Biogeosciences, 13, 4389–4410, 2016
www.biogeosciences.net/13/4389/2016/
doi:10.5194/bg-13-4389-2016
© Author(s) 2016. CC Attribution 3.0 License.
Seasonal variability of the oxygen minimum zone off Peru in a
high-resolution regional coupled model
Oscar Vergara1, Boris Dewitte1,3,4,5, Ivonne Montes2, Veronique Garçon1, Marcel Ramos3,4,5, Aurélien Paulmier1, and
Oscar Pizarro6,7
1Laboratoire d’Études en Géophysique et Océanographie Spatiales, CNRS/IRD/CNES/UPS, UMR5566, Toulouse, France
2Instituto Geofísico del Perú (IGP), Lima, Perú
3Departamento de Biología, Facultad de Ciencias del Mar, Universidad Católica del Norte, Coquimbo, Chile
4Millennium Nucleus for Ecology and Sustainable Management of Oceanic Islands (ESMOI), Coquimbo, Chile
5Centro de Estudios Avanzado en Zonas Áridas (CEAZA), Coquimbo, Chile
6Department of Geophysics, University of Concepción, Concepción, Chile
7Millennium Institute of Oceanography, University of Concepción, Concepción, Chile
Correspondence to: Oscar Vergara (oscar.vergara@legos.obs-mip.fr)
Received: 8 December 2015 – Published in Biogeosciences Discuss.: 18 January 2016
Revised: 16 June 2016 – Accepted: 17 June 2016 – Published: 8 August 2016
Abstract. In addition to being one of the most productive
upwelling systems, the oceanic region off Peru is embed-
ded in one of the most extensive oxygen minimum zones
(OMZs) of the world ocean. The dynamics of the OMZ off
Peru remain uncertain, partly due to the scarcity of data and
to the ubiquitous role of mesoscale activity on the circulation
and biogeochemistry. Here we use a high-resolution coupled
physical/biogeochemical model simulation to investigate the
seasonal variability of the OMZ off Peru. The focus is on
characterizing the seasonal cycle in dissolved O2 (DO) eddy
flux at the OMZ boundaries, including the coastal domain,
viewed here as the eastern boundary of the OMZ, consider-
ing that the mean DO eddy flux in these zones has a signif-
icant contribution to the total DO flux. The results indicate
that the seasonal variations of the OMZ can be interpreted
as resulting from the seasonal modulation of the mesoscale
activity. Along the coast, despite the increased seasonal low
DO water upwelling, the DO peaks homogeneously over the
water column and within the Peru Undercurrent (PUC) in
austral winter, which results from mixing associated with the
increase in both the intraseasonal wind variability and baro-
clinic instability of the PUC. The coastal ocean acts there-
fore as a source of DO in austral winter for the OMZ core,
through eddy-induced offshore transport that is also shown to
peak in austral winter. In the open ocean, the OMZ can be di-
vided vertically into two zones: an upper zone above 400 m,
where the mean DO eddy flux is larger on average than the
mean seasonal DO flux and varies seasonally, and a lower
part, where the mean seasonal DO flux exhibits vertical–
zonal propagating features that share similar characteristics
than those of the energy flux associated with the annual ex-
tratropical Rossby waves. At the OMZ meridional bound-
aries where the mean DO eddy flux is large, the DO eddy
flux has also a marked seasonal cycle that peaks in austral
winter (spring) at the northern (southern) boundary. In the
model, the amplitude of the seasonal cycle is 70 % larger at
the southern boundary than at the northern boundary. Our re-
sults suggest the existence of distinct seasonal regimes for
the ventilation of the OMZ by eddies at its boundaries. Im-
plications for understanding the OMZ variability at longer
timescales are discussed.
1 Introduction
In addition to hosting one of the most productive upwelling
systems, the South Eastern Pacific (SEP) is home to one of
the most extensive oxygen minimum zones (OMZs) of the
world ocean (Fuenzalida et al., 2009; Paulmier and Ruiz-
Pino, 2009). These oxygen-deficient regions are key to un-
derstanding the role of the ocean in the greenhouse gas bud-
get, in climate and in the presently unbalanced nitrogen cy-
Published by Copernicus Publications on behalf of the European Geosciences Union.
4390 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
cle (Gruber, 2008). The OMZs represent a net nitrogen loss
to the atmosphere in the form of N2O (particularly the SEP
OMZ; Farías et al., 2007; Arévalo-Martínez et al., 2015) in
addition with other toxic or climatically active gases, such
as H2S and CH4, respectively, in extremely low dissolved
oxygen (DO) concentrations (Libes, 1992; Law et al., 2013).
They might even limit the ocean carbon sequestration and
act as CO2 sources for the atmosphere (Paulmier et al., 2008,
2011). Furthermore, the OMZs contribute to the habitat com-
pression of marine organisms, in a zone that sustains 10 % of
the world fish catch (Prince and Goodyear, 2006; Chavez et
al., 2008). Therefore, understanding the dynamics behind the
OMZ becomes not just a matter of scientific interest but also
a major societal concern.
In general, these low-oxygen regions are considered to re-
sult from the interaction of biogeochemical and physical pro-
cesses (Karstensen et al., 2008). The SEP presents high bio-
logical productivity, inducing a significant DO consumption
mainly through the remineralization associated with a com-
plex nutrient cycle supported by the intense upwelling. In
addition, the SEP encompasses a so-called “shadow zone”, a
near stagnant/sluggish circulation region next to the eastern
basin boundary, not ventilated by the basin-scale wind-driven
circulation (Luyten et al., 1983). Assuming a steady state, lat-
eral oxygen fluxes from subtropical water masses and diapy-
cnal mixing are expected to balance the oxygen consump-
tion (Brandt et al., 2015). However, the diversity of environ-
mental forcings in the SEP and the variety of timescales at
which they operate (Pizarro et al., 2002; Dewitte et al., 2011,
2012) have eluded a proper understanding of the processes
controlling the OMZ structure and variability. On the one
hand, the scarcity of data and rare surveys have only per-
mitted the documentation of the DO temporal variability at a
few locations (e.g., Morales et al., 1999; Cornejo et al., 2006;
Gutiérrez et al., 2008; Llanillo et al., 2013). On the other
hand, the highly complex interaction between physical and
biogeochemical mechanisms makes modeling and prediction
of OMZ location, intensity and its temporal variability a chal-
lenging task (Karstensen et al., 2008; Cabré et al., 2015).
Low-resolution CMIP class coupled models still have severe
biases of physical and biogeochemical origins, particularly
in eastern boundary current systems (Richter, 2015), which
has eluded the interpretation of long-term trends in OMZ
(Stramma et al., 2008, 2012; Cabré et al., 2015). Regional
coupled biogeochemical modeling nonetheless has provided
a complementary approach to gain insight in the dynamics
of OMZ and its relationship with climate (Resplandy et al.,
2012; Gutknecht et al., 2013a). One recent modeling effort
to understand the dynamics behind the OMZ in the eastern
tropical Pacific comes from Montes et al. (2014). This study
provided a first regional simulation of the OMZ in the SEP
and summarized the elements involved in maintaining the
OMZ found off the coast of Peru as the result of a delicate
balance of (i) the equatorial current system dynamics – the
relatively oxygen-rich waters carried by the Equatorial Un-
dercurrent (EUC), the relatively oxygen-poor and nutrient-
rich waters carried by the primary and secondary Tsuchiya
Jets (primary and secondary southern subsurface countercur-
rents) – and (ii) the high surface productivity rates induced
by the coastal upwelling, which in turn triggers an intense
oxygen consumption in the subsurface. Their model experi-
ments also showed that different eddy kinetic energy (EKE)
levels, induced by different representations of the mean ver-
tical structure of the coastal current, may contribute to the
expansion or erosion of the upper boundary of the OMZ.
The study by Montes et al. (2014) established a bench-
mark in terms of numerical modeling of the OMZ in the SEP,
focusing on its permanent regime and connection with the
equatorial current dynamics. In the present study, we also
take advantage of the regional modeling approach in order
to investigate the mechanisms associated with the seasonal
cycle of DO within the OMZ. The motivation for focusing
on seasonal variability is threefold: (1) a better knowledge
of the processes acting on the OMZ at seasonal timescale is
viewed as a prerequisite for interpreting longer timescales of
variability (ENSO, decadal); (2) the scarcity of quality long-
term subsurface biogeochemical data in the SEP is a limita-
tion for tackling the investigation of OMZ variability at low
frequency; (3) to the authors’ knowledge, this issue has not
been addressed in the literature for the eastern tropical Pa-
cific, although it has been a concern for other tropical oceans
(Resplandy et al., 2012; Gutknecht et al., 2013a; Duteil et al.,
2014).
Here, besides investigating to what extent the seasonal
OMZ variability can relate to the variability of the environ-
mental forcing in the SEP (local wind, equatorial Kelvin and
extratropical Rossby waves, hereby referred to as ETRW),
our interest is on examining the DO budget (i.e., the bal-
ance between oxygen sources and sinks) and relating it to
the physical DO flux. In particular, since the Peruvian region
is the location of relatively intense eddy activity (Chaigneau
et al., 2009), the question of whether or not eddy activity is
involved in the seasonal variability of the OMZ arises and
calls for assessing its contribution to the DO flux. There is
growing evidence that the mesoscale activity plays a key
role in the biogeochemical cycles and the OMZ structure
in eastern boundary upwelling systems (Duteil and Oschlies,
2011; Nagai et al., 2015). Most studies addressing the role of
mesoscale processes in the OMZs have focused on the venti-
lation from the coastal domain, where the primary production
bloom provides nutrients and DO anomalies that are in turn
transported offshore (Stramma et al., 2013; Czeschel et al.,
2015; Thomsen et al., 2016). Gruber et al. (2011) showed that
mesoscale activity is prone to reducing the biological produc-
tion and offshore carbon export in upwelling systems by both
rectifying on the mean circulation (i.e., eddy-induced mix-
ing tends to flatten the isotherms nearshore and reduce the
upwelling) and changing its nutrient transport capacity. This
process has been to some extent supported by observations
in the Peruvian OMZ (Stramma et al., 2013). In this sense,
Biogeosciences, 13, 4389–4410, 2016 www.biogeosciences.net/13/4389/2016/
O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4391
the mesoscale activity represents a ventilation pathway for
the OMZ, through the offshore transport of oxygen-enriched
waters. The ventilation of the OMZ could also take place at
its meridional boundaries where strong mean DO gradients
are found along with eddy activity. Recently, Bettencourt et
al. (2015) proposed that mesoscale eddies shape the Peruvian
OMZ by controlling the diffusion of DO into the OMZ at the
meridional boundaries. Although it is likely that both pro-
cesses are important for understanding the OMZ structure, it
has not been clarified to which extent the variability of the
OMZ could be understood in terms of the changes in the DO
eddy flux into the OMZ through these different pathways.
The mesoscale activity also exhibits a significant meridional
variability off Peru (Chaigneau et al., 2009), which ques-
tions whether the offshore ventilation process can operate
effectively for modulating the whole OMZ. Another related
open question is at which timescales the ventilation process
through eddies-induced mixing can operate effectively. In
this paper we will tackle these issues from a regional model-
ing approach, focusing on the seasonal timescale.
The paper is organized as follows. After the introduction
(Sect. 1), we detail the observations and model configuration
used in the study, as well as the methodology employed in
the treatment of the information (Sect. 2). We also evaluate
the realism of the simulation against the available observa-
tions in reproducing the main characteristics of the OMZ.
The subsequent section (Sect. 3) characterizes the DO an-
nual cycle inside the OMZ. Section 4 opens with the anal-
ysis of the seasonal variability of the coastal OMZ and the
contribution of the DO budget terms associated with it. This
analysis is followed by the results of DO flux directed off-
shore and completed by the analysis of DO flux across the
OMZ meridional boundaries. Section 5 presents a discussion
of the main results and Sect. 6 presents a summary and the
concluding remarks.
2 Data description and methods
2.1 Data
2.1.1 Dissolved oxygen concentration from the CSIRO
Atlas of Regional Seas (CARS)
CARS is a climatological product derived from a quality-
controlled archive of historical subsurface ocean measure-
ments, most of which were collected during the past 50 years
(additional information might be found on the website
of the project: http://www.marine.csiro.au/~dunn/cars2009/).
For the present study, we use the CARS2009 version of the
CARS product (Ridgway et al., 2002), which has an hori-
zontal resolution of 0.5◦× 0.5◦ and 79 vertical levels, with
a 10 m resolution near the surface layer. We use CARS to
assess the model’s skills in simulating the OMZ mean state
and variability. One advantage of this product is its refined
interpolation treatment near steep topography in comparison
to other products such as the World Ocean Atlas (Dunn and
Ridgway, 2002). Also, it includes the annual and semiannual
oxygen cycles, although the semiannual cycle is available
only for the first 375 m over the region of interest due to the
scarcity of data.
2.1.2 Chlorophyll a concentration from SeaWiFS
Eight-day composites at 0.5◦× 0.5◦ resolution of the SeaW-
iFS chlorophyll product (version 4), between January 2000
and December 2008, are used to compute the surface chloro-
phyll seasonal cycle (McClain et al., 1998; O’Reilly et al.,
2000).
2.1.3 Sea surface temperature (SST)
The NOAA Optimum Interpolation SST (OISST V2) prod-
uct is contrasted against the simulation SST. This product is
an analysis constructed by combining observations from dif-
ferent platforms (satellites, ships, buoys) on a regular global
grid. More information about the methodology used to con-
struct this data set may be found in Reynolds et al. (2007) and
the product website (https://www.ncdc.noaa.gov/oisst). The
version used in this study corresponds to daily SST maps
with a spatial resolution of 0.25◦× 0.25◦, spanning the pe-
riod 2000–2008.
2.1.4 Sea level height (SLH)
The TOPEX/JASON1–2 merged SLH data set, distributed by
the Sea Level Research Group, University of Colorado (http:
//sealevel.colorado.edu/), is used to derive the geostrophic
velocity field and the mean EKE field. This data set corre-
sponds to a globally gridded 0.25◦× 0.25◦ weekly product.
The information used corresponds to the period 1993–2008.
Further details on this product may be found in Nerem et
al. (2010).
2.2 Model simulation
We use a high-resolution simulation of the southeastern Pa-
cific, based on the hydrodynamic Regional Ocean Mod-
eling System (ROMS) circulation model (see Shchepetkin
and McWilliams, 2005, 2009, for a complete description of
the model) coupled with a nitrogen-based biogeochemical
model developed for the eastern boundary upwelling systems
(BioEBUS; Gutknecht et al., 2013a, b), hereby referred as
CR BIO.
The model is used at an eddy-resolving resolution (1/12◦
at the Equator) for a region extending from 12◦ N to 40◦ S
and from the coast to 95◦W – although this study only
focuses on the domain spanning the latitudes of Peru and
Ecuador (Fig. 1) – with lateral open boundaries at its north-
ern, southern and western frontiers. The physical model re-
solves the hydrostatic primitive equations with a free-surface
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4392 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 1. Mean sea surface temperature (SST) between 2000 and 2008 for (a) OISST product (0.25◦× 0.25◦), (b) the simulation (1/12◦) and
(c) CARS data set (0.5◦× 0.5◦). (d) Difference between the OISST product and the simulation. Mean eddy kinetic energy (EKE) between
1993 and 2008, for (e) TOPEX/Poseidon Jason 1–2 merged product (0.25◦× 0.25◦), and (f) simulation (1/12◦). EKE was derived from the
interannual anomalies of the geostrophic velocity field.
explicit scheme and a stretched terrain-following sigma co-
ordinates on 37 vertical levels. The configuration is simi-
lar to Dewitte et al. (2012), that is the open boundary con-
ditions are provided by 5-day mean oceanic outputs from
SODA (Version 2.1.6) for temperature, salinity, horizontal
velocity and sea level for the period 1958–2008, while wind
stress and speed forcing at the air/sea interface come from the
NCEP/NCAR reanalysis. The atmospheric fields have been
statistically downscaled following the method by Goubanova
et al. (2011) in order to correct for the unrealistic wind stress
curl near the coast of the NCEP Reanalysis (see Cambon et
al., 2013, for a validation of the method for oceanic appli-
cations). Atmospheric fluxes were derived from the bulk for-
mula using the temperature from COADS 1◦× 1◦ monthly
climatology (daSilva et al., 1994). Relative humidity and
shortwave and long-wave radiations are also from COADS.
Bottom topography is from the GEBCO 30 arcsec grid data
set, interpolated to the model grid and smoothed as in Pen-
ven et al. (2005) in order to minimize the pressure gradient
errors and modified at the boundaries to match the SODA
bottom topography. This model configuration has been val-
idated from satellite and in situ observations in Dewitte et
al. (2012) with a focus on mean state interannual variabil-
ity. In general the model is skillful in simulating the mean
SST field (Fig. 1a, c) as well as other main aspects of the
mean circulation (e.g., Peru/Chile Undercurrent, EKE; see
Fig. 3 in Dewitte et al., 2012), although with a slight cold
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4393
Figure 2. Mean oxygen minimum zone core thickness (color scale
in meters) for (a) the simulation and (b) CARS. Depths of the lower
(white) and upper (black) limits of the OMZ core are also depicted.
The OMZ core is defined as [DO] < 20 µM. The red dots denote the
horizontal resolution of the DO field.
bias (∼ 1 ◦C) that could be partly attributed to the use of cli-
matological heat flux forcing (Fig. 1d).
The mesoscale activity diagnosed from the mean EKE, has
a comparable pattern than altimetry, although with a larger
amplitude (Fig. 1e, f). Similar levels of mesoscale activity
have been obtained by previous modeling studies in the Pe-
ruvian region (e.g., Echevin et al., 2011; Colas et al., 2012).
The ocean model within this configuration is coupled to
the BioEBUS model following similar methodology than
Montes et al. (2014). BioEBUS uses two compartments of
phytoplankton and zooplankton, small (flagellates and cili-
ates, respectively) and large (diatoms and copepods, respec-
tively), detritus, dissolved organic nitrogen and the inorganic
nitrogen forms nitrate, nitrite and ammonium, as well as ni-
trous oxide (see Gutknecht et al., 2013a, b, for a descrip-
tion of the model). The open-boundary conditions for the
biogeochemical model are provided by the climatological
CARS data set (nitrate and oxygen concentrations) and by
SeaWiFS archive (chlorophyll a concentration). Additional
biogeochemical tracers are computed following Gutknecht
et al. (2013a, b). Initial phytoplankton concentration is de-
fined as a function of vertically extrapolated satellite Chl a
following Morel and Berthon (1989). An offshore decreas-
ing cross-shore profile, following in situ observations, is ap-
plied for zooplankton, and a vertical constant (exponential)
profile is used for detritus (nitrite, ammonium and dissolved
organic nitrogen). In order to get a realistic solution for the
region, the model parameters were tuned to simultaneously
fit modeled oxygen and nitrate fields to observations (see Ta-
ble A1 of Montes et al. (2014) for parameter values). These
changes were motivated by the need to adjust the microbio-
logical rates to values observed in the SEP. Within this pa-
rameter configuration, BioEBUS has been shown to be skill-
ful for simulating the OMZ off Peru (Montes et al., 2014).
In particular the pattern correlations between the model and
observations for both the annual mean and the seasonal cy-
cle inside the OMZ present comparable scores (> 0.85, cf.
Montes et al., 2014) as well as low standard deviations (i.e.,
in the order of the observed values). The model was run over
the period 1958–2008 with a 10-year spin-up obtained by re-
peating the year 1958. Although, after the spin-up, the sim-
ulation has reached stable conditions and the OMZ volume
does not drift, we focus in the present study only on the pe-
riod 2000–2008.
The reason for focusing on the last 10 years of our simu-
lation is also motivated by the fact that the atmospheric mo-
mentum forcing is close to the satellite QuickSCAT winds
by construction (see Goubanova et al., 2011, for details) so
that this period of the simulation is the one when the model
is the most constrained by observations. Most previous mod-
eling studies for this region (Penven, et al. 2005; Montes et
al., 2010, 2014; Echevin et al., 2011; Colas et al., 2012) have
also used a wind forcing from the QuickSCAT scatterometer,
which provides a benchmark for assessing our simulation.
A monthly-mean climatology is calculated for all variables
over this period from the 3-day mean outputs of the model,
which can be compared to the CARS data.
Consistently with Montes et al. (2014), the coupled simu-
lation is skillful in simulating the mean characteristics of the
OMZ off the Peruvian coast (Figs. 2 and 3). In particular the
thickness and location of the model OMZ core limits are re-
alistic, and in good agreement with previous studies (Fig. 2;
e.g., Paulmier et al., 2006; Cornejo and Farías, 2012; Montes
et al., 2014). Note that the simulation reproduces a thinner
OMZ around 10◦ S in comparison to CARS, which agrees
with the results obtained by Montes et al. (2014) (see Fig. 2
in that study). Close to the western boundary of our model
domain, the simulated OMZ also exhibits a realistic verti-
cal structure (Fig. 3) with comparable concentration in DO
than observations in the vicinity of the Equatorial Undercur-
rent (∼ 100 m; Equator). Furthermore, the simulation is con-
sistent in reproducing the oxygen-consuming processes, as
supported by the apparent oxygen utilization (AOU; Fig. 4),
also in good agreement with previous studies (cf. Fig. 8 in
Cabré et al., 2015). AOU was computed as the difference be-
tween the DO concentration and the saturated oxygen (O2sat)
concentration (AOU=O2sat–O2) with O2sat computed fol-
lowing the methodology of García and Gordon (1992). The
realistic representation of the oxygen-consuming processes
is reflected by the particulate organic carbon flux as well
(Fig. 5a), whose values at 100 m fall within the observed
range for the region (30–60 gC m−2 yr−1 in the shelf area;
Dunne et al., 2005; Henson et al., 2012). In addition, the low
transfer efficiency of carbon (10–15 % or lower over and next
to the shelf; Henson et al., 2012), from the euphotic zone
to greater depths (Fig. 5b), implies that the remineralization
processes take place at realistic depths and therefore allow
for a correct vertical representation of the OMZ (cf. Fig. S2
in Cabré et al., 2015, for comparison).
The core of the OMZ, defined with a suboxic concentra-
tion ([DO] < 20 µM; µM will be used to refer to µmol L−1
in all the text and figures), occupies nearly 23 % of the
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4394 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 3. Mean oxygen concentration for a meridional section at 85◦W (a, b) and a cross-shore section at 12◦ S (c, d), for both the simulation
and CARS. Gray contours in (a) show mean zonal speed of 5, 10 and 15 cm s−1, respectively. The black dots denote the horizontal and vertical
resolution of the DO field.
domain volume (Fig. 6a), with the less oxygenated layers
comprised between 5 and 15◦ S, and 100 and 600 m depth
(Fig. 3). As expected, the simulation presents more details
than the climatological product (Fig. 3). Moreover, we com-
puted a geographical OMZ overlapping metric following
Cabré et al. (2015), which quantifies the spatial agreement
of the OMZ volume distribution between the simulation and
CARS, varying between 0 (no agreement) and 1 (perfect
collocation). We obtained a value of 0.79, which is ∼ 58 %
above the best CMIP5 models used in Cabré et al. (2015).
Despite the overall good agreement between the model
and observations, the simulation overestimates the oxygen
content in certain regions of the domain as compared to
CARS, particularly southwards of 20◦ S (Fig. 3a) and close
to the coast (Fig. 3d). The simulation also underestimates by
6 % the volume of suboxic water (Fig. 6a), which is com-
parable to the differences obtained by Montes et al. (2014)
using the same model within a different configuration and
boundary forcing.
The modeled DO distribution is also characterized by finer
spatial scales of variability inside the OMZ compared to ob-
servations (Fig. 3c and d). In particular, the model oxycline is
shallower and with a more intense DO gradient than the ob-
servations, which has been also observed in a regional sim-
ulation of the Arabian Sea OMZ (Resplandy et al., 2012).
While this could be partly due to CARS underestimating the
DO gradient, as a result of its relatively low vertical resolu-
tion, it could also be that the model underestimates the verti-
cal diffusivity in the vicinity of the oxycline. Also, it must be
kept in mind that CARS is built using all the available data
from the second half of the twentieth century (1940–2009),
whereas we focus on the period 2000–2008 for the simula-
tion, which is known to be a colder period than the previous
decades in the eastern tropical Pacific (Henley et al., 2015).
Other limitations for the comparison between model and
data include the errors associated with the scarcity of data
in some regions (Bianchi et al., 2012) and biases in model
parametrizations. Nonetheless, the simulation is in overall
good agreement with CARS in terms of mean characteristics
of the OMZ (Figs. 4, 6a).
In order to evaluate the realism of the seasonal cycle,
we estimate the seasonal variability of the volume of water
within the suboxic DO concentration range 0–20 µM in both
the model and data (Fig. 6b). The results indicate that, de-
spite a weaker amplitude (by 15 % on average), the seasonal
cycle of the OMZ core is relatively well simulated by the
model. For hypoxic DO volume in the range 40–50 µM, the
agreement is as good as inside the OMZ core, with a Pear-
son correlation value of 0.9 and a volume root mean square
(RMS) difference of 16 %, between the simulation and the
observations.
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4395
Figure 4. Mean apparent oxygen utilization (AOU) at 85◦W for both CR BIO and CARS. White contours denote the mean oxygen concen-
tration isopleths (in µM). The black dots denote the horizontal and vertical resolution of the DO field.
In order to summarize the model validation, we present a
Taylor diagram showing the statistics of the comparison be-
tween the model and observations for a depth range encom-
passing the OMZ (Fig. 7). This analysis indicates that within
the present model configuration, we reach a skill compara-
ble to the model configuration of Montes et al. (2014) (their
Fig. 1). The good agreement of the seasonal cycle between
CARS and the simulation, in addition to the consistency of
our results with those of Montes et al. (2014), provides con-
fidence in using the model outputs for investigating the pro-
cesses associated with the seasonal variability of the OMZ.
2.3 Methods
In this work, our approach is twofold: First, the biogeochem-
ical processes for DO are investigated explicitly through the
online oxygen budget. Although this methodology can pro-
vide a direct estimate of the seasonal variability in advec-
tion and mixing, it does not allow for a direct estimate of
the eddy contribution to DO change that can also vary sea-
sonally. The DO flux associated with different timescales of
variability is therefore estimated. This consists in computing
the temporal average of the cross-products between DO and
velocity anomalies. Anomalies can refer either to seasonal
anomalies (in that case, this provides the mean seasonal DO
flux: 〈˜u×O˜2〉, where∼ refers to the seasonal anomalies) or to
the intraseasonal anomalies, calculated here as the departure
from the monthly mean (in that case, this provides an esti-
mate of the mean DO eddy flux: 〈u′×O′2〉, where the apos-
trophe refers to the intraseasonal anomalies). In this paper
we are also interested in the seasonality of the DO eddy flux.
This is estimated from the monthly-mean seasonal cycle of
the mean DO eddy flux calculated over a 3-month running
window and is now referred to as 〈u′×O′2〉. The climatolog-
ical EKE activity is estimated similarly.
Figure 5. (a) Particulate organic carbon (POC) flux at 100 m and
(b) POC transfer efficiency between 100 and 2000 m (POC flux at
2000 m divided by POC flux at 100 m), computed from the simula-
tion. Integrated carbon flux at the depth of 100 m is 0.8 Pg C yr−1.
Black contours correspond to the 200, 1000 and 5000 m isobaths.
The DO budget consists in the following equation:
∂O2
∂t
=−u · (∇O2)+Kh · ∇2O2+ ∂
∂z
(
KZ
∂O2
∂z
)
+SMS(O2). (1)
The first three terms on the right-hand side represent the
physical processes involved in the changes in oxygen con-
centration. The first term stands for the advection of oxygen,
where u is the velocity vector (note that the model determines
the vertical velocity component from the continuity equa-
tion). The second term corresponds to the horizontal subgrid-
scale diffusivity (withKh the eddy diffusion coefficient equal
to 100 m2 s−1 in this version of the model), and the third term
corresponds to the vertical mixing (with turbulent diffusion
coefficient Kz calculated based on the K-profile parameter-
ization mixing scheme; Large et al., 1994). Note that the
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4396 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 6. (a) Domain volume distribution (25◦ S–5◦ N, 88–70◦W)
as a function of the oxygen concentration and (b) annual cycle, rel-
ative to the mean, of the volume distribution inside the OMZ core
(DO value range corresponding to 0–20 µmol L−1), for both CARS
and the simulation.
model also has numerical diffusion associated with inherent
spurious diapycnal mixing of the numerical scheme, so that
Kh is empirically adjusted.
The fourth term represents the “source-minus-sink” con-
tribution to the oxygen changes, directly due to biogeochemi-
cal activity. Biogeochemical processes correspond to the sum
of oxygen sources and sinks, namely the photosynthetic pro-
duction, and the aerobic processes (oxic decomposition, ex-
cretion and nitrification). In this study, for simplicity, those
will be considered as a summed-up contribution to the DO
rate of change, whereas physical processes will be divided
into advection and mixing terms. Each term of this oxy-
gen budget is determined online at each time integration.
While horizontal diffusion and vertical diffusivity are explicit
sources of mixing, they are not the only terms contributing
to mixing. Later on in the paper, unless stated otherwise,
the term mixing will refer to the integrated effect of all pro-
cesses contributing to mixing directly or indirectly. Besides
the horizontal diffusion (Kh×∇2O2) and vertical mixing(
∂
∂z
(
KZ
∂O2
∂z
))
, mixing can be also induced by nonlinear ad-
vection. The latter corresponds to
(
u′∂u′/∂x
)+(v′∂v′/∂y)+(
w′∂w′/∂z
)
, assuming the Reynolds decomposition for the
velocity field, i.e., u¯+u′, where u′ accounts for the intrasea-
sonal variability (periods shorter than ∼ 3 months).
In the SEP, the subthermocline seasonal variability can
be interpreted as resulting from the propagation of ETRW.
ETRW radiate from the coast and propagate vertically, induc-
ing a vertical energy flux whose trajectory follows the the-
oretical Wentzel–Kramers–Brillouin (WKB) ray paths (De-
witte et al., 2008; Ramos et al., 2008). The energy flux re-
sults from the phase relationship between vertical velocity
associated with the vertical displacement of the isotherms,
Figure 7. Taylor diagram of the seasonal mean (hourglass, dia-
mond, square and cross) and annual mean (circle) pattern of DO and
surface chlorophyll (25◦ S–5◦ N, 88–70◦W). Only annual mean
pattern comparisons are shown for temperature and salinity (same
spatial domain). DO, temperature and salinity were vertically aver-
aged between 100 and 600 m depth (focus on the OMZ core). Only
the surface chlorophyll values within 250 km next to the coast were
considered. The comparisons are made between the simulation and
CARS (for DO, temperature and salinity) and SeaWiFS (for sur-
face chlorophyll). Ordinate and abscissa axes represent the standard
deviation normalized by the observations standard deviation. Blue
dotted radial lines indicate the RMS difference between the obser-
vations and the simulation.
and the pressure fluctuations associated with them. In the re-
gions sufficiently below the thermocline for DO consump-
tion to become weak (that is DO can be considered a pas-
sive tracer), it is expected that changes in DO relate to the
anomalous velocity field and that the DO flux shares com-
parable characteristics than the Eliassen–Palm flux (Eliassen
and Palm, 1960). The trajectories of the WKB ray paths are
a function of latitude, local stratification and the phase speed
of the Rossby wave (see Ramos et al., 2008). The latter con-
sists in the superposition of a certain number of baroclinic
modes, in order to propagate vertically, so the phase speed
can range from c1 to cn, where cn is the theoretical phase
speed of a nth baroclinic mode, obtained from the vertical
mode decomposition of the local density profile.
3 Characteristics of the DO annual cycle
While the annual signal is a conspicuous feature inside the re-
gion (Fig. 6b), it could manifest differently across the OMZ.
As a first step towards investigating processes driving the rate
of DO change, it appears important to document the vertical
structure variability of the DO annual cycle within the OMZ.
The amplitude and phase of the annual harmonic of the
model DO climatology are presented along a zonal section
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4397
Figure 8. (a) Amplitude and (b) phase of the annual maximum (in
months) of the annual harmonic of the normalized DO concentra-
tion at 12◦ S. The slanted vertical lines indicate the theoretical WKB
ray paths at a frequency of ω = 2pi × 1 yr−1, for different values of
phase speed. The theoretical trajectories were computed using the
phase speed of the first (full), second (dashed) and third (dotted)
baroclinic modes of a long Rossby wave. Dashed contours in (a)
and (b) depict the 45 and 20 µM mean DO values. Land and the
region outside the 45 µM mean DO isopleth are masked in white.
(c) Annual harmonic of the DO concentration at 12◦ S, at 150 m
and (d) 700 m depth. Small color scale corresponds to 700 m and
the large color scale denotes the levels used in (c).
off central Peru (12◦ S, Fig. 8a, b), where the OMZ core is ex-
tensive (Fig. 2). The DO climatology has been normalized by
its RMS in order to emphasize the regions where the ampli-
tude in DO changes (and mean DO) is weak. The amplitude
reveals a complex pattern with three regions of large rela-
tive variability: (1) near the coast (i.e., fringe of ∼ 150 km)
between the oxycline and 400 m, (2) offshore between 82
and 84◦W in the upper 400 m and (3) below 500 m. The
phase lines over these three regions suggest distinct propa-
gating characteristics: whereas in the coastal region there is
no propagation, in the offshore and deep region there is indi-
cation of a westward propagation. In the region below 500 m,
the phase lines tend also to be parallel and slope downward,
suggestive of westward–downward propagation (estimated
phase speed of ∼ 2.5 cm s−1). These propagating character-
istics can be evidenced in the Hovmöller diagrams of the re-
composed annual cycle at the depth of 150 m (Fig. 8c) and
700 m (Fig. 8d). While at 150 m the annual signal does not
clearly propagate and only shows two domains of high am-
plitude, separated by low amplitude values (Fig. 8c) there is
a clear westward propagation of the DO anomalies at 700 m,
with the phase speed increasing westward. At 400 m, the
propagation is only observed west of 81◦W (Fig. 8b). In ad-
dition to the large vertical structure variability of the annual
cycle, the OMZ annual cycle is also characterized by a large
horizontal variability in particular at its northern and south-
ern boundaries. This is illustrated in Fig. 9, which displays
the amplitude of the annual cycle of the DO climatology at
400 m and evidences amplitude peaks at the OMZ meridional
boundaries (between the 20 and 45 µM isopleths).
The annual variability pattern evidenced above results
from a delicate balance between the physical processes
(namely advection and mixing, cf. Eq. 1) and the biogeo-
chemical processes (consumption versus production). As a
first step towards investigating each term of the DO budget,
it is interesting to evaluate the relative contribution of the
physical and biogeochemical fluxes to the DO variability at
seasonal scale. The RMS of the climatological fluxes along
a section at 12◦ S indicates that the maximum amplitude of
the seasonal fluxes takes place near the oxycline and along
the coast over the whole water column (Fig. 10). The rela-
tive importance of the physical processes against the biogeo-
chemical processes varies across the OMZ. At the coast and
near the oxycline, the annual variability of the biogeochemi-
cal processes reaches values almost half those of the variabil-
ity in physical processes (Fig. 10c), as a consequence of the
proximity to both the well-lit and highly productive part of
the water column, and the high remineralization activity that
occurs near the oxycline. Towards offshore and at depth, the
relative importance of the variability of the biogeochemical
processes reduces gradually. Near ∼ 300 m the variability of
the biogeochemical processes is nearly one-fifth of the phys-
ical processes variability. Below ∼ 300 m, and towards the
lower part of the OMZ core and below, the physical processes
variability is 1 order of magnitude larger. Consequently, the
distribution of DO in the lower part of the OMZ is rather
a function of advection/diffusion than a consequence of the
biogeochemical processes, although DO consumption even
at very low levels has the potential to generate local gradi-
ents and therefore induce advection. The spatial heterogene-
ity in the seasonal DO changes induced by the biogeochem-
istry and dynamics, as described above, appears as an ubiq-
uitous feature in the OMZ. To illustrate this, we estimate the
proportion of explained variance of the seasonal DO rate of
change by the physical fluxes as
R2Phys. = (1−RMS(biogeochemical fluxes)/
RMS(total fluxes)) · 100. (2)
Figure 11a and b present the results of R2Phys. at 100 and
450 m depth, which evidences that the relative importance
of the physical fluxes versus the biogeochemical fluxes in
the seasonal DO variability increases with depth and is en-
hanced at the OMZ boundaries. However, the biogeochemi-
cal fluxes explain more than 50 % of the variance in seasonal
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4398 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 9. Annual DO harmonic amplitude at 400 m depth. White
contours denote the 10, 20 and 45 µM mean oxygen isolines. Black
contours denote the 1, 2, 4, 6 and 8 µM levels.
DO change rate in a narrow (∼ 200 km width) coastal fringe
that extends more offshore to the north of the domain (around
8◦ S; Fig. 11a) and vertically down to 300 m (Fig. 11c).
Based on the above analysis, it is clear that the coastal re-
gion (first 200–300 km from the coast) below the oxycline
corresponds to a territory where the seasonal variability of
biogeochemical and physical fluxes have a comparable mag-
nitude, whereas outside this region, notably in the lower
part of the OMZ core, the physical fluxes variability domi-
nates over the biogeochemical fluxes variability at seasonal
timescale. Hereafter we examine the possibility of two dis-
tinct regimes of OMZ dynamics at seasonal timescale: one
associated with the upper OMZ (including coastal domain
and meridional boundaries), and one associated with the deep
OMZ. In the following we investigate the processes respon-
sible for the DO flux.
4 Seasonality of the OMZ ventilation
It has been shown for the SEP that the DO content near the
coast is set to a large extent from the transport of oxygen-
deficient waters from the equatorial current system, partic-
ularly the oxygen-depleted secondary southern subsurface
countercurrent (Montes et al., 2014). Therefore, the seasonal
variability of DO is likely to result in part from the seasonal
variability of the different branches of the EUC in the far
eastern Pacific. Local wind stress forcing (and its intrasea-
sonal activity) has also a marked seasonal cycle off Peru (De-
witte et al., 2011) which may impact both the upwelling dy-
namics – through Ekman pumping/transport – and mixing.
Some studies also argue that the DO exchange between the
coastal domain and the OMZ takes place through the off-
shore transport of DO-poor waters by eddies (Czeschel et al.,
2011), implying that the variability of such processes is set
up by coastal processes that determine the nature of the DO
source. As a first step, we investigate the mechanisms respon-
sible for the seasonal variability in DO along the coast, which
can be considered as the eastern boundary of the OMZ. This
is aimed at providing material for the interpretation of the
offshore DO flux variability.
4.1 The coastal domain as the eastern boundary of the
OMZ: variability and mechanisms
We analyze the seasonal variability along the coast, at a sec-
tion at 12◦ S. Similar results are obtained for latitudes be-
tween 7 and 14◦ S (not shown), which corresponds to the lat-
itude range where the Peru Undercurrent (PUC) is well de-
fined. The results are also presented in terms of the first em-
pirical orthogonal function (EOF) mode, in order to ease the
interpretation of the variability, reduced as a spatial pattern
modulated by a seasonal time series. It was verified in par-
ticular that the consideration of the first EOF mode of each
term leads to an almost perfect closure of the DO budget (see
below, Table 1). Figure 12 displays the first EOF mode of var-
ious climatological fields in a section at 12◦ S near the coast
and from the oxycline (45 µM isoline) to the depth of 300 m.
Figure 13 shows the principal components associated with
the first EOF-mode patterns. The seasonal DO cycle is dom-
inated by an annual component, with a peak centered in Au-
gust (Fig. 13a), and the largest variability at the coast below
the oxycline that extends offshore and downward, resulting
in an elongated tongue below 100 m near∼ 78◦W (Fig. 12a).
During the first quarter of the year, oxygen anomalies remain
relatively stable (oxygen rate nearly zero, Fig. 13b) and neg-
ative due to a high production of organic matter in austral
summer (cf. Fig. 1c of Gutiérrez et al., 2011) that stimulates
a subsurface oxygen consumption associated with the degra-
dation of this organic matter. DO anomalies start to increase
during the second quarter, become positive in June and reach
their maximum in August (Fig. 13a). The peak anomaly in
austral winter could be understood in terms of the increased
mixing (see Fig. 13a showing EKE peaking in July) asso-
ciated with the increase in baroclinic instability due to the
seasonal intensification of the PUC from June. Note that the
pattern of the first EOF mode of the alongshore current co-
incides with the mean position of the PUC (see Fig. 12b),
so that seasonal variations of the PUC can be interpreted in
terms of the variations in the vertical shear of the coastal cur-
rent system. Other processes that may explain the peak DO
anomaly in austral winter include the reduced productivity
and downwelling that peaks in June (Fig. 13c), associated
with seasonal equatorial downwelling Kelvin wave.
The following investigates the tendency terms of the DO
budget in order to quantitatively interpret the DO seasonal
cycle near the coast. Given that the analysis is performed
inside the 45 µM isopleth, the biogeochemical flux term is
largely dominated by the “sinks” terms (aerobic processes;
1 order of magnitude larger than “sources”), driven by or-
ganic matter remineralization and zooplankton respiratory
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4399
Figure 10. Root mean square of the seasonal cycle of (a) physical and (b) biogeochemical oxygen fluxes (in 10−5 and 10−6 µM s−1,
respectively) for CR BIO at 12◦ S. (c) Ratio between the RMS of the biogeochemical fluxes and the physical fluxes, expressed as percentage.
Dashed contours depict the 45 and 20 µM mean oxygen values. Note the vertical scale change at 300 m depth. Land and the region outside
the 45 µM mean DO isopleth are masked in white.
Figure 11. Percentage of the seasonal DO rate variance explained by the physical fluxes, at (a) 100 and (b) 450 m depth, and along a cross-
shore section at 12◦ S. Solid white lines (a, b) and dashed gray lines (c) denote the 10, 20 and 45 µM mean DO isopleths. Land and the region
outside the 45 µM mean DO isopleth are masked in white in (c).
metabolic terms (not shown). For clarity, the seasonal DO
budget is presented synthetically, from the first EOF mode
of the climatological advection, mixing (horizontal and ver-
tical diffusion) and biogeochemical fluxes terms. Although
this does not warranty a perfect closure, it eases the interpre-
tation. Note that the residual resulting from the difference be-
tween the first EOF mode of the rate of DO changes and the
summed-up contribution of all the other terms in Fig. 13b is
rather weak, validating to some extent our approach (see also
Table 1). First of all, we find that the largest amplitude of the
mode patterns is found near the coast and inside the mean
PUC core (Fig. 12d to g). During the first part of the year
(January to May), positive advection anomalies are compen-
sated by mixing (horizontal and vertical diffusion), and they
maintain the rate of DO change relatively low (Fig. 13b; Ta-
ble 1). Biogeochemical fluxes anomalies are positive during
that period, associated with a positive anomaly of primary
production in the well lit surface layers, implied by the high
chlorophyll a values (Fig. 13c). A positive oxygen anomaly
is sustained by the advection terms and the biogeochemical
terms and is balanced out by the mean advection of low DO
waters carried by the PUC (Montes et al. 2010, 2014), gener-
ating the relatively stable oxygen values (oxygen rate nearly
0).
From May, the rate of DO changes increases concomi-
tantly with EKE (Fig. 13a, b), followed 1 month later by
mixing (horizontal and vertical diffusion), whereas advection
and biogeochemical fluxes decrease. By June–July, the inten-
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4400 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Table 1. Austral summer (DJF mean) and winter (JJA mean) seasonal anomalies of the DO budget, averaged over the core of the Peru
Undercurrent at 12◦ S (as depicted by the red contour in Fig. 12). The values for the seasonal cycle and the reconstructed first EOF mode
(Figs. 12 and 13) are presented along with the difference between climatology and EOF. All values are in 10−6 µM s−1. Mixing here consists
in the summed-up contribution of horizontal diffusion (Kh×∇2O2) and vertical diffusivity
(
∂
∂z
(
KZ
∂O2
∂z
))
.
Climatology EOF Difference
Summer Winter Summer Winter Summer Winter
∂O2/∂t 1.10 −2.74 1.30 −2.67 −0.20 −0.07
Adv 0.61 −9.38 0.85 −9.30 −0.24 −0.08
Mixing −0.42 7.99 −0.35 7.99 −0.07 0.00
Biogeochemical flux 0.91 −1.35 1.00 −1.35 −0.09 0.00
Figure 12. First EOF-mode pattern of (a) DO, (b) alongshore currents component, (c) eddy kinetic energy, (d) oxygen rate, (e) biogeochem-
ical flux, (f) advective terms (sum of horizontal and vertical components) and (g) mixing terms (sum of horizontal and vertical components).
Percentage of explained variance by each EOF-mode pattern is indicated in parentheses on top of each panel. The red contour denotes the
mean position of the Peru Undercurrent core, defined here as alongshore southward current exceeding 4 cm s−1. The black dashed contour
denotes the mean DO 45 µM isopleth. Land and the region outside the 45 µM mean DO isopleth are masked in white. The EOF-mode patterns
were multiplied by the RMS of the principal component (PC) time series. Multiplying the EOF pattern by the PC time series plotted in Fig. 13
yields the contribution of the first EOF mode to the original field, in dimensionalized units (i.e., µM s−1 for the tendency terms).
sification in alongshore winds (Fig. 13c) starts to propel the
coastal upwelling, which has two compensating effects: on
one hand, it triggers photosynthesis in the lit surface layers
(DO rate turns to positive values); on the other hand, it uplifts
low-oxygen waters from the OMZ. The intraseasonal wind
activity also starts to increase at that time (cf. Fig. 13c; see
also Dewitte et al., 2011), which favors mixing and the down-
ward intrusion of positive DO anomalies (note the deepening
of the mixed layer in Fig. 13c). The overall effect is an in-
crease in DO, which leads to a peak anomaly in August. At
that time, the DO rate drops sharply due to the strong subsur-
face DO consumption (Table 1) associated with aerobic rem-
ineralization of organic matter produced earlier in the season
(DO rate moves sharply to negative values) and the high mix-
ing that brings DO-depleted waters from the subsurface into
the deepened mixed layer. Note that this is consistent with
the decrease in surface chlorophyll a (Fig. 13c) and the in-
terpretation proposed by Echevin et al. (2008) to explain the
austral summer minimum in surface chlorophyll a observed
off central Peru.
This change to oxygen-poor conditions combines with the
natural decrease in oxygen production towards the end of the
upwelling season and coincides with a restratification of the
water column, which restricts the oxygenated waters near the
surface (Echevin et al., 2008). This altogether contributes to
maintain a negative DO rate inside the coastal OMZ, despite
the increase in anomalous DO flux from the advective terms
and (later on) biogeochemical processes towards the end of
the year. As a result, oxygen returns to low values towards
the end of the year.
4.2 Offshore flux
While the coastal OMZ variability is heavily constrained
by the environmental forcings – coastal upwelling, coastal
current system and local wind – due to the shallow oxy-
cline there, the offshore OMZ, as embedded in the shadow
zone of the thermohaline circulation, is somewhat insensitive
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4401
to direct local forcing and rather experiences remote influ-
ence in the form of westward-propagating mesoscale eddies
(Chaigneau et al., 2009) and ETRW (Ramos et al., 2008; De-
witte et al., 2008). The influence of westward-propagating
mesoscale eddies on the OMZ translates as the transfer of
coastal water properties towards the open ocean (DO in-
cluded), while these properties are altered during transport
due to physical/biogeochemical interactions (Stramma et al.,
2014; Karstensen et al., 2015). Towards the end of their life-
time, hydrographic and biogeochemical anomalies carried by
eddies are redistributed in the ocean (Brandt et al., 2015),
linking the coast and the open ocean. Although most eddy
genesis takes place near the coast and seasonal ETRW have
a coastally forced component, we expect different character-
istics of the seasonal variability in DO between the coast and
the open ocean, given that oxygen demand will change from
one region to the other. We also distinguish the mean DO
flux associated with the annual component of the circulation
that represents the transport in DO associated with seasonal
changes in the large-scale circulation and the annual vari-
ability of the DO eddy flux that corresponds to the annual
changes in the transport due to eddies. These two quantities
are diagnosed at 12◦ S (Figs. 14 and 15). The DO has been
normalized by its climatological variability in order to em-
phasize variability patterns where DO is low.
4.2.1 Mean seasonal flux
We first document the mean DO flux associated with the an-
nual component of the circulation. It consists in the mean of
the cross-product of the annual harmonics of the climatolog-
ical velocity and DO (Fig. 14a). The results indicate that the
amplitude of the annual DO flux is maximum near the coast
and below ∼ 400 m and it tends to be orientated westward–
downward, following approximately the trajectories of theo-
retical WKB paths for the annual period Rossby wave. Note
that this is consistent with the westward-propagating pattern
of DO below 400 m evidenced earlier (Fig. 8). As a consis-
tency check, we also estimated the annual energy flux vector
in the (x,z) plane associated with a long extratropical Rossby
wave; that is
(〈p1yr× u1yr〉, 〈p1yr×w1yr〉) where the super-
script denotes the annual harmonics and the bracket the tem-
poral average (Fig. 14b). The flux vector indicates vertical
propagation of energy at the annual period and the pattern
of maximum flux coincides approximately with the region of
maximum amplitude of the mean seasonal DO flux. This sug-
gests that the annual ETRW are influential on the DO flux be-
low ∼ 400 m. This is interpreted as resulting from the advec-
tion of DO by the ETRW since biogeochemical fluxes have
much less influence on the DO rate of change below 400 m
(Fig. 10c) and the amplitude of the annual cycle of climato-
logical DO eddy flux has a much reduced amplitude below
that depth (Fig. 15a), suggesting a small contribution of hor-
izontal and vertical diffusion to the DO budget. Note that the
DO (Fig. 15a) was normalized prior to compute the DO eddy
Figure 13. (a, b) Non-dimensional principal components (PCs) as-
sociated with the EOF patterns in Fig. 12. Multiplying the principal
component by the associated EOF pattern (from Fig. 12) yields a
first EOF-mode reconstruction of the original field. RMS values of
the principal components are indicated in parenthesis (correspond-
ing units as in Fig. 12). The residual corresponds to the difference
between the rate of DO change and the sum of all the terms of the
right-hand side of Eq. (1) in terms of the normalized PC time se-
ries. The weak residual indicates that the seasonal DO budget can
be interpreted from the EOF decomposition. The EOF decomposi-
tion was performed over the climatological (mean seasonal cycle)
fields. (c) Normalized seasonal cycle of coastal alongshore wind
(AS wind) and coastal alongshore wind running variance (variance
over a 30-day running window) at 12◦ S, sea level at the coast at
12◦ S, surface chlorophyll a from CR BIO (Chl a) and from Sea-
WiFS (Chl a SW) averaged over a coastal band of 2◦ width at
12◦ S, and mixed layer depth (MLD) at the coast at 12◦ S. Mean
and RMS used to normalize each time series, are indicated in paren-
thesis. Original seasonal cycle is found by multiplying the normal-
ized series by its RMS and then adding the mean. Original units are
N m−2, m, mg m−3 and m, respectively.
flux, so it is possible to compare to Fig. 14a, and therefore
contrast the flux associated with the annual ETRW against
the annual DO eddy flux. It was verified that the vertical
structure variability of the annual DO flux described above
for the section of 12◦ S is comparable at other latitudes within
the OMZ. In particular, the annual DO flux tends to remain
homogeneous along trajectories mimicking the energy paths
of the ETRW at the annual period when the slope becomes
steeper to the south (not shown).
4.2.2 Seasonal eddy flux
As previously described, the annual amplitude of the clima-
tological DO eddy flux is the largest in the upper 400 m near
the coast at 12◦ S consistently with the high EKE in this re-
gion. Since EKE is large along the coast of Peru, exchange
of DO induced by eddies could be expected at all latitudes,
with a direction that depends on the sign of the DO gradient
at the coast. Figure 16 presents the annual harmonic of the
climatological DO eddy flux along the coast and averaged in
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4402 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 14. (a) Norm of the annual DO flux vector (i.e.,√(
〈u1yr×O1yr2 〉
)2+ (〈w1yr×O1yr2 〉)2) for a cross-shore
section at 12◦ S. Arrows indicate the vector direction(
i.e.,
[
〈u1yr×O1yr2 〉, 〈w1yr×O
1yr
2 〉
])
. The DO signal was
normalized by its root mean square value before computing
the annual harmonic in order to emphasize the flux patterns
where DO concentration is very low. (b) Norm of the annual
energy flux vector (i.e.,
√(〈p1yr× u1yr〉)2+ (〈p1yr×w1yr〉)2).
Arrows inside the 0.2 contour indicate the vector direction(
i.e.,
[
〈p1yr× u1yr〉, 〈p1yr×w1yr〉
])
. Some theoretical WKB
trajectories (1-year period) originating from near the coast at the
surface are drawn for phase speed values of a first (full), second
(dashed) and third (dotted) baroclinic modes. The range of phase
speed values (modes 1–3) is obtained from a vertical mode decom-
position of the mean model stratification. Dashed black contours
indicate the 45 and 20 µM mean DO isopleths. Land and the region
outside the 45 µM mean DO isopleth are masked in white.
a coastal fringe distant 1◦ from the coast and 2◦ width. The
maximum amplitude – reaching ∼ 1 cm s−1 µM – is concen-
trated in the upper oxycline (Fig. 16a) with a peak during
austral winter. The peak season is also confirmed by the EOF
analysis of the climatological DO eddy flux (not shown). De-
spite the relative large meridional variability in the ampli-
tude, the mean vertical structure of the DO eddy flux consists
in an approximate exponentially decaying profile with depth,
with a decay scale of ∼ 90 m (Fig. 16b) so that at 300 m the
seasonal DO eddy flux is on average only 19 % of that at
100 m along the coast. Figure 16a also reveals that the an-
nual DO eddy flux is larger towards the northern rim of the
domain and extends deeper than towards the south. The high
values are increasingly confined close to the surface towards
the southern part of the domain, in comparison to the north-
ern part, although the vertical attenuation displays a similar
scale.
4.3 Meridional boundaries
Here, our objective is to document the seasonality of the DO
eddy flux. As a first step, we estimate the distribution of mean
Figure 15. Zonal section of the annual harmonic of the module of
the seasonal DO eddy-flux vector
(
〈u′×O′2〉, 〈w′×O′2〉
)
at 12◦ S.
(a) Amplitude of the harmonic and (b) phase of the annual maxi-
mum (in months). Dashed white contours indicate the 45 and 20 µM
mean DO isopleths. DO was normalized by its RMS prior to carry-
ing out analysis. Land and the region outside the 45 µM mean DO
isopleth are masked in white.
Figure 16. (a) Amplitude (color shading) and phase (months, gray
contours) of the annual harmonic of the climatological DO eddy
flux along the coast. The climatology of DO eddy flux was averaged
over a coastal fringe of 2◦ width, starting from 1◦ from the coast.
(b) Meridional average vertical profile (black line), ±RMS (gray
shading). An exponential model fitted to the average vertical profile
(dashed blue line) yields a vertical decay scale of ∼ 90 m.
DO eddy flux in order to identify the regions where its mag-
nitude is large and thus where it is likely to vary seasonally
with a significant amplitude.
4.3.1 Mean seasonal flux
The horizontal distribution of mean DO eddy flux dis-
plays the highest values at the boundaries of the OMZ core
(Fig. 17) and adjacent to the 45 µM isopleth. Towards the
inner OMZ, the mean DO eddy-flux values decrease noto-
riously, with a factor of nearly 10 between the interior and
Biogeosciences, 13, 4389–4410, 2016 www.biogeosciences.net/13/4389/2016/
O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4403
Figure 17. Module of the mean DO eddy-flux vector(〈u′×O′2〉, 〈v′×O′2〉) at (a) 100 m, (b) 250 m, (c) 350 m and
(d) 700 m depth. Arrows displayed only for values above the
central value in each color bar denote the vector direction and
strength. White contours correspond to the 45, 20 and 10 µM mean
DO values. Red and blue lines denote the position of vertical
sections.
exterior of the 10 µM contour. In agreement with the obser-
vations reported in the previous section, the mean DO eddy
flux decreases sharply with depth (approximately 1 order of
magnitude between 100 and 700 m), with the highest values
concentrated near the oxycline, as expected from the increas-
ing oxygen concentration in this part of the OMZ. In this
sense, the pattern of DO eddy flux around the depth of the
oxycline encloses a region of high variability (not shown).
To gain further insight with respect to the vertical struc-
ture of the DO eddy flux and at the same time diagnose the
role of the mesoscale activity at the boundaries of the OMZ,
we compute the mean DO eddy flux across the two sections
that correspond to the northern and southern limits of the
OMZ (depicted in Fig. 18). These limits are defined based
on Fig. 17 and are located in the provinces of high amplitude
of the mean DO eddy flux.
The DO eddy flux across each of the northern and southern
boundaries was computed by averaging the product of the
fluctuating velocity component normal to the boundary in the
horizontal directions and the fluctuating DO concentration
component, thereby obtaining horizontal eddy fluxes.
As observed in Fig. 17, the highest values for both north-
ern and southern boundary sections are found between the
oxycline and the lower OMZ core limit (Fig. 18), be-
ing almost 1 order of magnitude smaller at greater depths
(Fig. 18c). These high values, located between ∼ 100 and
300 m, are followed by a sharp decrease (average decrease
of 1.5 cm s−1 µM in 100 m). At the range of depths between
100 and 300 m, the DO eddy flux displays higher values at
the southern boundary (nearly twice as large) when com-
pared with the northern boundary. This relationship is less
clear when analyzing the lower part of the OMZ. At both
meridional boundaries, the mean DO eddy flux in the upper
part of the OMZ is nearly 1 order of magnitude larger than in
the lower part.
4.3.2 Seasonal eddy flux
We now document the seasonal variability of the DO eddy
flux across the OMZ boundaries analyzed above (Fig. 18).
An EOF analysis of the mean seasonal cycle of the DO eddy
flux is performed at the boundary sections previously de-
fined. Figure 19 presents the first EOF-mode patterns along
with the associated time series. In order to estimate the uncer-
tainty associated with the location of the OMZ boundaries,
we repeated this analysis for 12 nearby sections parallel to
the boundaries and spaced by ∼ 20 km. This leads to an esti-
mated error (standard deviation across the different sections)
of the DO eddy flux. The error is represented as a colored
shading in Fig. 19b, d, e. At both locations, the first EOF
accounts for a well-defined seasonal cycle. At the northern
boundary (Fig. 19a), the seasonal cycle of the DO eddy flux
peaks in austral winter, in phase with the DO changes along
the coast (Fig. 16). Note that the seasonal cycle is in phase
with that of the intraseasonal activity of the horizontal cur-
rents normal to the section, which was estimated the same
way as the climatological eddy flux (see red line in Fig. 19b),
supporting the idea that the climatological DO eddy flux re-
sults from anomalous advection. The amplitude of the mode
pattern is maximum at the oxycline with DO between 20 and
45 µM and presents a sharp decrease below the OMZ core
depth (Fig. 19a). This sharp decrease is evidenced by the
mean vertical profile of the DO eddy-flux seasonal variabil-
ity estimated as the RMS across the section of the EOF-mode
pattern (Fig. 19e). The vertical structure of the DO eddy-flux
variability indicates that there is a difference of nearly 1 or-
der of magnitude between 100 and 300 m depth. From that
depth on, the DO eddy-flux variability decreases linearly.
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4404 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 18. (a) Mean DO eddy flux normal to the section denoted by the red line in Fig. 17. (b) Mean DO eddy flux normal to the section
denoted by the blue line in Fig. 17. (c) Horizontal mean of (a) and (b) (red and blue lines, respectively). Gray contours denote mean DO
concentrations, and light red/blue contours correspond to positive/negative values of mean currents normal to the section (1.0/−1.0 cm s−1 in
a and 0.4/−0.2 cm s−1 in b). White contour denotes the 0 value. The sign convention was chosen so that a positive horizontal flux indicates
transport towards the interior of the OMZ.
Figure 19. (a) First EOF mode of the seasonal cycle of the DO eddy flux normal to the section depicted in Fig. 17 by the dashed red
line (northern boundary). (b) Principal component (PC) time series associated with the first EOF mode (black line). The red line in (b)
corresponds to the PC time series associated with the first EOF mode of the seasonal cycle of the 30-day running variance of intraseasonal
currents normal to the section. (c) First EOF mode of the seasonal cycle of the DO eddy flux normal to the oblique section depicted in Fig. 17
by the dashed blue line (southern boundary). (d) PC time series associated with the first EOF mode (black line). The blue curves (full and
dashed lines) in (d) correspond to the PC time series associated with the first and second EOF modes of the seasonal cycle of the 30-day
running variance of the intraseasonal currents normal to the section (computed as in b). Percentage of explained variance and RMS value are
indicated in parentheses in the panels (b) and (d) (in cm s−1 µM and cm s−1 for DO eddy flux and currents, respectively). White contours
in (a) and (c) denote mean DO concentration values in µM. (e) RMS of the spatial patterns (a) and (c), computed along the horizontal
direction. Note the scale leap at 300 m. Red/blue shading in (b), (d) and (e) represents an estimate of the error associated with slight changes
in the location of the boundaries, i.e., when the EOF is performed over a section that is located at a distance from the original section (cf.
Fig. 17) compromised between ±120 km (see text). The error corresponds to the standard deviation among 12 PC time series (for b and d)
and EOF patterns (for e).
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O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4405
In contrast with the northern boundary, the seasonal vari-
ability at the southern boundary peaks during austral spring
(Fig. 19d), in phase with the intraseasonal activity of the hor-
izontal currents normal to the section. The amplitude of the
seasonal cycle is the largest around the depth of the oxycline
and remains high down to the vicinity of the OMZ core up-
per limit (Fig. 19c). Below the depth of the OMZ core, the
amplitude of the EOF mode decreases sharply (∼ 1 order of
magnitude in 100 m; Fig. 19c). This is evidenced by the pro-
file of the DO eddy-flux seasonal variability, estimated in
the same manner as for the northern boundary (Fig. 19e).
This profile shares some characteristics with its counterpart
at the northern boundary, meaning a sharp decrease between
the oxycline and the OMZ core depths, suffering a reduction
of nearly 90 % (Fig. 19e). In contrast, the variability along
the southern boundary is ∼ 70 % larger than along the north-
ern boundary. At both boundaries, the zonal wavelength of
the seasonal DO eddy-flux variability along the boundary is
estimated to be in the order of ∼ 102 km, a scale that falls
within the range of observed eddies diameter (Chaigneau and
Pizarro, 2005), which indicates that locally there can be an
injection or removal of DO across the boundary on average
over a season. The mean DO eddy flux across the boundaries
is nevertheless positive.
5 Discussion
We now discuss some limitations and implications of our re-
sults. While the model realistically simulates the main char-
acteristics of the OMZ (position, intensity, average volume
and seasonal variations), it still presents biases that could be
influential on our results. In particular, and since the coastal
domain is viewed here as a boundary of the OMZ, it is
important to have a realistic mean DO concentration there.
Compared to CARS, the simulated suboxic volume is, how-
ever, underestimated by ∼ 6 %, and 85 % of this error can
be attributed to the coastal domain (fringe of 3◦ from the
coast). This bias could be due to several factors. Montes et
al. (2014) observed variations of the suboxic volume in the
order of 5 % when contrasting two simulations that used dif-
ferent oceanic open boundary conditions, which indicates a
sensibility of the simulated OMZ to the physical parameters
and to the representation of the equatorial current system.
This bias could also be partly due to coastal sediments pro-
cesses (DO demanding processes) that are not represented
in our simulation. Using a similar configuration to the one
used in the present study on the Namibian OMZ, Gutknecht
et al. (2013a) observed that the differences between the sim-
ulated OMZ volume and CARS increased towards the shelf,
which could be related to the exclusion of the DO demand
from the sediments in the model. The role of benthic pro-
cesses in constraining the DO demand has been studied in
the northern California current system (Bianucci et al., 2012;
Siedlecki et al., 2015), indicating that locally such processes
might be essential to explain the hypoxic conditions. The in-
clusion of a sediment module in the current model setting is
planned for future work to address this issue.
Another process that could contribute to the underestima-
tion of the suboxic volume in the simulation is the higher
mesoscale activity in the model compared to the observations
(Fig. 1), which could in turn induce a higher ventilation of the
OMZ in the simulation.
Besides other likely sources of biases related to an im-
perfect model setting (e.g., use of relatively low-resolution
atmospheric forcings near the coast, absence of air/sea cou-
pling at mesoscale, absence of coupling with benthic oxygen
demand or consideration of N2 fixation), another inherent
limitation of our study is related with the difficulty in vali-
dating some aspects of the eddy field, in particular its vertical
structure. This might be overcome in the future as the Argo
coverage increases (cf. TPOS2020).
With the limitations of our regional modeling approach in
mind, it is worthwhile to discuss some implications of our re-
sults. While previous studies have mostly focused on the role
of the mean DO eddy flux in shaping the OMZ (Resplandy
et al., 2012; Brandt et al., 2015; Bettencourt et al., 2015),
we have documented here how the seasonal DO changes
inside the OMZ are essentially controlled by the DO eddy
flux at the OMZ limits, which means that the seasonality
of the OMZ can be interpreted as resulting from a modu-
lation of the mesoscale activity at seasonal timescales. We
infer that the seasonality of the DO eddy flux is regulated by
different physical processes depending on the region. At the
coast, there is a constructive coupling between eddies result-
ing from the instability of the PUC peaking in austral winter
and the enhanced DO along the coast resulting from an in-
creased horizontal and vertical diffusion at the same season.
At the northern part of the OMZ, the DO eddy flux is re-
lated to the strong EKE around 5◦ S that peaks in austral
winter. Despite the fact that the northern OMZ is embed-
ded in the equatorial wave guide, it can be ruled out that
the seasonal cycle in DO eddy flux is strongly linked to the
intraseasonal long equatorial waves since the intraseasonal
Kelvin wave activity tends to peak in austral summer (Illig
et al., 2014). The boundary forcing sensitivity model experi-
ments of Echevin et al. (2011) also suggest that the enhanced
mesoscale activity observed off northern Peru during winter
would be related to internal variability or local wind stress
rather than being connected to the equatorial Kelvin wave
activity. Whether or not the strong EKE found there results
from the instability of the coastal currents system or of the
EUC and the South Equatorial Current would need to be ex-
plored.
Regarding the southern boundary, it is interesting to note
that the DO eddy-flux peaks in austral spring, 3 months later
than at the northern boundary. A possible mechanism driv-
ing the local variability observed at the southern section
is the generation of local baroclinic instability and vortic-
ity input from wind stress curl as observed for the Califor-
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4406 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Figure 20. Schematic of the main processes driving the seasonal variability in the SEP OMZ: the DO eddy flux through the north–south
boundaries and the DO flux that takes place at the coastal boundary of the OMZ. The coastal band limits are defined by the light blue shading
adjacent to the coast. A scale of the seasonal amplitude of the eddy-driven DO flux at each OMZ boundary is indicated (units in cm s−1 µM).
The mean DO concentration (color shading) and the position of the 45 µM isopleth (thick black contour) at 100 m depth are also represented.
The vertical/offshore DO flux induced by the propagation of the annual ETRW across the 45 µM isopleth at 25◦ S is represented in the bottom
panel.
nia system (Kelly et al., 1998). The southern section lies
within the northeastern rim of the southeast Pacific anticy-
clone, and the peak in the seasonal DO eddy flux coincides
with the reported intensity peak of the seasonal cycle of
the Anticyclone, towards the end of the year (Rahn et al.,
2015; Ancapichún and Garcés-Vargas, 2015). Therefore, the
mesoscale activity in this region could be directly modulated
by the winds. An additional source of intraseasonal (inter-
nal) variability in the currents field could be the interaction
between the annual extratropical Rossby wave and the mean
circulation (Dewitte et al., 2008; Qiu et al., 2013). The actual
source of the eddy activity in this region would also deserve
further investigation.
Our study also reveals that the most prominent propagat-
ing features in DO inside the OMZ at annual frequency is
below ∼ 300 m, where the seasonal DO flux follows approx-
imately the theoretical WKB ray paths of the annual ETRW.
From that depth, the seasonal variability in physical fluxes
becomes 1 order of magnitude larger than that of the bio-
geochemical fluxes (Fig. 10c). This supports the observation
that DO tends to behave as a passive tracer so that verti-
cal displacements of the DO isopleths mimic those of the
isotherms, inducing a seasonal DO flux that resembles the
energy flux path of the ETRW. This mechanism adds a di-
mension to the understanding of the OMZ variability, consid-
ering that the vertical propagation of ETRW can take place
at frequencies ranging from annual (Dewitte et al., 2008) to
interannual (Ramos et al., 2008).
We now discuss some implications of our results with re-
gards to current concerns around OMZ variability at long
timescales. A recent study has suggested a trend in the OMZ
towards expansion and intensification (Stramma et al., 2008),
the forcing mechanism of which remains unclear (Stramma
et al., 2010). Observations in the Pacific Ocean also suggest
that the OMZ characteristics vary decadally (Stramma et al.,
2008, 2010). Since decadal variability can manifest as a low-
frequency modulation of the seasonal cycle, our study may
provide guidance for investigating OMZ variability at long
timescales. In particular, we show that the variability of the
OMZ is not only related to the fluctuations of the equatorial
currents system but also impacted by the subtropical vari-
ability. This view would link the OMZ low-frequency fluc-
tuations to changes in the midlatitude circulation, in addi-
tion to variations in the equatorial Pacific (Stramma et al.,
2010). Although we observe a larger amplitude of the sea-
sonal cycle in the subtropics compared to the equatorial re-
gion, which could denote a preferential OMZ ventilation
through the south, this result should be interpreted in light
of a possible overestimation of the ventilation at that bound-
ary in our simulation. We also note that the relative contri-
bution of the mean DO flux and the DO eddy flux exhibit
significant interannual fluctuations at the OMZ boundaries
(not shown), which suggests that eddy-induced DO flux may
not be the only key player for understanding long-term trend
in the OMZ. It is interesting to note that, so far, it has been
difficult to reconcile the observed trend in the OMZ with the
trend simulated by the current generation of coupled models
(Stramma et al., 2012), which has been attributed to biases in
the mean circulation and inadequate remineralization repre-
sentation (Cocco et al., 2013; Cabré et al., 2015). Our results
Biogeosciences, 13, 4389–4410, 2016 www.biogeosciences.net/13/4389/2016/
O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4407
support the view that such discrepancy may partly originate
from the inability of the low-resolution models to account for
the DO eddy flux and its modulation. Regional modeling ex-
periments also showed that eddy activity can be modulated at
ENSO and decadal timescales (Combes et al., 2015; Dewitte
et al., 2012). This issue would certainly require further inves-
tigation and could benefit from the experimentation with our
coupled model platform. This is planned for future work.
Lastly, the seasonal changes in the OMZ evidenced in this
work are associated with a seasonal change of the oxycline
depth (and an oxycline intensity change; not shown), which
can be considered a proxy for the production of greenhouse
gases (CO2 and N2O) inside the OMZ (e.g., Paulmier et al.,
2011; Kock et al., 2016). Our results suggest that the impact
of the OMZ on the atmosphere through the production of
climatically active gases, such as CO2 and N2O, would be
seasonally damped during austral winter due to a deepening
of the oxycline and a weakening of its intensity.
6 Summary and conclusions
A high-resolution coupled physical/biogeochemical model
experiment is used to document the seasonal variability of
the OMZ off Peru. The annual harmonic of DO reveals three
main regions with enhanced amplitude or specific propaga-
tion characteristics, suggesting distinct dynamical regimes:
(1) the coastal domain, (2) the offshore ocean below 400 m
and (3) at the southern and northern boundaries. In the
coastal portion of the OMZ, the seasonal variability is re-
lated to the local wind forcing and therefore follows to a
large extent the paradigm of upwelling triggered productiv-
ity, followed by remineralization. It is shown in particular
that DO peaks in austral winter, which is associated with hor-
izontal and vertical diffusion induced by both the increase in
baroclinic instability and intraseasonal wind activity. This is
counterintuitive with regards to the seasonality of the along-
shore upwelling favorable winds also peaking in austral win-
ter, which would tend to favor the intrusion of deoxygenated
waters from the open-ocean OMZ to the shelf. Instead, the
coastal domain can be viewed as a source of DO in austral
winter for the OMZ through offshore transport. The latter is
induced by eddies that are triggered by the instabilities of the
PUC. In the model, the offshore DO eddy flux has a marked
seasonal cycle that is in phase with the seasonal cycle of the
DO along the coast, implying that the coastal domain, viewed
here as the eastern boundary of the OMZ, is a source of sea-
sonal variability for the OMZ. This appears to operate effec-
tively in the upper 300 m. Below that depth, the DO eddy flux
is much reduced due to both a much weaker eddy activity
and a very low DO concentration. In contrast, a mean sea-
sonal DO flux is observed and exhibits propagating features
reminiscent of the vertical propagation of energy associated
with the annual ETRW.
In the upper 300 m, the OMZ seasonal variability is also
associated with the DO eddy flux at the OMZ meridional
boundaries where it is the most intense. We find that the sea-
sonal cycle in DO eddy flux peaks in austral winter at the
northern boundary, while it peaks a season later at the south-
ern boundary. Additionally, the amplitude of the seasonal cy-
cle in DO eddy flux is larger at the southern boundary than at
the northern boundary. The schematic of Fig. 20 summarizes
the main processes documented in this paper to explain the
seasonality of the OMZ.
Acknowledgements. Oscar Vergara was supported by a doctoral
scholarship from the National Chilean Research and Technology
Council (CONICYT) through the program Becas Chile (schol-
arship 72130138). The authors are thankful for the financial
support received from the Centre National d’Etudes Spatiales
(CNES). Boris Dewitte acknowledges support from FONDE-
CYT (project 1151185). Marcel Ramos acknowledges support
from FONDECYT (project 1140845) and Chilean Millennium
Initiative (NC120030). Oscar Pizarro acknowledges support from
the FONDECYT 1121041 project and the Chilean Millennium
Initiative (IC-120019). The authors thank the two anonymous
reviewers for their constructive comments that helped improving
the manuscript.
Edited by: K. Fennel
Reviewed by: two anonymous referees
References
Ancapichún, S. and Garcés-Vargas, J.: Variability of the South-
east Pacific Subtropical Anticyclone and its impact on sea sur-
face temperature off north-central Chile, Cienc. Mar., 41, 1–20,
doi:10.7773/cm.v41i1.2338, 2015.
Arévalo-Martínez, D., Kock, L. A., Löscher, C. R., Schmitz, R.
A., and Bange, R. A.: Massive nitrous oxide emissions from
the tropical South Pacific Ocean, Nat. Geosci., 8, 530–533,
doi:10.1038/NGEO2469, 2015.
Bettencourt, J. H., López, C., Hernández-García, E., Montes,
I., Sudre, J., Dewitte, B., Paulmier A., and Garçon, V.:
Boundaries of the Peruvian Oxygen Minimum Zone shaped
by coherent mesoscale dynamics, Nat. Geosci., 8, 937–940„
doi:10.1038/ngeo2570, 2015.
Bianchi, D., Dunne, J. P., Sarmiento, J. L., and Galbraith,
E. D.: Data-based estimates of suboxia, denitrification,
and N2O production in the ocean and their sensitivities
to dissolved O2, Global Biogeochem. Cy., 26, GB2009,
doi:10.1029/2011GB004209, 2012.
Bianucci, L., Fennel, K., and Denman, K. L.: Role of sediment den-
itrification in water column oxygen dynamics: comparison of the
North American East and West Coasts, Biogeosciences, 9, 2673–
2682, doi:10.5194/bg-9-2673-2012, 2012.
Brandt, P., Bange, H. W., Banyte, D., Dengler, M., Didwischus,
S.-H., Fischer, T., Greatbatch, R. J., Hahn, J., Kanzow, T.,
Karstensen, J., Körtzinger, A., Krahmann, G., Schmidtko, S.,
Stramma, L., Tanhua, T., and Visbeck, M.: On the role of circula-
tion and mixing in the ventilation of oxygen minimum zones with
www.biogeosciences.net/13/4389/2016/ Biogeosciences, 13, 4389–4410, 2016
4408 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
a focus on the eastern tropical North Atlantic, Biogeosciences,
12, 489–512, doi:10.5194/bg-12-489-2015, 2015.
Cabré, A., Marinov, I., Bernardello, R., and Bianchi, D.: Oxy-
gen minimum zones in the tropical Pacific across CMIP5 mod-
els: mean state differences and climate change trends, Biogeo-
sciences, 12, 5429–5454, doi:10.5194/bg-12-5429-2015, 2015.
Cambon G., Goubanova, K., Marchesiello, P., Dewitte, B., Illig, S.,
and Echevin, V.: Assessing the impact of downscaled winds on a
regional ocean model simulation of the Humboldt system, Ocean
Model., 65, 11–24, 2013.
Chavez, F. P., Bertrand, A., Guevara-Carrasco, R., Soler, P., and
Csirke, J.: The northern Humboldt Current System: Brief history,
present status and a view towards the future, Prog. Oceanogr., 79,
95–105, 2008.
Chaigneau, A. and Pizarro, O.: Eddy characteristics in the
eastern South Pacific, J. Geophys. Res., 110, C06005,
doi:10.1029/2004JC002815, 2005.
Chaigneau, A., Eldin G., and Dewitte, B.: Eddy activ-
ity in the four major upwelling systems from satellite
altimetry (1992–2007), Prog. Oceanogr., 83, 117–123,
doi:10.1016/j.pocean.2009.07.012, 2009.
Cocco, V., Joos, F., Steinacher, M., Frölicher, T. L., Bopp, L.,
Dunne, J., Gehlen, M., Heinze, C., Orr, J., Oschlies, A., Schnei-
der, B., Segschneider, J., and Tjiputra, J.: Oxygen and indicators
of stress for marine life in multi-model global warming projec-
tions, Biogeosciences, 10, 1849–1868, doi:10.5194/bg-10-1849-
2013, 2013.
Colas, F., McWillimas, J. C., Capet, X., and Kurian, J.: Heat balance
and eddies in the Peru-Chile current system, Clim. Dynam., 39,
509–529, doi:10.1007/s00382-011-1170-6, 2012.
Combes, V., Hormazabal, S., and Di Lorenzo, E.: Interannual vari-
ability of the subsurface eddy field in the Southeast Pacific,
J. Gephys. Res., 120, 4907–4924, doi:10.1002/2014JC010265,
2015.
Cornejo, M. and Farías, L.: Following the N2O consumption in
the oxygen minimum zone of the eastern South Pacific, Biogeo-
sciences, 9, 3205–3212, doi:10.5194/bg-9-3205-2012, 2012.
Cornejo, M., Farías, L., and Paulmier, A.: Temporal variabil-
ity in N2O water content and its air-sea exchange in an up-
welling area off central Chile (36◦ S), Mar. Chem., 101, 85–94,
doi:10.1016/j.marchem.2006.01.004, 2006.
Czeschel, R., Stramma, L., Schwarzkopf, F. U., Giese, B. S., Funk,
A., and Karstensen, J.: Middepth circulation of the eastern trop-
ical South Pacific and its link to the oxygen minimum zone, J.
Geophys. Res., 116, C01015, doi:10.1029/2010JC006565, 2011.
Czeschel, R., Stramma, L., Weller, R. A., and Fischer, T.: Circula-
tion, eddies, oxygen, and nutrient changes in the eastern tropical
South Pacific Ocean, Ocean Sci., 11, 455–470, doi:10.5194/os-
11-455-2015, 2015.
daSilva A., Young, A. C., and Levitus, S.: Atlas of surface marine
data 1994. Algorithms and procedures. vol. 1 Technical Report
6, US Department of Commerce, NOAA, NESDIS, 1994.
Dewitte B., Ramos, M., Echevin, V., Pizarro, O., and duPenhoat,
Y.: Vertical structure variability in a seasonal simulation of a
medium-resolution regional model simulation of the South East-
ern Pacific, Prog. Oceanogr., 79, 120–137, 2008.
Dewitte, B., Illig, S.,Renault, L., Goubanova, K., Takahashi, K.,
Gushchina, D., Mosquera, K., and Purca, S.: Modes of co-
variability between sea surface temperature and wind stress
intraseasonal anomalies along the coast of Peru from satel-
lite observations (2000–2008), J. Geophys. Res., 116, C04028,
doi:10.1029/2010JC006495, 2011.
Dewitte, B., Vazquez-Cuervo, J., Goubanova, K., Illig, S., Taka-
hashi, K., Cambon, G., Purca, S., Correa, D., Gutiérrez, D.,
Sifeddine, A., and Ortlieb, L.: Change in El Nino flavours
over 1958–2008: Implications for the long-term trend of the
upwelling off Peru, Deep-Sea Res. Pt. II, 77–80, 143–156,
doi:10.1016/j.dsr2.2012.04.011, 2012.
Dunn J. R. and Ridgway, K. R.: Mapping ocean properties in re-
gions of complex topography, Deep-Sea Res. Pt. I, 49, 591–604,
2002.
Dunne, J. P., Armstrong, R. A., Gnanadesikan, A., and Sarmiento,
J. L.: Empirical and mechanistic models for the parti-
cle export ratio, Global Biogeochem. Cy., 19, GB4026,
doi:10.1029/2004gb002390, 2005.
Duteil, O. and Oschlies, A.: Sensitivity of simulated extent and fu-
ture evolution of marine suboxia to mixing intensity, Geophys.
Res. Lett., 38, L06607, doi:10.1029/2011GL046877, 2011.
Duteil, O., Schwarzkopf, F. U., Böning, C. W., and Oschlies, A.:
Major role of the equatorial current system in setting oxy-
gen levels in the eastern tropical Atlantic Ocean: A high-
resolution model study, Geophys. Res. Lett., 41, 2033–2040,
doi:10.1002/2013GL058888, 2014.
Echevin, V., Aumont, O., Ledesma, J., and Flores, G.: The seasonal
cycle of surface chlorophyll in the Peruvian upwelling system: A
modelling study, Progr. Oceanogr., 79, 2–4, 167–176, 2008.
Echevin, V., Colas, F., Chaigneau, A., and Penven, P.: Sensitivity
of the Northern Humboldt Current System nearshore modeled
circulation to initial and boundary conditions, J. Geophys. Res.,
116, C07002, doi:10.1029/2010JC006684, 2011.
Eliassen, A. and Palm, E.: On the transfer of energy in stationary
mountain waves, Geofys. Publ., 22, 1–23, 1960.
Farías, L., Paulmier, A., and Gallegos, M.: Nitrous oxide and N-
nutrient cycling in the oxygen minimum zone off northern Chile,
Deep-Sea Res. Pt. I, 54, 164–180, doi:10.1016/j.dsr.2006.11.003,
2007.
Fuenzalida, R., Schneider, W., Garces-Vargas, J., Bravo, L., and
Lange, C.: Vertical and horizontal extension of the oxygen mini-
mum zone in the eastern South Pacific Ocean, Deep-Sea Res. Pt.
II, 56, 992–1003, doi:10.1016/j.dsr2.2008.11.001, 2009.
García, H. E. and Gordon, L. I.: Oxygen solubility in seawater – bet-
ter fitting equations, Limnol. Oceanogr., 37, 1307–1312, 1992.
Goubanova, K., Echevin, V., Dewitte, B., Codron, F., Takahashi, K.,
Terray, P., and Vrac, M.: Statistical downscaling of sea-surface
wind over the Peru–Chile upwelling region: diagnosing the im-
pact of climate change from the IPSL-CM4 model, Clim. Dy-
nam., 36, 1365, doi:10.1007/s00382-010-0824-0, 2011.
Gruber, N.: The marine nitrogen cycle: Overview of distributions
and processes, in: Nitrogen in the marine environment, second
Edn., edited by: Capone, D. G., Bronk, D. A., Mulholland, M.
R., and Carpenter, E. J., Elsevier, Amsterdam, 1–50, 2008.
Gruber, N., Lachkar, Z., Frenzel, H., Marchesiello, P., Münnich, M.,
McWilliams, J. C., Nagai, T., and Plattner, G. K.: Eddy-induced
reduction of biological production in eastern boundary upwelling
systems, Nat. Geosci., 4, 787–792, doi:10.1038/ngeo1273, 2011.
Gutiérrez, D., Enriquez, E., Purca, S., Quipuzcoa, L., Marquina, R.,
Flores, G., and Graco, M.: Oxygenation episodes on the conti-
Biogeosciences, 13, 4389–4410, 2016 www.biogeosciences.net/13/4389/2016/
O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru 4409
nental shelf of central Peru: remote forcing and benthic ecosys-
tem response, Prog. Oceanogr., 79, 177–189, 2008.
Gutiérrez, D., Bouloubassi, I., Sifeddine, A., Purca, S., Goubanova,
K., Graco, M., Field, D., Mejanelle, L., Velazco, F., Lorre, A.,
Salvatteci, R., Quispe, D., Vargas, G., Dewitte, B., and Ortlieb,
L.: Coastal cooling and increased productivity in the main up-
welling zone off Peru since the mid-twentieth century, Geophys.
Res. Lett., 38, L07603, doi:10.1029/2010GL046324, 2011.
Gutknecht, E., Dadou, I., Le Vu, B., Cambon, G., Sudre, J., Garçon,
V., Machu, E., Rixen, T., Kock, A., Flohr, A., Paulmier, A., and
Lavik, G.: Coupled physical/biogeochemical modeling includ-
ing O2-dependent processes in the Eastern Boundary Upwelling
Systems: application in the Benguela, Biogeosciences, 10, 3559–
3591, doi:10.5194/bg-10-3559-2013, 2013a.
Gutknecht, E., Dadou, I., Marchesiello, P., Cambon, G., Le Vu, B.,
Sudre, J., Garçon, V., Machu, E., Rixen, T., Kock, A., Flohr,
A., Paulmier, A., and Lavik, G.: Nitrogen transfers off Walvis
Bay: a 3-D coupled physical/biogeochemical modeling approach
in the Namibian upwelling system, Biogeosciences, 10, 4117–
4135, doi:10.5194/bg-10-4117-2013, 2013b.
Henley, B. J., Gergis, J., Karoly, D. J., Power, S. B., Kennedy, J.,
and Folland, C. K.: A Tripole Index for the Interdecadal Pacific
Oscillation, Clim. Dynam., 45, 3077–3090, doi:10.1007/s00382-
015-2525-1, 2015.
Henson, S. A., Sanders, R., and Madsen, E.: Global patterns
in efficiency of particulate organic carbon export and trans-
fer to the deep ocean, Global Biogeochem. Cy., 26, GB1028,
doi:10.1029/2011gb004099, 2012.
Illig, S., Dewitte, B., Goubanova, K., Cambon, G., Boucharel,
J., Monetti, F., Romero, C., Purca, S., and Flores, R.: Forc-
ing mechanisms of intraseasonal SST variability off cen-
tral Peru in 2000–2008, J. Geophys. Res., 119, 3548–3573,
doi:10.1002/2013JC009779, 2014.
Karstensen, J., Stramma, L., and Visbeck, M.: Oxygen minimum
zones in the eastern tropical Atlantic and Pacific oceans, Progr.
Oceanogr., 77, 331–350, 2008.
Karstensen, J., Fiedler, B., Schütte, F., Brandt, P., Körtzinger, A.,
Fischer, G., Zantopp, R., Hahn, J., Visbeck, M., and Wallace,
D.: Open ocean dead zones in the tropical North Atlantic Ocean,
Biogeosciences, 12, 2597–2605, doi:10.5194/bg-12-2597-2015,
2015.
Kelly, K., Beardsley, R., Limeburner, R., and Brink, K.: Variability
of the near-surface eddy kinetic energy in the California Current
based on altimetric, drifter and moored data, J. Geophys. Res.,
103, 13067–13083, 1998.
Kock, A., Arévalo-Martínez, D. L., Löscher, C. R., and Bange, H.
W.: Extreme N2O accumulation in the coastal oxygen minimum
zone off Peru, Biogeosciences, 13, 827–840, doi:10.5194/bg-13-
827-2016, 2016.
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic
vertical mixing: A review and a model with a nonlocal
boundary layer parameterization, Rev. Geophys., 32, 363–403,
doi:10.1029/94RG01872, 1994.
Law, C. S., Brévière, E., de Leeuw, G., Garçon, V., Guieu, C.,
Kieber, D. J., Kontradowitz, S., Paulmier, A., Quinn, P. K., Saltz-
man, E. S., Stefels, J., and von Glasow, R.: Evolving research
directions in Surface Ocean – Lower Atmosphere (SOLAS) sci-
ence, Environ. Chem., 10, 1–16, doi:10.1071/EN12159, 2013.
Libes, S. M.: An Introduction to Marine Biogeochemistry, John Wi-
ley and Sons, New York, 734 pp., 1992.
Llanillo, P. J., Karstensen, J., Pelegrí, J. L., and Stramma, L.: Phys-
ical and biogeochemical forcing of oxygen and nitrate changes
during El Niño/El Viejo and La Niña/La Vieja upper-ocean
phases in the tropical eastern South Pacific along 86◦W, Biogeo-
sciences, 10, 6339–6355, doi:10.5194/bg-10-6339-2013, 2013.
Luyten, J. R., Pedlosky, J., and Stommel, H.: The ventilated ther-
mocline, J. Phys. Oceanogr., 13, 292–309, 1983.
McClain, C. R., Cleave, M. L., Feldman, G. C., Gregg, W. W.,
Hooker, S. B., and Kuring, N.: Science quality SeaWiFS data
for global biosphere research, Sea Technol., 39, 10–16, 1998.
Montes, I., Colas, F., Capet, X., and Schneider, W.: On the pathways
of the equatorial subsurface currents in the Eastern Equatorial
Pacific and their contributions to the Peru-Chile Undercurrent, J.
Geophys. Res., 115, C09003, doi:10.1029/2009JC005710, 2010.
Montes, I., Dewitte, B., Gutknecht, E., Paulmier, A., Dadou, I.,
Oschlies, A., and Garçon, V.: High-resolution modeling of the
Eastern Tropical Pacific oxygen minimum zone: Sensitivity to
the tropical oceanic circulation, J. Geophys. Res.-Oceans, 119,
5515–5532, doi:10.1002/2014JC009858, 2014.
Morales, C. E., Hormazabal, S. E., and Blanco, J.: Interan-
nual variability in the mesoscale distribution of the depth
of the upper boundary of the oxygen minimum layer off
northern Chile (18–24S): Implications for the pelagic sys-
tem and biogeochemical cycling, J. Mar. Res., 57, 909–932,
doi:10.1357/002224099321514097, 1999.
Morel, A. and Berthon, J. F.: Surface pigments, algal biomass pro-
files, and potential production of euphotic layer: Relationship
reinvestigated in view of remote-sensing applications, Limnol.
Oceanogr., 34, 1545–1562, 1989.
Nagai, T., Gruber, N., Frenzel, H., Lachkar, Z., McWilliams, J.
C., and Plattner, G.-K.: Dominant role of eddies and filaments
in the offshore transport of carbon and nutrients in the Califor-
nia Current System, J. Geophys. Res.-Oceans, 120, 5318–5341,
doi:10.1002/2015JC010889, 2015.
Nerem, R. S., Chambers, D. P., Choe, C., and Mitchum, G. T.: Es-
timating mean sea level change from the TOPEX and Jason al-
timeter missions, Mar. Geod., 33, Supplement 1, 435–446, 2010.
O’Reilly, J. E., Maritorena, S., Siegel, D., O’Brien, M. O., Toole,
D., Mitchell, B. G., Kahru, M., Chavez, F., Strutton, P. G., Cota,
G. F., Hooker, S. B., McClain, C., Carder, K., Muller-Karger, F.,
Harding, L., Magnuson, A., Phinney, D., Moore, G., Aiken, J.,
Arrigo, K. R., Letelier, R. M., and Culver, M.: Ocean chloro-
phyll a algorithms for SeaWiFS, OC2, and OC4: Version 4, in:
SeaWiFS Postlaunch Calibration and Validation Analyses, Part
3, NASA Tech. Memo 2000–206892, vol. 11, edited by: Hooker,
B., and Firestone, E. R., NASA, Goddard Space Flight Center,
Greenbelt, Maryland, 9–19, 2000.
Paulmier, A. and Ruiz-Pino, D.: Oxygen minimum zones (OMZs)
in the modern ocean, Prog. Oceanogr., 80, 3–4, 113–128,
doi:10.1016/j.pocean.2008.08.001, 2009.
Paulmier, A., Ruiz-Pino, D., Garçon, V., and Farías, L.:
Maintaining of the East South Pacific Oxygen Minimum
Zone (OMZ) off Chile, Geophys. Res. Lett., 33, L20601,
doi:10.1029/2006GL026801, 2006.
Paulmier, A., Ruiz-Pino, D., and Garçon, V.: The Oxygen Minimum
Zone (OMZ) off Chile as intense source of CO2 and N2O, Cont.
Shelf Res., 28, 2746–2756, 2008.
www.biogeosciences.net/13/4389/2016/ Biogeosciences, 13, 4389–4410, 2016
4410 O. Vergara et al.: Seasonal variability of the oxygen minimum zone off Peru
Paulmier, A., Ruiz-Pino, D., and Garçon, V.: CO2 maximum in
the oxygen minimum zone (OMZ), Biogeosciences, 8, 239–252,
doi:10.5194/bg-8-239-2011, 2011.
Penven, P., Echevin, V., Pasapera, J., Colas, F., and Tam, J.: Average
circulation, seasonal cycle, and mesoscale dynamics of the Peru
Current System: a modeling approach, J. Geophys. Res., 110,
C10021, doi:10.1029/2005JC002945, 2005.
Pizarro, O., Shaffer, G., Dewitte, B., and Ramos, M.: Dy-
namics of seasonal and interannual variability of the
Peru-Chile Undercurrent, Geophys. Res. Lett., 29, 1581,
doi:10.1029/2002GL014790, 2002.
Prince, E. D. and Goodyear, C. P.: Hypoxia-based habitat com-
pression of tropical pelagic fishes, Fish. Oceanogr., 15, 451–464,
doi:10.1111/j.1365-2419.2005.00393.x, 2006.
Qiu, B., Chen, S., and Sasaki, H.: Generation of the North Equa-
torial Undercurrent jets by triad baroclinic Rossby wave inter-
actions, J. Phys. Oceanogr., 43, 2682–2698, doi:10.1175/JPO-D-
13-099.1, 2013.
Rahn, D., Rosenblüth, B., and Rutllant, J.: Detecting Subtle Sea-
sonal Transitions of Upwelling in North-Central Chile, J. Phys.
Oceanogr., 45, 854–867, 2015.
Ramos, M., Dewitte, B., Pizarro, O., and Garric, G.: Vertical prop-
agation of extratropical Rossby waves during the 1997–1998
El Niño off the west coast of South America in a medium-
resolution OGCM simulation, J. Geophys. Res., 113, C08041,
doi:10.1029/2007JC004681, 2008.
Resplandy, L., Lévy, M., Bopp, L., Echevin, V., Pous, S., Sarma,
V. V. S. S., and Kumar, D.: Controlling factors of the oxygen
balance in the Arabian Sea’s OMZ, Biogeosciences, 9, 5095–
5109, doi:10.5194/bg-9-5095-2012, 2012.
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K.
S., and Schlax, M. G.: Daily high-resolution blended analyses for
sea surface temperature, J. Climate, 20, 5473–5496, 2007.
Richter, I.: Climate model biases in the eastern tropical oceans:
causes, impacts and ways forward, WIREs Clim. Change, 6,
345–358, doi:10.1002/wcc.338, 2015.
Ridgway K. R., Dunn, J. R., and Wilkin, J. L.: Ocean interpola-
tion by four-dimensional least squares – Application to the wa-
ters around Australia, J. Atmos. Ocean. Tech., 19, 1357–1375,
2002.
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic
modeling system: a split-explicit, free-surface, topography-
following-coordinate ocean model, Ocean Model., 9, 347–404,
2005.
Shchepetkin, A. F. and McWilliams, J. C.: Correction and commen-
tary for “Ocean forecasting in terrain-following coordinates: For-
mulation and skill assessment of the regional ocean modeling
system” by Haidvogel et al., J. Comp. Phys., 227, 3595–3624,
2009.
Siedlecki, S. A., Banas, N. S., Davis, K. A., Giddings, S., Hickey,
B. M., MacCready, P., Connolly, T., and Geier, S.: Seasonal
and interannual oxygen variability on the Washington and Ore-
gon continental shelves, J. Geophys. Res.-Oceans, 120, 608–633,
doi:10.1002/2014JC010254, 2015.
Stramma, L., Johnson, G. C., Sprintall, J., and Mohrholz, V.: Ex-
panding oxygen-minimum zones in the tropical oceans, Science,
320, 655–658, 2008.
Stramma, L., Johnson, G. C., Firing, E., and Schmidtko,
S.: Eastern Pacific oxygen minimum zones: Supply paths
and multidecadal changes, J. Geophys. Res., 115, C09011,
doi:10.1029/2009JC005976, 2010.
Stramma, L., Oschlies, A., and Schmidtko, S.: Mismatch be-
tween observed and modeled trends in dissolved upper-ocean
oxygen over the last 50 yr, Biogeosciences, 9, 4045–4057,
doi:10.5194/bg-9-4045-2012, 2012.
Stramma, L., Bange, H. W., Czeschel, R., Lorenzo, A., and Frank,
M.: On the role of mesoscale eddies for the biological productiv-
ity and biogeochemistry in the eastern tropical Pacific Ocean off
Peru, Biogeosciences, 10, 7293–7306, doi:10.5194/bg-10-7293-
2013, 2013.
Stramma, L., Weller, R. A., Czeschel, R., and Bigorre, S.: Eddies
and an extreme water mass anomaly observed in the eastern south
Pacific at the Stratus mooring, J. Geophys. Res.-Oceans, 119,
1068–1083, 2014.
Thomsen, S., Kanzow, T., Krahmann, G., Greatbatch, R. J., Den-
gler, M., and Lavik, G.: The formation of a subsurface anticy-
clonic eddy in the Peru-Chile Undercurrent and its impact on the
near-coastal salinity, oxygen, and nutrients distributions, J. Geo-
phys. Res.-Oceans, 121, 476–501, doi:10.1002/2015JC010878,
2016. Thomsen, S., Kanzow, T., Krahmann, G., Greatbatch,
R. J., Dengler, M., and Lavik, G.: The formation of a sub-
surface anticyclonic eddy in the Peru-Chile Undercurrent and
its impact on the near-coastal salinity, oxygen, and nutri-
ents distributions, J. Geophys. Res.-Oceans, 121, 1, 476–501,
doi:10.1002/2015JC010878, 2016.
Biogeosciences, 13, 4389–4410, 2016 www.biogeosciences.net/13/4389/2016/