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    CTD cast showing temperature, salinity, density, and a thick barrier layer from the western equatorial Pacific. The cast was made at 0.01°S, 153.93°E on 29 Apr 1994 at 0427 UTC (1427 LT). Temperature and salinity have been scaled respectively by the coefficients of thermal expansion and saline contraction (computed for 29°C and 35 psu) to emphasize their relative impacts on density. The MLD and ILD are indicated. The layer between these two depths is defined as the barrier layer, whose thickness (BLT) is 36 m in this example (from Ando and McPhaden 1997).

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    Time–longitude estimate of (a) SST (°C), (b) SSS (psu), and (c) BLT (m) from Argo floats along the equator in the western central Pacific. The white lines are the 29°C, 34.8 psu, and 15-m contours, respectively (from Qu et al. 2014).

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    Monthly SSS (blue) plotted with the monthly annual cycle (red) from the TAO/TRITON array in the western equatorial Pacific at 2°S, 0°, and 2°N. This figure was constructed from data obtained from the TAO project office PMEL NOAA website (http://www.pmel.noaa.gov/tao/disdel/frames/main.html).

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    First EOF structure functions showing the depth dependence of S′ for seven stations near the equator in the TRITON western Pacific array. In each case, the structure function has been normalized by its surface value so that its depth dependence can be easily seen. The % variance described by the first EOF, the station location, and the amplitude (psu) of the SSS′ [S(0)] are marked on each panel. Here and elsewhere in this paper, each principal component is nondimensional and has a variance 0.5 so that the corresponding dimensional EOF structure function is indicative of the amplitude of the variability.

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    Isothermal layer depth along (top to bottom) 137°E (at 2°N), 147°E (at 0° and 2°N), and 156°E (at 2°S, 0°, and 2°N). Depths were estimated from in situ temperature data using the temperature difference method of Sprintall and Tomczak (1992) with temperature threshold ΔT = −0.5°C. Because of diurnal heating, values at 10-m depth were assumed to be at the surface. Average ILD is noted in each panel.

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    Observed AVISO SSH (cyan) and isothermal layer sea level anomaly estimated from (4.3) using monthly salinity and temperature anomaly data with time-dependent isothermal layer depth (red). The isothermal layer depth was estimated using the temperature difference criteria described in the text. In all cases, the AVISO sea level estimate was insignificantly correlated [correlations < rcrit(95%)] with , except at 2°N, 137°E.

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    (a) First EOF structure functions of (red), (black), and (cyan) near the equator at 156°E and (b) the corresponding principal components; (c),(d) the corresponding results at 147°E. The percentage of variance explained is color coded to the EOFs in (a) and (c), and the correlation coefficients between the principal components in (b) and (d) are also color coded; for example, r = 0.92 (with red “9” and cyan “2”) in (b) corresponds to the correlation of the and principal components at 156°E. In all cases, the correlation between and exceeded rcrit(95%). The correlations between and either or exceeded rcrit(95%) at 156°E [see (b)] but were weak or less than rcrit(95%) at 147°E [correlations with an asterisk in (d)].

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    (a) First EOF structure function and (b) corresponding first principal components for three separate EOF analyses of surface at longitudes 137°E (red), 147°E (cyan), and 156°E (black). The percentage of the variability explained by the first EOF at each longitude is shown in (a). The correlation between the 147° and 156°E first principal components is 0.86 [rcrit(95%) = 0.60], between the 137° and 156°E first principal components is 0.72 [rcrit(95%) = 0.65], and between the 137° and 147°E first principal component is 0.82 [rcrit(95%) = 0.67].

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    As for Fig. 8, but with replaced by the first EOF and principal component of at each longitude. The 147° and 156°E first principal components are correlated [r = 0.85, rcrit(95%) = 0.63], the 137° and 147°E first principal components are correlated [r = 0.62, rcrit(95%) = 0.58], but the 137°E principal component is not significantly correlated with the principal component at 156°E. Note that the EOF structure functions for 137° and 156°E between 3.5° and 6.5°N in (a) are almost identical, so the 137°E structure is not visible.

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    (left) First EOF structure function and the (right) corresponding principal component for the upper 74-m monthly anomalous salinity-driven flow at 0°, 156°E from March 1999 to December 2013. The first EOF describes 99% of the variance. The salinity-driven flow was estimated by in situ salinity and temperature data with time-independent ILD according to (5.3). The principal component has been normalized so that its variance is 0.5. The EOF then describes the amplitude of the variability in cm s−1.

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    Structure functions for the (a) first EOF and (b) first principal component of 156°E interannually filtered surface u′ for OSCAR (solid circles, black), in situ single-point Doppler (solid squares, cyan), and (solid circles, red). The percentage of variance described by each first-mode EOF is shown in the bottom panel. The correlation between the principal components of and is 0.86 [rcrit(95%) = 0.52], and is 0.81 [rcrit(95%) = 0.51], and between and is 0.93 [rcrit(95%) = 0.52].

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    As for Fig. 11, but for 147°E instead of 156°E. The correlation between the principal components of and is 0.68 [rcrit(95%) = 0.49], and is 0.67 [rcrit(95%) = 0.50], and and is 0.84 [rcrit(95%) = 0.49].

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    (a) Time–longitude plot of westerly wind stress anomalies (mPa) along the equator in the equatorial Pacific, the thick yellow line denoting the equatorial location of the 29°C isotherm, a proxy for the eastern edge of the warm/fresh pool; (b) as in (a), but for −OLR; and (c) equatorial longitudinal location of the 29°C isotherm (yellow) −40-mPa eastward equatorial wind stress isoline (black) and the 250 W m−2 OLR isoline (red). The break in the red and black curves near the end of 1997 occurs because the −40-mPa and 250 W m−2 values did not occur in the equatorial Pacific then. All data have been filtered with a 5-month running mean. The wind stress was averaged between 2.25°S to 2.25°N, the OLR between 2.5°S and 2.5°N, and the SST between 2°S and 2°N. In (c), the correlation between the 29°C and −40-mPa time series is 0.87 [rcrit(95%) = 0.46], between the 29°C and 250 W m−2 time series is 0.87 [rcrit(95%) = 0.42] and between the −40-mPa and 250 Wm−2 time series is 0.87 [rcrit(95%) = 0.43].

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    (a) Time-mean eastward wind stress τx along the equator in the Pacific. The point P (177.0°E) marks the location of the 29°C equatorial isotherm and point A (179.3°E) marks the location of the −40-mPa wind stress isoline. Both P and A are proxies for the eastern edge of the warm/fresh pool. (b) As in (a), but with OLR replacing τx. In this case, point A corresponds to the −250 W m−2 isoline.

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    Dynamic height anomalies (red) relative to the ILD at the equator at 156°E plotted with (a) the time integral of −OLR′ (black) and (b) −OLR′ (black). All data have been filtered with a 5-month running mean. The OLR data were averaged over the 6° (longitude) by 2° (latitude) box centered on 0°, 156°E.

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    The 5-month running mean of monthly −OLR′ (blue), (τx)′ (red), (black), and (green) at 0°, 156°E. All time series have been normalized by their standard deviation. These standard deviations are 13.9 W m−2 (OLR′), 15.9 mPa [(τx)′], 2.9 cm , and 22.0 cm s-1 . Correlations between the variables are given in Table 3.

  • View in gallery

    Time series of satellite-measured (red line) and in situ (blue line) SSS along 138°E (at 0°), 137°E (at 2°N), 147°E (at 0° and 2°N), 156°E (at 2°S, 0°, and 2°N), and 165°E (at 2°S and 2°N).

  • View in gallery

    Structure functions for the (a) first EOF and (b) first principal component of 156°E interannually filtered surface using S′(z, t) in (5.1) and (5.2) (red) and similarly calculated but with the salinity integral estimate from in situ SSS′ (blue) as described in the text. The percentage variance explained by each EOF is shown in the bottom panel, and the correlation between the principal components is 0.75[rcrit(95%) = 0.51].

  • View in gallery

    (a) First EOF of from in situ salinity (5.1) and (5.2) (red) and the corresponding first EOF from Aquarius satellite–estimated SSS′ (see text) (blue). The satellite estimate of time series at each of the 6 in situ latitudes was based on the 1° latitude satellite-estimated SSS′ (see main text). (b) The first principal component for from in situ salinity (red) and satellite-estimated (blue). The correlation between the principal components is 0.89 [rcrit(95%) = 0.62].

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Observations of Interannual Equatorial Freshwater Jets in the Western Pacific

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  • 1 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida
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Abstract

Observations of TRITON moored array salinity and temperature in the very wet western equatorial Pacific at 137°E, 147°E, and 156°E since the late 1990s reveal the importance of rainfall to the interannual flow and El Niño–Southern Oscillation (ENSO) dynamics. Past work has shown that in this region a fresher surface isohaline layer is embedded in a thicker isothermal layer. Array estimates of dynamic height relative to the 50–70-m isothermal layer depth (ILD) indicate a near-surface salinity-driven contribution to the monthly sea level anomaly that is uncorrelated with, and smaller than, monthly anomalous sea surface height (SSH) estimated from altimeter data. Despite the smaller size of , its meridional gradient dominates the total sea level meridional gradient. Thus, the corresponding shallow equatorially trapped interannual freshwater jet dominates the near-surface zonal interannual flow. This jetlike flow has a meridional scale of only about 2°–3° of latitude, an amplitude of 23 cm s−1, and is associated with the zonal back and forth displacement of the western equatorial warm/fresh pool that is fundamental to El Niño. The jet is not directly forced by the interannual freshwater surface flux but rather by wind stress anomalies that are mostly east of the warm/fresh pool edge during La Niña and mostly west of it during El Niño. A conceptual coupled ocean–atmosphere instability model is proposed to understand these observations. Calculations show that Aquarius satellite sea surface salinity (SSS) data match the TRITON in situ data well and that the satellite SSS can be used to estimate , and hence , geostrophically.

Corresponding author address: Allan J. Clarke, Department of Earth, Ocean and Atmospheric Science, Florida State University, 117 North Woodward Avenue, Tallahassee, FL 32306-4320. E-mail: aclarke@fsu.edu

Abstract

Observations of TRITON moored array salinity and temperature in the very wet western equatorial Pacific at 137°E, 147°E, and 156°E since the late 1990s reveal the importance of rainfall to the interannual flow and El Niño–Southern Oscillation (ENSO) dynamics. Past work has shown that in this region a fresher surface isohaline layer is embedded in a thicker isothermal layer. Array estimates of dynamic height relative to the 50–70-m isothermal layer depth (ILD) indicate a near-surface salinity-driven contribution to the monthly sea level anomaly that is uncorrelated with, and smaller than, monthly anomalous sea surface height (SSH) estimated from altimeter data. Despite the smaller size of , its meridional gradient dominates the total sea level meridional gradient. Thus, the corresponding shallow equatorially trapped interannual freshwater jet dominates the near-surface zonal interannual flow. This jetlike flow has a meridional scale of only about 2°–3° of latitude, an amplitude of 23 cm s−1, and is associated with the zonal back and forth displacement of the western equatorial warm/fresh pool that is fundamental to El Niño. The jet is not directly forced by the interannual freshwater surface flux but rather by wind stress anomalies that are mostly east of the warm/fresh pool edge during La Niña and mostly west of it during El Niño. A conceptual coupled ocean–atmosphere instability model is proposed to understand these observations. Calculations show that Aquarius satellite sea surface salinity (SSS) data match the TRITON in situ data well and that the satellite SSS can be used to estimate , and hence , geostrophically.

Corresponding author address: Allan J. Clarke, Department of Earth, Ocean and Atmospheric Science, Florida State University, 117 North Woodward Avenue, Tallahassee, FL 32306-4320. E-mail: aclarke@fsu.edu

1. Introduction

The western equatorial Pacific is the largest rainy region on Earth, with 2–4 m of rainfall per year falling on a region larger than the continent of Australia. The region is warm but rainfall greatly exceeds evaporation (see, e.g., Ando and McPhaden 1997) and, as a result, the near-surface water exists as a large fresh/warm “pool.” Lukas and Lindstrom (1991) pointed out that the heavy rainfall results in a relatively fresh surface mixed layer that is embedded in a deeper isothermal layer (see, e.g., Fig. 1). Lying between the bottom of the surface mixed layer and the bottom of the isothermal layer is a salt and density stratified “barrier” layer that inhibits vertical mixing. Estimates of the surface mixed layer depth (MLD), the isothermal layer depth (ILD), and the barrier layer thickness (BLT) have been given for the tropical Pacific by Ando and McPhaden (1997).

Fig. 1.
Fig. 1.

CTD cast showing temperature, salinity, density, and a thick barrier layer from the western equatorial Pacific. The cast was made at 0.01°S, 153.93°E on 29 Apr 1994 at 0427 UTC (1427 LT). Temperature and salinity have been scaled respectively by the coefficients of thermal expansion and saline contraction (computed for 29°C and 35 psu) to emphasize their relative impacts on density. The MLD and ILD are indicated. The layer between these two depths is defined as the barrier layer, whose thickness (BLT) is 36 m in this example (from Ando and McPhaden 1997).

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

Zonal movement of the western equatorial fresh/warm pool of water is fundamental to El Niño–Southern Oscillation (ENSO) dynamics (Picaut et al. 1996; Delcroix and Picaut 1998; Cronin and McPhaden 1998; Picaut et al. 2001; Bosc et al. 2009; Qu et al. 2014). Monthly salinity anomalies as large as 1 psu are common in the western equatorial Pacific as the warm/fresh pool and the barrier layer at its eastern edge move eastward during El Niño and westward during La Niña (see Fig. 2) over distances of thousands of kilometers. This movement is associated with interannual flow, but the strength, spatial structure, and dynamics of the near-surface interannual flow are not known. The existence of near-surface relatively fresh equatorial flow was first documented by Roemmich et al. (1994) when they observed a relatively fresh salinity anomaly (−1 psu) equatorial jet over several months in a surface layer 50 m thick. Beneath this layer was a sharp halocline in isothermal water so that the overall vertical structure of the temperature and salinity was similar to the boundary layer structure. But the Roemmich et al. theory is only valid for about 10 days, and for our study we are interested in interannual flows.

Fig. 2.
Fig. 2.

Time–longitude estimate of (a) SST (°C), (b) SSS (psu), and (c) BLT (m) from Argo floats along the equator in the western central Pacific. The white lines are the 29°C, 34.8 psu, and 15-m contours, respectively (from Qu et al. 2014).

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

In this paper, we will use western equatorial Pacific surface and subsurface salinity and temperature data collected by the TRITON array, mostly since the late 1990s, to calculate dynamic height relative to the ILD and use it and geostrophic balance to estimate the monthly zonal flow anomaly (departure from the seasonal cycle). [The subscripts T for temperature and F for fresher water (lower salinity) have been used because both correspond to positive equatorial sea level and eastward flow.] We find that although the monthly anomalous dynamic height is typically small compared to monthly anomalous sea levels, the meridional gradient of explains almost all of the anomalous meridional sea level gradient. Consequently the surface flow is almost entirely explained by the corresponding shallow near-surface flow over the ILD. This shallow interannual flow is jetlike in that it is strong (~23 cm s−1 in amplitude) and narrow, being confined to within about 2°–3° of latitude of the equator. We also find that and are mostly associated with anomalous salinity rather than anomalous temperature so we can think of the jet as an anomalous freshwater jet.

The overall structure of the paper is as follows: The data are described in section 2, and major features of the salinity observations in section 3. Monthly salinity variability is mainly due to salinity anomalies (departures from the seasonal cycle) rather than the seasonal cycle itself. Section 4 examines the anomalous dynamic height relative to the ILD and shows that it is mostly due to , the corresponding anomalous dynamic height due to anomalous salinity S′ alone. In section 5, we find that the near-surface jet is also dominated by the salinity anomaly and that this shallow equatorial jet explains most of the anomalous equatorial surface flow. The dynamics of the jet and its relationship to zonal interannual warm/fresh pool movement fundamental to ENSO is discussed in section 6, and then, in section 7, we examine whether the jet can be estimated using SSS observed remotely from space. A concluding section 8 summarizes the main results.

2. Data

a. Upper-ocean salinity and temperature

Monthly in situ temperature and salinity data were downloaded from the TAO Project Office/PMEL/NOAA website (http://www.pmel.noaa.gov/tao/disdelframes/main.html). We mainly used temperature and salinity data along the 137°E, 147°E, and 156°E meridians (Table 1), where the rainfall is enormous (about 2–4 m yr−1), and long surface and subsurface salinity and temperature records are available from the TRITON array deployed by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) after late 1999 (see, e.g., Ando et al. 2005). Long western Pacific salinity records are available at other longitudes (e.g., 165°E), but at these locations subsurface observations are either not available or gappy and shorter.

Table 1.

Western equatorial Pacific TAO/TRITON salinity and temperature measurement periods and parameters. The salinity and temperature data at 0°, 138°E were downloaded from JAMSTEC TRITON stations. At this station there is a data gap from November 2010 to July 2011 for salinity and only 7 months of useful data for temperature, so we did not calculate mean SST, RMS (SST′), and RMS seasonal SST there.

Table 1.

For sea surface temperature (SST), we used the monthly global 1° gridded dataset, NOAA optimum interpolation (OI) SST, version 2 (http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html).

In recent years, sea surface salinity (SSS) has been estimated by the Aquarius satellite with 1° resolution. In this paper, monthly SSS data available from the Aquarius website (http://aquarius.nasa.gov/index.html) will be compared with in situ data.

b. Sea level

For sea level we used the monthly ⅓° gridded Archiving, Validation, and Interpretation of Satellite Oceanography (AVISO) dataset (http://www.aviso.oceanobs.com).

c. Currents

Long western Pacific Doppler current records at 137°E, 147°E, and 156°E, either at a fixed depth or from an acoustic Doppler current profiler (ADCP) were downloaded from the TAO Project Office/PMEL/NOAA website (http://www.pmel.noaa.gov/tao/disdelframes/main.html). Monthly estimates of flows nominally averaged over the top 30 m of the ocean from the Ocean Surface Current Analyses–Real Time (OSCAR) dataset (http://www.oscar.noaa.gov/) were also utilized in our analyses. These data are available at ⅓° resolution since October 1992.

d. Wind stress data

To examine the western equatorial Pacific near-surface dynamics, monthly wind stress data were used from European Center for Medium-Range Weather Forecast (ECMWF) dataset (http://apps.ecmwf.int/datasets/data/interim-full-moda), the resolution of which is 0.75°.

e. Outgoing longwave radiation data

In the deep tropics, outgoing longwave radiation (OLR) data can be used as a proxy for rainfall. We used monthly mean OLR data available online (from http://www.esrl.noaa.gov/psd/data/gridded/data.interp_OLR.html). The data have a 2.5° latitude and longitude horizontal resolution and are available since June 1974.

In this paper, we will mainly be concerned with low-frequency departure from the seasonal cycle that is well characterized by monthly anomalous time series. For all the observed data, the monthly anomaly was calculated by removing the average value of each calendar month throughout the record; for example, January anomalies were obtained by subtracting the average January throughout the entire data record. Similar calculations for the other 11 calendar months yield anomalous time series. The anomaly of a specific variable will be denoted by the symbol of that variable with a prime.

3. TAO/TRITON salinity observations

a. SSS

Figure 3 shows monthly SSS TAO/TRITON observations near the equator on the 137°E, 147°E, 156°E, and 165°E meridians. Consistent with previous work discussed in the introduction, monthly salinity variations are large. As expected from Bosc et al. (2009) and Fig. 2, they are very strong at 156° and 165°E between 2°S and 2°N and are largely interannual rather than seasonal. This strong interannual signal is also seen at the equator at 147°E, but much less so at 2°N at 147° and 137°E. Table 1 and Figs. 2 and 3 support the conclusions that interannual variability dominates seasonal variability, that it decreases off the equator at 147°E, 156°E, and 165°E, and that, at least since the late 1990s, at the equator it is largest between about 147°E and 180°. The decrease off the equator at 156° and 165°E is weaker and so slight at 165°E that it may not be real.

Fig. 3.
Fig. 3.

Monthly SSS (blue) plotted with the monthly annual cycle (red) from the TAO/TRITON array in the western equatorial Pacific at 2°S, 0°, and 2°N. This figure was constructed from data obtained from the TAO project office PMEL NOAA website (http://www.pmel.noaa.gov/tao/disdel/frames/main.html).

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

b. Relationship between SSS(t) and S(z,t)

Figure 4 shows that, consistent with a coherent surface mixed layer, S′ is equal to SSS′ over the mixed layer depth (about 30–50 m). Beneath this surface mixed layer, the SSS′ signal decreases to a smaller amplitude near the ILD (about 50–80 m).

Fig. 4.
Fig. 4.

First EOF structure functions showing the depth dependence of S′ for seven stations near the equator in the TRITON western Pacific array. In each case, the structure function has been normalized by its surface value so that its depth dependence can be easily seen. The % variance described by the first EOF, the station location, and the amplitude (psu) of the SSS′ [S(0)] are marked on each panel. Here and elsewhere in this paper, each principal component is nondimensional and has a variance 0.5 so that the corresponding dimensional EOF structure function is indicative of the amplitude of the variability.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

4. Estimation of the sea level anomaly due to near-surface fresher water

The near-surface salinity anomalies described in section 3 are so large that they can contribute significantly to the dynamic height above the ILD. In this section, we will examine this contribution. Since a positive temperature anomaly might affect rainfall and hence S′, or a strong shallow barrier layer might affect the surface volume of water heated and hence the temperature anomaly T′, both S′ and T′ will be taken into account in our estimation of density and pressure changes in the isothermal layer.

a. Estimation of the dynamic height anomaly due to density anomalies in the isothermal layer

Over the ILD, the density anomaly ρ′ is approximately linear in salinity anomaly S′ and temperature anomaly T′, and we will write
e4.1
In (4.1), the reference density ρref = 1000 kg m−3, is the salinity contraction coefficient, and is the thermal expansion coefficient. For the near-surface temperatures and salinities in the near-surface western equatorial Pacific, βS and αT can be approximated as 7.5 × 10−4 psu−1 and 3.3 × 10−4 K−1. From (4.1) and hydrostatic balance, the pressure anomaly relative to the ILD is
e4.2
By dividing (4.2) by ref and evaluating p′ at z = 0, we determine that , the dynamic height relative to the ILD, is given by
e4.3
where and are the dynamic height contributions corresponding, respectively, to the T′ and S′ integrals. Physically, if (say) S′ < 0, corresponding to an anomalous freshening, the water in the isothermal layer is less dense and so, to keep the pressure at the isothermal layer depth constant, the sea level must be slightly increased [ in (4.3)]. Similarly, when T′ > 0 in the isothermal layer.

To determine , , and from (4.3) and the S′ and T′ TRITON data, we must first estimate ILD(t). Several criteria have been proposed, but based on the analysis by deBoyer Montégut et al. (2004) and Bosc et al. (2009), we estimated the monthly ILD(t) using the difference method of Sprintall and Tomczak (1992). Specifically, the ILD was taken to be the depth at which the temperature decreased from the surface by 0.5°C. Because of diurnal heating, the “surface” temperature is taken to be the temperature at 10-m depth.

Figure 5 shows the ILD(t) found in this way for the moorings near the equator. The ILD varies considerably, but typically the average ILD is near 60 m. The time-mean isothermal layer depths for all western Pacific moorings are listed in Table 1. Also listed in that table are the mean MLD and barrier layer thickness (BLT = ILD MLD). Similar to the ILD, the mixed layer depths were determined using the potential density difference method following Bosc et al. (2009), with ΔT = −0.5°C, αT = 3.3 × 10−4 K−1, and values at 10-m depth as the surface level of reference.

Fig. 5.
Fig. 5.

Isothermal layer depth along (top to bottom) 137°E (at 2°N), 147°E (at 0° and 2°N), and 156°E (at 2°S, 0°, and 2°N). Depths were estimated from in situ temperature data using the temperature difference method of Sprintall and Tomczak (1992) with temperature threshold ΔT = −0.5°C. Because of diurnal heating, values at 10-m depth were assumed to be at the surface. Average ILD is noted in each panel.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

b. Comparison of with sea level anomalies

Comparison of calculated from (4.3) with sea surface height (SSH) anomalies estimated from AVISO satellite data show (Fig. 6) that overall SSH′ has a much larger amplitude than . This is not surprising because SSH′ not only includes contributions from density anomalies in the isothermal layer but also dynamic height contributions from the large temperature fluctuations associated with thermocline variability. Only at 156°E are and SSH′ of comparable amplitude, and there they are uncorrelated. Note that here and elsewhere in this paper, significance estimates for correlation coefficients are based on Ebisuzaki (1997).

Fig. 6.
Fig. 6.

Observed AVISO SSH (cyan) and isothermal layer sea level anomaly estimated from (4.3) using monthly salinity and temperature anomaly data with time-dependent isothermal layer depth (red). The isothermal layer depth was estimated using the temperature difference criteria described in the text. In all cases, the AVISO sea level estimate was insignificantly correlated [correlations < rcrit(95%)] with , except at 2°N, 137°E.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

Although SSH′ dominates , this does not mean that meridional gradients of SSH′ dominate meridional gradients of , that is, that u′ near the surface dominates . In fact, in section 5 we will show that most of u′ near the surface is due to , and in the next subsection we will show that most of is due to meridional gradients of and the corresponding zonal velocity .

c. Comparison of and

The analysis of Bosc et al. (2009) suggested that S′ may be related to a T′ signal above the ILD. Figure 7 compares , , and near the equator by comparing the first empirical orthogonal function (EOF) modes of each at 156° and 147°E. At both longitudes for all three variables, the first EOF mode explains more than 86% of the variance and therefore its main time and spatial variability. Figure 7 shows that at both longitudes and are weakly or insignificantly correlated with except for with at 156°E. Figure 7 also shows that and its meridional gradients are largely due to rather than .

Fig. 7.
Fig. 7.

(a) First EOF structure functions of (red), (black), and (cyan) near the equator at 156°E and (b) the corresponding principal components; (c),(d) the corresponding results at 147°E. The percentage of variance explained is color coded to the EOFs in (a) and (c), and the correlation coefficients between the principal components in (b) and (d) are also color coded; for example, r = 0.92 (with red “9” and cyan “2”) in (b) corresponds to the correlation of the and principal components at 156°E. In all cases, the correlation between and exceeded rcrit(95%). The correlations between and either or exceeded rcrit(95%) at 156°E [see (b)] but were weak or less than rcrit(95%) at 147°E [correlations with an asterisk in (d)].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

5. Estimation of from the TAO/TRITON observations

a. Establishing equations for

Based on the idea that the zonal scale is much larger than the meridional, to a first approximation the low-frequency zonal flow is geostrophically balanced [Gill and Clarke 1974; see also Clarke (2008) for a detailed discussion]. With f as the Coriolis parameter, in (4.3) is thus associated with a low-frequency zonal geostrophic flow with
e5.1
Off the equator surface, can be found by dividing by f in (5.1). At the equator, differentiation of (5.1) with respect to y and then setting y = 0 gives
e5.2
where β = df/dy.
These expressions for at the surface can be generalized to any depth in the isothermal surface layer using (4.2). For example, at the equator at a depth −z, we have, from (4.2) and by analogy with (5.2),
e5.3

b. The horizontal structure of at the surface

The centered difference estimates of at the surface were calculated using the salinity and temperature integrals in (5.1) and (5.2) based on TRITON data at 137°E, 147°E, and 156°E. A separate EOF analysis for each of these longitudes showed that in each case EOF mode one explained almost all of the variability and so could be used to summarize the horizontal structure of the response. Figure 8 shows that at the surface at 156°E can be described as an anomalous equatorial jet with maximum at 1°N and large amplitude 23 cm s−1 at that point. We have referred to this flow as a “jet” because it is quite strong and its meridional scale is only about 2°–3° of latitude. At 147°E, the results are only available at 1° and 3.5°N, but there the amplitude structure resolved is essentially the same as 156°E. The variability at these two longitudes was also similar, the two principal component (PC) time series having a correlation r = 0.86 [rcrit(95%) = 0.60]. At 137°E, data are only available at 3.5° and 6.5°N, and again the observed structure is consistent with that at 156°E; the correlation between the 137° and 156°E principal component time series is 0.72 [rcrit(95%) = 0.65].

Fig. 8.
Fig. 8.

(a) First EOF structure function and (b) corresponding first principal components for three separate EOF analyses of surface at longitudes 137°E (red), 147°E (cyan), and 156°E (black). The percentage of the variability explained by the first EOF at each longitude is shown in (a). The correlation between the 147° and 156°E first principal components is 0.86 [rcrit(95%) = 0.60], between the 137° and 156°E first principal components is 0.72 [rcrit(95%) = 0.65], and between the 137° and 147°E first principal component is 0.82 [rcrit(95%) = 0.67].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

The meridional scale of the jet is comparable to the grid spacing used to calculate the flow, and so in the appendix we consider the likely error. We estimate that near the equator where the signal is large, the error is likely to be about 20%.

c. Comparison of with

Earlier in section 4 we noted that most of and its meridional gradients were due to , and so by geostrophy we expect to be dominated by ; that is, the interannual surface jet discussed in the previous section is mostly due to near-surface S′ rather than near-surface T′. Figure 9 is analogous to Fig. 8, except that it shows results for that are defined by (5.1) and (5.2) with the term involving T′ omitted. At 156° and 147°E, almost all the variance is described by the first EOF for both and ; at each longitude, the principal components are well correlated [r = 0.97, rcrit(95%) = 0.56 for 156°E and r = 0.97, rcrit(95%) = 0.66 for 147°E], and at each longitude, the structure function is the major contributor to the structure function.

Fig. 9.
Fig. 9.

As for Fig. 8, but with replaced by the first EOF and principal component of at each longitude. The 147° and 156°E first principal components are correlated [r = 0.85, rcrit(95%) = 0.63], the 137° and 147°E first principal components are correlated [r = 0.62, rcrit(95%) = 0.58], but the 137°E principal component is not significantly correlated with the principal component at 156°E. Note that the EOF structure functions for 137° and 156°E between 3.5° and 6.5°N in (a) are almost identical, so the 137°E structure is not visible.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

d. Vertical structure of

By construction, we expect that T′ will be nearly independent of z over the isothermal layer. Since S′ is nearly uniform over the mixed layer and then decreases and is of one sign until it reaches the bottom of the isothermal layer, we expect from (5.3) that will decrease from a maximum amplitude at the surface to zero at the ILD. Calculations bear this out, and Fig. 10 shows a typical example at the equator at 156°E for March 1999 to December 2013.

Fig. 10.
Fig. 10.

(left) First EOF structure function and the (right) corresponding principal component for the upper 74-m monthly anomalous salinity-driven flow at 0°, 156°E from March 1999 to December 2013. The first EOF describes 99% of the variance. The salinity-driven flow was estimated by in situ salinity and temperature data with time-independent ILD according to (5.3). The principal component has been normalized so that its variance is 0.5. The EOF then describes the amplitude of the variability in cm s−1.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

e. Comparison of surface with surface

The shallow near-surface jet is strong (see Fig. 8), and we wondered how much it contributed to the total anomalous equatorial flow. We compared two different estimates of surface u′ with surface from 5°S to 8°N at 156°E, where adequate data are available. One estimate of the total surface u′ was from the OSCAR dataset, and the other was from in situ single-point Doppler estimates.

Because the OSCAR “surface” currents are nominally an average over the upper 30 m of the water column, in comparing and the OSCAR currents, was averaged over the top 30 m of the water column. A similar average for in situ currents could not be done because these currents were only available at 10-m depth. In calculating this average for , there were some rare occasions when the monthly ILD was less than 30 m, and in those cases we averaged over the ILD. The time series of the monthly 30-m depth average and monthly surface flows showed that the 30-m depth average flows at the TRITON moorings were typically about 80% of the surface flow. Specifically, correlations between the surface and 30-m-depth average zonal flows at each mooring were all greater than or equal to 0.97, and most were 0.99; the ratios of the 30-m-depth standard deviation to that at the surface for the moorings ranged from 0.79 to 0.88. [Here and elsewhere in this paper, we use the ratio of standard deviations’ regression coefficient since it is an unbiased regression coefficient between two time series when noise and error in each time series are unknown (Clarke and Van Gorder 2013). For the surface and 30-m-depth average situations, the correlations are so high that least squares and the ratio of standard deviations regression coefficients differ negligibly, but in other cases where correlations are lower the least squares regression coefficient is biased.]

Zonal current monthly anomalies for all three datasets were filtered with a Trenberth (1984), 11-point, symmetric, nonrecursive, interannual filter and then a separate EOF analysis done on each. In all three cases, the first EOF describes over 80% of the variance (Fig. 11). The principal components are highly correlated, and the meridional structures are similar, indicating that, at least at 156°E, much of the interannual surface flow is associated with and hence .

Fig. 11.
Fig. 11.

Structure functions for the (a) first EOF and (b) first principal component of 156°E interannually filtered surface u′ for OSCAR (solid circles, black), in situ single-point Doppler (solid squares, cyan), and (solid circles, red). The percentage of variance described by each first-mode EOF is shown in the bottom panel. The correlation between the principal components of and is 0.86 [rcrit(95%) = 0.52], and is 0.81 [rcrit(95%) = 0.51], and between and is 0.93 [rcrit(95%) = 0.52].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

We also compared u′ and at 147°E, where is only available at two points. In this case (Fig. 12), and both estimates of were of lower amplitude and less well correlated with each other. However, it is also clear that contributes substantially to u′ even though low-frequency is dominated by the low-frequency sea level anomaly (see Fig. 6).

Fig. 12.
Fig. 12.

As for Fig. 11, but for 147°E instead of 156°E. The correlation between the principal components of and is 0.68 [rcrit(95%) = 0.49], and is 0.67 [rcrit(95%) = 0.50], and and is 0.84 [rcrit(95%) = 0.49].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

6. Freshwater jets and ENSO

We have shown that in the western equatorial Pacific, shallow, narrow freshwater jets dominate the interannual flow. Since the zonal interannual flow is a key to the movement of the western equatorial Pacific warm/fresh pool and ENSO dynamics, the freshwater jets are fundamental to the ENSO mechanism. In this section, we first discuss the statistical relationship of to ENSO (section 6a) and then the relationship of the wind and rainfall anomalies to the warm/fresh pool edge (section 6b). Observational evidence suggests that the anomalous freshwater flux does not directly force the freshwater jets but rather the rainfall anomaly generates zonal equatorial wind stress anomalies that then generate the freshwater jets (section 6c). The question then arises as to why there is a rainfall anomaly in the first place. We suggest that the whole process is associated with a coupled ocean–atmosphere instability (section 6d).

a. Relationship of to ENSO

Zonal equatorial advection by the anomalous zonal flow and associated zonal movement of the western equatorial Pacific warm/fresh pool is central to ENSO dynamics (see, e.g., Gill 1983; McPhaden and Picaut 1990; Picaut and Delcroix 1995; Picaut et al. 2001), with the warm/fresh pool moving eastward during El Niño and westward during La Niña. We checked on this expected relationship by lag–lead correlating monthly unfiltered equatorial with the commonly used El Niño monthly index Niño-3.4 (average SST anomaly from 5°S to 5°N, 170° to 120°W). The maximum correlation at the equator at 156°E is r = 0.60 [rcrit(95%) = 0.38] when leads by 3 months. Averaging in its region of maximum strength between 1°S and 1°N at 156°E does not improve the correlation or lead. The same latitudinal average for OSCAR u′ at 156°E for the same time period gives a slightly better correlation r = 0.71 [rcrit(95%) = 0.36] at a lead of 2 months.

b. Equatorial wind and rainfall anomalies and the zonal movement of the warm/fresh pool edge

Figure 13a shows zonal wind anomalies and the 29°C proxy for the eastern edge of the warm/fresh pool (thick yellow line). Notice that during an El Niño (westerly wind stress anomalies, red shading) most of the wind forcing is west of the warm/fresh pool edge (yellow line), while during La Niña (easterly wind stress anomalies, blue shading), most of the wind forcing is east of the warm/fresh pool edge. A similar relationship holds for the anomalous freshwater flux. The freshwater flux is dominated by rainfall rather than evaporation (see, e.g., Fig. 4d of Ando and McPhaden 1997), and Fig. 13b shows that during an El Niño (negative OLR anomalies, a proxy for rainfall anomalies, red shading) most of the anomalous rainfall is west of the warm/fresh pool edge, while during La Niña (anomalous drying, blue shading) most of the anomalous freshwater flux is east of the warm/fresh pool edge. Why should this be?

Fig. 13.
Fig. 13.

(a) Time–longitude plot of westerly wind stress anomalies (mPa) along the equator in the equatorial Pacific, the thick yellow line denoting the equatorial location of the 29°C isotherm, a proxy for the eastern edge of the warm/fresh pool; (b) as in (a), but for −OLR; and (c) equatorial longitudinal location of the 29°C isotherm (yellow) −40-mPa eastward equatorial wind stress isoline (black) and the 250 W m−2 OLR isoline (red). The break in the red and black curves near the end of 1997 occurs because the −40-mPa and 250 W m−2 values did not occur in the equatorial Pacific then. All data have been filtered with a 5-month running mean. The wind stress was averaged between 2.25°S to 2.25°N, the OLR between 2.5°S and 2.5°N, and the SST between 2°S and 2°N. In (c), the correlation between the 29°C and −40-mPa time series is 0.87 [rcrit(95%) = 0.46], between the 29°C and 250 W m−2 time series is 0.87 [rcrit(95%) = 0.42] and between the −40-mPa and 250 Wm−2 time series is 0.87 [rcrit(95%) = 0.43].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

Suppose that, to a first approximation, the observed large-scale zonal wind and rainfall anomalies result from the zonal displacement of a fixed mean wind stress and rainfall above the warm/fresh pool. Figures 14a and 14b show, respectively, the mean zonal equatorial wind stress and negative OLR as a function of longitude. In Fig. 14, point P identifies the mean longitudinal location of the 29°C proxy for the eastern edge of the warm/fresh pool, and point A identifies the mean longitudinal location for τx = −40 mPa (Fig. 14a) and −OLR = −250 W m−2 (Fig. 14b). If the wind stress and rainfall did have a mean fixed structure when the warm/fresh pool moved zonally, then we would expect the −40-mPa isoline and −250 W m−2 zonal displacements to be well correlated with the movement of the 29°C proxy for the eastern edge of the warm/fresh pool, and this is the case (see the yellow and black curves in Fig. 13c and the Fig. 13c caption).

Fig. 14.
Fig. 14.

(a) Time-mean eastward wind stress τx along the equator in the Pacific. The point P (177.0°E) marks the location of the 29°C equatorial isotherm and point A (179.3°E) marks the location of the −40-mPa wind stress isoline. Both P and A are proxies for the eastern edge of the warm/fresh pool. (b) As in (a), but with OLR replacing τx. In this case, point A corresponds to the −250 W m−2 isoline.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

Now consider the relationship between zonal equatorial wind stress anomalies, the rainfall anomalies, and the warm/fresh pool edge. When there is an El Niño and the warm/fresh pool moves eastward, the regions it anomalously covers are wetter with more westerly winds. Thus, west of the displaced warm/fresh pool edge, it is wetter and the winds are anomalously westerly (Figs. 13a,b). Conversely, when there is La Niña and the warm/fresh pool has moved westward, it is anomalously dry and winds are anomalously easterly to the east of the displaced warm/fresh pool edge.

c. Are the freshwater jets forced by the anomalous freshwater flux or anomalous wind stress?

How does anomalous freshwater flux affect the ocean? As noted earlier in section 6b, interannual rainfall dominates the interannual freshwater flux, which in the western equatorial Pacific is about 5 mm day−1 or 1.8 m yr−1 (see, e.g., Fig. 10 of Bosc et al. 2009). If the ocean responded like a large lake, this freshwater flux would result in interannual sea level fluctuations of a few meters per year, much larger than the 5–10 cm yr−1 observed interannual amplitude (see, e.g., Fig. 6). But, in fact, the ocean response is more than an order of magnitude smaller than 5–10 cm yr−1 rather than larger. To understand this, first note that the addition of mass due to the rainfall results in extra weight and therefore extra pressure felt throughout the ocean water column. As a result, the ocean response is barotropic, and, because it is not bounded like a lake, the barotropic signal propagates away rapidly (Huang and Jin 2002).

Specifically, for the barotropic mode, the long gravity wave speed c = ≈ 200 m s−1 for a typical ocean depth of 4 km. At ENSO frequencies ω, we have
e6.1
at all latitudes, and consequently long Rossby waves govern how the ocean adjusts barotropically at these frequencies. Even close to the equator within the equatorial waveguide, the large-scale ocean response is due to these waves [see, e.g., (7) and (8a) of Clarke and Shi (1991) under the approximation (6.1)]. These long barotropic Rossby waves travel at speeds of order βc2/f2 that are greater than 300 m s−1 in the tropics and about 30 m s−1 even at high latitudes. Thus, the ocean adjusts rapidly and globally to the freshwater flux and the barotropic pressure, and the corresponding sea level signal and excess mass are spread over the global ocean in a few weeks. Recent numerical calculations by Lorbacher et al. (2012) confirm this result.

The above rapid adjustment implies, for example, that even the interannual addition of water over an area comparable to the continent of Australia (as in the western equatorial Pacific) would only result in an effectively instantaneous rise of 1 cm or so in global (and western equatorial Pacific) sea level because the mass added is rapidly spread over the much larger global ocean. In practice, the sea level rise is even smaller because the excess interannual precipitation is compensated almost exactly by reduced interannual precipitation elsewhere in the tropics (Clarke and Kim 2005) so that globally the net freshwater flux is much smaller. Observations suggest that the net effect of this mechanism is only a few millimeters of change in sea level, much smaller than the observed interannual signal in the western equatorial Pacific. Such small interannual observed sea level changes related to El Niño have recently been discussed by Cazenave et al. (2012).

Another possible freshwater flux explanation of , , and associated geostrophic and is that these dynamic height and associated flows above the ILD are due to mixing of the monthly anomalous freshwater flux above the ILD. Thus, although the mass anomaly and associated barotropic pressure signal rapidly propagates away, mixing of the freshwater above the ILD when it rains results in lower salinity and hence less dense water there. Since the barotropic adjustment leaves the pressure at the ILD unchanged, to keep this unchanged pressure we must have an increased height of lower-density water above the ILD, that is, .

If and are due to such forcing, then they would be proportional to the net freshwater flux added, and therefore the time integral of the anomalous rainfall rate. Figure 15 compares and a time integral of −OLR′ (a proxy for the rainfall rate) as well as and −OLR′ at 0°, 156°E. The correlation of with the time integral of −OLR′ is insignificant (Fig. 15a), suggesting that is not due to a local anomalous surface flux mixing mechanism. On the other hand, Fig. 15b shows that is highly and significantly correlated with −OLR′. Similar results apply for at 156°E (see Table 2). Why should and be highly correlated with the rainfall rate?

Fig. 15.
Fig. 15.

Dynamic height anomalies (red) relative to the ILD at the equator at 156°E plotted with (a) the time integral of −OLR′ (black) and (b) −OLR′ (black). All data have been filtered with a 5-month running mean. The OLR data were averaged over the 6° (longitude) by 2° (latitude) box centered on 0°, 156°E.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

Table 2.

Correlation of monthly with monthly −OLR′ and with the time integral of monthly −OLR′ at near-equatorial TRITON moorings (columns 2 and 3). All anomaly data have been filtered with a 5-month running mean. The OLR data at each location are an average over a 6° longitude by 2° latitude box centered on each location. Columns 4, 5, and 6 show an estimate of the anomaly amplitude of each variable .

Table 2.

One possibility is that eastward wind stress anomalies (τx)′ generate eastward current anomalies ( and ) and, by geostrophy, and . At the same time, negative OLR anomalies, indicative of deep atmospheric convection, generate surface eastward wind anomalies and (τx)′ (see, e.g., Clarke 1994). In this way, , , and −OLR′ are linked. Comparison of (τx)′, , , and −OLR′ shows that these variables are significantly correlated at zero lag (or, in one case, 1-month lag; see Table 3 and Fig. 16), suggesting that the above physics is relevant to the −OLR′ and relationship seen in Fig. 15 and Table 2. The decreased correlation off the equator and decreased westward correlation at 147° and 137°E in Table 2 is consistent with the decreased and amplitude and signal-to-noise ratio there.

Table 3.

Correlation at zero lag with rcrit(95%) in brackets (upper triangle) and maximum correlation with lead in brackets (lower triangle) for the variables , , −OLR′, and (τx)′. All the data have been filtered with a 5-month running mean; the −OLR′ data were interpolated to 156°E and averaged between 2.5°N and 2.5°S; the (τx)′ data were also interpolated to 156°E and were averaged between 2.25°S and 2.25°N.

Table 3.
Fig. 16.
Fig. 16.

The 5-month running mean of monthly −OLR′ (blue), (τx)′ (red), (black), and (green) at 0°, 156°E. All time series have been normalized by their standard deviation. These standard deviations are 13.9 W m−2 (OLR′), 15.9 mPa [(τx)′], 2.9 cm , and 22.0 cm s-1 . Correlations between the variables are given in Table 3.

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

d. Coupled ocean–atmosphere instability

In the previous section, we suggested that anomalous rainfall −OLR′ drives (τx)′ that then generates and . But why does the anomalous rainfall occur in the first place? A possible mechanism is the coupled ocean–atmosphere instability mechanism similar to that discussed by Gill and Rasmusson (1983) and Clarke et al. (2000) and summarized by Clarke (2014). Specifically, if the warm/fresh pool undergoes a small eastward perturbation, then it follows from the warm/fresh pool displacement argument of section 6b (see Figs. 13 and 14) that anomalous rainfall and westerly (eastward) wind stress result. The anomalous westerly winds can be expected to generate the anomalous zonal flows that push the warm/fresh pool farther eastward, resulting in increased westerly wind anomalies and anomalous eastward flow, farther eastward warm/fresh pool displacement, and so on. The anomalous sea level arises through geostrophic balance with . The freshwater jets we have observed seem to be a major component of eastward surface flow in the coupled ocean–atmosphere instability.

What turns off the instability? Various mechanisms have been proposed [see Clarke (2014) for a review], but a key one is the movement of warm water south of the equator in the Southern Hemisphere summer (December–February). This moves the zonal wind anomalies south of the equator in December–February and typically terminates the El Niño near the beginning of the calendar year (Harrison and Vecchi 1999).

7. Can the anomalous freshwater jet be estimated from SSS′, even Aquarius satellite SSS′?

a. Comparison of Aquarius and in situ SSS

Having a 1° latitude and longitude resolution, the Aquarius satellite estimation of SSS has a far better spatial coverage than the TAO/TRITON moorings. The monthly satellite record is comparatively short, but Fig. 17 shows that the agreement with the in situ monthly SSS is very good.

Fig. 17.
Fig. 17.

Time series of satellite-measured (red line) and in situ (blue line) SSS along 138°E (at 0°), 137°E (at 2°N), 147°E (at 0° and 2°N), 156°E (at 2°S, 0°, and 2°N), and 165°E (at 2°S and 2°N).

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

b. Estimation of from SSS

To estimate from satellite SSS using geostrophic balance, we must first establish a relationship between SSS′ and . Since S′ near the surface is due to SSS′ (see Fig. 4), we might expect from (4.3) that would be well correlated with SSS′(x, y, t) in regions of strong SSS′ signal. Over the average isothermal layer depths of Table 1, Fig. 4 shows that SSS′ varies only slightly with depth and so from (4.3) we might expect that
e7.1
where is the time-averaged ILD for each horizontal location. We tested the usefulness of the above approximate expression by correlating SSS′ with calculated with time-dependent ILD as in (4.3). For the TRITON locations close to the equator where the SSS′ signal is strong, the correlation coefficients are generally close to 1 (see Table 4), indicating that (7.1) can be used to find from SSS′. The lowest correlation in Table 4 was at 2°N, 137°E, where the salinity anomaly signal was weakest. The regression coefficients based on from Table 1 are also close to 1, consistent with the (7.1) approximation.
Table 4.

Correlation and regression between the “exact” integral expression for by monthly salinity anomaly with time-dependent ILD [see (4.3)] and an approximate expression for based on SSS′ [see (7.1)] for TAO/TRITON stations at 2°N, 0°, and 2°S, where S′(x, y, z, t) was available. The regression coefficient is the ratio of the standard deviation of approximate from (7.1) to the exact from (4.3). The approximate (7.1) estimate used the mean ILD estimates in Table 1. All the correlations exceeded rcrit(95%).

Table 4.

The above analysis showed that when the SSS′ signal is strong, can be estimated from SSS′. Since can be obtained geostrophically from , might at the surface be obtained from SSS′? This case is more demanding than in that (5.1) and (5.2) require accurate single and double meridional derivatives of (4.3) to be valid. Using the average ILD from Table 1, Fig. 18 shows that near the equator at 156°E where the SSS′ signal is strong, can approximately be found from SSS′.

Fig. 18.
Fig. 18.

Structure functions for the (a) first EOF and (b) first principal component of 156°E interannually filtered surface using S′(z, t) in (5.1) and (5.2) (red) and similarly calculated but with the salinity integral estimate from in situ SSS′ (blue) as described in the text. The percentage variance explained by each EOF is shown in the bottom panel, and the correlation between the principal components is 0.75[rcrit(95%) = 0.51].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

We also attempted to calculate from Aquarius SSS′ for the short Aquarius SSS record. That record was too short to estimate the seasonal cycle of SSS so we used in situ SSS for the seasonal cycle based on the close agreement between satellite and in situ SSS (see Fig. 17). By this means we were able to estimate SSS′. Estimates of from Aquarius satellite SSS′ were calculated on the 156°E meridian at the same six latitudes as the in situ data. In this case, the regression coefficients between SSS′ and were calculated for the satellite period September 2011–December 2013. Separate EOF analysis of in situ and estimated from satellite SSS′ showed (Fig. 19) that in both cases the first EOF dominated the variability and that the EOF structures and PC time series are similar, showing that the satellite estimates of at 156°E are a good approximation to the in situ estimate.

Fig. 19.
Fig. 19.

(a) First EOF of from in situ salinity (5.1) and (5.2) (red) and the corresponding first EOF from Aquarius satellite–estimated SSS′ (see text) (blue). The satellite estimate of time series at each of the 6 in situ latitudes was based on the 1° latitude satellite-estimated SSS′ (see main text). (b) The first principal component for from in situ salinity (red) and satellite-estimated (blue). The correlation between the principal components is 0.89 [rcrit(95%) = 0.62].

Citation: Journal of Physical Oceanography 45, 11; 10.1175/JPO-D-14-0245.1

8. Concluding remarks

Analysis of the TAO/TRITON salinity data in the western equatorial Pacific showed that the monthly salinity anomalies were largest at the equator between 156° and 165°E over the period of observation from 1999 to December 2013. Salinity anomalies are usually larger than the seasonal cycle and are largest near the equator; at the equator at 156° and 165°E, they dwarf the seasonal cycle. Subsurface data show that they are shallow, being approximately equal to the SSS′ over the top 50–60 m and then usually decreasing with depth to much smaller amplitudes at 100-m depth.

Although the salinity variations are shallow, they are so strong that they affect the density and dynamic height, especially in the region of maximum salinity variability at the equator between about 156° and 165°E. Over the ILD ~50–70 m, these dynamic height perturbations are generally much smaller than the sea level variability. However, as we have shown, these comparatively small dynamic height perturbations are associated with a shallow equatorial surface jet ~23 cm s−1 within 2°–3° of latitude of the equator that dominates the observed equatorial interannual flow in this region. Our calculations suggest that the jet can be approximately estimated from both in situ and Aquarius satellite SSS′.

Analysis of anomalous monthly OLR data suggests that the interannual surface equatorial jets are not forced by the huge interannual surface freshwater flux. Instead, it is likely that they are generated by anomalous zonal equatorial winds that are part of a coupled ocean–atmosphere involving the zonal movement of the warm/fresh pool of water in the western equatorial Pacific. To a first approximation, mean easterly windstress decreases in strength and rainfall increases in strength westward along the equator in the western equatorial Pacific (see Fig. 14). As explained in section 6, if a small eastward displacement of the warm/fresh pool occurs, the eastward displacement of the wind stress structure will result locally in westerly wind stress anomalies largely west of the warm/fresh pool edge. These generate eastward zonal currents that move the warm/fresh pool farther eastward and the instability grows. The meridional displacement of warm water south of the equator in the Southern Hemisphere summer (December to February) causes the westerly wind anomalies to move south of the equator, typically halting the El Niño growth in December–February (Harrison 1987; Harrison and Vecchi 1999). Similar dynamics of opposite sign occurs during La Niña, the easterly wind anomalies generating the flow being largely east of the warm/fresh pool edge.

Several basic properties of the jets have not been addressed. For example, how exactly does the fresher surface water affect the ocean response to the wind? Specifically, how are the strength and spatial structure of the anomalous zonal surface flow affected? The analysis of Boulanger et al. (2001) considered the ocean response to the strong March 1997 westerly wind event and showed numerically that the presence of the warm/fresh pool had an one-order nonlinear effect on the zonal equatorial surface flow, increasing its strength by a factor of 3. The strong March 1997 westerly wind event lasted only about 2 weeks, a much shorter time scale than the interannual jet flows considered here. But the Boulanger et al. analysis nevertheless highlights the need to understand the effect on the interannual flow of the change in stratification at the warm/fresh pool edge. Brown et al. (2014) have recently shown that many of the state-of-the-art Coupled Model Intercomparison Project phase 5 (CMIP5) models poorly represent the salinity variability at the warm pool edge and so would seem to be missing a basic component of the coupling dynamics.

Acknowledgments

We gratefully acknowledge funding from the National Aeronautics and Space Administration (grant NNX14AH43G). Dr. Jaci Brown, Dr. Eric Lindstrom, and an anonymous reviewer provided helpful comments on the manuscript, and Dr. Tangdong Qu and Dr. Christophe Maes generously provided a high-resolution version of Fig. 2.

APPENDIX

Instrumental and Analysis Errors

The instrumental error for monthly in situ T is approximately 0.003°C and that for monthly S is 0.005 psu. Based on (4.3), these correspond to errors in and of approximately 0.007 and 0.01 cm, respectively. These errors are negligible since the signal to be resolved is about 2–3 cm. This instrumental error should not affect and either since over the meridional length scale ΔζTF ≈ 2–3 cm.

Of much more concern is the error in estimating by centered finite difference since the meridional station spacing at the equator is Δy ≈ 2° of latitude, and this is the same order as the meridional scale of . The error in approximating using centered difference is at the equator. We estimated this error by assuming . This gives an estimated error 0.5(Δy)2/a2 or about 20% for Δy = 2° of latitude and a = 3.3° of latitude based on a Gaussian fit to the structure of in Fig. 7a.

The above estimate is very crude, and the centered difference approximation may possibly result in random error larger than 20%. But the fact that most of the variability is captured by its first EOF argues against this.

Regarding errors in OSCAR, we note that the OSCAR equatorial interannual currents are likely to have small errors since their time variability and magnitude match quite well with the in situ results (see Fig. 11) and also our own along-track geostrophic calculations from altimeter height (not shown).

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