Seasonal Variability of the Pacific South Equatorial Current during the Argo Era

Lina Yang aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Lina Yang in
Current site
Google Scholar
PubMed
Close
,
Raghu Murtugudde dEarth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland

Search for other papers by Raghu Murtugudde in
Current site
Google Scholar
PubMed
Close
,
Shaojun Zheng aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Shaojun Zheng in
Current site
Google Scholar
PubMed
Close
,
Peng Liang aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Peng Liang in
Current site
Google Scholar
PubMed
Close
,
Wei Tan eCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, China

Search for other papers by Wei Tan in
Current site
Google Scholar
PubMed
Close
,
Lei Wang aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Lei Wang in
Current site
Google Scholar
PubMed
Close
,
Baoxin Feng aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Baoxin Feng in
Current site
Google Scholar
PubMed
Close
, and
Tianyu Zhang aLaboratory for Coastal Ocean Variation and Disaster Prediction, College of Ocean and Meteorology, Guangdong Ocean University, Zhanjiang, China
bKey Laboratory of Climate, Resources and Environment in Continental Shelf Sea and Deep Sea of Department of Education of Guangdong Province, Zhanjiang, China
cKey Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing, China

Search for other papers by Tianyu Zhang in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

The tropical Pacific currents from January 2004 to December 2018 are computed based on the gridded Argo temperatures and salinities using the P-vector method on an f plane and the geostrophic approximation on a β plane. Three branches of the South Equatorial Current (SEC) are identified, i.e., SEC(N) (2°S–5°N), SEC(M) (7°–3°S), and SEC(S) (20°–8°S), with the maximum zonal velocity of −55 cm s−1 and total volume transport of −49.8 Sv (1 Sv ≡ 106 m3 s−1) occurring in the central-east Pacific. The seasonal variability of each branch shows a distinct and different westward propagation of zonal current anomalies, which are well mirrored by the SLA differences between 2°S and 5°N, between 3°S and 6°S, and between 8°S and 15°S, respectively. Most of the seasonal variations are successfully simulated by a simple analytical Rossby wave model, highlighting the significance of the first-mode baroclinic, linear Rossby waves, particularly those driven by the wind stress curl in the central-east Pacific. However, the linear theory fails to explain the SEC(M) variations in certain months in the central-east Pacific, where the first baroclinic mode contributes only around 50% of the explained variance to the equatorial surface currents. A nonlinear model involving higher baroclinic modes is suggested for a further diagnosis. Considering the crucial role played by the tropical Pacific in the natural climate variability via the El Niño–Southern Ocean dynamics and the ocean response to anthropogenic forcing via the ocean heat uptake in the eastern tropical Pacific, advancing the process understanding of the SEC from observations is critical.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peng Liang, liangpeng0405@gmail.com

Abstract

The tropical Pacific currents from January 2004 to December 2018 are computed based on the gridded Argo temperatures and salinities using the P-vector method on an f plane and the geostrophic approximation on a β plane. Three branches of the South Equatorial Current (SEC) are identified, i.e., SEC(N) (2°S–5°N), SEC(M) (7°–3°S), and SEC(S) (20°–8°S), with the maximum zonal velocity of −55 cm s−1 and total volume transport of −49.8 Sv (1 Sv ≡ 106 m3 s−1) occurring in the central-east Pacific. The seasonal variability of each branch shows a distinct and different westward propagation of zonal current anomalies, which are well mirrored by the SLA differences between 2°S and 5°N, between 3°S and 6°S, and between 8°S and 15°S, respectively. Most of the seasonal variations are successfully simulated by a simple analytical Rossby wave model, highlighting the significance of the first-mode baroclinic, linear Rossby waves, particularly those driven by the wind stress curl in the central-east Pacific. However, the linear theory fails to explain the SEC(M) variations in certain months in the central-east Pacific, where the first baroclinic mode contributes only around 50% of the explained variance to the equatorial surface currents. A nonlinear model involving higher baroclinic modes is suggested for a further diagnosis. Considering the crucial role played by the tropical Pacific in the natural climate variability via the El Niño–Southern Ocean dynamics and the ocean response to anthropogenic forcing via the ocean heat uptake in the eastern tropical Pacific, advancing the process understanding of the SEC from observations is critical.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peng Liang, liangpeng0405@gmail.com

1. Introduction

The Pacific South Equatorial Current (SEC) is the most intense and most extensively distributed equatorial current that covers both the south and north tropical Pacific Ocean basins, roughly between 20°S and 5°N at the sea surface (Lumpkin and Johnson 2013). It plays a crucial role in the ocean general circulation of the tropical Pacific, whose role in global climate dynamics and the anthropogenic response of the climate system can hardly be overemphasized (Kessler 2006; Kosaka and Xie 2013). It is split into different branches by the Equatorial Undercurrent and the South Equatorial Countercurrent and thus has a complicated spatial structure (Lumpkin and Johnson 2013). Through the SEC as well as its bifurcation process off the Australian coast, the Pacific water mass and energy are redistributed between the eastern and western ocean basins and between the low and middle-to-high latitudes. The SEC branching is closely associated with the recharge–discharge process of the western Pacific warm pool as well as the evolution of El Niño–Southern Oscillation (ENSO) (Fine et al. 1994; Grenier et al. 2011; Davis et al. 2012; Gasparin et al. 2012; Ganachaud et al. 2014; Hu et al. 2015). In addition, the SEC serves as an important source of waters for the Indonesian Throughflow, both for the lower and upper thermocline layers (Gordon and Fine 1996; Sprintall and Révelard 2014; Yang et al. 2018). Thus, the fluctuations of the Pacific SEC are of great significance to the Indo-Pacific climate variability and change and thus for the global climate response as well.

Equatorial currents, e.g., Pacific North Equatorial Current (NEC) and North Equatorial Countercurrent (NECC), tend to fluctuate significantly in response to the trade winds and undergo a significant seasonal variability (e.g., Wyrtki 1974a,b; Qiu and Lukas 1996; Hsin and Qiu 2012; Liu and Zhou 2020; Liu et al. 2021). Due to scant observational data as well as the more complex structure of the Pacific SEC, there have been relatively few studies focusing on the spatiotemporal characteristics of the SEC across the entire Pacific Ocean basin. Nevertheless, earlier studies do provide us some illuminating knowledge using the limited data. For example, Johnson et al. (2002) defined a northern and a southern branch of the Pacific SEC, denoted by SEC(N) and SEC(S), based on 10 meridional sections of acoustic Doppler current profiler currents observed during the 1990s. Both branches are westward flows for potential densities of σθ < 26.0 kg m−3 that are located between the equator and the NECC and between the equator and 8°S (the southern limit of the observations), respectively. Specifically, the SEC(N) starts near 2°N in the east, extends to 3°N in the central Pacific, where the current is at its strongest, and shifts southward to 1°N in the west, where it weakens and sometimes vanishes. The SEC(S) is clearly manifested within 4°–5°S and maintains its strength in the central-western Pacific to the east of 165°E. Later, Lumpkin and Johnson (2013) identified, from global ocean drifter-deduced surface currents, a third branch of the Pacific SEC that lies along about 15°S, in addition to the most intense one at 2°N and the second strongest one between 2°–4°S. Thus, a dataset with a more extensive coverage is essential to acquire a relatively complete view of the three-dimensional structure of the Pacific SEC.

In the seasonal cycle, Wyrtki (1974a) found an overall stronger current in the central Pacific during the first half of the year. Johnson et al. (2002) provided further details that the SEC(N) varied roughly in opposite phase with the SEC(S); the SEC(N) is weakest in February–March in the east but around July in the west. Somewhat similarly, Kessler and Gourdeau (2007) also identified a maximum velocity of the SEC in the southwest Pacific Ocean during July–October from an ocean general circulation model, but with earlier anomalies at lower latitudes. These results suggest that a phase shift of the seasonal variability of the Pacific SEC occurs along both the latitudinal and longitudinal directions, which is likely to be related to the propagation of equatorial long waves. In fact, the previous studies have reported that the wind-stress-curl-forced first-mode baroclinic Rossby waves play an important role in the seasonal-to-interannual variabilities of the Pacific equatorial currents, e.g., the NEC (Qiu and Lukas 1996; Zhai and Hu 2013; Liu and Zhou 2020), the North Equatorial Subsurface Current (Yang et al. 2020), the NECC (Hsin and Qiu 2012; Tan and Zhou 2018), the South Equatorial Countercurrent (Chen and Qiu 2004), and the zonal jets in the Coral Sea which are formed when the SEC encounters islands in the southwest Pacific Ocean (Kessler and Gourdeau 2007; Kessler and Cravatte 2013). In this context, it should be noted that the characteristics and causes of the seasonal variability of the Pacific SEC have not been explored in detail at the basin scale.

The main purpose of this study is to derive a long time series of the Pacific SEC that covers areas between 25°S and 5°N based on Argo observational temperatures and salinities and to investigate its seasonal variability as well as the corresponding dynamics related to the Pacific trade winds. In the next section, the data and methods, including the ocean current inversion method, the 1.5-layer linear wave model, and the vertical mode decomposition method, are presented. The features and dynamics of the seasonal variability of the SEC across the entire ocean basin are described in section 3, followed by a discussion in section 4. The summary and conclusions are provided in section 5.

2. Data and methods

a. Data

Monthly ocean temperatures and salinities from 2004 to 2018, which are released by the Scripps Institution of Oceanography, are produced by optimal interpolation using Argo products only, with a horizontal resolution of 1° latitude × 1° longitude and 58 vertical levels down to 2000 dbar (Roemmich and Gilson 2009). Monthly in situ observational currents at 10-m depth are provided by the Tropical Atmosphere Ocean/Triangle TransOcean Buoy Network (TAO/TRITON) array from the Global Tropical Moored Buoy Array Program. Daily sea level anomalies (SLAs) and surface geostrophic currents from the year 1993 onward are provided by the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) and the Copernicus Marine Environment Monitoring Service, with a spatial resolution of 0.25° × 0.25°. Monthly means of daily wind velocities at 10-m height are provided by the European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim), which are available from January 1979 to August 2019, with a spatial resolution of approximately 80 km (Dee et al. 2011). The 2-min gridded global relief (ETOPO2) is provided by the National Centers for Environmental Information and is used to determine the eastern and western boundaries of the Pacific Ocean basin. Climatological profiles of temperatures and salinities for extracting the ocean vertical baroclinic modes are from the World Ocean Atlas 2018 (WOA18), on a 1° × 1° grid and in 102 vertical levels from the surface down to 5500 m (Locarnini et al. 2019; Zweng et al. 2019). Zonal currents at an interval of three days, available from January 1992 onward, are provided by the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2), with a horizontal resolution of 1/4° × 1/4° and 50 layers from 5 to 5900 m (Menemenlis et al. 2008; Fenty et al. 2017). Monthly zonal currents at 1° × 1° horizontal resolution and in 42 layers from 5 to 5350 m, are distributed by ECMWF Ocean Reanalysis System 4 (ORAS4), spanning the period from 1958 to 2015 (Balmaseda et al. 2013).

b. Geostrophic current in the tropical Pacific Ocean

The tropical Pacific Ocean currents out of and within the equatorial bands (5°S–5°N) are calculated by geostrophic approximation on an f plane and a β plane, respectively. The equations are as follows:
uf=1fρPy,
υf=1fρPx,
uβ=1βρ2Py2,
υβ=1βρ2Pxy,
where f, β, ρ, and P are the Coriolis parameter, meridional gradient of the Coriolis parameter, mean seawater density (set to a constant of 1028.0 kg m−3), and pressure, respectively. To remove the uncertainties introduced by the arbitrarily assumed level of no motion, uf and υf are calculated using the P-vector inverse method, which obtains absolute geostrophic current on the basis of conservation of mass and potential vorticity (Chu 1995). When calculating the uβ and υβ, multiple reference levels, i.e., 2000, 1500, 1000, and 800 m are used in this study to examine the robustness of the results, to verify that the references levels barely change the results. In the following analyses, the result calculated referenced to the 800-m depth is used. Finally, an exponential coefficient about latitudes is employed for a smooth transition from the f plane to the β plane. The corresponding equations are as follows:
ug=(1eλ2)uf+eλ2uβ,
υg=(1eλ2)υf+eλ2υβ,
where ug and υg are the final geostrophic velocities and λ denotes the latitude. The seasonal variability of geostrophic currents is represented by the climatological monthly anomalies from January 2004 to December 2018.

c. Axis and volume transport of the Pacific SEC

The axis (AX) and volume transport (UTran) of the SEC across the Pacific Ocean are calculated using the following formulas:
AX=ysynyU(x,y,t)dyysynU(x,y,t)dy,
UTran=ysyn26.0σθ0u(x,y,z,t)dzdy,
where x, y, z, and t are longitude, latitude, depth, and time, respectively; U is the vertical-mean westward zonal velocity above the 26.0σθ (kg m−3) isopycnal surfaces where the SEC core is mainly situated; u is the point-wise westward zonal velocity; and ys and yn are the southern and northern limits of each branch of the SEC, which are specified in detail in section 3b.

d. Effective degrees of freedom

When conducting correlation analysis of two time series x1 and x2, the effective numbers of degrees of freedom (DOF) are first estimated following Bretherton et al. (1999):
DOF=N1r1r21+r1r2,
where N is the sample number and r1 and r2 are the lag-one autocorrelation coefficients of x1 and x2.

e. The first-mode linear long Rossby and Kelvin wave models

The low-frequency evolution of large-scale off-equatorial SLA is governed by the long quasigeostrophic Rossby waves forced by the wind stress curl. The linear vorticity equation is given as (Meyers 1979; Kessler 1990; Chen and Qiu 2004):
hRtCRhRx=ggk×τρ0fεhR,
where hR is the modeled SLA (positive up); CR is the first-mode baroclinic long Rossby wave speed, which is set to the value provided by Chelton et al. (1998); g is the gravity (set to a constant of 9.81 m2 s−1); ρ0 is the mean density of the upper oceans (set to a constant of 1025.0 kg m−3); f is the Coriolis parameter; τ is the surface wind stress; g′ and ε are the reduced gravity coefficient and Newtonian dissipation rate, respectively, that are determined following Hsin and Qiu (2012) to ensure that the sum of absolute differences between hR and AVISO-SLA is minimum with 1/ε = 0–5 years and g′ = 0–0.3 m s−2. The hR is obtained by integrating Eq. (6) along the baroclinic Rossby wave characteristics from the eastern boundary (xe) toward the west, i.e.,
hR(x,y,t)=ggxex1CRk×τ(x,y,txxCR)ρ0f×exp(xxεCRdζ)dx+hR0exp(xexεCRdζ),
where hR0 is the SLA near the eastern boundary. The first term on the right-hand side of Eq. (7) is the interior wind-driven part and the second term is the one forced by the Rossby wave radiating from the eastern boundary.
In addition, the first-mode linear Kelvin wave is essential to study the variability of oceans near the equator. The model integrates the SLA in the following form:
(t+CKx)hK=gτxgρ0CK,
where hK is the Kelvin wave-induced SLA; CK is the first-mode baroclinic Kelvin wave speed, set to be 2.73 m s−1 (Kessler and McPhaden 1995); and τx is the zonal wind stress. The integration of (8) along the Kelvin wave characteristics from the western boundary (xw) gives the solution:
hk(x,t)=ggρ0CK2xwxτx(x,txxwCK)dx.
Here, the eastern and western boundaries are determined according to the ETOPO2 topography.

f. Vertical mode decomposition

Following Dewitte et al. (1999), the nth vertical mode function ψn satisfies the equation of
ddz(1N2dψndz)+1cn2ψn=0,
subject to the boundary conditions:
dψndz+N2gψn=0    at z=0
dψndz=0    at z=H,
where N is the Brunt–Väisälä frequency; cn is the eigenvalue, representing the nth vertical mode phase speed; H is the ocean depth (set to a constant of 5000 m). The three-dimensional zonal current anomalies u′ are then projected onto each baroclinic mode to extract the vertical mode coefficient un through
un=H0uψndzH0ψn2dz.

3. Results

a. Validation of derived equatorial currents

The Pacific surface geostrophic currents averaged from January 2004 to December 2018, derived from Argo temperatures and salinities, are shown in Fig. 1. The SEC is prominent in the latitudinal range of 15°S–5°N, with the NECC adjacent to its northern boundary and the South Equatorial Countercurrent embedded within it around 10°S. The maximum velocity of the SEC is located in the central-east Pacific north of 2°S and in the central Pacific around 5°S, where the current speed exceeds 30 cm s−1. In the western Pacific, the SEC nearly disappears to the north of the equator, where the opposing NECC becomes intensive, which is consistent with historical observations (Johnson et al. 2002).

Fig. 1.
Fig. 1.

The surface geostrophic currents averaged for the period of 2004–18 (cm s−1). SEC, South Equatorial Current; NECC, North Equatorial Countercurrent; SECC, South Equatorial Countercurrent; SPC, South Pacific Current. The westward currents are superimposed with light gray shading.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Previous studies have shown the reliability of the P-vector method in calculating the ocean currents away from the equator (e.g., Yuan et al. 2014; Yang and Yuan 2016; Zhou et al. 2018), given we mainly focus on the zonal currents, the zonal velocities within 5° of the equator are compared with measurements by TAO/TRITON in situ moorings (Fig. 2 and Table 1). The corresponding correlation coefficients and RMS differences are based on the monthly data during the overlapping period of the two currents from January 2004 to December 2018. To remove contributions from small-scale motions, a 9-point average is applied to the geostrophic currents at the equator. Figure 2 suggests that the variability of the geostrophic currents compares well with the mooring measurements, with the correlation coefficients at 11 stations (except at 5°N, 156°E) passing a 99% confidence level test at the effective degrees of freedom. At 5°N, 156°E, the corresponding correlation coefficient only passes a 95% confidence level test, with the discrepancy mainly occurring before the year 2008 when the Argo profiles are relatively sparser. Thus, the gridding and interpolation of the Argo data may introduce some uncertainties, for example, underestimating the magnitudes of zonal velocity at (5°N, 156°E). Table 1 summarizes the root-mean-squares (RMSs) of the in situ measurements and the derived geostrophic currents as well as their differences, showing a basically consistent RMS between the two currents. At 5°N, 137°E and 0°, 140°W, the RMSs of the two currents and their differences are all large, and the RMS differences remain nearly unchanged after subtracting the zonal Ekman velocity from the mooring currents. Here, the Ekman velocity is estimated based on ERA-Interim wind stress and the method of Lagerloef et al. (1999), and generally no stronger than 3 cm s−1 at 5°N, 137°E. Thus, the discrepancies there are probably due to the inefficiency of capturing transient eddies in the gridded Argo data within the equatorial band where the mesoscale eddies are active. But it will not significantly affect the annual mean structure and seasonal variations of each SEC branch after time averaging.

Fig. 2.
Fig. 2.

Comparison of the monthly zonal geostrophic currents (black lines) with the in situ measurements of the TAO/TRITON array (red lines) at 10-m depth during January 2004–December 2018. The correlation coefficient, based on monthly anomalies during the overlapping period of the two currents, and the effective degrees of freedom at each site are written in the right-lower corner of each panel.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Table 1

Root-mean-squares (cm s−1) of the observational currents (RMSTAO) and the derived geostrophic currents (RMSArgo) as well as their differences (RMSD; cm s−1).

Table 1

Moreover, the monthly geostrophic currents at the sea surface from January 2004 to December 2018 based on the AVISO sea level data are used to evaluate the derived equatorial currents at the basin scale (Fig. 3). The corresponding correlation coefficients are significant, passing a 99% confidence test, except near the eastern and western boundaries, where the Argo profiles are sparse (Riser et al. 2016). The situation is expected to be improved when taking into account the climatological seasonal variability. The differences between the respective RMSs of the Argo and AVISO geostrophic currents are less than 6 cm s−1 in most areas but have a maximum magnitude of 20 cm s−1 in the central-east basin around the equator. Two reasons are responsible for this discrepancy. First, the gridded Argo data with a 1° horizontal resolution cannot well resolve mesoscale eddies as thealtimetric data with a 0.25° horizontal resolution do. Second, the estimated errors of the altimetric geostrophic currents in the equatorial band are 10–15 cm s−1 (Rio and Hernandez 2004), comparable with the RMS differences. It is thus reasonable to argue that our derived geostrophic currents are valid for the present study.

Fig. 3.
Fig. 3.

(top) Correlation coefficients and (bottom) RMS differences (cm s−1) between the monthly zonal Argo geostrophic currents and the altimetric geostrophic currents during January 2004–December 2018. The correlation coefficients not passing a 99% confidence level test are stippled in black.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

b. Mean spatial structure of the Pacific SEC

Figure 4 displays the meridional sections of mean zonal currents above the 500-m depth during 2004–18. Three branches of the SEC are discernible among the alternating zonal currents, whose cores are concentrated roughly along the equator, 5°S, and 12°S, and denoted by SEC(N), SEC(M), and SEC(S), respectively. The latitudinal ranges of each branch of the SEC are identified as 2°S–5°N for SEC(N), 7°S–3°S for SEC(M), and 20°–8°S for SEC(S), in combination with the consideration of the current core positions as well as the natural separation by the South Equatorial Countercurrent and the Equatorial Undercurrent. SEC(N) is the strongest branch and is much stronger in the central-east Pacific, with a maximum speed of −55 cm s−1 around 110°W(the negative sign indicates a westward flowing current), which is unlike its counterpart in the North Pacific (i.e., the NEC). In addition, the latitudinal range of SEC(N) in the central-east Pacific is wider, extending from around the equator up to 5°N. Toward the west, the northern boundary as well as the core of SEC(N) gradually shifts southward to near the equator. As for its vertical extent, the SEC(N) in the west Pacific barely reaches the 24.0σθ (kg m−3) isopycnal while in the east it extends down to the 26.0σθ (kg m−3) isopycnal surface. Due to the outcrop of the Equatorial Undercurrent in the east Pacific, SEC(N) therein is separated from SEC(M). The second strongest branch is the SEC(M), whose maximum speed is −38 cm s−1 occurring in the central Pacific, around 155°W. SEC(M) is basically situated above the 26.0σθ (kg m−3) isopycnal surface and thus the current deepens, with the variations of isopycnal surfaces, from the east toward the west Pacific, where the SEC(M) is totally separated from SEC(S) by the South Equatorial Countercurrent. As for SEC(S), albeit the weakest branch, its maximum speed reaches up to ∼15 cm s−1 which occurs in the west Pacific, where the SEC penetrates down to the 27.0σθ (kg m−3) isopycnal surface. Toward the east Pacific, SEC(S) gradually shoals to the 26.0σθ (kg m−3) isopycnal surface, with its speed greatly decreasing.

Fig. 4.
Fig. 4.

Mean zonal geostrophic currents (cm s−1) along different meridional sections across the tropical Pacific Ocean between 20°S and 5°N during 2004–18. The black contour interval is 10 cm s−1. The gray contours represent potential density surfaces. The dotted lines mark the boundaries of the three branches of the SEC.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Using the vertical-mean westward velocity above the 26.0σθ (kg m−3) isopycnal surfaces, the axis of each branch of the SEC is calculated, as shown by black lines with error bars in Fig. 5a. As a whole, the axis of SEC(N) moves from around 2°N in the east Pacific to the equator in the west, where the current system is complicated with a large meandering NECC to the north of 3°N and vigorous eddies further south, although the axis of SEC(M) remains essentially along 5°S. According to a previous diagnosis in an ocean general circulation model, the mean structure of the equatorial zonal currents is the result of a combination of the linear Sverdrup dynamics driven by the wind stress curl and the nonlinear effects associated with the variations of the Equatorial Undercurrent (Kessler et al. 2003). As for SEC(S), its axis in the east and west Pacific, where an eastward flowing current emerges, is more poleward (at around 14°S) than in the central Pacific (at around 12°S), mainly controlled by the Sverdrup dynamics (Chen and Qiu 2004, their Fig. 1; Yang and Yuan 2016, their Fig. 4). The volume transport of each branch of the SEC above the 26.0σθ (kg m−3) isopycnal surface are displayed in Fig. 5b, showing that SEC(N) and SEC(M) have a larger volume transport in the central Pacific while SEC(S) reaches its maximum volume transport in the west Pacific. The maximum transport of SEC(N), SEC(M), and SEC(S) are −20.2 Sv at 137°W, −19.3 Sv at 162°W, and −20.3 Sv at 156°E, respectively (1 Sv ≡ 106 m3 s−1). The total volume transport of the SEC is largest in the central Pacific, reaching up to −49.8 Sv at 137°W. The standard deviations of the volume transport (shadings in Fig. 5b) show significant variabilities in each branch of the SEC. In the following sections, seasonal variabilities as well as the corresponding dynamical mechanisms of the three branches of the SEC are investigated.

Fig. 5.
Fig. 5.

(a) Zonal velocity averaged during 2004–18 at the sea surface in the tropical Pacific Ocean (color shading; cm s−1), superimposed with the three axes of the SEC which are represented by the black lines with error bars. (b) Mean volume transport (Sv) of SEC(N) (red line), SEC(M) (green line), SEC(S) (blue line), and the total (black line) above the 26.0σθ (kg m−3) isopycnal surfaces. The standard deviations are shaded with corresponding colors.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

c. Seasonal variations of the Pacific SEC

Seasonal variations of the vertical-mean zonal velocity of SEC(N), SEC(M), and SEC(S) above the 26.0σθ (kg m−3) isopycnal surface are shown in Fig. 6. Here, SEC(N), SEC(M), and SEC(S) are meridional-mean westward velocity over 2°S–5°N, 7°–3°S, and 15°–8°S [SEC(S) is already very weak to the south of 15°S], respectively. Westward propagations of velocity anomalies are notable in each branch of the SEC, but with different amplitudes and phases. Anomalies of SEC(N) in the central-east basin are the most significant, attaining up to ±12 cm s−1. SEC(N) in the east Pacific around 90°W weakens in the first half and strengthens in the second half of the year. Then the velocity anomalies propagate westward at a speed of about 0.94 m s−1, leading to corresponding changes in the west Pacific around 160°E lagging by about 4 months. For SEC(M), to the east of 140°W, the velocities tend to strengthen during January–March and June–August, and to weaken during the rest of the year. To the west of 140°W, stronger currents, with a maximum anomaly of −6 cm s−1, occur principally in the first half of the year, with a signature of westward propagation of velocity anomalies. SEC(S), as the weakest branch, has a maximum anomaly of ±3 cm s−1 in the central-west Pacific, whose seasonal cycle is significantly different from those of SEC(N) and SEC(M). In the east Pacific, SEC(S) shows a stronger velocity during the first half of the year, and the corresponding anomaly propagates westward with the propagation time of about 6 months to induce a stronger velocity in the west Pacific during the second half of the year. In this study, we find that the seasonal variabilities of SEC(N), SEC(M), and SEC(S) are well mirrored by the sea level anomaly (SLA) differences between 2°S and 5°N (SLA2°S − SLA5°N), between 3°S and 6°S (SLA3°S − SLA6°S), and between 8°S and 15°S (SLA8°S − SLA15°S), respectively (as shown in Fig. 7). The corresponding correlation and regression coefficients between monthly anomalies of each branch of the SEC and SLA differences for the period January 2004–December 2018 are shown in Fig. 8. Note that, the regression is performed using the original zonal velocities and SLA differences, without any normalization. Nearly all the coefficients (those represented by lines without black dots) pass a 99% confidence level test, except near the boundaries where the Argo floats are sparse thus the velocity errors are relatively large. Overall, the SLA differences are a good proxy of the seasonal variability of the Pacific SEC, which conforms to the geostrophic balance.

Fig. 6.
Fig. 6.

Climatological seasonal cycle of the vertical-mean zonal velocity (cm s−1) of (a) SEC(N) (averaged between 2°S and 5°N), (b) SEC(M) (averaged between 7°S and 3°S), and (c) SEC(S) (averaged between 15° and 8°S) above the 26.0σθ (kg m−3) isopycnal surfaces in the tropical Pacific.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Fig. 7.
Fig. 7.

Climatological seasonal cycle of SLA difference (cm) (a) between 2°S and 5°N, (b) between 3°S and 6°S, and (c) between 8°S and 15°S.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Fig. 8.
Fig. 8.

(top) Correlation and (bottom) regression coefficients between the monthly vertical-mean (above the 26.0σθ isopycnal surfaces) zonal velocity of (red solid line) SEC(N) and SLA difference between 2°S and 5°N, (green solid line) SEC(M) and SLA difference between 3°S and 6°S, and (blue solid line) SEC(S) and SLA difference between 8°S and 15°S. The black dots mean that the correlation or regression coefficients there do not pass the 99% confidence level test.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

d. Role of the first mode baroclinic oceanic Rossby and Kelvin waves in SEC

In this section, the analytical Rossby and Kelvin wave models described in section 2e are used to diagnose the roles of wind-driven long Rossby and Kelvin waves as well as the radiated Rossby waves from the eastern boundary in the seasonal variability of the Pacific SEC.

Climatological monthly anomalies of observational and modeled SLAs regarding SEC(N) are shown in Fig. 9. In comparison, combined with Fig. 7a, the seasonality of the SLA difference between 2°S and 5°N is dominated by the seasonal cycle of SLA along 5°N, where the modeled SLA coincides well with the observations. It means that the seasonal variability of SEC(N) mainly results from the forces at 5°N. Further analysis shows that the seasonal cycle of the SLA west of 160°W is mainly controlled by the wind-stress-curl-driven Rossby waves while that east of 160°W is forced by both the local Ekman pumping and remote Rossby waves (Figs. 10a,b). Furthermore, the SLA in the west Pacific originates from the Rossby waves east of 160°W (Figs. 10b,c). As for the reflected Rossby waves from the eastern boundary, their influence is evident only near the boundary (Figs. 10d and 9).

Fig. 9.
Fig. 9.

Climatological monthly anomalies of (left) observational AVISO-SLAs and (right) modeled SLAs (cm) during January 2004–December 2018 across 2°S and 5°N. The modeled SLAs along 2°S are based on the analytical Rossby and Kelvin wave models, while along 5°N only the Rossby waves are considered.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Fig. 10.
Fig. 10.

Climatological monthly anomalies of modeled SLA (cm) along 5°N during January 2004–December 2018 for different experiments: (a) Rossby wave, (b) local Ekman pumping is removed over the full domain, (c) Rossby wave is removed east of 160°W, (d) free Rossby wave emanating from the eastern boundary is removed. The corresponding Rossby wave speed (CR) and Ekman pumping velocity (WEK) are labeled.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Figure 11 shows that the magnitudes of the SLAs along 3°S and 6°S are comparable and thus the forcings at the two latitudes control the variations of SEC(M) together. Along 3°S, the phases of the simulated SLAs agree well with the observational AVISO-SLA except in the central Pacific where the modeled phases after linearly superposing Rossby and Kelvin waves are opposite from observations during austral winter. This suggests that the first-mode baroclinic linear long waves sometimes are insufficient to explain the seasonal variability of SEC(M) in the central Pacific. In view of the fact that the SLA signals across 3°S are characterized by a significant westward propagation from the east, the SLA along 3°S is reestablished using the analytical Rossby wave model only and the result still shows a good coherence with observations except in the central Pacific during austral winter (Fig. 12a). Thus, Rossby waves account for most of the annual variability across 3°S in the Pacific Ocean. Specifically, to the west of 170°W, the SLA is dominated by the remote Rossby waves that originate in the central-east Pacific (Figs. 12b,c). Between 170° and 110°W, local Ekman pumping and the remote wind-stress-curl-driven Rossby waves together control the SLA variations (Figs. 12c,d). To the east of 110°W, the reflected Rossby waves from the eastern boundary dominates the dynamics therein (Figs. 12a,d). Along 6°S, the simulated SLAs using the analytical Rossby wave model capture the AVISO-SLAs well except for failing to reproduce a positive anomaly during austral summer at the longitudes between 120° and 90°W, highlighting the importance of the first-mode baroclinic linear Rossby waves over a majority of the areas (Fig. 11). Similar to the cases along 3°S, the SLAs along 6°S in the west Pacific are mainly controlled by remote wind-stress-curl-driven Rossby waves while in the central Pacific the Ekman pumping also plays an important role (Figs. 12e–g), and in the east Pacific, the SLA variations are dominated by the reflected Rossby waves from the eastern boundary (Fig. 12h and Fig. 11).

Fig. 11.
Fig. 11.

Climatological monthly anomalies of (left) observational AVISO-SLAs and (right) modeled SLAs (cm) during January 2004–December 2018 across 3°S and 6°S. The modeled SLAs along 3°S are based on the analytical Rossby and Kelvin wave models, while along 6°S only the Rossby waves are considered.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Fig. 12.
Fig. 12.

Climatological monthly anomalies of modeled SLAs (cm) along (top) 3°S and (bottom) 6°S during January 2004–December 2018 for different experiments: (a) Kelvin wave is removed over the entire domain; (b) Kelvin wave and local Ekman pumping are removed over the entire domain; (c) as in (a), but with local Ekman pumping east of 170°W removed; (d) as in (a), but with the Rossby wave emanating from the eastern boundary removed; (e) Rossby wave; (f) local Ekman pumping is removed; (g) Rossby wave east of 170°W is removed; and (h) free Rossby wave emanating from the eastern boundary is removed. The corresponding Kelvin wave speed (CK), Rossby wave speed (CR), and Ekman pumping velocity (WEK) are labeled.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Climatological anomalies of observational and modeled SLAs along 8°S and 15°S are displayed in Fig. 13, with the AVISO-SLAs well reproduced by the analytical Rossby wave model. The seasonality of SEC(S) is controlled by the SLAs along the two latitudes. At 8°S, the SLA variations in the Coral Sea (west of 160°E) are mainly generated by the local Ekman pumping (Fig. 14a). Beyond the Coral Sea (east of 160°E), the Rossby waves become important and play a dominant role to the west of the date line. Toward the east, the local Ekman pumping and Rossby waves together control the SLA variations (Figs. 14b,c). Further analysis shows that the Rossby waves are primarily driven by the wind stress curl east of the date line (Figs. 14b,c), except near the eastern boundary where the Rossby waves radiated from the boundary become important (Fig. 14d and Fig. 13). At 15°S, the seasonal variability is most prominent in the western Pacific west of 150°W, where both the local Ekman pumping and remote Rossby waves which are driven by wind stress curl in the west Pacific (west of 150°W) are essential (Figs. 14e–g). East of 150°W, the amplitudes of seasonal variability are small and the phases are consistent with Rossby waves (Fig. 14f). In the east Pacific east of 90°W, the seasonal signals mainly result from the Rossby waves radiating from the eastern boundary (Fig. 14h).

Fig. 13.
Fig. 13.

Climatological monthly anomalies of (left) observational AVISO-SLAs and (right) modeled SLAs (cm) during January 2004–December 2018 across 8°S and 15°S. The modeled SLAs are based on the analytical Rossby wave model.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

Fig. 14.
Fig. 14.

Climatological monthly anomalies of modeled SLAs (cm) along (top) 8°S and (bottom) 15°S during January 2004–December 2018 for different experiments: (a) Rossby wave, (b) local Ekman pumping is removed over the whole domain, (c) Rossby wave is removed east of 180°, (d) free Rossby wave emanating from the eastern boundary is removed, (e) Rossby wave, (f) local Ekman pumping is removed over the entire domain, (g) Rossby wave is removed east of 150°W, and (h) free Rossby wave emanating from the eastern boundary is removed. The corresponding Rossby wave speed (CR) and Ekman pumping velocity (WEK) are labeled.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

4. Discussion

We have shown that the seasonal variability of the three branches of the Pacific SEC can be monitored by appropriate SLA differences. Wyrtki (1974a), using historical hydrographic data, reported a similar correlation between the zonal-mean geostrophic transport of the SEC in the southwestern Pacific and the sea level difference between Canton (3°S) and Pago Pago (14°S) islands. But our study identifies a good proxy of each SEC branch, which is varied with longitude. Using a series of model experiments, we demonstrate that the local Ekman pumping and remote Rossby waves, primarily driven by the wind stress curl in the central-east Pacific, together explain most of the seasonal variations of the SEC. According to Kessler and Cravatte (2013), on longer time scales, e.g., interannual scales, the wind-driven Rossby waves from the central-east Pacific are also critical to the SEC variability in the Coral Sea. Besides, as the counterpart of the SEC in the North Pacific, the seasonal variability of the NEC is suggested to be dominated by the joint effects of the local Ekman pumping and the first-mode baroclinic wind-driven Rossby waves in the interior ocean with obvious phase lags across the basin (Liu and Zhou 2020). This is consistent with our model results for the SEC, highlighting the dominant role of the geostrophic adjustment to the basin-scale wind forcing in the seasonal variations of the two wind-driven currents. Nevertheless, asymmetries are notable between the spatial–temporal variations of the Pacific SEC and NEC, by comparing our result with that of Liu and Zhou (2020). The SEC (∼20°S–5°N) is more extensively located than the NEC (∼8°N–18°N), and its strongest velocities occur within the equatorial band in the central-east Pacific. Thus, the equatorial dynamics are more important for the SEC, specifically the SEC(N) and SEC(M). Unlike the meridionally consistent seasonal variations of the NEC, the phase lags for the three branches of the SEC are significantly different, with the SEC(N) generally changing in phase with the NEC at the same longitudes between 140°E and 140°W. Due to the deceleration of the westward propagating Rossby waves toward higher latitudes, the overall southeast–northwest orientation of the western coastline of America tends to counteract but amplify phase differences respectively within the NEC and SEC.

In addition, previous studies have suggested that despite the dominant role of the first baroclinic mode, higher baroclinic modes also contribute to the equatorial surface current variability (e.g., Boulanger et al. 1997; Zhang and Clarke 2017). Based on the vertical mode decomposition method, we estimated the respective contributions of the first three and higher baroclinic modes to the surface zonal currents near the equator (Fig. 15). The baroclinic vertical modes were first calculated using the WOA18 climatological and zonal-mean density profile along each latitude. Then the three-dimensional zonal velocities were projected onto the vertical modes. Here, the zonal velocities were combined with the upper 2000-m data from the monthly geostrophic currents derived in this study and the lower 3000-m data from the monthly ECCO2 reanalyses. The result remains nearly identical when replacing the ECCO2 reanalyses with ORAS4 (figure not shown). As for the phase variations of the equatorial surface velocities, the first baroclinic mode alone describes them well enough except near the eastern boundary, where the higher modes also play a role. In terms of the explained variance, the dominant role of the first baroclinic mode gradually diminishes from around 80% in the western Pacific to around 50% in the eastern basin. The second baroclinic mode stands out in the central-east Pacific, particularly at 2°S and 3°S. Thus, the uncaptured variation characteristics of the SEC(M) in the central Pacific are partly attributable to the lack of contribution from higher baroclinic modes. In addition, using an ocean general circulation model, Kessler et al. (2003) found that the advective and friction terms within ±3° latitude band in the Pacific become important in the framework of vorticity balance, acting to intensify the mean currents there. We deem this to be beyond the scope of the present study and posit that a nonlinear model containing higher baroclinic modes is necessary for a further study to understand the dynamics of SEC(M).

Fig. 15.
Fig. 15.

(left) Correlation along the boundary latitudes of the SEC(N) and SEC(M) between the total surface zonal current anomaly and contributions from mode 1 (red line), mode 2 (green line), mode 3 (blue line), and from all the rest higher modes (black line). (right) Corresponding explained variance for each baroclinic mode.

Citation: Journal of Physical Oceanography 52, 10; 10.1175/JPO-D-21-0311.1

5. Summary and conclusions

In this study, the SEC speeds across the entire Pacific Ocean basin from January 2004 to December 2018 are derived based on the P-vector method on an f plane and the geostrophic approximation on a β plane using the gridded Argo profiling data. By comparison, our derived currents compare well with the moored current-meter measurements and the altimetric geostrophic currents, indicating a robust analysis of the mean spatial structure and the seasonal variability of the Pacific SEC. Three branches are identified; SEC(N) (2°S–5°N), SEC(M) (7°–3°S), and SEC(S) (20°–8°S), that are primarily located above the 26.0σθ (kg m−3) isopycnal surface. Unlike its counterpart in the North Pacific, SEC does not intensify all the way toward the west Pacific, but achieves the maximum zonal velocity of −55 cm s−1 and volume transport of −49.8 Sv in the central-east Pacific.

Seasonally, each branch of the SEC shows a notable westward propagation of velocity anomalies, but with different amplitudes and phases. It is found that the seasonal cycles of SEC(N), SEC(M), and SEC(S) are well mirrored by the SLA differences between 2°S and 5°N, between 3°S and 6°S, and between 8°S and 15°S, respectively. Most seasonal variations of the SEC are well explained by the analytical first-mode baroclinic linear Rossby wave model. Overall, the seasonal cycle of SEC(N) is dominated by the annual Rossby waves along 5°N which are mainly induced by the wind stress curl east of 160°W. For SEC(M), its seasonal variations are still closely associated with the annual Rossby waves originating from the wind stress curl east of 170°W along 3°S and 6°S, with the equatorial Kelvin waves playing a minor role. However, in the central Pacific during austral winter and in the eastern Pacific during austral summer, the SEC(M) anomalies are not well reproduced by the analytical linear long Rossby wave model. Further analysis shows that the explained variance of the surface zonal currents in the equatorial Pacific by the first baroclinic mode diminishes from around 80% in the west to around 50% in the east. Thus, higher baroclinic modes and nonlinear effects should probably be considered therein. For SEC(S), its seasonal variations in the Coral Sea are dominated by the local Ekman pumping while over the rest of the areas, the variability is mainly generated by the Rossby waves associated with the wind stress curl east of the date line along 8°S and west of 150°W along 15°S. In addition, the radiated Rossby waves from the eastern boundary govern the circumstances in the east Pacific near the boundary.

While our study focuses on the mean features and the seasonal variability, the intimate relation between the ocean–atmosphere dynamics of the region and the SEC, it is necessary to understand the detailed role of SEC in the recharge–discharge mechanism, especially for the different flavors of ENSO (Capotondi et al. 2015; Singh and Delcroix 2013). The question of the tropical Pacific heat balance in modulating the climate response to anthropogenic forcing, especially in terms of the trends in winds and the heat transport into the Indian Ocean, SEC’s role needs detailed understanding at multidecadal time scales (England et al. 2014; Lee et al. 2015). Chronic model biases in the Pacific will also be served well by validation against observed dynamics as presented here but Argo data remain relatively short in their time period for these tantalizing questions. Some model comparisons will be presented in a separate analysis along with the heat transports.

Acknowledgments.

We acknowledge the International Argo Program, the Global Tropical Moored Buoy Array Program, the AVISO+ and the Copernicus Marine and Environment Monitoring Service, the European Center for Medium-Range Weather Forecasts, the National Centers for Environmental Information, and the National Aeronautics and Space Administration for sharing the data. This study is supported by the National Natural Science Foundation of China (Grants 41706033, 42006023, and 41776031), the National Key Research and Development Program of China (Grant 2018YFC1506903), the program for scientific research start-up funds of Guangdong Ocean University (Grants R20020, R20023, R17051, R18023, R20019), the team project funding of scientific research innovation for universities in Guangdong Province (2019KCXTF021), and Guangdong Postgraduate Education Innovation Project (2022SQXX024). R. Murtugudde gratefully acknowledges the Emeritus position at the University of Maryland, College Park, and the Visiting Faculty position at the Indian Institute of Technology Bombay.

Data availability statement.

The gridded Argo profiling data are obtained from https://argo.ucsd.edu/data/argo-data-products/. The in-situ observational measurements at 10-m depth are available at https://www.pmel.noaa.gov/tao/drupal/disdel/. The satellite altimetric SLAs and surface geostrophic currents are downloaded from https://resources.marine.copernicus.eu/?option=com_sla. The wind speeds at 10 m high from ERA-Interim are available at https://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/. The ETOPO2 topography is shared at https://www.ngdc.noaa.gov/mgg/global/relief/ETOPO2. The WOA18 temperatures and salinities are obtained from https://www.nodc.noaa.gov/cgi-bin/OC5/woa18/woa18.pl. The ECCO2 and ORAS4 zonal currents are downloaded from https://ecco.jpl.nasa.gov/drive/files/ECCO2/cube92_latlon_quart_90S90N/ and ftp://ftp-icdc.cen.uni-hamburg.de, respectively.

REFERENCES

  • Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 11321161, https://doi.org/10.1002/qj.2063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boulanger, J. P., P. Delécluse, C. Maes, and C. Lévy, 1997: Long equatorial waves in a high-resolution OGCM simulation of the tropical Pacific Ocean during the 1985–94 TOGA period. Mon. Wea. Rev., 125, 972984, https://doi.org/10.1175/1520-0493(1997)125<0972:LEWIAH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., M. Widmann, V. P. Dymnikov, J. M. Wallace, and I. Bladé, 1999: The effective number of spatial degrees of freedom of a time-varying field. J. Climate, 12, 19902009, https://doi.org/10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921938, https://doi.org/10.1175/BAMS-D-13-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., R. A. deSzoeke, M. G. Schlax, K. El Naggar, and N. Siwertz, 1998: Geographical variability of the first-baroclinic Rossby radius of deformation. J. Phys. Oceanogr., 28, 433460, https://doi.org/10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., and B. Qiu, 2004: Seasonal variability of the south equatorial countercurrent. J. Geophys. Res., 109, C08003, https://doi.org/10.1029/2003JC002243.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P. C., 1995: P-vector method for determining absolute velocity from hydrographic data. Mar. Technol. Soc. J., 29, 314.

  • Davis, R. E., W. S. Kessler, and J. T. Sherman, 2012: Gliders measure western boundary current transport from the south pacific to the equator. J. Phys. Oceanogr., 42, 20012013, https://doi.org/10.1175/JPO-D-12-022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Dewitte, B., G. Reverdin, and C. Maes, 1999: Vertical structure of an OGCM simulation of the equatorial Pacific Ocean in 1985–94. J. Phys. Oceanogr., 29, 15421570, https://doi.org/10.1175/1520-0485(1999)029<1542:VSOAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • England, M., and Coauthors, 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Climate Change, 4, 222227, https://doi.org/10.1038/nclimate2106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fenty, I., D. Menemenlis, and H. Zhang, 2017: Global coupled sea ice-ocean state estimation. Climate Dyn., 49, 931956, https://doi.org/10.1007/s00382-015-2796-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fine, R. A., R. Lukas, F. M. Bingham, M. J. Warner, and R. H. Gammon, 1994: The western equatorial Pacific: A water mass crossroads. J. Geophys. Res. Oceans, 99, 25 06325 080, https://doi.org/10.1029/94JC02277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ganachaud, A., and Coauthors, 2014: The Southwest Pacific Ocean Circulation and Climate Experiment (SPICE). J. Geophys. Res. Oceans, 119, 76607686, https://doi.org/10.1002/2013JC009678.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparin, F., A. Ganachaud, C. Maes, F. Marin, and G. Eldin, 2012: Oceanic transports through the Solomon Sea: The bend of the New Guinea coastal undercurrent. Geophys. Res. Lett., 39, L15608, https://doi.org/10.1029/2012GL052575.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gordon, A. L., and R. A. Fine, 1996: Pathways of water between the Pacific and Indian oceans in the Indonesian seas. Nature, 379, 146149, https://doi.org/10.1038/379146a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grenier, M., S. Cravatte, B. Blanke, C. Menkes, A. Koch-Larrouy, F. Durand, A. Melet, and C. Jeandel, 2011: From the western boundary currents to the pacific equatorial undercurrent: modeled pathways and water mass evolutions. J. Geophys. Res., 116, C12044, https://doi.org/10.1029/2011JC007477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsin, Y. C., and B. Qiu, 2012: Seasonal fluctuations of the surface North Equatorial Countercurrent (NECC) across the pacific basin. J. Geophys. Res., 117, C06001, https://doi.org/10.1029/2011JC007794.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, D., and Coauthors, 2015: Pacific western boundary currents and their roles in climate. Nature, 522, 299308, https://doi.org/10.1038/nature14504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, G. C., B. M Sloyan, W. S. Kessler, and K. E McTaggart, 2002: Direct measurements of upper ocean currents and water properties across the tropical Pacific during the 1990s. Prog. Oceanogr., 52, 3161, https://doi.org/10.1016/S0079-6611(02)00021-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., 1990: Observations of long Rossby waves in the northern tropical Pacific. J. Geophys. Res., 95, 51835217, https://doi.org/10.1029/JC095iC04p05183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., 2006: The circulation of the eastern tropical pacific: A review. Prog. Oceanogr., 69, 181217, https://doi.org/10.1016/j.pocean.2006.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and S. Cravatte, 2013: ENSO and short-term variability of the South Equatorial Current entering the Coral Sea. J. Phys. Oceanogr., 43, 956969, https://doi.org/10.1175/JPO-D-12-0113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and L. Gourdeau, 2007: The annual cycle of circulation of the southwest subtropical Pacific, analyzed in an ocean GCM. J. Phys. Oceanogr., 37, 16101627, https://doi.org/10.1175/JPO3046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and M. J. McPhaden, 1995: Oceanic equatorial waves and the 1991–93 El Niño. J. Climate, 8, 17571774, https://doi.org/10.1175/1520-0442(1995)008<1757:OEWATE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., G. C. Johnson, and D. W. Moore, 2003: Sverdrup and nonlinear dynamics of the Pacific equatorial currents. J. Phys. Oceanogr., 33, 9941008, https://doi.org/10.1175/1520-0485(2003)033<0994:SANDOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., and S. P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lagerloef, G. S. E., G. T. Mitchum, R. B. Lukas, and P. P. Niiler, 1999: Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data. J. Geophys. Res., 104, 23 31323 326, https://doi.org/10.1029/1999JC900197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. K., W. Park, M. O. Baringer, A. L. Gordon, B. Huber, and Y. Liu, 2015: Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nat. Geosci., 8, 445449, https://doi.org/10.1038/ngeo2438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Zhou, H., 2020: Seasonal variations of the North Equatorial Current across the Pacific Ocean. J. Geophys. Res. Oceans, 125, e2019JC015895, https://doi.org/10.1029/2019JC015895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., H. Zhou, W. Yang, X. Liu, Y. Li, Y. Yang, X. Chen, and X. Li, 2021: A three-dimensional gravest empirical mode determined from hydrographic observations in the western equatorial Pacific Ocean. J. Mar. Syst., 214, 103487, https://doi.org/10.1016/j.jmarsys.2020.103487.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Locarnini, R. A., and Coauthors, 2019: Temperature. Vol. 1, World Ocean Atlas 2018, NOAA Atlas NESDIS 81, 52 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol1.pdf.

  • Lumpkin, R., and G. C. Johnson, 2013: Global ocean surface velocities from drifters: Mean, variance, El Niño–Southern Oscillation response, and seasonal cycle. J. Geophys. Res. Oceans, 118, 29923006, https://doi.org/10.1002/jgrc.20210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menemenlis, D., J. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguygen, M. Schodlock, and H. Zhang, 2008: ECCO2: high resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, No. 31, Mercator-Ocean, Ramonville-Saint-Agne, France, 1321.

    • Search Google Scholar
    • Export Citation
  • Meyers, G., 1979: On the annual Rossby wave in the tropical North Pacific Ocean. J. Phys. Oceanogr., 9, 663674, https://doi.org/10.1175/1520-0485(1979)009<0663:OTARWI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiu, B., and R. Lukas, 1996: Seasonal and interannual variability of the North Equatorial Current, the Mindanao Current, and the Kuroshio along the Pacific western boundary. J. Geophys. Res., 101, 12 31512 330, https://doi.org/10.1029/95JC03204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rio, M. H., and F. Hernandez, 2004: A mean dynamic topography computed over the world ocean from altimetry, in situ measurements, and a geoid model. J. Geophys. Res., 109, C12032, https://doi.org/10.1029/2003JC002226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riser, S. C., and Coauthors, 2016: Fifteen years of ocean observations with the global Argo array. Nat. Climate Change, 6, 145153, https://doi.org/10.1038/nclimate2872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roemmich, D., and J. Gilson, 2009: The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog. Oceanogr., 82, 81100, https://doi.org/10.1016/j.pocean.2009.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, A., and T. Delcroix, 2013: Eastern and central Pacific ENSO and their relationships to the recharge/discharge oscillator paradigm. Deep-Sea Res. I, 82, 3243, https://doi.org/10.1016/j.dsr.2013.08.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sprintall, J., and A. Révelard, 2014: The Indonesian Throughflow response to Indo‐Pacific climate variability. J. Geophys. Res. Oceans, 119, 11611175, https://doi.org/10.1002/2013JC009533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, S., and H. Zhou, 2018: The observed impacts of the two types of El Nino on the North Equatorial Countercurrent in the Pacific Ocean. Geophys. Res. Lett., 45, 10 49310 500, https://doi.org/10.1029/2018GL079273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1974a: Sea level and the seasonal fluctuations of the equatorial currents in the western Pacific Ocean. J. Phys. Oceanogr., 4, 91103, https://doi.org/10.1175/1520-0485(1974)004<0091:SLATSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1974b: Equatorial currents in the Pacific 1950 to 1970 and their relations to the trade winds. J. Phys. Oceanogr., 4, 372380, https://doi.org/10.1175/1520-0485(1974)004<0372:ECITPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., and D. Yuan, 2016: Absolute geostrophic currents in global tropical oceans. Chin. J. Oceanol. Limnol., 34, 13831393, https://doi.org/10.1007/s00343-016-5092-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., Z. Lei, S. Li, and Z. Wei, 2018: Spreading of the South Pacific tropical water and Antarctic Intermediate Water over the Maritime Continent. J. Geophys. Res. Oceans, 123, 44234446, https://doi.org/10.1029/2018JC013831.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Y., X. Li, J. Wang, and D. L. Yuan, 2020: Seasonal variability and dynamics of the Pacific North Equatorial Subsurface Current. J. Phys. Oceanogr., 50, 24572474, https://doi.org/10.1175/JPO-D-19-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, D., Z. Zhang, P. C. Chu, and W. K. Dewar, 2014: Geostrophic circulation in the tropical North Pacific Ocean based on Argo profiles. J. Phys. Oceanogr., 44, 558575, https://doi.org/10.1175/JPO-D-12-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, F., and D. Hu, 2013: Revisit the interannual variability of the North Equatorial Current transport with ECMWF ORA-S3. J. Geophys. Res. Oceans, 118, 13491366, https://doi.org/10.1002/jgrc.20093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., and A. J. Clarke, 2017: On the dynamical relationship between equatorial Pacific surface currents, zonally averaged equatorial sea level, and El Niño prediction. J. Phys. Oceanogr., 47, 323337, https://doi.org/10.1175/JPO-D-16-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, H., D. Yuan, L. Yang, X. Li, and W. K. Dewar, 2018: Decadal variability of the meridional geostrophic transport in the upper tropical North Pacific Ocean. J. Climate, 31, 58915910, https://doi.org/10.1175/JCLI-D-17-0639.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zweng, M. M., and Coauthors, 2019: Salinity. Vol. 2, World Ocean Atlas 2018, NOAA Atlas NESDIS 82, 50 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol2.pdf.

Save
  • Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 11321161, https://doi.org/10.1002/qj.2063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boulanger, J. P., P. Delécluse, C. Maes, and C. Lévy, 1997: Long equatorial waves in a high-resolution OGCM simulation of the tropical Pacific Ocean during the 1985–94 TOGA period. Mon. Wea. Rev., 125, 972984, https://doi.org/10.1175/1520-0493(1997)125<0972:LEWIAH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., M. Widmann, V. P. Dymnikov, J. M. Wallace, and I. Bladé, 1999: The effective number of spatial degrees of freedom of a time-varying field. J. Climate, 12, 19902009, https://doi.org/10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921938, https://doi.org/10.1175/BAMS-D-13-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chelton, D. B., R. A. deSzoeke, M. G. Schlax, K. El Naggar, and N. Siwertz, 1998: Geographical variability of the first-baroclinic Rossby radius of deformation. J. Phys. Oceanogr., 28, 433460, https://doi.org/10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., and B. Qiu, 2004: Seasonal variability of the south equatorial countercurrent. J. Geophys. Res., 109, C08003, https://doi.org/10.1029/2003JC002243.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P. C., 1995: P-vector method for determining absolute velocity from hydrographic data. Mar. Technol. Soc. J., 29, 314.

  • Davis, R. E., W. S. Kessler, and J. T. Sherman, 2012: Gliders measure western boundary current transport from the south pacific to the equator. J. Phys. Oceanogr., 42, 20012013, https://doi.org/10.1175/JPO-D-12-022.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Dewitte, B., G. Reverdin, and C. Maes, 1999: Vertical structure of an OGCM simulation of the equatorial Pacific Ocean in 1985–94. J. Phys. Oceanogr., 29, 15421570, https://doi.org/10.1175/1520-0485(1999)029<1542:VSOAOS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • England, M., and Coauthors, 2014: Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat. Climate Change, 4, 222227, https://doi.org/10.1038/nclimate2106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fenty, I., D. Menemenlis, and H. Zhang, 2017: Global coupled sea ice-ocean state estimation. Climate Dyn., 49, 931956, https://doi.org/10.1007/s00382-015-2796-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fine, R. A., R. Lukas, F. M. Bingham, M. J. Warner, and R. H. Gammon, 1994: The western equatorial Pacific: A water mass crossroads. J. Geophys. Res. Oceans, 99, 25 06325 080, https://doi.org/10.1029/94JC02277.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ganachaud, A., and Coauthors, 2014: The Southwest Pacific Ocean Circulation and Climate Experiment (SPICE). J. Geophys. Res. Oceans, 119, 76607686, https://doi.org/10.1002/2013JC009678.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gasparin, F., A. Ganachaud, C. Maes, F. Marin, and G. Eldin, 2012: Oceanic transports through the Solomon Sea: The bend of the New Guinea coastal undercurrent. Geophys. Res. Lett., 39, L15608, https://doi.org/10.1029/2012GL052575.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gordon, A. L., and R. A. Fine, 1996: Pathways of water between the Pacific and Indian oceans in the Indonesian seas. Nature, 379, 146149, https://doi.org/10.1038/379146a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grenier, M., S. Cravatte, B. Blanke, C. Menkes, A. Koch-Larrouy, F. Durand, A. Melet, and C. Jeandel, 2011: From the western boundary currents to the pacific equatorial undercurrent: modeled pathways and water mass evolutions. J. Geophys. Res., 116, C12044, https://doi.org/10.1029/2011JC007477.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsin, Y. C., and B. Qiu, 2012: Seasonal fluctuations of the surface North Equatorial Countercurrent (NECC) across the pacific basin. J. Geophys. Res., 117, C06001, https://doi.org/10.1029/2011JC007794.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, D., and Coauthors, 2015: Pacific western boundary currents and their roles in climate. Nature, 522, 299308, https://doi.org/10.1038/nature14504.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, G. C., B. M Sloyan, W. S. Kessler, and K. E McTaggart, 2002: Direct measurements of upper ocean currents and water properties across the tropical Pacific during the 1990s. Prog. Oceanogr., 52, 3161, https://doi.org/10.1016/S0079-6611(02)00021-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., 1990: Observations of long Rossby waves in the northern tropical Pacific. J. Geophys. Res., 95, 51835217, https://doi.org/10.1029/JC095iC04p05183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., 2006: The circulation of the eastern tropical pacific: A review. Prog. Oceanogr., 69, 181217, https://doi.org/10.1016/j.pocean.2006.03.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and S. Cravatte, 2013: ENSO and short-term variability of the South Equatorial Current entering the Coral Sea. J. Phys. Oceanogr., 43, 956969, https://doi.org/10.1175/JPO-D-12-0113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and L. Gourdeau, 2007: The annual cycle of circulation of the southwest subtropical Pacific, analyzed in an ocean GCM. J. Phys. Oceanogr., 37, 16101627, https://doi.org/10.1175/JPO3046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., and M. J. McPhaden, 1995: Oceanic equatorial waves and the 1991–93 El Niño. J. Climate, 8, 17571774, https://doi.org/10.1175/1520-0442(1995)008<1757:OEWATE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kessler, W. S., G. C. Johnson, and D. W. Moore, 2003: Sverdrup and nonlinear dynamics of the Pacific equatorial currents. J. Phys. Oceanogr., 33, 9941008, https://doi.org/10.1175/1520-0485(2003)033<0994:SANDOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kosaka, Y., and S. P. Xie, 2013: Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature, 501, 403407, https://doi.org/10.1038/nature12534.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lagerloef, G. S. E., G. T. Mitchum, R. B. Lukas, and P. P. Niiler, 1999: Tropical Pacific near-surface currents estimated from altimeter, wind, and drifter data. J. Geophys. Res., 104, 23 31323 326, https://doi.org/10.1029/1999JC900197.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. K., W. Park, M. O. Baringer, A. L. Gordon, B. Huber, and Y. Liu, 2015: Pacific origin of the abrupt increase in Indian Ocean heat content during the warming hiatus. Nat. Geosci., 8, 445449, https://doi.org/10.1038/ngeo2438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, X., and Zhou, H., 2020: Seasonal variations of the North Equatorial Current across the Pacific Ocean. J. Geophys. Res. Oceans, 125, e2019JC015895, https://doi.org/10.1029/2019JC015895.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, H., H. Zhou, W. Yang, X. Liu, Y. Li, Y. Yang, X. Chen, and X. Li, 2021: A three-dimensional gravest empirical mode determined from hydrographic observations in the western equatorial Pacific Ocean. J. Mar. Syst., 214, 103487, https://doi.org/10.1016/j.jmarsys.2020.103487.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Locarnini, R. A., and Coauthors, 2019: Temperature. Vol. 1, World Ocean Atlas 2018, NOAA Atlas NESDIS 81, 52 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol1.pdf.

  • Lumpkin, R., and G. C. Johnson, 2013: Global ocean surface velocities from drifters: Mean, variance, El Niño–Southern Oscillation response, and seasonal cycle. J. Geophys. Res. Oceans, 118, 29923006, https://doi.org/10.1002/jgrc.20210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Menemenlis, D., J. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguygen, M. Schodlock, and H. Zhang, 2008: ECCO2: high resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, No. 31, Mercator-Ocean, Ramonville-Saint-Agne, France, 1321.

    • Search Google Scholar
    • Export Citation
  • Meyers, G., 1979: On the annual Rossby wave in the tropical North Pacific Ocean. J. Phys. Oceanogr., 9, 663674, https://doi.org/10.1175/1520-0485(1979)009<0663:OTARWI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qiu, B., and R. Lukas, 1996: Seasonal and interannual variability of the North Equatorial Current, the Mindanao Current, and the Kuroshio along the Pacific western boundary. J. Geophys. Res., 101, 12 31512 330, https://doi.org/10.1029/95JC03204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rio, M. H., and F. Hernandez, 2004: A mean dynamic topography computed over the world ocean from altimetry, in situ measurements, and a geoid model. J. Geophys. Res., 109, C12032, https://doi.org/10.1029/2003JC002226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riser, S. C., and Coauthors, 2016: Fifteen years of ocean observations with the global Argo array. Nat. Climate Change, 6, 145153, https://doi.org/10.1038/nclimate2872.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roemmich, D., and J. Gilson, 2009: The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo Program. Prog. Oceanogr., 82, 81100, https://doi.org/10.1016/j.pocean.2009.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Singh, A., and T. Delcroix, 2013: Eastern and central Pacific ENSO and their relationships to the recharge/discharge oscillator paradigm. Deep-Sea Res. I, 82, 3243, https://doi.org/10.1016/j.dsr.2013.08.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sprintall, J., and A. Révelard, 2014: The Indonesian Throughflow response to Indo‐Pacific climate variability. J. Geophys. Res. Oceans, 119, 11611175, https://doi.org/10.1002/2013JC009533.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tan, S., and H. Zhou, 2018: The observed impacts of the two types of El Nino on the North Equatorial Countercurrent in the Pacific Ocean. Geophys. Res. Lett., 45, 10 49310 500, https://doi.org/10.1029/2018GL079273.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1974a: Sea level and the seasonal fluctuations of the equatorial currents in the western Pacific Ocean. J. Phys. Oceanogr., 4, 91103, https://doi.org/10.1175/1520-0485(1974)004<0091:SLATSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wyrtki, K., 1974b: Equatorial currents in the Pacific 1950 to 1970 and their relations to the trade winds. J. Phys. Oceanogr., 4, 372380, https://doi.org/10.1175/1520-0485(1974)004<0372:ECITPT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., and D. Yuan, 2016: Absolute geostrophic currents in global tropical oceans. Chin. J. Oceanol. Limnol., 34, 13831393, https://doi.org/10.1007/s00343-016-5092-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., Z. Lei, S. Li, and Z. Wei, 2018: Spreading of the South Pacific tropical water and Antarctic Intermediate Water over the Maritime Continent. J. Geophys. Res. Oceans, 123, 44234446, https://doi.org/10.1029/2018JC013831.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, Y., X. Li, J. Wang, and D. L. Yuan, 2020: Seasonal variability and dynamics of the Pacific North Equatorial Subsurface Current. J. Phys. Oceanogr., 50, 24572474, https://doi.org/10.1175/JPO-D-19-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, D., Z. Zhang, P. C. Chu, and W. K. Dewar, 2014: Geostrophic circulation in the tropical North Pacific Ocean based on Argo profiles. J. Phys. Oceanogr., 44, 558575, https://doi.org/10.1175/JPO-D-12-0230.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhai, F., and D. Hu, 2013: Revisit the interannual variability of the North Equatorial Current transport with ECMWF ORA-S3. J. Geophys. Res. Oceans, 118, 13491366, https://doi.org/10.1002/jgrc.20093.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, X., and A. J. Clarke, 2017: On the dynamical relationship between equatorial Pacific surface currents, zonally averaged equatorial sea level, and El Niño prediction. J. Phys. Oceanogr., 47, 323337, https://doi.org/10.1175/JPO-D-16-0193.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, H., D. Yuan, L. Yang, X. Li, and W. K. Dewar, 2018: Decadal variability of the meridional geostrophic transport in the upper tropical North Pacific Ocean. J. Climate, 31, 58915910, https://doi.org/10.1175/JCLI-D-17-0639.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zweng, M. M., and Coauthors, 2019: Salinity. Vol. 2, World Ocean Atlas 2018, NOAA Atlas NESDIS 82, 50 pp., https://data.nodc.noaa.gov/woa/WOA18/DOC/woa18_vol2.pdf.

  • Fig. 1.

    The surface geostrophic currents averaged for the period of 2004–18 (cm s−1). SEC, South Equatorial Current; NECC, North Equatorial Countercurrent; SECC, South Equatorial Countercurrent; SPC, South Pacific Current. The westward currents are superimposed with light gray shading.

  • Fig. 2.

    Comparison of the monthly zonal geostrophic currents (black lines) with the in situ measurements of the TAO/TRITON array (red lines) at 10-m depth during January 2004–December 2018. The correlation coefficient, based on monthly anomalies during the overlapping period of the two currents, and the effective degrees of freedom at each site are written in the right-lower corner of each panel.

  • Fig. 3.

    (top) Correlation coefficients and (bottom) RMS differences (cm s−1) between the monthly zonal Argo geostrophic currents and the altimetric geostrophic currents during January 2004–December 2018. The correlation coefficients not passing a 99% confidence level test are stippled in black.

  • Fig. 4.

    Mean zonal geostrophic currents (cm s−1) along different meridional sections across the tropical Pacific Ocean between 20°S and 5°N during 2004–18. The black contour interval is 10 cm s−1. The gray contours represent potential density surfaces. The dotted lines mark the boundaries of the three branches of the SEC.

  • Fig. 5.

    (a) Zonal velocity averaged during 2004–18 at the sea surface in the tropical Pacific Ocean (color shading; cm s−1), superimposed with the three axes of the SEC which are represented by the black lines with error bars. (b) Mean volume transport (Sv) of SEC(N) (red line), SEC(M) (green line), SEC(S) (blue line), and the total (black line) above the 26.0σθ (kg m−3) isopycnal surfaces. The standard deviations are shaded with corresponding colors.

  • Fig. 6.

    Climatological seasonal cycle of the vertical-mean zonal velocity (cm s−1) of (a) SEC(N) (averaged between 2°S and 5°N), (b) SEC(M) (averaged between 7°S and 3°S), and (c) SEC(S) (averaged between 15° and 8°S) above the 26.0σθ (kg m−3) isopycnal surfaces in the tropical Pacific.

  • Fig. 7.

    Climatological seasonal cycle of SLA difference (cm) (a) between 2°S and 5°N, (b) between 3°S and 6°S, and (c) between 8°S and 15°S.

  • Fig. 8.

    (top) Correlation and (bottom) regression coefficients between the monthly vertical-mean (above the 26.0σθ isopycnal surfaces) zonal velocity of (red solid line) SEC(N) and SLA difference between 2°S and 5°N, (green solid line) SEC(M) and SLA difference between 3°S and 6°S, and (blue solid line) SEC(S) and SLA difference between 8°S and 15°S. The black dots mean that the correlation or regression coefficients there do not pass the 99% confidence level test.

  • Fig. 9.

    Climatological monthly anomalies of (left) observational AVISO-SLAs and (right) modeled SLAs (cm) during January 2004–December 2018 across 2°S and 5°N. The modeled SLAs along 2°S are based on the analytical Rossby and Kelvin wave models, while along 5°N only the Rossby waves are considered.

  • Fig. 10.

    Climatological monthly anomalies of modeled SLA (cm) along 5°N during January 2004–December 2018 for different experiments: (a) Rossby wave, (b) local Ekman pumping is removed over the full domain, (c) Rossby wave is removed east of 160°W, (d) free Rossby wave emanating from the eastern boundary is removed. The corresponding Rossby wave speed (CR) and Ekman pumping velocity (WEK) are labeled.