# Surface ocean carbon dioxide during the Atlantic Meridional Transect (1995–2013); evidence of ocean acidification

Vassilis Kitidis, Ian Brown, Nicholas J. Hardman-Mountford, Nathalie Lefèvre

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Vassilis Kitidis, Ian Brown, Nicholas J. Hardman-Mountford, Nathalie Lefèvre. Surface ocean carbon dioxide during the Atlantic Meridional Transect (1995–2013); evidence of ocean acidification. Progress in Oceanography, 2017, 158, pp.65 - 75. 10.1016/j-pocean.2016.08.005. hal-01832117

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Surface ocean carbon dioxide during the Atlantic Meridional Transect (1995-2013); evidence
of ocean acidification.

Vassilis Kitidis¹,*, Ian Brown¹, Nicholas Hardman-Mountford¹,², Nathalie Lefèvre³

1: Plymouth Marine Laboratory, PL13DH, United Kingdom

2: CSIRO Oceans & Atmosphere, Centre for Environment and Life Sciences, Floreat 6014, WA, Australia

3: IRD-LOCEAN, Sorbonne Universités (Université Pierre et Marie Curie-CNRS-MNHN), 4 place Jussieu, 75252 Paris Cedex 05, France

*: corresponding author (vak@pml.ac.uk)
Abstract

Here we present more than 21000 observations of carbon dioxide fugacity in air and
seawater (fCO2) along the Atlantic Meridional Transect (AMT) programme for the period
1995-2013. Our dataset consists of 11 southbound and 2 northbound cruises in boreal autumn
and spring respectively. Our paper is primarily focused on change in the surface-ocean
carbonate system during southbound cruises. We used observed fCO2 and total alkalinity
(TA), derived from salinity and temperature, to estimate dissolved inorganic carbon (DIC)
and pH (total scale). Using this approach, estimated pH was consistent with
spectrophotometric measurements carried out on 3 of our cruises. The AMT cruises transect a
range of biogeographic provinces where surface Chlorophyll a spans two orders of magnitude
(mesotrophic high latitudes to oligotrophic subtropical gyres). We found that surface
Chlorophyll a was negatively correlated with fCO2, but that the deep chlorophyll maximum
was not a controlling variable for fCO2. Our data show clear evidence of ocean acidification
across 100 degrees of latitude in the Atlantic Ocean. Over the period 1995-2013 we estimated
annual rates of change in: a) sea surface temperature of 0.01 ± 0.05 degrees C, b) seawater
fCO2 of 1.44 ± 0.84 µatm, c) DIC of 0.87 ± 1.02 µmol per kg and d) pH of -0.0013 ± 0.0009
units. Monte Carlo simulations propagating the respective analytical uncertainties showed
that the latter were < 5% of the observed trends. Seawater fCO2 increased at the same rate as
atmospheric CO2.

Highlights

* Observations along the AMT track, over 19 years showed increase in seawater CO2.

* Seawater CO₂ increase followed the respective atmospheric increase

* Ocean Acidification rate of 0.0013 units per year was observed

* Chlorophyll *a* was a major control of surface CO₂ along-track

Keywords

* Carbon dioxide; Carbonates; Greenhouse effect; Climatic changes; Acidification; Ocean acidification

Regional Index Terms
* Atlantic Ocean
# 1. Introduction

Carbon dioxide ($CO_2$) is a long-lived trace gas in the atmosphere contributing to the greenhouse effect. Atmospheric $CO_2$ has increased by approximately 45% since pre-industrial times due to anthropogenic emissions from fossil fuel combustion, cement production and land use changes. While $CO_2$ has been accumulating in the atmosphere, the oceans and land have acted as a sink over the same period taking up about half of anthropogenic $CO_2$ emissions (Le Quéré et al., 2015). Thereby, for the last decade, the oceans have taken up 29% of anthropogenic $CO_2$ emissions (Le Quéré et al., 2015). While this uptake moderates the accumulation of $CO_2$ in the atmosphere, and by extension global warming, the dissolution of $CO_2$ in seawater causes ocean acidification (OA) (Caldeira and Wickett, 2003).

Net $CO_2$ uptake by the oceans is controlled by the solubility and biological pumps. The former describes dissolution of $CO_2$ in seawater which is driven by the sea-air concentration gradient, the gas transfer velocity and solubility of $CO_2$ in seawater (a function of temperature and salinity). The biological pump describes the net uptake of $CO_2$ by biota and is controlled by the balance between photosynthesis and respiration which in turn determine the trophic status of the ocean (autotrophy vs. heterotrophy). The majority of anthropogenic C ($C_{anth}$) uptake to date is thought to be driven by the solubility pump. Thereby, in the Atlantic Ocean, $C_{anth}$ uptake is largely restricted to surface waters (~400 m) with the exception of the North Atlantic where deep water formation transports $C_{anth}$ to the ocean interior (Gruber, 1998; Orr et al., 2001; Wanninkhof et al., 2010). Deep-water formation in the north Atlantic is a critical component of the meridional overturning circulation (MOC). and constitutes the biggest oceanic sink for $C_{anth}$ globally (Khatiwala et al., 2013).

Nevertheless, high spatial and temporal variability related to natural cycles is encountered across the Atlantic Ocean biogeochemistry. This is reflected in surface $pCO_2$ across different biogeographic provinces (Lefevre and Moore, 2000). The AMT programme, transects a number of these provinces from oligotrophic subtropical gyres to highly productive upwellings (Hooker et al., 2000). In the open ocean, at temperate latitudes along the AMT track, seasonal changes in photosynthesis and surface mixing result in high and highly-seasonal $pCO_2$ variability (Lefevre and Moore, 2000). Deep winter mixing entrains $CO_2$-rich deep water into the mixed layer leading to efflux of $CO_2$ to the atmosphere in winter, while the concomitant entrainment of nutrients enhances photosynthesis in spring/summer and drawdown of atmospheric $CO_2$ (Gruber et al., 2002). Coastal waters on the margins of the Atlantic Ocean are subject to terrestrial influences, tidal currents and show
greater short-term variability in pCO₂ than observed in the open ocean (Bianchi et al., 2005; Johnson et al., 2013; Marrec et al., 2013). The eastern margins of the Atlantic Ocean are dominated by upwelling systems: the Iberian, Mauritanian, Canary Current and Benguela upwellings. In these regions CO₂-rich deep water is upwelled resulting in efflux to the atmosphere, while the offshore transport of nutrients in upwelling filaments enhances photosynthesis and CO₂ drawdown (Gonzalez-Davila et al., 2009; Loucaides et al., 2012; Magdalena Santana-Casiano et al., 2009; Perez et al., 1999). The tropical Atlantic acts as a source of CO₂ to the atmosphere due to equatorial upwelling with some notable exceptions (Lefevre, 2009). Firstly, a persistent sink in the region of the inter-tropical-convergence zone (ITCZ; 5-8 °N) where low salinity increases the solubility of CO₂ (Lefevre et al., 1998). Secondly, a seasonal sink in the western tropical Atlantic driven by low salinity in the Amazon river plume (>40 °W) (Lefevre et al., 2010). The central North and South Atlantic, are dominated by the oligotrophic subtropical gyres where pCO₂ is generally close to equilibrium with the atmosphere (Lefevre and Moore, 2000).

The projected decrease in seawater pH under OA [up to 0.4 units by 2100 (Caldeira and Wickett, 2003; Feely et al., 2009)] is likely to impact biogeochemical cycles in the oceans, including calcification (Anthony et al., 2011), C-cycling (Riebesell et al., 2007) and N-cycling (Beman et al., 2011; Kitidis et al., 2011). It has been suggested that some genotypes of calcifying coccolithophores may be replaced by others which are able to compensate by calcifying more heavily (Beaufort et al., 2011). However, the ability to upregulate calcification has an associated energetic cost which may reduce the fitness of individuals (Wood et al., 2008).

Here, we focus on changes in pCO₂ and pH in surface waters of the Atlantic Ocean as sampled by the AMT programme between 1995 and 2013. There is recent evidence that the North Atlantic CO₂ sink may be decreasing in magnitude, though this may be part of natural variability. Long-term open ocean observations of CO₂ partial pressure (pCO₂) have shown that the magnitude of the North Atlantic sink is highly variable inter-annually driven by winter mixed layer depth variability (Bates, 2007; Gruber et al., 2002; Santana-Casiano et al., 2007; Schuster and Watson, 2007; Schuster et al., 2009). Progressive freshening of the North Atlantic over the last 50 years and into the future may slow the MOC critically (Curry and Mauritzen, 2005) and thereby impact on the capacity of the N. Atlantic to take up C<sub>anth</sub>. Furthermore, time-series observations in the North Atlantic have revealed an increase in pCO₂ and concomitant decrease in pH, with the latter decreasing at -0.002 units annually (Bates, 2007; Bates et al., 2014; Lauvset and Gruber, 2014; Olafsson et al., 2009; Santana-
Casiano et al., 2007). In contrast, the South Atlantic is generally less well sampled with a recent study reporting an annual pH change of -0.001 (Lauvset et al., 2015).

## 2. Methods

### 2.1 Cruise programme

The AMT programme has covered 20 years of observations in the Atlantic Ocean since 1995 over nearly 100 degrees of latitude (Rees et al., 2015). The majority of cruises have taken place onboard the RRS James Clark Ross (JCR) on its annual southbound passage (September/November) from the UK to Antarctica. Northbound cruises in April/May were carried out predominantly in the early phase of the programme. More recently, AMT cruises have also taken place on the RRS James Cook and RRS Discovery. Opportunistic pCO₂ observations have also taken place in years when JCR has not hosted an AMT or on recent northbound cruises. Here we present pCO₂ results from 11 southbound and 2 northbound cruises (Table 1). Of these, 8 cruises have been part of the AMT programme and a further 3 have used the pCO₂ system on JCR whilst on-passage south (4 cruises) or north (1 cruise) along the AMT track. In total, 21280 pCO₂ observations were made over 19 years.

<table><caption>**Table 1:** List of cruises presented here. Cruises denoted A# were part of the AMT programme (where # denotes the AMT number). Cruises denoted J#N or J#S denote opportunistic observations from RRS JCR along the AMT cruise track (north- and south-bound respectively; where # denotes the year). Direction denotes southbound (S) or northbound (N) and the number of pCO₂ observations per cruise is (n).</caption><thead><tr><th>Cruise</th><th>Direction (n)</th><th>Ship</th><th>Start</th><th>End</th></tr></thead><tbody><tr><td>A1 (AMT 1)</td><td>S (455)</td><td>JCR</td><td>21/9/1995</td><td>24/10/1995</td></tr><tr><td>A2 (AMT 2)</td><td>N (254)</td><td>JCR</td><td>22/4/1996</td><td>28/5/1996</td></tr><tr><td>A3 (AMT3)</td><td>S (1931)</td><td>JCR</td><td>16/9/1996</td><td>25/10/1996</td></tr><tr><td>A7 (AMT7)</td><td>S (1805)</td><td>JCR</td><td>14/9/1995</td><td>25/10/1998</td></tr><tr><td>J07S</td><td>S (648)</td><td>JCR</td><td>24/9/2007</td><td>23/10/2007</td></tr><tr><td>J10S</td><td>S (932)</td><td>JCR</td><td>1/10/2010</td><td>24/10/2010</td></tr><tr><td>A20 (AMT20)</td><td>S (1017)</td><td>James Cook</td><td>12/10/2010</td><td>25/11/2010</td></tr><tr><td>J11N</td><td>N (775)</td><td>JCR</td><td>30/4/2011</td><td>25/5/2011</td></tr><tr><td>J11S</td><td>S (1565)</td><td>JCR</td><td>27/9/2011</td><td>19/10/2011</td></tr><tr><td>A21 (AMT21)</td><td>S (6349)</td><td>Discovery</td><td>29/9/2011</td><td>14/11/2011</td></tr><tr><td>J12S</td><td>S (114)</td><td>JCR</td><td>12/10/2012</td><td>8/11/2012</td></tr><tr><td>A22 (AMT22)</td><td>S (1758)</td><td>James Cook</td><td>10/10/2012</td><td>24/11/2012</td></tr><tr><td>A23 (AMT23)</td><td>S (3677)</td><td>JCR</td><td>5/10/2013</td><td>8/11/2013</td></tr></tbody></table>
The ships stopped for one or two daily hydrocasts (usually pre-dawn and at solar noon), during which seawater was collected from the upper 300 m of the water column for various biogeochemical observations and experiments.

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Cruise tracks for southbound cruises (left), northbound (middle) and the respective latitude versus Julian day (right; noon on 1st January is Julian Day 1.5). Cruise names are followed "- ##" denoting the year (e.g. A1-95 was in 1995; see Table 1 for exact dates)</figcaption></figure>

## 2.2 CO₂ fugacity observations

Between 1995 and 1998 an autonomous CO₂ system was deployed encompassing a non-dispersive infrared detector (LiCor, Li6262), percolator-type equilibrator, mechanical and electronic hardware (Cooper et al., 1998). The system was calibrated using commercial CO₂ standards referenced to World Meteorological Organization primary standards (Cooper et al., 1998). The uncertainty of the system deployed in the period 1995-1998 was estimated as 1-2 µatm, during laboratory tests (Lefevre et al., 1998).

From 2007 onwards an evolution of this system, the PML-Dartcom *LivepCO₂* system was deployed. The system encompasses a non-dispersive infrared detector (LiCor, Li840), showerhead equilibrator (vented through a parallel second equilibrator), Peltier-dryer, gas-sampling and electronics hardware. The system was connected to the underway-seawater flow and set up to sample every 20 minutes. The intake of the ship's underway-seawater system is located at approximately 7 m depth for all three ships. The *LivepCO₂* system was calibrated using secondary CO₂ standards every hour (BOC gases Ltd; nominal 0, 250 and
450 ppmv CO₂ in synthetic air). In turn, these were calibrated against NOAA-certified primary reference standards (National Oceanic and Atmospheric Administration; 244.9 and 444.4 ppmv CO₂). Between 2007 and 2010, the system was fitted with a single ‘closed loop’ equilibrator which was prone to equilibration-headspace pressure changes (Kitidis et al., 2012). This effect was ship-specific and was not found to affect the system installed on RRS JCR (i.e. the data presented here). Laboratory inter-comparison tests showed that our system was in agreement with independent, similar systems (within ±0.5 µatm). An at-sea inter-comparison of the JCR system in 2008 showed agreement with an independent system within ±3 µatm (Jones et al., 2012). The 2010-onwards setup (with the vented equilibrator) was tested during a separate at-sea inter-comparison exercise against independent measurements of fCO₂ and other carbonate system parameters (TA, DIC, pH) with an accuracy of ±4 µatm (Ribas-Ribas et al., 2014). It should be noted that the latter was equivalent to the analytical uncertainty of fCO₂ derived from TA/DIC/pH (e.g. typical uncertainty of ±2 µmol L⁻¹ for DIC is equivalent to ±4 µatm fCO₂ at 20 °C and salinity 35).

Data reduction followed standard best practices (Dickson et al., 2007). Data are reported as CO₂ fugacity (fCO₂) rather than partial pressure in order to account for the non-ideal behaviour of CO₂. The sea-air difference in CO₂ fugacity (ΔfCO₂) was calculated by subtracting seawater fCO₂ (fCO₂ sea) from concurrent atmospheric measurements (fCO₂ air).

## 2.3 Carbonate observations

The concentrations of individual species of a CO₂-in-water solution can be determined by knowing two of the four parameters of the carbonate system (fCO₂, DIC, TA and pH). As CO₂ dissolves-in and reacts with water, it forms carbonic acid which further dissociates to carbonate and bicarbonate. The sum of these terms is called Dissolved Inorganic Carbon (DIC). Total Alkalinity (TA) is defined as the number of moles of hydrogen ion equivalents in excess of proton acceptors. Finally, pH is defined as negative log₁₀ of hydrogen ion concentration. During AMT 22 (cruise A22), samples were collected for TA/DIC from the daily hydrocasts. Further samples were collected for spectrophotometric pH analyses on AMT21, AMT22 and AMT23. Typically, 3 samples per cast were collected for TA/DIC (surface, DCM and deepest Niskin bottle) and 11 samples per cast for pH (focusing primarily at SML and pycnocline). TA/DIC samples were collected in 250 mL clear glass bottles straight from the Niskin bottles using dilute-HCl-cleaned Si-tubing and preserved using 100 µL of saturated HgCl₂ solution. The bottles were sealed with glass stoppers which were lightly greased using Apiezon H grease (Sigma-Aldrich, Z273562-1EA). The samples were
stored until analysis in the laboratory (within 12 months). DIC samples were analysed using an Apollo SciTech AS-C3 DIC analyser. The analyser draws a pre-defined volume of sample which is acidified with H₃PO₄ to convert all DIC into CO₂ which is sparged with N₂ and passed through a solid state infrared detector (LiCor, LI7000). TA was analysed immediately following DIC analysis using an Apollo SciTech AS-ALK2 analyser. The instrument follows an open-cell-titration of a known volume of sample to pH 3 using HCl (Dickson et al., 2007). DIC and TA measurements were calibrated using certified reference materials from the Scripps Institute of Oceanography (A.G. Dickson; batch 126 and 127). The precision and accuracy of replicate CRM analyses were better than ±2 µmol L⁻¹ (n=6). pH was determined spectrophotometrically onboard the ship using the m-cresol-purple dye (Clayton and Byrne, 1993). The dye has two absorbance maxima at 434 nm and 578 nm, the ratio of which is pH-, temperature- and salinity dependent. Samples were collected from the Niskin bottles using dilute-HCl-cleaned Si-tubing and placed in a water bath at 25 °C. 100 µL of dye were added to 35 mL of sample and absorbance was recorded on a spectrophotometer (Perkin Elmer, Lamda 35). The measurement temperature was recorded using a National Institute of Standards and Technology -traceable thermometer (VWR, VWRI620-2000). Sample salinity was taken from the conductivity temperature depth sensor (CTD) on the hydrocast (see below). Absorbance readings were corrected for the pH-change caused by the addition of the dye and pH is reported on the “Total” activity scale (pHₜ) (Dickson et al., 2007). The precision of triplicate pHₜ samples was ±0.001 units or better (triplicate samples were analysed on 17 occasions).

In order to extend our data, we used a generic function of salinity and temperature to obtain a best fit for our TA data (Lee et al., 2006). The fit is given by equation 1:

$$TA = 2305 + 53.97(S - 35) + 2.74(S - 35)^2 - 1.16(T - 20) + 0.04(T - 20)^2 \quad (1)$$

where T is the temperature and S is the salinity of the samples.

## 2.4 Other observations (Temperature, Salinity, Chlorophyll).

Sea surface temperature (SST), temperature at depth and salinity were recorded with a thermosalinograph underway and a conductivity temperature depth (CTD) instrument on the hydrocast (Sea-Bird Electronics, models: ocean logger / SBE45 / 9plus). Salinity was calibrated by discrete bottle sample analysis (Guildline, model: Autosal) using IAPSO standards. Temperature on the hydrocast was calibrated by discrete reversing thermometers (Sensoren Instrumente Systeme) until 1998. Subsequently, calibration of the platinum resistance thermometers was undertaken annually by the manufacturer. Discrete Chlorophyll
a samples were collected underway and at depth using the Niskin bottles on the hydrocast and analysed fluorometrically following acetone extraction (Welshmeyer, 1994). Briefly, Chlorophyll samples were filtered through 0.7 µm GF/F filters and placed in acetone for 18-36 hours before fluorescence was measured (Turner Designs, AU10 fluorometer). Post 1996 (AMT3 onwards), these samples were used to calibrate a fluorometer on the hydrocast sensor package. For each AMT hydrocast station (designation A* in Table 1), the depth of the surface mixed layer (SML) was computed as the depth where the derivative of σ<sub>T</sub> (σ<sub>T</sub> is defined as seawater density – 1000 kg m<sup>-3</sup>) (∂(σ<sub>T</sub>)/∂(Z), where Z is water depth in m) exceeded 0.01 m<sup>-1</sup> and 0.04 m<sup>-1</sup>. The former criterion identified short-term stratification of the water column (days to weeks), while the latter identified seasonal stratification over longer time scales (months).

2.5 Carbonate system internal consistency and trend uncertainty

The fit of TA predicted by equation 1 (TA<sub>predicted</sub>) against measurements is shown in
Figure 2. We used TA<sub>predicted</sub> and fCO<sub>2</sub> to calculate pH<sub>T predicted</sub> for surface samples collected
during AMT21-23 using CO2SYS software (.xls; v.14) (Lewis and Wallace, 1998) using the
H<sub>2</sub>SO<sub>4</sub> dissociation constants of Dickson et al. (Dickson, 1990) and the carbonic acid
dissociation constants of Millero et al. (Millero et al., 2006). The calculated pH<sub>T predicted</sub> values
were significantly correlated with pH<sub>T</sub> measurements on the three AMT cruises (Pearson
R<sup>2</sup>=0.94, p<0.001, df=184) with a slope and intercept indistinguishable from 1 and 0
respectively (slope = 1.02 ± 0.02 and intercept = -0.2 ± 0.2) (Figure 2). The standard error of
residuals was 0.0056 pH units. This internal consistency of a derived independent variable
with measurements across three independent cruises gives us confidence to apply equation 1
across our dataset and calculate TA<sub>predicted</sub> for all cruises. The combination of fCO<sub>2</sub> and
TA<sub>predicted</sub> gives two of the four carbonate system parameters required to calculate the
remaining two, here DIC and pH<sub>T</sub>. For selected surface samples on AMT22, we thereby had
all four carbonate system parameters (DIC,TA, pH<sub>T</sub> and pCO<sub>2</sub>). All possible pairs of these
were used to calculate the remaining pair and found to be consistent with observations.
<figure><img src="image_3.png" /></figure>

**Figure 2:** TA predicted from equation 1 versus measurements on AMT22 (left; TA<sub>predicted</sub>=1.00±0.02 × TA<sub>measured</sub> - 6±49) and pH<sub>T</sub> calculated from TA<sub>predicted</sub> and fCO<sub>2</sub> against measurements on AMT21, AMT22 and AMT 23 (right; pH<sub>T predicted</sub>=1.02±0.02 × pH<sub>T measured</sub> -0.2±0.2). Dashed lines represent the respective regression lines while solid lines represent the 1:1 line.

Our analysis of fCO₂ sea and pH_T trends includes analytical uncertainty which may bias their respective trends. As stated above, the analytical uncertainty for fCO₂ sea was ±4 µatm which is equivalent to ±0.0035 pH_T units at the mean SST and salinity for our dataset. Potentially larger sources of uncertainty are a) the analytical uncertainty of TA, b) uncertainty arising from equation 1 for TA<sub>predicted</sub> and c) uncertainty arising from the calculation of pH_T <u>predicted</u> (using CO2SYS). The combined effect of the latter uncertainties on pH_T is captured by the standard error of the regression for pH_T<sub>predicted</sub> (±0.0056 units; see above and Figure 2).

In order to estimate the effect of the fCO₂ sea and pH_T predicted analytical uncertainties (±4 µatm and 0.0056 units respectively) on the respective trends we carried out a Monte Carlo uncertainty propagation. We used the 'jitter' function in R to introduce random noise to our data (within 4 µatm and 0.0056 units respectively). As the noise introduced was by definition 'random', we ran 10³ iterations of the script, in order to ensure representative results. The mean trend for each 1-degree data-bin (fCO₂ sea data) including this random noise was statistically indistinguishable from the corresponding observations [paired T-test, n=99, the mean p-value of 10³ iterations of 'random noise addition' was 0.50±0.29]. Similarly, the mean trend in pH_T for each 1degree data-bin including this random noise was statistically indistinguishable from the corresponding observations [paired T-test, n=99, the mean p-value of 10³ iterations of 'random noise addition' was 0.49±0.29].
Subsequently, we performed a kernel density estimation (KDE) on the 10³ mean fCO₂ sea and pH_T trend-estimates using a Gaussian fit ('density' function in R). From the KDE we computed the respective 95 % confidence intervals for the effect of random noise addition on the mean trends. The corresponding density histograms are shown in (Figure 3) and the 95 % confidence intervals for the contribution of the analytical errors to the fCO₂ sea and pH_T trends from this analysis were ±0.05 µatm and ±7 × 10⁻⁵ pH units respectively (or < ±5 % of the observed trends). These 'random' analytical errors thereby cancelled each other out when computing the respective trends. We are therefore confident that the fCO₂ sea and pH_T analytical and calculation errors did not bias our results regarding observed trends in fCO₂ sea and pH_T.

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Density histogram of mean annual fCO₂ sea (left) and pH_T trends (right). Random noise (within the analytical uncertainty) was added to each of 10³ iterations used here. Solid vertical lines represent the observed trends and dashed lines represent the Gaussian fit from the kernel density estimation.</figcaption></figure>

## 2.6 Multiple linear regression: fCO₂ and ΔfCO₂

In order to identify the main controls on fCO₂ sea we carried out a multiple linear regression (MLR) analysis in R (version 3.1.2). The rationale was to build an MLR model with the least number of input variables, where SST, Salinity and fCO₂ air determine the physical dissolution of CO₂ in seawater (physical controls), while Chlorophyll *a* represents biological controls of CO₂ (as a first order approximation). A further variant of the MLR used depth integrated Chlorophyll *a*, using a dynamic maximum depth of integration (defined as $\partial(\text{Chlorophyll } a)/\partial(\text{depth})<$ standard deviation over the deepest 10 m of each cast). However,
this was found to covary with surface Chlorophyll a here and is therefore not discussed further. All MLR analysis was carried out on AMT cruises only (designation A* in Table 1) and from AMT3 onwards when a continuous fluorometric-sensor was fitted to the hydrocast. In total, 308 hydrocast profiles were processed in R and used in the MLR analysis.

### 3. Results

The water column typically exhibited stratification with a surface mixed layer (SML) which is more pronounced in the subtropical gyres (up to 135 m and 186 m in depth in the northern and southern sub-tropical gyres respectively). At the base of the SML, a deep chlorophyll maximum (DCM; 120 m and 150 m in the northern and southern gyres respectively) and $O_2$ maximum ($O_{2 max}$; 80 m and 90 m in the northern and southern gyres respectively) are typically found. At higher latitudes (north of 40 °N and south of 40 °S), highest chlorophyll and $O_2$ are observed near the surface (5-30 m in depth). For logistical and scientific reasons, the cruise tracks have varied over the last two decades (Smyth et al., submitted) (Figure 1). Therefore, it is important to note that some changes in $pCO_2$ over this period may be ascribed to track- or timing- variations. This is discussed in further detail below.

Salinity, surface Chlorophyll-$\alpha$ concentration ($Chl_{surf}$), atmospheric $fCO_2$ air, seawater $fCO_2$ sea and $\Delta fCO_2$ are shown in Figure 4 (southbound) and Figure 5 (northbound; $fCO_2$ air, $fCO_2$ sea and $\Delta fCO_2$ only) as averages in 1 degree of Latitude bins. The salinity distribution reflects the balance between freshwater inputs and evaporation, highlighting the North Atlantic and South Atlantic subtropical gyres (NAG: 25-40 °N and SAG: 15-30 °S respectively) where high salinity is typically found during AMT. Conversely, the subtropical gyres are characterised by low surface Chlorophyll-$\alpha$ concentration. In the equatorial region, high precipitation in the Inter Tropical Convergence Zone (ITCZ) reduces salinity, while equatorial upwelling supplies nutrients which fuel higher $Chl_{surf}$. $fCO_2$ air increased over the period of observations by 30-40 µatm (1995-2013). $fCO_2$ sea showed a similar increase in our data, although there is considerably more noise, particularly at higher latitudes along the cruise tracks. $fCO_2$ sea and $\Delta fCO_2$ showed a remarkably consistent distribution pattern with latitude in agreement with an earlier study describing the latitudinal distribution of $fCO_2$ for AMT1-3 (Lefevre and Moore, 2000). Thereby, at high latitudes (north of 40°N and south of 30 °S), surface waters were generally undersaturated with respect to atmospheric equilibrium (negative $\Delta fCO_2$). The North Atlantic subtropical gyre (NAG: 25-40 °N) was oversaturated in boreal autumn (southbound cruises) and undersaturated in boreal spring (northbound cruises).
The South Atlantic subtropical gyre (SAG: 15-30 °S) was undersaturated or in equilibrium in both spring and autumn. The region of 10-25 °N was oversaturated in boreal autumn, and over-/under-saturated in spring 1996 and 2011 respectively. The region of the NECC, North Equatorial Counter-Current (3-10 °N) (the ITCZ is at its southernmost position south of the equator (0-2°S) in March and migrates seasonally to reach about 10°N in July) was generally undersaturated in boreal autumn and in equilibrium with the atmosphere in boreal spring. The equatorial region and transition to the SAG (2 °N -15 °S) was generally oversaturated in both spring and autumn.
<figure><img src="image_5.png" /><figcaption>**Figure 4:** Salinity, Chlorophyll-α, CO₂ fugacity in air, seawater fCO₂ and ΔfCO₂ against latitude for southbound cruises in boreal autumn. Negative ΔfCO₂ in bottom panel denotes undersaturation of surface seawater with respect to atmospheric equilibrium.</figcaption></figure>
<figure><img src="image_6.png" /></figure>

**Figure 5:** CO₂ fugacity in air (top), seawater (middle) and sea-air difference (bottom) for northbound cruises in boreal spring. Negative ΔfCO₂ in bottom panel denotes undersaturation of surface seawater with respect to atmospheric equilibrium.

In order to determine rates of change over the 19 years covered by our observations, we grouped data from each cruise into 1 degree of latitude bins. The slope of each parameter with time was then calculated for each bin (e.g. fCO₂ sea vs. Year in 12-13 °N bin). This analysis was limited to southbound cruises (n=11) since the limited number of northbound cruises (n=2) would not give a statistically significant trend. The rate of fCO₂ sea change in each latitudinal bin is shown in Figure 6. Atmospheric fCO₂ air increased by 1.47±0.35 µatm y⁻¹ over the 19 years covered by our cruises. Seawater fCO₂ sea from our data increased by 1.44±0.84 µatm y⁻¹ (Figure 6). The mean rate of fCO₂ sea increase for 1 degree of latitude binned data was statistically indistinguishable from the respective mean rate of fCO₂ air increase (paired T-test, p=0.679, n=99). The increase in surface seawater CO₂ therefore followed the increase in atmospheric CO₂. Nevertheless, there were regional exceptions to this, notably between 30-35 °N, 7-12 °N and south of 40 °S. These exceptions may be due to cruise track differences, where the early cruises followed a more westerly track passing closer to the Canary- and Mauritanian-upwelling systems where one may expect higher fCO₂. Nevertheless, this was not reflected in SST or salinity. Park and Wanninkhoff found that winter fCO₂ sea did not follow the atmospheric increase in the Caribbean Sea and attributed
this to changes in SST and mixed layer depth via climatic forcing (Park and Wanninkhof, 2012). In our study, we did not observe changes in SST or MLD in the regions of low fCO₂ sea increase. As expected, the mean rate of ΔfCO₂ change was not significantly different from zero (mean ΔfCO₂ = -0.03±0.82 µatm y⁻¹, T-test, p=0.679, df=99) (Figure 6). The mean rate of DIC change was 0.87±1.02 µmol kg⁻¹ y⁻¹ and was significantly different from zero (T-test, p<0.001, df=99). The respective mean rate of pHₜ predicted change (see section 2.3) was –0.0013±0.0009 units y⁻¹ (Figure 6) and was significantly different from zero (T-test, p<0.001, df=99). A mean warming trend of 0.04±0.10 °C y⁻¹ was found for SST, but this was heavily biased by high apparent warming north of 38 °N. In this northernmost subset of our data there may be a bias introduced by cruise track and/or timing between early cruises (1995-1998) and later cruises (2010 onwards). The latter generally followed a more westerly track north of 38 °N. Excluding these data gave a mean SST warming of 0.01±0.05 °C y⁻¹. Both SST trends were statistically different from zero (T-test, p<0.001 and p<0.022 for df=99 and df=85 respectively).

<figure><img src="image_7.png" /><figcaption>**Figure 6:** Annual rate of change in CO₂ fugacity in seawater (fCO₂ sea) and annual rate of pHₜ change (bottom) for southbound cruises in boreal autumn.</figcaption></figure>

In the context of ‘change’ over this period, it is important to firstly consider differences
in cruise track and timing in order to identify any potential bias these may cause. For
example, a closer examination of the cruise tracks in Figure 1 shows that AMT20-22 (in 2010-2012) followed a more westerly track at these northern latitudes. These cruises would have entered the subtropical north Atlantic gyre earlier (and at higher latitude) where higher SST would be expected. Given that these cruises took place towards the latter part of our study, this may be a source of bias when estimating trends in our dataset. South of 38 °N, we observed a mean SST increase of 0.01 °C y⁻¹. In contrast the overall SST warming trend (0.04 °C y⁻¹), likely reflects some cruise track bias north of 38 °N. Based on CO₂ solubility calculations (Weiss, 1974), the corresponding annual change in fCO₂ sea would be +0.1 µatm y⁻¹ (for a 0.01 °C annual increase in SST). Since the observed increase in fCO₂ sea exceeds the effect of SST by one order of magnitude, we are confident that the latter does not substantially bias the trend in fCO₂ reported here. In order to further test the potential bias of different cruise tracks we compared the surface mixed layer (SML) and DCM depth across southbound AMT tracks. Our rationale for doing this was that if fCO₂ sea was influenced by track on particular cruises (e.g. upwelling filaments for more easterly tracks in the North Atlantic), then this influence would also manifest itself in the SML- and DCM-depths. Therefore, data were binned into 1 degree of latitude subsets and a linear fit of SML depth against time was applied to each subset. This analysis showed that the rate of change in SML depth was not statistically significant from zero for either ∂(σT)/∂(Z)=0.01 m⁻¹ or ∂(σT)/∂(Z)=0.04 m⁻¹ (T-test, p>0.05, n=83 in both cases). There was also no significant change in the depth of the DCM, surface Chlorophyll a or ChlDCM (T-test, p>0.05, n=83). Overall therefore we are confident that the track- and timing-differences of our cruises did not bias our results.

The MLR model used to identify controls of fCO₂ sea is given by:

$$f\text{CO}_2 \text{ sea} = f\text{CO}_2 \text{ air} + 2.6\text{SST} + 6.4\text{S} - 8.8 \text{ Chl}_{\text{surf}} \quad (\text{R}^2 = 0.51) \quad (2)$$

ΔfCO₂ showed a weaker correlation coefficient (R²=0.44). In both models fCO₂ sea was strongly and positively correlated with fCO₂ air, SST and salinity. Concurrently, fCO₂ sea was negatively correlated with surface Chlorophyll a. ΔfCO₂ followed the same pattern as fCO₂ sea in both models with the exception of fCO₂ air which was not significant.

## 4. Discussion

In this paper we focus on change in CO₂ fugacity (fCO₂), the air sea fugacity gradient (ΔfCO₂) and pHₜ of surface waters along the AMT track over a period of 19 years. In this context, it is important to establish that our observed changes in the carbonate system were indeed related to time and not analytical uncertainty or differences in cruise track. Random
error propagation, within the boundaries of combined analytical and calculation uncertainties,
showed that the effect of these uncertainties on the trends of fCO₂ sea and pH_T were less than 5
% of the observed trends (see section 2.5). The effect of cruise track variability is more
complex. The apparent SST warming trend of 0.01 °C y⁻¹ (excluding data north of 38 °N) was
consistent with the 1979-2012 trend reported by the Intergovernmental Panel on Climate
Change (Hartmann et al., 2013). It is also in agreement with the work of Aiken et al.
presented in this issue. The absence of significant differences in SML and DCM depth as well
as ChlSurf and ChlDCM, further corroborate our assumption that cruise track variability did not
bias our carbonate data and their interpretation.

For a given atmospheric CO₂ concentration, ΔfCO₂ can be affected by gas exchange, changes in solubility (a function of temperature and salinity) or biological activity (photosynthesis and respiration). Lateral transport and entrainment of deep water further determine fCO₂ sea and by extension ΔfCO₂. Where ΔfCO₂ is negative, surface waters are undersaturated with respect to equilibrium with the atmosphere, suggesting a source-sink imbalance. Net community production (NCP=photosynthesis-respiration) is frequently used to assess the net trophic status of the ocean (autotrophic vs. heterotrophic). Dissolved CO₂ in seawater is, to a certain extent, buffered against gas-exchange by the carbonate system. Therefore, unlike other gases, such as O₂, which have a surface mixed layer turnover time of days to weeks (through gas exchange), the turnover time of CO₂ is in the order of months (Broecker and Peng, 1974). Thereby, ΔfCO₂ retains a 'memory' of various processes, integrated over this timescale, such as NCP, particularly in regions such as the subtropical gyres where advective processes play a minor role.

The distribution of fCO₂ sea and its sea-air difference (ΔfCO₂) observed here were
consistent with previous AMT studies and the broader distribution of biogeographic
provinces (Hooker et al., 2000; Lefevre and Moore, 2000). In order to avoid repetition, the
reader is referred to these publications regarding the detailed distribution of fCO₂ sea and
ΔfCO₂ along biogeographic provinces sampled by the AMT programme. Nevertheless, it is
useful to make some broad observations on our 13-cruise dataset.

Firstly, the broad distribution of salinity with latitude consistently showed high salinity
in the subtropical gyres with lower values in temperate latitudes and around the equator
(Figure 3). Low salinity in the region of 5-10 °N, reflected high precipitation related to the
Intertropical Convergence Zone (ITCZ). Surface Chlorophyll a (Chlsurf) was lowest in the
subtropical gyres whilst higher Chlsurf was found in temperate waters and the equatorial
region where this coincided with carbon dioxide undersaturation (negative ΔfCO₂). These distribution patterns (Chlsurf and ΔfCO₂) were consistent with the distribution of productivity which is highest in mesotrophic temperate- and equatorial-waters along the AMT track (Serret et al., 2015; Tilstone et al., 2015). In contrast, near-equilibrium ΔfCO₂ was observed in the subtropical gyres consistent with lower productivity.

Secondly, given the slow turnover of CO₂ (via gas exchange), it is possible to infer on the trophic status of the open ocean from ΔfCO₂ data (Juranek et al., 2012). The topic of net autotrophy versus net heterotrophy in the open ocean is currently the subject of intense debate in the literature (Duarte et al., 2013; Williams et al., 2013). From our data, the north Atlantic gyre (NAG) showed a clear seasonal pattern with oversaturation in boreal autumn and undersaturation in boreal spring consistent with uptake of CO₂ by phytoplankton in spring (Bates, 2007). In contrast, ΔfCO₂ in the south Atlantic subtropical gyre (SAG) appears to be near equilibrium in austral autumn and undersaturated in austral spring. This difference between the two gyres is also reflected in AMT NCP data which show that the south Atlantic gyre is more frequently autotrophic while the northern gyre is in trophic balance or heterotrophic (Serret et al., 2015). While the in-vitro work of Serret et al. (2015) gives an instantaneous measure of NCP, ΔfCO₂ may give an integrated measure of NCP over longer time scales similar to geochemical measures of NCP. The inference of trophic status from ΔfCO₂ may be somewhat speculative, but is nevertheless consistent with previous work.

Thirdly, physical controls have a strong influence on fCO₂sea and hence ΔfCO₂ along the AMT track. For example, negative ΔfCO₂ in the NECC is consistent with this region acting as a sink for atmospheric CO₂ due to low salinity which in turn increases the solubility of CO₂ (Lefevre et al., 1998). Local hydrography also played a major part. For example, in the South Equatorial Current (1 °N to 15 °S) ΔfCO₂ was persistently positive, consistent with equatorial upwelling and advection of CO₂ rich waters from depth as well as the Benguela Upwelling system further East (Hooker et al., 2000). The major influence of physical and biological controls was also highlighted by our multi linear regression analysis (MLR).

The MLR analysis clearly identified SST and salinity as highly significant predictor variables for fCO₂sea (equation 2). The purpose of this analysis was to obtain qualitative rather than quantitative information. Differences in the turnover times of these variables precluded a more quantitative assessment. For example, the turnover time of Chlorophyll *a* may be days to weeks compared to months for fCO₂sea, so that low fCO₂sea may persist for some time after a phytoplankton bloom has collapsed. In this study, surface Chlorophyll *a*
was significantly and negatively correlated with fCO₂ sea (Equation 2) as one would expect from the drawdown of CO₂ by phytoplankton. Nevertheless, it is important to consider the limitations of using Chlorophyll *a* as a proxy for NCP or phytoplankton biomass here. While this is arguably a valid assumption as a first-order approximation along large productivity gradients (as sampled by AMT), the phytoplankton C: Chlorophyll *a* ratio is known to vary between species, with depth, light-field and nutrient status (Geider, 1987; Geider et al., 1998). It follows therefore that the Chlorophyll *a* – biomass relationship will also be variable. A direct measure of productivity (NCP or ¹⁴C-HCO₃⁻ uptake) may therefore be a better predictor of fCO₂ sea, but this is beyond the remit of this paper.

The increase in atmospheric CO₂ fugacity observed here (30-40 µatm) was consistent with the global mean atmospheric CO₂ increase of ~37 µatm over the same period, as measured by flask data (http://www.esrl.noaa.gov/gmd/ccgg/trends/). The concomitant increase in fCO₂ sea followed the atmospheric CO₂ increase over the 19-year period examined here (1995-2013). Indeed, the slope of fCO₂ sea versus fCO₂ air from the MLR (Equation 2, slope ~ 1.0) shows that an increase in atmospheric CO₂ was strongly correlated with a near-equal increase in seawater CO₂. This is further in agreement with the rate of change of fCO₂ sea and fCO₂ air between 1995 and 2013 in our data (1.44±0.84 µatm y⁻¹ and 1.47±0.35 µatm y⁻¹ respectively). Consequently, ΔfCO₂ has not changed significantly here (rate=-0.03±0.82 µatm y⁻¹). This implies that other processes controlling CO₂ in seawater have not changed over the period of 1995-2013 or that they have cancelled each other out. This does not contradict our earlier conclusion that the observed increase in SST would lead to an increase in fCO₂ sea of 0.1 µatm y⁻¹ since the latter is within the uncertainty of the overall trend in fCO₂ sea. Other processes controlling fCO₂ sea include NCP as well as local hydrography which may advect CO₂. Our data suggest that the increasing seawater-uptake of CO₂ across the Atlantic Ocean was largely driven by increasing atmospheric CO₂ through physical dissolution (gas exchange) and not enhanced uptake by biota. Nevertheless, satellite observations suggest that primary production may be decreasing over certain parts of the Atlantic Ocean (Tilstone et al., 2009). If so, then a concomitant decrease in respiration would also have to take place in order to maintain NCP and ΔfCO₂.

In order to determine the effect of increasing fCO₂ sea on the carbonate system of surface waters here, we used the fCO₂ sea data and TA, derived from salinity and SST. In the first instance, an increase in fCO₂ sea would be expected to lead to an increase in DIC and reduction in pHₜ. While the inclusion of SST in the calculation of TA improves the fit across
different water masses (compared to a fit of TA against salinity only) (Lee et al., 2006), this
also introduced an artificial temperature-dependence (equation 1) as TA is temperature-
independent. In order to quantify this potential error we used the apparent warming trend
from our data and found that our calculated TA would decrease by 1.84 µmol kg⁻¹ over 19
years using equation 1. This change in TA was at or below the detection limit of current
analytical methods (Ribas-Ribas et al., 2014). We therefore concluded that this potential
artifact would not influence our carbonate-system calculations substantially. In contrast, the
mean rate of change in DIC is 0.87 ± 1.02 µmol kg⁻¹ y⁻¹ or 16.53 µmol kg⁻¹ over the 19 year
period. Our analysis was consistent with spectrophotometric pHₜ measurements carried out
on three of our cruises and showed a decrease in pHₜ with a mean rate of 0.0013±0.0009
units y⁻¹. This is consistent with other observations and estimates in the Atlantic Ocean which
are in the range of -0.0017 to -0.0024 units y⁻¹ (Bates, 2007; Bates et al., 2014; Lauvset and
Gruber, 2014; Olafsson et al., 2009; Santana-Casiano et al., 2007). Previous studies reporting
ocean acidification have largely focused on the North Atlantic Ocean and suggest higher
acidification with increasing latitude. A further study, using 73 observations from the
SOCAT v.2 fCO₂ database (www.socat.info), reported an acidification rate for the South
Atlantic sub-tropical permanently stratified biome (represented by the SAG province here) of
-0.0011 units y⁻¹ for the period 1991-2011 (Lauvset et al., 2015).

Figure 7 shows pH<sub>T</sub> trends for 5 different regions along the AMT transect: a) 25-38 °N (NAG: predominantly the North Atlantic Gyre); 8-20 °N (TUR: transitional region influenced by the wider Canary Current/Mauritanian/Guinea Dome upwelling systems); 1-8 °N (ITCZ: dominated by the Inter Tropical Convergence Zone); 1 °N-15 °S (Equ: dominated by the Equatorial Upwelling and South Equatorial Current) and 15-31 °S (SAG: South Atlantic Gyre). This figure shows some inter-hemispheric differences in the rates of ocean acidification with higher rates in the southern hemisphere, ITCZ and equatorial region. This is most pronounced in the NAG (25-38 °N) and SAG (15-31 °S) regions representing the oligotrophic subtropical gyres with average ocean acidification rates of -0.0009 and -0.0019 units y<sup>-1</sup> respectively for our southbound cruises (Figure 7). This was somewhat surprising as we expected these two systems to show similar trends given their many other similarities (deep SML, persistent DCM and subsurface O<sub>2</sub><sub>max</sub>, oligotrophic nature). It is not clear whether this difference is genuinely interhemispheric or simply a reflection of season (boreal Autumn vs. austral Spring). Productivity in the oligotrophic, subtropical gyres is highest in Spring (Aiken et al., submitted; Smyth et al., submitted) and one may therefore expect to find higher pH<sub>T</sub> during this period (owing to the uptake of CO<sub>2</sub> by primary producers). This
seasonality in productivity was also reflected in ΔfCO₂ with negative values suggesting net autotrophy and uptake of CO₂ (e.g. contrast for NAG-region between Figures 4 and 5).

Indeed, during austral Spring, we observed higher pH<sub>T</sub> for any given year in our time-series in the SAG (Figure 7) and concomitantly recorded predominantly negative ΔfCO₂, suggesting net autotrophy (Figure 4). Nevertheless, this does not explain the different ocean acidification rates in these biomes. The reasons for this are unclear in the absence of seasonally-resolved data, but add to the growing body of evidence which suggests that the two subtropical gyre biomes are biogeochemically distinct despite their many similarities (Baker and Jickells, submitted; Goetze et al., submitted; Moore et al., 2009; Moore et al., 2013; Serret et al., 2015).

<figure><img src="image_8.png" /><figcaption>**Figure 7:** Average pH<sub>T</sub> for five latitudinal provinces during southbound cruises: 25-38 °N (North Atlantic Gyre); 8-20 °N (transitional region influenced by the wider Canary Current/Mauritanian/Guinea Dome upwelling systems); 1-8 °N (dominated by the Inter Tropical Convergence); 1 °N-15 °S (dominated by the Equatorial Upwelling and South Equatorial Current) and 15-31 °S (South Atlantic Gyre). Error bars represent standard deviation of binned data over these latitudinal bands.</figcaption></figure>

We also found ocean acidification for the regions which are influenced by upwelling systems (8-20 °N: Canary Current/Mauritanian/Guinea Dome and 1 °N-15 °S: Equatorial upwelling) (Figure 7). Conventional wisdom would suggest that regions where CO₂-rich deep-water is upwelled would be a source of CO₂ to the atmosphere and thereby not subject to ocean acidification. Whilst the former is clearly the case, as evidenced by positive ΔfCO₂ (Figures 4-5), the latter was not the case as OA was clearly observed (Figure 7). This is likely because as atmospheric CO₂ increases, the ΔfCO₂ is gradually decreasing (assuming constant CO₂ concentration in upwelled deep-waters). In turn, this would reduce the rate of CO₂
outgassing to the atmosphere, thus leading to retention of a higher proportion of deep water
$CO_2$ in surface waters.

There is evidence to suggest that further ocean acidification may have an impact on biogeochemical cycles, including the C-cycle. In this issue, Tilstone et al. describe a set of 48-hour experiments investigating primary production under ocean acidification conditions (760 ppmv $CO_2$). These authors found an increase in photosynthetic rates at the lower pH, attributed primarily to dinoflagellates (Tilstone et al., submitted-b). Such short-term exposure experiments do not allow for genetic adaptation of the existing phytoplankton communities, but they nevertheless demonstrate the expected physiological response. If these findings are applicable at the basin scale, then primary production may increase under future OA. Indeed, an increase in microphytoplankton primary production from a 12-year time series of satellite ocean colour observations is reported for some regions of the Atlantic Ocean in this issue (Tilstone et al., submitted-a). Much of the work on ocean acidification has focused on calcification and calcifying organisms. For example a recent study of Antarctic pteropods living in low pH waters found that their shell thickness was significantly reduced when compared with the shells of their counterparts in higher pH water (Bednarsek et al., 2012). As yet, there is no evidence that pH is a major controlling variable in the biogeographic distribution of calcifying pteropods along the AMT track (Peijnenburg et al., in prep.). This apparent contrast may be reconciled by differences in the aragonite saturation state in different waters – shell thickness only decreases once the saturation state approaches unity or below. Other work has shown that calcification may be negatively impacted in some species and enhanced in others (Meyer and Riebesell, 2015). This is consistent with palaeontological records around the Palaeocene-Eocene thermal maximum which show a species succession over the transition to a lower-pH environment (O'Dea et al., 2014). Longer experiments have shown that calcifying coccolithophores may evolve and that calcification may recover over multiple generations of exposure to OA (Lohbeck et al., 2012). As pH decreases further, calcifying organisms may either adapt to OA by increasing their calcification rates or be replaced by others which are capable of higher calcification rates. However, studies on benthic calcifying organisms have shown that the energetic cost of increasing calcification comes at the expense of other metabolic processes in the long term (Wood et al., 2008).

Through the AMT programme, our study has quantified changes in surface ocean $CO_2$ and the carbonate system at the basin-scale. These changes are driven by the atmospheric increase of $CO_2$ and are superimposed on regional and seasonal cycles at present. However, the effect of long-term climatic cycles is unclear. This understanding requires further
sustained observations and is itself essential for the development of numerical models which
can be used to predict the future impact of increasing atmospheric CO2. We expect that novel
technologies and further automation will make this task more cost-effective over the next two
decades of AMT.

5. Acknowledgements

We would like to thank two anonymous reviewers for their constructive comments on this
manuscript as well the officers and crew of the RRS James Clark Ross, Cook and Discovery
for the successful delivery of the AMT programme. We would also like to thank the scientific
personnel and principal scientists on our cruises for their support. Special thanks go to Dr.
Andrew Rees for the coordination of AMT latterly as well as previous coordinators, Dr Carol
Robinson and Prof. Jim Aiken. This study is a contribution to the international IMBER
project and was supported by the UK Natural Environment Research Council National
Capability funding, Greenhouse Gases Programme (grant no NE/K00249X/1), the European
Commission (CARBOCHANGE, grant no 264879) and EU Horizons 2020 (AtlantOS, grant
no 633211). This is AMT contribution no 300.
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