Progress in Oceanography 83 (2009) 117–123
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Progress in Oceanography
journal homepage: www.elsevier.com/locate/pocean
Eddy activity in the four major upwelling systems from satellite altimetry
(1992–2007)
Alexis Chaigneau a,b,*, Gérard Eldin c,d, Boris Dewitte b,c,d
a
Institut Pierre-Simon Laplace (IPSL), Laboratoire d’Océanographie et de Climatologie: Expérimentation et Analyse Numérique (LOCEAN), UPMC/CNRS/IRD/MNHN,
4 Place Jussieu, Case 100, 75252 Paris cedex 05, France
b
Instituto del MAR del PEru (IMARPE), Esquina general Gamarra y Valle, Callao, Peru
c
Université de Toulouse, UPS, LEGOS, 14 Av, Edouard Belin, 31400 Toulouse, France
d
IRD, LEGOS, 31400 Toulouse, France
a r t i c l e
i n f o
Article history:
Received 18 July 2008
Received in revised form 19 February 2009
Accepted 16 July 2009
Available online 24 July 2009
a b s t r a c t
Eddy activity in the four major eastern boundary upwelling systems (EBUS) is investigated using 15 years
of satellite altimetry data. Based on the analysis of more than 4000 long-lived eddy trajectories in every
EBUS, we show that mesoscale structures are mainly generated along the continental coasts and south of
the main archipelagos and propagate westward with velocities increasing toward the equator. These
mesoscale eddies, having radii of 70–160 km, are then frequently observed along the coastal transition
zones and frontal regions and some large oceanic areas are preferentially populated by cyclonic or anticyclonic eddies. Temporal variations of the number of newly-formed eddies and the associated eddy
activity index, defined as the mean eddy energy density, are finally examined at seasonal and interannual
scales. The strongest seasonal (interannual, respectively) variations are observed in the California (Benguela) upwelling systems. The proposed indices also exhibit contrasted long-term trends in each EBUS,
which suggests that eddy activity might be sensitive to a warming climate.
Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction
Superimposed on large-scale currents, mesoscale eddies are
ubiquitous features throughout the oceans that play a significant
role in the transfer of energy and water properties across different
spatial and temporal scales. They are known to transport physical
properties over long distances away from their region of formation,
but they also impact the distribution of chemical properties and
biogeochemistry. For instance, cyclonic eddies tend to upwell
nutrient-enriched deep waters into the euphotic zone, thereby
increasing biological community production (Falkowski et al., 1991;
McGillicuddy et al., 1998). A recent study also suggests that the
interaction of wind-driven currents with mesoscale structures could
dampen upwelling in cyclonic eddies and produce upwelling in anticyclonic eddies (McGillicuddy et al., 2007). Both cyclonic (CE) and
anticyclonic eddies (AE) have a profound effect on the physical and
biological environments and can impact the whole marine ecosystem from plankton distribution to higher trophic levels such as the
feeding and growth of eggs and larvae (Bakun, 2006), pelagic species
* Corresponding author. Address: Institut Pierre-Simon Laplace (IPSL), Laboratoire d’Océanographie et de Climatologie: Expérimentation et Analyse Numérique
(LOCEAN), UPMC/CNRS/IRD/MNHN, 4 Place Jussieu, Case 100, 75252 Paris cedex 05,
France. Tel.: +51 1 429 20 94;tel/fax: +51 1 441 32 23.
0079-6611/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.pocean.2009.07.012
(Logerwell and Smith, 2001; Seki et al., 2002; Domokos et al., 2007)
or seabirds (Spear et al., 2001; Yen et al., 2006).
Eastern boundary upwelling systems (EBUS) are very dynamic
and frequently populated with eddies which can extend the area
of high biological productivity offshore (Correa-Ramirez et al.,
2007) and actively participate to the cross-shore transport of coastal properties, forming a link with the open ocean environment.
Given the clear importance of eddies on marine ecosystem, this
study provides a comparative description of eddy characteristics
in the four major EBUS, namely the Peru-Chile (PCUS), California
(CALUS), Canary (CANUS) and Benguela (BENUS) upwelling systems. In particular, based on the analysis of 15 years of satellite
altimetry measurements, we detect, through an automatic procedure, well-formed and long-lived eddies (radius larger than
30 km, amplitude higher than 2 cm, and life times longer than
35 days). We then investigate: (1) the preferential areas for their
generation and their mean propagation characteristics; (2) the
mean frequency and the relative proportion of CE and AE; (3) the
temporal variations, at timescales larger than the seasonal cycle,
of the activity of newly-formed eddies. These results, which indicate large geographical heterogeneity in eddy activity both within
and between each EBUS, can be viewed as background materials
for the understanding of the observed differences in productivity
and biogeochemical properties of the EBUS (Carr and Kearns,
2003). Our study extends the work of Chelton et al. (2007) by spe-
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A. Chaigneau et al. / Progress in Oceanography 83 (2009) 117–123
cifically focusing on EBUS and providing maps of eddy genesis, frequency and polarity, followed by a discussion on the temporal variations of eddy activity over the last 15 years.
2. Data and methods
2.1. Altimetric sea-level anomaly data
Sea-level anomaly (SLA) data used in this study are based on the
gridded multimission altimeter product produced by Ssalto/Duacs
and distributed by CLS – Space Oceanography Division (Toulouse,
France). The combined altimeter data set, which provides the best
available spatio-temporal resolution for observing mesoscale features (Le Traon and Dibarboure, 1999; Pascual et al., 2006), spans
the period October 1992–December 2007 and corresponds to SLA
relative to a 7-year mean (1993–1999). SLA data are weekly
mapped onto a 1/3° 1/3° latitude/longitude grid by an improved
space/time objective analysis (Le Traon and Dibarboure, 1999; Ducet et al., 2000; Le Traon et al., 2001). From the spatial SLA
gradients and under the geostrophic approximation, residual seasurface geostrophic velocity components, eddy kinetic energy
(EKE) and vorticity (f) are computed (see Chaigneau et al. (2008)
for more details). Note that during the ERS-1 ice-monitoring and
geodetic mission (26 December 1993–31 March 1995), ERS data
were unavailable for ocean mesoscale studies. Consequently for
this period, only the Topex/Poseidon (T/P) data were used in the
merged product, which has been shown to cause a reduction in
EKE levels of around 30% globally (Ducet et al., 2000). We can thus
also expect in our study some decrease in EKE and in eddy activity
during the period with only T/P data.
2.2. Eddy detection and tracking procedure
The eddy identification/tracking algorithm used in this study is
a slightly modified version of the ‘‘winding-angle” method developed by Chaigneau et al. (2008). The CE (AE) detection algorithm
involves, first, searching for eddy centers associated with local
SLA minima (maxima) in a moving window of 4 4 grid points.
Then, for each possible cyclonic (anticyclonic) center, the algorithm searches for closed SLA contours with an increment (decrement) of 103 m. The outer closed SLA contour, embedding only
the considered center, corresponds to the eddy edge. Instead of
using SLA contours, Chaigneau et al. (2008) detected eddy edges
from closed streamlines in the geostrophic current field. However,
in the geostrophic approximation, streamlines are parallel to SLA
contours and closed streamlines correspond to closed SLA contours. In order to optimize computation time, eddy edges are thus
detected here using closed SLA contours.
For each identified eddy, the apparent radius (R) corresponds to
the radius of an equivalent circular vortex having the same area,
whereas the eddy intensity (EI), or energy density, corresponds
to the mean EKE over the vortex normalized by its area. To measure the overall eddy activity in an EBUS for a given date, we define
an eddy activity index (EAI) as the average of the eddy intensities
over each considered domain for this particular time.
In a second stage, each vortex is tracked from the time of its
appearance to its dissipation. Eddy tracking is performed by comparing each eddy at the current time t with those at time t + dt
(dt = 1 week) within a radius of 150 km. We evaluate a cost function CF of each eddy pair having the same polarity which depends
on the mismatch between their distances, vorticities, kinetic energies and radii:
CF ¼
2 2 2
2 dD
dn
dEKE
dR
þ
þ
þ
50
10
10
1:3 106
where dD, dn, dEKE and dR, are the differences in distance, vorticity,
EKE and radius between the vortex at time t and any vortex at time
t + dt. The relative weights are determined by estimating average
departures for successfully formed tracks for limited trials. In the
cost function matrix so obtained, we find the minimum cost and
we classify the eddy at time t + dt as a continuation of the one at
time t. As eddies may disappear between consecutive maps, in particular if they pass into the gaps between satellite groundtracks, we
search for the same eddy for 3 weeks after its disappearance.
3. Results
Given the accuracy of satellite measurements and AVISO product (Le Traon and Ogor, 1998; Ducet et al., 2000; Chelton and Schlax, 2003), and in order to quantify and track persistent features,
we only consider here long-lived eddies, having amplitudes
P2 cm and lifetimes P35 days. In every EBUS, around 4000
long-lived trajectories are identified during the 15-year period,
which represent 40–45% of the total tracks (Table 1). At the scale
of EBUS basins, there is no significant difference between the number of CE and AE trajectories. On average, around 60–100 eddies
are observed per week in every EBUS, and 4–7 are generated (Table
1). The total area covered by CE and AE represents 25–30% of the
considered oceanic region (Table 1). These percentages are independent of the size of the basin, as also illustrated by Chaigneau
et al. (2008) who obtained similar values in a smaller region along
the Peruvian coast. Although the mean statistics are similar in the
four EBUS, the next sections indicate large geographical heterogeneity of eddy properties within and between each EBUS.
Table 1
Eddy statistics in the four major EBUS. Numbers in brackets in the fourth column denote the number of eddies generated each week.
Number of eddies per
weekly map
Area occupied by eddies
(106 km2)
Basin area
(106 km2)
Percentage of basin area covered
by eddies (%)
Peru-Chile (5–40°S; 70–100°W)
1946
2008
44.9
76 (5)
2.21
9.13
24.2
California (20–50°N; 105–140°W)
2264
2348
39.4
75 (5)
1.75
5.94
29.5
Benguela (10–40°S; 0–30°E)
1787
1664
46.5
58 (4)
1.55
5.72
27.1
Canary (10–45°N; 40–5°W)
2756
2631
40.9
99 (7)
2.52
8.65
29.1
Number of trajectories
Cyclonic
eddies
Anticyclonic
eddies
Percentage of long-lived
trajectories
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A. Chaigneau et al. / Progress in Oceanography 83 (2009) 117–123
a] Eddy genesis
20
18
16
14
12
10
8
6
4
2
0
45ºN
30ºN
15ºN
0º
15ºS
30ºS
120ºW
90ºW
60ºW
30ºW
0º
30ºE
c] Eddy radius
b] Eddy propagation velocity
10
30ºN
8
15ºN
6
0º
4
15ºS
2
30ºS
120ºW
90ºW
60ºW
30ºW
0º
30ºE
0
d] Radius and velocity as a function of latitude
km
160
45ºN
45ºN
cm s -1
45ºN
140
Eddy radius
Eddy westward speed
Rossby radius
Rossby westward speed
30ºN
30ºN
15ºN
120
15ºN
0º
100
0º
15ºS
15ºS
80
30ºS
30ºS
120ºW
90ºW
60ºW
30ºW
0º
30ºE
60
0
50
100
km
150
0
5
cm s -1
10
15
Fig. 1. Long-lived eddy genesis and propagation. (a) Number of newly generated eddies. (b) Propagation velocity vectors of both cyclonic and anticyclonic eddies. (c) Mean
radius of long-lived eddies. (d) Zonal average of radii (left) and propagation speeds (right) of long-lived eddies (green lines), with red lines indicating the Rossby radius of
deformation (left) and propagation speed of first baroclinic mode Rossby waves (right).
3.1. Mean eddy genesis and propagation
Fig. 1a shows the geographical distribution of the eddy genesis,
which is defined at each grid point as the number of times a new
eddy is formed over the 15-year period. In the four EBUS, eddies
are mainly generated along the coasts and some localized ‘‘hot
spots” can be discerned. For instance in the PCUS, higher numbers
of eddies are formed off Pisco (15°S), but also between 25 and 30°S
and south of Concepción (37°S); in the CALUS, eddies are born principally in the southern part of the sea of Cortez (20°S), but also off
Punta Eugenia (28°N), Conception Point (34°N) and Cape Blanco
(43°N); in the BENUS, numerous eddies are generated around
Lüderitz (26°S) off Namibia and at 15°S off Angola; finally, in the
CANUS, the main eddy formation sites are found south of the three
archipelagos (Azores, Canary and Cape Verde islands), west of Portugal, and around Cape Ghir (32°N) and Dakar (15°N) along the
North-African coast. The mechanisms responsible for eddy generation in these different regions may be multiple: interaction of the
large-scale currents with the bottom topography, coastline geometry or islands (Røed and Shi, 1999; Tseng and Ferziger, 2001;
Arístegui et al., 1994); local baroclinic instability and wind-forcing
near the coast (Pares-Sierra et al., 1993); vorticity input from windstress curl through Ekman pumping (Kelly et al., 1993); vorticity
conservation and instability of coastal currents (Leth and Shaffer,
2001; Willett et al., 2006); coastal-trapped-waves of equatorial
origin (Zamudio et al., 2008), etc. However, it is noteworthy that,
due to the coarse resolution of the altimeters, only well-formed
eddies can be detected and tracked. This may imply some uncertainties in the identification of preferential areas for eddy genesis
where smaller or complex structures may not be captured by
remote sensing.
Once generated and according to quasi-geostrophic theory, eddies on a b-plane should propagate westward with a speed proportional to the vertical stratification (Cushman-Roisin, 1994). The
vertical stratification also leads to an expected eddy size, the Rossby radius of deformation (Rr), which can be estimated from in situ
observations (Chelton et al., 1998). Both Rr and the westward drift
are expected to decrease toward the poles but also eastward due to
geographical variations of the stratification, which tends to be
weakest in the eastern basins due to the spreading of the isotherms
in upwelling areas. As expected, eddies drift westward and their
propagation velocities increase toward the equator (Fig. 1b). Also,
the meridional velocity component indicates a divergence of the
eddy field from the equator and high-latitude regions and a convergence at around ±15–30° (Fig. 1b). When distinguishing longer
lived eddies, with lifetimes longer than 3 months, CE and AE tend
to move slightly poleward and equatorward, respectively (not
shown), as previously observed in EBUS (Morrow et al., 2004;
Chaigneau and Pizarro, 2005a; Chaigneau et al., 2008) and globally
(Chelton et al., 2007). The general distribution of eddy radii is also
in agreement with the quasi-geostrophic theory with eddy size
decreasing toward eastern edges and toward the poles related to
a change in stratification (Fig. 1c).
However, as mesoscale eddies generation is often associated
with non-linear processes, we examine the departure from the linear theory by comparing R to Rr and the propagation velocities to
the phase speeds of the first baroclinic Rossby waves (Fig. 1d).
When zonally averaging over every EBUS, R increases from
70 km at ±40° to 140 km at ±10°. Poleward of ±15° these typical
scales are around twice Rr, whereas equatorward of ±15° the latitudinal increase of eddy sizes is weak compared to the slope of the
Rossby radius curve. In terms of velocity, eddy westward speeds
are rather in agreement with the theoretical values south of 25°S
in the Southern Hemisphere and north of 35°N in the Northern
Hemisphere with values of 1–3 cm s1. In contrast, equatorward
of ±30°, eddy propagation velocities increase from 5 cm s1 at
±30° to 10 cm s1 at 10°S, with a weaker slope than the theoretical
phase speeds of baroclinic Rossby waves. As also suggested from
inspection of Fig. 1b and c, no significant difference was observed
in the latitudinal variations of eddy radii and propagation velocities neither between PCUS and BENUS nor between CALUS and
CANUS.
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A. Chaigneau et al. / Progress in Oceanography 83 (2009) 117–123
a]Long-lived eddy frequency
%
b] Polarity
60
0.5
45ºN
45ºN
0.4
50
0.3
30ºN
30ºN
0.2
40
0.1
15ºN
15ºN
30
0º
0º
20
15ºS
10
30ºS
120ºW
90ºW
60ºW
30ºW
0º
30ºE
0
-0.1
15ºS
-0.2
-0.3
30ºS
-0.4
120ºW
90ºW
60ºW
30ºW
0º
30ºE
-0.5
Fig. 2. Long-lived eddy frequency (left) and polarity (right). See text for definitions.
3.2. Mean eddy frequency and polarity
The mean geographical distribution of eddy frequency EF
(EF = FAE + FCE), which corresponds at each location to the percentage of time that the point is located within a vortex, is depicted in
Fig. 2a. In every EBUS, long-lived eddies are frequently observed
in the coastal transition zones (CTZ) and in frontal regions where
they also explain a relatively large part of SLA variance (Chelton
et al., 2007). In contrast, equatorward of ±15–20°, EF decreases,
probably due to the faster eddy propagation in these regions
(Fig. 1b) which weakens the probability of being inside an eddy.
The high SLA variance observed in these latitude bands (not
shown) is also more likely to be associated with long-wave
dynamics rather than mesoscale eddies. In the PCUS, eddies are
frequently observed in the CTZ extending from the coast of Chile
to 600–1000 km offshore. This CTZ is wider and more diffuse in
the north and narrower but better defined off central Chile. Strong
EF is also observed along the subtropical front which is southeastward oriented between 100°W and the Chilean coast (Chaigneau
and Pizarro, 2005b). In the CALUS, high values of EF are observed
within 600 km of the coast south of 42°N with the maximum
offshore signal between 32 and 38°N. In contrast, long-lived
eddies are rarely observed north of 42°N where EKE levels are also
weak (Strub and James, 2000). In the BENUS, EF is particularly
high south of 20°S due to the northwestward advection of eddies
shed into the South Atlantic from the retroflection of the Agulhas
Current (Gordon and Haxby, 1990; Byrne et al., 1995). These high
EF levels are also reinforced by westward advection of eddies
generated off Lüderitz (Fig. 1). Finally, in the CANUS, EF is relatively high southwest of the Canary and Cape Verde islands, but
also overall, along and north of the Azores Current, or Subtropical
Front at 35°N.
High EF along the subtropical fronts of both the PCUS and CANUS are not associated with strong local eddy generation
(Fig. 1a). Eddies seem to be rather developed away from the fronts
and propagate and converge (Fig. 1b) along them. The westward
propagation and intensification of eddies and Meddies along the
North Atlantic Subtropical Front have been previously observed
(Pingree, 1997; Pingree et al., 1999; Drillet et al., 2005; Siedler
et al., 2005). The Azores Current is also known to meander and
shed eddies, which can be re-absorbed into the jet, merged or coalesced with other rings (Pingree, 1997; Pingree et al., 1999). It is
thus unexpected to observe relatively weak eddy genesis along
the main axis of the subtropical fronts, particularly in the CANUS
(Fig. 1a). This problem may be attributed to a deficiency of our
tracking algorithm which can underestimate the number of generated eddies by automatically extending trajectories which end near
a newly formed eddy. In fact, when considering short-lived eddies
(lifetime <35 days), more eddies are generated along the fronts
(not shown).
Eddy polarity represents the probability of a point, inside a vortex, being inside a CE (eddy polarity <0) or AE (eddy polarity >0).
The mean polarity distribution, computed as (FA FCE)/(FAE + FCE)
over the 15-year period of the altimetry dataset is presented in
Fig. 2b. It shows the existence of large-scale polarized regions in
the four EBUS: In the PCUS, a wide cyclonic band between 10
and 30°S is surrounded by two anticyclonic regions, in agreement
with Chaigneau and Pizarro (2005a) who also observed more CE
than AE trajectories between 10° and 30°S. In the CALUS, south
of 30°N eddies tend to be rather cyclonic, whereas north of 30°N
the distribution is unclear. Along the Namibia and Angola coasts
in BENUS, CE (AE) are frequently observed south (north) of
20°S. In the offshore ocean of this EBUS, the opposite is observed.
Finally, Fig. 2b shows that the region North of 32°N in CANUS is
rather anticyclonic, whereas three latitudinal cyclonic bands are
centered at 15°N, 27°N and 32°N. Along the North-African coast,
eddy polarity is rather anticyclonic. The eddy polarity distribution
in the four EBUS is consistent with other regional studies
(DiGiacomo and Holt, 2001; Stegmann and Schwing, 2007;
Chaigneau and Pizarro, 2005a) and with global maps of cyclonic
and anticyclonic motions computed from near-surface drifters
(Griffa et al., 2008). Note that the patterns of Fig. 2 are not significantly altered when eddy frequency and polarity are estimated
over the periods 1992–1997, 1998–2002 or 2003–2007 (not
shown), which suggests that their distribution is tightly linked to
some aspects of the mean circulation.
3.3. Temporal variations of eddy activity
Seasonal and interannual variations of the number of newly
generated eddies (N) and the corresponding eddy activity index
PN EIi
are displayed in Fig. 3. In general, the variations
EAI ¼ i¼1
N
of N are relatively weak (±10–15% around the mean) whereas the
EAI can experience important temporal variability (up to ±50%
around the mean). On average, 20–30 eddies are formed per month
in every EBUS (see also Table 1) with a mean EAI of 3–
4 103 cm2 s2 km2 except in the BENUS where this index
reaches 30 103 cm2 s2 km2. The stronger EAI in the BENUS
is related to energetic Agulhas eddies which evidence large values
of kinetic energy.
At seasonal scales, the PCUS exhibits the weakest variability and
neither N nor EAI shows a marked seasonal cycle (Fig. 3a). The
CALUS shows the strongest seasonal cycle with a clear boreal summer maximum in EAI from June to September. The positive phase
of EAI may be related to the presence of an equatorward coastal
jet which moves offshore from spring to fall developing energetic
mesoscale structures (Strub and James, 2000). The variance of
the annual harmonic fitted to the EAI seasonal cycle explains 60%
of the seasonal EAI variance observed in this EBUS (not shown).
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A. Chaigneau et al. / Progress in Oceanography 83 (2009) 117–123
%
%
a] Seasonal cycle
50
40
30
20
10
0
−10
−20
−30
−40
−50
50
40
30
20
10
0
−10
−20
−30
−40
−50
PCUS:
N=21.4
EAI=2.5
J F M A M J J A S O N D
Month
J F M A M J J A S O N D
Month
CANUS:
N=28.2
EAI=4.0
BENUS:
N=18.7
EAI=29.5
CALUS:
N=21.4
EAI=4.4
J F M A M J J A S O N D
Month
J F M A M J J A S O N D
Month
b] Interannual variations
CALUS:
N: 1.7%
EAI: -2.8%
PCUS:
N: -0.7%
EAI: -12.7%
F=244.7
93
95
97
99 01
Year
03
p<0.01
05
07 93
F=4.0
95
97
99 01
Year
BENUS:
N: -2.7%
EAI: -16.5%
F=37.5
p=0.05
03
05
07
93
95
97
99 01
Year
03
CANUS:
N: 0.7%
EAI: 1.3%
F=0.4
p<0.01
05
07 93
95
97
99 01
Year
p=0.5
03
05
07
Fig. 3. Temporal variations of the number of generated eddies (N) and the corresponding eddy activity index (EAI in 103 cm2 s2 km2) in the four major EBUS. (a) Seasonal
cycle of N (gray bars) and EAI (black bars), computed in percentage around the annual mean; bold numbers in the upper right corners indicates the average number of eddies
generated each month (N) and the associated EAI. (b) Interannual variations of N (gray thin line) and EAI (black thin line), computed in percentage around a 12-year mean
(April 1995–March 2007, outside the shaded gray areas); Black thick lines show linear trends for EAI whereas shaded gray areas correspond to the periods not taken into
account for the computation of the 12-year mean and linear trends. Bold numbers in the upper right corners indicate the slope of the linear trends observed over the 12-year
period whereas the results of the f-test (F and p values), testing the significance of the EAI trends, are displayed at the bottom. Interannual variations were computed by
applying a 6-month forward/backward moving average to the timeseries.
In the BENUS, N does not show a marked seasonal cycle, whereas
EAI seasonal variance is explained by both the annual harmonic
(40% of the seasonal variance) and the semi-annual component
(20% of the seasonal variance). This may reflect the influence of distinct eddy sources. In particular, there is an area of year round
coastal upwelling between 15 and 30°S, two regions of seasonal
upwelling between 30 and 34°S and between 18 and 26°E with
maximum intensity during spring and summer, and the regular
advection of mesoscale eddies pinched off from the Agulhas retroflection. Finally, the CANUS exhibits a semi-annual cycle in both N
and EAI with two maxima in March–April and October–November,
and two minima from December to February and from July to September. In this EBUS, the semi-annual harmonic explains more
than 60% (45%, respectively) of the EAI (N) seasonal variance
whereas the annual component explains around 10% (30%). This
semi-annual signal may originate from the upwelling variability
in the central part of the northwest African coast (21–32°N) where
persistent upwelling throughout the year reaches its maximum
intensity also during fall and spring (Santos et al., 2005). Also,
the EAI maximum observed in October–November coincides with
higher energy levels and generation of cyclonic eddies associated
with the Azores Current (Mouriño et al., 2003).
At interannual scales, every EBUS exhibits a significant decrease
of EAI in 1994 ranging from 20% to 40% (Fig. 3b). This is consistent
with the global EKE reduction of 30% in SLA products between
January 1994 and March 1995, when only one satellite was
available (Ducet et al., 2000). Both upwelling systems of the Pacific
Ocean (PCUS and CALUS) show relatively weak interannual
variations of less than ±15% around the mean. However in the
CALUS, the mean eddy energy density increased significantly in
2003–2005 whereas it has been strongly reduced since 2005. In
the South-Atlantic Ocean, the BENUS shows strong interannual
variations with a negative phase in 1994–1996 followed by two
strong positive phases in 1997 and from mid 1998 to mid 2000
reaching an EAI anomaly of +50%. Since 2001, the EAI is reduced
by 15–30% except in 2004 where a slight positive anomaly is
observed. Finally in the North-Atlantic Ocean, the EAI in the CANUS
shows aperiodic oscillations, with positive phases in 1995–1997
and 2001–2004, and two negative phases in 1997–2000 and in
2004–2006.
In an attempt to relate the observed interannual EAI variations
to some aspects of the climate variability, a 6-month backward/
forward moving average (similar to what was used to filter EAI)
is also applied to ‘classical’ climatic indexes, namely the Niño3.4
index (sea-surface temperature averaged over 180–130°W; 5°S–
5°N), the Southern Oscillation Index (SOI) and the North Alantic
Oscillation index (NAO) (source: http://climexp.knmi.nl/). We find
that in the PCUS, the mean eddy energy density associated with
newly-formed eddies is significantly anti-correlated to the
Niño3.4 Index at 39% whereas it is strongly and significantly correlated to the SOI with a value of 53%. In the CANUS the EAI is significantly anti-correlated to the NAO index at 29%.
Superimposed on the seasonal and interannual EAI variations, we
observe significant long-term linear trends (black lines in Fig. 3b).
The latter was computed from April 1995 to March 2007, a 12-year
period excluding the time interval when only one satellite was
available. The EAI associated with newly-formed eddies decreased,
respectively, by 13% in the PCUS, 3% in the CALUS, 17% in the
BENUS. Using the f-test, we find that these trends are statistically
significant at the 0.01 level for the PCUS and BENUS and at the 0.05
level for the CALUS. In contrast the EAI in the CANUS remained rather
stable with a slight and non-significant increase of 1%. Interestingly,
these changes are not associated with significant trends in the
number of eddies nor in their size but with a reduction in EKE.
4. Summary
Based on 15 years of satellite altimetry measurements, the
dynamics and some of the characteristics of long-lived mesoscale
eddies are investigated in the four major upwelling systems. On
average, and despite significant differences in physical and topo-
122
A. Chaigneau et al. / Progress in Oceanography 83 (2009) 117–123
graphic properties, all the EBUS exhibit similar statistics. The most
important discrepancies are found in the distribution of long-lived
eddy frequency/polarity and eddy genesis which may be a sign of
different forcing mechanisms. Eddies are generated at some ‘‘hot
spots” along the western coasts of each continent and south of
the main archipelagos in the CANUS. They propagate offshore
and are frequently observed along the coastal transition zones
and the major fronts. Poleward of around ±30°, eddy propagation
speeds are in agreement with the linear theory. In contrast, at
lower latitudes, where they propagate faster, non-linear processes
and interaction with large-scale circulation may dissipate energy
and slow down eddy propagation; this could result in observed
westward speeds much weaker than the ones predicted by the linear theory. In every EBUS, regions of different polarities are identified, which may indicate that preferential ‘‘vortex streets” exist,
depending on polarity. This could have important implications
for both the horizontal and vertical transport of physical/biogeochemical properties. For instance in the Chilean upwelling, high
levels of chlorophyll-a concentration have been observed along cyclonic eddy paths far from coastal upwelling zones, associated to
both eddy chlorophyll advection and eddy nutrient pumping
(Correa-Ramirez et al., 2007). Conversely, high chlorophyll-a values along anticyclonic eddy trajectories are only observed near
the coast being mainly linked to eddy advection of chlorophyll-rich
waters from coastal upwelling. However, these results contrast
with the findings of Rossi et al. (2008) who noted in the BENUS
and CANUS negative correlation between horizontal mixing and
chlorophyll standing stocks suggesting that in upwelling regions
eddies do not tend to enhance biological productivity. Future
investigations are thus necessary to better understand the impact
of mesoscale structures on biological activity in the EBUS.
The temporal variations of eddy genesis and their associated
intensity have shown that the PCUS experiences relatively weak seasonal and interannual variations. In contrast, the CALUS (BENUS)
shows the strongest seasonal (interannual) variations and a relatively weak or moderate variability at interannual (seasonal) scale.
The CANUS shows moderate modulation of eddy energy density at
both the seasonal and interannual scales. The interannual EAI variations in the PCUS (CANUS, respectively) are rather anti-correlated
with the Niño3.4 index (NAO index) and correlated with the SOI. Despite the limitation of the relatively short record, linear trends show
significant decreases of the energy density of newly-formed eddies
in the PCUS, the CALUS and the BENUS. This could indicate that warmer large scale oceanic conditions, as observed in recent years, are
associated with a reduction of the mean eddy energy density in most
of the EBUS. This hypothesis is reinforced by recent results showing
that the atmospheric EKE at mid-latitudes exhibits a maximum for a
climate with mean temperature similar to that of present-day earth,
with significantly smaller values both for warmer and for colder climates (O’Gorman and Schneider, 2008). However, as also mentioned
by these authors, an increase of the meridional temperature gradients could lead to an increase in EKE. These results, obtained from
general circulation atmospheric models, raise the issue of whether
or not eddy activity in EBUS can be used as an index for measuring
the impact of climate change on the ocean circulation. This will deserve further study as longer records and high-resolution regional
oceanic reanalyses become available. At this stage, our study provides a benchmark for the validation and interpretation of regional
high-resolution oceanic simulations, which in turn could allow
investigating the modulation of the mesoscale activity associated
to the change in large-scale circulation.
Acknowledgements
The AVISO altimeter product was produced by Ssalto/Duacs and
distributed by Aviso with support from CNES. We thank two anony-
mous reviewers for their constructive comments, and A.D. McIntyre for the English edition of the text. A. Chaigneau and B.
Dewitte benefited from funding from the PCCC program (Peru Chile
Climate Change) of the ANR (Agence Nationale de la Recherche).
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