Ocean carbon cycling in the Indian Ocean:

1. Spatiotemporal variability of inorganic carbon and

air-sea CO2 gas exchange

Nicholas R. Bates,1 A. Christine Pequignet,1 and Christopher L. Sabine2

Received 18 February 2005; revised 27 October 2005; accepted 17 February 2006; published 9 September 2006.

[1] The spatiotemporal variability of upper ocean inorganic carbon parameters and air-sea CO2 exchange in the Indian Ocean was examined using inorganic carbon datacollected as part of the World Ocean Circulation Experiment (WOCE) cruises in 1995.Multiple linear regression methods were used to interpolate and extrapolate thetemporally and geographically limited inorganic carbon data set to the entire IndianOcean basin using other climatological hydrographic and biogeochemical data. Thespatiotemporal distributions of total carbon dioxide (TCO2), alkalinity, and seawaterpCO2 were evaluated for the Indian Ocean and regions of interest including the ArabianSea, Bay of Bengal, and 10N35S zones. The Indian Ocean was a net source of CO2 tothe atmosphere, and a net sea-to-air CO2 flux of +237 132 Tg C yr

1 (+0.24 Pg C yr1)was estimated. Regionally, the Arabian Sea, Bay of Bengal, and 10N10S zoneswere perennial sources of CO2 to the atmosphere. In the 10S35S zone, the CO2 sinkor source status of the surface ocean shifts seasonally, although the region is a net oceanicsink of atmospheric CO2.

Citation: Bates, N. R., A. C. Pequignet, and C. L. Sabine (2006), Ocean carbon cycling in the Indian Ocean: 1. Spatiotemporal

variability of inorganic carbon and air-sea CO2 gas exchange, Global Biogeochem. Cycles, 20, GB3020,

doi:10.1029/2005GB002491.

1. Introduction

[2] Determining the past, present and future fate ofanthropogenic carbon dioxide (CO2) requires detailedknowledge of the exchange between and transformationsof carbon within the mobile reservoirs of the atmosphere,terrestrial biosphere and ocean. This entails improvementsin knowledge about ocean carbon sources and sinks, theirgeographic and temporal variability, and their modulationby biological and physical processes.[3] The Indian Ocean, influenced by seasonal monsoonal

forcing, is an important component of the global oceansystem and a modulator of heat and salinity transport, andthe biogeochemical cycling of carbon and nutrient elements(e.g., nitrogen, phosphorus, silicon). Biological productivityin the Indian Ocean, for example, accounts for 1520% ofglobal ocean productivity [e.g., Chavez and Barber, 1987;Behrenfield and Falkowski, 1997], with large variationobserved in the timing and spatial distribution of new andexport production.[4] The Indian Ocean has been the focus of previous

studies that evaluated inorganic carbon cycling and rates ofair-sea CO2 gas exchange. The southwestern Indian Ocean,

south of 35S, has been the focus of several survey andtime series (e.g., Kerguelen time series) investigations [e.g.,Metzl et al., 1991; Poisson et al., 1993; Metzl et al., 1995,1998]. Similarly, the Arabian Sea and Bay of Bengal hasbeen investigated during the U.S. Joint Global Ocean FluxStudy (JGOFS) process cruises of 1995 [Millero et al.,1998a; Goyet et al., 1998] and Indian JGOFS programs, inparticular [e.g., George et al., 1994; Kumar et al., 1996;Sarma et al., 1998, 2003; Sarma, 2003, 2004]. For theentire Indian Ocean basin, synthesis of surface partialpressure of CO2 (pCO2) data collected primarily on WorldOcean Circulation Experiment (WOCE) cruises, and theNational Oceanographic and Atmospheric Administration(NOAA) Ocean Atmosphere Carbon Exchange Study(OACES) Indian Ocean cruises have led to improvedknowledge of the variability of pCO2 and air-sea CO2fluxes [Louanchi et al., 1996; Sabine et al., 2000], whileothers have focused on the distribution of anthropogenicCO2 in the ocean interior [Sabine et al., 1999; Sabine andFeely, 2001; Coatanoan et al., 2001]. Analysis of thevariability of ocean sinks and sources of CO2 in the IndianOcean, however, has been somewhat limited by coarsespatial resolution employed in previous studies (2 2[Louanchi et al., 1996; Sabine et al., 2000], 4 5[Takahashi et al., 1993, 2002]).[5] The present work examines the seasonal and spatial

variability of upper ocean inorganic carbon and the ex-change of CO2 between ocean and atmosphere in the IndianOcean. As pointed out by a number of authors [e.g.,

GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 20, GB3020, doi:10.1029/2005GB002491, 2006ClickHere

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1Bermuda Biological Station For Research, Inc., Ferry Reach, Bermuda.2Pacific Marine Environmental Laboratory, NOAA, Seattle, Washing-

ton, USA.

Copyright 2006 by the American Geophysical Union.0886-6236/06/2005GB002491$12.00

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Takahashi et al., 2002; Sabine et al., 2000], previous studiesof the region have been somewhat limited by spatially andtemporally insufficient data coverage. Here multiple linearregression (MLR) approaches were used to interpolate andextrapolate inorganic carbon data, collected as part of theWOCE CO2 survey in 1995, to the entire Indian Oceanbasin. Similar MLR approaches have been widely used inrecent studies of inorganic carbon cycling [e.g., Goyet andDavis, 1997; Goyet et al., 2000; Lee, 2001; Lee et al.,2002], inorganic carbon data quality issues [e.g., Lamb etal., 2002]; calcium carbonate dissolution [Chung et al.,2003; Feely et al., 2002], anthropogenic CO2 inventories[Sabine et al., 1999; 2002a, 2002b; Sabine and Feely,2001], and global pCO2 climatotologies [Takahashi et al.,2002]. Here total carbon dioxide (TCO2), alkalinity (TA)and seawater pCO2 data set were extrapolated to the basinscale (Indian Ocean; 35S!15N) using a spatial resolu-tion of 1 1, vertical differentiation into 14 layers in theupper 500 m, and temporal resolution of 1 month in order toevaluate the spatiotemporal distribution of hydrographicand seawater CO2 properties. The seasonal and annual ratesof air-sea CO2 exchange were then quantified to determinethe regional and basin sources and sinks of CO2. In acompanion paper [Bates et al., 2006], the analyses pre-sented herein are used to quantify the annual and spatial-temporal pattern of new production (or net communityproduction, NCP) and export production, and examine thenet metabolism (heterotrophy versus autotrophy) for theIndian Ocean.

2. Indian Ocean Overview

[6] The Indian Ocean is dominated by monsoon forcingand intermonsoon transitions. During the Northeast Mon-soon (NEM; DecemberFebruary), the Northeast Mon-soon Current (NMC) brings waters from east to west acrossthe north Indian Ocean and into the Arabian Sea (Figure 1)[e.g., Wyrtki, 1973; Weller et al., 1998; Schott andMcCreary, 2001]. At the equator, the South EquatorialCounter Current (SECC) returns water to the east, whilesouth of the equator (5S20S), the South EquatorialCurrent (SEC) transports water from east to west. Thecoolest surface temperatures (24 1C) are typicallyobserved in this period, particularly in the Arabian Sea[e.g., Weller et al., 1998; Morrison et al., 1998]. Duringthe Spring Intermonsoon (SIM; MarchMay) transition,the westward flow of NMC declines and stops prior to theSouthwest Monsoon (SWM; JuneSeptember). A broadswath of warm surface temperatures (>28C) occupies theIndian Ocean north of 10S (Figure 1), with temperaturesreaching their seasonal maxima in the Arabian Sea (>28C[see Weller et al., 1998; Morrison et al., 1998]) (see alsoauxiliary materials, Figures S2 and S31). In the southernIndian Ocean south of 30S, the surface layer cools duringthe austral fall period. During the SWM period, there isintense coastal upwelling off the Arabian Peninsula with theSomali Jet (SJ) [Schott and McCreary, 2001]. At the

equator, the Somali Current (SC) forms part of a stronganticyclonic gyre, while the South West Monsoon Current(SWMC) transports waters to the east, north of the equator(Figure 1). Notable hydrographic features include the pres-ence of cooler surface temperatures (

[8] In addition, data from the US JGOFS Arabian SeaExpedition [e.g., Smith et al., 1998; Millero et al., 1998a],and NOAA-OACES CO2 repeat hydrographic transectsurvey (I8N) in the Indian Ocean in 1995, were notincorporated into the primary WOCE CO2 data set forinterpolation and extrapolation purposes, but rather usedas independent validation of the modeled DIC and TA data.Climatological hydrographic and biogeochemical data (i.e.,T, S, NO3, PO4), with a spatial resolution of 1 1,vertical resolution of 14 layers in the upper 500 m, andtemporal resolution of 1 month, were also used in thisanalysis (please see auxiliary material).

3.2. Interpolation and Extrapolation ofInorganic Carbon Data

[9] Determining the time and space scale of variability ofthe carbon cycle requires interpolation of discrete watercolumn data to a basin scale. As stated earlier, inorganiccarbon parameters (i.e., TCO2 and TA) were sampled onnine cruises across the Indian Ocean. Although the surveycruises were broadly distributed, large swaths of the regionwere not sampled in time or space (Figure 1). However, inthe Indian Ocean there is an abundance of other climato-logical and hydrographic data (e.g., temperature, salinity,inorganic nutrients). Thus TCO2 and TA can be interpolatedand extrapolated as a function of other water mass proper-ties, such as potential temperature and salinity.[10] In previous studies, interpolation of TCO2 and TA

distributions using the MLR approach has an uncertainty of515 mmoles kg1 when applied to data below the mixedlayer [e.g., Goyet and Davis, 1997; Sabine et al., 1999;Goyet et al., 2000; Sabine and Feely, 2001; Coatanoan etal., 2001]. In the mixed layer, the interpolation of TCO2 inparticular, has a greater uncertainty due to seasonal vari-ability. However, Lee et al. [2000, 2002], applied MLRanalyses (albeit using a much more limited data set thanused here) to interpolate DIC from temperature, salinity andnitrate data, and extrapolated to produce 4 4 maps ofglobal TCO2 and TA (including the Indian Ocean).[11] Here different interpolation schemeswere investigated

using data available for all cruises, including: T, potentialtemperature (q), S, DO, density, apparent oxygen utilization

(AOU), nitrate (NO3), phosphate (PO4), sample depth, lati-tude and longitude. AOU was computed from bottle DO, T,and S data sets. Various combinations of parameters wereexamined in order to improve the quality of the fit andreduce the residual errors between the measured and syn-thetic data. Initially, carbon parameters were fit simply as afunction of q, salinity and AOU, similar to the approach ofGoyet et al. [1999, 2000].

TCO2 a1 a2q a3AOU a4S; 1

where a1 to a4 are constants. However, the uncertaintiesof the interpolation for TCO2 and TA were relatively high(>10 mmoles kg1) for the upper ocean. Various combina-tions of data were evaluated and the quality of the MLR fitwas determined by the RMS error, comparison of cruise andsynthetic data and examination of the spatial pattern of theresiduals. The optimal interpolation (with the lowestassociated uncertainty) for TCO2 and TA in the upperocean (0150 m) was a function of several properties,

TCO2 a1 a2T a3AOU a4S a5NO3 a6PO4 a7latitude a8sample depth 2

TCO2 b1 b2T b3AOU b4S b5NO3 b6PO4 b7latitude b8sample depth; 3

where a and b are constants (Table 1). The optimalinterpolation for TCO2 and TA in the water column (150m-bottom) is reported in auxiliary materials (auxiliary Table 1in Text S1). The best fits for the upper ocean were generatedwith the following considerations in mind.[12] First, the MLR approach was initially applied to

Indian Ocean TCO2 and TA data from the entire watercolumn. However, the best fits for the upper ocean occurredif the MLR approach was used for data from the 0- to 150-mlayer only rather than the entire water column. Thus MLRequations are reported for 0- to 150-m layer and deeper>150 m layer (see auxiliary Table 1 in auxiliary Text S1).The error analysis outlined in section 2.3 is focused on the0- to 150-m layer data only.

Table 1. Multiple Linear Regression Equations for the Best Fit Between Observed Inorganic Carbon Data and Modeled Inorganic

Carbon Data Computed From Other Hydrographic Parameters for the Upper 0150 ma

Season Mean N Optimal Multiparameter Fit

Total Carbon Dioxide (TCO2 0150 m)DecFeb 5.13 700 722.27 + (6.473.T) + (40.348.S) + (1.513.NO3) + (0.064.dep) + (0.009.lat) + (0.464.AOU) + (59.299.PO4)MarchMay 5.66 311 743.93 + (7.587.T) + (40.506.S) + (0.743.NO3) + (0.107.dep) + (0.008.lat) + (0.374.AOU) + (57.45.PO4)JuneAug 5.99 703 661.38 + (6.058.T) + (41.361.S) + (1.393.NO3) + (0.014.dep) + (0.044.lat) + (0.073.AOU) + (68.737.PO4)SepNov 4.40 681 779.56 + (5.331.T) + (37.712.S) + (0.262.NO3) + (0.028.dep) + (0.034.lat) + (0.159.AOU) + (79.882.PO4)

Alkalinity (TA) 0150 mDecFeb 6.43 659 735.73 + (2.308.T) + (46.136.S) + (0.568.NO3) + (0.023.dep) + (0.032.lat) + (0.022.AOU) + (4.455.PO4)MarchMay 4.99 299 416.23 + (1.552.T) + (54.673.S) + (1.179.NO3) + (0.013.dep) + (0.004.lat) + (0.059.AOU) + (12.954.PO4)JuneAug 4.48 733 338.55 + (1.481.T) + (57.046.S) + (0.550.NO3) + (0.008.dep) + (0.114.lat) + (0.051.AOU) + (8.116.PO4)SepNov 4.78 672 508.56 + (1.239.T) + (51.923.S) + (1.041.NO3) + (0.007.dep) + (0.081.lat) + (0.117.AOU) + (3.152.PO4)

aThe best multiparameter fits for TCO2 and alkalinity were generated as a function of temperature (T), salinity (S), apparent oxygen utilization (AOU),nitrate (NO3), phosphate (PO4), depth, latitude and season. RMS values are mmoles kg

1. N equals number of data. For example, a salinity error of 0.1would increase (decrease) TCO2 and TA by 5.5 and 6.5 mmoles kg1, respectively. This would either increase (decrease) seawater pCO2 by 1 matm.

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[13] Second, TCO2 and TA data from regions where thebottom depth was less than 200 m were excluded from theMLR analysis. This step effectively excludes the continentalshelf of the Indian Ocean from the analysis. Recent reviewsindicate that there is much greater spatiotemporal variabilityof the CO2 in the coastal ocean compared to the open ocean[Borges, 2005; Borges et al., 2005; Ducklow and McAllister,2005]. Given the paucity of CO2 data collected in thecoastal ocean of the Indian Ocean during the WOCEprogram, there were insufficient data to characterize thespatiotemporal variability of inorganic carbon and air-seaCO2 exchange within the complex coastal zone of theIndian Ocean.[14] Third, this study was limited to the Indian Ocean

region north of 35S. Within the region of 35S42S,WOCE CO2 survey data were insufficient to adequatelycharacterize the seasonal and temporal variability of inor-ganic carbon in the region of the Subtropical Front (STF).[15] Fourth, the smallest interpolation errors were pro-

duced if the data were grouped into respective monsoon andintermonsoon seasons relevant to the Indian Ocean, i.e.,December to February (NEM); March to May (SIM); Juneto August (SWM); and September to November (FIM).Greater differences between the observed and MLR mod-eled data occurred if the MLR approach was applied to thefull year of TCO2 and TA data. Similarly, greater errorsoccurred when the MLR approach was applied to TCO2 andTA data grouped into each month. This reflected a paucityof data in different months in different regions of the IndianOcean.[16] Fifth, AOU values rather than DO, improved the

MLR fit, while inclusion of PO4 and depth improved the fitthe least. Dissolved oxygen concentrations are influencedby physics, biology and air-sea O2 exchange. As AOU is apartial function of the dominant physical forcing (i.e.,temperature and salinity), and since AOU (rather thanDO) exhibits a stronger covariance with TCO2 in responseto a variety of biological processes, the inclusion of AOUsignificantly improved the MLR fit. Both nitrate and phos-phate were included in the MLR approach, reflecting thevariability of N:P elemental stoichiometry in the upperocean in response to processes such as nitrogen fixationand denitrification in parts of the Indian Ocean. Thiscontrasts to the deeper water column, where the elementalstoichiometric ratios of N and P have a very restricted range[e.g. Redfield et al., 1963; Anderson and Sarmiento, 1994].[17] Last, the inclusion of latitude in the interpolation

improved the fit, but longitude did not (reflecting thestronger meridional gradients compared to zonal gradients).In situ temperature (T) was used rather than potentialtemperature (q) since there was negligible differencebetween T and q in the 0- to 150-m layer. The sampledepth of the observed TCO2 and TA data was alsoincluded in the MLR approach, since this helped toimprove the MLR fits. The inclusion of sample depthprobably reflects the contribution of sinking and dissolvedorganic matter remineralized to DIC vertically through thewater column.[18] The optimal interpolation of surface layer (0150 m)

TCO2 had an uncertainty of 5.06.5 mmoles kg1 for the

four monsoonal/intermonsoonal seasons (Table 1; see aux-iliary material Figure S1). These error estimates were smallcompared to the large range of TCO2 (300 mmoles kg1)values observed in the upper 150 m during the surveycruises. It should also be noted that owing to the relativelyshort period over which TCO2 samples were collected in theIndian Ocean, no correction was applied to account forsecular increase in seawater TCO2 due to uptake of anthro-pogenic CO2 from the atmosphere. The optimal interpola-tion of surface layer (0150 m) TA had an uncertainty of4.05.5 mmoles kg1 for the four seasons (Table 1, see aux-iliary material Figure S2). Again, this error was small com-pared to the range of TA (150 mmoles kg1) in the upper150 m observed during the survey cruises. In the deeperdepths (i.e., >150 m), TCO2 had an uncertainty of 6.68.6 mmoles kg1 for the four monsoonal/intermonsoonalseasons (Table 1). Additionally, in the deeper depths (i.e.,>150 m), TA had an uncertainty of 3.76.1 mmoles kg1for the four monsoonal/intermonsoonal seasons (Table 1).

3.3. Extrapolation to the Indian Ocean

[19] In the extrapolation step, optimal MLR equations(Table 1) were applied to gridded, 1 1 climatologicaldata of hydrographic parameters each month (i.e., T, S,AOU, NO3, PO4) to produce basin-wide maps of carbonparameters (e.g., DIC and TA). In addition, basin-widedistributions of seawater partial pressure of CO2 (pCO2)were calculated each month from the 1 1 monthly gridsof DIC, TA, T and S (see auxiliary materials Figures S2S4and S10S14). Seawater pCO2 was calculated from DICand TA using the program of Lewis and Wallace [1998] (seeauxiliary materials for more information).

3.4. Sensitivity Tests, Errors, and Caveats

[20] The interpolation of DIC and TA in the upper 0150 m from hydrographic and biogeochemical properties(equations (2) and (3)) had a relatively small error (46 mmoles kg1; Table 1). However, there may be additionalsystematic uncertainties associated with extrapolating dataoutside of the defined interpolation range, particularly in theregions of the Indian Ocean where there is limited data intime and space. This problem is inherent to other studiesusing the MLR approach including: carbon cycle and datastudies [e.g., Goyet and Davis, 1997; Sabine et al., 1999;Goyet et al., 2000; Lee, 2001; Sabine and Feely, 2001; Leeet al., 2002; Takahashi et al., 2002; Chung et al., 2003;Feely et al., 2002]. In order to get some quantitative senseof the potential errors associated with extrapolation, twoerror analyses were attempted.[21] First, modeled DIC and TA (generated from the

primary CO2 data sources) were compared with observa-tional data collected on the I8N NOAA OACES repeatsection and during the JGOFS program in the Arabian Sea.As stated earlier, these secondary data sets were not includedwith the WOCE CO2 data in the MLR interpolation andthus represent independent data sets with which to compare.The MLR regressions (for the 0- to 150-m layer) were usedto determine model DIC and TA data for each 1 1 gridin the Indian Ocean. The DIC and TA data (determinedfrom climatological hydrographic data and at depths equiv-

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alent to the observed data) were compared with observedDIC and TA data from each CTD sample depth collected onthe I8N and Arabian Sea cruises. For the I8N NOAAOACES repeat section, the mean difference between modeland observed TCO2 and TA data was small (11.3 and4.1 mmoles kg1, respectively; see auxiliary materialsTable 2 in Text S1 and Figure S3). This repeat section wasconducted in a different season than the primary WOCE CO2survey cruises that were used in the multiparameter analysis.In the Arabian Sea, modeled DIC and TA data werecompared to observed data collected from four differentArabian Sea cruises, along the separate north and southrepeat transects (see Smith et al. [1998] for CTD stationlocations). Along the south transect, for example, the meandifference between calculated and observed DIC and TAwas810 mmoles kg1 (see auxiliary materials Table 2 inText S1 and Figure S5). For TA, there was a large difference(19 mmoles kg1) between model and observed data for theTN43 cruise. This indicates that the MLR approach may nothave captured all the processes that influence alkalinityduring the NEMonsoon period (e.g., upwelling of alkalinity,calcification due to pelagic calcifying plankton). In summary,these comparisons suggest that the extrapolation errors(9.011.3 mmoles kg1 for DIC; auxiliary materialsTable 2 in Text S1) were typically larger than the interpo-lation errors (4.46.0 mmoles kg1; Table 1). However, thedifferences between model and observed data may partlyrelate to differences imparted by the hydrographic data setsused (i.e., climatological hydrography versus observedWOCE CTD hydrography), and scale incompatibility ofthe comparison (1 1 model data compared to observeddata from 1 or more CTD station within a 1 1 grid).[22] Second, a comparison of observed and modeled

seawater pCO2 was attempted for the entire Indian Ocean.To facilitate comparison, model seawater pCO2 data werecalculated from model TCO2 and TA data for each 1 1grid every month, and compared to seawater pCO2 datacalculated from observed TCO2 and TA data at colocatedand contemporaneous WOCE CTD stations in the IndianOcean. This approach tests whether there are systematicerrors in the TCO2 or TA data that present themselves in themodel pCO2 data. For the 0- to 150-m layer, the meandifference between model and observed seawater pCO2 wassmall (1 matm; see auxiliary material Table 3 in Text S1and Figure S6), although the standard deviation was quitevariable (1221 matm). This comparison indicates that themodel pCO2 data simulate the observed spatiotemporalvariability pCO2 quite well [Sabine et al., 2000]. If thereare underlying systematic differences between model andobserved TCO2 and TA data, these differences do notmanifest themselves in the model pCO2 data.[23] Unfortunately, there are no other CO2 data sets that

can be entrained to test whether there are systematic differ-ences between model and observed CO2 data outside thetime and space frame used to generate the MLR analysis.Systematic errors in the model TCO2 and TA data could notbe clearly identified with the available observational datasets. The error analysis conducted here indicates that theerrors associated with interpolation and extrapolation are10 mmoles for model TCO2 and TA data.

3.5. Quantifying the Rates of Air-Sea CO2Gas Exchange (Cgasex)

[24] Basin-wide estimates for the rate of air-sea flux ofCO2 (C

gasex) were estimated from seawater pCO2 andatmospheric pCO2, and wind data (see auxiliary materialfor more information). The flux of CO2 (F) across the air-sea interface is typically determined from the bulk formula,

Ft k s DpCO2 t 4

where Ft is the CO2 flux averaged over a certain time period(t), k is the gas transfer velocity, s is the solubility of CO2 inseawater, and DpCO2 is the difference between seawaterand atmospheric pCO2, respectively. The DpCO2, or air-seaCO2 disequilibrium, sets the direction of CO2 gas exchangewhile k and the magnitude of DpCO2 determines the rate ofair-sea CO2 transfer.[25] Wind speed is currently the most robust parameter

available to determine air-sea CO2 exchange and several gastransfer velocity-wind speed relationships are frequently used[Liss and Merlivat, 1986;Wanninkhof, 1992;Wanninkhof andMcGillis, 1999]. Here a quadratic dependency between windspeed and k is used [Wanninkhof, 1992],

k 0:39 U210 Sc=660 0:5; 5

where U10 is wind speed corrected to 10 m, and Sc is theSchmidt number for CO2.[26] The flux of CO2 due to gas exchange was computed

using equation (5) and monthly wind speed data, facilitatingcomparison with the air-sea CO2 fluxes determined byLouanchi et al. [1996], Louanchi and Najjar [2000] andSabine et al. [2000]. The F (equation 4) or Cgasex term wasquantified every month for each 1 by 1 box in the IndianOcean (see auxiliary material for more information). Theerrors for both air-sea CO2 flux terms were determinedassuming a systematic 5 matm uncertainty for the seawaterpCO2 values.

4. Results and Discussion

4.1. Spatiotemporal Distributions of CO2 in the UpperOcean of the Indian Ocean

[27] The spatiotemporal distributions of TCO2, TA, andseawater pCO2 were determined monthly but presented here(Figures 24) as averages for the four different monsoon/intermonsoon seasons. The Indian Ocean, particularly thenorthern sector and Arabian Sea, is influenced by theseasonal transition between the NE and SW monsoon. Thisphysical forcing provides the context for interpreting thespatiotemporal variability of inorganic and organic carbonin the Indian Ocean.[28] The spatiotemporal distribution of TA was similar to

salinity. This finding is not unexpected since TA is typically aconservative function of salinity, particularly in the subtrop-ical gyres [e.g.,Millero et al., 1998b]. The range of alkalinityacross the Indian Ocean was >200 mmoles kg1, with thehighest salinity and TA found in the Arabian Sea (>36salinity; >2300 mmoles kg1 TA) and in the region betweenthe SEC and STF (>35 salinity; >2300 mmoles kg1 TA)

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(Figure 2).The lowest salinity andTAvalueswere found in theequatorial region (3435 salinity;

During the SIM (AprilJune), pCO2 values greater than400 matm are localized to the Arabian Sea, and pCO2 valuesare close to or below equilibrium values across much of theIndian Ocean. South of 15S25S, surface pCO2 valueswere close to equilibrium values or below during allseasons. The highest seasonal variability of surface pCO2was observed south of 30S (>100 matm), within the Bay ofBengal (>100 matm) and localized within the Arabian Sea,close to the Oman coast (Figure 4d).[31] Although considerable complexity is evident in the

spatiotemporal variability of TCO2, alkalinity and pCO2,simple generalizations can be made (with exceptions in theArabian Sea and Bay of Bengal). In the 10S35S region,the annual amplitude of surface temperature and seawaterpCO2 was 6 2C and 65 15 matm, respectively(Figure 5). The thermodynamic effect of temperature onpCO2 is 4.21% pCO2 change C1 (1517 matm C1).Similar to the subtropical gyres of the North Pacific andNorth Atlantic Oceans [Bates et al., 1996], the seasonalinfluence of temperature on seawater pCO2 (i.e., 6 16 =90 matm) was offset by seasonal influence on pCO2 by

TCO2 changes. About one third of the annual change ofTCO2 (35 10 mmoles kg1) was due to salinity changes(i.e., annual change of nTCO2 was 255 mmoles kg1; seeauxiliary materials Figure S12). The remaining annualnTCO2 change was primarily caused by primary production(two thirds [Bates et al., 2006]) and sea-to-air CO2 gasexchange (one third), respectively.[32] In the 10N10S region, the annual amplitude of

surface temperature and seawater pCO2 was 2 1C and40 10 matm, respectively (Figure 5). Here the annualamplitude of pCO2 was mostly caused by temperaturevariability.

4.2. Indian Ocean Air-Sea CO2 Fluxes

[33] Air-sea CO2 flux rates (Figure 6) were calculatedfrom monthly maps of DpCO2 (Figure 7) and wind speed(not shown). From these monthly air-sea CO2 flux maps,annual rates were computed for the entire Indian Ocean andregions of interest (e.g., Arabian Sea) (Table 2). The netannual sea-to-air CO2 flux for the entire Indian Ocean(35S20N) was estimated at +237 132 Tg C yr1 (or

Figure 3. Spatial distribution of surface TCO2 (mmoles kg1) in the Indian Ocean each season.

(a) December to February (NEM period). (b) March to May (SIM period). (c) June to August (SWMperiod). (d) September to November (FIM period).

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+0.24 0.13 Pg C yr1) using the quadratic wind speed-kdependency (equation (5)) of Wanninkhof [1992]. Meanmonthly winds speeds in the Indian Ocean typically rangedbetween 6 and 11 m s1, with an annual mean of 7.8 m s1.At this range of wind speeds, the cubic wind speed-kdependency of Wanninkhof and McGillis [1999] predictslower rates of air-sea CO2 exchange [e.g., Bates andMerlivat, 2001]. If the cubic wind speed-k dependency isused, a net annual sea-to-air CO2 flux for the entire IndianOcean (35S20N) was estimated at +190 106 Tg C yr1

(or +0.19 0.11 Pg C yr1),80% of the value predicted bythe quadratic equation.[34] Previous studies have reported a wide range of air-

sea CO2 fluxes for the Indian Ocean. Louanchi et al.[1996], using 2 2 spatial resolution for maps ofseawater pCO2, estimated a smaller net annual sea-to-airflux of +22 Tg C yr1 for the 35S20N zone. The majordifferences between the annual CO2 flux estimates of thisstudy and the Louanchi et al. [1996] study appear to beassociated with higher sea-to-air CO2 fluxes in the 20S20N region (estimated in this study). In a recent synthesisof surface seawater pCO2 measurements from the Indian

Ocean during the WOCE survey in 1995, Sabine et al.[2000] estimated a net annual sea-to-air CO2 flux of+150 Tg C yr1 (or +0.150 Pg C yr1) for the 36S20N zone of the Indian Ocean. In a recent study ofanthropogenic CO2 in the Indian Ocean, Hall et al.[2004] suggested that the rate of sea-to-air CO2 flux inthe Indian Ocean was 250 Tg C yr1. Both the Sabine etal. [2000] and Hall et al. [2004] estimates of annual sea-to-air CO2 flux were similar to the estimates reported in thisstudy. No estimates of error were given for the Louanchi etal. [1996] or Sabine et al. [2000] air-sea CO2 flux rates.

4.3. Arabian Sea

[35] In the Arabian Sea, TCO2 ranged from approximately2000 to 2050 mmoles kg1 (Figure 3). The seasonal DTCO2changes were relatively modest (80 mmoles kg

1)close to the NE Arabian Peninsula off Oman. During theSW monsoon period, high TCO2 and nTCO2 was observedoff the coast (Figure 4) and associated with coastal upwell-ing. On the US JGOFS Arabian Sea expeditions, surface

Figure 4. Spatiotemporal distribution of surface seawater pCO2 (matm). (a) December to February(NEM period) surface pCO2. (b) March to May (SIM period) surface pCO2. (c) June to August (SWMperiod) surface pCO2. (d) September to November (FIM period) surface pCO2.

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temperatures were 46C cooler during the SWM than theproceeding Spring Intermonsoon period [Morrison et al.,1998; Weller et al., 1998; Gundersen et al., 1998]. Thespatiotemporal distribution of nTCO2 in the Arabian Seawere similar to the observations of Millero et al. [1998a],collected on the US JGOFS Arabian Sea expeditions(although the US JGOFS Arabian Sea TCO2 was notincluded in the MLR fits, the modeled nTCO2 agrees within

1015 mmoles kg1). Both the Arabian Sea data and themodeled data show high nTCO2 (>2050 mmoles kg

1) offthe coast of Oman during the SWM period. Over the rest ofthe Arabian Sea, the seasonal range of nTCO2 was 19251975 mmoles kg1, with the lowest values (19251940 mmoles kg1) observed during the Spring Intermon-soon (SIM) period [Millero et al., 1998a, Figure 8].

Figure 5. Seasonal variability of hydrographic properties, TA, TCO2 and pCO2 (mmoles kg1) in the

surface layer of the Indian Ocean. (a) Salinity (DS). (b) Alkalinity (DTA) (mmoles kg1). (c) Surface TCO2(orDTCO2) (mmoles kg

1). (d) Seawater pCO2 (matm). (e) Temperature (C). (f) mixed layer depth (m). Theseasonal range was calculated for each parameter in every 1 1 box. Mixed layer depth was calculatedusing a 0.1 sigma theta density change criterion.

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[36] The large seasonal TCO2 (and TA) changes south-west of the Indian continent, reflect a seasonal increase insalinity during the SWM and FIM. It appears that the SWMonsoon Current (SWMC; Figure 1) transports highersalinity (and high TCO2 and TA) waters from the northernArabian Sea toward the equator. Such seasonal advection ofhigh-salinity water to the southeast from the Arabian Seawas also indicated during the US JGOFS Arabian Seaexpeditions [e.g., Morrison et al., 1998].[37] The geographic and temporal variability observed

compared well with other observed pCO2 data from basin[Louanchi et al., 1996; Sabine et al., 2000] and regionalstudies [Goyet et al., 1998]. For example, high (>400 matm)but localized pCO2 values were observed in the ArabianSea off Pakistan during the NEM period in the model data(Figure 4a) and observed data [Sabine et al., 2000]. Thelowest pCO2 values found during the NEM period occurredclose to the southwest coast of India, a feature also observedby Sarma [2003] who ascribed this feature to low-salinitywater inflow from the Bay of Bengal. Similarly, high(>400 matm) but localized pCO2 values were observed inthe Arabian Sea off Oman during the SIM in both model dataand observed data [Goyet et al., 1998; Sabine et al., 2000]. As

pointed out by others [Goyet et al., 1998; Sarma et al., 1998;Sabine et al., 2000; Sarma, 2004], this reflects upwelling ofpCO2 rich waters close to the coast of Oman. During theSWM, seawater pCO2 values decreased to a seasonal mini-mum (350400 matm) throughout much of the Arabian Sea(higher near to the coast), although the region remainedsupersaturated with respect to the atmosphere.[38] In the Arabian Sea, DpCO2 values were generally

positive each month (Figure 6) and this region was aperennial source of CO2 to the atmosphere (Figure 7). Inthe Arabian Sea north of 10N, fluxes ranged from +2 to+9 Tg C month1, with a net annual sea-to-air flux of +64 30 Tg C yr1 (Table 2). For comparison, Sarma et al.[1998] estimated an annual flux of +47 Tg C yr1 for theArabian Sea from limited pCO2 observations. More recently,Sarma [2003] estimated an annual net air-sea CO2 flux of90 Tg C yr1 north of 10N using a model approach. In bothstudies, no estimates of error were given for the air-sea CO2flux rates, but it is expected that there is a similar level ofuncertainty as reported herein. Seasonally, the highest fluxesoccurred during both monsoon (i.e., SWM and NEM)periods presumably associated with higher wind speedsprevalent in this region. The lowest sea-to-air flux period

Figure 6. Monthly distribution of air-sea CO2 flux (mmoles CO2 m2) in the Indian Ocean. Positive

values denote sea-to-air CO2 flux, whereas negative values denote air-to-sea CO2 flux. (top, left to right)January to April; (middle, left to right) May to August; (bottom, left to right) September to December.

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occurred during the Spring Intermonsoon, a feature observedin several other studies [George et al., 1994; Louanchi et al.,1996; Sabine et al., 2000].

4.4. Bay of Bengal

[39] In the Bay of Bengal, there was a general northwarddecrease in TCO2 (Figure 3), with the strongest gradientspresent during the SWM and FIM periods. George et al.[1994] observed similar gradients of TCO2 (19201800 mmoles kg1) between 10N and 17N in MarchApril 1991. The seasonal DTCO2 and DnTCO2 changeswere greater than 100 mmoles kg1 across most of the Bayof Bengal (Figure 5). In the Bay of Bengal, large variabilityof surface pCO2 was observed but there are very few data tocompare directly [George et al., 1994]. From January toSeptember, pCO2 values are close to equilibrium or below,similar to a very limited calculated pCO2 data set collectedin MarchApril 1991 [George et al., 1994]. In contrast,pCO2 values were seasonally higher (>400 matm) during theOctoberDecember period. This feature coincides with theseasonally low salinities and high salinity normalized TCO2values observed in the Bay of Bengal. Presumably, high

river (and carbon) output after the SWM elevates seawaterpCO2, thereby switching the Bay of Bengal from neutral/sink status to a source of CO2 status.[40] In the Bay of Bengal, fluxes ranged from 1 to

+2 Tg C month1 and the net annual sea-to-air CO2 fluxwas +13 6 Tg C yr1 (Table 2). The highest sea-to-airCO2 fluxes occurred during the SWM and FIM period,when DpCO2 values were higher (i.e., seawater pCO2higher). River discharge into the Bay of Bengal primarilyoccurs during this period (e.g., June to September

Figure 7. Monthly distribution of DpCO2 (matm; pCO2sea- pCO2atm) in the Indian Ocean. (top, left toright) January to April; (middle, left to right) May to August; (bottom, left to right) September toDecember.

Table 2. Annual Sea-to-Sea CO2 Flux for the Indian Ocean and

Regions of Interesta

Area Air-Sea CO2 Flux, Tg C yr1

Indian Ocean +237Arabian Sea +64Bay of Bengal +1310N10S +18010S20S +11020S35S 130

aOcean sources of CO2 to the atmosphere are given as positive values.Ocean sinks for atmospheric CO2 are given as negative values.

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[Subramanian and Ittekkot, 1991]), and the higher sea-to-airCO2 fluxes is presumably associated with enhanced trans-port of riverine TCO2 and remineralization of riverinedissolved organic carbon within the Bay of Bengal.

4.5. 10N35S Region of the Indian Ocean[41] In the region of 10N10S, seawater pCO2 values

were also higher than atmospheric values (Figure 7; DpCO2values were positive) and the area was a perennial source ofCO2 to the atmosphere. Sea-to-air CO2 fluxes ranged from+822 Tg C month1 and the net annual sea-to-air CO2flux was estimated at +180 98 Tg C yr1. As a compar-ison, Louanchi et al. [1996] estimated a flux of +169 Tg Cfor the 10S20N zone. The highest CO2 fluxes wereobserved in the FIM/NEM periods (October to February)associated with highest seawater pCO2 values (and highestpositive DpCO2 values) across this region. A secondarypeak in CO2 flux occurred in May and June at the onset ofthe SWM (i.e., when wind speeds increase).[42] Compared to the equatorial region, Arabian Sea and

Bay of Bengal, different seasonal patterns of air-sea CO2fluxes were observed in the 10S20S and 20S35Szones. During the NEM to SIM period (December May), DpCO2 values were positive and these regions(i.e., 10S35S) were sources of CO2 to the atmosphere(Figure 7). However, during the SWM to FIM period (i.e.,austral winter and spring), DpCO2 values become negativeand these regions switch to oceanic sinks for atmosphericCO2. In the 10S20S zone, the reversal from CO2 sourceto sink status occurs from July to September, and it wasestimated that the annual sea-to-air flux CO2 was 110 59 Tg C yr1 (Table 2). In a previous study, Louanchi et al.[1996] estimated a zero flux for this region (i.e., 10S20S).[43] In the 20S35S zones, air-to-sea fluxes were high-

est (40 24 Tg C month1) during the SWM/FIMtransition period. In contrast to other regions of the IndianOcean, this region is a net annual sink for atmospheric CO2 of130 72 Tg C yr1 (Table 2). In a previous study, Louanchiet al. [1996] estimated a CO2 flux of 147 Tg C yr1 forthe 20S35S region of the Indian Ocean.

5. Conclusions

[44] Spatiotemporal variability of ocean carbon cyclingand air-sea CO2 exchange in the Indian Ocean are examinedusing inorganic carbon data collected as part of the WOCEand OACES cruises in 1995. The spatiotemporal distribu-tions of TCO2, alkalinity and seawater pCO2 were estimatedfor the Indian Ocean and regions of interest includingthe Arabian Sea, Bay of Bengal and 10N35S zones.The range of alkalinity across the Indian Ocean was200 mmoles kg1. The highest seasonal variability wasobserved in the Bay of Bengal (>100200 mmoles kg1) andin the Arabian Sea (up to 60 mmoles kg1). In other regions,seasonality of alkalinity was small (

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