":"https://doi.org/10.1029/2024GB008193","journal":{"identity":"global-biogeochemical-cycles","isVorOnly":true,"title":"Global Biogeochemical Cycles"},"publishedOn":"2025-04-28 00:00:00","publishedOnDateReadable":"April 28th, 2025"},"versionCreatedAt":"2024-04-03 16:31:45","video":"","vorDoi":"10.1029/2024GB008193","vorDoiUrl":"https://doi.org/10.1029/2024GB008193","workflowStages":[]},"version":"v2","identity":"rs-3937570","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3937570","identity":"rs-3937570","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}