In this study, we use a joint observation–model approach to investigate the mixed-layer heat and salt annual mean as well as seasonal budgets in the eastern tropical Atlantic. The regional PREFCLIM (PREFACE Climatology) observational climatology provides the budget terms with a relatively low spatial and temporal resolution compared to the online NEMO (Nucleus for European Modeling of the Ocean; Madec, G., 2014) model, and this is later resampled as in PREFCLIM climatology. In addition, advection terms are recomputed offline from the model as PREFCLIM gridded advection computation. In the Senegal, Angola, and Benguela regions, the seasonal cycle of mixed-layer temperature is mainly governed by surface heat fluxes; however, it is essentially driven by vertical heat diffusion in the equatorial region. The seasonal cycle of mixed-layer salinity is largely controlled by freshwater flux in the Senegal and Benguela regions; however, it follows the variability of zonal and meridional salt advection in the equatorial and Angola regions, respectively. Our results show that the time-averaged spatial distribution of NEMO offline heat and salt advection terms compares much better to PREFCLIM horizontal advection terms than the online heat and salt advection terms. However, the seasonal cycle of horizontal advection in selected regions shows that NEMO offline terms do not always compare well with PREFCLIM, sometimes less than online terms. Despite this difference, these results suggest the important role of small-scale variability in mixed-layer heat and salt budgets.
The interaction between ocean and atmosphere plays a crucial role in the climate system. This interaction involves heat and freshwater fluxes, which affect temperature and salinity variations in the upper-oceanic mixed layer. Therefore, sea surface temperature and salinity (SST, SSS) are two key climate variables, and understanding the balance of processes determining their variability is a key requirement to accurately simulating the climate system.
In the eastern tropical Atlantic, seasonal climate variability is mostly associated with the West African monsoon (WAM) and strongly linked with SST and SSS variations. SST is characterized here by a strong seasonal cycle, which influences the regional climate, particularly the large-scale atmospheric circulation and rainfall over the ocean and African continent (Carton and Zhou, 1997; Foltz et al., 2013; Kushnir et al., 2006). In the Gulf of Guinea, the seasonal formation of the Atlantic cold tongue (ACT), associated with equatorial upwelling, is the main feature associated with SST seasonal variations (Chang et al., 2006; Peter et al., 2006; Caniaux et al., 2011). This seasonal cooling creates an intense meridional SST front that enhances southern trade winds and shifts the Intertropical Convergence Zone (ITCZ) northward, which triggers the WAM (Philander and Pacanowski, 1981; Picaut, 1983; Waliser and Gautier, 1993; Caniaux et al., 2011). In the Angola–Benguela frontal zone off southwestern Africa, SST variability also has an impact on coastal precipitation (Reason and Rouault, 2006). Conversely, the atmospheric conditions in the eastern tropical Atlantic impact SST, as trade winds are the main driver of eastern boundary upwelling systems found along the coasts of Angola–Namibia and Senegal–Mauritania.
Like SST, SSS is an essential climate variable. Its variations are closely linked to the global hydrological cycle, as freshwater exchanges between the ocean and atmosphere control its mean large-scale distribution (Durack and Wijffels, 2010; Bingham et al., 2012): regions of high SSS are dominated by evaporation, while regions of low SSS are dominated by precipitation. In the eastern tropical Atlantic, the intense precipitation under the ITCZ has a strong impact on seasonal variability of SSS. For example, off Senegal and Guinea, SSS decreases in boreal fall following the period when the ITCZ reaches its most northern position, which leads to a maximum in precipitation and runoff from Senegal and Gambia rivers (Camara et al., 2015). The eastern tropical Atlantic is indeed characterized by low SSS plumes due to strong river discharges, including the Congo River (the second-largest outflow in the world after the Amazon River) and the Niger River in the Gulf of Guinea. SSS variations are also associated with upwelling. In the equatorial Atlantic, SSS increases during the development of the Atlantic cold tongue (ACT) in boreal spring–summer (Schlundt et al., 2014; Da-Allada et al., 2014). Summer upwelling at the northern coast of the Gulf of Guinea also increases coastal SSS (Alory et al., 2021). Off Angola, relatively high SSS is observed during the upwelling season in August, while low SSS appears in March and October–November (Kopte et al., 2017; Awo et al., 2022). In the Benguela upwelling system further south, the maximum in SSS appears in April and the minimum in October (Junker et al., 2017).
SST and SSS have already been the focus of several studies in the eastern tropical Atlantic. Studies only based on observations (in situ and/or satellite), and others combining observational data and model data, show a variety of physical processes contributing to the heat and salt budget with a different balance from one region to another (Foltz et al., 2003; Foltz and McPhaden, 2008; Da-Allada et al., 2013, 2014).
In the northern tropical Atlantic far from the coast, the heat budget is
largely driven by surface heat fluxes, essentially solar radiation varying
with the cloudy ITCZ position (Carton and Zhou, 1997).
Off northwestern Africa in the Senegal region, the seasonal cycle of SST is
associated with coastal upwelling modulated by the seasonal variations of
alongshore winds. In the equatorial zone, the mixed-layer heat budget is
mainly controlled by surface heat fluxes, of which mainly the solar
radiation is important
(Carton
and Zhou, 1997; Foltz et al., 2003; Yu et al., 2006). The dominance of the
surface heat flux is highlighted by a recent study based on PIRATA buoy data
off the Equator, in particular at the 6
In the northeastern tropical Atlantic, including in the Senegal region, the salt budget is controlled by freshwater fluxes. The net mixed-layer salinity variations are, however, weak because of the compensation between the atmospheric and oceanic terms (Camara et al., 2015). In the eastern equatorial Atlantic and Gulf of Guinea, horizontal advection and vertical mixing play a dominant role in determining the seasonal cycle of salt budget, which cannot be explained by freshwater fluxes only (Tzortzi et al., 2013; Da-Allada et al., 2013, 2014). This dominance of zonal advection and vertical processes extends southward and in the Angola coastal region (Camara et al., 2015).
In addition, it can be noted that various approaches have been used to
estimate the heat and salt budgets in the tropical Atlantic, in
particular the advection terms. Foltz
et al. (2003) analyzed the mixed-layer heat balance at PIRATA mooring
locations, where they computed the heat advection from monthly gridded
climatologies of near-surface horizontal velocity, based on ship drifts and
Lagrangian drifters, and SST gradient fields based on a combination of ship,
buoy, and satellite data. Wade et al. (2011)
used a similar SST product but satellite-derived currents to estimate the heat advection every
10 d at positions of Argo profiles. Then monthly
averages in nine boxes covering the Gulf of Guinea were used to study the
seasonal cycle of mixed-layer heat as observed by Argo.
Da-Allada et al. (2013) developed
an original mixed-layer model of the tropical Atlantic at monthly,
1
Nonlinear terms are associated with turbulence. In eddy-resolving models,
they represent mesoscale activity related to eddies and tropical instability
waves (TIWs). The characteristic size of eddies is given by the Rossby
radius, which increases equatorward and has a minimum value of 30–40 km in
the 30
In this paper, we exploit for the first time a recently produced tropical Atlantic mixed-layer heat and salt budget observation-based climatology. Moreover, we use a joint observation–model approach that has rarely been used: we compare the mixed-layer heat and salt budget terms estimated from observations to those simulated by a high-resolution OGCM simulation in the eastern tropical Atlantic. A sensitivity test to the spatiotemporal resolution at which advection terms are computed in the model is conducted. This comparison should allow providing a high-level model validation, isolating the contribution of mesoscale advection in the mixed-layer budgets, and quantifying the uncertainty in the different budget terms. We particularly focus for the mixed-layer budgets on the upwelling regions where oceanic processes are expected to be dominant. The observational product, the model, and the methodology used are presented in Sect. 2. Section 3 contains the results of observation–model comparison regarding mean heat and salt budgets, as well as their seasonal variability in selected regions. In Sect. 4, a discussion and conclusion are presented.
We use the PREFCLIM (PREFACE Climatology) observed seasonal climatology of
mixed-layer heat and salt budgets covering the eastern tropical Atlantic
(Rath et al., 2016). It has been produced in the
framework of the European PREFACE (Enhancing prediction of tropical Atlantic
climate and its impacts) project, which aimed at improving climate models in
the tropical Atlantic. This monthly climatology is derived from all
hydrographic data publicly available covering the region, including Argo
float data (Argo, 2000) and glider measurements conducted by GEOMAR
between 2002 and 2015 (for more details, see
These data have been gridded using an interpolation scheme including
isobath-following and front-sharpening components
(Schmidtko et al.,
2013). Mixed-layer properties like temperature, salinity, and depth are
provided with a spatial resolution of
We use a regional configuration of the NEMO (Nucleus for European Modeling
of the Ocean; Madec, G., 2014) oceanic model version 3.6. This regional
simulation covers the tropical Atlantic (35
In this paper, the driving processes of the seasonal variability of mixed-layer
temperature and salinity in selected regions are quantified through heat and
salt budgets from the NEMO model. This approach has been already used in several
studies based on observations and models
(Da-Allada
et al., 2013; Hasson et al., 2013). In the following, as mixed-layer
temperature and salinity are very close to SST and SSS, respectively, we
indifferently use the vocabulary. The heat budget evolution and salt budget
evolution within the mixed layer are respectively given by
Eqs. (1) and (2), already used in previous studies
(Peter
et al., 2006; Jouanno et al., 2011; Da-Allada et al., 2014; Schlundt et al.,
2014):
The left-hand side of Eq. (1) represents the mixed-layer temperature
tendency term, and the right-hand side represents all terms contributing to
the heat budget. Namely, term
The left-hand side of Eq. (2) represents the mixed-layer salinity
tendency term, and the right-hand side represents all terms contributing to the
salt budget. Namely, term
Annual mean of mixed-layer depth from observations
The budget computation slightly differs between the observation-based
climatology and the model data. All terms are computed online in the NEMO
model, explicitly from Eqs. (1) and (2), except for the entrainment term
that is estimated (using the online advection term) as a residual. In the
PREFCLIM observed climatology, equations for the heat and salt budgets are
simplified, as done in other studies
(Stevenson
and Niiler, 1983; Foltz et al., 2003, 2004; Delcroix and Henin, 1991;
Schlundt et al., 2014). Only the tendency, surface heat or freshwater flux
(term
A preliminary validation of the NEMO regional simulation is done by
comparing modeled and observed annual means of mixed-layer depth,
mixed-layer temperature, and mixed-layer salinity, as well as the standard deviation
of the latter two. The model (Fig. 1b) reproduces the large-scale properties
of observed MLD (Fig. 1a) in the eastern tropical Atlantic. In both
observations and the model, the shallowest mixed layer is found along the
Equator and the coasts of Africa, while the deepest mixed layer is found
towards the northern and southern subtropical gyres. The main differences
between the modeled and observed MLD are found along the northern coast of
the Gulf of Guinea and along 24
The model (Fig. 2b) reproduces the observed mean SST (Fig. 2a) in the
eastern tropical Atlantic well. The highest SST is found along the zonal band between
0 and 12
The model (Fig. 3b) also represents the main observed features of SSS (Fig. 3a) in the eastern tropical Atlantic. The highest SSS is found towards the
center of both the northern and southern subtropical gyres. In contrast,
the lowest SSS is found slightly north of the Equator and in the Gulf of
Guinea due to the strong precipitation associated with the ITCZ and river
runoff. SSS is also relatively low in the Benguela upwelling region. SSS is
lower in the model than in observations in the eastern part of the Gulf of
Guinea, but higher along 12
Annual
In the following part, the heat and salt budgets will be investigated in detail
for four boxes, covering oceanic regions of similar size, selected for their
particularly low mean SST and SSS and/or strong SST and SSS variability, as well as
generally being associated with upwelling. These are, from north to south, the
Senegal box (6–20
Annual mean
We compared the mean mixed-layer heat budget from the model and observations.
The surface heat flux and horizontal heat advection maps are presented in
Fig. 4. Surface heat flux from observations is positive everywhere in the
eastern tropical Atlantic, with a maximum along the Equator that gets the
strongest solar flux, and along the west coasts of Africa (Fig. 4a). Along
West African coasts, the heat flux is strong as solar flux can concentrate
in a thin mixed layer (Fig. 1), notably due to the strong salinity
stratification induced by the Niger and Congo rivers in the Gulf of Guinea. In
addition, the temperature difference between the ocean cooled by coastal
upwelling (Benguela, Senegal, see Fig. 2) and the atmosphere leads to a
reduced latent heat flux. The model reproduces the observed patterns with
higher resolution (Fig. 4c) and, when resampled similarly (Fig. 4b), shows
good spatial agreement with the PREFCLIM climatology (
As expected, there are important differences between the maps of offline
heat advection, calculated based on the coarsened model currents and
hydrography (Fig. 4f), and the online heat advection taking into account the
full spatiotemporal variability (Fig. 4g, resampled to
Mean heat flux from PREFCLIM
In this part, we analyze the individual contributions of different physical processes to the heat budget during a seasonal cycle in selected regions. We present the seasonal variability of mixed-layer temperature and of the heat tendency term (Fig. 5) and try to identify the dominant processes. Taylor diagrams are used to evaluate the consistency of the global terms of the budget between PREFCLIM climatology and the NEMO model (Fig. 6). In the following, the observed gridded advection, rather than the observed Lagrangian advection, is used because it is generally better correlated with the model advection (see Figs. A3 and A4 for more details).
In the Senegal region, observed and modeled seasonal mixed-layer temperature
variations (Fig. 5a) are largely consistent (
In the equatorial region, Fig. 5d presents the seasonal evolution of
mixed-layer temperature in the model and observations. There is a strong
consistency (with
In the Angola region, the model reproduces the seasonal evolution of
observed mixed-layer temperature well (Fig. 5g,
In the Benguela region (Fig. 5j), the model reproduces the observed
seasonal cycle of the mixed-layer temperature well, which is maximum in
February–May and minimum from July to October, but with a positive bias
close to 1
Seasonal mixed-layer heat budget terms from observations
(dashed line) and the model (full line and full dotted line for pseudo-residual
associated with offline advection) in selected regions: SST (
Taylor diagram of global terms of the heat budget in selected
regions. Heat flux, horizontal advection (gridded advection for observations
and online advection for model), and (pseudo-)residuals are represented by
squares, circles, and triangles, respectively. Empty circles and triangles are
offline advection and associated (pseudo-)residuals. Senegal, Benguela,
equatorial, and Angola regions are designed by blue, red, yellow, and magenta, respectively. Correlations are 95 % significant when
We now compare the mean salt budget from the model and from observations through
freshwater flux and horizontal salt advection (Fig. 7). The PREFCLIM
freshwater flux acts to decrease the mixed-layer salinity along the
0–12
Mean freshwater flux from PREFCLIM
As previously done for the heat budget (see Figs. A5 and A6 for more details), we evaluate the individual contributions of different physical processes to the salt budget during a seasonal cycle (Fig. 8) and try to identify the dominant processes. Taylor diagrams are used to evaluate the consistency of budget terms between PREFCLIM climatology and the NEMO model (Fig. 9).
In the Senegal region, observed and modeled mixed-layer salinity seasonal
cycles (Fig. 8a) are very different (
In the equatorial region, the modeled and observed seasonal cycles of the
mixed-layer salinity are largely in phase (Fig. 8d,
In the Angola region, Fig. 8g presents the seasonal evolution of mixed-layer
salinity in the model and observations. The model reproduces (
In the Benguela region, the model follows the observed seasonal cycle
of mixed-layer salinity well (
Seasonal mixed-layer salt budget terms from observations (dashed line) and the model (full line and full dotted line for pseudo-residual associated with offline advection) in selected regions: SSS in practical salinity units and tendency terms in practical salinity units per month.
Taylor diagram of global terms of the salt budget in selected
regions. Freshwater flux, horizontal advection (gridded advection for
observations and online advection for model), and (pseudo-)residuals are
represented by squares, circles, and triangles, respectively. Empty circles and
triangles are offline advection and associated (pseudo-)residuals. Senegal,
Benguela, equatorial, and Angola regions are designed by blue, red, yellow,
and magenta, respectively. Correlations are 95 % significant when
In this paper, we examined the dominant physical processes controlling the seasonal variability of mixed-layer heat and salt budgets in selected coastal regions of the eastern tropical Atlantic, namely the Senegal, equatorial, Angola, and Benguela regions. First, we used both a regional configuration of the NEMO model and the PREFCLIM observation-based climatology to analyze the spatial variations of the annual mean mixed-layer heat and salt budgets in the eastern tropical Atlantic (see Figs. 4 and 7, respectively). The model outputs were resampled to the PREFCLIM time–space resolution to compare maps of the mean processes contributing to mixed-layer heat and salt budgets, according to both sources. Second, we analyzed the seasonal variation of the mixed-layer temperature and salinity, their related tendencies, and potential driving processes: heat and freshwater flux, horizontal heat and salt advection, and other processes estimated from observations as a residual but explicitly resolved in the model for the selected regions. As the PREFCLIM climatology does not capture the mesoscale physical processes, we relied on the high-resolution model outputs to evaluate their contribution to the mixed-layer heat and salt budget.
For the preliminary validation, the results have shown that the model
consistently reproduces the mean features of observed mixed-layer depth,
temperature, and salinity in the eastern tropical Atlantic (see Figs. 1, 2, and 3, respectively). The existing differences between modeled outputs
and the PREFCLIM climatology can be explained by the different heat and salt
flux products that are used for forcing the model or for estimating the
PREFCLIM budget terms. There are also differences in the method to define
the MLD. The PREFCLIM climatology uses the algorithm of
Holte and Talley (2009), whereas the model
uses the density criterion (0.03 kg m
For the secondary validation, we have used both the model and the PREFCLIM
climatology to analyze the annual mean of heat and freshwater flux as well as
horizontal heat and salt advection. The model heat and freshwater fluxes largely
agree with the PRECLIM climatology (Figs. 4a–b and 7a–b, respectively),
except for differences in a few regions that can again be due to different
flux products or MLD biases. There are important differences between the
model heat and salt advection terms computed either offline (Figs. 4f and 7f,
respectively), at PREFCLIM spatiotemporal resolution (2.5
At seasonal timescales, the monthly mixed-layer heat and salt tendency terms in the selected regions are very weak in both the PREFCLIM climatology and the model compared to individual terms contributing to the heat and salt budgets that tend to compensate for each other, as also found in previous studies (Da-Allada et al., 2013, 2014; Camara et al., 2015).
Surface heat fluxes, especially the solar flux, dominate the seasonal mixed-layer heat budget in the Senegal, Angola, and Benguela regions (Fig. 5b, h, and k, respectively). In the Senegal region, this result, and the secondary contribution of oceanic processes such as vertical diffusion and zonal advection, which add to latent heat flux to drive the observed winter cooling, confirms previous studies (Carton and Zhou, 1997; Yu et al., 2006).
In the equatorial region, the heat flux remains positive and nearly constant
throughout the seasonal cycle. This shows the dominance of the shortwave
flux that warms the mixed layer from September to April, although this
warming weakens between November and December. Although our selected box
slightly differs from previous regional studies, this result is in agreement
with earlier studies (Peter
et al., 2006; Wade et al., 2011). The variability of mixed-layer
temperature, in particular the observed spring–summer cooling during the
formation of the ACT, is mainly controlled by vertical heat diffusion (Fig. 5e), confirming other studies
(Yu
et al., 2006; Jouanno et al., 2011). Recently,
Scannell and McPhaden (2018) also confirmed the
role of turbulent vertical mixing from a PIRATA buoy located at the
southeastern edge of the ACT. While it does not compensate for the cooling
effect of vertical diffusion, zonal heat advection is positive all year long
in the equatorial region, the only one among analyzed regions where it is
so. This is the consequence of a negative zonal temperature gradient as the
mixed-layer temperature decreases toward the coast, advected by westward
currents associated with the SEC. When associated with meridional heat
advection, this leads to a positive horizontal heat advection throughout all
year except in the month of May. We note a nearly similar variability of
horizontal advection in Wade et al. (2011),
although this term is negative in their study except for June–July, when we
both observe a positive maximum. This difference can be linked to either
products used or the criterion used to define the MLD (temperature vs.
density criterion) and maybe to the slightly different boxes of study.
Jouanno et al. (2017) also found, like us, a
permanent warming effect of horizontal advection in an equatorial box
shifted west compared to ours using the same model configuration. In our
box, according to the model this warming is largely due to mesoscale
advection, probably by eddies as TIW activity sharply decreases east of
15
In the Angola region, the dominant role of heat flux in the mixed-layer heat
budget was also found in previous studies
(Carton
and Zhou, 1997; Yu et al., 2006). In this region, the incoming shortwave
flux warms the mixed layer from August until March against the action of
latent heat flux. The competition between the shortwave flux and the latent
heat flux is also mentioned in Scannell and
McPhaden (2018), although at the 6
In the Benguela region, the heat tendency variations are roughly in phase with the heat flux variations. The heat budget is mostly driven by the shortwave flux, as found previously in the neighboring southern Angola upwelling system. The cooling that occurs from March to August can be associated with cloud cover, which reduces the incoming solar flux, and also a small contribution of oceanic processes. The observed coldest temperatures correspond to the July–October upwelling season (Hagen et al., 2001; Muller et al., 2014).
There are much larger differences between PREFCLIM and NEMO in the seasonal variations of the mixed-layer salinity (Fig. 8a, d, g, j) compared to temperature. These seasonal variations generally have a larger amplitude and lower minima in the model, as also seen in Fig. 3. This can be due to several factors. First, although the PREFCLIM product benefited from newly available hydrographic data in the Senegal, Angola, and Namibia coastal waters, the data density is still low in the equatorial Gulf of Guinea (Dengler and Rath, 2015), the freshest waters associated with heavy rain as well as the large Congo and Niger River plumes; hence, the largest difference in seasonal salinity variations is in the equatorial box (Fig. 8d). Poor data density can also be associated with a seasonal bias that may prevent capturing the full seasonal cycle. Second, hydrographic profiles, notably those from Argo floats, do not sample the salinity minimum found in the upper few meters of the ocean in regions highly stratified by rain and river plumes, which induces SSS estimations higher than those observed from satellites (Boutin et al., 2016; Houndegnonto et al., 2021). This leads to overestimation of mixed-layer salinity too. Third, while the NEMO model configuration has high vertical resolution in the upper few meters and homogeneous spatial coverage, the way it reproduces mixed-layer salinity highly depends on its freshwater forcing, including river runoff, and its own dynamics that are of course not perfect. Despite these differences between observations and the model, the comparison is instructive.
The Senegal region is the only one among the four analyzed regions where the
salt budget is clearly controlled by the surface freshwater fluxes, with an
added runoff effect (Fig. 8b). From March to October the observed freshening
can be explained by the combined effect of precipitation and Senegal and
Gambia rivers inputs. From October to November, zonal salt advection adds
its contribution to existing freshwater inputs to freshen the mixed layer.
Vertical salt diffusion, with an additional contribution of meridional and
vertical salt advection, tends to increase mixed-layer salinity and partly
compensates for the previous freshening effect. Although our selected regions
are slightly different, these results are consistent with those
of Camara et al. (2015). However, in our
study, evaporation plays a dominant role in increasing salinity for the rest
of the year, even if there is also a weak contribution of oceanic processes.
This disagrees with the study of Camara
et al. (2015), wherein the contribution of evaporation to the mixed-layer salt
budget is very weak compared to our results. This contradiction can be
explained by different model configurations as described in
Da-Allada et al. (2017).
Camara et al. (2015) use for model
forcing an older version of the DRAKKAR Forcing Set (DFS4) compared to the
one (DFS5.2) used in the present study, and their model has fewer vertical
levels than ours (46 vs. 75). They also use a smaller density criterion (0.01 kg m
In the equatorial region, the seasonal variability of mixed-layer salinity is mainly due to oceanic processes as shown in other studies (Da-Allada et al., 2013, 2014). In our study, we found vertical salt diffusion and zonal salt advection as dominant oceanic processes (Fig. 8f). From October to December and March to July, zonal advection is the most important freshening contribution, stronger that precipitation. It is explained by the westward South Equatorial Current (SEC), which transports low-salinity waters from the Gulf of Guinea associated with the Niger and Congo River plumes (Houndegnonto et al., 2021). The major role played by vertical salt diffusion in increasing the salinity in the mixed layer, demonstrated in previous studies (Da-Allada et al., 2014, 2017), is confirmed by our results for boreal spring–summer in particular. This strong vertical salt diffusion is the consequence of the vertical shear between the westward SEC and the eastward EUC (which transports high-salinity waters) but can be reduced by the strong salinity stratification caused by the Niger and Congo River plumes (Jouanno et al., 2011). Note that vertical diffusion, however, is strongly compensated for by zonal advection, above all in May. These results agree with the other studies covering the region despite slight differences in the limits of selected boxes (Berger et al., 2014; Da-Allada et al., 2014, 2017; Camara et al., 2015). Although the contribution of surface freshwater fluxes and runoff remains weak in our study, its seasonal variations follow those described by Da-Allada et al. (2017), with some time lag. During the period when the ITCZ is close to the Equator between November and April, the freshwater flux is dominated by precipitation and decreases the mixed-layer salinity, whereas the rest of the year, the freshwater flux is dominated by evaporation and increases salinity.
As in the equatorial region, horizontal and vertical oceanic processes drive the mixed-layer salinity in the Angola region too (Fig. 8g–i), in agreement with previous studies (Camara et al., 2015; Awo et al., 2022). Meridional salt advection explains most of the variability in salt budget, particularly its semi-annual cycle, as it freshens the mixed layer in February–April and September–October, when the southward Angola Current brings low-salinity water from the Congo River plume (Gordon and Bosley, 1991; Awo et al., 2022). However, for the rest of the seasonal cycle, a combined action of meridional salt advection, vertical salt advection, and vertical salt diffusion increases the salinity of the mixed layer. Although vertical advection is stronger than vertical diffusion, they remain in phase throughout the cycle and act against the runoff and zonal salt advection. Seasonal variations in both vertical salt diffusion and advection are driven by changes in the vertical salinity gradient related to the semi-annual intrusion of low-salinity surface waters (Camara et al., 2015; Awo et al., 2022).
In the Benguela region, individual contributions of physical processes are relatively weak in comparison to the other regions. The mixed-layer salinity variability is partly controlled by freshwater fluxes, particularly evaporation (Fig. 8j–k). Zonal advection remains negative throughout the year, and from March to December, it acts to decrease salinity, against the action of evaporation that is reinforced by vertical salt diffusion between September and December. The increase in salinity corresponds to the upwelling season in the southern part of Benguela upwelling system in summer (Muller et al., 2014).
Although increasing resolution in oceanic models intends to produce more
realistic simulations by explicitly resolving mesoscale variability, and
models are the only way to estimate all terms of the heat and salt budget in the
mixed layer, it is difficult to directly validate such model budgets with in
situ data. One problem is that globally available in situ data can only
explicitly resolve near-surface horizontal processes, particularly
advection, not vertical processes that have to be estimated as a residual. A
second problem is that in situ observation density does not allow estimating
horizontal advection at the high resolution available from models.
Therefore, to be properly compared with those available from observations,
model horizontal advection terms must be computed offline at the
spatiotemporal resolution of observations. Our results indeed show that the
time-averaged spatial distribution of NEMO offline heat and salt advection terms
compares much better to PREFCLIM horizontal advection terms than the online
heat and salt advection terms. However, when examining the seasonal cycle of
horizontal advection in selected boxes, NEMO offline terms do not always
compare well with PREFCLIM, sometimes less than online terms. This suggests
that temporal coverage of in situ observations is more critical than spatial
coverage, particularly for salinity, and especially in coastal areas of
Africa where Argo profiles are relatively scarce and in the equatorial region where
Lagrangian drifters do not stay long due to Ekman divergence. Another
possibility would be to estimate advection from satellite products of SST,
SSS, and currents, the latter estimated from altimetry and satellite wind for
their geostrophic and Ekman components, respectively
(Bonjean and Lagerloef, 2002), which
are available at a resolution of a few tens of kilometers and a few days.
The new Surface Water and Ocean Topography (SWOT) mission
(Morrow et al., 2019) should soon
further improve the resolution of geostrophic currents. The Soil Moisture
Ocean Salinity High Resolution (SMOS-HR) mission project
(Rodriguez-Fernandez et al., 2022) would also help to capture
SSS gradients. The often large differences between offline and online
advection terms in the model suggest an important role of small-scale
(
Difference between observations and the model in
mixed-layer depth
Spatial correlation (
Taylor diagrams comparing seasonal variations of
horizontal, zonal, and meridional heat advection (HADV, UADV, VADV) from
observations (gridded advection named Obs
Seasonal cycle of horizontal (cyan), zonal (blue), and meridional (purple) heat advection from observations (dashed line for gridded advection and dotted line for Lagrangian advection) and the model (full line for online and full dotted line for offline) in selected boxes. All others terms are in degrees Celsius per month.
Taylor diagram of horizontal salt advection and these
components between observations (gridded advection for Obs
Seasonal cycle of horizontal (cyan), zonal (blue), and meridional (purple) salt advection from observations (dashed line for gridded advection and dotted line for Lagrangian advection) and the model (full line for online and full dotted line for offline) in selected boxes. All others terms are in practical salinity units (psu) per month.
The PREFCLIM climatology used here is available from
RDN performed the data analysis and wrote the paper with a strong contribution from GA. OEK did some preliminary analysis under the supervision of GA, CYDA, and JJ. WR and JJ produced the PREFCLIM climatology and the NEMO model simulation, respectively. All co-authors contributed to the scientific improvement of the paper.
The contact author has declared that none of the authors has any competing interests.
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This work is part of the PhD thesis of Roy Dorgeless Ngakala, funded by the DAAD (Deutscher Akademischer Austauschdientst/German Academic Exchange Service) in the framework of the “In-Country/In-Region Scholarship Programme” for Sub-Saharan Africa. The PREFCLIM climatology was produced in the framework of the European Union FP7 PREFACE project. This study was supported by the TRIATLAS project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement 817578. This study is also supported by the TOSCA SMOS and SWOT-GG projects funded by CNES.
This research has been supported by the H2020 TRIATLAS project (grant no. 817578).
This paper was edited by Karen J. Heywood and reviewed by two anonymous referees.