Introduction
The eastern tropical North Atlantic (ETNA: 4 to 22∘ N
and from the shelf at the eastern boundary to 38∘ W; Fig. 1) off
northwestern Africa is one of the biologically most productive areas of the
global ocean (Chavez and Messié, 2009; Lachkar and Gruber, 2012). In
particular, the eastern boundary current system close to the northwestern
African coast is a region where northeasterly trade winds force coastal
upwelling of cold, nutrient-rich waters, resulting in high productivity
(Bakun, 1990; Lachkar and Gruber, 2012; Messié et al., 2009; Pauly
and Christensen, 1995). The ETNA is characterized by a weak large-scale
circulation and instead dominated by mesoscale variability (here referred to
as eddies; Brandt et al., 2015; Mittelstaedt, 1991). Traditionally the
ETNA is considered to be “hypoxic”, with minimal oxygen concentrations of
marginally below 40 µmol kg-1 (e.g., Stramma et al.,
2009; Fig. 1a). The large-scale ventilation and oxygen consumption
processes of thermocline waters in the ETNA result in two separate oxygen
minima (Fig. 1b): a shallow one with a core depth of about 80 m and a deep
one at a core depth of about 450 m (Brandt et al., 2015; Karstensen et
al., 2008). The deep minimum is the core of the so-called oxygen minimum zone (OMZ) and is primarily created
by sluggish ventilation of the respective isopycnals (Luyten et al.,
1983; Wyrtki, 1962). It extends from the eastern boundary into the open
ocean and is located in the so-called shadow zone of the ventilated
thermocline, with the more energetic circulation of the subtropical gyre in
the north and the equatorial region in the south (Karstensen et al.,
2008; Luyten et al., 1983). The shallow oxygen minimum intensifies from the
Equator towards the north with minimal values near the coast at about
20∘ N (Brandt et al., 2015; Fig. 1a). It is assumed that the shallow oxygen minimum originates from enhanced biological
productivity and an increased respiration associated with sinking particles
in the water column (Brandt et al., 2015; Karstensen et al., 2008;
Wyrtki, 1962).
(a) Map of the ETNA including contour lines of the oxygen minimum
of the upper 200 m (in µmol kg-1) as obtained from the MIMOC
climatology (Schmidtko et al., 2013). The color indicates the
percentage of “dead-zone” eddy coverage per year. The black triangle
defines the SOMZ. (b) Mean vertical oxygen profile of all profiles within the
SOMZ showing the shallow oxygen minimum centered around 80 m depth and the
deep oxygen minimum centered at 450 m depth.
The eddies act as a major transport agent between coastal waters and the
open ocean (Schütte et al., 2016a), which is a well-known
process for all upwelling areas in the world oceans (Capet et al., 2008;
Chaigneau et al., 2009; Correa-Ramirez et al., 2007; Marchesiello et al.,
2003; Nagai et al., 2015; Schütte et al., 2016a; Thomsen et al., 2015).
In the ETNA, most eddies are generated near the eastern boundary; Rossby
wave dynamics and the basin-scale circulation force these eddies to
propagate westwards (Schütte et al., 2016a). Open-ocean
eddies with particularly high South Atlantic Central Water (SACW) fractions
in their cores have been found far offshore in regions dominated by the much
saltier North Atlantic Central Water (NACW; Karstensen et al., 2015;
Pastor et al., 2008). Weak lateral exchange across the eddy boundaries is
most likely the reason for the isolation (Schütte et al.,
2016a). The impact of eddy transport on the coastal productivity (equivalent
to other upwelling-related properties) was investigated by
Gruber et al. (2011), who were able to show that high (low) eddy-driven transports of nutrient-rich water from the shelf into the open-ocean
results in lower (higher) biological production on the shelf. Besides acting
as export agents for coastal waters and conservative tracers, coherent
eddies have been reported to establish and maintain an isolated ecosystem
changing non-conservative tracers with time (Altabet et al., 2012;
Fiedler et al., 2016; Hauss et al., 2016; Karstensen et al., 2015;
Löscher et al., 2015). Coherent/isolated mesoscale eddies can exist over
periods of several months or even years (Chelton et al.,
2011). During that time the biogeochemical conditions within these eddies
can evolve very different from the surrounding water masses
(Fiedler et al., 2016). Hypoxic to suboxic oxygen
levels have been observed in cyclonic eddies (CEs) and anticyclonic
mode-water eddies (ACMEs) at shallow depth and just beneath the mixed layer
(ML, about 50 to 100 m; Karstensen et al., 2015). The
creation of the low-oxygen cores in the eddies have been attributed to the
combination of several factors (Karstensen et al.,
2015): high productivity in the surface waters of the eddy (Hauss et al.,
2016; Löscher et al., 2015), enhanced respiration of sinking organic
material at subsurface depth (Fiedler et al., 2016; Fischer et al., 2016)
and an “isolation” of the eddy core from exchange with surrounding and
better-oxygenated water (Karstensen et al., 2016).
The intermittent nature of the oxygen depletion and the combination of high
respiration with sluggish oxygen transport resembles what is known as
“dead zone” in other aquatic system (lakes, shallow bays), and therefore
the term “dead-zone eddies” has been introduced
(Karstensen et al., 2015). So far the profound impacts
on behavior of microbial (Löscher et al.,
2015) and metazoan (Hauss et al., 2016) communities has
been documented inside the eddies. For example, the appearance of
denitrifying bacteria, typically absent from the open tropical Atlantic, has
been observed (Löscher et al., 2015) via the detection of nirS gene
transcripts (the key functional marker for denitrification). However, the
close-to-Redfield N : P stoichiometry in ACMEs in the ETNA
(Fiedler et al., 2016) does not suggest a
large-scale net loss of bioavailable nitrogen via denitrification. The key
point in changing non-conservative tracers in the eddy cores is the
physical-biological coupling, which is strongly linked to the vertical
velocities of submesoscale physics, stimulating primary production (upward
nutrient flux) in particular under oligotrophic conditions (Falkowski et
al., 1991; Levy et al., 2001; McGillicuddy et al., 2007). The detailed
understanding of the physical and biogeochemical processes and their linkage
in eddies is still limited (Lévy et al., 2012).
Consequently the relative magnitude of eddy-dependent vertical nutrient
flux, primary productivity and associated enhanced oxygen consumption or
nitrogen fixation/denitrification in the eddy cores and accordingly the
contribution to the large-scale oxygen or nutrient distribution is fairly
unknown.
In order to further investigate the physical, biogeochemical and ecological
structure of “dead-zone” eddies, an interdisciplinary field study was
carried out in winter 2013/spring 2014 in the ETNA, north of Cape Verde,
using dedicated ship, mooring and glider surveys supported by satellite and
Argo float data. The analysis of the field study data revealed surprising
results regarding eddy metagenomics (Löscher
et al., 2015), zooplankton communities (Hauss et al.,
2016), carbon chemistry (Fiedler et al., 2016) and
nitrogen cycling (Karstensen et al., 2016). Furthermore, analyses of
particle flux time series, using sediment trap data from the Cape Verde
Ocean Observatory (CVOO), were able to confirm the impact of highly
productive “dead-zone” eddies on deep local export fluxes
(Fischer et al., 2016). In this paper we investigate
“dead-zone” eddies detected from sea level anomaly (SLA) and sea surface
temperature (SST) data based on methods described by
Schütte et al. (2016a). We draw a connection between the
enhanced consumption and associated low-oxygen concentration in eddy cores
and the formation of the regional observed shallow oxygen minimum. To
assess the influence of oxygen-depleted eddies on the oxygen budget of the
upper water column, a sub-region between the ventilation pathways of the
subtropical gyre and the zonal current bands of the equatorial Atlantic was
chosen and investigated in more detail. This region includes the most
pronounced shallow oxygen minimum zone (SOMZ; Fig. 1a). The probability of
“dead-zone” eddy occurrence per year is more or less evenly distributed in
the ETNA (Fig. 1a). Particularly in the SOMZ there seems to be neither a
distinctly high nor an explicitly low “dead-zone” eddy occurrence. Due to
the absence of other ventilation pathways in this zone, the influence of
“dead-zone” eddies on the shallow oxygen minimum budget may be important
and a closer examination worth the effort. We determine the average
characteristics of “dead-zone” eddies in the ETNA, addressing their
hydrographic features as well as occurrence, distribution, generation and
frequency. Based on oxygen anomalies and eddy coverage we estimate their
contribution to the oxygen budget of the SOMZ. The paper is organized as
follows. Section 2 addresses the different in situ measurements, satellite
products and methods we use. Our results are presented in Sect. 3,
discussed in Sect. 4 and summarized in Sect. 5.
Map of the ETNA containing all available profiles between 1998 and
2014. The green cross marks the CVOO position, blue dots mark shipboard conductivity–temperature–depth (CTD)
stations, red dots mark the locations of glider profiles and black dots
locations of Argo float profiles.
Data and methods
In situ data acquisition
For our study we employ a quality-controlled database combining shipboard
measurements, mooring data and Argo float profiles as well as autonomous
glider data taken in the ETNA. For details on the structure and processing
of the database see Schütte et al. (2016a). For this study
we extended the database in several ways. The region was expanded to now
cover the region from 0 to 22∘ N and 13 to
38∘ W (see Fig. 2). We then included data from five recent ship
expeditions (RV Islandia ISL_00314, RV Meteor M105, M107,
M116, M119), which sampled extensively within the survey region. Data from
the two most recent deployment periods of the CVOO mooring from October 2012
to September 2015 as well as Argo float data for the years 2014 and 2015
were also included. Furthermore, oxygen measurements of all data sources
were collected and integrated into the database. As the last modification of
the database we included data from four autonomous gliders that were
deployed in the region and sampled two ACMEs and one CE. Glider IFM11
(deployment ID: ifm11_depl01) was deployed on 13 March 2010.
It covered the edge of an ACME on 20 March and recorded data in the upper
500 m. Glider IFM05 (deployment ID: ifm05_depl08) was
deployed on 13 June 2013. It crossed a CE on July 26 and recorded data down
to 1000 m depth. IFM12 (deployment ID: ifm12_depl02) was
deployed on 10 January 2014 north of the Cape Verde island São Vicente
and surveyed temperature, salinity and oxygen to 500 m depth. IFM13
(deployment ID: ifm13_depl01) was deployed on 18 March 2014
surveying temperature, salinity and oxygen to 700 m depth. IFM12 and IFM13
were able to sample three complete sections through an ACME. All glider data
were internally recorded as a time series along the flight path, while for
the analysis the data was interpolated onto a regular pressure grid of 1 dbar resolution (see also Thomsen et al., 2015). Gliders collect a large
number of relatively closely spaced slanted profiles. To reduce the number
of dependent measurements, we limited the number of glider profiles to one
every 12 h. All four autonomous gliders were equipped with Aanderaa
optodes (3830) installed in the aft section of the devices. A recalibration
of the optode calibration coefficients was determined on dedicated conductivity–temperature–depth (CTD)
casts following the procedures of (Hahn et al., 2014). These
procedures also estimate and correct the delays caused by the slow optode
response time (more detailed information can be found in Hahn
et al., 2014, and Thomsen et al., 2015). As gliders move through the water
column the oxygen measurements are not as stable as those from moored
optodes analyzed by Hahn et al. (2014). We thus estimate their measurement
error to about 3 µmol kg-1. The processing and quality control
procedures for temperature and salinity data from shipboard measurements,
mooring data and Argo floats has already been described by Schütte et al. (2016a). The processing of the gliders' temperature and salinity
measurements is described in Thomsen et al. (2015). Oxygen measurements
of the shipboard surveys were collected with Seabird SBE 43 dissolved oxygen
sensors attached to Seabird SBE 9plus or SBE 19
CTD systems. Sampling and calibration
followed the procedures detailed in the GO-SHIP manuals (Hood et al., 2010).
The resulting measurement error were ≤ 1.5 µmol kg-1. Within
the CVOO moorings, a number of dissolved oxygen sensors (Aanderaa optodes
type 3830) were used.
Calibration coefficients for moored optodes were determined on dedicated CTD
casts and additional calibrated in the laboratory with water featuring 0 %
air saturation before deployment and after recovery following the procedures
described by Hahn et al. (2014). We estimate their measurement error at
< 3 µmol kg-1. For the few Argo floats equipped with
oxygen sensors a full calibration is usually not available and only a visual
inspection of the profiles was done before including the data into the
database. The different manufacturers of Argo float oxygen sensors specify
their measurement error at least better than 8 µmol kg-1 or
5 %, whichever is larger. Note that early optodes can be significantly
outside of this accuracy range, showing offsets of 15–20 µmol kg-1, in some cases even higher.
As a final result the assembled in situ database of the ETNA contains 15 059
independent profiles (Fig. 2). All profiles include temperature, salinity
and pressure measurements while 38.5 % of all profiles include oxygen
measurements. The database is composed of 13 % shipboard, 22.5 % CVOO
mooring, 63 % Argo float and 1.5 % glider profiles. To determine the
characteristics of different eddy types from the assembled profiles, we
separated them into CEs, ACMEs and the “surrounding area” not associated
with eddy-like structures following the approach of
Schütte et al. (2016a).
Satellite data
We detected and tracked eddies following the procedures described in
Schütte et al. (2016a). In brief we used 19 years of the delayed-time
“all-sat-merged” reference dataset of SLA (version 2014). The data are
produced by Ssalto/Duacs and distributed by AVISO (Archiving, Validation,
and Interpretation of Satellite Oceanographic), with support from CNES
(http://www.aviso.altimetry.fr/duac/). We used the multi-mission product,
which is mapped on a 1/4∘ × 1/4∘ Cartesian grid and has
a temporal resolution of 1 day. The anomalies were computed with respect
to a 19-year mean. The SLA and geostrophic velocity anomalies also
provided by AVISO were chosen for the time period January 1998 to December
2014.
For SST the dataset “Microwave Infrared Fusion Sea Surface Temperature”
from Remote Sensing Systems (www.remss.com) is used. It is a combination of
all operational microwave (MW) radiometer SST measurements (TMI, AMSR-E,
AMSR2, WindSat) and infrared (IR) SST measurements (Terra MODIS, Aqua
MODIS). The dataset thus combines the advantages of the MW data
(through-cloud capabilities) with the IR data (high spatial resolution). The
SST values are corrected using a diurnal model to create a foundation SST
that represents a 12:00 LT temperature (www.remss.com). Daily data with 9 km
resolution from January 2002 to December 2014 are considered.
For sea surface chlorophyll (Chl) data we use the MODIS/Aqua Level 3 product
available at http://oceancolor.gsfc.nasa.gov provided by the NASA. The data
were measured via IR and are therefore cloud cover dependent. Daily data
mapped on a 4 km grid from January 2006 to December 2014 are selected.
(a) Salinity–σθ diagram with color indicating
the oxygen concentrations. The black line separates the 173 profiles with
minimum oxygen concentration of < 40 µmol kg-1 (left
side/more SACW characteristics) from profiles of the surrounding water
(right side/more NACW characteristics), taken from the same devices
shortly before and after the encounter with a low-oxygen eddy. (b) Mean
oxygen concentration vs. depth of the coastal region (east of
18∘ W, solid black line), of all CEs (solid blue line) and all
ACMEs (solid green line) with available oxygen measurements. The dashed line
represents the reconstructed mean oxygen concentration for the same CEs
(blue) and ACMEs (green). (c) Difference between the reconstructed and
measured oxygen concentrations in CEs (blue) and ACMEs (green) with
associated standard deviation (shaded area).
Low-oxygen eddy detection and surface composites
In order to verify whether low-oxygen concentrations (< 40 µmol kg-1) at shallow depth (above 200 m) are associated with eddies we
applied a two step procedure. First, all available oxygen measurements of
the combined in situ datasets are used to identify negative oxygen anomalies
with respect to the climatology. Next, the satellite-data-based eddy
detection results (Schütte et al., 2016a) were matched in
space and time with the location of anomalously low-oxygen profiles. In this
survey the locations of 173 of 180 low-oxygen profiles coincide with surface
signatures of mesoscale eddies. Schütte et al. (2016a) showed that ACMEs
can be distinguished in the ETNA from “normal” anticyclonic eddies by
considering the SST anomaly (cold in case of ACMEs) and sea surface salinity
(SSS) anomaly (fresh in case of ACMEs) in parallel to the respective SLA
anomaly. The satellite-based estimates of SLA and SST used in this study are
obtained by subtracting low-pass filtered (cutoff wavelength of
15∘ longitude and 5∘ latitude) values
from the original data to exclude large-scale variations and preserve only
the mesoscale variability (see Schütte et al., 2016a for more detail).
All eddy-like structures with low-oxygen profiles are visually tracked in
the filtered SLA (sometimes SST data) backward and forward in time in order to
obtain eddy propagation trajectories. The surface composites of
satellite-derived SLA, SST and Chl data consist of 150 km × 150 km
snapshots around the obtained eddy centers. For construction of the
composites the filtered SLA and SST are used as well.
Reconstruction of oxygen concentrations in low-oxygen eddy cores
About 30 % of the profiles from the combined in situ dataset conducted in
CEs or ACMEs do not have oxygen measurements available. However, we are only
interested in oxygen measurements in isolated CE or ACME cores. These
isolated eddy cores carry anomalously low-salinity SACW of coastal origin,
while the surrounding waters are characterized by an admixture of more
saline NACW (Schütte et al., 2016a). All eddies that show
a low-salinity and cold core indicate that (i) they have been generated near
the coast and (ii) their core has been efficiently isolated from surrounding
waters. The salinity–σθ diagram (Fig. 3a) of open-ocean
(west of 19∘ W) profiles shows a correlation between low-salinity
eddy cores and low-oxygen concentrations. Moreover, it indicated that the
oxygen content in the isolated eddies is decreasing from east to west. In
order to compensate for missing oxygen measurements on many of the profiles
we derive a salinity–oxygen relation but also consider the “age” of the
eddy (time since the eddy left the eastern boundary) and an oxygen
consumption rate within the eddy core. The oxygen consumption rate is
estimated from the difference between the observed oxygen and a reference
profile (the mean of all profiles east of 18∘ W in the eastern
boundary region; Fig. 3a), the distance from the eastern boundary, and the
propagation speed (3 km d-1; see Schütte et al., 2016a). The mean
eddy consumption rate is now the difference from the initial oxygen
condition and the observed oxygen concentration in the eddy core divided by
the eddy age (distance divided by propagation speed). For eddy profiles
without oxygen measurements but SACW water mass characteristics (less saline
and colder water than surrounding water) we can assume a strong isolation of
the eddy and thus a lowering in oxygen. Using the coastal reference profile
(Fig. 3), oxygen consumption rate and the distance from the coast an oxygen
profile is reconstructed for all isolated CEs and ACMEs. To validate the
method we reconstructed the oxygen profiles for the eddies with available
oxygen measurements and compared them (Fig. 3b). On average an uncertainty
of ±12 (16) µmol kg-1 is associated with the
reconstructed oxygen values (Fig. 3c) of CEs (ACMEs). Depending on the
intensity of isolation of the eddy core, lateral mixing could have taken
place, which is assumed to be zero in our method. However, this approach
enables us to enlarge the oxygen dataset by 30 %. We considered the
reconstructed oxygen profiles only to estimate the mean structure of oxygen
anomaly.
Mean vertical oxygen anomaly of low-oxygen eddies and their impact on
the SOMZ
To illustrate mean oxygen anomalies for CEs and ACMEs as a function of depth
and radial distance, all oxygen profiles (observed and reconstructed) were
sorted with respect to a normalized distance, which is defined as the actual
distance of the profile from the eddy center divided by the radius of the
eddy (the shape and thus the radius of the eddy are gained from the
streamline with the strongest swirl velocity around a center of minimum
geostrophic surface velocity). The oxygen profiles were grouped and averaged
onto a grid of 0.1 increments between 0 and 1 of the normalized radial
distance. Finally a running mean over three consecutive horizontal grid
points was applied. A mean oxygen anomaly for the CEs and the ACMEs was
constructed by the comparison with the oxygen concentrations in the
surrounding waters. To illustrate the influence of the reconstructed oxygen
values, the mean oxygen anomaly is also constructed based only on original
measured oxygen values, and both anomalies are shown for comparison.
An oxygen deficit profile due to “dead-zone” eddies in the SOMZ is derived
by building an oxygen anomaly on density surfaces (O2′) separating
CEs and ACMEs. The derived anomalies are multiplied by the mean number of
eddies dissipating in the SOMZ per year (n) and weighted by the area of
the eddy compared to the total area of the SOMZ (ASOMZ= triangle in
Fig. 1a). Differences in the mean isopycnal layer thickness of each eddy
type and the SOMZ are considered by multiplying the result with the ratio of
the mean Brunt–Väisälä frequency (N2) outside and inside
the eddy, resulting in an apparent oxygen utilization rate (µmol kg-1 yr-1) due to “dead-zone” eddies in the SOMZ on density
layers:
aOUR=nO2′πrEddy2NSOMZ2ASOMZNEddy2,
where rEddy is the mean radius of the eddies.
Minimum oxygen concentration (contour lines, µmol kg-1) in the ETNA between the surface and 200 m depth as obtained from
the MIMOC climatology (Schmidtko et al., 2013). Superimposed
colored dots are all low-oxygen measurements (below 40 µmol kg-1
in the upper 200 m) which could be associated with eddy-like structures. The
size of the dots represents a typical size of the mesoscale eddies. The
associated trajectories of the eddies are shown in green for ACMEs and in
blue for cyclones. The oxygen concentrations are from the combined dataset
of shipboard, mooring, glider and Argo float measurements.
Results
Low-oxygen eddy observation from in situ data
Several oxygen measurements in the ETNA with anomalously low-oxygen
concentrations, which is defined here as an oxygen concentration below 40 µmol kg-1 (Stramma et al., 2009) could be identified
from Argo floats, ship surveys, glider missions and from the CVOO mooring
(Fig. 4). In total, 27 independent eddies with oxygen values < 40 µmol kg-1 in the upper 200 m were sampled with 173 profiles
from 25 different platforms (Table 1). Almost all of the observed anomalous
low-oxygen values could be associated with mesoscale structures at the sea
surface (CEs or ACMEs) from satellite data.
In situ measurements for meridional velocity, temperature, salinity and
oxygen of the CVOO mooring during the westward passage of one CE and one
ACME with low-oxygen concentrations are chosen to introduce the two
different eddy types and their vertical structure based on temporally high-resolution data (Fig. 5). From October 2006 to December 2006 (Fig. 5a), a CE
passed the CVOO mooring position on a westward trajectory. At its closest,
the eddy center was located about 20 km north of the mooring. The meridional
velocities show a strong cyclonic rotation (first southward, later
northward) with velocity maxima between the surface and 50 m depth at the
edges of the eddy. In the core of the CE, the water mass was colder and less
saline than the surrounding water, the ML depth is reduced and
the isopycnals are shifted upwards. The oxygen content of the eddy core was
reduced by about 60 µmol kg-1 at 115 m depth (or at the
isopycnal surface 26.61 kg m-3) compared to surrounding waters, which
have a mean (±1 standard deviation) oxygen content of 113 (±38) µmol kg-1 at around 150 m depth
or 26.60 (±0.32) kg m-3 during the mooring period between 2006 and 2014. Schütte et al. (2016a) showed that around
52 % of the eddies in the ETNA represents CEs.
They have a marginally smaller radius, rotate faster and have a shorter
lifetime compared to the anticyclonic eddies, which is also shown in other
observational studies of Chaigneau et al. (2009),
Chelton et al. (2011), and theoretically suggested by
Cushman-Roisin et al. (1990).
Available oxygen measurements below 40 µmol kg-1 in
the ETNA. The * indicates recent observations which are not included in Fig. 4 due to not existent delayed time satellite products.
Time
Minimum O2 between
Associated eddy
0 and 200 m
type
11 ship cruises:
(81 profiles)
Meteor 68/3
Summer 2006
17
CE
L'Atalante GEOMAR 3
Winter 2008
25
ACME
Meteor 80/2
Winter 2009
32
ACME
Meteor 83/1
Winter 2010
20
ACME
Meteor 96
Spring 2013
38
ACME
Meteor 97
Summer 2013
28
ACME
Islandia
Spring 2014
10
ACME
Meteor 105
Spring 2014
4
ACME
Meteor 116
Spring 2015
17
ACME*
Meteor 119
Autumn 2015
30
ACME*
Maria S. Merian 49
Winter 2015
35
CE*
9 Argo floats:
(24 profiles)
6900632
Autumn 2008
14
CE
1900652
Winter 2008
26
ACME
1900650
Summer 2009
27
ACME
1901360
Autumn 2014
34
CE
1901361
Autumn 2014
21
CE
1901362
Autumn 2014
26
CE
1901363
Autumn 2014
37
CE
1901364
Autumn 2014
24
ACME
1901365
Autumn 2014
24
ACME
4 gliders:
(32 profiles)
IFM 11
Spring 2010
19
ACME
IFM 05
Summer 2013
9
CE
IFM 12
Winter 2014
1
ACME
IFM 13
Spring 2014
1
ACME
9 CVOO events:
(36 profiles)
Optode at 127 m depth
Winter 2007
15
ACME
Optode at 79 m depth
Autumn 2008
38
CE
Optode at 54 m depth
Winter 2010
2
ACME
Optode at 53 m depth
Winter 2012
17
ACME
Optode at 53 m depth
Spring 2012
30
CE
Optode at 45 m depth
Summer 2013
29
ACME
Optode at 45 m depth
Winter 2013
9
CE
Optode at 43 m depth
Winter 2015
2
ACME*
Optode at 43 m depth
Summer 2015
6
ACME*
∑173 profiles
∑27 different eddies
Meridional velocity, temperature, salinity and oxygen of an
exemplary (a) CE and (b) ACME at the CVOO mooring. Both eddies passed the CVOO
on a westward trajectory with the eddy center north of the mooring position
(CE 20 km, ACME 13 km). The CE passed the CVOO from October to December 2006
and the ACME between January and March 2007. The thick black lines in the
velocity plots indicate the position of an upward looking ADCP. Below that
depth calculated geostrophic velocity is shown. The white lines represent
density surfaces inside the eddies and the thin grey lines isolines of
temperature and salinity, respectively. Thin black lines in the temperature
and salinity plot mark the vertical position of the measuring devices. On
the right a time series of oxygen is shown from the one sensor available at
nominal 120 m depth.
From January 2007 to March 2007 (Fig. 5b), an ACME passed the CVOO mooring
position. The core of the westward propagating eddy passed about 13 km north
of the mooring. The velocity field shows strong subsurface anticyclonic
rotation at the depth of the core, i.e., between 80 and 100 m. In contrast to
“normal” anticyclonic eddies, the water mass in the core of an ACME is
colder and less saline than the surrounding waters. The isopycnals above the
core are elevated resulting in shallower MLs both resembling a cyclone.
Beneath the core, the isopycnals are strongly depressed as in a normal
anticyclone. Thus, dynamically this resembles a mode-water anticyclone, an
eddy type which is well known from local single observations in almost all
ocean basins (globally: Kostianoy and Belkin, 1989;
McWilliams, 1985 (“submesoscale coherent vortices”); in the
North Atlantic: Riser et al., 1986; Zenk et al., 1991 and Bower et al., 1995;
Richardson et al., 1989; Armi and Zenk, 1984 (“Meddies”); in the Mediterranean Sea: Tauper-Letage et al., 2003 (“Leddies”);
in the North Sea: Van Aken et al., 1987; in the
Baltic Sea: Zhurbas et al., 2004; in the Indian Ocean:
Shapiro and Meschanov, 1991 (“Reddies”); in the North Pacific:
Lukas and Santiago-Mandujano, 2001; Molemaker et al., 2015 (“Cuddies”); in the South Pacific: Stramma et al., 2013;
Colas et al., 2012; Combes et al., 2015; Thomsen et al., 2015 and Nof et al., 2002 (“Teddies”); in the
Arctic:
D'Asaro, 1988; Oliver et al., 2008). For the
majority of the observed mode-water-type eddies the depressed isopycnals in
deeper water mask the elevated isopycnals in the shallow water in terms of
geostrophic velocity, resulting in an anticyclonic surface rotation and a
weak positive SLA (Gaube et al., 2014).
Composites of surface signature for SLA, SST and Chl from all
detected low-oxygen eddies: (a) ACMEs and (b) CEs. The solid black cross marks
the eddy center and the solid black circle the average radius. Due to
significant cloud cover the number of Chl data are much less when compared
to the SLA and SST data; thus there is more lateral structure.
Depth profiles of a mean apparent oxygen utilization rate (aOUR,
µmol kg-1 d-1) within CEs (blue) and ACMEs (green) in the
ETNA with associated standard deviation (shaded area). Derived by using the
propagation time of each eddy, an initial coastal oxygen profile and the
assumption of linear oxygen consumption (based on depth layers).
In contrast to most of the ACMEs reported, the CVOO ACME eddy core is located
at very shallow depth, just beneath the ML. The oxygen content in the eddy's
core recorded from the CVOO mooring is strongly decreased with values around
19 µmol kg-1 at 123 m depth (or 26.50 kg m-3) compared to
the surrounding waters (113 (±38) µmol kg-1). Within the
entire time series, the CVOO mooring recorded the passage of several ACMEs
with even lower oxygen concentrations (for more information see
Karstensen et al., 2015, or Table 1). Recent model
studies suggest that ACMEs represent a non-negligible part of the worlds
eddy field, particular in upwelling regions (Combes et al., 2015; Nagai
et al., 2015). Schütte et al. (2016a) could show, based on observational
data, that ACMEs represent around 9 % of the eddy field in the ETNA. Their
radii are in the order of the first baroclinic-mode Rossby radius of
deformation and their eddy cores are well isolated (Schütte et al.,
2016a).
Vertical structure of oxygen from the composite (a) CE and (b) ACME
in the ETNA presented as a half section across the eddies. The left side of
both panels (-60 to 0 km) is based on reconstructed and measured oxygen
profiles whereas the right side (0 to 60 km) is based on measured oxygen
profiles only. Both methods are shown against the normalized radial
distance. The blackb lines represents the density surfaces inside the eddies.
(c) Mean profiles of the oxygen anomalies based on measured profiles only;
green lines are associated with ACMEs and blue to CEs. Horizontal lines
indicate the standard deviation of the oxygen anomaly at selected depths.
The thick dashed lines indicates the mean ML within the different eddy
types. The grey vertical dashed line represents zero oxygen anomaly.
Combining in situ and satellite data for low-oxygen eddy detection in
the ETNA
Combining the location and time of in situ detection of low-oxygen eddies
with the corresponding SLA satellite data reveals a clear link to the surface
manifestation of mesoscale structures, CEs and ACMEs (Fig. 4).
Composite surface signatures for SLA, SST and Chl from all anomalous
low-oxygen eddies as identified in the in situ dataset are shown in Fig. 6. The ACME composites are based on 17 independent eddies and on 922 surface
maps. The detected ACMEs are characterized by an elevation of SLA, which is
associated with an anticyclonic rotation at the sea surface. The magnitude
of the SLA displacement is moderate compared to normal anticyclones and CEs
(Schütte et al., 2016a). More distinct differences to
normal anticyclones are the cold-water anomaly and the elevated Chl
concentrations in the eddy center of the ACMEs. Normal anticyclones are
associated with elevated SST and reduced Chl concentrations. Through a
combination of the different satellite products (SLA, SST, SSS) it is
possible to determine low-oxygen eddies from satellite data alone (further
details of the ACME tracking and the average satellite surface signatures
(SLA, SST, SSS) of all eddy types (CEs, anticyclones and ACMEs) identified
in 19 years of satellite data in Schütte et al., 2016a).
The composite mean surface signature for low-oxygen CEs is based on 10
independent eddies and on 755 surface maps. The CEs are characterized by a
negative SLA and SST anomaly. The observed negative SST anomaly of the
low-oxygen CEs is twice as large (core value CE: -0.12 (±0.2) ∘C; core value ACME: -0.06 (±0.2) ∘C) as the
corresponding anomaly of the ACMEs. The Chl concentration in the eddy center
is also higher for CEs compared to ACMEs (core value CE: 0.35 (±0.22) log mg m-3; core value ACME: 0.21 (±0.17) log mg m-3).
Note that we only considered the measured low-oxygen ACMEs and CEs from
Table 1 to derive the composites.
Using the eddy-dependent surface signatures in SLA, SST and Chl the
low-oxygen eddies could be tracked and an eddy trajectory could be derived
(e.g., Fig. 4). All detected eddies were propagating westward into the open
ocean. North of 12∘ N, most of the eddies set off near the coast,
whereas south of 12∘ N the eddies seem to be generated in the open
ocean. Detected CEs have a tendency to deflect poleward on their way into
the open ocean (Chelton et al., 2011), whereas ACMEs seem
to have no meridional deflection. However, during their westward propagation
the oxygen concentration within the low-oxygen eddy cores decreases with
time. Using the propagation time and an initial coastal oxygen profile (Fig. 3b), a mean apparent oxygen utilization rate per day could be derived for all
sampled eddies (Fig. 7). On average the oxygen concentration decreases by
about 0.19 ± 0.08 µmol kg-1 d-1 in the core of an
isolated ACME but has no significant trend in the core of an isolated CE
(0.10 ± 0.12 µmol kg-1 d-1). This is in the range of
recently published aOUR estimates for single observations of CEs
(Karstensen et al., 2015) and ACMEs
(Fiedler et al., 2016).
Mean oxygen anomalies from low-oxygen eddies in the ETNA
In Fig. 8 we compare the mean oxygen anomalies based purely on
observations with those based on the extended profile database, including
observed and reconstructed oxygen values (see Sect. 2.4). It shows the
mean oxygen anomalies against the surrounding water for CE (Fig. 8a) and
ACME (Fig. 8b) vs. depth and normalized radial distance. On the left side
of each panel the anomaly is based on the observed and reconstructed oxygen
values (736 oxygen profiles; 575 in CEs; 161 in ACMEs), whereas on the right
side the anomaly is based only on the observed oxygen measurements (504
oxygen profiles; 395 in CEs; 109 in ACMEs). The distinct mean negative
oxygen anomalies for CEs and ACMEs indicate the low-oxygen concentrations in
the core of both eddy types compared to the surrounding water. The strongest
oxygen anomalies are located in the upper water column, just beneath the ML.
CEs feature maximum negative anomalies of around -100 µmol kg-1
at around 70 m depth in the eddy core, with a slightly more pronounced
oxygen anomaly when including the reconstructed values (left side of Fig. 8)
compared to the oxygen anomaly based purely on observation (right side of
Fig. 8a). This is contrary for the ACME with stronger oxygen anomalies on
the right part than on the left (Fig. 8b). Both methods deliver maximum
negative anomalies of around -120 µmol kg-1 at around 100 m
depth in the ACME core. At that depth, the diameter of the mean oxygen
anomaly is about 100 km for ACMEs and 70 km for CEs (the eddy core is
defined here as the area of oxygen anomalies smaller than -40 µmol kg-1). Beneath 150 m depth, magnitude and diameter of the oxygen
anomalies decrease rapidly for both eddy types. Figure 8c is based on both
the in situ and reconstructed oxygen values and shows the horizontal mean
oxygen anomaly profile of each eddy type against depth obtained by
horizontally averaging the oxygen anomalies shown in Fig. 8a and b. The maximum
anomalies are -100 µmol kg-1 at around 90 m for ACMEs and -55 µmol kg-1 at around 70 m for
cyclones. Both eddy types have the
highest oxygen variance directly beneath the ML (in the eddy core) or
slightly above the eddy core. The oxygen anomaly (and associated variance)
decreases rapidly with depth beneath the eddy core and is smaller than
around -10 ± 10 µmol kg-1 beneath 350 m for both eddy
types.
Discussion
The pelagic zones of the ETNA are traditionally considered to be
“hypoxic”, with minimal oxygen concentrations of marginally below 40 µmol kg-1 (Brandt et al., 2015; Karstensen et al., 2008; Stramma
et al., 2009). This is also true for the upper 200 m (Fig. 1). However,
single oxygen profiles taken from various observing platforms (ships,
moorings, gliders, floats) with oxygen concentrations in the range of severe
hypoxia (< 20 µmol kg-1) and even anoxia
(∼ 1 µmol kg-1) conditions and consequently below
the canonical value of 40 µmol kg-1 (Stramma et
al., 2008) are found in a surprisingly high number (in total 180 profiles)
in the ETNA. In the current analysis we could associate observations of
low-oxygen profiles with 27 independent mesoscale eddies (10 CEs and 17
ACMEs). Mesoscale eddies are defined as coherent, nonlinear structures with
a lifetime of several weeks to more than a year and radii larger than the
first baroclinic-mode Rossby radius of deformation (Chelton
et al., 2007). In reference to the surrounding water, the eddies carry a
negative oxygen anomaly which is most pronounced right beneath the ML. The oxygen anomaly is attributed to both an elevated primary
production in the surface layers of the eddies (documented by positive
chlorophyll anomalies estimated from satellite observations, Fig. 6) and the
subsequent respiration of organic material (Fiedler
et al., 2016), as well as the dynamically induced isolation of the eddies with
respect to lateral oxygen resupply (Fiedler et al., 2016; Karstensen et
al., 2015). In contrast to the transport of heat or salt with ocean eddies,
the oxygen anomaly intensified with the time the eddy existed (eddy age). The
oxygen-depleted eddy cores are associated with either CEs or ACMEs. In the
ETNA both eddy types have in common that in their center the ML
base rises towards shallow depth (50 to 100 m), which in turn favors biological
productivity in the euphotic zone (Falkowski et al., 1991; McGillicuddy
et al., 1998). In addition, an enhanced vertical flux of nutrients within or
at the periphery of the eddies due to submesoscale instabilities is expected
to occur (Brannigan et al., 2015; Karstensen et al., 2016; Lévy et
al., 2012; Martin and Richards, 2001; Omand et al., 2015).
As a consequence the eddies establish a specific ecosystem of high primary
production, particle load and degradation processes, and even unexpected
nitrogen loss processes (Löscher et al., 2015). The combination of high
productivity and low-oxygen supply resembles the process of “dead-zone”
formation, known from other aquatic systems. As for other aquatic systems,
specific threats to the ecosystem of the eddies are observed such as the
interruption of the diurnal migration of zooplankters
(Hauss et al., 2016).
We observed low-oxygen cores only in ACMEs (also known as submesoscale coherent vortices in D'Asaro, 1988, and McWilliams, 1985, or
intra-thermocline eddies in Kostianoy and Belkin,
1989) and CEs but not in normal anticyclonic rotating eddies. In fact the
ML base in normal anticyclonic eddies is deeper than the
surroundings, bending downward towards the eddy center as a consequence of
the anticyclonic rotation. Therefore the normal anticyclones create a
positive oxygen anomalies when using depth levels as a reference. However,
when using density surfaces as a reference, the anomalies disappear.
Moreover, normal anticyclonic eddies have been found to transport warm and
salty anomalies (Schütte et al., 2016a) along with the positive oxygen
anomaly, which is very different from the ACMEs (and CEs) with a low-oxygen
core.
(a) Depth profile of the apparent oxygen utilization rate (aOUR,
µmol kg-1 yr-1) for the Atlantic as published from
Karstensen et al. (2008; dashed black line). The oxygen
consumption profile due to low-oxygen eddies referenced for the SOMZ region
(solid black line) and the separation into CEs (blue) and ACMEs (green). The
solid black line in (b) represents the observed mean vertical oxygen profile
of all profiles within the SOMZ against depth, whereas the dashed black line
represents the theoretical vertical oxygen profile in the SOMZ without the
dispersion of low-oxygen eddies. Naturally due to the dispersion of negative
oxygen anomalies, the observed values (black line) are lower than the
theoretical oxygen concentrations in the SOMZ without eddies (dashed black
line). The impact of the dispersion of low-oxygen eddies on the oxygen
budget in the depth of the shallow oxygen minimum zone is also indicated by
the thick black arrows.
The ETNA is expected to have a rather low population of long-lived eddies
(Chaigneau et al., 2009; Chelton et al., 2011), we could identify 234 CEs
and 18 ACMEs per year in the ETNA with a radius > 45 km and a
tracking time of more than 3 weeks. For the eddy detection we used an
algorithm based on the combination of the Okubo–Weiß method and a
modified version of the geometric approach from Nencioli et al. (2010) with an adjusted tracking for the ETNA (for more information see
Schütte et al., 2016a). Schütte et al. (2016a) found an eddy-type-dependent connection between SLA and SST (and SSS) signatures for the ETNA
that allowed a detection (and subsequently closer examination) of ACMEs.
Because of weaker SLA signatures, the tracking of ACMEs is rather difficult
due to the small signal-to-noise ratio (not the case for the CEs) and
automatic tracking algorithms may fail in many cases. Note that all tracks of
ACMEs and CEs shown in Fig. 4 were visually verified. Similar to
Schütte et al. (2016a), we derived “dead-zone” eddies surface
composites for SST, SSS (not shown here) and Chl (Fig. 6). It revealed that
the existence of an ACMEs is very associated with low SST (and SSS) but also
with high Chl (see also single maps in Karstensen et al., 2015). Analyzing
jointly SLA, SST and Chl maps we found that ACMEs represent a non-negligible
part of the eddy field (32 % normal anticyclones, 52 % CEs, 9 %
ACMEs; Schütte et al., 2016a).
It has been shown (Fig. 4) that the low-oxygen eddies in the ETNA could be
separated into two different regimes: north and south of 12∘ N.
The eddies north of 12∘ N are generally generated along the coast
and in particular close to the headlands along the coast. Schütte et al. (2016a) suggested that CEs and normal anticyclones north of 12∘ N
are mainly generated from instabilities of the northward directed alongshore
Mauritania Current (MC), whereas the ACMEs are most likely generated by
instabilities the Poleward Undercurrent (PUC). However, the detailed
generation processes need to be further investigated. The low-oxygen eddies
south of 12∘ N do not originate from a coastal boundary upwelling
system. Following the trajectories it seems that the eddies are generated in
the open ocean between 5 and 7∘ N. In general, the
occurrence of oxygen-depleted eddies south of 12∘ N is rather
astonishing, as due to the smaller Coriolis parameter closer to the Equator
the southern eddies should be more short-lived and less isolated compared to
eddies further north. In addition, the generation mechanism of the southern
eddies is not obvious. The eddy generation could be related to the presence
of strong tropical instabilities in that region (Menkes et al., 2002; von
Schuckmann et al., 2008). However, in particular the generation of ACMEs is
complex and has been subject of scientific interest for several decades
already (D'Asaro, 1988; McWilliams, 1985). The low stratification of the
eddy core cannot be explained by pure adiabatic vortex stretching alone as
this mechanism will result in cyclonic vorticity, assuming that f dominates
the relative vorticity. Accordingly, the low stratification in the eddy core
must be the result of some kind of preconditioning induced by for example
upwelling, deep convection (Oliver et al., 2008) or
diapycnal mixing near the surface or close to boundaries (D'Asaro,
1988) before eddy generation takes place (McWilliams, 1985).
D'Asaro (1988), Molemaker et al. (2015) and Thomsen et al. (2015) highlight the importance of flow separation associated with headlands
and sharp topographical variations for the generation of ACMEs. This notion
is supported by the fact that low potential vorticity signals are usually
observed in the ACMEs (D'Asaro, 1988; McWilliams, 1985; Molemaker et al.,
2015; Thomas, 2008). The low potential vorticity values suggest that the
eddy has been generated near the coast as – at least in the tropical
latitudes – such low potential vorticity values are rarely observed in the
open ocean. These theories seem to be well suitable for the ACME generation
north of 12∘ N but do not entirely explain the occurrence of ACMEs
south of 12∘ N. However, more research on this topic is required.
Because we expect “northern” and “southern” eddies to have different
generation mechanisms and locations and because they have different
characteristics we discuss them separately. The core of the eddies generated
north of 12∘ N is characterized by less saline and cold SACW
(Schütte et al., 2016a) and thereby forms a strong hydrographic anomaly
against the background field. In contrast, the core of the eddies
generated south of 12∘ N does not show any significant
hydrographic anomalies. However, a low-oxygen core in eddies is observed in both regions, indicating that the
combination of the isolation of the eddy core and the high productivity in the eddy
surface waters also occurs in both regions.
The oxygen content decreases on average by about 0.19 ± 0.08 µmol kg-1 d-1 in an ACME and by about
0.10 ± 0.12 µmol kg-1 d-1 in an CE, based on 504 oxygen measurements in CEs and
ACMEs. Note that these apparent oxygen utilization rates (aOUR) are in the
range of recently published aOUR estimates for CEs
(Karstensen et al., 2015) and ACMEs
(Fiedler et al., 2016), which are based on single
measurements in “dead-zone” eddies. In particular for CEs we take that as
an indication that no significant trend in aOUR exists. An important point
regarding the method and the associated inaccuracies in deriving the aOURs
is the initial coastal oxygen concentration, which is highly variable in
coastal upwelling regions (Thomsen et al., 2015). In addition one should
mention that the relative magnitude of eddy-dependent vertical nutrient
flux, primary productivity and associated oxygen consumption or nitrogen
fixation/denitrification in the eddy cores strongly varies among different
eddies because of differences in the initial water mass in the eddies'
core, the eddies' age and isolation and the experienced external forcing (in
particular wind stress and dust/iron input).
However, the mean oxygen profiles from the eastern boundary and inside of
all CEs and ACMEs (Fig. 3b) indicate no pronounced oxygen difference beneath
250 m depth. The largest anomalies have been observed in the eddy cores at
around 100 m depth (Fig. 8). As a result of the dynamic structure, the core
water mass anomalies of the ACMEs are more pronounced than the one of the CE
(Karstensen et al., 2016) and consequently the oxygen anomalies are
stronger. This is supported by the differences in the oxygen anomaly based
on the measured plus reconstructed and the measured oxygen values. The
reconstruction of oxygen values assumes a complete isolation of the eddy
core. The left side of Fig. 8a, which includes the reconstructed oxygen
values, features a larger oxygen anomaly than the right side based on
measured oxygen values only. Consequently the CEs are probably not
completely isolated and the evolving oxygen anomaly is affected by some
lateral flux of oxygen. In contrast, the oxygen anomaly of ACMEs (Fig. 8b) is smaller for the reconstruction than for the measured oxygen values.
This suggests that the ACMEs are more effectively isolated, resulting in
enhanced apparent consumption in the ACME core. However, another source of
error in the reconstructed oxygen values is the assumption of a linear
decrease of oxygen with time. All observed CEs or ACMEs contain a negative
oxygen anomaly, partly because they transport water with initial low-oxygen
concentrations and additionally because the oxygen consumption in the eddies
is more intense then in the surrounding waters (Karstensen et al., 2015;
Fiedler et al., 2016). D'Asaro (1988), Molemaker et al. (2015) and Thomsen et al. (2015) argued that the core waters of ACMEs
generated near the coast originate to a large extent from the bottom
boundary layer at the continental slopes. At the shelf off northwestern
Africa,
low-oxygen concentrations (around 30 µmol kg-1) in
the depth range between 50 and 150 m could occasionally locally identified (M.
Dengler,
personal communication, 2016). Consequently it is certainly possible that the
eddies have initially low-oxygen concentrations in their cores. This is not
the case for the short-lived southern eddies, which seem to be generated in
the open ocean. It would suggest that, to achieve similarly strong negative
oxygen anomalies, the oxygen consumption in the eddies south of
12∘ N must be even stronger than in the ACMEs further north.
Pronounced productivity patterns in tropical instability waves and vortices
have been reported in the past (Menkes et al., 2002) but were not
connected to low-oxygen eddies before.
In the following, an estimate of the contribution of the negative oxygen
anomalies of low-oxygen eddies to the oxygen distribution of the SOMZ is
presented. The satellite-based eddy tracking reveals that on average each
year 14 (2) CEs (ACMEs) are propagating from the upwelling system near the
coast into the SOMZ and dissipate there. By deriving the oxygen anomaly on
density surfaces an oxygen loss profile due to low-oxygen eddies in the SOMZ
is derived (Fig. 9). Note that due to the lower oxygen values within the
eddies compared to the surrounding waters in the SOMZ, the release of
negative oxygen anomalies to the surrounding waters is equivalent to a local
(eddy volume) enhancement of the oxygen utilization by -7.4 (-2.4) µmol kg-1 yr-1 for CEs (ACMEs) for the depth range of the shallow
oxygen minimum in the SOMZ, i.e., 50 to 150 m depth. Instead of describing
the effect of the low-oxygen eddies on the oxygen consumption an equivalent
view is to consider a box model approach for the SOMZ. The basis of this box
model is the mixing of high-oxygen waters (the background conditions) with
low-oxygen waters (the low-oxygen eddies). The average oxygen concentrations
within the eddies in the considered depth range, i.e., 50 to 150 m, are
73 (66) µmol kg-1 for CEs (ACMEs). The average oxygen
concentration of the background field averaged over the same depth range
(between 50 and 150 m) derived from the MIMOC climatology
(Schmidtko et al., 2013) is 118 µmol kg-1. This climatological value includes the contribution of low-oxygen eddies.
If we now consider the respective oxygen concentrations and volumes of the
SOMZ and the eddies (multiplied by their frequency of occurrence per year),
we are able to calculate the theoretical background oxygen concentration for
the SOMZ without eddies to be 125 µmol kg-1. Naturally due to
the dispersion of negative oxygen anomalies, the oxygen concentrations in
the SOMZ without eddies must be higher than the observed climatological
values. Attributing the difference of these oxygen concentrations, on the one
hand in the SOMZ without eddies (125 µmol kg-1) and on the other
hand the observed climatological values in the SOMZ with eddies (118 µmol kg-1), solely to the decrease induced by the dispersion of eddies,
we find that an equivalent reduction of around 7 µmol kg-1 of
the observed climatological oxygen concentration in the SOMZ box. To
visualize that a depth profile of oxygen in the SOMZ without the dispersion
of low-oxygen eddies is equally derived and compared to the observed oxygen
profile in the SOMZ (Fig. 9b). Consequently, the oxygen consumption in this
region is a mixture of the large-scale metabolism in the open ocean
(Karstensen et al., 2008) and the enhanced metabolism in low-oxygen eddies
(Karstensen et al., 2016; Fiedler et al., 2016). Note that a small
compensating effect, for example due to diapycnal oxygen fluxes in normal
anticyclones, can probably be expected. However, our estimates should be
considered as a lower limit for the contribution of ACMES because of the
problem in detecting and tracking ACMEs (weak SLA anomaly) and because of
the assumption of zero lateral ventilation within the eddies. Moreover, we
identified a few occurrences of ACMEs based on shipboard ADCP as well as
hydrographic measurements (e.g., during the research cruises of Ron Brown
2009 and Meteor 119) that did not have a significant SLA signature. In
addition, only eddies are considered which could be followed with tracking
algorithms directly from the coast into the transition zone and have a
radius greater than 45 km and a lifetime of more than 21 days.
Although a reduction of 7 µmol kg-1 seems to be small, one
may note that the peak difference is a reduction of 16 µmol kg-1
at 100 m depth (Fig. 9) in the core depth of the shallow oxygen minimum
zone in the ETNA. The additional respiration due to the presence of
low-oxygen eddies can be important as well in numerical simulations, where
up to now only the large-scale consumption is taken into account. In turn it
is important to investigate the eddy occurrence and eddy cycling in
numerical simulation of the OMZ given they have a sufficient resolution.
Our results question the assumption that the oxygen consumption is
determined by the metabolism of the large-scale community alone. The
observations presented here suggest instead that also hot spots of locally
enhanced consumption may possibly need to be considered in the future.