**MIXING ANALYSIS OF NUTRIENTS, OXYGEN**

**AND DISSOLVED INORGANIC CARBON IN THE UPPER AND MIDDLE**

**NORTH ATLANTIC OCEAN east of the Azores**

Fiz F. PÉREZ , Aida F. RÍOS, Carmen G. CASTRO and Fernando FRAGA

*Instituto de Investigacións Mariñas de Vigo (CSIC),Eduardo Cabello, 6 ,
36208 Vigo (SPAIN)*

**ABSTRACT**

We show the distribution of nutrients, oxygen and dissolved inorganic
carbon along two perpendicular sections in the Northeast Atlantic,
between the Azores Islands and the Iberian Peninsula. A mixing model has
been established based on the thermohaline properties of water masses
according to the literature. It can explain most of the variability
found in the distribution of the chemical variables. The model is
validated using conservative parameter \"NO\" (Broecker, 1974).

From nutrients, oxygen, alkalinity and DIC, the chemical
characterisation of the water masses was performed calculating the
concentration of them in the previously defined end-members. From the
thermohaline and chemical concentrations of the end-members, the mixing
model can determine the chemical field the same and other oceanic areas
with comparative and predictive purposes. The relative variation of
nutrients concentrations, due to the regeneration of organic matter, was
estimated. In addition, from the model residuals, the ventilation
pattern described for North Atlantic Central Water (NACW) shows a
north-south gradient associated to the Subtropical gyre and the Azores
Current.

**INTRODUCTION**

Many different water masses mixing models have been used in the study of
the variability of both nutrients and oxygen. One of the most widely
used techniques is that working along isopycnic layers considering only
the existence of lateral mixing (Takahashi et al., 1985; Kawase and
Sarmiento, 1985). Other authors (Broenkow, 1965; Minas et al., 1982) do
not assume any restriction in the modelling of nutrients in various
upwelling systems. Tomczak (1981) develops an analysis of water masses
from mixing triangles with no assumption of isopycnal mixing. This type
of analysis can only resolve mixing with three end-members, considering
that only salinity and temperature will be used as conservative
variables. Each water end-member is defined by a single and fixed
temperature and salinity water, while a water mass is conventionally
characterised by the mixing of two end-members, showing a rather fixed
θ-S relationship. When there are four end-members -as it happens in the
frontal zones between North Atlantic Central Water (NACW) and South
Atlantic Central Water (SACW) off the Northwest coast of Africa-
triangular mixing analysis cannot be applied and so, it is necessary
either to use other conservative parameter or to assume isopycnal mixing
(Tomczak, 1981; Fraga et al., 1985).

In general, dissolved oxygen and nutrient distributions do not behave in
a conservative way, due to biological activity. Broecker (1974), brought
forward the concept of \"NO\" (\"NO\"=RN·NO3+ O2), a conservative tracer
which balances the effect of nutrient regeneration by the associated
oxygen consumption. The RN factor proposed by him was 9, but a set
different values between 9 and 10.5 has been reported (Redfield et al.,
1963; Takahashi et al., 1985; Minster and Boulahdid, 1987; Ríos et al.,
1989).

From Tomczak\'s work, some authors have recently developed
multiparametrical models that, assuming a nutrient conservative
behaviour, characterise and resolve mixing of more than three
end-members (Mackas et al., 1987; Tomzack and Large, 1989). The
characteristics and proportional importance of the end-members are also
estimated by Hamann and Swift (1991) by means of the exploratory
multivariate Q-mode factor analysis (QMFA) in which they include the
\"NO\" and \"PO\" conservative tracers. As both conservative (S, θ,
\"NO\") and non-conservative variables (nutrients, oxygen, alkalinity
and DIC) are handled in the same way in multivariate analyses, it cannot
be discerned which part of the nutrient content is due to mineralization
or ventilation processes. In this way, any variability in the
non-conservative tracer could led to incorrectly define new water masses
in areas of very intense biological activity.

Alternatively, if the profile of water masses is completely defined by
the thermohaline variability it is possible define a mixing model based
in a set of mixing triangles vertically ordered. This mixing model can
be tested with other conservative tracer as \"NO\". Using the observed
non-conservative chemical variables, this model could allow the chemical
characterisation of the water masses involved and described the
ventilation and mineralization patterns from the residuals (Pérez et
al., 1993).

Ríos et al. (1992) have described the thermohaline variability and the
water masses involved in the upper ocean of the region comprised between
the Azores Islands and the Iberian Peninsula (Fig. 1). From previous
water masses studies (Harvey, 1982; McCartney and Talley, 1982; Fiúza,
1984; Pollard and Pu, 1985) and from the thermohaline distribution
obtained during ANA cruise, Ríos et al. (1992) characterised different
varieties of NACW east of Azores Islands: ENACWT (Eastern North Atlantic
Central Water of subtropical origin), ENACWP (Eastern North Atlantic
Central Water of subpolar origin), and WNACW (Western North Atlantic
Central Water), showing also their displacements. Dynamics and
distributions of these NACW varieties present in the work area are
summarised in Fig. 1. North of the Subtropical Front (STF), well
characterised by the Azores Current, different mode waters (McCartney
and Talley, 1982) are involved in different isopycnical levels, those of
subtropical origin ENACW with winter mixed layer about 150-200 meters
(σ~θ~\<27.1) show a north-eastward displacement (Käse and Siedler, 1982;
Fiúza, 1984) while those of the subpolar origin with deep mixed layer,
300-400 meter and σ~θ~\>27.1, move southward below the subtropical one
(Pérez et al., 1993). Off Cape Finisterre these oppositte-displacing
water masses formed a subsurface front (Fraga et al. 1982). In the
subtropical gyre, two components of subtropical central water with
θ\>13ºC were recorded (WNACW and ENACW~T~). WNACW exists just in the STF
and surroundings. South of 32°N, it is found ENACW~T~ , specifically
Madeira Mode Water (MMW) as also described Siedler et al (1987). The
ENACW~T~ or MMW is a salinization of the WNACW. Also, in the subtropical
gyre, it was found the least salinity minima of NACW due to the
northward spreading of Antarctic Intermediate Water (AAIW).

**MATERIAL AND METHODS**

During the \"ANA\" cruise of the \"Biomass-IV\" expedition on R/V
\"Professor Siedlecki\" in November 1988, 20 stations were occupied
between 42º53\'N - 9º28.5\'W and 23º29\'N - 23º40.1\'W. Nine stations
lay on a perpendicular section to the NW coast of Galicia (Spain); the
other eleven stations lay on a meridional section perpendicular to the
first. The positions of stations are shown in Fig. 1.

Salinity, temperature and pressure were measured with a \"Neil Brown\"
CTD model SN-01/1132 at each station. Bottle samples for salinity,
nutrients, pH and alkalinity determinations were collected from surface
to 1100 m depth. Salinity was measured with an induction salinometer
(Plessey Environmental Systems Model 6230N) with a accuracy of 0.005.
Oxygen samples were measured using an automated and potentiometric
titration as a slight modification of the original Winkler method. The
standard error for five replications was less than 2 µmol·kg^-1^. The
apparent oxygen utilisation (AOU) defined by the deficit of oxygen
concentration with regard to the saturation concentration at atmospheric
pressure is used to describe the oxygen distributions. Nutrients were
determined by colorimetric methods, using a Technicon Autoanalyser AAII.
For silicate, a modified Hansen and Grasshoff (1983) method was used, in
which ß-silicomolybdenic acid is reduced with ascorbic acid. Nitrate was
determined after reduction to nitrite in a Cd-Cu column. The standard
deviation for duplicates was 0.07 µmol·kg^-1^ for silicate, 0.06
µmol·kg^-1^ for nitrate and 0.01 µmol·kg^-1^ for phosphate. This is
equivalent, respectively to 0.3%, 0.5% and 0.8% (full scale)
reproducibility.

A Ross Orion 81-04 electrode calibrated with 7.413 NBS buffer, was used
to determine pH. The temperature was also measured by means of a Pt-100
probe. pH values were normalised to 15°C to avoid the temperature effect
over pH (Pérez and Fraga, 1987a). Automatic titration was used to
measure alkalinity to final pH 4.44 with HCl (Pérez and Fraga, 1987b).
The precision was 2 µmol·kg^-1^ (0.1%) for alkalinity and 0.005 for pH.
In order to correct the drift and bias during the cruise due to slight
changes in the reference electrodes, routine and daily measurements of
both variables for big container (25l.) were made. Dissolved inorganic
carbon (DIC) and partial pressure of CO~2~ (pCO~2~) were estimated from
pH15 and alkalinity using the equations of the carbonate system
(Dickson, 1991) and the constants determined by Mehrbach et al. (1973)
and Weiss (1974). We use Mehrbach's constants because they are
determined in natural sea water and reproduce very well the experimental
temperature effect on pCO2 (Takahashi et al., 1993; Millero et al.,
1994). In addition, the NBS scale was used in the TTO cruise, whose data
are here compared with ANA data. In any case, the use of the new set of
constants (Roy et al., 1993; Lee and Millero, 1995) give only a positive
difference of 1.4[+]{.underline}0.15 µmol·kg^-1^ in the DIC calculations
which is lower than the precision of the analytical determination. The
total error propagation of alkalinity and pH15 over DIC and pCO~2~ is 4
µmol·kg^-1^ and 6 µatm respectively (Millero, 1995; Ríos and Rosón,
1996). The normalised DIC (NDIC) defined by NDIC=DIC·35/S is used to
describe the carbonic variability.

**RESULTS AND DISCUSSION**

*Distribution of nutrients and water masses.*

Vertical distribution of pressure, salinity, nutrients, NDIC and AOU
versus σ~θ~ (potential density -1000) of both sections below the surface
layer are shown together (Fig. 2). The STF was close to 34ºN (Ríos et
al.,1992) showing a strong haline change in the subsurface layer (Fig.
2b) and being a boundary to the extension of more saline NACW to the
north. The first vertical maximum of salinity is generally used to
define the upper limit of NACW (Fiúza, 1984; Ríos et al., 1992). The
isohaline of 35.6, following the isopycnal 27.1, defines the limit that
separates the saline ENACWT from ENACWP (Harvey, 1982; Pollard and Pu,
1985). North of the STF, the salinity minimum of NACW traces the highest
presence of ENACWP while the northwards and eastwards extension of
ENACWT is limited to the most shallow layers of NACW. Mediterranean
Water (MW) is clearly characterised by a salinity maximum, located north
of the STF and at the easternmost edge of the zonal section at 10ºW and
limiting the extension of ENACWP towards the south and south-east
(Pollard et Pu, 1985; Ríos et al., 1992). The salinity minimum at 41ºN
corresponds to ENACWP (Harvey, 1982; McCartney and Talley, 1982; Ríos et
al., 1992). The salinity minimum (S\<35.4) at 24ºN is due to the
influence of Antarctic Intermediate Water (AAIW) according to
Willenbrink (1982).

Nitrate, silicate and NDIC distributions (Figs 2c-e) show a strong
linear covariance between them (r2 =0.91 in both regressions, n=220).
The molar ratios SiO2:NO3 and NDIC:NO3 are 0.91 and 7.1 respectively.
Concentrations of nutrients and NDIC show a gradual increase with
density. This increase is stronger in the southern side of the
meridional section than in the northern side and along the zonal
section. There is a gradual increase of nutrients concentrations from
north to south at a same isopycnal. At levels deeper than 27.3, the
salinity maximum of MW shows relative minimum of nutrients (St. 2 to 5
and 12), particularly in the northeast. South of 28ºN, the strong
increase of nutrients concentrations is due to the influence of AAIW
(Emery and Meincke, 1986; Tsuchiya et al., 1992).

North of 31°N, concentrations of nitrate, silicate and NDIC associated
with the salinity minimum at levels σ~θ~ \> 27.2, show very little
variability. Tsuchiya et al. (1992), also describe a low salinity water
overlying MW for a section along 20°W from 3°S to 60°N. According to
these authors, this salinity minimum corresponds to the northward
spreading of AAIW, characterised by high silicate content (Tsuchiya,
1989). Due to the relatively low and constant levels of nutrients and
NDIC at this salinity minimum, it is difficult to confirm a northward
extension of AAIW in the ANA sections.

From the AOU vertical distribution (Fig. 2f) we can distinguish the
waters recently formed from those aged by their high AOU values. The AOU
vertical distribution is similar to nutrients and NDIC vertical
distributions. The direct correlation between AOU and nitrate, silicate
and NDIC gives r2 of 0.85, 0.77 and 0.64 with molar ratios of
AOU:NO3=5.4[+]{.underline}0.15, AOU:SiO~2~ =7.0[+]{.underline}0.25 and
AOU:NDIC=0.66[+]{.underline}0.3, respectively. However the covariation
of AOU with the thermohaline properties and salinity is less than 0.45.
The maximum oxygen values (244 µmol·kg^-1^) are found along the zonal
section at 41.3ºN corresponding to ENACW~P~. The oxygen levels are near
180 µmol·kg-1 (90 µmol·kg^-1^of AOU) in the MW cores. South of the STF,
the AOU progressively increases reaching values higher than 140
µmol·kg-1, together with the highest values of nitrate and silicate (24
and 16 µmol·kg^-1^, respectively) in the domain of AAIW.

*Mixing Model and its validation by \"NO\"*

Following the water masses description given by Ríos et al. (1992), we
define a set of end-members in order to capture the thermohaline
variability due to physical mixing. It need not assume either isopycnal
or diapycnal mixing here. Fig. 3 shows the θ-S diagram with all samples
and the end-members selected for the mixing model (Table 1). For
Labrador Sea Water (LSW), we have adopted those thermohaline properties
given by Talley and McCartney (1982) when the LSW crosses the Mid
Atlantic Ridge (3.40ºC and 34.89). We have chosen the thermohaline
characteristics of MW (11.74ºC and 36.5) reported by Wüst and Defant
(1936) near to Cape St. Vicente. Taking into account the different
varieties of NACW (Harvey, 1982; McCartney and Talley, 1982; Ríos et
al., 1992), the typical θ-S segment that defines NACW (Sverdrup et al.,
1942) has been divided into two segments, one from NACWT to H and other
from H to ENACWP (Fig. 3). We keep the same acronyms for the deep
end-members of ENACWP. Although, Ríos et al. (1992) clearly described
two tropical components of NACW with θ\>13ºC (WNACW and ENACW~T~), the
strong thermohaline covariability (r^2^=0.988, n=85) does not enable to
introduce two end-members for distinguishing them. Following Worthington
(1976), we take 18ºC and 36.5 for NACWT end-member and resolve the
mixing of both tropical NACW component using only the salinity as
conservative variable. At the same salinity the ENACW~T~ is cooler than
WNACW. For the same salinity ENACW~T~ is 0.7ºC colder than WNACW which
produces an additional incertitude in the estimations of end-member
nutrients lower that twofold their standard error. Pollard and Pu (1985)
took 35.7 for the salinity minimum of ENACWT, and Harvey (1982)
characterised the upper limit of ENACWP with 12ºC and 35.66 of salinity.
Thus, this last θ-S point, represented by H, has been selected to
separate NACWT from ENACWP. The ENACWP end-member is 8.58ºC and 35.23 of
salinity (Pérez et al., 1993), establishing the mixing triangle between
ENACWP and MW without LSW contribution (Fig. 3), as the mixing with LSW
is below the salinity maximum of MW. The mixing of water bodies under
the core of MW is quantified from the triangle ENACWP, MW and LSW. Then,
the ENACWP-MW line join the MW maximum in each profile. As it was
previously discussed, south of 31ºN (St. 15) AAIW influence is evident,
at least for salinity lesser than 35.5. To evaluate the influence of
AAIW in this region, the ENACWP point is replaced by the AA end-member
(Fig. 3) whose thermohaline characteristics (S=34.9, θ=6.5ºC) have been
defined by Fraga et al. (1985) off Cape Blanc, being similar to those
measured by Tsuchiya et al. (1992) at 20ºN, 20ºW.

The contribution of the water masses considered (Mk,i) to a given sample
"i" can be calculated solving the following determined system of three
linear equations

**1 = Σ Mk,i**

**Si = Σ Mk,i·Sk (1)**

**θi = Σ Mk,i·θk**

where "k" is the end-member (NACW~­T~, H, ENACW~P~, MW, LSW, AA) and "i"
is the sample number (from 1 to 220). Sk and θk are the thermohaline
characteristics of the "k" end-member. As each sample is comprised
within the limits of an unique triangle, Mk,i must be set to zero for
the other three end-members. Once Mk,i has been calculated for the 220
samples, the expected concentration of any chemical variable for the six
end-members in the study area (Ck) was obtained solving the
corresponding 220 equations by a least-squares approach:

**Ci = Σ Mk,i·Ck (2)**

As any multilinear fitting, this procedure also provides the theoretical
values of the variable Ck and the residual or anomaly for every sample
(Pérez et al., 1993).

In order to support the proposed mixing model, we have applied the
equations system (2) for a conservative tracer. Following Broecker
(1974), we have used the \"NO\" tracer with a RN rounded 10 (Emerson and
Hayward,1995). The mixing model adjusts more than 97% of the variability
(Table 1) and the distributions of anomalies or residuals ("NO"~model~
-"NO"~real~) from the multilinear adjustment are low without any
well-defined geographical pattern (Fig. 4). The mean square error of
adjustment is 7µmol·kg-1 of \"NO\", which is about twice the expected
error due to reproducibility of nitrate and oxygen (0.06\*10+2=2.6
µmol·kg-1). Probably, the actual error in the reproducibility between
stations is higher than that obtained in the same sample bottle. Also
the error about 5% in the RN determination (Minster and Boulahdid,1987)
could be other factor which impede to get even a best fitting. Due to
the high variability of \"NO\" explained by the model, it is very
difficult to define new water masses increasing the numbers of
end-members using the \"NO\" as new conservative tracer. Only it would
be possible use the \"NO\" as a third independent variable when the
residuals of "NO" given by the model were a significant percentage of
its variability. In any case, the high explained NO variability assure
us about the goodness in the election of the end-members.

Opposite to "NO", nitrate, oxygen and NDIC in subsurface waters vary due
to the remineralisation of organic matter (ROM). In addition, SiO~2~
concentrations increase due to the opal dissolution without oxidation of
organic matter but hereinafter as the two processes act on the same
substrate we are going to referred as ROM (Spencer, 1975). Therefore,
they do not completely behave as conservative variables. However, on a
first stage, we shall apply the model to them, assuming a conservative
behaviour. As the ROM covaries with thermohaline distribution, part of
the nutrients NDIC and O2 variability caused by the ROM will be
explained by the mixing model increasing the nutrient concentration of
the end-members. In this way we distinguish two parts in the biological
effects on nutrients distributions, one included in the nutrient
end-members and the other included in the residuals. This partition
depend on the size of the studied area. As the residuals vary
independently of θ and S, their distributions can be related with the
variability of the ROM inside of the area.

In table 1, we show the nutrient end-members obtained after applying the
mixing model. The variance explained by the model for the distributions
of nitrate, silicate, DIC and alkalinity is higher than 85%, while for
oxygen is much lower (36%). This difference had been noted in the
distributions shown in Fig. 2, and it is likely due to a lower
variability due to the mixing of the end-members compared with
variability generated by the ventilation processes. Therefore, in the
distribution of oxygen, ventilation and ROM processes are much more
evident than in the distribution of nutrients. Also it suggests that the
oxygen distributions may arise as much from mixing as from biological
variability (Jenkins, 1987).

The nutrient end-members obtained resume the chemical variability of the
water masses. The oxygen end-members show high concentrations (young
waters) in LSW and H end-member, while the lowest concentration is
obtained in AA. This pattern is transferred to nitrate and silicate. The
high nutrients (low oxygen and pH) in AA contrast with those of ENACWP
end-member with similar temperature revealing their different
hemispheric origins. However, the temperature governs in some way the
nutrient end-members in nutrients and pH. The warm water tends to
content lower nutrients and higher pH than cold water. To regard the
alkalinity and DIC, their naturally covariations with salinity is
clearly recorded, but once this is removed using the normalised
alkalinity and NDIC, both chemical variables have a trend to decrease
with the temperature. The high silicate end-member obtained to AA
reveals its Antarctic origin.

Mathematically speaking in a mixing triangle, the chemical variable
end-member obtained by the model (Ck) and the residuals do not depend on
the choice of the end-members, but depend on the data population present
in each triangle. In this way, the transmission of errors due to the
end-members choice is practically minimal.

*Remineralisation of organic matter and residuals distributions.*

The distribution of residuals (real minus modelled values) shows a
defined, non-randomised behaviour and resemblance between nutrients,
oxygen and DIC (Fig. 5). Once the variability caused by mixing is
removed through the mixing model, the covariability among residuals show
that the misfit is due to ROM processes not correlated with thermohaline
properties. The anomalies in oxygen and nutrients show high covariance
between them with slopes near to those expected in a Redfield type model
of ROM (Table 2). The RN value of 9.5 determined here is very similar to
those estimated by other authors (Redfield et al., 1963; Takahashi et
al., 1985, Minster and Boulahdid, 1987; Ríos et al., 1989) reinforcing
the usefulness of \"NO\" as conservative tracer.

Silica is not expected to show a close stoichiometric relationship with
the other nutrients and oxygen consumption. The proportion of diatoms in
phytoplankton varies considerably and their degree of silicification
depends on the species involved (Spencer, 1975). However, this author
reported ratios of Si:N between 0.5 to 1.2, which implies a ratio RSi =
∆O~2~:∆Si from 8 to 20. The ratio RSi of 18 adjusts correctly the
residuals due to the ROM and opal dissolution. This ratio is slightly
higher than that estimated by Pérez et al. (1993) with a series of data
from cruises off the Iberian Peninsula.

Fraga and Pérez (1990) from the chemical composition of phytoplankton
obtained a theoretical RC value between 1.0 and 1.60. The RC of 2.27
determined here from the residuals is too high (Table 2). Takahashi et
al. (1985) also present high RC values (1.95) at the isopycnal level of
27.2 for the Indic and Atlantic oceans. Other processes besides the ROM
must be present to produce such high values of RC. Takahashi et al.
(1985) suggested that the anthropogenic increase of CO~2~ could be
explain this deviation. The long-scale increase of CO~2~ partial
pressure (pCO~2~) in the atmosphere gives rise to a relative increase of
carbonic concentrations in the recently formed water masses as compared
to the old ones. This process reduces the range of variability of DIC
anomalies with regard to the rest of nutrients and oxygen. This point
will be explained below.

The similarity between the ratios calculated here and those showed in
the literature, supports the idea that the residuals of the mixing model
are mainly due to ROM or opal dissolution, which are strongly dependent
of the residence time of the water masses in the area. Taking into
account that the geographical distribution of the anomalies (Fig. 5)
shows a very similar behaviour, the results of nutrients and oxygen
anomaly will be described in terms of ageing or ventilation. The
positive anomalies of oxygen show the waters recently arrived at the
studied area, while the negative anomalies matched waters with long
residence time. As it was explained above the residuals represent only
the part of the biological processes not included in the nutrients
end-members, ie. not correlated with thermohaline properties.

At isopycnal levels above 27.3, oxygen anomalies show strong changes
(Fig. 5a) due to horizontal ventilation gradients between the core of
old water located at 26°N and the recently ventilated water in the upper
levels to the north. This water outcrops in a wide zonal region
comprising the whole thermohaline variation of NACWT and the upper part
ENACWP. Central waters south of the STF present a longer ageing with
regard to those located north and those near the Iberian Peninsula, the
later showing a maximum of ventilation (St. 4) just . The oxygen,
nitrate and silicate anomaly distributions show a layer of maximum
ageing (high inorganic nutrients and low oxygen) stretching northwards
between 27 and 27.1 isopycnals and splitting downward of STF in two
maximum ageing layers along 27.1 and 27.3 isopycnals. These
distributions suggest the northward spreading of the less saline
components of subtropical NACW (ENACWT and WNACW), together with a
southward stretching of ENACWP in the lower level (McCartney and Talley,
1982; Ríos et al., 1992). ENACWP shows its highest degree of ventilation
in the north part (St. 4), whereas southwards it reaches the highest
values of ROM. However the isopycnal southwards spreading of young
ENACWP seems to insert between layers of relatively old water suggesting
a preferential interchange of water between the subtropical gyre and the
young surroundings waters at different isopycnal levels.

The deep minima of nutrients and DIC anomalies close to the deep maximum
of oxygen anomalies, about 27.6 horizon, join the maximum of the MW,
ENACWP and AA end-members. These three end-members are water sources,
and so, are relatively recent in the area comparing with the mixed water
among them. The maximum of nutrient anomalies and minimum of oxygen
anomalies located between the MW maximum and the ENACWP minimum, about
27.3 isopycnal in the 41ºN zonal section, trace a layer relatively older
than those expected by the mixing of endmembers. This layer was
carefully described by Pérez et al. (1993) along off Iberian Peninsula.

The analysis of inorganic nutrients variability allows to describe
regions and layers of water with different degree of ventilation and
probably also with different displacements. Taking into account that an
important variability of nutrients described here is due to ROM and opal
redissolution, it does not seems adequate to use them to characterise
water masses, because the discrimination obtained over non-conservative
distributions would probably generate some new end-members or sources of
water from the others physically equals with different degree of ROM or
ventilation (Mackas et al., 1987, Tomczack and Large, 1989).

*Comparison with TTO and Atlor data*

We have applied both mixing model and nutrients end-members values
previously obtained with ANA data set to give a further validation of
the model. We are going to applied the model to the data set collected
during TTO (Transient Tracers in the Oceans, 1981) cruise off Iberian
Peninsula coast (solid squares in Fig. 1) and to the data set obtained
during the ATLOR II (Fraga and Manriquez, 1974) and ATLOR VII (Manriquez
and Fraga, 1978) in the upwelling region off NW Africa (solid triangles
and crosses respectively in Fig. 1).

We have obtained the theoretical nutrient concentration (C~i~) of each
sample by means of equation 2 considering the nutrients end-members
(C~k~) of Table 1 and the contributions of each end-members (M~k,i~)
applying equation 1. On the other hand, theoretical nitrate
concentration of each sample can also be estimated considering its
theoretical "NO" calculated and its oxygen concentration in the
following way,

NO~3~ = ("NO"-O~2~)/R~N~

It is going to be referred as theoretical nitrate from "NO" tracer, to
discern from theoretical nitrate directly estimated from the mixing
model.

Fig. 6a shows the two different set of theoretical nitrate
concentrations versus measured nitrate for TTO stations 110 to 114 (TTO,
1981). Although the agreement between theoretical nitrate concentrations
estimated from the model (white points) and actual nitrate
concentrations is high (r^2^ =0.82 , std(y-x)=1.2 µmol·kg^-1^), nitrate
concentrations estimated from "NO" (solid circles) get a better fit
(r^2^ =0.98, std(y-x)= 0.5 µmol·kg^-1^). Theoretical nitrate levels
calculated from ANA end-members is slightly higher than actual nitrate
showing the lower degree of regeneration in the water masses sampled
during the TTO cruise regarding to ANA cruise. The TTO stations are
north of the subtropical gyre where water masses are aged, as it was
previously discussed. Theoretical nitrate concentrations estimated for
the ATLOR II and ATLOR VII data set are much lower than real values,
showing that water masses located off the NW Africa coast have suffered
strong ROM due to upwelling processes. The use of "NO" tracer and the
actual oxygen gives theoretical nitrate values more similar to the
measured ones, showing that the use of "NO" tracer is the best device to
get accurate extrapolated results even in such extreme conditions.

*Recalculation of stoichiometric RC ratio from ANA data set.*

The high R~C~ calculated here of 2.2 is similar to those estimated by
Takahashi et al. (1985) in the North Atlantic, but it is too high taking
into account the expected RC from the decomposition of organic matter
(R~C~=1.36, Fraga and Pérez, 1990; R~C~=1.4[+]{.underline}0.1, Laws,
1991; R~C~=1.41, Anderson**,** 1995). We have suggested before that the
pCO2 time variation can produce an increase in DIC concentrations in the
modern 'vintages'. To remove the anthropogenic effect on DIC, we have
used the age of the water and the atmospheric pCO2 annually course. The
oxygen utilisation rates (OUR=AOU/age, in µmol·kg^-1^y^-1^) given by
Doney and Bullister (1992) from CFC-age allow us to determine the age of
each sample. In this way, the pCO~­2~ during the formation of the water
masses is determinate using the yearly atmospheric pCO2 variations
(Keeling and Whorf, 1991). Afterwards, we correct the DIC concentration
due to the pCO~2~ atmospheric change. For that, we use the factor de
Revelle (β=(δln(pCO~2~)/δln(DIC); Broecker and Peng, 1982) to convert
into at constant pCO~2~ of 348 µatm.

This procedure assumes that the formation of water occurs at the same
degree of air-sea equilibrium in oxygen and CO2 concentrations. Fig. 7
shows the pCO2 versus AOU values for all the samples of ANA cruise. The
major axis fitting shows an y-intercept of 347[+]{.underline}17 µatm,
which is close to atmospheric pCO2 of 348 µatm in 1988, suggesting that
in some manner the new 'vintages' of water formed have oxygen and CO~2~
close to saturation or partially mixed with prior 'vintages'.

In their Fig. 11, Doney and Bullister (1992) give OUR values for the
isopycnal levels between 26.6 to 27.6 assuming that oxygen saturation
levels are reached at the time of water formation. OUR values fitted to
the following lineal equation:

**OUR (µmol·kg^-1^·y^-1^) = 3.3 + 3.4 · (27.6 -**σ~θ~**) (3)**

From the AOU measured (Fig. 2f) and this equation we obtain the age of
the water masses in the study area (Fig. 8) dividing AOU by OUR. The
logical pattern of this distribution show old waters inside of
subtropical gyre mainly in the AAIW core and the young water in the
north side where new mode ENACW is formed.

Keeling and Whorf (1991) have reported the annual atmospheric pCO2 data
at Mauna Loa Station, which are linearized according to the following
equation:

**pCO2 (µatm) = 279 + (e^0.134·(y-1850)^)0.7 (4)**

where y is the year. From the age calculated by AOU, we have determine
the atmospheric pCO2 when the water sample was formed.

Finally to remove the DIC increase due to the anthropogenic increasing
of pCO2, the factor the Revelle relates the chemical variability of
pCO~2~ and DIC. This factor is expanded to:

**DIC' = DIC \[1 + (1/β)·(348/pCO~2~ -1)\] (5)**

where DIC' is the DIC converted to 348 µatm of atmospheric pCO~2~.

Applying the mixing model to the corrected DIC' concentrations, the new
recalculated DIC' anomalies show a better correlation and a lower RC
than those obtained with the DIC affected by the CO2 anthropogenic
increase (Fig. 9). The corrected RC of 1.77[+]{.underline}0.05 is still
a little higher than the expected from the ROM (Fraga and Pérez, 1990;
Laws, 1991; Anderson, 1995). Although, the proposed algorithm is a rough
approach of the effect of the anthropogenic CO2 input, it is a evidence
that this effect must be taking into account in estimations of RC ratios
(Takahashi et al., 1985). Also Takahashi et al. (1985) suggested that
the organic matter regenerated below the photic layer is dominated by
hydrogenated forms like fatty acids. However the oxidation of natural
lipids compounds in marine organisms never overpass the RC of 1.6 (Fraga
and Pérez, 1990; Laws, 1991, Anderson, 1995). Other mechanism do not
take into account here is the possible increase of alkalinity in the
modern vintages due to anthropogenic increase of pCO2 which decreases
the oversaturation of aragonite and calcite. This effect would increase
R~C~.

The simple models to calculate the uptake of anthropogenic CO2 (Chen,
1993; Krozingher et al., 1997) are very sensitive to the value of RC
considered. Low values of RC, like that proposed by Redfield et al.
(1963) could produce high estimations of anthropogenic inputs. The good
estimations of anthropogenic CO2 uptake by the ocean and the RC are
experimentally linked.

**CONCLUSIONS**

By applying a simple mixing model, we have described the local
remineralisation pattern in the frontal zone of Azores in November 1988
during the ANA cruise. Nutrients types obtained by the model strongly
reaffirm the influence of AAIW south of the STF.

The distributions of nutrients, oxygen and DIC anomalies clearly discern
the two hydrographic domain in the surveyed area. South of the STF, in
the Subtropical gyre we found maxima of nutrientes and DIC anomalies,
accompanied by negative oxygen anomalies, suggesting stronger local
remineralisation associated with the recirculation (Rhines and Young,
1982; Kawase and Sarmiento, 1985; Sarmiento et al., 1990). On the hand,
nutrients and DIC anomalies are negative -positive oxygen anomalies-
north of the STF, in the domain of recently ventilated central waters
(Pollard and Pu, 1985).

Calculated ratios anomalies, similar to the Redfield ratios, support the
remineralisation model previously assumed. However, for δO~2~:δDIC we
have obtained higher values than the R~C~ expected from decomposition of
organic matter (Fraga and Pérez, 1990; Laws, 1991; Anderson, 1995; which
probably is caused by the effect of the anthropogenic CO~2~ at the time
of formation of water masses. Removing the effect of anthropogenic CO~2~
with a rough approach we have recalculated δO~2~:δDIC closer to the
expected R~C~.

**ACKNOWLEDGEMENTS**

We thank participants in the ANA expedition and the "Professor
Siedlecki" crew for their help. We would like to acknowledge Trinidad
Rellán for the oxygen, pH and alkalinity measurements and R. Prego for
the nutrients determinations. The ANA cruise was supported by a Polish
Academic of Science and Consejo Superior Investigaciones Científicas
agreement and the data processing and the modelling work was supported
by the MAS3-CT96-0060 project of UE. We thank to two anonymous reviewer
for their valuables suggestions and comments on an earlier version of
this paper.

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Table 1.- Definition of the six water types studied and their chemical
characterisation. Correlation coefficients between actual values and
those obtained through the model, and the average square of residual are
also shown. The concentration units are µmol·kg^-1^ except for S, θ and
pH~15~ . The number of data are 220 except for alkalinity (n=180).

S θ Osat \"NO\" O2 ALK DIC pH15 NO3 SiO2

NACWT 36.50 18.00 231 231±2 195 5 23802 21002 8.2360.005 3.60.5 2.00.3

H 35.66 12.00 262 325±1 213 3 23382 21311 8.1120.003 11.20.3 4.50.1

ENACWP 35.23 8.58 283 393±2 185 6 23231 21743 7.9920.006 20.70.6 11.90.3

AA 34.90 6.50 297 452±3 116 9 23061 22114 7.8660.009 33.60.9 24.10.5

MW 36.50 11.76 262 318±3 16310 24133 22124 8.0790.010 15.51.0 10.60.6

LSW 34.89 3.40 320 450±8 26624 22975 21539 7.9830.023 18.42.5 12.21.3

r2 0.97 0.36 0.91 0.90 0.92 0.85 0.92

STD residuals 7 22 5 9.5 0.020 2.3 1.2

Table 2. Teissier linear regression between oxygen anomalies and
nitrate, DIC and silicate anomalies (Fig 6), respectively.

δ\[O2\] = (-9.5[+]{.underline} 0.2) \* δ\[NO~3~^-^\] (n= 220) r^2^=0.90
RN = 9.5

δ \[O2\] = (-2.27[+]{.underline} 0.1) \* δDIC (n= 220) r^2^=0.74 RC =
2.27

δ\[O2\] = (-18[+]{.underline} 0.7) \* δ\[SiO2\] (n= 220) r^2^ =0.63 RSi
= 18

**FIGURE CAPTIONS**

Fig. 1. Location of stations of ANA cruise(•)and the TTO (), ATLOR II
(), ATLOR VII (x) stations used to validate the model. The circulation
scheme of NACW varieties according to Ríos et al. (1992) is also
superimposed. The main hydrographic features are also represented: NAC
(North Atlantic Current), F (Subsurface Front between ENACW~P~ and
ENACW~T~; Fraga et al., 1982), AC (Azores Current), STF (Subtropical
Front) and KS (Frontal Band; Käse and Siedler, 1982). The displacement
of East North Atlantic Central Water of subtropical (ENACW~T~) and
subpolar (ENACW~P~) origin, and the Madeira Mode Water (MMW) are shown.

Fig. 2. Composite distributions of the meridional and zonal section,
separated by the vertical line at St. 9, of pressure (a), salinity (b),
nitrate (c), silicate (d), NDIC (e) and AOU (f) versus density anomaly.
Units in µmol·kg^-1^ except for pressure (dbar) and salinity. Surface
waters are removed. The polygonal upper line shows the upper limit of
NACW.

Fig. 3. θ-S diagram of subsurface samples of ANA cruise with mixing
triangles employed. The thermohaline properties of the end-members are
shown in Table 1. The white squares represent the seawater samples with
AAIW influence.

Fig. 4. Composite distributions of the meridional and zonal sections,
separated by the vertical line at St. 9, of \"NO\" anomalies
("NO"~real~ - "NO"~model~ in µmol·kg-1). See details in Fig. 2 caption.

Fig. 5. Composite distributions of the meridional and zonal sections,
separated by the vertical line at St. 9, oxygen, nitrate, silicate and
DIC anomalies versus density anomaly. The vertical maximum (+) and
minimum (-) are also shown. Units in µmol·kg^-1^. See details in Fig. 2
caption.

Fig. 6. Graphs of theoretical nitrate concentrations estimated from the
model () and theoretical nitrate concentrations estimated from "NO" ()
versus measured nitrate concentrations for TTO (a) and ATLOR II and
ATLOR VII (b) data set. The x=y line is also shown.

Fig. 7. Partial pressure of carbon dioxide (pCO~2~) versus apparent
oxygen utilisation (AOU) from the whole ANA cruise data set.

Fig. 8. Age (years) calculated by apparent oxygen utilisation and the
oxygen utilisation rates estimated from CFC data during Oceanus 202
cruise (Doney and Bullister, 1992). See details in Fig. 2 caption.

Fig. 9. Plot of DIC () and corrected DIC () anomalies versus oxygen
anomalies in µmol·kg^-1^. The major axes slope fitted and correlation
coefficients are shown.