Introduction
The excess of CO2 in the atmosphere, largely responsible for global
climate change, has prompted research on the role of the oceans in the carbon
cycle. The aim in recent decades has been to assess how the oceans act as
sources or sinks within the carbon cycle. To achieve this goal, highly resolved spatial
and temporal observations representative of the distribution of CO2
fluxes between the ocean and atmosphere are necessary. Automated instruments
on volunteer observing ships (VOSs) serve to provide as many observations
throughout the global ocean as possible. This is in addition to data collected
on scientific cruises and at long-term moorings (i.e., Astor et al., 2005:
Lüger et al., 2004, 2006; González-Dávila et al., 2005, 2009;
Schuster et al., 2009; Ullman et al., 2009; Watson et al., 2009; Padin
et al., 2010; Gruber et al., 2002; Dore et al., 2003; Santana-Casiano et al.,
2007; Bates et al., 2014).
With the amount of data already gathered (http://www.socat.info/; Pfeil
et al., 2013), climatologies that present average
CO2 fluxes between the
atmosphere and the ocean have been developed, identifying areas acting as a
source or sink (Key et al., 2004; Takahashi et al., 2009). However, the low
spatial resolution of these databases limits the applicability, especially in
coastal areas. Upwelling regions are particularly under-represented in such
large databases. Upwelling presents a dynamic process that raises nutrient
and CO2-rich water from relatively deep areas to the surface. The
nutrients reaching the photic zone promote primary production, which consumes
CO2. This process generates a CO2 flux into the ocean. On the other
hand, upwelling also brings up CO2 from deep seawater, which generates
uncertainty about the actual role of upwelling areas as a source or sink of
CO2 (Michaels et al., 2001). Indeed, upwelling areas may act as a source
or sink of CO2 depending on their location (Cai et al., 2006; Chen et
al., 2013), where upwelling regions at low latitudes mainly act as a source
of CO2 (Feely et al., 2002; Astor et al., 2005; Friederich et al., 2008;
Santana-Casiano et al., 2009; González-Dávila et al., 2009) and those
at midlatitudes mainly act as a sink of CO2 (Frankignoulle and Borges,
2001; Hales et al., 2005; Borges and Frankignoulle, 2002; Borges et al.,
2005; Santana-Casiano et al., 2009; González-Dávila et al., 2009).
Several anthropogenic interactive effects strongly influence eastern boundary
upwelling systems (EBUSs), including upper ocean warming, ocean
acidification, and ocean deoxygenation (Gruber, 2011; Feely et al., 2008;
Keeling et al., 2010). Moreover, evidence of increasing wind speed that would
favor upwelling (Bakun, 1990; Demarcq, 2009; Oerder et al., 2015) supports
the possibility of a change in the dynamics of these highly productive areas.
Recently, eddy-resolving regional ocean models have shown how upwelling
intensification can cause a major impact on the system's biological
productivity and CO2 outgassing (Lachkar and Gruber, 2013; Oerder et
al., 2015). Wind observations and reanalysis products are controversial
regarding the Bakun intensification hypothesis (Bakun, 1990). Using different
wind databases for the Canary region, Barton et al. (2013) concluded that
there was no evidence for a general increase in the upwelling intensity off
northwest Africa. Marcello et al. (2011) found an intensification of the
upwelling system in the same area during a 20-year period, while the
alongshore wind stress remained almost stable. Cropper et al. (2014) found
that coastal summer wind speed increased, resulting in an increase in
upwelling-favorable wind speeds north of 20∘ N and an increase in
downwelling-favorable winds south of 20∘ N. Santos et al. (2005,
2012) showed that sea surface temperature (SST) was not homogeneous either
along latitude or longitude and depended on the upwelling index
(UI) intensity. Varela et al. (2015) demonstrated opposite results worldwide
depending on the length of data, season evaluated, and selected area within
the same wind data set or between data sets. For the Mauritanian region, when
wind stress data were used (Varela et al., 2015), a more persistent
increasing trend in upwelling-favorable winds north of 21∘ N and a
decreasing trend south of 19∘ N was determined.
Starting in June 2005, the QUIMA-VOS line visited the Mauritanian–Cap Vert
upwelling region northwest of Africa on a monthly basis (Fig. 1 and Table S1
in the Supplement) producing for the first time a high-resolution database of
SST and partial pressure of CO2 expressed as fugacity fCO2. This
database shows the variations in the CO2 system under changes in the
upwelling conditions in the Canary ecosystem from 27 to 10∘ N for
the period 2005 to 2012. More data for the region from other surveys exist
(http://www.socat.info/; Pfeil et al., 2013) but they were not
considered in this study as they do not follow the same track as the
QUIMA-VOS line. Those data are strongly influenced by the distance to the
upwelling cells with the corresponding physical effects in the partial
pressure of CO2.
Ship track (black line) in the area from 28∘ N (Gran Canaria, the Canary
Islands) to 10∘ N. The locations of Cap Blanc and Cap Vert are indicated. Monthly
OceanColor Web (https://oceancolor.gsfc.nasa.gov/) data for
average chlorophyll a concentration (mg m-3) were included in a
MATLAB routine and annually averaged. The map has been generated using
MATLAB 7.12 R2011a.
Experimental
Study region
The VOS line crosses the east Atlantic Ocean from the north of Europe
(English Channel) to South Africa, calling at Gran Canaria, the Canary
Islands, with a periodicity of 2 months, which provides monthly data
(southward or northward sections). In this work, the area between Gran
Canaria at 27 and 10∘ N has been selected in order to study the
Mauritanian–Cap Vert upwelling region. On its route south (Fig. 1), the
ship leaves Gran Canaria and goes straight to 100 km off Cap Blanc at
21∘ N, 17∘45′ W. It then follows this longitude, passing
at 100 km off Cap Vert until 12∘ N, where it changes direction to
Cape Town, reaching 10∘ N, 17∘ W at 330 km off the coast
of Guinea. Between 22 and 20∘ N, the ship reaches the 500 m
isobath. South of 15∘ N, the ship moves between the 1000 and 500 m
isobaths. On its route north, the ship follows the reverse track.
Experimental data
Experimental data were obtained under the EU projects CARBOOCEAN and
CARBOCHANGE (www.CarboOcean.org and
https://carbochange.b.uib.no/) and now also available at
http://www.socat.info/ (Pfeil et al., 2013). An autonomous instrument
for the determination of the partial pressure of CO2 developed by Craig
Neill following NOAA recommendations was installed on a VOS line. This was
operated by the Mediterranean Shipping Company S.A. from 2005 to 2008
and Maersk from 2010 to 2012. This VOS line (QUIMA-VOS) ran between the
UK and Cape Town from July 2005 to January 2013 (Table S1 in the
Supplement). Temperature was measured at three positions along the sampling
circuit: in the intake (Sea-Bird SBE38L), in the equilibrator (Sea-Bird
thermosalinograph SBE21 and internal PT100 thermometer), and in the oxygen
sensor (Optode 3835, Aanderaa™). After the
seawater pump, the intake is divided into two lines, one feeding the CO2
system and the other feeding the oxygen sensor, the fluorometer, and the Sea-Bird
thermosalinometer. Differences between equilibrator and intake temperatures were constant
in time due to the high seawater flow but varied among ships due to the
different locations of the equipment. Values varied between 0.06 ∘C
when the equipment was placed close to the intake and 0.35 ∘C when
the equipment was one floor above and inside the engine room. The SST was also
obtained from the NOAA_OI_SST-V2 data provided by the NOAA/OAR/ESRL
PSD from Boulder, Colorado, USA (http://www.esrl.noaa.gov/psd). These data
had a spatial resolution of 1∘ latitude and 1∘ longitude and
monthly averages were used. The correlation between our experimental SST data
and satellite data was better than ±1 ∘C, and improved
to ±0.4 ∘C after removing the most affected upwelling regions
(19–22 and 14–16∘ N), which related to the high variability imposed by
the upwelling.
The CO2 molar fraction, xCO2, was obtained every
150 s in seawater, while atmospheric xCO2 data were obtained every 180 min. The
seawater intake was located at a 10 m depth. The system was calibrated every 3 hours by measuring four different standard gases with mixing ratios in
the ranges of 0.0, 250–290, 380–410, and 490–530 ppm of CO2 in the
air, provided by NOAA and traceable to the World Meteorological Organization
scale. The precision of the system is greater than 0.5 µatm and the
accuracy estimated with respect to the standard gases is of 1 µatm
inside the standards' range. For xCO2 values higher than the highest
standard (532.04 ppm), the accuracy will be reduced, even when linearity was
observed in all cases inside the standards range. The fugacity of CO2
(fCO2, µatm) was calculated from xCO2 after correcting
for temperature differences between intake and equilibrator, according to the
expressions for seawater given by DOE (1994). Normalized fCO2
(NfCO2) derived from the mean SST for the area (Tmean) was computed following Takahashi
et al. (1993) as
NfCO2=fCO2⋅exp0.0423Tmean-SST.
In order to compute a second carbonate system variable, the surface total
alkalinity (AT) was computed from sea surface salinity (SSS) and SST (Lee et al.,
2006). pHT at the in situ temperature was computed from fCO2 and
AT and with average annual surface ocean total phosphate and total
silicate concentrations of 0.5 and 4.8 µmol kg-1,
respectively, from the World Ocean Atlas 2009, using the carbonic acid
acidity constants by Mehrbach et al. (1973) refitted by Dickson and
Millero (1987).
Air–sea CO2 fluxes (FCO2, mmol m-2 d-1) were evaluated
as
FCO2=0.24⋅k⋅s⋅(fCO2sw-fCO2atm),
where 0.24 is the scale factor, k is the gas transfer velocity, s is the
CO2 solubility, fCO2sw is the seawater fugacity of
CO2, and fCO2atm is the atmospheric fugacity of
CO2. In order to evaluate
(fCO2sw-fCO2atm), fCO2atm
data were linearly interpolated to the fCO2sw time vector. A
positive value for FCO2 corresponds to CO2 outgassing from the
ocean. k (cm h-1) was evaluated with the following parameterization (Nightingale
et al., 2000):
k=(0.222⋅W2+0.333⋅w)⋅(Sc/660)-1/2,
where W is the wind speed at 10 m above the sea surface (m s-1) and
Sc is the Schmidt number.
The variables involved in estimating FCO2 data (i.e., fCO2sw, fCO2atm, SST, and
SSS) were fitted to sinusoidal expressions (Lüger et al., 2004) for a
given latitude as follows:
Xlat∗=a0+a1t-2005+a2sin2πt+a3cos2πt+a4sin4πt+a5cos4πt,
where ai are the fitting coefficients, t is the sampling time
expressed as year fraction, and X(lat)* represents any of the four fitted
variables. This procedure allowed us to reconstruct the series of
experimental data for periods without monthly data. The variables were
decomposed into an interannual term X(lat)t∗=a0+a1t-2005 plus a periodical term
X(lat)p∗=a2sin2πt+a3cos2πt+a4sin4πt+a5cos4πt, that is, X(lat)∗=X(lat)t∗+X(lat)p∗. The periodical term accounts for the high-frequency seasonal variability, while the interannual term marks the
year-to-year trend. First, observations were grouped in a natural year for a
given latitude, as if they had been taken in a single year (no correction was
done for interannual variability). The mean seasonal climatology data
associated with the periodic coefficients (i.e., a2, a3, a4,
and a5) throughout the sampling period were determined. Next, the
interannual coefficient a1 was calculated by fitting the residuals
resulting from subtracting the periodical component, X(lat)p∗, from the original variable X(lat). By fixing these five
coefficients (a1–a5), new distributions for
fCO2sw∗, fCO2atm∗, SST∗,
and SSS∗ were constructed with a daily resolution based on the curve
fits given for each variable as in Eq. (4), providing the coefficient
a0. The accuracy of this fitting procedure was checked by both computing
the correlation between experimental and reconstructed values and by
determining the mean residuals. The Pearson coefficients were always over
0.87 for SST (average 0.94 ± 0.03), over 0.69 for both
fCO2sw and fCO2atm (average of
0.79 ± 0.07 and 0.82 ± 0.04, respectively), and over 0.67 for SSS
(average 0.79 ± 0.07). The mean residual on the determination of those
four variables were ±3.7 µatm, ±1.5 µatm,
±0.22 ∘C, and ±0.05 for fCO2sw∗,
fCO2atm∗, SST∗, and SSS∗, respectively.
When the monthly satellite SST values were considered, the new SST*
function averaged for each month produced values within
±0.47 ∘C, confirming that this procedure was able to fit
non-sampled periods. It was assumed that the same procedure was valid for
non-sampled fCO2. Finally, daily FCO2∗ time series between
10 and 27∘ N with a latitudinal resolution of 0.5∘ were
calculated with a standard error of estimation of
0.5 mmol m-2 d-1 (15 % of error) that produced mean
residuals (experimental FCO2–FCO2∗) of
0.4 mmol m-2 d-1 and Pearson correlation coefficients between
experimental and computed FCO2∗ of r > 0.6, p < 0.01.
Chlorophyll a was calculated from measurements made by the Moderate-resolution Imaging Spectroradiometer (MODIS) aboard NASA's Aqua satellite. We
used monthly averages with a spatial resolution of 9 km supplied by OceanColor Web (https://oceancolor.gsfc.nasa.gov).
Wind data were downloaded from the NCEP CFSR database at
http://rda.ucar.edu/pub/cfsr.html developed by NOAA and retrieved from
the NOAA National Operational Model Archive and Distribution System and
maintained by the NOAA National Climatic Data Center. The spatial resolution
is approximately 0.3×0.3∘ and the temporal resolution is
6 h. The reference height for the wind data is 10 m.
Rainfall data were collected by the precipitation radar installed on the
Tropical Rainfall Measuring Mission (TRMM) satellite
(http://precip.gsfc.nasa.gov). Monthly averages with a spatial
resolution of 0.5 × 0.5∘ (product 3A12, version 07)
were used (Fig. S1 in the Supplement) in order to explain changes in seasonal
surface salinity distributions.
Results and discussion
Physical properties
The variability of the Mauritanian–Cap Vert upwelling was analyzed in terms
of the upwelling index (Nykjaer and Van Camp, 1994) (Fig. 2) using satellite
wind data.
Negative UI values correspond to upwelling-favorable conditions and positive values to downwelling-favorable conditions. The lowest negative values of the
index correspond to more intense upwelling. Results clearly distinguish two
main subareas in the upwelling system: (1) north of 20∘ N, the
upwelling conditions were favorable throughout the year, although the highest
upwellings were observed from March to September with a northward shift from
20 to 22∘ N. (2) South of 20∘ N, a marked seasonality was
observed with favorable upwelling conditions during autumn and winter, with
the maximum intensity observed during January and February. In this region, a
downwelling regime is present between May and November when the summer trade
winds are replaced by the monsoonal winds advecting warm water (Fig. 3a)
northward along the shore (Nykjaer and Van Camp, 1994). Our results (Fig. 2)
are quite consistent with previous research (Nykjaer and Van Camp, 1994;
Marcello et al., 2011; Santos et al., 2005, 2012; Cropper et al., 2014) but
include the years 2010 to 2012, when the UI at around 20–21∘ N
presented a shift of the upwelling intensity from high
(-2000 m2 s-1) to strong (-2800 m2 s-1). The
analysis of upwelling trends along this route has been controversial since it
is highly dependent on the selected region (Santos et al., 2012). The
interannual evolution of the UI over the period 2005 to 2012 (Fig. 4, green
line) for each degree in latitude indicates an increase in the UI (mean
confidence interval of 9 m2 s-1) as showed by Santos et
al. (2012).
Time series of upwelling index
(UI, × 10-3 m2 s-1) in the Mauritanian–Cap Vert
upwelling region along the ship track computed following Nykjaer and
Van Camp (1994). Blue colors are related to upwelling events and red
colors to downwelling events.
In situ data of column (a) SST and column (b) SSS in the
Mauritanian–Cap Vert coastal region grouped by seasons: winter (W;
December, January, and February), spring (Sp; March, April, and May), summer
(Sm; June, July, and August), and autumn (Au; September, October, and November).
The averaged values for all cruises in Table S1 are shown in black for each
season including the 95 % confidence limits. The color code for each cruise
is indicated in Table S1.
North of 15∘ N, the upwelling index confirmed the stronger upwelling
observed since 1995–1996 in this region after more than a 10-year (from at
least 1982 to 1995) period of weaker upwelling (Santos et al., 2012). Local
zonal differences between ocean and coastal SST trends determined with
satellite data confirmed the intensification of the upwelling regime along
the African coast for the period 1982 to 2000 (Santos et al., 2005) and extended
by Santos et al. (2012) until 2010 and further extended in this study until 2012
(data not shown). This has been described as a decadal-scale shift of the
upwelling regime intensity (Marcello et al., 2011; Santos et al., 2012).
South of 15∘ N, the annual UI values and trends (Figs. 2 and 4) both
for the upwelling (values close to -2800 m2 s-1 in January) and
downwelling (values reaching 1850 m2 s-1 in July) periods are
becoming stronger. At 11–12∘ N, where downwelling is becoming
stronger, this results in negative annual temperature rates that approach zero.
The UI serves as an indication of decadal variability of the
summer monsoon winds and associated northward advection of warm water along
the coast (Santos et al., 2012).
Latitudinal distribution of the interannual trends for the upwelling
index (UI) and for the four experimental variables along
the QUIMA-VOS line integrated over every degree between 2005 and 2012.
Panel (a) presents the trends for upwelling index
(UI, × 10-3 m2 s-1, mean confidence interval of
9 m2 s-1), SST (∘C yr-1, confidence interval
0.13 ∘C), and SSS (yr-1, confidence interval 0.06) and
(b) the trends for fCO2sw and
fCO2atm (confidence intervals 4.23 and
0.44 µatm).
The highest upwelling intensity along the VOS line was located at the capes,
Cap Blanc and Cap Vert. From satellite chlorophyll a data, especially off
Cap Blanc, giant filaments with chlorophyll concentrations above
1 mg m-3 persist year-round, spreading from the coast to several hundred
kilometers offshore (Fig. 1). North of Cap Blanc the upwelled water
originates from the North Atlantic Central Water, and mixes with South
Atlantic Central Water (SACW) towards the south (Mittelstaedt, 1983). South
of Cap Blanc, the upwelling of nutrient-rich SACW (Mittelstaedt, 1983)
promotes phytoplankton growth between Cap Blanc and Cap Vert. Towards
12∘ N, upwelling is also fed by the North Equatorial Undercurrent
(Hagen and Schemainda, 1984). Moreover, the entire northwest African coast is
also influenced by the African desert dust transport by the mid-tropospheric
Harmattan winds originating from the central Sahara, which supplements the
levels of micronutrients (such as iron) to the adjacent marine ecosystem
(Mittelstaedt, 1983; Neuer et al., 2004).
The study area is also affected by the migration of the Intertropical
Convergence Zone (ITCZ), related to maximum precipitation rates (Hastenrath,
1995). To have a significant satellite precipitation record in our region of
interest, precipitation data were integrated longitudinally between 25.25 and
9.75∘ W. Time series for the latitudinal distribution of integrated
precipitation (Fig. S1 in the Supplement) identified the average position of
the ITCZ related to maximum precipitation rates. The ITCZ was located at its
southernmost position (2∘ N) during winter, reaching its
northernmost position (14–16∘ N) around summer. The ITCZ reached
our area of interest (> 10∘ N) from late spring to late summer.
The latitudinal distributions of measured SST and SSS along the vessel track
are shown in Fig. 3, grouped by seasons (labeled W, Sp, Sm, and Au). The temperature generally decreased
from 10 ∘ N to about 20–21∘ N, where the ship meets the Mauritanian
upwelling. From there to the north, the temperature rises as the ship leaves
the upwelling area on its way to the Canary Islands. In situ temperature at
27∘ N shows temperatures in the range of 18 to 24 ∘C with
the minimum in winter and maximum in late summer to early autumn. The annual
temperature range was somewhat higher at 20∘ N, with a summer maximum
of around 26 ∘C and minimum in spring of about 17 ∘C. At
10∘ N, temperatures were the highest throughout the year
(> 25 ∘C), with minimum values in winter and maximum in late
spring and late autumn. The low values observed during the end of summer are
related to the arrival of the ITCZ (Fig. S1 in the Supplement) at those
latitudes. The thermal distribution shows a temperature increase as we move
to the Equator and a notable cooling at the upwelled waters off Mauritania.
The upwelling of cold water from the Cap Vert area was only detected during winter time and the beginning of spring.
Salinity minimum values were normally located at 10∘ N, increasing to maximum values at the
Canaries' latitude. The minimum values of salinity were exceptionally low
during autumn from 10 to 16∘ N by both the freshwater input from
rivers that increase their outflow during this season (Nicholson, 1981) and
by the northward shift of the ITCZ during this time of the year.
Anomaly fields for temperature and salinity (data not shown) were calculated
as the difference between the observations and the mean values at each season
for individual latitudes. For temperature, the largest anomalies in winter
and spring were located south of 18∘ N, with values of
±2 ∘C, related to the seasonal cycle of the Cap Vert
upwelling. During summer the pattern changed and the largest anomalies were
detected in the upwelling area at 18–22 ∘N, with values of
±5 ∘C when the upwelling index for the Mauritanian area was
highest (Fig. 2). In autumn the temperature anomalies were shifted slightly
to the north, 20–24∘ N, with values of ±3 ∘C related
to the observed pulses in upwelling-favorable winds that affected the surface
seawater properties. On the other hand, salinity anomalies showed a very
homogeneous pattern in all latitudes for winter, spring, and summer, with
values generally within ±0.5. However, during autumn important anomalies
south of 18∘ N were observed, with values in the range of ±1.5.
In this region, the upwelling development, the river discharge, and the rainy
season controlled the observed distribution (Yoo and Carton, 1990).
Fugacity of CO2 data in the Mauritanian–Cap Vert coastal
region grouped by seasons: winter (W; December, January, and February), spring
(Sp; March, April, and May), summer (Sm; June, July, and August), and autumn
(Au; September, October, and November). Column (a) fCO2sw latitudinal distribution.
Column (b), difference between measured and fCO2sw values normalized to a constant temperature of
22 ∘C. The averaged values for all cruises in Table S1 are shown in
black for each season including the 95 % confidence limits. The color code
for each cruise is indicated in Table S1.
To conclude, the data show a permanent annual upwelling regime observed north of
20∘ N and a seasonal regime across 10–19∘ N, in accordance
with the climatology of previous studies. The data also confirm an increase
in upwelling conditions north of 20∘ N and an increase in
downwelling conditions south of 20∘ N.
Carbon dioxide variability
The latitudinal distribution of the seasonal fCO2sw data
(Fig. 5a) showed the highest values between 18 and 23∘ N for all
seasons due to the variability imposed by the upwelling off Mauritania.
fCO2sw was consistently greater than the
fCO2atm. During winter, when the Cap Vert upwelling
develops (Fig. 2), the 12–15∘ N region also presented higher
fCO2sw values than those in the atmosphere.
fCO2sw data showed a latitudinal shift between the seasons
following the shift observed in the upwelling index: in winter, the
largest values were located between 19 and 24∘ N; in spring, they
were located between 16 and 22∘ N; and during summer and autumn, the
largest fCO2sw values were recorded in the range 20 to
23∘ N. The difference between fCO2sw normalized to
the mean SST of 22 ∘C for the region (NfCO2sw) and
fCO2sw (ΔfCO2 = NfCO2sw-fCO2sw, Fig. 5b)
reinforced the variability at 20–23∘ N all year around and at
12–17∘ N during winter and spring, indicating that upwelling is the
major factor contributing to the fCO2 variability.
According to Takahashi et al. (1993), fCO2sw increases with
temperature at a rate of 4.3 % µatm ∘C-1 (between
15 and 26 µatm ∘C-1 in this area) in a
thermodynamically controlled system. At 27∘ N, as SST increases, the
rate was only 7.45 µatm ∘C-1 due mainly to
biological uptake and also to CO2 outflux.
At 20∘ N the rate became negative with a value of
-10.9 µatm ∘C-1, clearly indicating the important
injection of cool and CO2-rich seawater at the upwelling area. The
injection is not being compensated for by the solubility nor by the biological
carbon pumps. At 10∘ N, the rate was still negative but only
-4.3 µatm ∘C-1 as a result of the seasonal
upwelling. NfCO2sw was related with SST (data not shown) in
order to account for effects not removed during normalization. At latitudes
19 to 21∘ N, in the upwelling vicinity of Cap Blanc, an inverse
relationship of 70–100 µatm ∘C-1 was found during
winter and spring, while in summer and autumn the inverse relationship rate was
reduced to 12–18 µatm ∘C-1. While the upwelling
indexes at those latitudes were quite constant throughout the year, different
rates observed should be related to biological consumption of the CO2
excess. However, during winter and spring the injection of CO2 in the
upwelling is not decreased by the biological activity in the area. But during
the Chlorophyll a maximum (late spring and summer), most of the CO2 was
consumed and/or exported and, therefore, the rate was strongly reduced.
Figure 4 depicts the observed interannual trends (a1 coefficient in
Eq. 4) for the four experimentally recorded detrended parameters, together
with the UI trend. Confidence intervals of the computed mean annual values
for SST, SSS, fCO2atm, and fCO2sw were
0.13 ∘C, 0.06, 0.44, and 4.23 µatm, respectively. There was
a clear SST trend whereby seawater along the VOS line track was getting
cooler with maximum cooling rates at the location of Cap Blanc
(21∘ N) and Cap Vert upwellings (15∘ N) with rates higher
than -0.2 ∘C yr-1. Data from the first 3 years (2005 to
2008) at 21∘ N showed lower temperatures with higher cooling rates
that reached -0.7 ∘C yr-1, although 3 years of data are
not representative. The area crossed by the VOS line along
17∘45′ W from 22 to 10∘ N is located inside the 1000 m
isobath that is well inside the mean frontal activity in the Canary region,
about 200 km wide (Wang et al., 2015). The different changes in temperature
in the coastal slope and offshore waters are related to the different origins
of the waters upwelled from depths of about 100 m to the surface
(Mittelstaedt, 1983) that spread off the coastal area. The offshore water SST
is less variable owing to longer residence time in the ocean surface. These
effects and the fact that the VOS line keeps a track line that crossed the
upwelling cells at a distance to the coast that varies among cells
contribute to the observed spatial variability. There was no attempt to
compare latitudinal and longitudinal effects on the observed values. Our
experimental data, however, do not show any positive SST rates in the
upwelling affected area, and only when the ship approached the Canary
Islands did the trends become less negative, reaching a value of
+0.02 ∘C yr-1 at 27∘ N, similar to those obtained
for oceanic Atlantic water (Bates et al., 2014).
fCO2atm for the area showed the interannual increase of
about 2 ± 0.3 µatm yr-1 observed in atmospheric
stations, while fCO2sw presented a heterogeneous
distribution. South of 18∘ N, the rate of increase was always higher
than that in the atmosphere reaching a maximum value of
4.1 ± 0.4 µatm yr-1 at 10∘ N. At
27∘ N, fCO2sw increased at a rate of
1.7 ± 0.2 µatm yr-1 similar to that determined at the ESTOC time series site (González-Dávila et al., 2010) located at
29∘10′ N, 15∘30′ W. In the Cap Blanc area,
fCO2sw increased at an average rate of
2.5 ± 0.4 µatm yr-1 with the highest values in the
period 2005 to 2008 (a rate of 4.6 ± 0.5 µatm yr-1 was
computed with only those years). Around Cap Blanc, fCO2sw
always presented lower rates of increase than in the atmosphere with values
well below 1 µatm yr-1. The observed decrease in SST and the
trends in fCO2sw can only be explained by a reinforced
upwelling. North of 18∘ N, the lowest rate of increase in
fCO2sw compared to fCO2atm, together with a
decrease in temperature, indicated that upwelling is also favoring an
increase in the net community production around the Mauritanian upwelling,
consuming and/or exporting the CO2-rich upwelled waters favored by the
lateral transport of the Mauritanian current (Lachkar and Gruber, 2013;
Varela et al., 2015). The upwelling intensification effects observed in the
trends of our experimental data support the recent wind stress trends
(Cropper et al., 2014; Varela et al., 2015; Santos et al., 2012) of increased
upwelling-favorable winds, at least for the period 2005–2012 in the Canary
upwelling region (Figs. 2 and 4). The intensification of the upwelling
results in a change in the measured upwelled water properties due to either
higher upwelling velocities or deeper source upwelled waters. However, what
remains unclear from these records is to what extent those changes reflect
upwelling variations due to climate change forcing versus natural decadal
variability in the upwelling areas occurring over interannual timescales.
pH of surface waters in total proton scale and at in situ SST computed from total
alkalinity (based on regional correlations with SST and SSS; Lee et al.,
2006) and fCO2 at 21 ± 0.25∘ N. The error bars
represent the standard deviation of the computed data for each cruise for
the selected latitude. The black curve shows the harmonic fitting of Eq. (4) for
the data and the corresponding linear trend is also shown.
Latitudinal distributions of seasonal and annual CO2 fluxes (FCO2, mol m-2). Fluxes of CO2 were computed using Nightingale
et al. (2000) parameterization and satellite winds with a resolution of 6 h.
(a) Integrated year to year from 2005 to 2012 and
(b) latitudinally integrated for 2005 to 2012 together with annual
values for the North Atlantic Oscillation (NAO) index. Latitudinal distributions of FCO2 seasonally
integrated from 2005 to 2012 are depicted for (c) winter (December,
January, and February), (d) spring (March, April, and May), and (e) summer (June, July, and August).
Because the upwelling intensity is changing, other variables will also be
affected. pHT,is at 21 ± 0.25∘ N was computed from
fCO2 and alkalinity pairs of data. Alkalinity was computed from
regional correlations with SST and SSS (Lee et al., 2006), which could
underrepresent seasonal and interannual variations in upwelling areas.
However, pH computed from fCO2 values are relatively insensitive to
errors in AT, and fCO2 controls the magnitude and variability of
pH (a 60 µmol kg-1 change in AT will affect a 0.1 %
in pH, that is, about 0.01 pH units). Figure 6 depicts the computed
pHT,is(AT, fCO2) data and the harmonic fitting of
Eq. (4) providing the seasonal variability and interannual trend. Considering
the small systematic biases in interannual dynamics, we determined a decrease
in pH at a rate of -0.003 ± 0.001 yr-1 (Fig. 6). This decrease
is one of the highest rate values determined in several time series stations
(Bates et al., 2014), where oceanic SST has only slightly increased in the
last decades. However, at the Mauritanian upwelling area and at the location
where our VOS line approached this region, SST decreased at a rate of
-0.22 ± 0.06 ∘C yr-1 (Fig. 4). Solely this decrease in
temperature would increase the pH by a rate of +0.004 yr-1 and the
fCO2 would decrease by 4 µatm yr-1. The net effect of
the increase in the amount of rich CO2 and lower pH upwelled waters in
the Mauritanian upwelling would be, therefore, a decrease in the pH rate of
over -0.007 ± 0.002 units yr-1 and an increase in fCO2
of +6.5 ± 0.7 µatm yr-1 (with periods where those
rates could reach values of -0.015 yr-1 in pH and
+10.5 µatm yr-1 in fCO2 as recorded during
2005–2008). Those values are greatly compensated for by the important
decrease in the SST resulting in the determined rates of
-0.003 ± 0.001 pH units and +2.5 ± 0.4 µatm of
fCO2 per year.
This new data set of experimental values confirmed a decrease in SST and
trends in fCO2sw that can only be explained by reinforced
upwelling conditions that favor an increase in the net community production
around the Mauritanian upwelling together with a more corrosive environment
with pH rates that change by more than -0.007 ± 0.002 yr-1 at
21∘ N. However, the decrease in SST in the upwelling cell buffers
this pH rate to values around -0.003 ± 0.001 yr-1 and
+2.5 ± 0.4 µatm yr-1 in fCO2, still among the
highest observed in other time series.
Fluxes of CO2
The annual air–sea CO2 flux for the full domain was positive (Fig. 7a),
with the area off Cap Blanc with values close to 3.3 mol CO2 m-2
(Fig. 7a). North of 24∘ N, in the area not affected by the coastal
upwelling, an average flux of +0.14 ± 0.03 mol CO2 m-2
was determined. The ingassing observed during winter and spring of
-0.16 ± 0.03 mol CO2 m-2 for the full period (Fig. 7)
was surpassed by the outgassing during summer and autumn of
0.28 ± 0.14 mol CO2 m-2. South of 24∘ N, it was
observed that during spring (Fig. 7d) the photosynthetic activity was not
intense enough to uptake the CO2 injected by the strongest upwelling in
the surface waters and thus the area acted as a source of CO2 with
values reaching 1.9 mol CO2 m-2 in 2012. During summer
(Fig. 7e), primary producers and lateral advection of warm waters by the
Mauritanian current could consume and/or export the CO2-rich waters reaching
values of 0.5 mol CO2 m-2. During autumn (Fig. 7f), only the area between
20 and 23∘ N acted as a source of 1–1.5 mol CO2 m-2,
while the rest was almost in equilibrium. Late autumn–winter upwelling in the
14 to 17∘ N region contributed to an increased outgassing with a
second annual submaximum of about 0.4 mol CO2 m-2 in winter
(Fig. 7c). South of 14∘ N, annual CO2 fluxes decreased from
about 0.7 mol CO2 m-2 at 14∘ N to being roughly in equilibrium at
10∘ N.
The integrated CO2 fluxes for the area between 10 and 27∘ N along the
VOS line section for the years 2005 to 2012 (Fig. 7b) were between 1.6 and
2.1×106 mol of CO2, with an important annual variability. FCO2 increased during
the studied period by 0.05 ± 0.02 × 106 mol yr-1.
The increase in FCO2 is related to the observed increase in wind speed
(Fig. 4, indicated as UI) north of 16∘ N. North of 19∘ N,
the influence of wind speed far surpassed the effect of the smaller annual
rate of increase in fCO2sw relative to
fCO2atm, with an exception at 21∘ N (Fig. 4). South
of 16∘ N, the decrease in wind speed did not exceed the effect of
the incremental change in (fCO2sw-fCO2atm)
associated with the increased downwelling indexes (Fig. 4; Santos et al.,
2012), resulting in a slightly increasing FCO2. The variability observed
in the annual integrated CO2 fluxes (Fig. 7b) was related with the
basin-scale oscillations, the North Atlantic Oscillation (NAO) index and the
east Atlantic pattern (EA) (http://www.cpc.ncep.noaa.Gov/data/teledoc/
telecontents.shtml). Cropper et al. (2014) found winter upwelling
variability was strongly correlated with the winter NAO (r values ranged from
0.50 at 12–19∘ N to 0.59 at 21–26∘ N), due to the
influence of the Azores semi-permanent high-pressure system on the strength
of the trade winds. The annual integrated FCO2 was related with the
annual NAO index (Fig. 7b) with a similar r=0.54, even when fluxes are
not only controlled by wind strength. However, Fig. 7a clearly indicates that
the Mauritanian upwelling area was the most important contributor to
FCO2 in the study area. The FCO2 was not significantly correlated
with the winter NAO (r=0.23). Also, the EA index, which represents a
southward-shifted NAO-like oscillation, presented a lower significant value
(r=0.48) (trends not shown), in agreement with the upwelling index
(Cropper et al., 2014). Overall, the correlation between fluxes and climate
indexes describing the main mode of variability across the Atlantic sector
may be directly related to the Azores High and its influence on the trade
wind strength.
FCO2 values along the QUIMA-VOS line were used in order to compute a
flux budget for the Mauritanian–Cap Vert region. The observed values were
assumed to be valid for at least 100 km on both sides of the QUIMA-VOS line.
In this case, the total flux of CO2 being ejected to the atmosphere
would reach a value of 16 Tg of carbon dioxide a year for the period
2005–2012, with a rate of increase of +0.6 Tg yr-1. However, it should
be considered that the export of the rich fCO2 upwelled water with
high nutrient concentration off the coastal areas would promote a decrease in
surface fCO2 values during productive seasons (as those observed north
and south of 21∘ N) that will result in an ingassing of CO2. This
could balance the observed outgassing increase on a more global scale.