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A Geodetic Analysis of the Volume Transport
in the ACC Region Based on Satellite Data
Juan A. Vargas-Alemañy , M. Isabel Vigo , David García-García ,
and Ferdous Zid
Abstract
Geostrophic currents, driven by the Coriolis and pressure gradient forces, are crucial for
understanding ocean circulation. The Antarctic Circumpolar Current (ACC) in the Southern
Ocean, which surrounds Antarctica, has a significant global impact, and its volume transport
(VT) remains a challenge to measure. We use satellite data, combining altimetry and
gravity satellite missions, to estimate VT within the ACC region. Our study provides
a comprehensive spatial and temporal analysis, including both barotropic and baroclinic
VT components. The spatial analysis reveals a mean VT of 210.44 ˙3.4 Sv for the
entire study area, with maxima near critical choke points. Focusing on the time-varying
component, we identify a mean VT of 15.86 ˙0.05 Sv per 1ıgrid cell, a linear trend of
0.007 ˙0.002 Sv per month, and significant seasonal and biannual signals. The baroclinic
component drives low-frequency variability, while the barotropic component controls high-
frequency variability. We propose a specific ACC zonal VT of 201.63 ˙0.71 Sv. We
validate our results with in situ measurements from the Drake Passage. In conclusion, our
satellite-based approach provides valuable insights into the ACC VT. This methodological
extension improves our understanding of the ocean circulation dynamics of the ACC and
demonstrates the utility and robustness of satellite data in oceanographic research.
Keywords
Antarctic circumpolar current Satellite altimetry Satellite gravity Volume transport
1Introduction
Geostrophic currents (GC) are the movements of ocean water
that are primarily influenced by the balance between two
important forces: the Coriolis force and the pressure gradient
force. The Coriolis force, caused by the Earth’s rotation,
deflects moving objects, such as water, to the right in the
Northern Hemisphere and to the left in the Southern Hemi-
sphere. The pressure gradient force, on the other hand, is the
result of variations in pressure over an area. The balance
J. A. Vargas-Alemañy () · M. I. Vigo · D. García-García · F. Zid
Department of Applied Mathematics, University of Alicante, Alicante,
Spain
e-mail: juan.vargas@ua.es
between these two forces gives rise to GC, which play a
crucial role in understanding ocean circulation patterns.
The Antarctic Circumpolar Current (ACC) is located in
the Southern Ocean (SO) region. The ACC is the largest
and most powerful current system in the world, encircling
the entire continent of Antarctica (Carter et al. 2008). It is
characterized by strong and continuous eastward flow, driven
by the combined effects of wind, density differences, and the
Earth’s rotation. The ACC area is of great scientific interest
because of its influence on global climate, ocean circulation,
and marine ecosystems.
The ACC covers a large latitudinal range, extending from
approximately 40ıSto60
ıS. Within this region, the ACC
is known for its distinct fronts, including the Subantarctic
Front, the Polar Front, and the Southern ACC Front (Orsi et
al. 1995; Sokolov and Rintoul 2009; Tarakanov 2021). These
International Association of Geodesy Symposia,
https://doi.org/10.1007/1345_2024_261, © The Author(s) 2024
J. A. Vargas-Alemañy et al.
fronts mark the boundaries between different water masses
and play a crucial role in the exchange of heat, carbon, and
nutrients between the ocean and the atmosphere.
The remote and challenging nature of the ACC presents
a considerable challenge when it comes to obtaining direct
measurements of its VT. However, the Drake Passage (DP)
provides a critical corridor. It is situated between the southern
tip of South America (Cape Horn) and the northernmost
point of the Antarctic Peninsula, where the ACC flows from
the Pacific Ocean into the Atlantic Ocean. This location
serves as the most suitable gateway for the study of the ACC,
resulting in numerous monitoring programs in the region
aimed at estimating its VT (Whitworth III 1983; Whitworth
III and Peterson 1985; Cunningham et al. 2003; Firing et al.
2011; Chidichimo et al. 2014; Koenig et al. 2014; Donohue et
al. 2016). These studies have reported ACC transport across
the DP in the range of 136–173 Sv.
Estimating oceanic VT using satellite data has several
advantages. Satellite observations provide a valuable tool for
studying VT and their variability on large time and spatial
scales. These satellite-based techniques allow continuous
monitoring of VT and ocean circulation dynamics. By inte-
grating altimetry and gravity satellite data, we can estimate
the GC and their associated VT (Wunsch and Gaposchkin
1980). Previous studies (Kosempa and Chambers 2014;Vigo
et al. 2018; Vargas-Alemañy et al. 2023), have successfully
used this methodology to analyze GC and VT in the SO
region.
In this study, we extend the application of this method-
ology to perform a thorough analysis of VT within the
ACC region. This analysis includes both spatial and temporal
aspects, as well as an investigation of the barotropic and
baroclinic components of VT. To ensure the validity of our
estimates, we compare them with in situ measurements in
the DP region as provided by Cunningham et al. (2003).
2 Methodology
Utilizing the same methodology as Vigo et al. (2018),
we estimate the geostrophic ocean flow by synergizing
space data, incorporating measurements from satellites for
altimetry and gravity, along with in situ data (Wunsch and
Gaposchkin 1980). First, we define the Absolute Dynamic
Topography (ADT) as follows:
ADT .x; y; t/DSSH.x; y;t/N.x;y/;(1)
where Nis a time-averaged geoid, and xand yare longitude
and latitude.
Here, ADT represents the instantaneous sea height above
the geoid and can be computed, as shown in Eq. (1), by taking
the difference between Sea Surface Height (SSH) and the
geoid. SSH, in turn, can be derived from satellite altimetry
data by combining Sea Level Anomalies (SLA) with the
corresponding Mean Sea Surface (MSS):
SSH.x; y;t/DSLA.x;y; t/CMSS.x; y/;(2)
where tis time.
Furthermore, by calculating the disparity between the
geoid and the MSS, the Mean Dynamic Topography (MDT)
can be determined:
MDT .x; y/DMSS.x;y/N.x; y/:(3)
If both MDT and SSH are referenced to the same MSS, we
can express ADT, according to Eqs. (1), (2), and (3), as:
ADT .x; y; t/DMDT .x; y/CSLA.x; y; t/:(4)
We will use Eq. (4) to compute the ADT by integrating
the satellite gravity estimates of the MDT with the satellite
altimetry estimates of the SLA. Both MDT and SLA products
are referenced to the same MSS. A description of the datasets
used is given in Sect. 3.
The Surface Geostrophic Current (SGC) can be computed
by determining the directional derivatives of the ADT
by applying of the geostrophic equation. This equation
describes the balance between the pressure gradient force
and the Coriolis force at the ocean surface:
us.x;y; t /Dg.y/
f
ıADT
ıy .x;y; t /;
vs.x;y; t /Dg.y/
f
ıADT
ıx .x;y; t /;
(5)
where usis the zonal surface velocity (positive eastward) and
vsis the meridional surface velocity (positive northward).
The parameter fD2!sin(y) represents the Coriolis param-
eter, where wis the mean angular rate of the Earth’s rotation
and gis the gravitational acceleration (latitude dependent).
In order to derive GC at different depths, it is first
necessary to establish the concept of Relative Dynamic
Topography (RDT):
RDT .x; y; z;t/D1
g.y/Z0
P.z/
dP
.x;y; z;t/;(6)
where P(z) is the pressure at depth z(in Pascal units), and
is the water density.
Using the SGC as the reference level and considering the
directional derivatives of the RDT, the value for the GC and
any depth zican be obtained as follows:
us.x;y; t /Dg.y/
f
ıRDT
ıy .x;y; zi;t/Cu.x; y; zi;t/;
vs.x;y; t /Dg.y/
f
ıRDT
ıx .x;y; zi;t/Cv.x; y; zi;t/:
(7)
A Geodetic Analysis of the Volume Transport in the ACC Region Based on Satellite Data
By vertically integrating the three-dimensional geostrophy
from a depth D to the surface within a cell of a regular grid,
we can determine the volume of water transported by the
geostrophic flow from the surface to that specific depth. For
accurate volume calculations, it is essential to consider the
width of the grid cell perpendicular to the flow direction.
VTu.x;y; t/DwNSR0
Du.x;y; z;t/dz;
VTv.x;y; t/DwEW .y/R0
Dv.x;y; z;t/dz;
(8)
where VTuis the zonal VT, which is positive eastward, while
VTvis the meridional VT, which is positive northward. wNS
and wEW denote the North-South and East-West widths of
the grid cell, respectively. It is important to note that wEW
depends on the latitude y, while wNS remains constant. The
VT is measured in Sverdrups (Sv), where 1 Sv is equivalent
to 106m3/s.
To distinguish between the barotropic and baroclinic com-
ponents of the VT, we follow the definition provided by
Fofonoff (1962). According to this definition, the barotropic
transport refers to the part of the VT attributed to a water
column moving uniformly and at the same speed as the bot-
tom current. Conversely, the baroclinic transport represents
the remaining component of VT, which accounts for the non-
uniform movement of the water column.
To estimate the barotropic component for the zonal VT,
we identify zmax(x,y) as the depth zof the deepest current
at each point (x,y). We then define uBT , the geostrophic
component of the bottom current relative to the barotropic
component:
uBT .x;y; t /Du.x; y;zmax .x; y/;t/:(9)
For each depth zi, the geostrophic current relative to the
baroclinic components is obtained as:
uBC .x;y; zi;t/Du.x; y; zi;t/uBT.x; y; t/:(10)
The barotropic component of the VT at point (x,y)from
surfate to a depth zmax(x,y) is obtained as:
VT
uBT .x;y; t /DwNS Z0
zmax .x;y/
uBT .x;y; t /dz
DwNSuBT .x; y; t/zmax .x; y/;
(11)
while the baroclinic component is given by:
VT
uBC.x; y; t/DwNS Z0
zmax .x;y/
uBC .x;y; z;t/dz:(12)
Note that with this definition, the following equality follows:
VT
u.x;y; t /DVT
uBC .x;y; t /CVT
uBT .x;y; t /:(13)
The barotropic and baroclinic components of the meridional
VT are obtained analogously.
Following Vargas-Alemañy et al. (2023), we define the
ACC region as the grid points within the SO region where
the mean zonal full-depth VT exceeds 12 Sv, with certain
outliers removed. The boundaries of this region, as defined
by these criteria, are outlined in Fig. 1.
3Data
In this study, we calculated the ADT according to Eq. (4),
using both the MDT and the SLA with reference to the
high-resolution MSS model DTU18MSS – developed by the
Danish National Space Center. This model is based on a 25-
year dataset collected from various multi-mission satellite
altimeters, including a 3-year record from Sentinel-3A and
an enhanced 7-year record from Cryosat-2 LM. Further
details can be found in the work of Andersen et al. (2018).
For the MDT, we used on the DTUUH19MDT geodetic
model, also developed by the Danish National Space Center.
This model is based on the OGMOG geoid model, expanded
with EIGEN-6C4 coefficients up to degree and order 2,160,
along with the aforementioned DTU18MSS mean sea sur-
face model. This integration incorporates drifter data to
enhance the MDT resolution; see Knudsen et al. (2019)for
comprehensive information.
To derive the SLA, we used the CCI-Sea Level Project
(http://www.esa-sealevel-cci.org) product of sea-level maps
available as a monthly merged solution from different altime-
try satellites (Jason 1 and 2, TOPEX/Poseidon, Envisat, ERS-
1 and -2, and GEOSAT-FO). These maps, with a spatial
resolution of 0.25ı, cover the period from 1 January 1993 to
31 December 2015 (version v2.0, downloaded in December
2019), and are presented as anomalies with respect to the
same DTU18MSS model used for the MDT.
For the calculation of the RDT, we used the EN4.1.1
objective analyses dataset, of subsurface ocean temperature
(T) and salinity (S) data sourced from the Met Office
Hadley Centre. These profiles include T and S measurements
incorporating ARGO data and extend to depths of 5,500 m,
allowing us to compute near full-depth VT. Within this
dataset, T and S measurements have been optimally
interpolated onto a regular 1ı1ıgrid across 42 depth
layers (see Good et al. 2013 for more information). The
EN.4.1.1 data were obtained from https://www.metoffice.
gov.uk/hadobs/en4/ (© British Crown Copyright, Met
Office, [2021]) and are available under a Non-Commercial
Government License (http://www.nationalarchives.gov.uk/
doc/non-commercial-government-licence/version/2/). We
use the seawater state equation provided by the Gibbs
Seawater Oceanography Toolbox (Mcdougall and Barker
J. A. Vargas-Alemañy et al.
Fig. 1 Bathymetry (in meters) of
the SO region from https://
download.gebco.net/ (GEBCO
Compilation Group 2020). The
area delineated as the ACC
region is highlighted in black
2011) to derive density based on ocean temperature, salinity,
and pressure.
4 Results
To deepen our comprehension of the studied area, Fig. 1
illustrates the bathymetry of the SO. We have demarcated
the identified ACC region with black dots for clarity.
In Fig. 2a, we present the mean GC speeds for the entire
study period. Notably, maximum values of up to 51.99 cm/s
are observed in close proximity to the Agulhas Current
([40ıS] [30ıE, 60ıE]) and the Brazil-Malvinas Current
([40ıS, 50ıS] [300ıE, 330ıE]).
Figure 2b illustrates the depth dependent decrease in mean
speed from 5 m to 2,000 m. This was achieved by applying
a linear fit to each data point based on its depth level. The
slope of the fitted model is shown for each data point
The blue values represent a decrease with depth, which
is to be expected. However, there are some positive (red)
values, indicating an increase in speed with depth. These
positive values represent only 1.96% of the total (the value
0 is at the 98th percentile). These positive values indicate
regions characterized by the presence of meandering strong
currents.
The mean slope for the entire region is 0.0034 ˙3.8
105(cm/s)/m, with an average GC speed of 11.01 ˙
0.11 cm/s at 5 m depth and 5.55 ˙0.14 cm/s at 2,000 m
depth. These averages are expressed in cm/s per 1ıcell and
are latitude weighted.
In Fig. 3we present two different plots of the time-
averaged VT. In Fig. 3a, the colored background indicates the
vector norm, while the black arrows representing direction
indicate the mean VT vector, calculated by taking the tempo-
ral average of each component. In addition, at each grid point
we compute a monthly time series of VT vector norms, and
Fig. 3b displays the temporal average of this series, which
we refer to as the monthly VT norm.
It’s important to note the differences between these two
visualizations. The mean VT vector (a) becomes null, indi-
cating no VT at all, when there are two opposite vectors of
equal magnitude at the same grid point for two consecutive
months. Conversely, the monthly VT norms (b) will show the
norm of the two vectors, representing the mean VT through
the grid point in both directions. The monthly VT norms will
never be less than the norm of the mean VT vector.
The mean VT for the entire region is 23.7 ˙0.3 Sv
per 1ıcell, and the mean of the monthly VT norms is
51.68 ˙0.5 Sv per 1ıcell. These means are latitude-
weighted.
A Geodetic Analysis of the Volume Transport in the ACC Region Based on Satellite Data
Fig. 2 (a) Mean (GC) for the
whole study period (2004–2015)
at a depth of 5 m (cm/s).
Maximum values reach 51 cm/s.
Color saturated at 30 cm/s to
better visualize the results. (b)
Depth dependent mean speed
decrease from 5 m to 2,000 m
J. A. Vargas-Alemañy et al.
Fig. 3 Mean geostrophic volume
transport (2004–2015): (a)
Arrows depict the mean vectors,
with color indicating their
magnitude. Arrows are only
shown for mean vectors with a
magnitude greater than 15 Sv for
enhanced clarity. Units are
expressed in Sv, with the color
scale capped at 100 Sv, while the
highest values can extend to
193 Sv. (b) Mean of monthly
vector magnitudes. Each grid
point has a monthly time series of
VT norms, and this plot
represents the mean of these
monthly time series. Units are in
Sv, and the color scale is capped
at 170 Sv, with maximum values
reaching up to 338 Sv
A Geodetic Analysis of the Volume Transport in the ACC Region Based on Satellite Data
Fig. 4 Longitudinal series of
total (indicated by the black line
with asterisks), zonal
(represented by the red curve
with triangles), and meridional
(depicted as the blue curve with
dots) VT. These longitudinal
series are generated by averaging
the data over time and integrating
them latitudinally at each
longitude across the ACC region
For the monthly VT norms, maximum values can reach
up to 248.93 Sv and are mainly concentrated near the
Brazil-Malvinas and the Agulhas currents. For the mean
VT, maximum values also reach up to 193.04 Sv and are
also concentrated near the Brazil-Malvinas and the Agulhas
currents. However, there are also some points in the [50ıS,
60ıS] [150ıS, 240ıS] region that can reach these high
values.
For a more comprehensive analysis of the spatial variabil-
ity of VT, the longitudinal series are shown in Fig. 4.This
series is derived by first averaging the data over time and
then, for each longitude, integrating all latitudes within the
ACC region. This process is applied to the total, zonal, and
meridional VT.
The total VT exhibits an average of 210.44 ˙3.4 Sv. In
particular, three prominent maxima are observed near 30ıE,
170ıE, and 300ıE, corresponding to the choke points near
South Africa, South Australia, and the DP. These choke
points essentially mark the boundaries between the three
major ocean basins. There is also a gradual decrease from
west to east.
The zonal VT, with a mean of 198.47 ˙2.95 Sv, accounts
for 94.31% of the total VT and mainly influences the long-
wave variations. On theother hand, the meridional VT, which
primarily contributes to the high-frequency spatial variations,
has a mean of 2.03 ˙4.06 Sv, indicating that it is not
significantly different from zero. This phenomenon can be
attributed to the north-south shifts that occur in the ACC due
to the meandering of its branches.
To analyze the barotropic and baroclinic components of
VT, Fig. 5shows the contributions of the barotropic (green
dashed line) and baroclinic (magenta dashed line) compo-
nents for both the zonal and meridional variables presented
in Fig. 4.
Regarding the zonal component (Fig. 5a), the baroclinic
component accounts for 69.53% of the zonal VT and has a
mean signal of 137.99 ˙2.29 Sv. Meanwhile, the barotropic
component has a mean signal of 60.48 ˙2.38 Sv.
Both components show a strong correlation with the zonal
VT, with a correlation coefficient of 0.65 for the baroclinic
component and 0.61 for the barotropic component, both
statistically significant (p-value 0.05). However, their con-
tributions to the total zonal VT are different; the baroclinic
component mainly influences long-wave spatial variability,
while the barotropic component plays a key role in driving
high-frequency spatial variability.
The barotropic and baroclinic components of the
meridional VT have mean values of 0.23 ˙4.02 and
1.79 ˙1.71, respectively. These values are not significantly
different from zero, mirroring the behavior of the meridional
component itself.
For a more detailed analysis of the temporal variation in
the ACC, Fig. 6shows the time series of the mean VT per 1ı
grid cell for the total (black asterisks), zonal (red triangles),
and meridional (blue circles) components.
The total VT shows a mean signal of 15.86 ˙0.05 Sv,
with a linear trend of 0.007 ˙0.002 Sv per month. It also
shows an annual signal with an amplitude of 0.42 ˙0.11 Sv
peaking in late May, and a biannual signal with an amplitude
of 0.12 ˙0.11 Sv peaking in the 5th month of the 24-month
period.
The zonal component, with a mean signal of 15.85 ˙
0.05 Sv, accounts for 99.98% of the total VT, as expected
from to the zonal nature of the ACC driving circulation.
J. A. Vargas-Alemañy et al.
Fig. 5 Longitudinal series data obtained using the same methodology
as in Fig. 4.(a) zonal VT (represented by the red curve with triangles),
and (b) meridional VT (depicted as the blue curve with dots). Each of
these series includes both its barotropic component (green dashed line)
and its baroclinic component (magenta dashed line)
Fig. 6 VT within the ACC region per 1ıgrid, including total (black
asterisks), zonal (red triangles), and meridional (blue circles) VT. Thick
lines represent a 12-month running mean, with units in Sv
Conversely, the meridional VT maintains a mean value of
0.15 ˙0.02, which is not significantly different from zero.
This behavior, as seen in Fig. 5, is due to north-south shifts
within the ACC.
From this time series, we can derive an estimate of the
zonal VT at any given meridional section within the ACC.
This is done by multiplying the zonal value at a given time
by the number of grid points corresponding to that section.
This method allows us to calculate the mean zonal VT for
any section at a given longitude. Importantly, for the entire
ACC region, where the average number of grid points for a
given longitude is 12.7, our estimate indicates that the mean
zonal VT across the entire ACC zone is 201.63 ˙0.71 Sv.
To examine the barotropic and baroclinic components,
Fig. 7shows these components desegregated for both the
zonal (Fig. 7a) and meridional (Fig. 7b) components.
The baroclinic component of the zonal VT maintains a
mean value of 11.02 ˙0.01 Sv, which represents 69.49%
of the total zonal VT. Conversely, the barotropic component,
with a mean value of 4.84 ˙0.06 Sv, plays a pivotal role
in driving the variability and shows a strong correlation
with the zonal component (correlation coefficient of 0.99, p-
value 0.05).
Regarding the meridional component, the baroclinic com-
ponent has a mean value of 0.15 ˙0.01 Sv, while the
barotropic component has a mean value of 0.003 ˙0.031 Sv.
Neither value is significantly different from zero. Similar to
the zonal component, the variability of the meridional com-
ponent is primarily influenced by the barotropic transport,
with a correlation coefficient of 0.83 (p-value 0.05).
To validate our results, we performed a comparative
analysis with the results of Cunningham et al. (2003). They
provided an estimate for the ACC based on in situ data focus-
ing on measurements from the DP. The DP, located between
South America and Antarctica, acts as a natural passage,
which makes the ACC narrower. This has led to considerable
A Geodetic Analysis of the Volume Transport in the ACC Region Based on Satellite Data
Fig. 7 VT within the ACC
region per 1ıgrid as in Fig. 6.(a)
Zonal VT (represented by the red
curve with triangles), and (b)
Meridional VT (represented by
the blue curve with dots). Each of
these series includes both its
barotropic component (green
dashed line) and its baroclinic
component (magenta dashed
line). Thick lines represent
12-month running means
interest in studying ocean circulation in this area. Transport
through this critical point has been extensively investigated
by several monitoring programs. Cunningham et al. (2003)
reported a canonical estimate of 136.7 ˙7.8SvfortheACC
VT.
In the same region, our method gave an estimate of
142.7 ˙41 Sv. Although our estimate aligns with the in situ
results, it shows greater variability. The comparison between
our estimate and Cunningham et al.’s results highlights the
consistency of our approach with the in situ data and rein-
forces the validity of our satellite-based results, especially
within the specific region of the DP.
5 Conclusions
In this study, we apply a geodetic methodology to investigate
the full-depth VT within the ACC region. This approach
relies on a fusion of data derived from altimetry and gravity
satellite missions, allowing the determination of the SGC. In
addition, we use T and S profiles to assess flows at different
depths, providing a comprehensive analysis of the dynamics
of the ACC. By using these different datasets and methods,
our study provides a thorough investigation of the ocean
circulation in the ACC.
In our analysis, we found that the spatial variability of
the total VT remains relatively steady at 210.44 ˙3.4 Sv,
characterized by three prominent maxima near the choke
points around South Africa, South Australia, and the DP.
Examining the temporal variability, we found a mean VT of
15.86 ˙0.05 Sv per 1ıgrid cell, along with a linear trend of
0.007 ˙0.002 Sv per month. Furthermore, we observed a
periodic signal with an amplitude of 0.42 ˙0.11 Sv, peaking
in late May, and a biannual signal with an amplitude of
0.12 ˙0.11 Sv, peaking in the 5th month of the 24-month
period.
Both analyses confirm that the total VT is mainly influ-
enced by the zonal VT, while the meridional VT does not sig-
nificantly deviate from zero. When examining the barotropic
and baroclinic components, it becomes evident that the baro-
clinic component is responsible for low-frequency variations,
whereas the barotropic component drives high-frequency
variations.
To validate our results, we compare them with the estab-
lished canonical value of 136.7 ˙7.8 Sv provided by
Cunningham et al. (2003), which is derived from in situ
measurements in the DP region. Using our results, we obtain
a similar estimate of 142.7 ˙41 Sv for the same area, which
is in good agreement with the in situ results.
A major advantage of our satellite-based geodetic
approach is its capability to estimate the full-depth VT
over the entire ACC region, in contrast to previous studies
that have often focused only on the DP. This extended spatial
coverage is crucial for a more comprehensive understanding
of the ACC dynamics, as it enables the characterization of
VT variability along its entire path, rather than in isolated
regions. By leveraging this extensive spatial analysis, we
propose a mean value for the ACC region zonal VT of
201.63 ˙0.71 Sv. This estimate is calculated by multiplying
the mean zonal VT per 1ıgrid cell by the average number of
grid points covering the entire ACC region.
In addition, it is important to acknowledge the poten-
tial sources of uncertainty inherent in our methodology.
The paucity of observations, particularly at depths below
2,000 m, introduces uncertainties in the T and S data in the
ACC region (Kosempa and Chambers 2016). We are aware
of the limitations imposed by the restricted availability of
observational data in this region. Despite these challenges,
J. A. Vargas-Alemañy et al.
our study provides valuable insights into the dynamics of the
VT within the ACC region. Addressing these uncertainties
remains a key area for further refinement in future research.
In summary, our study has employed a geodetic method-
ology using satellite-based gravity and altimetry data to
estimate VT within the ACC region. This approach has pro-
vided a comprehensive spatial and temporal analysis of the
dynamics of the ACC. Furthermore, our results are in close
agreement with in situ estimates, reinforcing the robustness
of our methodology. This study contributes valuable insights
into the understanding of ocean circulation patterns within
the ACC, and highlights the power of satellite data in advanc-
ing our knowledge of this critical region.
Ackowledgments The authors acknowledge the supported of all data
providers: ESA in the frame of the CCI Sea Level Project for Altimetry
data; DTU SPACE from the Danish National Space Center for MDT
and MSS products; and the Met Office Hadley Center for EN4.1.1 data
product.
Funding This research is supported by grant PID2021-122142OB-
I00 funded by MCIN/AEI/10.13039/501100011033, grant PROME-
TEO/2021/030 funded by Generalitat Valenciana, and grant GVA-
THINKINAZUL/2021/035 funded by Generalitat Valenciana and
“European Union NextGenerationEU/PRTR”.
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A Geodetic Analysis of the Volume Transport in the ACC Region Based on Satellite Data
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