The equatorial undercurrent, meridional overturning ... - Takeshi Izumo

Ocean Dynamics (2005) 55: 110–123 DOI 10.1007/s10236-005-0115-1

Takeshi Izumo

The equatorial undercurrent, meridional overturning circulation, and their roles in mass and heat exchanges during El Ninõ events in the tropical Pacific ocean Received: 1 February 2005 / Accepted: 6 April 2005 / Published online: 21 July 2005 Springer-Verlag 2005

Abstract The equatorial undercurrent (EUC), the shallow meridional overturning cells feeding it, and their role in El Ninõ and decadal variability in the equatorial Pacific are studied using both in situ data and an ocean general circulation model. Using temperature and current data from the TAO/TRITON moorings at the equator, their data gaps are filled and it was shown that continuous time series of mass transport, temperature, depth, and kinetic energy of the EUC could be constructed for the period 1980–2002 with an excellent accuracy. This dataset was analysed and used to validate the output from an oceanic general circulation model (OGCM). The OGCM was then used to find that variations in the strength of the EUC, shallow meridional overturning (pycnocline convergence and surface divergence), and equatorial upwelling had the same variations in mass transport on interannual and longer time scales within the period 1951–1999. These variations are all caused by variations of the zonal wind stress zonally integrated, in agreement with simple linear and steady dynamics theories. Impact of these mass transport variations and of temperature variations on heat budgets in the entire equatorial band of the Pacific and in its eastern part are quantified. Keywords Equatorial undercurrent (EUC) Æ El Ninõ Southern Oscillation (ENSO) Æ Shallow subtropical/ tropical meridional overturning cells (STCs/TCs) Æ Equatorial upwelling Æ Mass and heat transport Æ Heat budget Æ Recharge/discharge mechanism Æ Interannual and decadal variations of the tropical Pacific Æ Climate change Æ TAO/TRITON moorings Æ Ocean general circulation model (OGCM)

T. Izumo Laboratoire d’E´tudes en Geóphysique et Oceánographie Spatiales (LEGOS), Universite´ Paul Sabatier, 14, avenue Edouard Belin, 31400 Toulouse, France E-mail: [email protected]

1 Introduction The equatorial undercurrent (EUC), which was first discovered by J.Y. Buchanan in 1886 (McPhaden 1986), feeds equatorial upwelling (Bryden and Brady 1985) and may have strong influences on SST in the eastern Pacific and thus on El Ninõ Southern Oscillation (ENSO). The EUC is a quasi-permanent feature of the equatorial Pacific. Its thinness and length are remarkable. It has a meridional width of about 200–400 km, is centered on the equator in the thermocline between about 50 and 200 m depth, and extends nearly across the whole length of the basin. The EUC reaches a speed of 1 m/s in its core and transports around 30–40 Sv (1 Sv = 106 m3/s). Moreover, the EUC has strong interannual variations in mass transport (Picaut and Tournier 1991; Johnson et al. 2000) and temperature (Izumo et al. 2002), and is part of both subtropical and tropical shallow meridional overturning cells (STCs and TCs, Liu et al. 1994; McCreary and Lu 1994; Lu et al. 1998; Sloyan et al. 2003). EUC cold waters come mainly from the subduction regions in the subtropics, brought by meridional convergence in the pycnocline through the interior ocean or the western boundary currents (Blanke and Raynaud 1997; Goodman et al. 2004). The waters then upwell in the eastern equatorial Pacific and diverge in the surface layer towards the subtropics. The transit time is about 10–20 years. The STCs have thus been involved in theories on Pacific decadal variability, considering temperature variations (Gu and Philander 1997) or mass transport variations (Kleeman et al. 1999). Other papers have studied decadal mass transport variations (Klinger et al. 2002; McPhaden and Zhang 2002; Nonaka et al. 2002) and temperature variations (Deser et al. 1996; Schneider et al. 1999; Pierce et al. 2000; Hazeleger et al. 2001) in these cells using observations or models. In these four studies, temperature anomalies propagating in the pycnocline through the STCs seem to decrease strongly before they reach the equatorial region, whereas mass transport decadal variations seem to be well

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anti-correlated with equatorial SST variations. Some studies have thus analysed the associated heat transport variations on decadal time scales (Nonaka et al. 2002), or when varying the mean state (Hazeleger et al. 2004). However, these studies have mainly focused on the decadal variations of the meridional overturning. This paper will extend the analysis to an interannual time scale, since the EUC and meridional overturning variations can also have an important role on this shorter time scale. It is also of great interest to augment these studies by analysing how the EUC and meridional overturning variations are linked. In this paper, both in situ data and an ocean general circulation model (OGCM) are used to better understand the role of ocean circulation in El Ninõ variability. In Section 2.1, we construct continuous time series of EUC variability since 1980 using subsurface current and temperature data from tropical atmosphere/ocean (TAO) array (Hayes et al. 1991; McPhaden et al. 1998) moorings right at the equator. In Section 2.2, the continuous time series of EUC characteristics are analysed and dynamical causes of temporal variations are inferred with the help of a simple theoretical model of the EUC. In Section 3, the EUC, shallow meridional overturning (pycnocline convergence and surface divergence) and equatorial upwelling are estimated in an OGCM. Their transport variations are analysed and compared between each other and with data particularly on an interannual time scale. The results from the OGCM are compared with simple linear theories (Sverdrup and Ekman dynamics) that use only wind products, to verify that the numerical results are physically consistent and to diagnose quantitatively the physical origin of model variability. The physical causes of mass transport variations are found using simple linear theories and mechanisms are explored using lagged-correlation analysis. In Section 3.3, consequences of the variations of mass transports and transport-weighted temperatures of EUC and of meridional overturning on the heat transports are quantified through heat budgets on interannual and longer time scales. Conclusions and consequences are discussed in Section 4.

equator (Fig. 1) at 170W (starting in 1988), at 140W (starting in 1983), and at 110W (starting in 1980). For the purpose of this study, the EUC is here defined with the following criteria for zonal current (U) and temperature (T): U > 0 m/s and T < T (z = 15 m) 0.1C and T < 27C and 25 m < z < 300 m (or less depending on data availability). The sensitivity to the choice of criteria was tested and appeared unimportant for variability. The following relevant characteristics are defined for theR EUC right at the equator: UEUC=eq ¼ EUC u dz where UEUC/eq is the mass transport per unit width in m2/s obtained by integrating the zonal current at the equator over all depths where the EUC is defined. R uT dz TEUC=eq ¼ REUC where TEUC/eq is the transportEUC

u dz

weighted temperature in C obtained by integrating the product T * UR over all depths where the EUC is defined. uz dz where ZEUC/eq is the transportZEUC=eq ¼ REUC EUC

u dz

weighted depth in m obtained by integrating the product z * U over all depths where the EUC is defined. R KEEUC=eq ¼ EUC 12 u2 dz where KEEUC/eq is the kinetic energy per unit width and per unit mass in J/kg/m obtained by integrating 12 U 2 over all depths where the EUC is defined. Definitions of the full EUC characteristics (UEUC, TEUC, ZEUC, KEEUC) are similar to those defined right on the equator except for extending the integration over the meridional extent of the EUC (the integration is limited between 2N and 2S near the surface and 4N and 4S at 200 m depth: 2 z/100 < y < 2 + z/100,

2 Study of the EUC using TAO moored data over 1980–2002 2.1 Construction method of EUC time series from TAO data 2.1.1 Data and definitions The TAO array consists of approximately 70 moorings measuring subsurface temperature. At some locations, currentmeters and/or acoustic Doppler current profiler (ADCP) measurements are available. The region where the EUC is fed by both STCs and TCs is the central and eastern Pacific. To study the EUC there, current and temperature data are used from TAO moorings on the

Fig. 1 The different boxes and sections used in the present study: the equatorial band over the whole basin (5N–5S, west to east), the central to eastern equatorial basin (5N–5S, 170W to the eastern boundary) where equatorial upwelling is defined at 80 m depth, the Ninõ-3.5 region (5N–5S, 180W–120W), the TAO moorings at 170W, 140W, and 110W (red circles for ADCP and light blue squares for currentmeters when available) used in part 2, 170W section where EUC and SEC transports are estimated for part 3, and 5N and 5S sections where pycnocline convergence and surface divergence are defined. Mean SST is also shown in colour in the background (over the period 1980–1999 in the model forced with NCEP winds)

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with y = latitude. These limits exclude the North and South Equatorial Counter Currents). RR It should be noted that since EUC uT dz dy ¼ UEUC TEUC ; it is easy to distinguish between the contributions of transport-weighted temperature and mass transport variations in the heat transport variations of the EUC. 2.1.2 Method and validation Three problems exist for the use of TAO data to estimate EUC characteristics: data gaps, coarse vertical sampling over the depth range of the EUC for currentmeter measurements, and no meridional information except during TAO cruises. Four steps are therefore used to obtain continuous time series of EUC characteristics:

Fig. 3 Comparison for the mass transport of the EUC at the equator (m2/s) between the estimate using ADCP current measurements available every 5 m (blue line) and the estimate using only currentmeters depth levels (black line) for the longest current and temperature time series available at 0, 110W TAO mooring, 3-months Hanning filter

1. Filling gaps in T and U (currentmeters + ADCP) using bilinear or linear regression with adjacent depth levels following the same method as Johnson and McPhaden (1993) (Fig. 2). 2. Vertical interpolations of U (spline) and T (linear) every 5 m. 3. Integration over the depth of the EUC to calculate mass transport (UEUC/eq), transport-weighted temperature (TEUC/eq), transport-weighted depth (ZEUC/eq) and kinetic energy (KEEUC/eq) of the EUC, right at the equator. Tests using only currentmeter depth levels, or all ADCP levels, when both measurements are available, were made. The resulting time series of UEUC/eq, TEUC/eq, ZEUC/eq, and KEEUC/ eq were insignificantly different, showing the efficiency of spline interpolation for the zonal current in the EUC (Fig. 3). The continuous time series are therefore obtained with surprisingly good precision (eUeuc=eq 4 m2/s (4%); eTeuc=eq 0.11C (0.6%) ; eZeuc=eq 1.7 m (1.3%) ; eKEeuc=eq 6 J/kg/m (10%)), considering the few levels in the vertical for

currentmeters (e.g. 10, 25, 45, 80, 120, and 200 m at 110W for the 1988–1999 period). 4. Meridional extrapolation to the full section of the EUC (see also Izumo et al. 2002), justified theoretically by the linear stationary analytical model of McPhaden (1993). In his model, there is an exact proportionality between the zonal current integrated only vertically right at the equator over the depth of the EUC (UEUC/eq) and the one integrated over the whole EUC section (UEUC), with a meridional width L of about 230 km ðUEUC ¼ L UEUC=eq Þ: The present analysis confirms the efficiency of meridional extrapolation for the time-varying EUC for all its characteristics. The correspondence between the exact estimates over the full EUC section (UEUC, TEUC, ZEUC, and KEEUC) and the extrapolated ones (UEUC/eq, TEUC/eq, ZEUC/eq, and KEEUC/eq) is remarkable in both in situ data

Fig. 2 Subsurface zonal current (left, cm/s) and temperature (right, C) at the equator at 140W from TAO mooring data, after filling gaps and interpolating vertically (spline for U and linear for T). The EUC vertical integrated characteristics can be still calculated for the gap in current under 120 m in 1995–1996 using linear regression with the estimates integrated only between 30 and 120 m, and for

the gap in temperature in 1999 by using linear regression with temperature data available from TAO mooring at 2S, 140W. Note that for the three moorings at the equator at 170W, 140W, and 110W, current data are not always available near the surface (z < 30 m) or under 250 m but that does not affect calculations in the EUC, which is in the appropriate range of depth

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Fig. 4 Mass transport (left, Sv, meridional width L used: 270 km) and transport-weighted temperature (right, C) of EUC at 140W, estimated from TAO data right at the equator and meridionaly

extrapolated (5 days averages). Red circles and crosses are estimated from CTD/ADCP meridional sections (from G. Johnson, NOAA/ PMEL). The comparison works also very well at 170W and 110W

(Fig. 4 and Table 1) and OGCM (see Section 3.1 for the description of the OGCM). Correlations between 0.90 and 0.98 are found at the different longitudes for all EUC characteristics (by data filtered with a 3months Hanning filter). Moreover, the same order of the meridional width is found in both observations and the OGCM, although observational studies of Johnson et al. (2000, 2002) use different criteria for defining the EUC: using the time series constructed here from TAO data combined with EUC estimations of Johnson et al. (2000, 2002) from CTD/ADCP meridional sections, the fitting width is L = 300 km at 170W, 270 km at 140W, and 250 km at 110W (e.g. see Fig. 4, left), and for the OGCM, in the simulation forced by NCEP winds, L = 420 km at 170W, 350 km at 140W, and 390 km at 110W (lower values were found in the more realistic simulation forced by ERS winds).

variability and to validate numerical models. Moreover, mass transport UEUC and transport-weighted temperature TEUC of the EUC will be very useful for heat budgets analysis, since their product UEUC*TEUC is by definition equal to EUC heat transport.

To sum up, by combining the vertical interpolation and this meridional extrapolation method, continuous time series for the EUC are obtained with a great precision from TAO data. They can be used to study EUC

Table 1 EUC means, from in situ data right at the equator/over EUC meridional extent

UEUC/eq* L/UEUC (Sv) TEUC/eq/TEUC (C) ZEUC/eq/ZEUC (m) KEEUC/eq/KEEUC (J/kg/m)

170W

140W

110W

26/30 19.2/17.8 164/165 28.0/–

34/30 18.1/17.4 133/130 56.5/–

25/26 16.3/16.3 108/110 39.9/–

Time means of EUC characteristics; the first value is obtained from TAO data right at the equator (with L = 300 km at 170W, 270 km at 140W, and 250 km at 110W), the second being the estimate over the whole meridional extent of the EUC (from Johnson et al. 2002). Note the different criteria for the EUC used in the present study (see Section 2.1.1) and in Johnson et al. (2002) study (U > 0 m/s and 23 kg/m3 < rh < 26.5 kg/m3)

2.2 EUC variability 2.2.1 Description The EUC time series obtained from in situ data right at the equator show for the temporal means an EUC becoming cooler and shallower towards the east, in agreement with former studies (Table 1). The time series all show large variations, especially on seasonal to interannual time scales (Figs. 3 and 4). Spectral analysis (not shown) for mass transport and temperature of the EUC gives different peaks corresponding to ENSO at a 3.8 years period, seasonal and semi-annual cycles, and intraseasonal variations at 120 days (see Cravatte et al. 2003) and 75 days (due to Madden-Julian oscillation, see Kessler et al. 1995). 2.2.2 Physical causes Qualitatively, the EUC is known to vary with the zonal wind in the central equatorial Pacific. To quantify this relationship, the EUC mass transport is compared to equatorial zonal wind (from NCEP 1948–1999 reanalysis) integrated over different regions, with linear regression analysis being used to find the optimal region with the highest correlation. For interannual time scale (defined with a low-pass temporal Hanning filter of 2 years, to filter out seasonal and shorter time scales), the EUC responds quasi-linearly to zonal wind stress integrated zonally over its fetch westward Rof the considered longitude (Fig. 5a): UEUC ðSvÞ ¼ K fetch sx ; dx; with K varying from 0.73 to 1.7 · 10 4 Sv/Pa/m, depending on

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Fig. 5 Physical causes of EUC variability: a interannual variations of EUC transport at 170W from TAO data (Sv, black) and of equatorial zonal wind stress (NCEP, blue) integrated over 120E– 160W and multiplied by a fitting factor K 1.2 * 104 Sv/Pa/m (K = 2 * 104 Sv/Pa/m in the linear model). Correlation = 0.94, 2 years Hanning filter. 1 Sv = 106 m3/s. b EUC transport-weighted

temperature anomaly (C, black) and its linear regression estimate with the difference between EUC transport-weighted depth and thermocline depth (red, correlation = 0.96) from TAO data at 0W, 170W. The linear regression factor is 0.088C/m. The deeper the EUC is, compared to the thermocline, the colder the EUC is. Correlation = 0.96, 3-months Hanning filter

EUC longitude and fetch region. The best correlations (0.94 at 170W and 0.91 at 140W) are obtained when equatorial zonal wind stress sx is integrated over the region 120E–160W for the EUC at 170W and over 170W–140W for the EUC at 140W. This linear relationship is in quantitative agreement with the analytical and simplified model (linear and steady stratified model, constant vertical gradient of temperature, no meridional currents, and no boundaries) for the EUC of McPhaden (1993), with a proportional coefficient of the same order (K = 2 · 10 4 Sv/Pa/m in theory). Qualitatively, anomalous westerlies in the western and central equatorial Pacific create baroclinic Kelvin waves, which produce negative zonal current anomalies at the depth of the EUC. The Kelvin waves propagate eastward and change EUC transport eastward of the fetch. On the contrary, the same analysis shows that seasonal variations of EUC transport can not be explained simply by wind variations (not shown). Other processes are involved, such as the Rossby waves propagation associated with the seasonal cycle. These results confirm that equatorial dynamics is quasi-linear and quasi-stationary on interannual but not on seasonal time scales, in agreement with Yu and McPhaden (1999a, b). Considering the importance of transport-weighted temperature HTEUC for heat transport TEUC (HTEUC = UEUC*TEUC) and thus for the heat budget of the eastern equatorial Pacific, it is very important to understand EUC temperature variations. A linear regression analysis shows that these variations are due to variations in both thermocline depth (defined here simply as depth of the 20C isotherm Z20 in TAO data) and EUC transport-weigShted depth (ZEUC). At 170W, the EUC temperature varies linearly with the difference (Z20 ZEUC) on both interannual and seasonal time scales (Fig. 5b, correlation: 0.96 over the period 1988–2001). It

is the local position of the EUC relative to the thermocline that controls EUC temperature in the central Pacific, and not EUC depth or thermocline depth alone. The proportionality coefficient of 0.088C/m is of the same order as the temperature gradient in the thermocline and is thus physically realistic. The result is different in the eastern Pacific where the EUC is closer to the surface: thermocline depth variability is dominant at 110W, where EUC temperature varies linearly with Z20, with a coefficient of 0.063C/m (correlation = 0.89 over the period 1980–2001). To summarize, the carefully constructed time series of different EUC characteristics obtained from in situ data over the period 1980–2002 have allowed us to study EUC variability and its physical causes. They will now be used to validate the simulated EUC in an OGCM. And this OGCM will then be used to understand the link between EUC variability and meridional overturning variability, and their roles in heat exchanges.

3 Study of the meridional overturning cells 3.1 Model description and validation Because of the low density of data, an OGCM is useful for assessing meridional and vertical currents and computing heat budgets. The OGCM used in this study is the OPA (Oceán Paralle´lise´) model from LODYC (Madec and Imbard 1996; Madec et al. 1998), in its global version ORCA2. The zonal resolution is 2, the meridional one varies from 0.5 at the equator to 2 poleward of 20, and the vertical one varies from 10 m to 15 m over the first 200 m. Two simulations were mainly used for this analysis, one forced by NCEP reanalysis (Kistler et al. 2001) wind stress, heat and

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Fig. 6 Comparison for the vertical transport meridional distribution between estimates from drifting buoys (black line, mean divergence in the surface layer, left scale, from Johnson 2001) and from the OGCM over 165E–85W. The right scale (m2/s) is for the mean vertical transports per unit width at 40 m in the OGCM, forced by ERS winds (red line, mean over 1993–1999) and forced by NCEP winds (dark blue line for the mean over 1993–1999 and light blue line for the mean over 1980–1999). Note that 40 m is the optimal depth to catch the strongest downwellings associated with the TCs (this optimal depth varies weakly with longitude, and this behaviour justifies the zonal integration at this fixed depth to have an approximated value of TCs). With Johnson (2001) supposition of a mean surface layer depth of 30 m, a 107/s divergence corresponds to a vertical transport per unit width of 37 m2/s

(e.g. see Fig. 6 for equatorial upwelling). The OGCM variability is validated for the only branch of the meridional overturning cells that we can assess through in situ data: the EUC in the central Pacific since the 1980s. EUC interannual variability is very well simulated in the central Pacific, with the same amplitude for EUC temperature. However, there is an underestimation of about 25% for EUC mass transport (Fig. 7). This validation gives confidence in the OGCM for interannual variations over the period 1980–1999. Note that for simulating long-term variability over the period 1948– 1999, an unrealistic overestimated long-term decrease in NCEP easterlies (Putman et al. 2000; Wu and Xie 2003) causes too strong trends in the simulated mass transports (slowdown of the circulation) and temperatures (such as a warming of the eastern equatorial Pacific surface, Fig. 8). The following results will thus be given for the validated period 1980–1999 (means and correlations given for time series filtered with a 2-years Hanning lowpass filter that removes frequencies higher than 1/year to retain interannual time scales and filter out seasonal and intraseasonal time scales). 3.2 Study of the different branches of the meridional overturning 3D cells 3.2.1 Definitions

freshwater fluxes over the period 1948–1999 (similar simulation to the one used by Rodgers et al. 2003), another in which the more realistic winds from ERS satellite scatterometers were used since mid-1992 (see Izumo et al. 2002, for further description and validations of the OGCM). In the present paper, we will mainly show results from the first simulation, even if analysis of the second simulation gives similar but more realistic results over the shorter period 1992–1999. For validating the OGCM, temporal means of the EUC, the equatorial upwelling and its meridional distribution have been used. A good comparison with estimates from in situ data is found for the simulation forced by ERS winds, but mass transports in the simulation forced with NCEP winds are slightly underestimated (e.g. at 170W, UEUC = 28 Sv for ERS simulation, 24 Sv for NCEP simulation and 30 Sv from in situ data of Johnson et al. 2002) and not so well-defined meridionaly

Meridional overturning cells have been historically defined from analysis of layer model results and there is no consensus on their definition. The cells have different branches: meridional convergence in the pycnocline (defined here from the western to the eastern boundaries, including western boundary currents (WBCs)), EUC in the central Pacific, equatorial upwelling in the central to eastern Pacific, meridional surface divergence (Fig. 9). All branches of the cells have the same mean mass transports if they are well-defined, on the right range of latitude. Therefore, for the stationary state, looking either at pycnocline meridional convergence (mean = 14 Sv at 5N, 14 Sv at 5S and 28 Sv for the total, see the definitions described further in the text), EUC transport in the central Pacific (mean = 24 Sv at 170W), equatorial upwelling (mean = 25 Sv), surface meridional divergence (mean = 13 Sv at 5N, 14 Sv at 5S, and 27 Sv for the

Fig. 7 Comparison for the interannual variations (Hanning filter of 2 years) for the mass transport (left, Sv) and transport-weighted temperature (right, C) of the EUC at 140W between the model (blue) and the extrapolated TAO data. A meridional width L =

200 km, about 25% smaller than the optimal 270 km value evaluated from in situ data (see Fig. 4), is used for the extrapolated mass transport, because of the underestimation of the EUC in the model forced with NCEP winds

116 Fig. 8 Difference (Tdiv Tconv) of the transport-weighted temperatures of warm surface divergence Tdiv (mean: 27.0C) and cold pycnocline convergence Tconv (mean: 21.6C) compared to Ninõ-3.5 SST. Mean (5.4C) has been removed and a 2-years Hanning filter has been applied to show the remarkable similarity on interannual time scale. All the available period (except the first spin-up years) 1951–1999 is given, to show the too strong long-term trend of the simulation forced with NCEP winds

total), or by using stream functions, should be equivalent. For the steady state, STCs and TCs can be distinguished, even if debates exist (Hazeleger et al. 2000). In the model, TCs appear well in the mean and are close to the equator, with upwelling at the equator and downwelling maxima around 3–4 of latitude (Figs. 6 and 9). Their main role is to homogenize the waters of the mixed layer near the equator rather than to exchange heat with the extraequatorial regions. For the study of cells variability, all branches may not vary equally and in phase. Here, we therefore study the different branches independently, being aware that our goal is not to distinguish STCs and TCs variabilities. Moreover, the results may be dependent

of the meridional width of the band used for the calculations. We thus choose to be not too far from the equator (5N–5S), where the patterns of wind interannual anomalies are meridionally uniform to a reasonable extent (and also to be able to compare to studies using in situ data, such as the ones of Meinen and McPhaden (2000) and Meinen et al. (2001)). Criteria of definition of pycnocline convergence (22.5 < rh < 26 kg/m3 and z > 50 m, with rh being potential density) and of surface divergence (rh < 22.5 kg/m3 or z < 50 m) are equivalent to other authors studies (McPhaden and Zhang 2002; Lee and Fukumori 2003). These criteria appear adequate since the same means are then obtained for the different branches of the cells. No current criteria are used, since the currents may reverse during strong El Ninõ events and we want to catch all the transport variability in pycnocline and surface layers. The convention here is to define mass transports of all the branches as positive when in the same direction as the mean circulation (pycnocline convergence transport is, for example, counted positively towards the equator, whereas surface divergence transport is counted positively towards the poles). 3.2.2 Pycnocline convergence variability

Fig. 9 The different branches of the shallow meridional cells in the simulation forced with NCEP winds. Zonal current (in colour, cm/s) and potential density (blue contours every 0.5 kg/m3) at 170W evidences the EUC and SEC in the central Pacific. Meridional and vertical currents zonally integrated from the west to the east of the Pacific basin (black arrows, m2/s) evidence the meridional convergence in the pycnocline, the equatorial upwelling, and the surface divergence. Arrows orientation has been adjusted to meridional and vertical scales. Black lines highlight 5N and 5S sections where surface divergence and pycnocline convergence are defined. Means over the period 1980–1999

Very strong variations of mass transports are seen in the model, with strong decreases of the pycnocline convergence during El Ninõ events and the opposite during La Ninã events: a strong anti-correlation (see Table 2, lags will be discussed later) is found with eastern equatorial SST (defined as SST over Ninõ-3.5 region: 180E– 120W, 5N–5S, region shown in Fig. 1, time series shown in Fig. 10b for the period 1980–1999 and in Fig. 8 for the period 1951–1999), which is a good ENSO indicator. Additional analysis (not shown) has been made to distinguish WBCs and interior ocean pycnocline transports. The variations of the total pycnocline convergence are dominated by variations of the convergence through the interior ocean (mean = 10 Sv,

Lags and lagged correlation with simulated Ninõ-3.5 SST of the different branches of the meridional overturning 3D cells and of their physical causes (determined in Sections 2 and 3): zonal wind sx (NCEP winds) integrated from west to east of the basin at 5N and 5S (-Ekman divergence), over 5N–5S (=> geostrophic convergence) and equatorial zonal wind integrated over 120E–160W (that forces mass transport variations of EUC at 170W). The values have been classed considering their lead on SST. Results are given for the 1980–1999 period in the model forced by NCEP winds for values filtered with a 2-years Hanning filter. Precision on lags is of ±1 month

0.85 0.97 0.92 0.92 0.82 0.77

4.5 months 5.5 months

Lag with Ninõ-3.5 SST Lagged-correlation

6.5 months

5 months

0.78

0.85

0.93

2 months 3 months 4.5 months

0.5 months 1 month

EUC at 170W sx at 0 integrated over 120E–160W SEC at 170W Equatorial upwelling (170W–East, 5N–5S) Surface divergence 5N + 5S sx from west to east at 5N

Table 2 Lags and lagged correlations with Ninõ-3.5 SST

sx from west to east over 5N–5S

Pycnocline convergence 5N + 5S

sx from west to east at 5S

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rms = 4.4 Sv for interannual variations). These variations are as expected slightly counteracted (correlation = 0.71) by the smaller variations of the pycnocline convergence through the WBCs (stronger mean: 18 Sv, smaller rms: 2.2 Sv), in agreement with other studies using linear models (Zebiak 1989; Springer et al. 1990; An and Kang 2000), box models (Meinen and McPhaden 2001; Meinen et al. 2001) and OGCMs (Lee and Fukumori 2003). Convergences at 5N and 5S vary relatively symmetrically and with the same amplitudes on interannual time scale (correlation = 0.51), since their variations are dominated by geostrophic transport interannual variations, which are dominated by their symmetric part between 5N and 5S (Kug et al. 2003, see also Yu and McPhaden 1999b). On the basis of quasi-steady Sverdrup dynamics, geostrophic transport over the whole water column is the difference between Sverdrup and Ekman transports. It can thus be directly estimated from zonal wind stress and its curl, integrated zonally at 5N and 5S over the whole basin, from the western (W) to the eastern (E) boundaries: Z E curl s sx VSv - Ek ¼ þ dx 1=q b f W where q is the constant reference density, s is the wind stress, f is the Coriolis parameter and b is the meridional derivative of f. As expected, if only the part of this transport corresponding to the pycnocline is taken (about 40% for a good fit), it is nearly equal to the interior ocean convergence in the model, for the mean as well as for its interannual variations (correlation = 0.96, Fig. 10a). With the same approach, WBCs should exactly oppose Sverdrup meridional transport through the interior ocean in the steady state. However, only about 25% of Sverdrup transport interannual variations are needed for a good fit with pycnocline convergence variations in the WBCs, and the correlation (0.76) is lower. Oceanic waves and non-linearities are important in the WBCs region on interannual time scales, and the Sverdrup steady dynamics is thus less pertinent there. 3.2.3 Surface divergence variability Surface meridional transports at 5N and 5S also vary strongly with ENSO, but with a strong asymmetry and low correlation between 5N and 5S, (correlation = 0.26, Fig. 10b). This is caused by asymmetric Ekman transports, due, for example, during the mature phase of the El Ninõ event end-1997, to the anomalous displacement of the intertropical convergence zone (ITCZ) in the southern hemisphere, increasing the easterlies in the northern hemisphere (thus Ekman divergence) and decreasing the easterlies in the southern hemisphere. This strong asymmetry is also found in other studies (Meinen and McPhaden 2001, Alory and Delcroix 2002, Kug et al. 2003). But very interestingly, if northern and southern surface transports are summed, coherence is found again and the total surface divergence

118 Fig. 10 Meridional overturning interannual variations in the model and their physical causes: (a) variations of pycnocline convergence (5N + 5S) through the ocean interior in the model (black) and geostrophic convergence over the whole water column estimated from Sverdrup balance and multiplied by 0.38 (blue, see Section 3.2 for more details). (b) Meridional mass transport associated with the surface divergence at 5N (in Sv, red line), at 5S (blue) and the total surface divergence (5N + 5S, black line). Ninõ3.5 SST anomaly multiplied by a negative factor of 4 is superposed (green line), zero of the anomaly being represented by the black straight line. (c) Variations of the surface divergence (5N + 5S) in the model (black), Ekman divergence (red) and divergence using Ekman and Sverdrup transports (blue)

is highly correlated to Ninõ-3.5 SST, and thus to ENSO and associated equatorial wind variations (Fig. 10b and Table 2). With the same method as for pycnocline convergence, quasi-steady Sverdrup dynamics allows the estimation of surface meridional transport by adding Ekman transport VEk and the counteracting geostrophic transport in the surface layer 0.3 * VSv-Ek (about 30% of the geostrophic transport over the whole water column is taken for a good fit). The comparison of this estimation (VEk + 0.3 * VSv-Ek) with surface divergence in the OGCM is then very good, whereas considering only Ekman transport would overestimate divergence interannual variations (Fig. 10c). To sum up, pycnocline convergence and surface divergence variations can both be quantitatively very well explained by linear and steady Ekman and Sverdrup theories. As in the previous section for the EUC, this confirms the quasi-linear and

quasi-steady equatorial dynamics in the interior ocean on interannual time scales. 3.2.4 Covariability of the different branches of meridional overturning cells. Comparison with eastern equatorial SST Interannual variations of EUC mass transport at 170W, pycnocline convergence and surface divergence at 5N and 5S, are very similar, remarkably with the same amplitudes, and with some expected monthly lags due to ocean dynamical adjustment (Fig. 11a and Table 2). As seen in the previous sections, they have all approximately the same physical origin: interannual variations of zonal wind stress and its curl, integrated zonally over a great part or over the whole basin, over 5N–5S, or at 5N and 5S. This explains the covariability. Note that this covariability on interannual time

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Fig. 11 Covariability of the different branches of the shallow meridional overturning cells in the model: interannual variations of EUC mass transport UEUC (black, positive towards the east) compared to pycnocline convergence Vconv (red, left, positive towards the equator), surface divergence Vdiv (blue, left, positive towards the poles), SEC USEC (green, right, positive towards the

west) and equatorial upwelling Wupw (purple, right, positive towards the surface). EUC mass transport is on the two figures and same scale is used to highlight the similarity in both correlation and amplitude between the different mass transports. Anomalies in Sv are filtered with a 2-years Hanning filter

scale exists only in a latitudinal band close enough to the equator, where the dynamics is quasi-steady, and where the spatial pattern of wind interannual variations is sufficiently meridionally uniform. The same analysis for 9N–9S only shows good covariability on longer time scales than interannual (in agreement with Nonaka et al. 2002), the wind variation pattern associated to Pacific decadal variability being meridionally wider than on interannual time scales. Comparison can also be made with the other branches of the meridional overturning cells 3D circulation in the OGCM: equatorial upwelling (defined as vertical transport at 80 m, east of 170W, 5N–5S) and south equatorial current (SEC) at 170W. Their interannual variations are all quite similar, with same amplitudes, and also with some monthly lags (Fig. 11b). The variations of all the branches of the cells are strongly correlated to ENSO (a slowdown during El Ninõ events and a strengthening during La Ninã events): their comparison with Ninõ-3.5 SST is very good (Table 2), with leading lags that give hints about the physical mechanism. For an El Ninõ event (and conversely during a La Ninã event), anomalous westerly zonal wind at 5N (i.e., a decrease of the usual easterlies) cause a decrease in poleward Ekman meridional transport, leading chronologically to decreases in surface divergence, SEC, equatorial upwelling, and finally in EUC and in geostrophic meridional transports, thus in pycnocline convergence. These mass transport decreases strongly affect the heat balance and cause later an increase in SST of the eastern Pacific (see Section 3.3 for heat budgets). Note that due to the decrease in NCEP easterlies, a too strong long-term decrease of about 30% is seen in all the branches of the meridional overturning cells over the 50 years of the simulation, greater than the slowdown of about 25% estimated by McPhaden and Zhang (2002)

from both hydrological and wind data sets. This decrease can also be tested by using a proxy of equatorial zonal wind variations sx¢ over the whole basin such as the pressure difference Dp between the western and eastern equatorial Pacific available since 1920 (Clarke and Lebedev 1996). With this proxy, the slowdown of the equatorial circulation can be estimated: using Sverdrup and Ekman dynamics, an approximated estimate for mass transport anomalies is V’div(Sv) V’conv(Sv) 103*sx 4*Dp’(hPa). The comparison shows that the slowdown is realistic in the model between the 1970–1979 and 1980– 1989 decades (V¢div 4 Sv and V’conv 5 Sv in the model, similar to V’conv_McPhaden_and_Zhang (2002) 7 Sv and 4 *Dp¢ 4 hPa), contrary to the unrealistic slowdown seen in the model between the 1956–1965 and 1970– 1979 decades (V’div 3 Sv and V’conv 5 Sv in the model compared to V¢conv _McPhaden_and_Zhang (2002) +2 Sv and 4 *Dp¢ +1 hPa). The long-term decrease in NCEP easterlies thus appears unrealistic before the mid-1970s (see also Alory et al. 2005).

3.2.5 Variability in temperature of convergence and divergence Pycnocline convergence brings cold water to the equatorial band and surface divergence removes warm water from the equatorial band. Another physical term, other than mass transport, is therefore needed to quantify the meridional heat transport towards the poles: the difference in transport-weighted temperature (same definition as for TEUC, the weight being meridional current V instead of U) between surface divergence and pycnocline convergence (Tdiv Tconv). In the OGCM, variations of this difference are very strong on interannual and longer time scales (Fig. 8) and can greatly influence the heat

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budget of the equatorial band. The comparison of this difference with Ninõ-3.5 SST gives a remarkable result: its interannual and longer variations are nearly equal to those of SST. Note that this relationship is also valid using 9N and 9S for divergence and convergence calculations. The strong correlation can be expected, since the strongest temperature interannual variations in the mixed layer are those associated to ENSO. However, the similarity in amplitude is surprisingly very good and can be useful to estimate the variations of the difference (Tdiv Tconv) in the real ocean from SST in situ data. 3.3 Consequences for heat exchanges 3.3.1 Heat exchanges due to meridional overturning In the OGCM, an exact heat budget for the equatorial band (from west to east, 5N–5S, rh < 26 kg/m3) has been computed. Diffusion appears negligible at the boundaries of the box defined here, and heat content variations are only due to oceanic advective heat transports and to surface heat transport from the atmosphere. This heat budget allows quantification of the role of meridional overturning for the recharge and discharge of the equatorial band, and to compare the consequences of mass transports and transport-weighted temperatures variations on heat transports. Since mass transports of surface divergence Vdiv and of pycnocline convergence Vconv vary similarly (Fig. 11a), heat transport towards the equatorial band due to meridional overturning HTmer.overt. can be estimated simply: Vd i v Vc o n v ) H Tm e r: o v e r t. qCv Vd i v ðTd i v Tc o n v Þ where Cv is the specific heat of water (note that the use of Vconv instead of Vdiv as the mass transport reference of meridional overturning does not change the results). This estimation is of great interest because it is independent of the reference temperature choice and it is

Fig. 12 (a) Interannual variations of the total oceanic heat transport (black, 1 PW = 1015 W) towards the equatorial band and of heat transports due to meridional overturning, taking into account its variations in mass transport (red), in temperature (blue) or both (green). (b) Interannual variations of the total oceanic heat

easily interpreted physically. HTmer._overt. explains a great part of the total oceanic heat transport into the equatorial band, for the mean (70 PW compared to 85 PW, 1 PW = 1015 W) as well as for the interannual variations (Fig. 12a). The other part of the cooling oceanic heat transport is due to upwelling of cold intermediate waters in the eastern Pacific at the bottom of the box (where rh = 26 kg/m3), compensated by an outflow of warmer waters through the Indonesian throughflow towards the Indian ocean (in agreement with the observational study of Sloyan et al. 2003). Moreover, heat transport variations due to mass transport variations qCv Vdiv ðTdiv Tconv Þand those due to temperature variations qCv Vdiv ðTdiv Tconv Þvary differently and both are important. During the onset of an El Ninõ, the slowdown of the meridional overturning in the narrow 5N–5S equatorial band, due to the decrease in easterlies, tends to heat the band (recharge of heat), whereas the subsequent increase of eastern equatorial SST and thus of the difference (Tdiv Tconv) peaking during the mature phase of the event tends to cool the band (discharge of heat). To sum up, the result of both effects will be usually a recharge of the band, firstly due to an anomalously small (Tdiv Tconv) during normal or La Ninã conditions before an El Ninõ event (this recharge may help its onset), and then due to the meridional overturning slowdown during the onset of El Ninõ. This recharge is rapidly followed by a strong discharge, caused firstly by the maximum of (Tdiv Tconv) during the mature phase, then by the meridional overturning strengthening associated with the reestablishment of the equatorial easterlies. This discharge helps the transition to La Ninã. This is in agreement with the recharge–discharge mechanism (Wirtky 1985; Jin 1997a, b) and with warm water volume observations (Meinen and McPhaden 2000, 2001), but with a complementary point of view. Note that the warming surface heat transport from the atmosphere (+85 PW) balances

transport (black, PW) towards the eastern equatorial Pacific and of zonal heat transports associated to EUC at 170W, taking into account its variations in mass transport (red), in temperature (blue) or both (green)

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the total oceanic heat transport for the mean (85 PW). It acts as a damping term for interannual variations, being well anti-correlated to Ninõ-3.5 SST (correlation = 0.93), as expected from former studies. During an El Ninõ event, it will tend to discharge heat from the equatorial band (and the opposite during La Ninã events), in agreement with Sun (2000, 2003). 3.3.2 Zonal heat exchanges associated with the EUC With the same method, an exact budget for the central to eastern equatorial Pacific (defined as the box east of 170W, 5N–5S, z < 300 m) has been computed to quantify the influences of mass transport and temperature variations of the EUC on heat budget. Since mass transports of SEC USEC and of EUC UEUC vary relatively similarly (Fig. 11b), zonal heat transport towards the eastern Pacific due to EUC (bringing cold water) and SEC (removing warm water) zonal circulation, HTEUC, can be estimated simply: U 0EUC U 0SEC ) HTEUC qCv UEUC ðTSEC TEUC ÞThe mean temperature TSEC of the SEC is used here, since the goal is to quantify the influence of EUC variations on the heat budget. The approximated heat transport compares relatively well with the exact total oceanic heat transport into the box, with however a lag (note that using USEC instead of UEUC in the previous formulae gives a better comparison, with more similar amplitudes, and less lag, but we are more interested in EUC variations than in SEC). Variations of HTEUC are mainly due to mass transport variations on interannual time scales (Fig. 12b). However, on longterm time scale, over the 50 years of the simulation, the EUC transport-weighted temperature trend is not negligible: the long-term decrease of EUC temperature (1C), due to a shallower thermocline, counteracts about a third of the warming trend caused by the strong decrease in EUC mass transport.

4 Discussion Firstly, TAO moorings current and temperature data have been used to derive EUC mass transport, transport-weighted temperature and depth, and kinetic energy. Even if time series are gappy and available only at some depths and right at the equator, it has been shown here, using regression analysis, that vertical interpolation and then meridional extrapolation allow together a precise estimation of EUC characteristics and of their variability since the 1980s. This result is very important, since the continuous time series obtained over the period 1980–2002 can be useful for future studies on the EUC and its dynamics, and to validate the EUC in numerical models (filled TAO data and EUC time series can be obtained freely by asking the author). In the present paper, these EUC time series are quantitatively analysed to find the physical causes of EUC variability. The series are then used for a successful validation of the simulated

EUC and to quantify the role of the EUC in zonal heat exchanges. Moreover, Section 3 showed that the interannual and long-term variations of the different branches of the shallow meridional overturning cells are all nearly equal in amplitude and phase in the OGCM (with some monthly lags due to wave propagation). Mass transport of the EUC is therefore an indicator of the cells strength. Consequently, knowing the EUC and its variability from TAO in situ data over a long period is useful in assessing these cells variability and especially equatorial upwelling (of great importance for SST in the equatorial cold tongue). Simple analytical theories with linear and steady-state dynamics have been used to successfully derive mass transports from interannual variations in wind stress alone and to explain the covariability of the different branches of the cells. Ocean dynamics appears quasilinear and quasi-stationary on interannual time scales in the 5N–5S equatorial band. Mass transport variations of the EUC and of meridional overturning are mostly explained by linear ocean adjustment to zonal wind and its curl integrated zonally over the equatorial Pacific. This same physical origin is the reason for the covariability of the different branches of the cells. Analysis of transport-weighted temperatures also shows simple physical linear responses: EUC temperature varies linearly with the local difference in depth between thermocline and EUC in the central Pacific, and the difference in transport-weighted temperatures (Tdiv Tconv) has the same interannual and long-term anomalies as Ninõ3.5 SST. As for the EUC, the remarkable comparison for meridional overturning between OGCM outputs and estimates using only wind products gives confidence in the model. Further studies would still be interesting to estimate pycnocline convergence and surface divergence variations using only hydrographic data. The present study has evidenced how all the branches of the shallow meridional overturning cells strongly covary with ENSO, slowing down and even vanishing during strong El Ninõ events, and strengthening during La Ninã events, leading to important changes in zonal and meridional heat exchanges. Mass transport variations of the different branches of the cells are indeed all very well correlated with eastern Pacific SST. Surface divergence and upwelling lead Ninõ-3.5 SST variations by about 5 months. Moreover, the heat budget analysis shows that mass transport variations are the main causes of heat transport variations towards the eastern equatorial Pacific. This corroborates the mechanism inferred from time lag analysis, in which wind variations cause mass transport variations, first in surface divergence, equatorial upwelling and SEC (changes in pycnocline convergence and in EUC transport appear shortly later), leading to changes in SST in the equatorial cold tongue. The exhaustive present study, combining an analysis of zonal and meridional mass transports and of their temperatures, heat budgets, and a time lag analysis, confirms Kleeman et al. (1999) hypothesis for Pacific decadal variability (see Section 1), extending it with the

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same kind of mechanism to interannual time scales in the narrow 5N–5S equatorial band and giving details on the mechanism involved. In the forced simulations used here, the origin of the leading wind changes at 5N cannot be determined, and studying coupled models would be useful for this purpose. Results from this study indicate that heat transports interannual variations are due to EUC and meridional overturning variations. Taking these circulations into account in conceptual models of ENSO such as the recharge oscillator may help to better understand ENSO variability and associated recharges and discharges that are different for the entire equatorial band and for the eastern equatorial Pacific. Compared to two-layer oceanic models used in conceptual models of ENSO, the surface divergence and the SEC in the OGCM are in an upper layer which corresponds to the upper part of the surface layer, and the pycnocline convergence and the EUC are in a lower layer which roughly corresponds to both the lower part of the surface layer and to a part of the bottom layer. Using three-layer oceanic models would thus be interesting for simple coupled models. The two heat budget analyses, for the entire equatorial band and only for the eastern Pacific, show a great difference in the relative importance of mass transport and temperature variations for the heat budget. A simultaneous slowdown of all the branches of the meridional overturning cells, for example, during an El Ninõ event, will lead to a warming of the eastern Pacific caused by the decrease of equatorial upwelling, SEC, and EUC, the changes in temperature of the water masses transported being negligible. Based on this result, an estimate for the time scale of positive feedback between the ocean and the atmosphere in the cold tongue can be derived. The time scale, inferred from the response of the EUC to zonal wind in the central Pacific and from the role of the EUC in equatorial upwelling, is about 3 months, consistent with estimates derived in simple coupled ocean-atmosphere models of ENSO variability (e.g. see Jin 1997a, b), but with a different and complementary point of view. Contrary to the eastern Pacific, for all the equatorial band, the temperature changes in converging and diverging water masses are as important as the changes in mass transport of the cells for the heat budget, and thus for recharge and discharge of heat. Note that important asymmetries are also observed between the variations of the northern and southern upper branches of the cells (the covariability of the surface divergence with the other branches of the cells appears when the sum of 5N and 5S divergences is considered). This may lead to asymmetric and complex recharge/discharge mechanisms, in agreement with former studies (see Section 3.2.3.), that may be important for the understanding of ENSO and of its interaction with the seasonal cycle. Finally, the simulation forced by NCEP reanalyses over the period 1948–1999 shows long-term variations and long-term trends in mass transports and transportweighted temperatures of the EUC and of meridional

overturning. These trends over the 50 years are exaggerated since they are caused by the long-term decrease in NCEP easterlies, which appears unrealistic before the mid-1970s. However, the simulation can still be used to study the strong link between the observed global climate warming trend in the equatorial Pacific and atmospheric and oceanic circulations changes. In the OGCM, the heat budget analysis evidences and quantifies the physical interactions. The decrease in easterlies causes a slowdown of the equatorial circulation, leading to a decrease of the cooling oceanic heat transport and thus to a warming of the eastern equatorial SST in the Pacific, partly compensated by an anomalous cooling due to the decrease in EUC temperature. It would be interesting to study these interactions in coupled simulations to better understand decadal variations and the link with global warming. Such problems of trends with wind products may be avoided by using a proxy such as pressure difference Dp between the western and eastern equatorial Pacific to estimate the variations of the equatorial circulation (see Section 3.2.4). These problems and also all the present study show the importance of continuing TAO/TRITON moorings maintenance and high-quality satellite altimeter missions, and of implementing new measurements such as Argo profiling floats, to address issues related to the role of tropical Pacific ocean circulation in seasonal to decadal climate variability. Acknowledgments I would first like to thank Joe¨l Picaut (now retired), who followed this work as my Ph.D. advisor, for all his enthusiam and his great knowledge. I would also like to thank in particular B. Blanke for his following of this work and the many productive discussions, M.J. McPhaden, G. Reverdin, and Y. Tourre for their careful lecture of the Ph.D. manuscript (available in line at http://tel.ccsd.cnrs.fr) associated to this work and their precious comments. I would like to thank G. Alory and G. Lorand for the outputs of the OPA model, forced with NCEP winds and with ERS winds, respectively, and G. Johnson for the in situ data for the EUC, used in the present analysis. I would also like to thank PMEL staff for their work on TAO data (available at http:// www.pmel.noaa.gov) and for the development of the very useful Ferret software. Support for this work was provided by the French Department of Education, Research and Technology, CNES, IRD and PNEDC.

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