Interannual Sea Level Variations in the South Pacific ... - AMS Journals

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JOURNAL OF PHYSICAL OCEANOGRAPHY

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Interannual Sea Level Variations in the South Pacific from 5° to 28°S JIANKE LI*

AND

ALLAN J. CLARKE

Department of Oceanography, The Florida State University, Tallahassee, Florida (Manuscript received 29 June 2006, in final form 25 January 2007) ABSTRACT Ocean Topography Experiment (TOPEX)/Poseidon/Jason-1 satellite altimeter observations for the 11-yr period from January 1993 to December 2003 show that in the South Pacific Ocean most of the interannual sea level variability in the region 5°–28°S is west of 160°W. This interannual variability is largest from about 5° to 15°S and from 155°E to 160°W, reaching a root-mean-square value of over 11 cm. Calculations show that this interannual sea level signal can be described by first and second baroclinic vertical mode Rossby waves forced by the curl of the interannual Ekman transport. This curl, which tends to be positive during El Niño and negative during La Niña, generates positive (negative) sea level anomalies during El Niño (La Niña) that increase westward in amplitude in accordance with Rossby wave dynamics. The sea level anomalies are not exactly in phase with the curl forcing because Sverdrup balance does not hold—vortex stretching also contributes to the response. East of 160°W is a large “quiet” region of low interannual sea level variability, especially south of about 15°S. This is surprising because there is no flow into the coast, so the interannual sea level amplitude of equatorial origin should be constant along the coast, resulting in a source of westward-propagating Rossby waves of considerable amplitude. The large low-variability region results because coastal sea level amplitude falls between 5° and 15°S, so the Rossby wave source south of 15°S is weak. During El Niño the sea level is higher than normal at the coast, so the southward fall in anomalous sea level implies, by geostrophy, that there is an anomalous onshore flow. This flow feeds an anomalous southward El Niño current of up to 20 cm s⫺1 above the 30–50-km-wide shelf edge. During La Niña the sea level is lower than normal at the coast and the flows reverse: a narrow anomalously northward shelf-edge flow feeding a broad offshore flow between 5° and 15°S. South of 16°S the coastal flow is much weaker.

1. Introduction Interannual variations in the equatorial Pacific Ocean waveguide (5°N–5°S) have been the focus of research in the Pacific for the last several decades because it is there that the El Niño–Southern Oscillation (ENSO), the world’s leading short-term climate fluctuation, is generated. Comparatively little is known about the vast tropical South Pacific south of 5°S. However, now that near-global satellite altimeter measurements have been available for more than a decade, analysis of the interannual variability in this region is possible.

* Current affiliation: College of Marine Science, University of South Florida, St. Petersburg, Florida.

Corresponding author address: Allan J. Clarke, Dept. of Oceanography, The Florida State University, Tallahassee, FL 323064320. E-mail: [email protected] DOI: 10.1175/2007JPO3656.1 © 2007 American Meteorological Society

JPO3149

Previous work on the dynamics of interannual variability in the tropical South Pacific is largely confined to the region near the coast of South America. At this coast, as shown by Enfield and Allen (1980) and Pizarro et al. (2001), coastal interannual sea level fluctuations are of equatorial origin. Wang et al. (1998) analyzed Ocean Topography Experiment (TOPEX)/ Poseidon altimeter data and found evidence of westward propagating interannual Rossby waves at 20°S, but the record was only 4 years long and therefore short on an interannual time scale. Vega et al. (2003) used a damped long Rossby wave model and 8 years and 3 months of altimeter-estimated sea level observations to suggest that, from the eastern boundary to about 90°W, the interannual variability was mainly due to westward Rossby wave propagation from the boundary and that west of 90°W the interannual variability was generated by wind stress curl forcing. In their analysis, like that of Qiu et al. (1997), Vega et al. assumed that the Rossby waves were strongly damped with a damping time scale

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FIG. 1. Southern Pacific study area. Rms interannual sea level anomaly (cm) from 1° ⫻ 1° T/P and J1 estimates over the period from January 1993 to December 2003. The solid circle at 9°30⬘S, 160°E denotes the location of Honiara. The white regions are not plotted owing to inaccurate sea level height estimates near islands. The north–south scale has been stretched so that the north–south structure can be seen clearly.

of 300 days for the first vertical mode and 150 days for the second vertical mode near the eastern boundary. In section 5 of this paper we suggest that north of approximately 15°S, the coastal signal penetrates to about 130°W, much farther west than 90°W. In other work focusing on the South American coast, Shaffer et al. (1999) and Pizarro et al. (2001) discussed interannual coastal currents at 30°S off Chile based on data gathered from current meter moorings. Pizarro et al. found that these flows had an amplitude of only a few centimeters per second and showed how they were related to sea level through Rossby wave dynamics. All of the above work is concentrated near the eastern ocean boundary. Just recently Qiu and Chen (2006) used a 11⁄2-layer reduced-gravity, wind-forced long Rossby wave model to examine the decadal variability of 12 years of satellite altimetry sea level estimates on a much bigger scale, namely, over the South Pacific between 10° and 60°S. Qui and Chen’s focus was mainly on decadal trends, whereas ours is on interannual and lower frequencies in the tropical South Pacific. There is some overlap, mainly near the eastern tropical ocean boundary, which we will discuss in section 4. Bowen et al. (2006) also recently published an analysis of southwest Pacific satellite altimeter sea level height estimates as well as a bathythermographic line of measurements between New Zealand and Fiji. Their 11⁄2-layer forced long Rossby wave analysis shows that, to understand the low-frequency dynamics in this region, it is necessary to include the net surface heating. However, the net surface heating contributes mainly to the annual cycle rather than the anomalous sea level of interest in this paper. In the next section we will show, using satellite al-

timeter estimates of sea level, that the biggest ENSO signal in the South Pacific from 5° to 28°S is actually in the western Pacific. Following a discussion of our application of the wind-forced long Rossby wave model to the South Pacific in section 3, we find, in section 4, that the western Pacific signal is mostly driven by the strong interannual wind stress curl there. Section 5 discusses the interannual variability near the eastern boundary and the analysis of the surprisingly narrow and strong interannual El Niño currents near the shelf edge. Section 6 contains some concluding remarks.

2. Altimeter-estimated interannual sea level variability Figure 1 shows the root-mean-square interannual sea level variability in the South Pacific from 5° to 28°S using the combined 1° ⫻ 1° TOPEX/Poseidon (T/P) and Jason-1 (J1) altimeter products. We stop our analysis at 28°S because wind data are necessary to understand the sea level data and there are too few wind data south of 28°S. The T/P and J1 sea level datasets for the period January 1993–December 2003 are provided by the National Aeronautics and Space Administration Pathfinder project and the Jet Propulsion Laboratory (available online at http://podaac-www.jpl.nasa.gov/). The interannual variations were found by removing the mean and seasonal cycle of the monthly data and then filtering with a low-pass filter (Trenberth 1984). This filter passes greater than 80% of the amplitude at frequencies lower than 2␲/2 yr and passes essentially no amplitude at frequencies higher than 2␲/8 months. In all that follows an “interannual” time series of a given physical variable refers to variability calculated in this way.

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Figure 1 shows that the largest-amplitude interannual variability is west of about 160°W, especially north of 15°S, the rms amplitude in this region reaching more than 11 cm. A region of smaller amplitude variability extending from about 160°E to 160°W and from 20°S to 28°S, reaches an rms amplitude of about 7 cm. In the huge region east of 160°W the signal is much weaker except near 5°S and near the eastern boundary north of about 15°S. It is ironic that most of the analysis of the sea level interannual variability in the South Pacific has been done south of 15°S and near the eastern boundary, a region of comparatively small interannual variability. What is the main cause of the large-amplitude interannual variability west of about 160°W? In the next section we analyze this variability using long windforced Rossby wave dynamics.

3. The western Pacific large-scale interannual variability and wind-forced Rossby waves a. The wind-forced long Rossby wave model The quasigeostrophic, long, wind-forced Rossby wave model has been used many times in the past to analyze large-scale, low-frequency variability (e.g., White 1977; Meyers 1979; Kessler 1990). The ocean response is the sum of an infinite set of vertical modes, but previous analyses (Cane 1984; Busalacchi and Cane 1985) and our own calculations (see section 4b) suggest that the first two baroclinic modes dominate the response. Each mode satisfies a forced long Rossby wave equation of the form ⭸␩j ⫺ ⭸x

冉冊 f2

␤cj2

bj f ⭸␩j ⫽ ⭸t ␤g␳ *

冉

冊

⭸␶ y ⭸␶ x ␤␶ x ⫺ ⫹ , ⭸x ⭸y f

共1兲

where ␩j is the jth-mode contribution to the interannual sea level ␩, x is distance eastward, y is the distance northward from the equator, f is the Coriolis parameter and ␤ is its northward gradient, t is the time, g is the acceleration due to gravity, cj is the long internal gravity wave speed due to the jth vertical mode, ␳ is the mean * water density, and ␶ x and ␶ y are the x and y components of wind stress. In (1) the forcing coefficient bj for each vertical mode is given by bj ⫽

冕

0

⫺Hmix

j ⫽ 1, 2,

Fj 共z兲 dz

冒再冕 Hmix

0

⫺H

冎

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FIG. 2. (a) Correlation coefficients between ERS wind stress forcing F [see (4)] and the El Niño index Niño-3.4 for the time interval January 1992–December 2000. (b) Regression of ERS zonal wind stress forcing F onto the El Niño index Niño-3.4 for the same time. Contours: 10⫺2 Pa °C⫺1. Niño-3.4 time series are normalized with variance 0.5 so that regression coefficients are representative of actual wind amplitude. For both (a) and (b) negative contours are thick gray, positive contours are thick black, and the zero line is thin black. The north–south scale has been stretched so that the north–south structure can be seen clearly.

level being forced by the wind is in phase with the wind forcing. Any phase difference between the sea level and the wind forcing therefore indicates that vortex stretching is influencing the dynamics. Equation (1) can be solved for each vertical mode by integrating westward following long, westward propagating Rossby waves using the method of characteristics. For some general point x ⬍ 0 the solution is

冋

␩j x, t共0兲 ⫺

册

x bj ⫽ ␩j关0, t 共0兲兴 ⫹ ␥j g␳ *

冕冋 x

F ␰, t 共0兲 ⫺

0

册

␰ d␰, ␥j 共3兲

␤c2j /f 2

where ␥j ⫽ is the long Rossby wave speed for vertical mode j, x ⫽ 0 refers to the eastern ocean boundary, and the forcing term F ⫽ ␶x ⫹

冉冊冉 f ␤

冊

⭸␶ y ⭸␶ x ⫺ . ⭸x ⭸y

共4兲

b. Interannual wind forcing in the South Pacific

关Fj 共z兲兴2 dz ,

共2兲

with –z as the water depth, Hmix as the mixed layer depth, and Fj (z) as the eigenfunction for the jth vertical mode. When there is no vortex stretching, the second term on the left-hand side of (1) is absent, and the sea

The interannual variability of the forcing F in (4) for the tropical South Pacific region is dominated by ENSO. The structure of the interannual ENSO wind forcing (see Fig. 2) can be found by the regression of F onto the El Niño index Niño-3.4 (the sea surface temperature anomaly averaged over the equatorial Pacific region 5°S–5°N, 170°–120°W). The contributions of the

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FIG. 3. (a) Correlation coefficients between ERS zonal wind stress and the El Niño index Niño-3.4 for the time interval January 1992–December 2000. (b) Regression coefficient of ERS zonal wind stress anomaly on the El Niño index Niño-3.4 for the same time. Contours: mPa °C⫺1. Niño-3.4 time series are normalized with variance 0.5 so that regression coefficients are representative of actual wind amplitude. For both (a) and (b) negative contours are thick gray, positive contours are thick black, and the zero line is thin black. The north–south scale has been stretched so that the north–south structure can be seen clearly.

zonal wind stress and ( f /␤) times the wind stress curl to the ENSO wind forcing F in (4) can also be found by correlation and regression of these forcing components onto Niño-3.4 (see Figs. 3 and 4, respectively]. We have used the European Remote Sensing (ERS) scatterometer-derived winds (available online at http://www. ifremer.fr/) because their resolution and accuracy enable better wind stress curl estimates. The satellite record is short, but calculations with other much longer wind datasets (see section 3c, below) give similar wind structures. Figures 2a, 3a, and 4a show that there is an ENSO wind-forcing signal in both the eastern and western Pacific, but the regression coefficients (Figs. 2b, 3b, 4b) indicate that the eastern Pacific signal is much smaller. Figure 3 shows that the western Pacific wind anomalies tend to be westerly (easterly) near the equator during El Niño (La Niña), consistent with previous work. As noted by Clarke et al. (2007), the rapid decrease and reversal of these wind anomalies to the south results in strong negative El Niño wind stress curl anomalies and hence, because of the factor f /␤, the positive anomalies in the northwest part of Fig. 4. Farther to the south, the zonal winds reverse again and this reversal is largely responsible for the El Niño positive curl anomaly and, by the factor f /␤, the negative values in the southwest of our region in Fig. 4. The similarity of Figs. 2b and 4b shows that the curl-forcing term in (4) dominates the forcing.

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FIG. 4. (a) Correlation coefficients between ( f /␤) multiplied by the ERS wind stress curl and the El Niño index Niño-3.4 for the time interval January 1992–December 2000. (b) Regression coefficient of ( f /␤) multiplied by the ERS wind stress curl anomaly on the El Niño index Niño-3.4 for the same time. Contours: (10⫺2 Pa) °C⫺1. Niño-3.4 time series are normalized with variance 0.5 so that regression coefficients are representative of actual wind amplitude. For both (a) and (b) negative contours are thick gray, positive contours are thick black, and the zero line is thin black. The north–south scale has been stretched so that the north–south structure can be seen clearly.

c. Model inputs Because of the longer record, we used the Florida State University (FSU) wind data (available online at http://www.coaps.fsu.edu/RVSMDC/SAC/index.shtml) instead of the satellite wind stress when running our forced wave model. The more-than-40-yr-long FSU dataset combines wind measurements from ships and buoys to provide monthly wind pseudostress on a 2° ⫻ 2° grid. When we repeat the correlation and regression calculations of section 3b on the FSU winds instead of the satellite winds, the results are very similar to those in Figs. 2–4. The extreme southern latitude of the FSU dataset is 29°S, so, when we calculate the wind stress curl, 28°S is the southernmost latitude for data, and we end our southern boundary at 28°S, as mentioned in section 2. The FSU pseudostress was converted to stress using a constant air density of 1.2 kg m⫺3 and a constant drag coefficient of 1.1 ⫻ 10⫺3 (Gill 1982). For simplicity, we assumed that the mixed layer depth Hmix is constant and equal to 50 m based on the World Ocean Atlas 1994 monthly mixed layer depth data [provided online by the International Research Institute for Climate and Society (IRI)/LamontDoherty Earth Observatory (LDEO) Climate Data Library at http://ingrid.ldeo.columbia.edu/SOURCES/ LEVITUS94/MONTHLY/]. We averaged c longitudi-

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FIG. 5. (a) First-mode EOF of the interannual sea level along the eastern coast of the South Pacific from 5° to 15°S using the 1° ⫻ 1° TOPEX/Poseidon and Jason-1 altimeter data. The first mode explains 97% of the total variance. (b) The principal component time series corresponding to the EOF in (a). The time series is normalized with variance 0.5.

nally so that c is a function of latitude alone. Longitudinal variations in c are small enough that using a constant average value negligibly affects the model sea level results. Latitudinal variations in longitudinally averaged c are also small. First-vertical-mode longitudinally averaged c varies less than 10% from 2.6 m s⫺1, while the second vertical longitudinally averaged mode varies less than 10% from 1.6 m s⫺1. Except where noted otherwise, our integration in (3) begins at the eastern boundary, so ␩[0, t(0)] must be given for each latitude along the boundary. The principal component time series of the first empirical orthogonal function (EOF) of the interannual boundary sea level (Fig. 5) is highly correlated (r ⫽ 0.93) with the interannual coastal tide gauge sea level at Callao (12°S). According to the method of Ebisuzaki (1997), which we use here and elsewhere in this paper, this correlation is significantly different from zero [rcrit (95%) ⫽ 0.65]. Based on the good correlation between the principal component time series and the Callao sea level record, we obtained a long record of sea level all along the coast by weighting the long interannual record at Callao according to the alongshore structure of the EOF in Fig. 5. To solve (3), not only must the coastal sea level be known at each latitude, it also must be known for each vertical mode. Clarke and Van Gorder (1994) used a forced wave model to show that the interannual sea level along the eastern Pacific boundary near the equator is dominated by the first two baroclinic vertical modes and that mode-2 sea level is about 1.5 times that

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FIG. 6. Interannual sea level time series at Honiara (solid line) and from the model discussed in section 4 (dashed line). The 95% critical correlation coefficient is 0.42 based on the method of Ebisuzaki (1997). The regression coefficient between the simulation and the observed time series is 0.77 with 95% confidence interval (0.71, 0.84).

of mode 1. Based on their result, we assumed that 60% of the interannual sea level amplitude was in vertical mode 2 and 40% in vertical mode 1.

4. Comparison of the long forced Rossby wave theory with observations a. Comparison of observed and modeled sea level at Honiara We first compare model and observed interannual tide gauge sea level at Honiara (9°–26°S, 159°–57°E) (Fig. 6) because of the long record there and because it is located in the heart of the northwestern region where the largest interannual amplitudes occur. The tide gauge record, from January 1975 to December 2003, was provided by the Sea Level Center, University of Hawaii (from their Web site at http://www.soest.hawaii. edu/UHSLC/). Visually the modeled and observed interannual Honiara time series agree quite well. The correlation coefficient between them is r ⫽ 0.77 with a 95% critical correlation coefficient of 0.42. The regression coefficient between the simulation result and the observed time series is 0.77 with 95% confidence limits of 0.71 and 0.84. Figure 6 suggests, and lag correlation calculations confirm, that the observed Honiara sea level slightly leads that of the model. The maximum lagged correlation coefficient is r ⫽ 0.86 with the observational time series leading by 3 months and the corresponding lag regression coefficient is 0.86 with 95% confidence interval (0.81, 0.91). One possible reason for the lag might be that our theory underestimates the Rossby wave speed. If we increase the Rossby wave speeds by a factor of 2.7 in our model, we get a maximum corre-

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FIG. 7. (a) Rms interannual sea level anomaly (cm) based on the T/P and J1 satellite sea level height estimates. Data near islands are inaccurate and are not shaded. (b) As for (a) but using interannual sea level from the wind-forced long Rossby wave model with two vertical modes. (c) As for (b) but with the free-wave Rossby contribution from boundary sea level [the first term on the right-hand side of (3)] missing. (d) As for (b) but with wind forcing [the second term on the right-hand side of (3)] missing. In all cases the north–south scale has been stretched so that the north–south structure can be seen clearly.

lation of r ⫽ 0.88 with zero lag. However, the cause of this Rossby wave speed increase, if indeed it is real, is unclear. Such an increase is not, for example, explained by zonal mean flow in this region (see, e.g., Killworth et al. 1997). We checked for possible dissipation in our calculations by adding a linear damping term to the left-hand side of (1). Vega et al. (2003), consistent with Qiu et al. (1997), found that damping was strong near eastern boundaries. However, when we used damping time scales comparable to those used by Qiu et al. (1997) and Vega et al. (2003)—namely, 300 days for vertical mode 1 and 150 days for vertical mode 2—our model results compared less favorably with observations. We found a similar degraded model performance for the large signals in the northwest of our region (section 4b) with dissipation included.

b. Comparison of observed and modeled sea level in the northwestern part of our region Figure 7a shows rms observed satellite interannual sea level estimates and Fig. 7b the rms model interan-

nual sea level between 5° and 15°S. The structure and amplitude of both plots are similar. Contributions to the total sea level amplitude (Fig. 7b) from the wind stress forcing (Fig. 7c) and free-wave propagation from the coast (Fig. 7d) show that most of the sea level amplitude west of 160°W is due to wind stress forcing in the basin rather than free-wave propagation from the coast. Figure 8 shows that the pointwise correlation between observed and model interannual sea level is higher than 0.6 for almost the entire region west of 160°W, larger signal amplitude generally coinciding with higher correlation. Figures 7 and 8 show that, mostly where the signal is large, model and observed interannual sea levels agree both spatially and temporally. The contributions of the wind stress curl and zonal wind to the sea level variations [see (3) and (4)] are compared in Fig. 9. The similarity of rms patterns in Figs. 9a and 7a–c suggests that the wind stress curl is the primary factor generating the interannual sea level in the northwestern region. However, Fig. 9b shows that the zonal wind stress contributes substantially north of

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FIG. 8. Pointwise correlation between observed and simulated anomalous monthly sea level filtered with the interannual Trenberth (1984) filter. Negative contours are thick gray, positive contours are thick black, and the zero line is thin black. The north–south scale has been stretched so that the north–south structure can be seen clearly.

7°S where the zonal wind stress anomalies are strong (Fig. 3). We checked the relative contributions of the vertical modes to the sea level and found, in agreement with previous work (Cane 1984; Busalacchi and Cane 1985), that the first two baroclinic modes comprise almost all of the response. The reason for the dominance of the lower-order modes can be seen from (1). Because (cj)⫺2 ⬃ j 2, the vortex stretching term [the second term on the left-hand side of (1)] dominates for large j, so, by (1) and (4), for forcing of interannual frequency ␻,

␩ j ⬃ ␤ bj cj2FⲐ共g␳ f 2␻兲. *

共5兲

But bj ⱗ ⬃b1 and, since (cj)2 ⬃ j⫺2, the lower-order modes dominate. Calculations show that modes 3 and higher contribute negligibly to sea level, but Fig. 10 shows that the mode-2 contribution is comparable in size to that of mode 1 and cannot be neglected. Note that, if vortex stretching were not important to the dynamics for the first two vertical baroclinic modes, which dominate the response, the left-hand side of (1) would be approximately ⳵␩j /⳵x and the sea level response and wind forcing would be in phase. Since calculations show that vortex stretching is important, the

sea level response is not in phase with the wind forcing. However, although vortex stretching is important, it is not dominant—both terms on the left-hand side of (1) contribute to the sea level response.

c. Comparison of observed and modeled sea level in the southwestern part of our region Interannual sea level variability is also strong in the box 20°–28°S, 160°E–160°W with a rms maximum sea level of nearly 7 cm (Figs. 1 and 11b). If we were to begin our model integration at the eastern boundary, in theory we would need more than 40 years of accurate wind stress curl data for the integration because secondvertical-mode Rossby waves propagate so slowly at 28°S. However, Fig. 1 shows that the low-frequency sea level amplitude in the 20°–28°S latitude band is very small in the vast region east of 120°W, so the large-scale unforced sea level signal propagating westward from the boundary contributes negligibly to the response. So, since the wind stress forcing (Fig. 2) is negligible east of about 140°W, we begin our westward integration at 140°W with ␩ ⫽ 0. The model and observed rms interannual sea level variability are shown in Fig. 11. The observed and mod-

FIG. 9. (a) Rms interannual sea level anomaly (cm) response of the wind-forced long Rossby wave sea level model discussed in section 4 with two vertical modes and only wind stress curl forcing. The meridional scale has been stretched so that the north–south variations can be seen clearly. (b) As for (a) but using zonal wind stress forcing and no curl forcing.

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FIG. 10. (a) Rms of the first-vertical-mode interannual sea level anomaly (cm) from the wind-forced long Rossby wave sea level model discussed in section 4. The meridional scale has been stretched so that the north–south variations can be seen clearly. (b) As for (a) but for the second vertical mode.

eled variability does not agree as well as in Fig. 7. However, east and south of New Caledonia (marked approximately by the unplotted white space at about 167°E north of 24°S) observed and modeled sea level variability have similar large-scale structure and amplitude. The model sea level is generated by the interannual wind stress curl forcing shown in the southwestern part of Fig. 4. However, model and observed sea levels are poorly correlated and are even of the wrong sign over much of the region (see Fig. 12). We compared the interannual FSU wind forcing with the ERS satelliteestimated winds and found reasonable agreement during their time of overlap, so probably the wind forcing is adequately accurate. The low correlation has also been noted by Qiu and Chen (2006), who used a similar model to compare observed and simulated time series. They suggested that this low correlation may be due to

the regionally enhanced eddy variability associated with the instability of the South Tropical Current and the East Australian Current (Qiu and Chen 2004). The sea level response to steric heating, which is not included in our model, in theory could also partly account for the low correlation in this region (Bowen et al. 2006). However, Fig. 3c of Bowen et al. (2006) suggests that the anomalous steric heating response, of relevance here, is small. Our model also does not include the island of New Caledonia: such an island can affect the interannual sea level pattern (Clarke 1991). The blocking of the Rossby wave by New Caledonia in the real world may at least partly explain why the observed interannual sea level variability west of New Caledonia is much lower than the model interannual sea level. We wondered whether the poor agreement between

FIG. 11. Rms interannual sea level anomaly (cm) for (a) the wind-forced long Rossby wave model and (b) the T/P and J1 satellite sea level estimates. Data near islands are inaccurate and are not shaded. “N. Cal.” stands for the island of New Caledonia and its vicinity.

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FIG. 12. As in Fig. 8 but for the southwest region shown in Fig. 11. Negative contours are thick gray, positive contours are thick black, the zero line is thin black, and the contour interval is 0.2. The north–south scale has been stretched so that the north–south structure can be seen clearly.

model and observed interannual sea level and the short spatial scales in the correlation coefficient in Fig. 12 could be blamed on the brevity of the satellite sea level record. Fortunately, a long monthly record of tide gauge sea level is available for Noumea (22°18⬘S, 166°26⬘E) at the southern end of New Caledonia (see Fig. 12). After correcting this sea level for atmospheric pressure and then forming monthly Trenberth-filtered interannual sea level anomalies, we compared this observed interannual sea level with a corresponding model sea level calculated using the no-mean-flow theoretical Rossby wave speed. The correlation between model and observed sea level is negligible [r ⫽ ⫺0.03; rcrit (95%) ⫽ 0.33]. If we double the long Rossby wave speed the correlation improves, but it is still only 0.48 [rcrit (95%) ⫽ 0.32]. It does, therefore, seem that standard wind-forced long Rossby wave dynamics does not describe the sizable interannual sea level variability in the southwestern part of the tropical South Pacific.

5. Interannual variability near the eastern boundary a. The vast “quiet” region east of 160°W The huge region of low interannual variability east of about 160°W in Fig. 1 is surprising because it contradicts known theory. Specifically, analysis by Pizarro et al. (2001) suggested that the interannual sea level variability along the eastern boundary of the Pacific south of the equator was mainly due to the ENSO signal originating at the equator rather than alongshore coastal wind stress forcing. Based on this, we would expect the amplitude of the coastal signal to be approximately constant all along the coast in order to satisfy a condition of no normal geostrophic flow into an impermeable coastal wall. But if the interannual sea level amplitude were constant along the coast, then at these low frequencies Rossby waves would propagate westward into the ocean interior and spread a largeamplitude interannual signal into much of the interior

basin. However, the prediction that the sea level is constant spatially along the South American coast is false; Fig. 1 shows that the sea level amplitude falls along the coastline so that the boundary source for the Rossby waves is of comparatively low amplitude. This smallamplitude coastal source explains or partly explains the huge “quiet” region of low interannual variability south of 12°S and east of 160°W (huge dark blue region in Fig. 1). Pizarro et al. (2001) showed that the interannual alongshore wind generates sea level fluctuations that are too small and are uncorrelated with the observed interannual sea level, so the alongshore wind cannot explain the fall in coastal sea level in Fig. 1. The Pizarro et al. (2001) analysis of coastal sea level suggests that bottom friction near the coast may explain the fall in coastal sea level amplitude, but the fall in altimeterestimated interannual sea level amplitude in Fig. 1 is steeper than that of the coastally measured sea level of Pizarro et al. Perhaps interannual changes in the coastal alongshore flow affect the energy loss of the large-scale flow to smaller-scale eddies and this causes the interannual coastal sea level fall. Further detailed investigation, beyond the scope of this paper, is needed to check this possibility.

b. Westward propagation of free Rossby waves Figure 13 shows the maximum correlations of lagcorrelated interannual satellite-estimated interannual sea level near the coast with offshore satellite-estimated interannual sea level at the same latitude. All sea level estimates were derived from 1° ⫻ 1° TOPEX/Poseidon and Jason-1 altimeter products. There is a general tendency for westward propagation since lag in Fig. 13c tends to increase westward. The westward propagation is comparable to, but faster than, the theoretical propagation for long, baroclinic Rossby waves since the theoretical lags in Fig. 13d increase faster than those in Fig. 13c. These results are consistent with those of Vega et al. (2003), who also discussed westward propagation from this coast using satellite altimeter data. Vega et al.

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FIG. 13. (a) Maximum lag correlation coefficients between the interannual sea levels in the ocean and the same latitude “coastal” sea level: All data derived from 1° ⫻ 1° TOPEX/ Poseidon and Jason-1 measurements. At each latitude the coastal sea level is the sea level for the 1° ⫻ 1° box closest to the coast. (b) As in (a) but for regression coefficients. (c) The lags at maximum correlation. Lags greater than 5 months are not plotted. (d) As in (c) except that the interior interannual sea level is due to first-vertical-mode, westward-propagating long Rossby waves.

found that at 27°S the speed was about 30% faster than theoretical for the period from 1992 to 2000. Vega et al. (2003) also suggested that “[w]est of 90°W, the wind forcing becomes dominant because the free waves forced at the boundary are dissipated and replaced by propagating forced waves.” We also find that the coastal signal is dissipated strongly south of about 20°S but north of 15°S the coastal signal extends westward to about 130°W, much farther west than 90°W (see Fig. 13).

c. Coastal El Niño current The fall in interannual sea level amplitude along the South American coast (Figs. 1 and 5) suggests, by geostrophy, that between about 5° and 15°S there is an interannual flow perpendicular to the coast. When the

sea level is higher than normal at the coast (e.g., during the 1997–98 El Niño) the fall in anomalous sea level leads to a geostrophic anomalous flow toward the coast that blocks the flow and deflects it alongshore. This anomalous alongshore geostrophic flow should be southward, driven by the southward alongshore pressure gradient. During a La Niña when the sea level is lower than normal at the coast, the currents should reverse, resulting in lower than normal sea level near the equator rising toward zero southward, consistent with anomalous offshore geostrophic flow and an anomalous northward coastal flow. To investigate this geostrophic coastal flow we can use the high-resolution along-track (Fig. 14) T/P and J1 interannual sea level estimates near the eastern Pacific boundary. Figure 15 shows the correlation and regres-

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FIG. 14. Ground tracks near the eastern Pacific coast. The 200-m isobath (dashed line) marks the approximate position of the shelf edge.

sion coefficients between the tide gauge interannual sea level at the coastal station Callao and the interannual satellite sea level estimates along track 191. There are no estimates within about 30 km of the coast because the satellite measurements are not accurate there. However, seaward of 30 km the correlation is greater than 0.8 for about 500 km. The regression coefficient drops rapidly from the shelf edge, suggesting a strong alongshore flow there with a width of about 20 km. By geostrophy, this anomalous flow will be southward when the sea level is anomalously high at the coast (El Niño) and northward when the sea level is anomalously low at the coast (La Niña). Since the regression coefficient between the first satellite sea level 30 km from the coast and the coastal tide gauge measurement at Callao is 0.65 and the correlation between these time series is high (greater than 0.9: see Fig. 15), it is likely that the interannual sea level drops sharply from the coast and that, therefore, there is a coastal interannual current on the shelf in the same direction as the shelf-edge flow. Figure 16 shows the structure functions and principal component time series for the first EOFs for satelliteestimated interannual sea levels along tracks 039, 115, 191, and 013 for measurement points near the coast. In each case the first-mode EOF explains almost all of the variance and the principal component is highly correlated with the Callao coastal interannual sea level. The interannual sea level amplitude decreases seaward from the coast on the continental shelf for tracks 039 and 115,

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FIG. 15. (a) Correlation of the interannual sea level estimates along satellite track 191 shown in Fig. 12 with the interannual coastal tide gauge time series at Callao, Peru. The 200-m isobath (dashed line) marks the approximate position of the shelf edge. The 95% critical correlation coefficients marked by the curved thin lines were found using the method of Ebisuzaki (1997). (b) Regression coefficients corresponding to (a).

consistent with the fall in amplitude from the Callao tide gauge to the first satellite estimate along track 191, as discussed above. Tracks 191 and 013 also suggest that there is a fall in interannual sea level from the shelf edge, but this does not appear to be present for the two northernmost tracks, 039 and 115. By geostrophy, a rapid drop in interannual sea level amplitude is consistent with a strong interannual flow. If we assume that this interannual flow is parallel to the coast or shelf edge as appropriate then, as has been done elsewhere (e.g., Clarke and Li 2004; Li and Clarke 2004), we can calculate the size of the flow as well as its direction. When the principal components in each case have a value of ⫹1, the geostrophic coastal flows estimated in this way are about 20 cm s⫺1 for tracks 039, 115, and 191 and about 10 cm s⫺1 for track 013. Similar analyses were also carried out on the tracks to the south (not shown). These analyses showed that the flow there is much weaker. These flows vary interannually as the principal component varies; because the flow is in one direction along the coast for a long time, particles like fish eggs and larvae can be moved large distances and the ecosystem can be greatly affected. There appear to be no directly measured currents in this region to check the interannual geostrophic surface flow during the satellite measurements. However, anomalously strong poleward alongshore currents of about 14 cm s⫺1 at 55-m depth near the coast at 15°S were observed from 27 March to 30 May 1976 and about 21 cm s⫺1 from 7 October to 10 December 1982 at 100-m depth near the coast at 10°S (Smith

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FIG. 16. (a) First-mode EOF of the interannual sea level along track 191 for the first accurate 10 points closest to the coast in Fig. 12. The first mode explains 97% of the total variance. The dashed line is the 200-m isobath and denotes the approximate position of the shelf edge. (b) The principal component time series corresponding to the EOF in (a). The time series is normalized with variance 0.5 so that the EOF represents sea level amplitude. The correlation coefficient r between the principal component time series and the interannual Callao time series is 0.91. (c), (d) As for (a) and (b) but for track 115. In this case the EOF explains 94% of the total variance and r ⫽ 0.91. The shelf here is wide and 30 points have been used so that the shelf edge (dashed line) is included. (e), (f) As for (a) and (b) but for track 191 with 20 points. In this case the EOF explains 95% of the total variance and r ⫽ 0.92. (g), (h) As for (a) and (b) but for track 013. In this case the EOF explains 86% of the total variance and r ⫽ 0.78.

1983). But these estimates are based on only a few years of data. The EOF analyses are dominated by the 1997–98 El Niño signal. We checked whether there really is a geostrophic flow with similar structure at other times by removing the 1997–98 signal period (February 1997 to August 1998) from the interannual data and then repeating the EOF analysis. If the 1997–98 signal is not distorting the results, then the first EOF multiplied by the principal component should be similar in both the 1997–98 removed and included cases during their time of overlap. This result holds for the northernmost tracks 039, 115, and 191 (see Fig. 14) but not for track 013 in the south, which, when the 1997–98 signal is removed, has a nearly horizontal structure function and, correspondingly, no large interannual flow. We do not know whether the southernmost track really does

have no interannual flow or whether the sea level signal in the south is too small to be measured accurately. In any case, overall it does seem that the strong interannual flow, at least for the three northern tracks, is not an artifact of the huge 1997–98 El Niño. To summarize, between about 5° and 15°S during El Niño there is a broad anomalous flow toward the South American coast. This shoreward flow results in a strong southward shelf edge and coastal southward anomalous flow. These anomalous flows reverse during La Niña.

6. Conclusions Observations of 11 years of T/P and J1 altimeter measurements enable us to examine interannual ocean variability in the tropical South Pacific (5°–28°S). In the northwestern part of the region (5°–15°S, 155°E–

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160°W), during El Niño, strong anomalous westerly wind stress that decreases southward from the equator forces the ocean. Since the wind curl anomaly is negative and f is negative, the curl of the anomalous Ekman transport is positive. This results in positive sea level anomalies that increase in amplitude westward in accordance with forced, long Rossby wave dynamics. The sea level anomalies are not exactly in phase with the wind forcing because Sverdrup balance does not hold— vortex stretching also contributes to the response. The first baroclinic vertical mode dominates the response, although the contribution from vertical baroclinic mode 2 is not negligible. During La Niña, the wind anomalies reverse and the sea level anomalies are negative. As in the western Pacific, the main interannual signal in the eastern tropical Pacific is strongly related to ENSO. The interannual sea level amplitude decreases rapidly along the South American coast. Rossby waves propagate westward into the interior, but because the Rossby wave source at the coast is small along most of the coast and because the curl forcing is weak over much of the eastern Pacific, there is a vast region of the eastern tropical South Pacific that is “quiet” interannually. Near the boundary from 5° to 15°S, the fall in ENSO sea level amplitude along the coast causes an anomalous onshore transport and strong anomalous southward coastal current during El Niño. During La Niña the flow reverses. Acknowledgments. We gratefully acknowledge the support of the National Science Foundation (Grants OCE-0220563 and ATM-0326799). REFERENCES Bowen, M. M., P. J. H. Sutton, and D. Roemmich, 2006: Winddriven and steric fluctuations of sea surface height in the southwest Pacific. Geophys. Res. Lett., 33, L14617, doi:10.1029/2006GL026160. Busalacchi, A. J., and M. A. Cane, 1985: Hindcasts of sea level variations during the 1982–83 El Niño. J. Phys. Oceanogr., 15, 213–221. Cane, M. A., 1984: Modeling sea level during El Niño. J. Phys. Oceanogr., 14, 1864–1874. Clarke, A. J., 1991: On the reflection and transmission of low-frequency energy at the irregular western Pacific Ocean boundary. J. Geophys. Res., 96, 3289–3305. ——, and S. Van Gorder, 1994: On ENSO coastal currents and sea levels. J. Phys. Oceanogr., 24, 661–680.

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——, and J. Li, 2004: El Niño/La Niña shelf edge flow and Australian western rock lobsters. Geophys. Res. Lett., 31, L11301, doi:10.1029/2003GL018900. ——, S. Van Gorder, and G. Colantuono, 2007: Wind stress curl and ENSO discharge/recharge in the equatorial Pacific. J. Phys. Oceanogr., 37, 1077–1091. Ebisuzaki, W., 1997: A method to estimate the statistical significance of a correlation when the data are serially correlated. J. Climate, 10, 2147–2153. Enfield, D. B., and J. S. Allen, 1980: On the structure and dynamics of monthly mean sea level anomalies along the Pacific coast of North and South America. J. Phys. Oceanogr., 10, 557–578. Gill, A. E., 1982: Atmosphere–Ocean Dynamics. Academic Press, 662 pp. Kessler, W. S., 1990: Observations of long Rossby waves in the northern tropical Pacific. J. Geophys. Res., 95, 5183–5217. Killworth, P. D., D. B. Chelton, and R. A. de Szoeke, 1997: The speed of observed and theoretical long extratropical planetary waves. J. Phys. Oceanogr., 27, 1946–1966. Li, J., and A. J. Clarke, 2004: Coastline direction, interannual flow, and the strong El Niño currents along Australia’s nearly zonal southern coast. J. Phys. Oceanogr., 34, 2373–2381. Meyers, G., 1979: On the annual Rossby wave in the tropical North Pacific Ocean. J. Phys. Oceanogr., 9, 663–674. Pizarro, O., A. J. Clarke, and S. Van Gorder, 2001: El Niño sea level and currents along the South American coast: Comparison of observations with theory. J. Phys. Oceanogr., 31, 1891– 1903. Qiu, B., and S. Chen, 2004: Seasonal modulations in the eddy field of the South Pacific Ocean. J. Phys. Oceanogr., 34, 1515– 1527. ——, and ——, 2006: Decadal variability in the large-scale sea surface height field of the South Pacific Ocean: Observations and causes. J. Phys. Oceanogr., 36, 1751–1762. ——, W. Miao, and P. Muller, 1997: Propagation and decay of forced and free baroclinic Rossby waves in off-equatorial oceans. J. Phys. Oceanogr., 27, 2405–2417. Shaffer, G., S. Hormazabal, O. Pizarro, and S. Salinas, 1999: Seasonal and interannual variability of currents and temperature off central Chile. J. Geophys. Res., 104, 29 931–29 962. Smith, R. L., 1983: Peru coastal currents during El Niño: 1976 and 1982. Science, 221, 1397–1399. Trenberth, K. E., 1984: Signal versus noise in the Southern Oscillation. Mon. Wea. Rev., 112, 326–332. Vega, A., Y. du-Penhoat, B. Dewitte, and O. Pizarro, 2003: Equatorial forcing of interannual Rossby waves in the eastern South Pacific. Geophys. Res. Lett., 30, 1197, doi:10.1029/ 2002GL015886. Wang, L., C. Koblinsky, S. Howden, and B. Beckley, 1998: Largescale Rossby wave in the mid-latitude South Pacific from altimetry data. Geophys. Res. Lett., 25, 179–182. White, W. B., 1977: Annual forcing of baroclinic long waves in the tropical North Pacific Ocean. J. Phys. Oceanogr., 7, 50–61.