In this work, we provide ship and satellite comparisons of PIC in the Atlantic and Indian sectors of the SO, comparing them to other data sampled globally to demonstrate the validity of the PIC algorithm for defining the GCB. We use satellite and ship data to show the large‐scale pattern of coccolithophores in the GCB and discuss the effects of macronutrient and iron control on the presence of the feature. We describe an index (residual nitrate potential growth (RNPG)) that scales the excess of nitrate versus silicate to the potential growth of phytoplankton, as trace metal limitation is relieved. We then examine the effect of GCB coccolithophores on the CO 2 source/sink balance of the GCB waters and the sinking flux of POC, and we contrast this with regions containing abundant diatoms. Finally, we synthesize these points into a conceptual view on the regulation of GCB growth as well as its biogeochemical significance.

The importance of a ballast effect on atmospheric CO 2 is less clear. Observed correlations between deep (>2000 m) PIC flux and the absolute and relative flux of deep POC have led to hypotheses about the role of PIC in ballasting POC export [ Francois et al ., 2002 ; Klaas and Archer , 2002 ]. Increasing both the magnitude of POC flux to depth [ Volk and Hoffert , 1985 ] and its remineralization length scale [ Kwon et al ., 2009 ] should lower ocean (and ultimately atmospheric) CO 2 . Model studies have shown that atmospheric CO 2 is sensitive to modest changes in remineralization length scales, such that a 24 m increase in the depth at which 63% of sinking carbon is remineralized led to a decrease in atmospheric CO 2 of 10–27 ppm [ Kwon et al ., 2009 ].

The seasonal presence of the GCB in the Southern Ocean may have substantive implications for carbon biogeochemistry in the region [ Freeman and Lovenduski , 2015 ]. The distribution of coccolithophores in the GCB may affect the efficiency of the biological carbon pump in two opposing ways: (1) as part of the carbonate (PIC) pump, lowering total alkalinity (TA) in the surface ocean during calcification and thereby increasing seawater p CO 2 via changes in CO 2 ‐carbonate equilibria [e.g., Bates et al ., 1996a ; Zeebe and Wolf‐Gladrow , 2001 ], and (2) through a ballasting effect that increases the magnitude and/or transfer efficiency of the soft tissue (particulate organic carbon (POC)) pump, which would decrease surface ocean p CO 2 [e.g., Tanhua et al ., 2013 , and references within]. The balance of the POC and PIC pumps determines the net effect of the biological carbon pump on surface p CO 2 , exchanges of CO 2 across the air‐sea interface, and ultimately feeds back to atmospheric p CO 2 . Model studies have shown that atmospheric CO 2 is highly sensitive to the PIC:POC export ratio [ Archer and Maier‐Reimer , 1994 ; Archer et al ., 2000 ; Matsumoto et al ., 2002 ], particularly in the SO. Halving the global PIC:POC export ratio can lead to an atmospheric CO 2 decrease of 55 ppm [ Sigman et al ., 1998 ]. Despite the importance of the PIC:POC export ratio, however, its magnitude is not well constrained. Sarmiento et al. [ 2002 ] argued that the often‐used global PIC:POC export ratio of 0.25 [ Archer et al ., 2000 ] is too high. They calculated a global average export ratio of 0.06 ± 0.03, with the SO (defined as south of 45°S) having the lowest export ratio (CaCO 3 :C org ~0.01, where C org is equivalent to sinking POC). Measurements of PIC:POC across the GCB would help assess these model findings.

The co‐location of the GCB with major oceanographic fronts would suggest that bottom‐up supply of upwelled nutrients along these frontal boundaries plays a dominant role in controlling coccolithophore productivity. Because of the importance of nitrate and silicate for phytoplankton growth, several indices have been used to describe their covariability (or lack thereof) in the sea. The Si:N ratio reveals the relative overabundance of silicate in deep waters of the SO [ Sarmiento et al ., 2007 ] associated with differences in remineralization of nitrate and silicate and effects of iron on sinking particulate Si and N. Residual nitrate (nitrate–silicate) also has been used to describe the fertility of water masses in coastal waters such as the Gulf of Maine for supporting diatoms versus nonsiliceous phytoplankton [ Townsend et al ., 2010 ]. Specifically, in HNLC waters such as the SO, the issue for either of these indices is that for understanding the potential effect of nitrate and silicate on phytoplankton growth, an index is needed that scales the dominant nutrient concentration to the potential growth that they could sustain should growth limitation (e.g., by a trace metal) be removed.

Iron availability also influences phytoplankton growth and community structure in large sectors of the ocean [ Boyd et al ., 2007 ; Moore et al ., 2013 ], and this, indeed, may influence the GCB. Although coccolithophores generally appear less prone to iron limitation compared to diatoms and other microplankton [ Brand , 1991 ; Lam et al ., 2001 ; Sunda and Huntsman , 1995 ], iron has been reported to limit coccolithophore growth in regions of the North Atlantic and Pacific Oceans [ Crawford et al ., 2003 ; Nielsdottir et al ., 2009 ]. In the southern hemisphere, higher PIC concentrations are found near continental landmasses and islands, suggesting that associated trace metal supply (either aeolian or upwelled) may help support seasonal coccolithophore production. Furthermore, strong correlations have been noted in sediment records from the SAF zone between fluxes of dust and alkenones (coccolithophore biomarkers) on glacial time scales [ Jaccard et al ., 2013 ; Martínez‐Garcia et al ., 2009 , 2014 ], suggesting that coccolithophores are stimulated by nutrients from dust.

The spatiotemporal success of one algal group over another group in the GCB is likely related to physical and biogeochemical factors involving the bottom‐up supply of nutrients or top‐down grazing. Significant portions of the SO in the region of the GCB are considered high nutrient, low chlorophyll (HNLC) [ Minas and Minas , 1992 ], with abundant macronutrients and limiting iron [ Boyd et al ., 1999 ]. Diatoms usually require Si(OH) 4 and NO 3 in roughly equal portions [ Brzezinski , 1985 ]. Typically, if diatom growth becomes limited by Si (concentrations < ~2 μM), non‐Si users are able to grow faster [ Dugdale and MacIsacc , 1971 ; Egge and Aksnes , 1992 ] and can dominate. Moreover, simulation models have suggested that when Si(OH) 4 reaches a critical, low concentration, coccolithophorids can dominate [ Aksnes et al ., 1994 ].

Satellite estimates indicate that 26% of global suspended PIC is found in the GCB, which represents 16% of the global ocean by area [ Balch et al ., 2005 ]. This would suggest that the GCB is arguably the largest ocean coccolithophore‐rich biome. Indeed, supporting evidence of its presence (and domination by the coccolithophore, Emiliania huxleyi ) has been observed in seawater samples across all sectors of the SO, as far south as 65°S with shipboard observations since the 1960s [see Holligan et al ., 2010 , their Table 1].

Satellite observations enable quantitative estimates of coccolithophore PIC concentrations in the upper ocean through remote sensing algorithms [ Balch et al ., 2005 ; Gordon et al ., 2001 ]. The PIC algorithm has been validated previously, mostly in the North Atlantic Ocean. In the Southern Ocean, satellite‐derived PIC concentrations suggest that a substantive region of elevated PIC, previously defined as the Great Calcite Belt (GCB) [ Balch et al ., 2011 ], occurs each year in the austral summer near the Subtropical Front (STF), Subantarctic Front (SAF), and Polar Front (PF). The GCB appears seasonally in austral summer south of ~38°S and extends poleward to ~60°S with an area of ~52 × 10 6 km 2 . Ship observations have confirmed that the brightest, highest PIC concentrations are found northeast of the Drake Passage, in the region of the Patagonian Shelf and Falkland Islands [ Balch et al ., 2014b ; Garcia et al ., 2011 ; Painter et al ., 2010 ; Poulton et al ., 2013 ]. Satellite‐derived PIC concentrations gradually diminish eastward, remaining discernable throughout the Indian and Pacific sectors of the Southern Ocean (SO).

The pool of particulate inorganic carbon (PIC) is an important reservoir for carbon in the global ocean, with its rates of production, storage, and export providing complex feedback to the global ocean carbon cycle. Estimates of annual PIC production are highly unconstrained, ranging from ~0.4 to 1.8 Pg C yr −1 based on ocean sediment trap estimates [ Balch et al ., 2007 ; Berelson et al ., 2007 ; Feely et al ., 2004 ; Milliman , 1993 ; Morse and Mackenzie , 1990 ; Wollast , 1994 ]. Other observational approaches such as satellite remote sensing of PIC [ Balch et al ., 2005 ; Iglesias‐Rodríguez et al ., 2002 ; Moore et al ., 2012 ], modeling [ Pinsonneault et al ., 2012 ], and global alkalinity tracer variability [e.g., Fry et al ., 2015 ] offer further evidence on the location, timing, and magnitude of PIC production, PIC to organic carbon rain ratio, and contributions to the global carbon cycle. The aim of these approaches is to elucidate the climate change‐ocean acidification feedback on calcium carbonate (CaCO 3 ) production and dissolution [ Ridgwell et al ., 2007 ; Riebesell et al ., 2000 ; Zondervan et al ., 2001 ] and ultimately carbonate‐organic matter ballasting of anthropogenic CO 2 [ Tanhua et al ., 2013 ].

Eight metal incubation experiments were performed along and around the GCB to assess potential for Fe to limit the growth of coccolithophores, diatoms, and other phytoplankton taxa. Mixed‐layer water was collected from 20 m with a 30 L GO‐FLO bottle deployed on Kevlar line. Bottles were immediately transported into a positive‐pressure trace metal clean van, and water was dispensed gently into 10 L low‐density polyethylene cubitainers. A 200 µm mesh was placed in‐line to remove mesozooplankton grazers. All bottles and plastic ware were stringently acid cleaned prior to use, and trace metal clean techniques [ Bruland et al ., 1979 ] were employed for all steps including conducting the work in a trace metal clean van. Cubitainers and polycarbonate incubation bottles were soaked in 1% Micro detergent for several days, then rinsed copiously with ultrapure >18.2 megohm water, and soaked in 1 M reagent HCl for several more days before being rinsed again and dried before use. Cubitainers were spiked with a Fe solution produced from a traceable AAS standard diluted into 0.01 M Optima HCl. The first two incubation experiments (experiments 1 and 2) inadvertently involved Fe additions of 0.02 nM, due to a calculation error. Experiments 3 through 8 involved Fe additions of 2 nM. Treatment cubitainers were gently homogenized, and water was decanted into triplicate 2.5 L polycarbonate bottles. Bottles were filled completely (minimizing headspace) and sealed with parafilm and vinyl tape, then placed in deckboard incubators at 50% surface irradiance. For experiments 1 through 4 (Atlantic sector cruise), incubator temperature was maintained with surface water circulated through the ship's through‐hull system. There were some situations where variations in surface temperature (as we transited north or south) changed the temperature from initial conditions at which the phytoplankton community was collected. For experiments 5 through 8 (Indian sector cruise), a deckboard heater/chiller unit was used to control the temperature of recirculating seawater as the ship transited through oceanographic regions with different temperatures. The responses of phytoplankton to added Fe were assessed through measurements of total chlorophyll, biogenic Si, and PIC at the end of the incubation. Incubation experiments lasted between 3 and 6 days.

As a cross‐check on theassumption, we examined the number frequency of silicate concentrations measured in our Southern Ocean cruises and found that the most common silicate concentration below 5 μM was 0.5–1 μM (7.7% of the 1495 samples), while only 1.1% of the data were a true 0 μM (data not shown). This is entirely consistent with Paasche's supposition ofof 0.7 μM. RNPG was then calculated as

The subscript,, in equations 2 and 3 denotes the nitrate‐related growth kinetics. It can be seen that in the growth rate prediction of equation 3 , the maximal growth rates would asymptote at ~3 d. Silicate uptake by diatoms differs from phytoplankton nitrate uptake; in that, diatoms typically cannot completely deplete all the silicate. The residual silicate substrate concentration,, averages about 0.7 μM and was integrated into the Michaelis‐Menten equation [] to predict silicate‐dependent growth using equation 4 where the subscript Si denotes the silicate‐specific process. In the absence of comparative data on nitrate and silicate uptake kinetics across a wide spectrum of phytoplankton groups, we here assume that physiological kinetic response to variable substrate concentration will be similar for limiting nutrients, in order to maintain balanced growth [.,]. We therefore used the same formulation given in equations 2 and 3 to approximate silicate growth kinetics, but we included, in the formulation, the minimal reactive silicate concentration below which diatoms cannot utilize the nutrient (equation 4 ) as observed previously by].

The similarity of half‐saturation coefficients for nutrient uptake and growth was noted previously [], which then allowed potential growth rates to be calculated for nutrient‐limited algae as shown in equation 1 whereandare the specific growth rate and maximum growth rate (both units d), respectively;is the substrate concentration (μM); andis the half‐saturation constant for growth (similar to that of uptake; units of μM). Nonetheless, it is well known that species vary in their values of, but in a comparison of the range ofvalues for nitrate uptake by diatoms, dinoflagellates, prymnesiophytes, and chlorophytes, the averagefor 13 species, with 27 individual determinations, was ~1.77 μM (SE = ±0.46 μM) [.,]. The best known estimates of the variability of maximum growth rate of phytoplankton were made as a function of temperature [.,]. In this case, maximum growth rates within the 0–25°C range varied from 1 to 3 dfor nearly 200 different measurements. A more recent appraisal of 1500 different published estimates ofshowed a similar shaped curve but with a slightly larger variability of 1–4 d.,].] noted that the values ofandwere a function of the degree of eutrophy of the environments that the phytoplankton were sampled in, and they used the following simple empirical approximations to predict both parameters:

The calculation of residual nitrate potential growth (RNPG) involves recasting the observed nitrate and silicate concentrations into potential algal growth rates, then subtracting the silicate‐dependent growth rate from the nitrate‐dependent growth rate, the sign of which determines which algal groups would dominate if relieved from trace metal limitation. If the difference is positive, then this implies that potential nitrate‐dependent growth will exceed silicate‐dependent growth, should trace‐metal limitation be removed. A negative difference implies that potential silicate‐dependent (diatom) growth exceeds nitrate‐dependent growth as trace metal limitation is relieved. The basic Michaelis‐Menten formulation was used to describe nutrient uptake [ Caperon , 1967 ; Dugdale , 1967 ].

We used the 238 U‐ 234 Th disequilibrium technique coupled with measurements of size‐fractionated 234 Th, POC, PIC, and BSi to determine 234 Th‐based export fluxes of POC, PIC, and BSi. Samples for 234 Th and size‐fractionated particles were collected at a subset of stations (Figure 10 c). Total 234 Th activity profiles were measured using the small‐volume technique [ Pike et al ., 2005 ]. 234 Th export flux from the base of the euphotic zone (“ z PAR ”), defined as the depth of the 0.3% light level, and 100 m below that (“ z PAR + 100 ”) were determined by integrating the 234 Th activity deficit to those depths. Note that the 0.3% light level was used here because the control of the light levels in our shipboard incubators with neutral‐density screen could only be achieved to 0.3%, instead of the more typical definitions of the euphotic zone between the 1% and 0.1% light depths [ Marra et al ., 2014 ]. 234 Th fluxes were converted to POC, PIC, and BSi fluxes using measured ratios of those components to 234 Th in >51 µm particles collected by dual‐flow, in situ filtration at z PAR and z PAR + 100 [ Rosengard et al ., 2015 ; Thomalla et al ., 2008 ]. The PIC:POC export ratio was determined from the PIC:POC ratio of >51 µm particles at z PAR . A dual‐flow version of battery‐operated pumps by McLane Research, Inc. was used to allow simultaneous collection of particles on precombusted quartz fiber filters (for PIC, POC, and 234 Th) and on polyethersulfone filters (for BSi) [ Cutter et al ., 2010 ; Lam et al ., 2012 ; Ohnemus and Lam , 2014 ; Owens et al ., 2015 ]. Total and particulate 234 Th activities were counted using low‐level Risø beta counters at sea and again after six half‐lives on land. Full methodological details are reported in Rosengard et al. [ 2015 ].

Analytical methods followed standardized protocols [ Bates et al ., 1996b , 2001 ; Dickson et al ., 2007 ; Knap et al ., 1993 ]. Both DIC and TA samples were sampled shipboard with replicate samples analyzed ashore for QC/QA purposes. TA was determined using a Versatile Instrument for Detection of TA (VINDTA) analytical system (2011 cruise) and Automated Infrared Inorganic Carbon Analyzer (AIRICA). A 25 mL or 10 mL sample volume was used for analysis of DIC analyzer samples on the VINDTA and AIRICA, respectively. Samples were also analyzed ashore using a highly precise (0.02%; 0.4 µmol kg −1 ) VINDTA system [ Bates , 2007 ; Bates and Peters , 2007 ; Bates et al ., 1996b ]. Both DIC and TA analyses had a precision and accuracy of ~1 µmol kg −1 (precision estimates were determined from 300 between‐bottle and within‐bottle replicates and accuracy assessed using calibrated reference materials for shipboard and lab work from A. Dickson, Scripps Institution of Oceanography, La Jolla, CA). Normalized alkalinity (nTA) is defined as the total alkalinity normalized to a salinity of 35. The ships were outfitted with a flow‐through “SAMI” p CO 2 sensor (Sunburst Sensors) based on colorimetric methods ( http://www.sunburstsensors.com/products/oceanographic‐carbon‐dioxide‐sensor.html ). Carbonic acid dissociation constants were appropriate for temperate/polar seas, and temperature, salinity, and depth data were used to compute inorganic carbon parameters (i.e., p CO 2 ; note that pH, [CO 3 2− ], and Ω for aragonite/calcite were not used directly in the paper) using the software, CO2calc [ Robbins et al ., 2010 ].

Water was sampled at six depths per station, and the microdiffusion technique [ Balch et al ., 2000 ; Paasche and Brubak , 1994 ] was used with simulated in situ, 24 h incubations in deck incubators cooled with ambient surface seawater (Atlantic sector cruise) and a deckboard heater/chiller unit (Indian sector cruise) to estimate net coccolithophore calcification (NCC) and net community organic production. Samples were filtered through 0.4 µm polycarbonate filters, rinsed, and prepared for microdiffusion analysis [ Balch et al ., 2000 ].

Total coccolith and coccolithophore abundances were measured by the HA filter/optical‐adhesive technique [ Poulton et al ., 2010 ] with polarized light microscopy and image analysis [ Balch and Utgoff , 2009 ; Balch et al ., 2011 ]. Discrete water samples were run through a FlowCAM imaging cytometer for measuring particle backscattering, chlorophyll, phycoerythrin fluorescence, and imaging of nanoplankton and microplankton (4–100 µm). Particle volume was converted to cellular C according to Menden‐Deuer and Lessard [ 2000 ].

Regional PIC concentrations (mol m −3 ) were estimated by satellite using the merged two‐band/three‐band algorithm [ Balch et al ., 2005 ; Gordon et al ., 2001 ] with Moderate Resolution Imaging Spectroradiometer (MODIS)‐Aqua data. The performance of the algorithm in the GCB was assessed by comparing the satellite‐derived PIC concentration (mol m −3 ) to the shipboard‐derived PIC concentration (same units, estimated by multiplying the ship‐measured acid‐labile backscattering (b b ′; units of m −1 ), by the average coccolithophore backscattering cross section of PIC, currently used in the NASA PIC algorithm (1.628 m 2 mol −1 )). This algorithm comparison was made with the data taken in the cruises described here and compared to other global cruises, in which these measurements have been made previously (see Results section). Satellite‐derived current velocities were estimated using satellite altimetry from the Jason‐2 mission (Ocean Surface Current Analyses–Real Time (OSCAR); http://www.oscar.noaa.gov/index.html ). Current velocities and directions were downloaded with 1° × 1° resolution between 30°S and 65°S around the entire SO for January 2012, for comparison to MODIS‐Aqua‐derived PIC patterns from the same time period, averaged at the same time and space resolutions.

A semicontinuous underway sampling system was used to measure hydrographic and bio‐optical properties of surface seawater [ Balch et al ., 2010 ]. The system measured temperature, salinity, pH, and chlorophyll fluorescence. Total backscattering at 532 nm (b b tot ) was measured using a WetLABS ECO‐VSF (three angles). Backscattering also was iteratively measured following acidification of seawater with glacial acetic acid, dropping the pH below the p K 1 for dissolution of calcite and the more soluble aragonite [ Millero , 1996 ] (b b acid ), and by difference, acid labile backscattering (b b tot − b b acid = b b ′) [ Balch and Drapeau , 2004 ], which was calibrated to PIC concentration. About every 3 h, underway, discrete water samples were collected for chlorophyll a [ Joint Global Ocean Flux Study , 1996 ], PIC, and POC [ Poulton et al ., 2006 ] to calibrate the underway sensor system. Biogenic silica also was sampled using the technique of Brzezinski and Nelson [ 1989 ].

This study was done on research cruises between the Patagonian Shelf and Cape Town, South Africa (January–February 2011; R/V Melville no. 1101), and between Durban, South Africa, and Fremantle, Australia (February–March 2012; R/V Revelle no. 1202). Underway measurements were made for temperature, salinity, partial pressure of CO 2 ( p CO 2 ), total particulate, and PIC‐specific backscattering. Discrete measurements were made for chlorophyll, PIC, POC, biogenic silica (BSi), coccolithophore concentration, calcification, and photosynthesis. Measurements were also made of the seawater CO 2 ‐carbonate system (i.e., dissolved inorganic carbon (DIC) and total alkalinity (TA)), iron limitation of phytoplankton (via deck experiments), and 234 Th‐based vertical flux rates. Each of these measurements is described in detail below.

Relationships between temperature, RNPG, and PIC:POC. (a) Map showing sea surface temperature (numbers mark the locations of thorium flux measurements and their magnitude referring to groupings in Figure 10 d), (b) map showing the surface values of residual nitrate potential growth (RNPG) along the cruise track, (c) map showing the PIC:POC export ratio at 100 m, and (d) scatterplot of RNPG versus SST (color represents the PIC:POC export ratio). PIC:POC export ratio was the ratio of PIC flux to POC flux at the base of the euphotic zone. Stations were grouped by similarities in RNPG and temperature (ovals), and the mean (1 standard deviation) PIC:POC export ratios are indicated for each group. Leftmost group is designated as the polar group (1), top middle group is the subpolar group (2), and the rightmost group is the subtropical group (3); locations and group number are indicated on the SST map in Figure 10 a. Station marked in Figure 10 a as “(3)” is undefined for RNPG because NO= 0. If included in the subtropical group, PIC:POC export ratio in that group would be 0.36 ± 0.15. Climatological frontal boundaries are indicated in Figures 10 a– 10 c as in Figure 2

The minimum, median, and maximum PIC:POC export ratios (the ratio of PIC flux to POC flux at z PAR ) were 0.018, 0.176, and 1.03, respectively (Figure 10 d). PIC:POC export ratios were grouped on the basis of observed RNPG and sea surface temperature. Polar regions (defined as RNPG < 1 and SST < 10°C) and subtropical regions (defined as RNPG < 1 and SST > 10°C) had low (0.094 ± 0.089) and high (0.31 ± 0.093) PIC:POC export ratios, respectively (Figure 10 ). The highest PIC:POC export ratios (>0.7) were in subpolar regions (RNPG > 1), which also had the highest mean PIC:POC export ratio but with high variability (0.33 ± 0.35; Figure 10 ).

(a)Th‐derived POC flux as a function of BSi flux at. (b) POC flux transfer efficiency between the base of the euphotic zone () and 100 m below (defined as POC flux atdivided by POC flux at) as a function of PIC flux. Significant linear relationships are plotted as a solid black line. Transfer efficiency values at stations GB1‐25 and GB2‐106 were excluded from all correlations because of suspected sampling issues at those stations (figure adapted from]).

During both cruises, the 238 U‐ 234 Th disequilibrium results showed that the magnitude of shallow POC export flux at the base of the euphotic zone ( z PAR ~100 m) was not correlated with PIC export flux (not shown [ Rosengard et al ., 2015 ]) but rather was correlated with BSi export flux ( r 2 = 0.74; Figure 9 a). In contrast, we found a significant correlation between the transfer efficiency of POC flux in the upper mesopelagic zone—defined as the ratio of the POC fluxes 100 m below the base of the euphotic zone (at z PAR + 100 ) to the POC fluxes at the base of the euphotic zone (at z PAR )—and the PIC export flux (Figure 9 b; r 2 = 0.39, p < 0.001), but not the BSi export flux (not shown [ Rosengard et al ., 2015 ]).

(a) Locations of incubation experiments. White lines represent the climatological locations of the Subtropical (northmost) and Polar (southmost) Fronts. (b) Relative response of total Chl a , PIC, and BSi relative to unamended control for eight different experiments, in which Fe additions were made (0.2–2 nM). Error bars represent 1 standard deviation for triplicate measurements. Asterisks indicate the values significantly greater than 1.

Incubation experiments showed significant ( p < 0.05, one‐tailed t test) responses of phytoplankton to added Fe mostly in the Indian sector of the GCB, with generally negligible responses in the Atlantic sector (Figure 8 ). Experiment 1 (closest to Patagonian shelf) showed a small (6 ± 3% enhancement over the control) but significant enhancement of Chl in response to added Fe. The remaining three Atlantic experiments did not show significant Chl responses. Accumulation of BSi and PIC were not stimulated, relative to controls, in any of the Atlantic experiments. In the Indian sector, Fe stimulated significant Chl accumulation over the unamended controls in each of the experiments conducted south of subtropical waters (which had a starting nitrate concentration of just 0.1 μM). The addition of Fe also stimulated growth of coccolithophores (assessed via accumulation of PIC) in the Indian sector of the GCB. PIC responded significantly to Fe in experiment 6 (in between Crozet and Kerguelen Islands) and experiment 8 (southwest of Australia). Furthermore, in both of these experiments control‐normalized response of PIC was approximately 45% greater than the response of total chlorophyll (Figure 8 ), suggesting that coccolithophores were preferentially stimulated by Fe compared to the overall phytoplankton community. In contrast, coccolithophores were not stimulated in the experiments performed in the Atlantic sector of the GCB (experiments 1, 3, and 4) nor did Fe stimulate PIC in the experiments conducted either north of the GCB (experiments 2 and 5: nitrate <1 μM) or south of the GCB (experiment 7; ambient water 1.1°C), suggesting that something other than Fe was constraining coccolithophore growth. Biogenic silica (a proxy for diatom biomass) was significantly stimulated by Fe only in experiment 6 (in the GCB between Crozet and Kerguelen).

Results fromno. 1202 cruise, near Crozet Islands (Indian sector of the SO) superimposed on 12 year MODIS‐Aqua climatology for PIC using merged two‐band, three‐band PIC algorithm [.,.,]. (a) The color bar gives PIC concentration in mol m, with white lines denoting approximate regions of higher PIC. Streamlines of current velocity (sverdrups = 10) also superimposed from] (solid black lines are 0, 40, and 140 Sv isolines; from the CROZEX experiment, 2004–2005). Solid red lines give 20 Sv increments, while dashed red lines provide further intermediate levels. ARC designates the Agulhas Return Current. SeawaterCOfrom our cruise (circle symbols) is shown in Figure 7 a along with star symbols that denote approximate locations of high PIC or low PIC features. (b) SurfaceCOdata from the Great Belt cruise are shown against underlying, gridded (1° × 1°) surfaceCOdata collected during the months of January and February from 2010 to 2015. These latter data represent the limited SOCAT data (version 3 [.,.,]) collected in the region. The approximate regions of higher PIC in the region are superimposed from Figure 7 a. Location of Crozet Islands is also marked.

(a) Salinity‐normalized total alkalinity versus acid‐labile backscattering (b′) from region near Crozet Islands (circled in inset at bottom) and (b) in deep water offshore of the Patagonia shelf in the South Atlantic Ocean. In Figure 6 a (data from cruise RR1202), the top inset shows the seawaterCO(color scale goes from 325 to 475 ppm), where A and B mark the start and end of the transect (also shown in bottom inset map). This inset illustrates that the surfaceCOfeatures extend through the mixed layer. Multiple linear regression between nTA (independent variable) and b′ andCO(dependent variables) is shown with dashed black line: nTA [±6.678] = 2432.527 [±17.369] − 6650.91 [±2047.77] (b′) − 0.18785 [±0.04736] ×CO. (Numbers in square brackets are the SE of each fit term.) Other statistics for multiple linear regression:= 0.529; degrees of freedom = 62;< 0.01. In Figure 6 b (including data from cruise MV1101 as well as COPAS'08 (Knox 22RR)) [.,], the top inset shows the locations of underway and CTD‐hydrocast stations for similar data as in Figure 6 a but around the Patagonian Shelf region. The scatterplot for total alkalinity versus b′ is also given, as in Figure 6 a. Multiple linear regression between nTA (independent variable) and b′ andCO(dependent variables) is shown with dashed black line: nTA [±12.0810] = 2279.64 [±12.8357] − 10776.85 [±2568.10] (b′) − 0.1628 [±0.0428] ×CO. Other statistics for multiple linear regression:= 0.522; degrees of freedom = 50;< 0.01.

Seawater p CO 2 was highly variable in SAF waters, ranging from <300 to >420 µatm (Figure 2 d). Large regions of the SAF zone (e.g., near the Falklands, Crozet, Kerguelen, and Heard Islands—with elevated coccolithophore biomass during January and February) had p CO 2 values up to 100 µatm higher when compared to adjacent low coccolithophore biomass areas in regions of similar temperature in the SAF. Near the Crozet Islands, a multiple linear regression of nTA as a function of b b ′ and p CO 2 showed a moderate correlation ( r 2 = 0.52; p < 0.01; Figure 6 a). In this same region, the fraction of backscattered light contributed by PIC (b b ′/b bptot , i.e., PIC:POC; Figure 2 e) was accompanied by elevated surface seawater p CO 2 in a number of cases (Figure 2 d). These areas also coincided with low‐salinity normalized TA values and TA:DIC ratios, suggestive of alkalinity uptake due to calcification (temperature effects on p CO 2 are minimal and do not contribute to this contrast). These biogeochemical and optical features were also pronounced near the Falklands (Figure 6 b) and Kerguelen (not shown here) in the SAF. Superimposing the p CO 2 concentrations over the 12 year climatological PIC concentrations generally showed highest p CO 2 concentrations in regions with historically elevated PIC concentrations (Figure 7 b). Moreover, the boundaries of the elevated PIC regions near Crozet corresponded to the streamlines of current velocity [ Pollard et al ., 2007b ]. The regions of highest surface seawater p CO 2 observed during the Great Belt cruise are also co‐located with regions of higher p CO 2 collected in January and February from 2010 to 2015. In Figure 7 b, gridded Southern Ocean CO 2 Atlas (SOCAT) data set (version 3 [ Bakker et al ., 2014 ; Pfeil et al ., 2013 ]) corresponds to mean p CO 2 in each 1° × 1° box using higher‐frequency surface p CO 2 collected from the R/V Marion Dufresne by N. Metzl (partial data reported elsewhere [ Lourantou and Metzl , 2011 ]). Figure 7 b shows that limited p CO 2 data have been collected in the region over the past 6 years since 2010.

Residual nitrate potential growth (RNPG) plotted versus (a) temperature (axis) and latitude (color of points); (b) surface acid‐labile backscattering, b′ (axis; least squares linear fit to the data: b= 2.108 × 10× RNPG + 3.661 × 10= 0.386;= 65; RMSE = 4.005 × 10) and coccolith concentration (color of data points); (c) coccolithophore concentration (per mL) and PIC:BSi molar ratio (color of points); and (d) dinoflagellate abundance and concentration of 4–12 µm diameter nanophytoplankton (color of data points). Data from Figures 5 a, 5 c, and 5 d are from multiple depths, while data from Figure 4 b were collected from the surface only.

To assess the potential for relative growth of diatoms versus nonsilicifying phytoplankton, we examined the potential for growth (RNPG) on nitrate versus silicate in the absence of trace metal limitation. RNPG was greatest in waters with temperatures of 5 to 7°C (SAF) and 10 to 12°C (STF). RNPG values were lowest south of the PF and north of the STF (Figures 3 a, 5 a, and 10 b). Coccolith backscattering (b b ′; also called acid‐labile backscattering [ Balch et al ., 2001 ]; only measured along track in surface waters) increased with RNPG (Figure 5 b). Coccolithophore concentration within the euphotic zone was highest in areas of positive RNPG, but silicifiers exceeded calcifiers (low PIC:BSi ratios) when RNPG was negative (Figures 3 a, 3 c, and 5 c). In contrast, negative RNPG occurred north of the STF (where reduced nitrate concentrations limited diatom growth more than growth of nonsilicious phytoplankton) and south of the PF waters (where high silicate allowed more diatom growth relative to calcifiers; Figures 5 a and 5 c). As shown in Figure 3 , diatoms cooccurred with coccolithophores near the STF off Australia (near sections K and L) and in the SE Atlantic (along section F) but were most abundant south of the PF (Figure 3 b; between sections I and J), where silicate‐rich water reached the surface and RNPG was zero or negative, with diatoms increasing relative to coccolithophores (Figure 3 c).

(a) Altimetry‐based currents of Southern Ocean (from OSCAR program) color coded to velocity (scale bar on the right). (b) Average PIC concentration from MODIS‐Aqua plotted against altimetry‐derived current velocity (black diamonds, left y axis). Numbers of data points in each binned average shown as open squares (right y axis). Data show that highest PIC concentration is associated with fastest currents (which lie along frontal boundaries). Least squares power fit to the data also is shown.

Satellite‐derived PIC and shipboard measurements of coccolithophore concentration showed highest values within the fast‐moving waters of the ACC (Figure 4 ), in moderate salinity Atlantic sector waters, decreasing eastward into the Indian sectors of the GCB (Figures 3 c and 4 ). The regression between altimetry‐based water velocity and PIC concentration is given in Figure 4 b. There was a highly significant relationship between the binned current velocity (m s −1 ) and binned PIC concentration (mol m −3 ; Figure 4 ) ( Y [±0.078] = 6.93 × 10 −4 [±2.8 × 10 −5 ] X 0.202 [±0.0175] ; r 2 = 0.759; p < 0.001).

Vertical sections fromno. 1101 andno. 1202 cruises. The top inset shows the cruise track with letters designating sections of the cruise track. (a) Sections of RNPG (d) where positive values are regions where nonsiliceous phytoplankton growth would be expected to be favored, while negative values represent regions and depths where potential diatom growth would be expected. (b) Diatom abundance (mL) as determined with the FlowCAM. (c) Sections of coccolithophore concentration (mL) as determined with polarized light microscopy. In all plots, vertical lines and letters designate the sections shown in inset. Frontal climatologies are indicated in top inset with different line patterns (from north to south): thick solid line = Subtropical Front, dashed line = Subantarctic Front, fine dash = Polar Front, thin solid line = ACC boundary [.,]. Density isopleths overlaid onto Figures 3 a– 3 c.

Geographic distribution of surface properties. (a) Cruise track ofno. 1101 overlaid on the monthly MODIS‐Aqua PIC product for January 2011 (9 km binned; mol m). (b) Cruise track ofno. 1202 overlaid on average satellite‐derived PIC for February 2012 (same spatial bins as in Figure 2 a). (c) Acid‐labile backscattering (backscattering from CaCO) of surface mixed layer for both GCB cruises, as well as COPAS'08 cruise over the Patagonian Shelf [.,]. (d) Regions withCO> 390 µatm (ochre to red colors) represent a source of COto the atmosphere. Anything ≤390 µatm (ochre to indigo) represents a COsink. (e) Fraction of total backscattering contributed by PIC. (f) Integrated calcification/integrated photosynthesis measured using microdiffusion technique. For all plots, the climatological positions of the frontal boundaries are indicated with various black lines: heavy solid = Subtropical Front, medium dash = Subantarctic Front, fine dash = Polar Front, thin solid = ACC boundary [.,].

The highest PIC concentrations in the algorithm comparison were found south of 38°S in the GCB (Figure 1 ). Moreover, satellite‐derived PIC and shipboard measurements of acid‐labile backscattering showed highest concentrations within the Atlantic sector of the SO, decreasing eastward into the Indian sector of the GCB (Figures 2 a– 2 c). Diversity of coccolithophore populations increased to the east: scanning electron microscope and light microscopy showed that the number of coccolithophore species increased from 1 ( E. huxleyi ) off Patagonia to 8 off Africa and 13 SW of Australia [ Smith , 2014 ]. In both Atlantic and Indian sectors, PIC and coccolithophore concentrations were generally, but not always, elevated near the climatological and actual positions of the STF, SAF, PF, and the Antarctic Circumpolar Current (ACC) front (Figures 2 a– 2 c; see overlaid density isopleths in Figure 3 for actual locations of fronts). RNPG was highest between the STF and SAF frontal regions and north of the PF. It was lowest south of the PF and in ACC waters (Figure 3 a). Diatom concentration was highest inshore of the shelf front on the Patagonian Shelf, in PF and SAF waters in the Atlantic sector of the SO, and PF and ACC waters of the Indian sector SO. Lowest diatom concentrations were observed north of the STF (Figure 3 b). Diatom abundance was not exclusively low in regions of positive RNPG. There were a few regions (section B and the northernmost part of section F) where there were both elevated diatoms and coccolithophores in a region of positive RNPG. However, south of the PF in both Atlantic and Indian sectors, regions of negative RNPG were characterized by a stronger diatom (than coccolithophore) response.

Validation of MODIS‐Aqua‐derived, merged two‐band/three‐band PIC algorithm [.,.,] (only designed for case I waters, where phytoplankton dominate the optical signal []). Dashed line is the 1:1 line. Solid line is the least squares fit. Note that the logarithmic axes allow the performance of the algorithm to be assessed over ~3 orders of magnitude of PIC concentration. The least squares fit to the data (95% CI in square brackets) is log() = 0.837 [±0.037] × log() − 0.751 [±0.141];= 0.575; RMS error = ±11%. Open symbols are the matchup points from latitudes north of the 38°S parallel and closed points are for matchups from south of the 38° parallel (in the GCB). Inset in the bottom right shows the global map of the matchup database, with observations from the GCB cruises described here, plus other cruises where the same technique was applied: Patagonian Shelf COPAS'08 [.,], Atlantic Meridional Transect [.,], Western Arctic [.,], and Gulf of Maine North Atlantic Time Series [.,]. Note that the Atlantic GCB cruise in 2011 was anomalously cloudy and overcast. There were no synchronous satellite‐ship matchups for PIC.

The accuracy of the MODIS‐Aqua estimates of PIC concentrations was assessed using cruise data, collected from the world ocean since 2002, the year of the launch of MODIS‐Aqua (see inset to Figure 1 ). Satellite‐derived PIC was compared to shipboard measurements of acid‐labile backscattering (b b ′; units m −1 ) that were converted to equivalent PIC concentration using the average PIC backscattering cross section (1.628 m 2 (mol PIC) −1 ; value currently implemented in the official NASA PIC algorithm). The matchups (which include data from the Southern Ocean) are shown in Figure 1 . The results show an RMS error of ±11% for the algorithm globally.

4 Discussion

Our results confirm that within Atlantic and Indian sectors of the SO, satellite measurements of the GCB are matched by elevated acid‐labile backscattering and coccolithophore concentrations, consistent with a coccolithophore PIC source of the elevated reflectance. This finding is also in agreement with prior coccolithophore observations in the SO [Balch et al., 2014b; Holligan et al., 2010; Rembauville et al., 2016; Smith, 2014]. Furthermore, our observations showed highest (a) coccolith light scattering, (b) PIC:BSi, and (c) abundance of coccolithophores and nonsiliceous phytoplankton when RNPG was positive (Figures 3 and 5) [Eynaud et al., 1999], suggesting that potential growth of nonsilicious phytoplankton was greater than growth of diatoms in these waters. This is the first observation over basin scales in the SO that PIC concentration covaries with regions of greatest current velocity. We suggest that this occurs due to Ekman pumping along the frontal boundaries of the SO current system, and possible upwelling of iron [de Baar et al., 1995], which combined with the elevated nitrate relative to silicate (+RNPG) made for optimal conditions for increased abundance of nonsilicious phytoplankton (including coccolithophores) as opposed to diatoms (Figures 3 and 5). The iron incubation results demonstrate that iron can control the distribution and growth of coccolithophores in the GCB, with iron limitation of coccolithophores observed in the Indian sector of the GCB, where dust sources are most remote and ambient PIC levels are lower (Figures 2a and 2b). Iron availability may thus influence the balance between calcification (e.g., NCC) and POC production (e.g., NCP) by other phytoplankton groups, as shown by the bigger response of PIC than Chl to added Fe near Crozet, with the balance also impacting seawater pCO 2 .

2 dynamics near the SAF. This was also seen in data from CROZet natural iron bloom and EXport experiment (CROZEX) and the Southern Ocean CO 2 Atlas (SOCAT version 3) [Bakker et al., 2014 Pfeil et al., 2013 Fry et al., 2015 −1) is calculated as m is the observed alkalinity and Alk r is the riverine TA end‐member [Fry et al., 2015 The seasonal occurrence of coccolithophore calcification in the GCB has a significant impact on COdynamics near the SAF. This was also seen in data from CROZet natural iron bloom and EXport experiment (CROZEX) and the Southern Ocean COAtlas (SOCAT version 3) [.,.,]. Deepwater TA supply to the Southern Ocean increases alkalinity tracers such as TA* [.,] with drawdown of TA observed in the GCB co‐located with regions of high seasonal coccolithophore biomass. TA* (in units of µmol kg) is calculated aswhereand Alkis the observed alkalinity and Alkis the riverine TA end‐member [.,].

Ignoring gas exchange and horizontal transport, the summer cooccurrence of high coccolithophore biomass with elevated pCO 2 (i.e., seasonal change in pCO 2 = CO 2SEASONAL ) reflects the dominant influence on CO 2 due to a combination of upwelling (δCO 2UPWELLING ), net community organic production (NCP; δCO 2NCP ), and net community calcification (NCC; δCO 2NCC ), such that CO 2SEASONAL = δCO 2UPWELLING + δCO 2NCP + δCO 2NCC . In areas where upwelling and Ekman pumping bring up nutrients and CO 2 , seasonal organic carbon production (i.e., NCP), δCO 2NCP , is likely greater than δCO 2UPWELLING with the net result that seawater pCO 2 decreases. However, in areas of high coccolithophore growth and biomass, generation of CO 2 by calcification offsets the drawdown of CO 2 by organic production. Thus conceptually, there are three scenarios explaining the range of seawater pCO 2 observed across the SAF waters in the GCB: (1) where NCP > NCC, seawater pCO 2 is likely to decrease; (2) where NCP~NCC, seawater pCO 2 is likely to remain unchanged; and (3) where NCP < NCC, seawater pCO 2 is likely to increase. These three scenarios all likely occur in the GCB.

In the third scenario, coccolithophore calcification in the SAF can potentially shift waters from a CO 2 sink to source (Figures 2d, 6, and 7), hinted at in a few previous studies in the Sargasso Sea, Bay of Biscay, and the North Atlantic Ocean [Bates, 2007; Robertson et al., 1994; Smith et al., 2012]. Recently, Salter et al. [2014] highlighted the importance of the carbonate counter pump (long‐term production of CO 2 associated with CaCO 3 precipitation [Zeebe, 2012]) in regions of the Southern Ocean, including around Crozet. We confirm this directly for the first time with measurements of seawater pCO 2 . Such shifts in the CO 2 sink‐source status of SAF surface waters have direct relevance to global air‐sea CO 2 fluxes and coccolithophore‐CO 2 feedback.

Another diagnostic for the importance of the carbonate counter pump is the PIC:POC export ratio (rain ratio), which is an important control for atmospheric CO 2 [Archer et al., 2000]. Using an ocean biogeochemical‐transport box model, Sarmiento et al. [2002] determined a global PIC:POC export ratio of 0.06, with highest values in subtropical gyres (~0.08 in the subtropical Atlantic and Indian Oceans) and lowest values in the Southern Ocean (~0.01 in the Southern Ocean Atlantic and Indian subpolar gyres, defined as waters south of 45°S). Our estimates of PIC:POC export ratio using the PIC:POC ratio of >51 µm particles were considerably higher (0.08–1.08), but the patterns we found were broadly similar, with polar and subtropical waters having consistently low and high PIC:POC export ratios, respectively (Figure 10). One notable difference was that we also found variable PIC:POC export ratios in subpolar waters. Differences in the magnitude of our estimates are likely due to the different time scales of integration represented by the two methods. The Sarmiento et al. [2002] estimate uses observed profiles of DIC, alkalinity, and NO 3 , usually taken during summertime cruises, for their calculations, and represents an integration over the growing season. In contrast, our estimate is based on a snapshot of the large particle population at the time of sampling by in situ filtration. Since our cruises were timed to maximize expected coccolithophore production, our estimate may represent times of highest PIC:POC export ratio.

Time scales of integration aside, we still find that the highest PIC:POC export ratios (>0.7) were all in subpolar waters south of 45°S adjacent to the Patagonia Shelf or Crozet Island, with RNPG > 1 (Figure 10). Away from these putative trace metal sources in waters with RNPG > 1, PIC:POC export was low (Figure 10c). Export was generally not measured in fronts because of the strong current velocities and logistical difficulties in sampling in these dynamic regions. Given the high PIC detected in regions of high velocity (Figure 4), we speculate that these would also be regions of high PIC:POC export ratio. Subpolar waters with RNPG > 1 thus have very variable PIC:POC export ratios (Figure 10)—high when near a source of trace metals, and potentially in fronts, and low otherwise.

Recent global estimates of the Alk* tracer [Fry et al., 2015], which isolates the effects of CaCO 3 cycling on alkalinity, show elevated Alk* in the Southern Ocean [Carter et al., 2014; Fry et al., 2015]. Elevated Alk* in the Southern Ocean is thought to result from the upwelling of high Alk* waters, and does not preclude calcification in surface waters, which would otherwise tend to decrease Alk*. Indeed, further evidence of sustained PIC production in the GCB comes from the sediment composition in the STF and SAF zones dominated by CaCO 3 [Longhurst, 1998]. Overall, evidence suggests that the STF and SAF zones have a relatively high PIC:POC export ratio, which would thus act to increase ocean CO 2 .

Our 234Th flux results (Figure 9) add to the growing body of evidence that there is not a universal CaCO 3 ballast effect operating in the upper ocean [Le Moigne et al., 2012; Riley et al., 2012; Thomalla et al., 2008]. Instead, the correlation between PIC flux and the transfer efficiency of POC flux through the upper mesopelagic (Figure 9b) suggests a role for PIC in extending the remineralization length scale of sinking POC. However, regions of more efficient transfer of the exported POC flux to the mesopelagic were not high POC flux regions. We suggest that the CO 2 ‐enhancing role of the carbonate pump is more important than any ballast‐induced enhancement of the soft‐tissue POC pump. A corollary to this is that any reduction in calcification due to ocean acidification should be expected to have a greater impact on the carbonate pump than the soft tissue pump and thus be an overall negative feedback on pCO 2 [Riebesell et al., 2009].

Based on this work, we propose a conceptual model on the growth of phytoplankton in the SO. Our results can be divided into a four‐way mandala (Figure 11), where RNPG primarily determines whether diatoms can outcompete nonsiliceous phytoplankton (including coccolithophores) when relieved of a limiting micronutrient (Figure 8), while iron further regulates the growth of all phytoplankton in ACC waters. Each quadrant has different biogeochemical ramifications to the seawater pCO 2 and export carbon fluxes.

Figure 11 Open in figure viewer PowerPoint Conceptual model for balance of coccolithophores and noncalcifying phytoplankton growth in GCB versus diatom growth (determined as RNPG) and effects on CO 2 source/sink dynamics and sinking particle fluxes. Abbreviations used in table: coccos = coccolithophores; dinos = dinoflagellates; nanophytopl. = nanophytoplankton; picophytopl. = picophytoplankton; eff. = efficiency; ARC = Agulhas Return Current.