BGBiogeosciencesBGBiogeosciences1726-4189Copernicus GmbHGöttingen, Germany10.5194/bg-12-5793-2015Air-sea CO2 fluxes and the controls on ocean surface pCO2 seasonal
variability in the coastal and open-ocean southwestern Atlantic Ocean: a modeling studyArrudaR.cadoarruda@gmail.comhttps://orcid.org/0000-0002-8324-3301CalilP. H. R.https://orcid.org/0000-0001-6361-1747BianchiA. A.DoneyS. C.GruberN.https://orcid.org/0000-0002-2085-2310LimaI.TuriG.Laboratório de Dinâmica e Modelagem Oceânica (DinaMO), Instituto de Oceanografia, Universidade Federal do Rio Grande, Rio Grande, RS, BrazilDepartamento de Ciencias de la Atmósfera y los Oceános, Universidad de Buenos Aires, Buenos Aires, ArgentinaDepartamento Oceanografía, Servicio de Hidrografía Naval, Av. Montes de OCA2124-Buenos Aires, ArgentinaDepartment of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USAInstitute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerlandnow at: CIRES, University of Colorado at Boulder, and NOAA/ESRL, Boulder, CO, USAR. Arruda (cadoarruda@gmail.com)12October20151219579358095March201519May201526September20152October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/12/5793/2015/bg-12-5793-2015.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/12/5793/2015/bg-12-5793-2015.pdf
We use an eddy-resolving, regional ocean biogeochemical model to investigate
the main variables and processes responsible for the climatological
spatio-temporal variability of pCO2 and the air-sea CO2 fluxes in the
southwestern Atlantic Ocean. Overall, the region acts as a sink of
atmospheric CO2 south of 30∘ S, and is close to equilibrium with
the atmospheric CO2 to the north. On the shelves, the ocean acts as a weak
source of CO2, except for the mid/outer shelves of Patagonia, which act as
sinks. In contrast, the inner shelves and the low latitude open ocean of the
southwestern Atlantic represent source regions. Observed
nearshore-to-offshore and meridional pCO2 gradients are well represented
by our simulation. A sensitivity analysis shows the importance of the
counteracting effects of temperature and dissolved inorganic carbon (DIC)
in controlling the seasonal variability of pCO2. Biological production
and solubility are the main processes regulating pCO2, with biological
production being particularly important on the shelves. The role of
mixing/stratification in modulating DIC, and therefore surface pCO2,
is shown in a vertical profile at the location of the Ocean Observatories
Initiative (OOI) site in the Argentine Basin (42∘ S, 42∘ W).
Introduction
Shelf regions are amongst the most biogeochemically dynamical zones of the
marine biosphere . Even though they comprise only
7–10 % of the global ocean area , continental shelves
could contribute to approximately 10–15 % of the ocean primary production
and 40 % of the ocean's carbon sequestration through particulate organic
carbon . Global discussions about the role of continental
margins as a sink of atmospheric CO2 gained momentum after
suggested that these shelf regions take up as much as 1 Pg C yr-1 of atmospheric CO2. Recent estimates range from 0.2 Pg C yr-1 to roughly 0.6 Pg C yr-1, somewhat more
modest than initially thought , but still relevant to the
global ocean sink estimated around 2.3 Pg C yr-1.
Continental shelves tend to act as a sink of carbon at high and medium latitudes (30–90∘),
and as a weak source at low latitudes (0–30∘) , i.e.,
they tend to follow similar meridional trends as the open ocean CO2 fluxes .
However, continental shelves present a higher spatio-temporal variability of
air-sea CO2 fluxes than the adjacent open ocean, with the inner shelf and
near coastal regions generally acting as a source of CO2 to the
atmosphere, while the mid/outer shelf and the continental slope generally act
as sinks . This pattern can be explained by the increased
primary production and decreased terrestrial supply towards the outer shelf
. Seasonality of the upper ocean (e.g. mixing and
stratification) may also be important to the air-sea exchange of carbon. For
example, the United States southeast continental shelf acts as a sink of
CO2 in the winter and as a source in the summer .
In the southwestern Atlantic Ocean, the shelf region presents distinct
features. To the south, the Patagonian shelf is one of the world's largest
shelves with an area close to 106 km2, broadening to more than 800 km
from the coastline . To the north, the Brazilian shelf
narrows to around 100–200 km from the coastline. This region is one of the
most energetic regions of the world's ocean with the confluence of the warm
southward-flowing Brazil Current (BC) and the cold Malvinas Current (MC)
flowing northward . The extension of the confluence roughly
divides the subtropical and subantarctic oceanic gyres in the South Atlantic
and might be a hotspot for shelf-open ocean exchange .
In the open-ocean, the South Atlantic is thought to absorb between 0.3–0.6 Pg C yr-1
south of 30∘ S, while acting as a source to the atmosphere
north of 30∘ S . Aside from global open-ocean
estimates, only a few local studies were conducted on the continental shelves
in this region. The Patagonia shelf was characterized as a source of CO2
to the atmosphere on the inner shelf, and as a sink in the mid-outer shelf
. The southeast Brazilian shelf and continental slope were
characterized as sources of CO2 to the atmosphere during all seasons
. Such regions are often neglected, or poorly resolved, on
relatively coarse global modeling assessments, although they may contribute
up to 0.2 Pg C yr-1 of global ocean CO2 uptake .
Regional marine biogeochemical models have been used to assess the ocean
carbonate system and CO2 fluxes, including the continental margins. For
example, along the US east coast, the seasonality of pCO2 was found to
be controlled mainly by changes in the solubility of CO2 and biological
processes . Along the California coast, biological
production, solubility and physical transport (e.g. circulation) were found
to be the most influential processes on pCO2 variability, both spatially
and temporally .
In this study we use a regional marine biogeochemical model coupled to a
hydrodynamic model to investigate the parameters and processes regulating the
variability of ocean surface pCO2 in the southwestern Atlantic Ocean.
Our model domain includes the location of the global node mooring that is
soon to be deployed as part of the Ocean Observatories Initiative (OOI) at
42∘ S, 42∘ W (www.oceanobservatories.org).
We compare modeled surface pCO2 distribution with observations and use
the results to investigate the relative importance of the parameters (DIC,
temperature, alkalinity and salinity) and processes (biological production,
air-sea CO2 flux, CO2 solubility and physical transport) in controlling
surface pCO2 distribution and variability on the continental shelf and
open ocean in the southwestern Atlantic Ocean.
Materials and methodsModel
The physical model used in this study is the Regional Ocean Modeling System
(ROMS) . Our model domain spans from 15 to
55∘ S, and from 70 to 35∘ W, i.e., covering the
southwestern Atlantic from its subtropical to subantarctic latitudes and from
the continental shelf all the way out to the open ocean. The horizontal grid
resolution is 9 km, with 30 vertical levels with increasing resolution
towards the surface.
The biogeochemical model is an NPZD type, including the following state
variables: phytoplankton, zooplankton, nitrate, ammonium, small and large
detritus, and a dynamic chlorophyll-to-carbon ratio for the phytoplankton
. A carbon component is also coupled to the model, with the
addition of calcium carbonate, DIC and alkalinity to the system of state
variables . Parameters and values used
in the biogeochemical model are listed in Table 1 of . The
CaCO3 cycle was parametrized as in . Phytoplankton types
as parametrized in the model correspond to the microplankton with large
nutrient requirements and relatively fast growth rates .
Since our domain encompasses several ecological provinces
, we may not represent all regions equally well
with only one phytoplankton functional type.
Statistical indicators of model skill for surface ocean pCO2 in the three areas (A1, A2 and A3 – Fig. 4).
The indicators are the following: ME (Model Efficiency); CF (Cost Function) and PB (Percentage of Bias). Additionally,
showing total bias (µatm), correlation and total number of observations (N) available on each area.
Bold values indicate “good/reasonable” model skill.
Areas utilized for the temporal analysis, (a) shows the three continental
shelves (SEBS, SBS and PS) analyzed in a map with annual mean ocean surface pCO2.
The green circle represents the location of the vertical profile at the OOI site. (b) shows the two oceanic regions (ST and SA) in a map with bathymetry.
The initial and boundary conditions used for the physical variables were
obtained from a climatology of the Simple Ocean Data Assimilation (SODA)
, and for the biogeochemical variables from a
Community Earth System Model (CESM) climatological model product
. The model is forced at the surface with climatological
winds from QuikSCAT and heat and freshwater surface fluxes
from the Comprehensive Ocean-Atmosphere Data Set (COADS) .
We used a fixed atmospheric pCO2 of 370µatm without CO2
incrementation throughout the years and without seasonal variations. We ran
the model for 8 years and used a climatology from years 5 through 8 in our
analyses.
Even though processes such as river runoff and tides are locally relevant
(i.e., La Plata River, and Patagonia shelf), we are not considering them in
the present study (see conclusions section). The low salinity waters from the
La Plata river are included in the climatological forcing from COADS which
are “nudged” into the model. These shortcomings may affect the results in
some regions, but it is unlikely that they will affect the overall pCO2
results in the wider domain.
Analysis
Ocean surface pCO2 is the most important variable determining the
air-sea CO2 flux. This is because the variability of ocean pCO2 is
much greater than that of atmospheric pCO2, and the impact of variations
in the gas transfer coefficient are usually several times smaller than those
of ocean surface pCO2. Seawater pCO2 is
regulated by the concentration of dissolved inorganic carbon (DIC),
alkalinity (ALK), temperature (T) and salinity (S). While T and S
are controlled solely by physical factors, DIC and ALK are affected both
by biological production and physical transport. DIC concentration is also
affected by air-sea CO2 fluxes .
In our model, ocean surface pCO2 is calculated through a full model implementation
of the seawater inorganic carbon system, i.e., as a function of the state variables T, S, DIC, and ALK, with the dissociation constants k1 and k2 from .
In order to assess the impact of different parameters on pCO2 variability, we decompose pCO2 with
respect to T, S, DIC and ALK, following the approach of and
,
ΔpCO2=∂pCO2∂DICΔDICs+∂pCO2∂ALKΔALKs+∂pCO2∂TΔT+∂pCO2∂FWΔFW,
where the Δ's are anomalies, either spatial or temporal, relative to a
domain or an annual mean, respectively. DICs and ALKs are the variable
concentrations normalized to a domain-averaged surface salinity of 34.66,
therefore the effects of dilution on DIC and ALK through freshwater input
are not included in DICs and ALKs. The dilution effect is considered
instead in the freshwater component (FW) that includes the effects of
precipitation and evaporation on DIC and ALK concentrations.
The partial derivatives were calculated following .
pCO2 was recalculated four times adding a small perturbation to the
spatial, or temporal, domain average for each variable (T, S, DIC,
ALK) while maintaining the other three variables fixed to the domain-averaged
surface values. The perturbation applied here was 0.1 % of the domain mean.
In order to investigate the parameters and processes controlling pCO2 on
the continental margin, we limited our temporal analysis to three regions
with depths shallower than 1000 m: the Southeast Brazilian Shelf (SEBS) in
the northern part of the domain, the South Brazilian Shelf (SBS) in the
middle of the domain that encompasses the Uruguayan Shelf, and the Patagonia
Shelf (PS) to the south of the domain (Fig. 1a). We also selected two open
ocean regions for comparison with the continental shelves: a subtropical (ST)
and a subantarctic (SA) region (Fig. 1b). In each of these regions, we
estimated the monthly contribution of each parameter to the modeled pCO2
variability by spatially averaging the parameters within each region, and
using the temporal anomalies (subtracting the annual mean) on Eq. (1). For the
spatial analysis, we used the whole study area and then calculated in each
grid cell the spatial anomalies (subtracting the domain mean of that grid
cell), finally applying it to Eq. (1).
In order to identify the main processes responsible for the variability of
surface pCO2, we used a progressive series of sensitivity experiments as
in , focusing on the processes of biological production,
CO2 solubility, air-sea CO2 fluxes, and physical transport. To quantify
these processes, we made three additional model runs, progressively excluding
each process. In the first experiment (E1), we set the CO2 gas exchange
flux coefficient between the atmosphere and the ocean to zero, inhibiting gas
exchange in the surface layer. In the second experiment (E2), we started from
E1 and also turned off the photosynthetically available radiation (PAR),
preventing phytoplankton growth. Finally, in experiment E3, the CO2
solubility was set to a constant value, calculated with the domain-averaged
surface salinity and temperature of 34.66 and 12.33∘C,
respectively, while maintaining the changes of E1 and E2. The control run
minus E1 represents the impact of gas exchange between ocean and atmosphere,
E1 minus E2 represents the impact of biology, E2 minus E3 represents the
impact of variable solubility. The last experiment (E3), in which there is no
air-sea flux, no biology and constant solubility represents the impact of
physical transport .
Seasonal climatology of modeled ocean surface pCO2 (upper row) and
observations of pCO2 from the SOCAT database (lower row). The white separation
between red and blue is set to 370 µatm which is the atmospheric pCO2
used in this study. Blue represents a sink of atmospheric CO2 and red a source.
Model evaluation on the Patagonia Shelf (PS) (zoom in from model domain in Fig. 2a).
Seasonal climatology of modeled ocean surface pCO2 (upper row) and pCO2 observations
from ARGAU and GEF3 cruises(lower row) . The white separation between red
and blue is set to 370 µatm which is the atmospheric pCO2 used in this study.
Blue represents a sink of atmospheric CO2 and red a source.
Location of the three areas used for the monthly comparison with SOCAT database (a) in a
map with annual mean eddy kinetic energy. In panels (b), (c) and (d), green lines are the modeled
monthly mean pCO2 and black lines are the monthly mean pCO2 from SOCAT. Error bars are two standard deviations.
Given the short model integration times, the vertical gradients in the E3
simulation have not come in to steady-state with the processes. So our
physical transport is working on the vertical DIC gradients established by
the biological pump. Since the lateral boundary conditions are the same for
all experiments, these simulations are therefore only approximations of the
impact of each process on pCO2. Furthermore, this separation assumes a
linear additionality of each process, which is clearly a strong
simplification given the non-linear nature of the inorganic carbonate system
. The same spatial and temporal analysis
described for the variables (ALK, DIC, T and FW) was also applied for
the processes experiments (air-sea CO2 flux, biology, CO2 solubility,
physical transport).
Model evaluation and validation
Model results were evaluated against data from the Surface Ocean CO2 Atlas (SOCAT)
version 2 . SOCAT fCO2 observations were
converted into pCO2 using the set of equations from
and then compared with modeled pCO2 to assess the
overall skill of the model. Due to the paucity of in situ observations,
particularly on the continental shelves, we used monthly climatologies for
the comparison. The seasonal model evaluation was made over the whole domain
(Fig. ). On the Patagonia Shelf, data from the Argentinian
cruises ARGAU and GEF3 were used for a more focused comparison of the model
results . For the Brazilian continental shelves no data
were found for local comparisons.
Overall, our model simulates reasonably well the seasonality of ocean surface
pCO2, with the latitudinal and cross-shelf gradients represented during
all seasons (Fig. 2). Since our simulation has a fixed atmospheric pCO2
of 370µatm, this value separates the source from the sink regions. In
the northernmost oceanic region, between 16 and 30∘ S, the
observations show pCO2 close to 370–380 µatm. Therefore this
region acts as a weak source of CO2 to the atmosphere. This tendency is
well captured by the model, particularly during summer and autumn. From
30 to 55∘ S, the whole offshore region acts as a CO2
sink, with pCO2 ranging from 250 to 350 µatm during all
seasons in the model results. The observations show the same pattern down to
50∘ S. However in the southernmost region the observed pCO2
rises to values close to 400 µatm. On the Southeast Brazilian Shelf,
there were no data for model evaluation, but the overall behavior of
pCO2 agrees with previous results from , who suggested
that the continental shelf in this region acts as a source to the atmosphere
across both inner and outer shelves during all seasons. The southernmost and
northernmost regions are where our model has the largest biases,
underestimating the ocean surface pCO2. These biases could be due to a
variety of reasons, including the high variability of the Antarctic
Circumpolar Current and/or proximity to the model boundary with potential
biases in the lateral boundary conditions used to force the model.
On the Patagonia Shelf the model was evaluated using in situ observations
from during the years 2000 to 2006 (Fig. 3). The model
agrees very well with the seasonality of the observations of this shelf
region, in particular the high pCO2 values along the inner shelf, which
make these regions a source of CO2 during all seasons, but more intense
during autumn/winter (Fig. 3b, c, f, g). In the mid-outer shelf the ocean
generally acts as a sink, while to the north the ocean is in equilibrium with
the atmosphere particularly during winter.
The monthly analysis was restricted to three offshore areas (A1, A2 and A3 in
Fig. 4a). We compared the spatial monthly mean modeled surface pCO2 with
the monthly average of the SOCAT pCO2 data available in each area.
Within these areas, we applied the following statistical indicators used in
in order to quantitatively assess model skill: model
efficiency ME =1-(Σ(O-M)2)/(Σ(O-O¯)2), cost function CF =(Σ∣M-O∣)/(nσo) and percentage of bias PB =∣(Σ(O-M).100)/ΣO∣, where M stands for modeled
pCO2 and O for observations from SOCAT database, n is the number of
observations and σo is the standard deviation of all observations.
These statistics are indicators of the model's performance and provide
complementary information of the model skill. ME relates model error with
observational variability, CF is the ratio of mean absolute error to standard
deviation of observations, and PB is the bias normalized by the observations
. Basically if ME >0.5, CF <1
and PB <20, this indicates that the model is “good/reasonable”
when comparing to observations. If ME <0.2, CF >3 and PB >40 the model is classified as “poor/bad”.
Modeled pCO2 results for A1 agree very well with the observations,
representing the pCO2 evolution throughout the year with maximum values
in summer (Fig. 4b). All statistical indicators characterized the model with a
good/reasonable skill in A1 (Table ).
A2 is the region with the largest pCO2 standard deviation from both
model and observations (Fig. 4c). This region is near the confluence between
the warm Brazil Current and the cold Malvinas Current, generating one of the
most energetic regions of the world's oceans. Moreover, this region comprises
the shelfbreak front, with differences in stratification, local dynamics and
salinity between shelf waters and Malvinas current waters
(Fig. a). Consequently, ME was estimated as poor/bad in this
region, probably due to the high pCO2 data variability. But CF and PB
were both rated as “good/reasonable” (Table ).
Taylor Diagram showing the three areas used for comparison with
SOCAT observational data. A1 is the only area with statistically significant correlation.
pCO2 spatial anomalies – difference between annual mean and domain mean (a) and
the contribution of the main drivers: ALKs(b), FW (c), T(d) and
DICs(e). Computed using spatial anomalies for Δ.
Processes driving the annual mean surface pCO2. Contribution of Air-sea
flux of CO2 [Control–E1] (a), CO2 solubility [E2–E3] (b), physical transport [E3] (c) and biological production [E1–E2] (d).
In A3 the model consistently underestimated pCO2 (Fig. 4d). This bias is
seen in the seasonal comparison and in the monthly analysis, where summer is
the only season for which modeled pCO2 is within the standard deviation
of the observations. ME was estimated as poor/bad in A3, but PB and CF rated
our model as reasonable and good, respectively. (Table ). Both A2 and A3 regions are close to an area of
elevated eddy kinetic energy (Fig. 4a), which could explain the large standard
deviation and biases in these regions.
The Taylor diagram is consistent with the model efficiency (ME) estimate,
showing good/reasonable results in A1, with a correlation of 0.8, and poor
results in A2 and A3, with negative correlations (Fig. 5). Only in A1 the
correlation was found to be statistically significant. Aside from greater
pCO2 variability in these regions, the poor results found in A2 and A3
could also be due to the paucity of the observational data both in space and
time.
Furthermore, in order to validate the baseline of our model, seasonal
climatologies of modeled sea-surface temperature and chlorophyll a were
compared with climatologies from AVHRR and MODIS-aqua, respectively. Results
and a detailed discussion of this validation are shown in the Appendix.
In conclusion, our model reproduces the most important north-south and
inner-outer shelf gradients seen in the pCO2 observations. While there
is clearly room for improvement, we deem this level of agreement as
sufficient for proceeding to the analysis of the processes and parameters
affecting pCO2 variability in this region.
Results and DiscussionpCO2 drivers – spatial analysis
Modeled pCO2 spatial anomalies relative to the domain average are shown
in Fig. 6a, with positive anomalies prevailing on the Brazilian continental
shelves, inner-mid Patagonia Shelf and North of 32∘ S, while the
negative anomalies are found in the open ocean south of 32∘ S and in
the mid-outer Patagonia Shelf. DICs has the highest impact on the spatial
variations, being counteracted by ALKs and T (Fig. 6). In contrast, the
fresh water flux has a minor influence on the spatial anomalies of pCO2,
agreeing with and . Despite its smaller
role, the influence of ALKs on pCO2 anomalies was higher (-100 to
100 µatm) than those found in previous studies in other regions
. The higher contribution of both DICs and
ALKs to the spatial variations in pCO2 could be explained by the more
heterogeneous domain that encompasses several distinct surface water masses
and frontal zones. Also, the elevated contribution of ALKs could be due to
our relatively high CaCO3 to biological production ratio of 0.07.
Sensitivity of pCO2 computed with grid point anomalies in time to local annual means.
Annual mean contribution of the main drivers: ALKs(a), T(b) and DICs(c).
Temporal evolution of pCO2 anomalies and their drivers in each continental shelf
(right hand side of Eq. (1) using temporal anomalies), red line represents the effects of
Temperature, blue line the effects of DICs, green line FW, and yellow line ALKs.
Temporal evolution of the monthly anomalies of each process in regulating pCO2
anomalies, green line represents the biological production, red line the physical transport,
light blue line the air-sea CO2 fluxes and dark blue line the CO2 solubility. Black lines represent
the temporal pCO2 anomalies.
Panels (a) and (b) show the temporal evolution of pCO2 anomalies and its drivers in each oceanic regions
(ST and SA) (right hand side of Eq. (1) using temporal anomalies), red line represents the effects of T, blue line the
effects of DICs, green line the FW and yellow line ALKs. Panels (c) and (e) show the
temporal evolution of the monthly anomalies of each process in regulating temporal pCO2 anomalies, green line
represents the biological production, red line the physical transport, light blue line the air-sea CO2 fluxes
and dark blue line the CO2 solubility. Black lines represent the temporal pCO2 anomalies.
Panel (a) is the annual mean of air-sea CO2 fluxes. Panels (b), (c) and (d) show
the monthly average of surface CO2 fluxes constrained to bathymetry levels of 100, 200 and 1000 m.
The changes in the state variables affecting pCO2 are ultimately being
driven by physical and biogeochemical processes. We investigate the role of
each of these processes in controlling the changes in surface pCO2 from
our sensitivity experiments (E1, E2, E3). The most important processes
affecting the spatial distribution of pCO2 are biological production (E1–E2) and physical transport (E3) (Fig. 7). When physical transport (vertical
and horizontal) is the only process altering pCO2, we observe an
increase in pCO2 of up to 800 µatm on the continental shelves, due
to the upwelling and vertical mixing of DIC-rich subsurface waters. At the
same time, the effect of biological production on the uptake of DIC and
changes in ALK due to nitrate uptake and production/dissolution of CaCO3
accounts for a decrease of up to -600µatm on the continental shelves.
Solubility effects (E2–E3) are responsible for a decrease in pCO2
south of 45∘ S and an increase in pCO2 to the north, ranging
from -50 to 50µatm. Finally, air-sea CO2 fluxes (Control–E1)
have little impact on regulating the ocean surface pCO2. The effect of
both biological production and physical transport is highest on the
continental shelves. The balance between these processes also largely control
pCO2 in the open ocean. North of 45∘ S, biological production is
counteracted by physical transport and, to a minor extent, solubility, whereas
south of 45∘ S physical transport is counteracted by biological
production and solubility.
The strong effect of biological production on the shelf region is a result of
the elevated nutrient supply and high primary production found in these
regions, with increasing contribution towards the inner shelves. Physical
transport also presents a higher contribution on the continental shelves,
where the mixed layer often spans the entire water column, showing the
importance of vertical mixing in bringing DIC as well as nutrients to the
surface waters, therefore increasing pCO2. These results are in
agreement with previous studies (c.f. ), showing the
importance of the biological net community production and advection of ALK
and DIC (physical transport) in controlling ocean surface pCO2. This
suggests a major role of net community production in reducing ocean pCO2
in the region.
pCO2 drivers – temporal analysis
In order to identify the seasonal variability of the contribution of each
parameter, we used local grid temporal anomalies over the seasonal cycle
(Fig. 8). DICs and T are still the most influential parameters, with
increasing importance on the continental shelves. The contribution by ALKs
is relevant only on continental shelves south of 32∘ S, and FW have a
minor influence (not shown). It is important to highlight that the magnitude
of the signals seen in this analysis is one order of magnitude smaller than
the previous spatial analysis. This is likely due to our large and
heterogeneous domain, which results in large spatial gradients as compared to
the range found over the seasonal cycles.
The contribution of the state variables in each continental shelf region
(Fig. 9) shows that these three regions have distinct characteristics, with
different contributions from each parameter. In all three regions, DICs
and T are the most important parameters affecting pCO2 anomalies,
albeit with opposing and seasonally varying contributions. While in summer
the T contribution increases pCO2, that of DICs acts to diminish
pCO2. The opposite occurs in winter. The Southeast Brazilian Shelf (SEBS)
is the region with the least variability in pCO2 anomalies, with the
contributions of both DICs and T in this region ranging from
-10 to 10 µatm.
The South Brazilian Shelf (SBS) is the region with the largest variability in
pCO2 anomalies, with ALKs having the most prominent impact on
pCO2 as compared to the other regions (up to 15 µatm in spring).
DICs is the most important parameter in this area, with a contribution of
up to 70 µatm, followed by temperature, with a contribution of up to
60µatm in the winter. On the Patagonia Shelf (PS) and South Brazilian
Shelf (SBS), although the contributions by DICs and T are large, the
tendency of these two terms to cancel each other out results in smaller
pCO2 anomalies. In both SBS and PS, pCO2 is predominately
controlled by T and DICs, with small contributions from ALK and FW.
Seasonal warming/cooling largely controls pCO2 anomalies throughout the
continental shelves. This signal is dampened by DICs, but also by ALKs
in the case of the South Brazilian Shelf (SBS). This pattern of seasonal
variation of the parameters on continental shelves agrees with the results
from and , although with different
absolute values. The pattern of diminishing variability towards subtropical
continental shelves is also shown by .
This pattern of opposing contributions of T and DIC was also found along
the North American east coast by , who attributed winter
mixing and the spring-summer biological drawdown as the processes responsible
for pCO2 and DIC variability. In the offshore subtropical region (ST)
the pCO2 anomalies have higher amplitudes than in the adjacent
continental shelf (SEBS), and are driven mainly by temperature, with the
other variables having minor contributions (Fig. 11). In the offshore southern
region (SA), DICs controls pCO2 variability, with T and ALKs
dampening pCO2 anomalies (Fig. 11), similar to the adjacent shelf (PS).
The analysis of the processes underlying this seasonal variability using our
progressive sensitivity simulations shows that on all shelf regions,
biological production and CO2 solubility mostly control pCO2
variability (Fig. 10). Physical transport, although weaker than biological
production, acts to diminish the pCO2 variability by counteracting the
effects of biology and increasing DIC concentrations. In our case, physical
transport controls pCO2 spatially, but the temporal effects of physical
transport are much weaker than in along the California
coast. This is probably due to the much stronger upwelling in that region
that dampens the effects of biology by bringing DIC rich waters to the
surface. Along western boundaries, upwelling is weaker and more localized.
Physical transport is therefore more related to processes that modulate
vertical mixing and stratification (thereby controlling the seasonal
enrichment of surface waters) and horizontal advection due to the presence of
two major western boundary currents. Finally, air-sea CO2 fluxes show only
a minor contribution to the pCO2 anomalies.
In conclusion, on the Patagonia Shelf (PS), the biological production is the
most important contributor to pCO2 variability, with a peak summer
contribution of -80µatm and a maximum in the winter of 70µatm.
On the South Brazilian Shelf (SBS), solubility is the most influential
process (up to 90 µatm), followed by biological production and physical
transport, during all seasons. On the Southeast Brazilian Shelf (SEBS), the
pattern is the same as in the SBS, although with a smaller magnitude and
variability. Physical transport, although large in absolute contributions in
the spatial analysis, has a lower contribution to pCO2 variability in
the temporal analysis.
In the subtropical region, processes that control the temporal variability of
pCO2 on the shelf and offshore are different. In the open ocean (ST)
(Fig. 11) pCO2 is mainly controlled by solubility, with biological
production having the least effect on pCO2. This contrasts with the
importance of biology at mid/low latitude continental shelves (SEBS). In the
subantarctic region, the processes controlling pCO2 are similar for both
the offshore region (SA) and the adjacent continental shelf (PS) (Figs. 10 and 11). In
this case biological production is the most important process countered
mainly by solubility, although with a smaller magnitude in the offshore
region.
Vertical profile at 42∘ S, 42∘ W. Upper panels showing monthly mean surface
pCO2 (solid black line), pCO2 anomalies (dashed black line) and the contribution from
T and DICs (red and blue dashed lines) and the contribution of biology and solubility
(green and cyan dashed lines). Lower panels showing vertical profiles of DIC (a), T(b), and
chlorophyll a(c), black line represents the mixed layer depth.
Air-sea CO2 fluxes
On the continental margins, we investigate monthly averaged air-sea CO2
fluxes on the inner shelf (0–100 m depth), mid-outer shelf (100–200 m depth) and
shelf break-slope (200–1000 m depth) (Fig. 12a). As
shown in the previous sections, the inner shelves have a potential to act as
a source of CO2, while the mid/outer shelves tend to act as a sink of
CO2. On the Brazilian shelves (SBS and SEBS) the flux density of CO2 in
the inner shelves is around 0 and 0.5 mol C m-2 yr-1, thus this
region acts as a weak source. On the mid/outer shelf a shift towards CO2
sink occurs, with a flux density of between -1 and 0 mol C m-2 yr-1 on the
Southeast Brazilian shelf (SEBS) (Fig. 12c). On the mid/outer South Brazilian
Shelf (SBS) the sink is slightly stronger with an average flux between -1.5
and -0.5 mol C m-2 yr-1 (Fig. 12b). The Patagonia Shelf (PS) acts on
average as a sink of CO2, with fluxes larger than on the Brazilian
shelves. CO2 uptake intensifies from the inner Patagonian shelf (-1.0 to -0.5 mol C m-2 yr-1)
to the outer shelf and continental slope
(-2.0 to -4.0 mol C m-2 yr-1) (Fig. 12d). Although, overall the PS
acts on average as a sink, there are some coastal regions that act as a
source of CO2, in agreement with the observations of .
Annual mean modeled air-sea CO2 fluxes agreed reasonably well with global
climatologies in the oceanic regions (not shown) .
South of 30∘ S, the open ocean acts on average as
a sink of atmospheric CO2, uptaking up to 4 mol C m-2 yr-1.
North of 30∘ S, the open ocean is on average in equilibrium with the
atmosphere (Fig. 12a). On the continental margins, our annual mean air-sea
CO2 fluxes compare well with the global estimate from
, with the Patagonia Shelf acting as a CO2 sink
(-1.0 to -3.0 mol C m-2 yr-1) and the Brazilian shelves as weak
sources (0 to 1 mol C m-2 yr-1). Nevertheless, we found
variability in these areas, with regions on the inner Patagonia Shelf acting
as a source or in equilibrium with the atmosphere (0 to 2.0 mol C m-2 yr-1), and regions on the outer Brazilian shelves acting as
sinks of CO2.
Vertical structure – case study at Argentine OOI site
Seasonal variations in mixing and stratification control the evolution of the
mixed layer depth and consequently the vertical structure of the state
variables of the carbonate system. Diapycnal fluxes and uptake of DIC by
primary producers are important processes regulating ocean surface pCO2. Therefore, surface pCO2 variability is linked to
variations in mixed layer depths.
In order to understand the seasonal evolution of the upper ocean vertical
distribution of the state variables in the region and how it affects surface
pCO2, we chose the location of the Ocean Observatory Initiative (OOI)
site in the Argentine Basin at 42∘ S, 42∘ W (Fig. 1a), as it
will soon become a test-bed for the validation of biogeochemical models
globally and regionally. We extracted modeled climatological vertical
profiles of DIC concentration, temperature and chlorophyll a, and compared
with the modeled surface pCO2 and mixed layer depth (Fig. 13).
During the entire year, this location acts in our model as a sink for
atmospheric CO2, with modeled surface pCO2 ranging from 280 to 320 µatm. The contribution of DICs and T are again
driving surface pCO2 anomalies. In this case DICs is controlling the
anomalies signal, being modulated by temperature. The main processes
affecting pCO2 in this location is biological production and solubility.
Minimum pCO2 in summer coincides with strong stratification and elevated
subsurface biological production, respectively, with the opposing
contribution of DICs and T leading to pCO2 anomalies near zero.
Maximum pCO2 occurs when the mixed layer depth deepens, during fall and
winter, causing an increase in DIC concentrations in surface waters. This
has a larger effect on pCO2 than the decrease in temperature, resulting
in positive pCO2 anomalies. The excess of DIC is consumed by
biological fixation during spring and summer, thus reducing surface
pCO2.
Conclusions
In this study, we used climatologies derived from a regional hydrodynamic
model coupled to a biogeochemical model to investigate the main parameters
and processes that control ocean surface pCO2 and air-sea CO2 fluxes
in the southwestern Atlantic Ocean. Modeled ocean surface pCO2 compared
well with the available in situ data, reproducing the expected meridional and
cross-shelf gradients of pCO2, with elevated pCO2 in the inner
shelves and at lower latitudes. Our results highlight that the most important
variables controlling the spatio-temporal variability of pCO2 are T
and DICs. These two variables have opposing effects on pCO2 and have
been shown to be the main drivers of pCO2 both in global
and in other regional studies
. ALKs is of secondary
importance as a spatial regulator of pCO2, with larger impacts
particularly in the South Brazilian Shelf (SBS) and in the southern open
ocean region (SA).
The most important processes underlying changes on the state variables and
thus on pCO2 are biological production and CO2 solubility. Biological
production is particularly important on the continental shelves, with higher
contribution at high latitudes. In the open ocean, CO2 solubility is the
main process driving pCO2 variations in the subtropics, while in the
subantarctic both CO2 solubility and biological production are important
drivers of pCO2 variability.
The southwestern Atlantic Ocean acts, on average, as a sink of atmospheric
CO2 south of 30∘ S, and is close to equilibrium to the north. In
the inner continental shelves the ocean acts either as a weak source or is in
equilibrium with the atmosphere. To the outer shelf the ocean shifts to a
sink of CO2. The entire Patagonian shelf acts, on average, as a sink, but
there are some particular regions in the inner shelf that acts as a source of
CO2. The total integrated flux agrees well with ,
particularly on the Brazilian Shelves (SEBS and SBS). In the Patagonia Shelf (PS),
we found a slightly stronger sink on the mid/outer Patagonian Shelf
(-1.0 to -3.0 mol C m-2 yr-1) and more variability towards the
inner shelf.
Our model does not include river inputs of carbon, which are known to be an
important factor regulating pCO2. The lack of tides
may adversely affect our model results in the inner shelf of Patagonia, where
tidal amplitudes can reach up to 12 m
and tidal fronts are known to impact oceanic pCO2.
In future regional studies focused on the Patagonia shelf, tides and river
run-off should be included.
Modeling studies such as this one depend heavily on in situ observations, the
lack of which hampers our ability to properly refine our model. This will
certainly be improved by future efforts in data assimilation from vertical
profiles of biogeochemical and physical variables collected at the OOI site
in the Argentine basin. This study is a first step towards understanding the
processes controlling surface pCO2 in an undersampled, yet highly
important, region of the world's ocean.
Model validation (SST and chlorophyll a)
Seasonal climatology of modeled sea surface temperature ∘C – 4 years average (upper row),
and climatology from AVHRR sensor – from 1985 to 2002 (lower row).
Seasonal climatology of modeled chlorophyll a concentration mg Chl a m-3 – 4 years average (upper row),
and climatology from Aqua-Modis sensor – from 2003 to 2013 (lower row).
Seasonal climatologies of 4 years of modeled sea surface temperature and
chlorophyll a concentration were compared with climatologies from the sensors
AVHRR (1985–2002) and Modis-aqua (2003–2013), respectively (Figs. A1 and A2).
Modeled sea surface temperature compared well with AVHRR (Fig. A1)
representing both subantarctic and subtropical oceanic regions during all
seasons.
Modeled chlorophyll a concentration reproduces the general pattern from
MODIS-aqua (Fig. A2), with low concentrations in the oceanic regions and
higher concentrations on the continental shelves. However, modeled
chlorophyll a concentrations are overestimated in the open ocean regions
(0.5 mg Chl a m-3), especially in the spring season (up to 1 mg Chl a m-3).
In the coastal regions, we underestimate chlorophyll a on
the Patagonia Shelf during spring and summer seasons. Expectedly, there was
an underestimation in the La Plata region, since we are not modeling the
nutrient and organic loads from the river. Finally, on the Brazilian shelf
our model overestimates chlorophyll a, particularly during summer and spring
seasons. These biases may be due to our application of a relatively simple
ecosystem model with only one phytoplankton functional type in such a wide
region, which encompasses several ecological provinces. Nevertheless, the
general pattern is well reproduced in this first effort in modeling the
biogeochemistry of the southwestern Atlantic Ocean, and the biases may not
significantly compromise our analysis of drivers and processes of pCO2
variability.
Acknowledgements
P. H. R. Calil acknowledges support from the Brazilian agencies Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq), grants 483112/2012-7 and
307385/2013-2, and the Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior (CAPES Process 23038.004299/2014-53). R. Arruda acknowledges support from a
CAPES scholarship. S. C. Doney and I. Lima acknowledge support from the National Science
Foundation (NSF AGS-1048827). N. Gruber and G. Turi received support from ETH Zurich and
from the EU FP7 project CarboChange (264879).
The Surface Ocean CO2 Atlas (SOCAT) is an international effort, supported
by the International Ocean Carbon Coordination Project (IOCCP), the Surface
Ocean Lower Atmosphere Study (SOLAS), and the Integrated Marine
Biogeochemistry and Ecosystem Research program (IMBER), to deliver a
uniformly quality-controlled surface ocean CO2 database. The many
researchers and funding agencies responsible for the collection of data and
quality control are thanked for their contributions to SOCAT.
We are greatly indebted with the Ministero de Defensa de Argentina that
supported the project “Balance y variabilidad del flujo mar-aire en el
Mar Patagónico” (PIDDEF 47/11). This work was carried out with the aid
of a grant from the Inter-American Institute for Global Change Research (IAI)
CRN3070 which is supported by the US National Science Foundation (Grant GEO-1128040).
Supported by Global Environmental Facilities (GEF) in the frame of PNUD
ARG/02/018-GEF BIRF No. 28385-AR, subproject B-B46, and by Servicio de
Hidrografía Naval. Additional support was provided by the ARGAU Project,
Instituto Antártico Argentino, Institut National de Sciences de l'Univers,
Processus Biogeochimiques dans l'Océan et Flux, Université Pierre et
Marie Curie.
Edited by: C. Klaas
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