Progress in Oceanography 120 (2014) 230–242

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Progress in Oceanography

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Anthropogenic CO2 estimates in the Southern Ocean: Storagepartitioning in the different water masses

0079-6611/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.pocean.2013.09.005

⇑ Corresponding author. Tel.: +34 986231930.E-mail address: pconde@iim.csic.es (P.C. Pardo).

Paula C. Pardo a,⇑, F.F. Pérez a, S. Khatiwala b, A.F. Ríos a

a Spanish National Research Council (CSIC), Instituto de Investigaciones Marinas (IIM), Eduardo Cabello, 6, 36208, Vigo, Pontevedra, Spainb Department of Earth Sciences, University of Oxford, Oxford, UK

a r t i c l e i n f o a b s t r a c t

Article history:Received 15 October 2012Received in revised form 13 September2013Accepted 14 September 2013Available online 1 October 2013

The role of the Southern Ocean (SO) remains a key issue in our understanding of the global carbon cycleand for predicting future climate change. A number of recent studies suggest that 30 to 40% of oceanuptake of anthropogenic carbon (CANT) occurs in the SO, accompanied by highly efficient transport of CANT

by intermediate-depth waters out of that region. In contrast, storage of CANT in deep and bottom layers isstill an open question. Significant discrepancies can be found between results from several indirect tech-niques and ocean models. Even though reference methodologies state that CANT concentrations in deepand bottom layers of the SO are negligible, recent results from tracer-based methods and ocean modelsas well as accurate measurements of 39Ar, CCl4 and CFCs along the continental slope and in the Antarcticdeep and bottom waters contradict this conclusion. The role of the SO in the uptake, storage and transportof CANT has proved to be really important for the global ocean and there is a need for agreement betweenthe different techniques. A CO2-data-based (‘‘back-calculation’’) method, the C0

T method, was developedwith the aim of obtaining more accurate CANT concentration and inventory estimates in the SO region(south of 45�S). Data from the GLODAP (Global Ocean Data Analysis Project) and CARINA databases wereused. The C0

T method tries to reduce at least two of the main caveats attributed to the back-calculationmethods: the need for a better definition of water mass mixing and, most importantly, the unsteady stateof the air-sea CO2 disequilibrium (DCdis) term. Water mass mixing was computed on the basis of resultsfrom an extended Optimum Multi-Parametric (eOMP) analysis applied to the main water masses of theSO. Recently published parameterizations were used to obtain more reliable values of DCdis and also ofpreformed alkalinity. The variability of the DCdis term (dCdis) was approximated using results from anocean carbon cycle model. Results from the C0

T method are compared with those from the DC* method,the TrOCA method, and two different tracer-based approaches, the transit-time distribution (TTD) andGreen’s function (GF) methods. We find that the TTD, GF and C0

T methods give very similar estimatesfor the SO’s inventory (with reference to the year 1994) of 30 ± 2, 22 ± 2, 29 ± 3 PgC, respectively. Impor-tantly, Antarctic Bottom Water shows CANT concentrations of 9 ± 1, 3 ± 0.3, 6 ± 1 lmol kg�1, contributing6–12% of the SO’s inventory. The DC* and TrOCA methods seem to underestimate and overestimate,respectively, both the total CANT inventory and CANT concentrations in deep and bottom layers. Resultsfrom the C0

T method suggest that deep and bottom layers of the water column in the SO contain, in gen-eral, low concentrations of CANT compared with subsurface and intermediate layers but higher than thoserecorded in the global databases. It is important to note that, as deep and bottom layers in the SO fill twoof the most voluminous water masses of the global ocean, even these relatively low values of CANT can beof considerable importance when computing the inventories in the water column, mostly in the SO butalso in outer regions where bottom waters spread.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Since the beginning of industrialisation, the increase of anthro-pogenic atmospheric carbon dioxide (CANT) concentrations (Keelingand Whorf, 2000) has been mitigated by its uptake through theocean and land biosphere (Enting et al., 1995; Keeling et al.,

1996; Sarmiento and Gruber, 2002). In order to understand the glo-bal carbon cycle, quantification of the distribution and uptake ofCANT in the ocean is crucial. Additionally, the interest in this subjecthas been reinforced by the quasi-direct progressive acidification ofthe ocean by carbon dioxide uptake and its effect on marine eco-systems (Feely et al., 2004; Orr et al., 2005).

The fundamental problem of quantifying the capacity of theocean to sequester CANT stems from the fact that CANT concentra-tions in the ocean cannot be measured directly. It is difficult to

P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242 231

separate the small anthropogenic signal (order 3%) from the largepool of total dissolved inorganic carbon. Several competing indirectmethods have been developed for more than 30 years, which canbe generally divided into two groups: CO2-data-based methods(Brewer, 1978; Chen and Millero, 1979; Gruber, 1998) and tra-cer-based methods (Hall et al., 2002; Waugh et al., 2006; Khatiwalaet al., 2009). CO2-data-based methods estimate CANT by correctingthe measured carbon in a water sample for the biogeochemicalchanges incurred since it was isolated from the surface and by sub-tracting the preformed industrial carbon concentration. Histori-cally, the DC* method (Gruber et al., 1996; Gruber, 1998), hasbeen considered the reference approach of CO2-data-based meth-ods. This technique has been used for global ocean CANT estimates(Sabine et al., 1999, 2002, 2004) that are included in the WOA/GLO-DAP database, as a basis for studies of air-sea CO2 flux and CANT

transport in the global ocean (Gloor et al., 2003; Sabine et al.,2004; Mikaloff Fletcher et al., 2006), and also to evaluate oceanmodels (Orr et al., 2001; Matsumoto et al., 2004). On the otherhand, tracer-based methods consider CANT as a conservative tracer.These methodologies rely on tracer measurements to estimate CANT

transport from the surface mixed layer into the interior. Typically,this transport is mathematically described by a transit-time distri-bution (TTD) or, more generally, a Green’s function (GF).

The uptake and accumulation of CANT is controlled – to a largeextent – by ocean circulation and water mass mixing (Sarmientoet al., 1992; Siegenthaler and Sarmiento, 1993). In general terms,the shallowest penetration of CANT is associated with upwelling re-gions, while the deepest penetration is associated with conver-gence zones. In terms of the uptake, storage and transport ofCANT, the Southern Ocean (SO) is the most conspicuous place ofthe global ocean (Fig. 1). The upwelling of older waters in this re-gion is closely related with the formation of intermediate, deep andbottom water masses through complex dynamical processes

Fig. 1. Dataset downloaded from GLODAP and CARINA databases (n = 82,792). The differdotted lines indicate the path of the section surrounding the Antarctic continent (AA). Ththe three major oceans: Atlantic (A), Pacific (P), and Indian (I).

(Fig. 2). Several studies have pointed out that the SO is a relativelylarge sink of CANT, representing approximately 30–40% of the oceanCANT uptake (Gruber et al., 2009; Khatiwala et al., 2009). Resultsfrom ocean models (Caldeira and Duffy, 2000; Orr et al., 2001;Ito et al., 2010) suggest that, despite the significant CANT uptakeby the SO, the efficient export of water towards northern latitudesleads to a low CANT storage (Fig. 2). Sabine et al. (2004) concludedthat the SO contains only 9% of the CANT inventory of the globalocean. van Heuven et al. (2011) compiled several CANT estimatescomputed in deep (Warm Deep Water) and bottom (Weddell SeaBottom Water and/or Antarctic Bottom Water) layers of the SO,making clear that results depend very much on the methodologyapplied. Differences of as much as 10–20 lmol kg�1 in the samelayer can be found depending on the technique. The underlyingreason for all these controversial results lies in the particulardynamics of the SO (Fig. 2).

The dynamical features of the SO (Fig. 2) determine the two dif-ferent paths by which CANT penetrates the water column (Tréguerand Jacques, 1992; Marinov et al., 2006; Ito et al., 2010; Iudiconeet al., 2011). North of the PF (�51�S), deep winter ventilation asso-ciated with the formation of Sub-Antarctic Mode Water (SAMW)and Antarctic Intermediate Water (AAIW) makes possible theintrusion of CANT down to more than 1000 m of depth (McNeilet al., 2001; Wang and Matear, 2001). South of the PF, the complexprocesses involved in the formation of bottom waters favour thepumping of CANT to deep and bottom layers (>2000 m, Hoppemaet al., 1998; Rintoul and Bullister, 1999; McNeil et al., 2001).

The first application of CO2-data-based methods (Gruber et al.,1996;Sabine et al., 1999, 2002, 2004) suggested that CANT

concentrations are almost zero or below the accuracy level(5–6 lmol kg�1) in deep and bottom layers of the SO. Among thearguments supporting those results are the short residence timeof surface-waters, the dissolution by mixing of older waters, the

ent sections chosen to describe the distributions of CANT are also shown. The whitee white solid lines indicate the three meridional sections, each one located in one of

Fig. 2. Dynamics of the SO (based on Anderson et al., 2009). The scheme shows the upwelling of older-deep waters (CDW) in the SO region. It also tends to reproduce theirimplication, together with shelf and surface waters (HSSW and AASW), in the formation of bottom (AABW), subsurface (SAMW) and intermediate (AAIW) waters in the SO.The colours qualitatively indicate the CANT concentrations found in the present study. (For interpretation of the references to colour in this figure legend, the reader is referredto the web version of this article.)

232 P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242

highly efficient export at intermediate levels of the water columnand the inhibition of air-sea exchange by sea-ice (Poisson andChen, 1987; Schlosser et al., 1987). Additionally, low values of dif-ferent tracers (CFCs, bomb-radiocarbon, tritium, etc.) were re-ported at that time as confirming the idea of CANT – free deep tobottom waters in the SO (Weiss et al., 1979; Roether et al.,1993). Nevertheless, studies of 39Ar, CCl4 and CFCs concentrationsdo not support the idea of those negligible deep and bottom con-centrations of CANT (Schlosser et al., 1991, 1994; Warner and Weiss,1992; Archambeau et al., 1998; Meredith et al., 2001; Orsi et al.,2002). Lo Monaco et al. (2005a, 2005b) first considered a meanoxygen under-saturation value (12%) to correct the estimates ofCANT in the SO. This correction was applied to various CO2-data-based methodologies, resulting in a significant amount of CANT indeep and bottom Antarctic waters. Results from tracer-basedmethods such as the TTD method (Hall et al., 2002; Waugh et al.,2006), also established significant concentrations of CANT below2000 m depth in the SO. Using a GF approach, Khatiwala et al.(2009) also found higher CANT concentrations in the deep to bottomlayers of the SO than previous estimates. van Heuven et al. (2011),using a so-called transient steady state (TSS) approach, obtainedclose to zero values in deep waters but notable concentrations(�6 lmol kg�1) in bottom waters. On the other hand, CANT invento-ries in the water column are usually very similar between methodsand ocean model results. Tanhua et al. (2007) reported that thereason for this stems from the cancelling of different biases.

The majority of the recent studies pointing to significant CANT

concentrations in deep and bottom waters of the SO come fromtracer-based methods (Waugh et al., 2006; Khatiwala et al.,2009) and ocean models (Orr et al., 2001). The reason for the differ-ences between these results and those obtained with the DC*

method were attributed to fundamental biases of the back-calcula-tion techniques. These main biases can be attributed to: (a) a poorestimate of the mixing of water masses and (b) the low accuracy offundamental terms (preformed variables, stoichiometric ratios andair-sea disequilibrium term).

Given the great efforts to create international databases withhighly exigent quality controls, the use of CO2-data-based methodsshould be maintained. In this study we present a CO2-data-basedmethod (C0

T method), which considerably reduces the fundamentalbiases of previous back-calculation methods. Therefore, the aim ofthe manuscript will be to obtain more accurate estimates of the

CANT concentrations in different layers of the water column of theSO using a back-calculation technique.

The C0T method is described in Section 2.3. The improvements of

the methodology include: (i) estimating the mixing of watermasses by using an improved extended Optimum Multi-Paramet-ric (eOMP) analysis (Pardo et al., 2012); (ii) more accurately esti-mating the preformed alkalinity (A0

T ) and the air-sea CO2

disequilibrium (DCdis) for the main water masses using recentlyimproved global parameterizations (Pardo et al., 2011; Vázquez-Rodríguez et al., 2012); and (iii) including the variability of theDCdis term over time (dCdis) which was approximated by comput-ing results in an ocean carbon cycle model (Khatiwala et al.,2009). The CANT concentrations are estimated using data from theGLODAP and CARINA databases. The distributions of the CANT con-centrations obtained are shown in meridional sections located inthe Atlantic, Pacific and Indian Ocean sectors of the SO and alsoin a section surrounding the Antarctic continent (Section 3.1). CANT

estimates were interpolated to a WOA09 domain (1� latitude � 1�longitude with 33 vertical layers) through a Water Mass Properties(WMP) interpolation method (Velo et al., 2010) and subsequentlyvolumetrically averaged for each of the water masses as well asfor the whole SO (Section 3.2). Lastly, the results from the integra-tions were compared with previous published methodologies suchas the back-calculation reference method, DC* (Gruber, 1998), theTrOCA method (Touratier et al., 2007), and two tracer-based tech-niques: the TTD (Waugh et al., 2006), and GF (Khatiwala et al.,2009) methods (Section 4).

2. Observations and methods

2.1. Data

Measurements of potential temperature (h), salinity (S), oxygen(O2), nitrate (NO3), phosphate (PO4), silicate (SiO2), total alkalinity(AT) and total carbon (CT) were taken from the GLODAP (Key et al.,2004; http://cdiac.ornl.gov/oceans/glodap/Glodap_home.html)and CARINA (http://store.pangaea.de/Projects/CARBOOCEAN/cari-na/index.htm, NDP 091) databases, covering the area from the Ant-arctic continent north to 45�S (Fig. 1) and with a total of 82,792data points. These data represent an internally consistent datasetof open ocean subsurface measurements subjected to extensive

P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242 233

quality control (Key et al., 2004; Hoppema et al., 2009; Sabineet al., 2009; Key et al., 2010; Lo Monaco et al., 2010).

2.2. Water masses

The SO is the place of convergence of the two cells of the Merid-ional Overturning Circulation (MOC), the most important mecha-nism for ventilating the global ocean. The MOC brings watersfrom the ACC to the Antarctic shelf (Fig. 2), where they are up-welled, mixed with the near-freezing local shelf waters (Gordonand Huber, 1984, 1990) and modified to produce dense transitionalwaters that flow to bottom layers forming AABW (Gill, 1973; Fos-ter and Carmack, 1976; Fahrbach et al., 1995; Gordon et al., 2001).The counter-clockwise cell of the MOC is driven by the AABW andit mainly transports water from the Antarctic continent to northernlatitudes.

A total of nine main water masses are considered to representthe dynamics of the SO (Fig. 3; Table 1). In subsurface layers ofthe SO, Sub-Tropical Central Water (STCW) accounts for the influ-ence of subtropical waters from the three major oceans (Fig. 3, Ta-ble 1). SAMW is formed north of the SAF and at intermediate levelsof the water column (Figs. 2 and 3, Table 1). It spreads northwardsto the subtropical gyres, renewing the water of the lower thermo-cline (McCartney, 1977; McCartney, 1982; Talley 2003). The cold-est varieties of SAMW in the Pacific and Atlantic oceans supplyAAIW (Fig. 3, Table 1) of those basins (Piola and Gordon, 1989; Tal-ley, 1996). AAIW is also relevant in ventilating the Sub-AntarcticZone (SAZ, between SAF and the Subtropical Front) and other re-gions of the ocean (Rintoul and Bullister, 1999), including regionsof northern latitudes.

North Atlantic Deep Water (NADW) drives the clockwise circu-lation cell of the MOC which goes southward from the Arctic(Fig. 3, Table 1). NADW forms in the northern North Atlantic (Mau-ritzen, 1996; Rudels et al., 2002) and flows southward within theNorth Atlantic deep western boundary current until it reachesthe Antarctic Circumpolar Current (ACC) in the SO. The ACC flowseastward around the Antarctic continent between the SAF

Fig. 3. (a) T–S diagram of the SO using data from GLODAP and CARINA databases. The twOcean (SWM2) and the warm branch from the Indian Ocean (SAMW1). (b) Zoom for theacronyms for each water mass.

(�42�S) at the north and the PF (�51�S, Orsi et al., 1995). CDW(Fig. 3) occupies the thick deep layer within the ACC and mixeswith the arriving NADW at its densest portion. In fact, CDW com-prises deep waters flowing from the Atlantic, Pacific and Indian ba-sins (Table 1) and eventually mixes (Fig. 2) with ventilated waterssurrounding the Antarctic continent (Withworth III et al., 1998;Rintoul et al., 2001).

AABW (Fig. 3, Table 1) comprises bottom waters formed at dif-ferent places around the Antarctic continent (Orsi et al., 2002):Weddell Sea Bottom Water (WSBW), Ross Sea Bottom Water(RSBW) and Adélie Bottom Water (ADLBW) (Fig. 3, Table 1).

The coldest surface–subsurface waters are those found over theAntarctic shelf (Fig. 3): High Salinity Shelf Water (HSSW) and Ant-arctic Surface Water (AASW). HSSW and AASW can be differenti-ated by their relatively high and low salinity values (Table 1),respectively (Whitworth and Orsi, 2006), and both are involvedin the formation of bottom waters (Fig. 2).

2.3. Methodology

The back-calculation method used here is constructed on thebasis of the original approach proposed by Brewer (1978) and Chenand Millero (1979). Following these authors, CANT can be consid-ered (Eq. (1)) as the difference between the total amount of pre-formed carbon measured at a certain point in space and time(C0

T ) and the concentration that could be measured at that locationin preindustrial times (C0p

T ):

CANT ¼ C0T � C0p

T ð1Þ

The preformed carbon at a given time (C0T ) can be estimated as

the total carbon measured (CT) corrected for changes due to theremineralisation of organic matter and CaCO3 dissolution:

C0T ¼ CT �

1rC� AOU� 1

2AT � A

�

T þ AOU1rNþ 1

rP

� �� �ð2Þ

where AOU ¼ O�2sat � O2, with O�2sat being the O2 saturation;rC = 1.45, rN = 9.3 and rP = 135 are the stoichiometric coefficients

o branches of SAMW are differentiated: the cold branch from the southeast Pacificthree source water masses of AABW. The table on the right indicates the names and

Table 1Upper part: Potential temperature (h) and salinity (S) defining the main water masses of the SO (>45�S). Central part: Parameterisations used to estimate the values of thepreformed alkalinity (A0

T ) and air-sea CO2 disequilibrium (DCdis) for each water mass. Bottom part: Decompositions of CDW, AABW, WSBW, RSBW and ADLBW used in theiterative routine in order to obtain the values of both before mentioned variables for CDW and AABW.

h S A0T

DCpdis dCdis

�C psu lmol kg�1

WSPCW 15.000 ± 0.005 35.660 ± 0.005 2326 ± 6 �14 ± 7 �6 ± 2SAMW1 8.750 ± 0.005 34.580 ± 0.005 2279 ± ± 6 �6 ± 7 �4 ± 3SAMW2 5.000 ± 0.005 34.140 ± 0.005 2264 ± 6 �1 ± 6 �4 ± 1HSSW �1.910 ± 0.005 34.820 ± 0.005 2359 ± 4 �10 ± 6 �38 ± 4AASW �1.850 ± 0.005 33.800 ± 0.005 2278 ± 4 �16 ± 5 �10 ± 1AAIW 3.140 ± 0.005 34.140 ± 0.005 2287 ± 4 �8 ± 5 �6 ± 1NADW 3.280 ± 0.005 34.910 ± 0.005 2312 ± 5 �11 ± 7 �2 ± 3CDW* 0.650 ± 0.005 34.707 ± 0.005 2326 ± 2 �11 ± 4 �18 ± 1AABW* �0.753 ± 0.005 34.660 ± 0.005 2336 ± 3 �11 ± 4 �26 ± 2

STCW, SAMW1, SAMW2, AAIW:

A0T � 6:1 ¼ 2288:3þ 62:8ðS� 35Þ � ð0:9==1:6Þðh� 16Þ þ 0:1 � ðPO-300Þ

DCdis � 4:9 ¼ ð�47:9==þ 51:3Þ4:34 � hþ 0:35ðPO-300ÞHSSW, AASW:

A0T � 4:3 ¼ 2296:7þ 94:7 � ðS-35Þ þ 0:3ðPO-300Þ

DCdis � 4:9 ¼ ð�47:9==þ 51:3Þ þ 4:34 � hþ 0:35ðPO-300ÞNADW:

PASSLT � 4:6 ¼ 585:7þ 46:2 � Sþ 3:27 � hþ 0:24 � NOþ 0:73 � Si

DCdis � 5:8 ¼ �15þ 3:45 � ðh� 10Þ þ 20 � ðS-35Þ þ 0:11 � ðNO-300Þ þ 0:14 � ðPO-300Þ � 0:405 � Si

CDW* = 0.65 � AABW + 0.30 � NADW + 0.05 � AAIWWSBW = 0.5 � HSSW + 0.10 � AASW + 0.40 � CDWRSBW = 0.30 � HSSW + 0.05 � AASW + 0.65 � CDWADLBW = 0.40 � HSSW + 0.05 � AASW + 0.55 � CDWAABW* = 0.77 �WSBW + 0.20 � RSBW + 0.03 � ADLBW

234 P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242

or Redfield ratios (Broecker and Peng, 1974;Anderson and Sarmien-to, 1994; Gruber and Sarmiento, 1997); AT is the total measuredalkalinity and A0

T is the preformed alkalinity.On the other hand, the total preformed carbon in preindustrial

times (C0pT , Eq. (1)) results from the sum of the total concentration

of carbon saturated at the corresponding pCO2 of preindustrialtimes (Cp SAT

T ) and the air-sea CO2 disequilibrium (DCpdis) term repre-

senting the difference in the CO2 partial pressure between theatmosphere and ocean (Broecker and Peng, 1974) that is reflectedin the oceanic carbon concentrations (Gruber et al., 1996):

C0pT ¼ CpSAT

T þ DCpdis ð3Þ

The above steps (Eqs. (1)–(3)) are common to all back-calcula-tion methods. Two of the most important terms in the basic equa-tions (Eqs. (1)–(3)) are A0

T and DCpdis (Pardo et al., 2011) and both

terms need to be obtained accurately in order to reduce biases. Par-do et al. (2011) have obtained accurate regionalized parameteriza-tions for A0

T in the Pacific and Indian oceans by using the subsurfacelayer as the data source. Subsurface layer measurements have beenshown to be very lowly contaminated by the seasonal variabilityaffecting the sea surface and therefore are the best data for infer-ring characteristics of the water masses at the moment of forma-tion. The subsurface data were also used for obtaining the multi-parametric equation of A0

T in the work by Vázquez-Rodríguezet al. (2012) for the Atlantic Ocean. According to the location ofthe zone of formation of the different water masses of the SO, A0

T

values can be obtained for each of them (Table 1). Since CDWand AABW do not form at the sea surface but are formed by mixingof other water masses, the A0

T values for these two water massesare obtained through an iterative process, beginning with a defaultvalue in AABW, which takes into account the volumetric relation-ships of combination found by Pardo et al. (2012) (Table 1). Theiteration stops when the difference between the A0

T value of AABWat the iteration i and that at the iteration i � 1 is less than 0.005.

One of the most important assumptions in this method is thatDCp

dis (Eq. (3)) is not constant over time:

DCpdis ¼ DCdis � @Cdis ð4Þ

DCdis is the air-sea CO2 disequilibrium term typically estimated inback-calculation techniques when DCp

dis is assumed to be in steadystate, i.e., DCdis ¼ DCp

dis. In the present work DCdis was computed byreliable parameterizations (Table 1) that, in the same way as for A0

T ,can be found in the studies from Pardo et al. (2011) and Vázquez-Rodríguez et al. (2012). Values of DCdis were obtained for eachwater mass except CDW and AABW, whose values also resultedfrom an iterative process (Table 1). In this case, the iteration processfinishes when the difference between values of DCdis in AABW attwo consecutive iteration times is less than 0.05.

The variability of the DCpdis term (dCdis) was estimated as:

dCdis ¼ DCactualdis � DCpreind

dis ð5Þ

where the air-sea CO2 disequilibrium for actual (DCactualdis ) and prein-

dustrial (DCpreinddis ) times was taken from results of an ocean carbon

cycle model (Khatiwala et al., 2009). This carbon model computesthe oceanic and atmospheric pCO2 evolution from preindustrialtimes through 2008. Preindustrial and actual (results for year2008) values of pCO2 were both transformed in CO2 concentrationsfor the atmosphere and the ocean in order to obtain the air-sea CO2

disequilibrium terms (Eq. (5)). In accordance with the previousDCdis estimates, dCdis values for each of the water masses consid-ered were obtained as the mean values in the area of formation ofeach of them (Table 1). Lastly, DCp

dis was computed for each one ofthe water masses (Eq. (4), AABW and CDW by the iteration pro-cesses, Table 1).

Once the values of A0T and DCp

dis were obtained for the mainwater masses of the SO, the next step was to obtain these valuesfor each data point, for which an accurate estimate of the mixingof the water masses was required. As previously mentioned, repre-senting the effect of water mass mixing on CANT concentrations is,together with the variability of DCp

dis, one of the major sources ofuncertainty in widely used back-calculation methods. Here, weused an Optimum Multi-Parametric (OMP) analysis (Tomczak,

P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242 235

1981; Mackas et al., 1987; Tomczak and Large, 1989) to infer therelative contributions, or mixing coefficients (Xi), of the variouswater masses contained in the sample. In the OMP, each measuredproperty is related with the water masses involved in the mixingthrough linear mixing processes. More concretely, the extendedOMP (eOMP) analysis used here resulted in more accurate coeffi-cients since non-linear processes (remineralisation of organic mat-ter, dissolution of opal) affecting many water properties (nutrients,oxygen and alkalinity) were also taken into account. eOMP analy-ses have been used in earlier studies dealing with CANT (Goyet et al.,1999; Coatanoan et al., 2001; Sabine et al., 2002; Lee et al., 2003;Steindfeldt et al., 2009). In a previous study, Pardo et al. (2012) ob-tained Xi for the main water masses of the SO for each data point.We applied these mixing coefficients to compute A0

T and DCpdis (Eqs.

(6) and (7)) and subsequently CANT (Eq. (1)), for the entire SO data-set (n = 56,447 data points because of unavailable or poor qualityCT data). The overall uncertainty of the CANT estimated by the C0

T

method is ±6 lmol kg�1. A description of the process of estimationof the relative errors of CANT is given in the Appendix A section.

A0T ¼ R9

i¼1Xi � A0T ð6Þ

DCpdis ¼ R9

i¼1Xi � DCpdis ð7Þ

The Water Mass Properties (WMP) interpolation method devel-oped by Velo et al. (2010) was used to interpolate the database to aWOA09 domain (1� latitude � 1� longitude � 33 vertical levels).The WMP interpolation method takes into account the propertiesof the main water masses involved in the dynamics of a certain re-gion in order to assign the interpolated value for a certain datapoint. Since this study strongly depends on water mass character-istics and distributions, the WMP interpolation method is likely togive more consistent results than other techniques. The volumetriccensus from Pardo et al. (2012) in the WOA09 domain allows esti-mation of the CANT concentrations and inventories in the differentwater masses as well as for the entire SO. Results of the concentra-tions and inventories of CANT obtained by the C0

T method in eachlayer of the water column and for the entire SO are discussed inSection 3.2.

For comparison, we also computed CANT using the TrOCA meth-od (Touratier and Goyet, 2004a, 2004b; Touratier et al., 2007), aCO2-data-based technique which estimates CANT on the basis ofthe so-called TrOCA tracer, which is defined so as to vary with timein a manner directly related to the accumulation of CANT in theocean:

CANT ¼ðTrOCA� TrOCA0Þ

1:279ð8Þ

where the TrOCA tracer is defined by:

TrOCA ¼ O2 þ 1:279 CT �12

AT

� �ð9Þ

and TrOCA0 is the corresponding value of TrOCA in preindustrialtimes:

TrOCA0 ¼ e7:511� 1:087�10�2ð Þh�7:81�105

A2T

� �ð10Þ

Values of CANT were estimated by the TrOCA method (Eqs. (8)–(10))from the GLODAP and CARINA datasets and interpolated to theWOA09 domain. CANT estimates using the DC* method were alsodownloaded for comparison from GLODAP and CARINA databasesand also interpolated to the WOA09 domain.

Lastly, we compared our estimates to those computed byWaugh et al. (2006) using the TTD method and Khatiwala et al.(2009) using the GF approach. As reviewed recently by Khatiwalaet al. (2012), the TTD and GF methods are based on a mathematical

description of how the ocean’s circulation ‘‘propagates’’ sea surfaceconcentrations of conservative tracers into the interior. This math-ematical construct, which can be regarded as a continuous, jointdistribution of the time and surface location at which a water par-cel was last exposed to the atmosphere, is known as a ‘‘boundarypropagator’’ (Holzer and Hall, 2000), a type of Green’s function,i.e., a solution to the advection–diffusion equation for the oceanwith an impulse boundary condition at the surface of the ocean.Convolution of the Green’s function, which is an intrinsic propertyof ocean circulation rather than a particular tracer, with the timehistory of a given tracer in the surface mixed layer allows us tocompute the interior concentration of that tracer at any point inspace and time. By treating the anthropogenic perturbation (CANT)as a conservative tracer this approach can be used to estimate thedistribution of CANT in the ocean.

The TTD scheme is a simplified application of the above formal-ism. Specifically, Waugh et al. (2006) assumed that there is negli-gible mixing between water masses, that is, CANT at a givenlocation comes from a single source region. Thus, the Green’s func-tion depends only on the time elapsed since a water parcel was lastin contact with the surface and is known as a transit time distribu-tion. A second simplification was to assume that the ocean’s TTDcan be approximated by an analytical function (Hall et al., 2002)known as the ‘‘inverse Gaussian’’ (Seshadri, 1999) that is parame-terized by two variables (mean and width) which they estimatedusing CFC-12 observations from the GLODAP dataset. A thirdassumption made in the TTD scheme is that air-sea disequilibriumhas remained constant over the industrial period. Equilibrium car-bonate chemistry can then be used to estimate surface boundarycondition for CANT. In contrast, Khatiwala et al. (2009) have devel-oped an inverse method that does not require making the aboveassumptions. Specifically, they applied a maximum entropy decon-volution technique to constrain the full Green’s function with mul-tiple steady and transient tracers. This approach accounts for themixing of waters both of different ages and different end-membertypes, as well as allowing the air-sea disequilibrium to evolve inspace and time. The latter requires assuming that the change insurface disequilibrium of CO2 relative to the preindustrial disequi-librium is proportional to the CANT perturbation in the atmosphere,an assumption justified by simulations in an ocean carbon cyclemodel (Khatiwala et al., 2009; Wang et al., 2012).

Estimates of CANT based on the TTD method were downloadedfrom the GLODAP site and WMP-interpolated to the WOA09 do-main. Similarly, the GF estimates were also interpolated onto theWOA09 domain. All the estimates of CANT were referenced to theyear 1994. Results from all the methodologies used and the simi-larities and differences between them are discussed in Section 4.An evaluation of the uncertainties of the integrated concentrationsand inventories is given in the Appendix A section.

3. Results

3.1. Distributions of CANT

Three meridional sections were selected to describe the CANT

distributions in the SO, estimated by the C0T method. The sections

are located between 10 and 40�W in the Atlantic sector (A,Fig. 1), between 170 and 180�E in the Indian sector (I, Fig. 1), andat 90�W in the Pacific sector (P, Fig. 1) (Fig. 4a–c). Additionally,the distribution of CANT in a section surrounding the Antarctic con-tinent (AA, Fig. 1) is also shown to provide a more completedescription (Fig. 4d).

In section A (Fig. 4a), high concentrations of CANT are located inthe northern part of the section but from intermediate layers to thesurface (>20 lmol kg�1), where the results from the eOMP indicate

Fig. 4. Distributions of CANT (in lmol kg�1) estimated using the C0T method. A, P and I (a–c) are meridional sections (yellow lines in the map) in the Atlantic (A), Pacific (P) and

Indian (I) basins, respectively. AA is the section surrounding the Antarctic continent (blue line in the map). White circles indicate the presence of more than 90% of CDW. Theblack solid lines indicate the presence of more than 50% of AAIW while the dotted black lines indicate the presence of more than 50% of AABW. Grey dotted line indicates thepresence of more than 50% SAMW. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

236 P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242

the presence of more than 50% AAIW (from the solid black line tothe surface). CANT concentrations achieve �7 lmol kg�1 (highervalues can be due to biases in the measured data) in bottom layersin the Weddell Sea (>65�S), where the presence of AABW is greaterthan 50% in each sample (between the dotted black line and thebottom, Fig. 4a). The minimum values of CANT (<5 lmol kg�1) areobserved in the northern part of the section in deep layers, wheremost of the volume (more than 90% contribution) is occupied byCDW (white circles). The concentration of CANT in CDW increasestowards the coast due to the progressive shoaling of the layer(Fig. 4a), achieving a mean value of 7 lmol kg�1 between 60 and70�S.

In Section 1 (Fig. 4b), high values of CANT (>20 lmol kg�1) areobtained in subsurface layers in the northern part of the section,mostly occupied by AAIW (from the solid black line to the surface)and also close to the Antarctic continent, where shelf waters arefound. CDW (white circles) contains older water than in other sec-tions, with CANT concentrations of <5 lmol kg�1 in the major partof its volume in the section. CDW achieves concentrations as highas �7 lmol kg�1 in some places (mainly close to the Antarcticshelf) but it is also free of CANT in other areas. In bottom layers,close to the Antarctic shelf (>65�S), the mean concentration of CANT

in AABW (from the dotted black line to the bottom) is also�7 lmol kg�1.

In Section P (Fig. 4c), shelf surface waters also show relativelyhigh values of CANT (�10 lmol kg�1) but not as high as waters offthe coast at subsurface and intermediate depths. The presence ofAAIW, clearly defined between the solid black line and the dashedgrey line by the eOMP analysis (Fig. 4c), presents values of CANT inthe range of 25–40 lmol kg�1. The water immediately above(>30 lmol kg�1), where SAMW (from the dashed grey line to thesurface) is located, contains values of CANT higher than30 lmol kg�1. The points with more than 90% of CDW are locatedin deep layers of the water column throughout the section (whitecircles in Fig. 4c) and with a mean value of �7 lmol kg�1 of CANT.

The values of CANT in AABW are very low between 63 and 55�Sand around �4 lmol kg�1 in bottom layers close to the Antarcticshelf.

In the section surrounding the Antarctic continent (Section AA,Fig. 4d), the highest values of CANT can be found in waters occupiedby AAIW (from the solid line to the surface), which present valuesof CANT between 20 lmol kg�1 and 40 lmol kg�1 and are located inthe Pacific and Atlantic Ocean sectors, between 150 and 0�E. CDWshows low values of CANT concentration (�5 lmol kg�1) in certainareas: to the north of the Ross Sea (150–180�E and 180–150�W),between 30 and 60�E and between 10 and 30�W approximately.The rest of the volume presents a mean value of �7 lmol kg�1. Itis also important to note that bottom layers (AABW) to the northof the Weddell Sea (60–30 �W) and between 60 and 150�E showaverage CANT values of �7 lmol kg�1. Only the bottom layers lo-cated to the north of the Ross Sea (150–180�E and 180–150�W)are depleted in CANT relative to the others, with an average concen-tration of 5 lmol kg�1.

3.2. CANT concentrations and inventories in the water masses

CANT concentrations were vertically averaged ([CANT]) for eachwater mass volume by using the volumetric census reported byPardo et al. (2012). The inventory of CANT in the SO was also per-formed, together with the contributions of each of the watermasses considered to the total inventory (Fig. 5; see alsoTable A1). The total inventory obtained in the SO was 29 ± 3 PgC.

The highest [CANT] is found in subsurface and upper intermedi-ate waters (STCW + SAMW) with a value of 37 ± 18 lmol kg�1

(Table A1). This layer constitutes 6 ± 0.4% of the SO volume andcontains 22% of the total SO inventory (Table A1). AAIW shows[CANT] of 19 ± 4 lmol kg�1, almost equal to those of shelf watersaltogether (HSSW + AASW, 18 ± 4 lmol kg�1), but the contribu-tions to the total SO inventory are quite different (Table A1). AAIWcontributes 20 ± 5% of the SO inventory while shelf waters are only

Fig. 5. Bar graphs representing: (a) the percentages respect to the total SOinventory of CANT and (b) the [CANT] for deep (CDW and NADW) and bottom (AABW)waters of the SO. Different colours indicate different methods of estimating CANT.The error bars are also shown in each of the plots. (For interpretation of thereferences to colour in this figure legend, the reader is referred to the web version ofthis article.)

P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242 237

3 ± 1% due to the large difference in their volumes (AAIW means10 ± 0.5% of the SO volume and shelf waters less than �2%) (Pardoet al., 2012).

Considering deep and bottom layers of the SO (Fig. 5), NADWcontributes significantly to the SO inventory of CANT with approxi-mately 3–4 PgC (�12% of the total SO inventory; Fig. 5b). The [CANT]in this layer is the highest of the deep and bottom layers(10 ± 1 lmol kg�1; Fig. 5a). On the other hand, the highest contri-bution to the SO inventory of CANT is that of CDW (Fig. 5b), the mostvoluminous water mass of the SO (51 ± 6% of the total SO volume).CDW traps 34 ± 4% (�10 PgC) of the SO inventory of CANT and pre-sents a [CANT] of 7 ± 0.5 lmol kg�1. AABW presents a [CANT] verysimilar to that of CDW (6 ± 1 lmol kg�1; Fig. 5a) even though itscontribution to the SO inventory is only 9 ± 1% (�3PgC; Fig. 5b).

4. Discussion

The meridional distributions of the estimated CANT in each sec-tion (Fig. 4) show special features that reveal the unique nature ofthe dynamics of the SO (Fig. 2). For instance, the presence of CDW,which is a combination of waters reaching the ACC and thereforean old water mass with a very low expected CANT inventory, is cru-cial in establishing the [CANT] found in bottom layers. As CDW is in-volved in the formation of bottom waters, it has long beenconsidered to be responsible for the near-zero concentrations inbottom layers (Gruber et al., 1996; Sabine et al., 1999; van Heuvenet al., 2011). Instead, we have found considerable amounts of CANT

in bottom layers of the SO (Figs. 4 and 5), with a mean value(6 ± 1 lmol kg�1) almost equal to that found in the CDW layer(7 ± 0.1 lmol kg�1).

Subsurface points near the Antarctic shelf where CDW upwells(between 60�S and 70�S approximately, Fig. 4a–c), present [CANT] -P 10 lmol kg�1. This could indicate that, even though upwelling

of CDW near the shelf dilutes [CANT] in surface waters, someenrichment could also be possible due to the continuous advectivecirculation beneath the formation of AABW (Orsi et al., 2001). Fur-thermore, CDW could also be enriched in CANT by intrusions fromintermediate and deep waters. In fact, along Section P (Fig. 4c),[CANT] in the layers occupied by CDW also display concentrationsof �7 lmol kg�1. Section P is located in the zone of formation ofSAMW and AAIW in the Pacific Ocean and the importance of bothwater masses in the water column is clearly seen in the plot(Fig. 4c). SAMW and AAIW seem to accompany the path of CDW to-wards the shelf, probably favouring enrichment of CANT in the CDWlayer. CDW is the most voluminous water mass of the whole ocean(12.6 ± 0.6 � 1016 m3, Pardo et al., 2012) and despite its lowanthropogenic carbon concentrations it represents 34% of the SOinventory (Fig. 5b).

The significant [CANT] found in bottom waters are even lowerthan those found by McNeil et al. (2001) and Lo Monaco et al.(2005a, 2005b). McNeil et al. (2001) estimated concentrations ofCANT using an MLR approach, obtaining values in AABW as greatas 13 ± 10 lmol kg�1, close to the value obtained by the TrOCAmethod, which also concurs with results from CFC data (Rintouland Bullister, 1999). McNeil et al. (2001) also found [CANT] in shelfwaters (18 ± 9 and 14 ± 9 lmol kg�1) and in upper-intermediatewaters (20 ± 9 lmol kg�1) very similar to those found here(Table A1). Alternately, Lo Monaco et al. (2005b) obtained high val-ues in bottom waters by considering an under-saturation of �12%in the oxygen. However, this under-saturation value could be toohigh leading to too-high CANT concentrations. Recent results fromLoose and Schlosser (2011) indicate that gas exchange under seaice cover conditions of the SO (>50�S) is greater than generally ex-pected, even in 100% sea ice cover, because of different turbulentprocesses in the water layer close to the ice. Additionally, Looseand Schlosser (2011) also concluded that the restriction of CO2 fluxin winter results in larger than expected CO2 flux in spring as thesea ice cover retreats, compensating to some extent the sea-icecover blockade.

The concentrations and inventories of CANT of the main watermasses computed using the C0

T method were compared with esti-mates from other methodologies (Fig. 5; see also the Appendix Asection). Except for the DC* method, all the methodologies esti-mate similar inventories in the region of the SO south of 45�S(Table A1). An overall mean value of 29 ± 4 PgC could be consid-ered, which is very similar to the results obtained by Waughet al. (2006). The DC* method leads to a value of 14 ± 2 PgC, quitelower than the other estimates.

Regarding the different water masses, the main differences be-tween results are located in deep and bottom layers of the watercolumn (Fig. 5a), therefore these are the focus of this discussion.In deep layers, the DC* method estimates [CANT] values of 2 ± 0.2and 3 ± 0.4 lmol kg�1 in the CDW and NADW layers, respectively,both of them much lower than the other values obtained with theother methodologies (Fig. 5a). Additionally, results from the GFmethod (5 ± 0.3 lmol kg�1 in the CDW layer and 7 ± 1 lmol kg�1

in the NADW layer) are slightly lower than those from the C0T ,

TTD and TrOCA methods (Fig. 5a). The differences between theestimates of the GF method and those of the TTD method are tobe expected since an assumption of constant disequilibrium madein the latter typically leads to an overestimate of [CANT] (Khatiwalaet al., 2012). In the AABW layer, the values of [CANT] depend muchon the methodology. The DC* and TrOCA methods show the lowerand upper limit, respectively, of the estimates of [CANT] in theAABW layer (1 ± 0.2 lmol kg�1 obtained by the DC* method and12 ± 1 lmol kg�1 obtained by the TrOCA method) (Fig. 5a). TheC0

T , DC* and GF methods estimate [CANT] in the AABW layer as lowerthan in deep waters, while TTD and TrOCA methods estimate [CANT]in bottom waters as very similar (TTD) or even higher (TrOCA) than

Fig. 6. Distributions in the Section AA. The different figures show the differences (in lmol kg�1) between the estimates of CANT using the different methodologies respect tothe estimates of CANT using the DC* method: (a) TrOCA – DC*, (b) C0

T – DC*, (c) TTD – DC* and (d) GF – DC*. White circles indicate the presence of more than 90% of CDW. Theblack solid lines indicate the presence of more than 50% of AAIW while the dotted black lines indicate the presence of more than 50% of AABW.

Fig. 7. Bar graph representing the percentage of CANT inventory respect to the totalof the Atlantic Ocean sector for the deep (CDW, NADW) and bottom (AABW) watersof the SO. Different colours indicate different methods of estimating CANT. The errorbars are also shown. (For interpretation of the references to colour in this figurelegend, the reader is referred to the web version of this article.)

238 P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242

those in deep waters (Fig. 5). These findings, together with resultsfrom previous comparisons (Hall et al., 2002; Sabine et al., 2002;Matsumoto and Gruber, 2005; Lo Monaco et al., 2005b; Vázquez-Rodríguez et al., 2009; van Heuven et al., 2011; Khatiwala et al.,2012) lead to the conclusion of a more than likely underestimationof [CANT] by the DC* method in deep and bottom waters of the SO.This is presumably due to problems in the DC* method associatedwith CFC estimating water mass ages older than �50 years, andwith not sufficiently accounting for mixing processes. Conversely,the TrOCA method appears to overestimate [CANT] in deep and bot-tom waters of the SO by �5 lmol kg�1 (Lo Monaco et al., 2005b;van Heuven et al., 2011) probably due to errors in the parameter-ization of TrOCA�. Considering this overestimation of�5 lmol kg�1

in the TrOCA estimates, the corresponding value of CANT in bottomwaters of the SO (Fig. 5a) once corrected would be �7 lmol kg�1,much more similar to those found by the C0

T method. Additionally,the TTD method is also considered to overestimate [CANT] in deepto bottom waters of the ocean to the order of �2 lmol kg�1 andeven more in the region of the SO (Wang et al., 2012). Once again,considering this overestimation, the [CANT] estimated by the TTDmethod in the AABW could be more similar to the [CANT] obtainedby the C0

T method.Considering the CANT inventories in deep and bottom layers

(Fig. 5b), the important contribution of CDW to the total SO inven-tory with respect to the other deep and bottom waters is clearlyshown in all the methodologies, except in the DC* method. Alsoquite noticeable is the relevance of the contribution of AABW inmost of the estimates (Fig. 5b). GF, C0

T , TTD and TrOCA methodsestimate that deep and bottom layers constitute altogether halfthe total inventory (or more) of the SO, whereas the DC* methodestimates that these layers contribute 29 ± 3% of the total SOinventory.

Fig. 6 shows the distributions of the differences CMETHODANT – DC*

around the Antarctic continent (section AA). Differences with re-spect to the DC* estimates in the CDW layer are around10 lmol kg�1 in most of the cases. The TTD method also shows dif-ferences around 10 lmol kg�1 in bottom waters (Fig. 6c). The dif-

ferences between the TrOCA and the DC* methods (Fig. 6a) are of10–20 lmol kg�1 in deep and bottom waters of a region close tothe Weddell Sea (the highest differences are found in bottomwaters). The relevant differences between the C0

T and DC* methodsin deep and bottom waters are mainly distributed through the Pa-cific and Atlantic Ocean sectors, from 180�W to 30�E. The differ-ences between the GF method (Fig. 6d) ant the DC* method aredistributed very similarly to C0

T – DC* (Fig. 6b), with positive differ-ences between 180�W and 30�E in deep and bottom waters andnegative differences in the region close to the Ross Sea. These lastnegative differences (Fig. 6d) have quite higher values than thosewhich can be seen in the C0

T – DC* distribution (Fig. 6b).Since the Atlantic Ocean contents the highest CANT storage of the

global ocean, estimates of the total inventory and the contributionof each layer to the Atlantic Ocean sector of the SO have been alsocomputed (Fig. 7; see also Table A2) using the different methodol-

Table A1CANT concentrations (in lmol kg�1) and inventories (in %) estimated by the C0

T , GF, TTD, DC* and TrOCA methods in the different layers of the water column occupied by the mainwater masses of the SO. The total SO inventory of CANT is also shown in PgC. SAMW corresponds to SAMW1 + SAMW2.

CANT (C0T ) CANT (GF) CANT (TTD) CANT (DC*) CANT (TrOCA)

% lmol kg�1 % lmol kg�1 % lmol kg�1 % lmol kg�1 % lmol kg�1

STCW + SAMW 22 ± 11 37 ± 18 24 ± 10 31 ± 12 18 ± 7 32 ± 13 37 ± 15 30 ± 11 21 ± 10 42 ± 18HSSW + AASW 3 ± 1 18 ± 4 4 ± 1 20 ± 4 3 ± 1 22 ± 5 5 ± 1 17 ± 4 3 ± 1 25 ± 4AAIW 20 ± 5 19 ± 4 23 ± 5 16 ± 3 18 ± 4 18 ± 3 29 ± 8 13 ± 3 18 ± 5 21 ± 4CDW 34 ± 4 7.0 ± 0.5 32 ± 3 4.9 ± 0.3 38 ± 4 8.5 ± 0.5 17 ± 3 1.8 ± 0.2 34 ± 4 8.3 ± 0.5NADW 12 ± 2 10 ± 1 11 ± 1 7 ± 1 11 ± 1 9 ± 1 9 ± 2 3.4 ± 0.4 10 ± 1 9 ± 1AABW 9 ± 1 6 ± 1 6 ± 1 3.3 ± 0.3 12 ± 2 9 ± 1 3 ± 1 1.0 ± 0.2 14 ± 1 12 ± 1SO (PgC) 29 ± 3 22 ± 2 31 ± 2 14 ± 2 34 ± 2

Table A2(a) CANT inventories (in %) in the different layers of the water column and total inventory (in PgC; last row) estimated by the C0

T , GF, TTD, DC* and TrOCA methods for the AtlanticOcean sector of the SO (67.7�W–30�E).

CANT (C0T ) CANT (GF) CANT (TTD) CANT (DC*) CANT (TrOCA)

% % % % %

STCW + SAMW 5.8 ± 0.2 6.3 ± 0.2 4.3 ± 0.1 12.3 ± 0.3 5.0 ± 0.2HSSW + AASW 4.50 ± 0.01 6.30 ± 0.01 5.30 ± 0.01 9.40 ± 0.01 4.70 ± 0.01AAIW 19.4 ± 0.4 22.0 ± 0.4 16.7 ± 0.4 39.0 ± 0.7 15.6 ± 0.4CDW 41 ± 2 42 ± 1 44 ± 2 28 ± 1 40 ± 2NADW 8.3 ± 0.2 7.4 ± 0.1 7.8 ± 0.1 7.5 ± 0.2 5.7 ± 0.1AABW 21.0 ± 0.3 16.0 ± 0.3 21.9 ± 0.5 3.8 ± 0.3 29.0 ± 0.3Sector Atlántico (PgC) 6 ± 1 6 ± 1 8 ± 2 3 ± 1 9 ± 1

P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242 239

ogies. Results from the GF, C0T , and TTD methods (Fig. 7) are rather

more similar than is the case for the estimates of the whole SO(Fig. 5 b). The TTD method estimates a slightly higher inventoryof CANT (8 ± 2 PgC; Table A2) in the Atlantic Ocean sector thanthe GF and C0

T methods (6 ± 1 PgC), mainly because of the differ-ences between the relative contributions of CDW to the AtlanticOcean sector inventory of CANT (41% and 43% in the GF and C0

T

methods, respectively and 45% in the TTD method, Fig. 7). The TrO-CA method estimates the highest inventory of CANT in the AtlanticOcean sector (Fig. 7), which is mainly due to the high contributionof the AABW layer (30%). The DC* method estimates an inventoryof 3 PgC, in accordance with results obtained by Lee et al. (2003)(�4 PgC south of 45�S), but only half the inventory obtained bythe GF and C0

T methods (Table A2).Additionally, the value of the rate of increase of [CANT] in bottom

waters can be approximated. Considering the rate of increase of[CANT] in surface waters of 1.69% y�1 obtained by Steindfeldtet al. (2009) assuming transient steady state (TSS, Tanhua et al.,2007), the rate of increase of [CANT] in the AABW in the AtlanticOcean sector is of 0.11 lmol kg�1 y�1. This result concurs withthe value obtained by Ríos et al. (2012) and by van Heuven et al.(2011) in the western South Atlantic region. When the computa-tion was made for the AABW layer of the whole SO, the same valuewas obtained.

5. Conclusions

A back-calculation method, C0T , has been used to estimate the

mean concentrations and inventories of CANT in the SO in differentlayers of the water column according to the location of the mainwater masses involved in the SO circulation. The main biases ofthe back-calculation methods were reduced in three main aspects:(a) a better approximation of the water mass mixing by an eOMPanalysis; (b) a more accurate estimation of A0

T and DCdis; and (c)the computation of the temporal variability of the air-sea CO2 dis-equilibrium (dCdis).

A non-negligible amount of CANT of the order of 3 PgC (�9% ofthe SO inventory) is found in the AABW layer demonstrating theimportance of considering this layer in the global carbon cycle of

the oceans. The importance of this layer in the Atlantic Ocean sec-tor (67.5�W – 30�E) is also noticeable, constituting 22% of the totalinventory of the sector (�6.5 PgC). On the other hand, concentra-tions of CANT estimated in the CDW layer are similar to those foundin the AABW layer (�7 lmol kg�1). This is also an important resultto consider because of the large volume of the CDW with respect tothe other water masses of the global ocean. In fact, it constitutes34% of the SO inventory of CANT, higher than the contribution ofintermediate and subsurface waters. Our results demonstrate thatvertical water transport in the Antarctic Shelf is efficient enough totrap considerable quantities of CANT in bottom layers. Additionally,the global connection between the SO and the Arctic Region in theNorth Atlantic (Toggweiler and Samuels, 1998) together with therelevant atmosphere–ocean interaction processes affecting the SO(Anderson et al., 2009) makes it important to differentiate betweenthe CANT estimates in the different water masses, in order to betterunderstand the global carbon cycle.

Acknowledgements

We thank the scientists and also the crew for participating inthe different cruises and collecting data for the CARINA and GLO-DAP databases. The research leading to these results was sup-ported through EU FP7 project CARBOCHANGE ‘‘Changes incarbon uptake and emissions by oceans in a changing climate’’which received funding form the European Commission’s SeventhFramework under grant agreement no. 264879 and by the SpanishMinistry of Sciences and Innovation, and was co-founded by theFondo Europeo de Desarrollo Regional 2007–2012 (FEDER) throughthe CATARINA Project (CTM2010-17141/MAR). SK was funded byU.S. NSF grant OCE 10-60804. The corresponding author is con-tracted by the CARBOCHANGE Project.

Appendix A

Uncertainties. of the estimates

Errors associated with the estimations primarily come fromconsidering the value of the AOU parameter and not that of the real

240 P.C. Pardo et al. / Progress in Oceanography 120 (2014) 230–242

under-saturation of oxygen (Sabine and Feely, 2001). Nevertheless,these errors can be considered small. Loose and Schlosser (2011)observed that the reduction in the air-ocean interchange was notas much as was speculated and that reinforcement of the flux insummer with the melting of sea-ice even existed. Again, errorsassociated with variations of stoichiometric ratios should also bekept in mind (Wanninkhof et al., 1999), even though these can alsobe considered small compared with those of the methodology(±6 lmol kg�1). In order to better considerer all the possible biases,the relative errors of the terms involved in the estimation of CANT

(Section 2.3, Eqs. (1)–(5)) are considered in each data point:

frCANTg2 ¼ frCmeasT g2 þ frC0

Tg2þ fAOU � rrCg2

þ fðrC þ 0:5rN þ 0:5rPÞ � rO02g

2

þ fð�rC � 0:5rN � 0:5rPÞ � rOmeas2 g2 þ f0:5 � rAmeas

T g2

þ fð@C0T=@Ameas

T þ 0:5Þ � rA0Tg

2� frDCp

disg2

where most of the values are equal to those applied by Lee et al.(2003) except for those of rN, rC, rP and the relative errors corre-sponding to A0

T (rA0T ) and DCp

dis (rDCpdis), which are obtained for each

point at the same time as the proper values of the variables by usingthe eOMP analysis (Table 1). Additionally, another factor was takeninto consideration; the upper limit for AOU values is 80 lmol kg�1

(instead of 50 lmol kg�1 considered by Lee et al., 2003) becausethe errors of CANT estimates due to uncertainties of rC are significantin waters with AOU higher than this upper-limit value (Gruberet al., 1996). After applying this procedure to each point of the data-set a mean error of ±6 lmol kg�1 was obtained for the whole data-base in the estimates of CANT by the C0

T method.Even though, in general, model results show relatively high dif-

ferences at regional scales, their results are generally sufficientlytested and the values of air-sea carbon fluxes are quite well-approximated (Orr et al., 2001; Khatiwala et al., 2009). Thus, spec-ulating the value of dCdis by considering model results is a quitereliable technique. The value of dCdis was obtained for each oneof the water masses as the mean value obtained in each zone offormation. The uncertainties associated with dCdis were obtainedas the standard deviation of the values considered in each one ofthese zones (Table 1).

Errors associated with the eOMP analysis are discussed in Pardoet al. (2012) and they mostly depend on the volume of each one ofthe water masses included in the analysis (Steindfeldt et al., 2009;Pardo et al., 2012). Furthermore, it must be noted that this studywas completed with the unrealistic consideration of a constant glo-bal circulation as many other previous studies have done (Sarmi-ento et al., 1995; Holfort et al., 1998). Nevertheless, severalresults indicate that important changes in the circulation patternsare occurring in the SO due to global warming and also as a resultof the variability of the forcing mechanisms (Manabe and Stouffer,1993; Toggweiler and Samuels, 1998). This could lead to changesin the volume of intermediate, deep and bottom waters formedand also in their respective defining properties (Fahrbach et al.,1995; Jacobs and Giulivi, 1998). Nevertheless, results from McNeilet al. (2001) indicate that at least for intermediate waters, climate-change related changes to circulation and ventilation of the watermasses would produce insignificant errors in CANT accumulationestimates.

Uncertainties. in [CANT] and CANT inventories

As for the errors in the vertical integrations (concentrations andinventories), a mean error was assigned to each data point beforethe WMP interpolation. The mean error assigned to the C0

T , TTD,GF and TrOCA methods was ±6 lmol kg�1 (Waugh et al., 2006;

Touratier et al., 2007; Khatiwala et al., 2009) and ±9 lmol kg�1 inthe case of the DC* method (Gruber, 1998). Considering theseuncertainties, the values of CANT in the dataset and for each oneof the methodologies were perturbed by considering a normal dis-tribution function with the mean value corresponding to the CANT

estimated at each point and the standard deviation of the distribu-tion equal to the mean uncertainty of the method. 100 perturba-tions runs were performed and the mean values together withthe corresponding standard deviations are shown in Table A1 forthe whole SO region and in Table A2 for the Atlantic Ocean sector.

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