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  • View in gallery

    (a) Locations of XBT profiles used in the study. The names of the primary lines and ports are indicated. Water depths shallower than 200 m are shaded gray. Inset shows place names in the Indonesian archipelago

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    Data distribution along the three XBT lines as a function of position and time: (a) IX1, (b) PX2, and (c) IX22.

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    (a) The mean temperature along the IX1 section, (b) the standard deviation of the mapped temperature on depth surfaces along IX1, and (c) the standard deviation of the residuals of the mapping procedure

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    As for Fig. 3 but for the PX2 section.

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    As for Fig. 3 but for the IX22 section.

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    Temperature as a function of time and depth along IX1 at two latitudes: (a) off the coast of Java at the Sunda Strait and (b) off the coast of Western Australia near 25°S

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    Temperature as a function of time and depth along PX2 at two longitudes: (a) off the Java shelf break near 116°E and (b) at the Arafura Shelf break near 133°E

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    The percentage of mapped temperature variance in four spectral bands along IX1: (a) intraseasonal (periods less than 0.4 yr), (b) seasonal (periods between 0.4 and 1.1 yr), (c) quasi-biennial band (periods between 1.1 and 3.2 yr), and (d) interannual (periods longer than 3.2 yr)

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    As for Fig. 8 but for PX2

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    As for Fig. 8 but for IX22

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    (a) Time series of interannual anomalies of along path wind averaged along the equatorial and coastal waveguides in the Indian and Pacific Oceans (see text). (b) Variance-preserving spectra of the wind series in (a). The line frequencies plotted are, from left to right, for 10, 5, 2, 1, and 0.5 yr

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    The percentage of total variance accounted for by lagged partial regression. Overplotted in white is the mean salinity along the lines with a contour interval of 0.1. Above each plot is the fitted percent variance of dynamic height relative to 700 m. (a), (b), and (c) are for IX1, PX2, and IX22, respectively

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    (a), (c), (e) Fitted temperature coefficients (°C) and (b), (d), (f) lags in months for the wind indices along IX1: (a), (b) Pacific wind index; (c), (d) Indian wind index, and (e), (f) the local wind index, here the cross-shelf Ekman flux between Fremantle and the equator. Blank areas in the top panels reflect the presence of topography while blank areas in the plots on the bottom are where the total variance accounted for is less than 30%. Above the main panels is the coefficient in meters or lag in months of surface dynamic height relative to 700 m based on the XBT data

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    As for Fig. 13 but for PX2. The local wind index used is the zonal wind stress averaged over the Banda Sea

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    As for Fig. 13 but for IX22. The local wind index used is as for Fig. 13

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    (a) Standard deviation of low-frequency anomaly of sea surface height (cm), and (b) percent variance captured by the lagged linear partial regression model

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    (a) Coefficient (cm) and (b) lag in months of the low-frequency anomaly of SSH for Pacific wind index

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    As for Fig. 17 but for the Indian Ocean wind index

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    Theoretical zonal Rossby wave speeds based on the atlas of Chelton et al. (1998) (solid line) and the zonal phase speed deduced from the partial regression fits to the Pacific Ocean wind index (see text)

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    Schematic of remotely forced wave pathways into the throughflow region. Thin broken lines show the waveguide from the equatorial Indian Ocean, with energy spreading into the internal seas through both Lombok and Ombai Straits. Solid black arrows show the pathways for equatorial Pacific wind energy traveling down the Papuan/Australian shelf break and radiating westward-propagating Rossby Waves into the Banda Sea and South Indian Ocean

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An Intersection of Oceanic Waveguides: Variability in the Indonesian Throughflow Region

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  • 1 CSIRO Marine Research, Hobart, Tasmania, Australia
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Abstract

Temperature and sea level variability within the Indonesian seas and southeast Indian Ocean are described based on expendable bathythermograph deployments along volunteer merchant shipping lines under way since 1983. These data resolve variability at time scales ranging from the intraseasonal to the interannual. A lagged partial regression technique reveals that anomalies from a mean seasonal cycle of temperature and sea level for seasonal to interannual time scales can be largely understood in terms of free Kelvin and Rossby waves generated by remote zonal winds along the equator of the Indian and Pacific Oceans, with local wind forcing appearing to play a minor role. About 60%–90% of sea level variability and 70% of thermocline temperature variability can be accounted for in this way. Variations in zonal Pacific equatorial winds force a response along the Arafura/ Australia shelf break through Pacific equatorial Rossby waves exciting coastally trapped waves off the western tip of New Guinea, which propagate poleward along the Australian west coast. The signature of this Pacific energy radiating westward across the Banda Sea and into the subtropical south Indian Ocean within 1500 km of the coast is also prevalent. Equatorial Indian Ocean wind energy propagates along the Sumatra–Java–Nusa Tenggara waveguide to penetrate the Savu Sea, the western Banda Sea and Makassar Strait, thus having an impact on the western internal seas. Hence the region comprises the intersection of two ocean waveguides, as first predicted by Clarke and Liu.

Corresponding author address: Dr. Susan Wijffels, CSIRO Marine Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia. Email: susan.wijffels@csiro.au

Abstract

Temperature and sea level variability within the Indonesian seas and southeast Indian Ocean are described based on expendable bathythermograph deployments along volunteer merchant shipping lines under way since 1983. These data resolve variability at time scales ranging from the intraseasonal to the interannual. A lagged partial regression technique reveals that anomalies from a mean seasonal cycle of temperature and sea level for seasonal to interannual time scales can be largely understood in terms of free Kelvin and Rossby waves generated by remote zonal winds along the equator of the Indian and Pacific Oceans, with local wind forcing appearing to play a minor role. About 60%–90% of sea level variability and 70% of thermocline temperature variability can be accounted for in this way. Variations in zonal Pacific equatorial winds force a response along the Arafura/ Australia shelf break through Pacific equatorial Rossby waves exciting coastally trapped waves off the western tip of New Guinea, which propagate poleward along the Australian west coast. The signature of this Pacific energy radiating westward across the Banda Sea and into the subtropical south Indian Ocean within 1500 km of the coast is also prevalent. Equatorial Indian Ocean wind energy propagates along the Sumatra–Java–Nusa Tenggara waveguide to penetrate the Savu Sea, the western Banda Sea and Makassar Strait, thus having an impact on the western internal seas. Hence the region comprises the intersection of two ocean waveguides, as first predicted by Clarke and Liu.

Corresponding author address: Dr. Susan Wijffels, CSIRO Marine Research, GPO Box 1538, Hobart, Tasmania, 7001, Australia. Email: susan.wijffels@csiro.au

1. Introduction

Recent observations from the Indonesian Throughflow (hereinafter throughflow) region have revealed a rich spectrum of variability with high energy found from the intraseasonal to the interannual bands, typical of the tropical oceans. Also, as found elsewhere in the Tropics, remote forcing plays a large role in producing local variability. What is atypical of the throughflow region is that the remote forcing originates from two ocean basins and not just one, and here we demonstrate that the throughflow region can be thought of as the intersection of two remote equatorial wave guides: that of the Pacific and the Indian Oceans.

The idea that the Pacific and Indian equatorial winds must force variability in the throughflow was elegantly argued by Clarke and Liu (1994) who considered time scales well beyond one year, by which time all equatorial and coastal wave transients would have disappeared. Using wind observations and a simple modal wave model, they predicted interannual pressure changes in the coastal waveguides on either side of the throughflow, and thus transport changes. However, they lacked sea level observations on the northern side of the throughflow to help confirm their theory.

An analysis of variation in depth of the thermocline between Australia and Indonesia was consistent with the theory (Meyers 1996) but did not identify the waves. In addition, it is not understood how far east Indian Ocean wind-driven energy penetrates along the broken waveguide of the Nusa Tenggara island arc. Sprintall et al. (2000) observed a semiannual Kelvin wave, excited on the Indian Ocean equator, pass southeastward along the Sumatra/Java coasts, through Lombok Strait, and northward to 3°S in the Makassar Strait to reverse currents at a mooring set there during 1997/98. They also showed that a simple wind-forced Kelvin wave model could account for much of the sea level variability along the Nusa Tenggara through to Ombai Strait, indicating that remote equatorial Indian Ocean winds contribute to variability there. Similarly, Hautala et al. (2001) recognized that the South Java Current, a shallow coastal eastward flowing jet associated with equatorially generated downward Kelvin waves, also penetrates as far eastward as Ombai Strait. They found that variability in Timor Strait, however, was quite distinct from that found in the straits farther west and north, suggesting that the Indian Ocean wind energy did not reach the east coast of Timor. Molcard et al. (1996, 2001) find large intraseasonal energy in both Ombai and Timor Straits. As found by Sprintall et al. (2000) and Potemra et al. (2002), they observed flow reversals in Ombai Strait associated with Kelvin waves forced in the Indian Ocean.

It has been long observed that remotely driven low-frequency (interannual) Pacific wind energy penetrates into the throughflow region and southward along the Western Australian coast (Pariwono et al. 1986; Clarke 1991; Clarke and Liu 1994; Meyers 1996) and modulates both sea level and thermocline depth nearly in phase with the El Niño–Southern Oscillation (ENSO). This remote energy is believed to arise from the transmission of equatorial Rossby waves generated by zonal wind anomalies in the central equatorial Pacific into coastally trapped waves where the New Guinea coast intersects the equator. These coastal-trapped waves then propagate around the western tip of New Guinea and poleward along the Arafura/Western Australian continental margin. This Pacific wind-driven wave energy also propagates westward across the Banda Sea and southern tropical Indian Ocean as free Rossby Waves and within months is augmented by the local response to the strong forcing of the monsoon winds (Meyers 1996; Ffield et al. 2000; Masumoto and Meyers 1998). A 1½-layer model used by Potemra (2001) suggests that significant energy can “leak” from the central equatorial Pacific into the Indian Ocean at semiannual and longer time scales. However, Potemra (2001) had difficulty obtaining observational evidence for this process, especially for the higher frequencies.

Here we employ a dataset with broader spatial coverage and a longer time span than those used previously to address the question: how much of the variability in the throughflow region is remotely driven by the winds in the equatorial Pacific and Indian Oceans? What is the spatial extent of its penetration into the archipelago and beyond?

2. Data

As part of the Tropical Ocean and Global Atmosphere (TOGA) upper-ocean observing system, a network of expendable bathythermograph (XBT) sections was established in 1983–86 along commercial shipping lines between the western coast of Australia and various ports. The lines used here are detailed in Fig. 1 and are designated as IX1, which is from Fremantle on the south coast of Western Australia to Sunda Strait on the western tip of Java, Indonesia; PX2, which crosses the Banda Sea just north of the Nusa Tenggara island arc; and IX22 from Port Hedland on the north coast of Western Australia through the Banda and Flores Seas to the east coast of Mindanao in the Philippines.

On all lines, Sippican T-4 and T-7 XBTs were dropped roughly every 4 hours (∼80 nm) from volunteer observing vessels. Sampling has occurred on average at fortnightly frequency along IX1 and monthly frequency on the other lines, though it can be less because of changes in shipping and the time required to reequip the line (Fig. 2). In mid-1986 the probe type changed from Sippican T-4s to T-7s, and thus, after this date, the maximum depth of the profiles collected changed from 450 to 760 m.

The profiles were carefully quality controlled (Bailey et al. 1994). Profiles were depth corrected using the algorithm determined by Hanawa et al. (1995) and then filtered and subsampled to 5-m intervals. In order to study the variability along the section, the data were gridded in space and time. This was achieved by applying a weighted average at each time and grid point. The weight function was a Gaussian with an e-folding scale of 18 days for IX1 and 30 days for PX2 and IX22. Weighted averaging was also applied in space with widths adjusted to increase resolution near coasts where boundary currents are present, but were typically about 100 km.

Because the region is typified by very tight shallow thermoclines and large tidal energy, we reduced the “smearing” of these tight thermoclines during gridding by forming averages of the depth of a temperature surface, rather than averages of the temperature on a depth surface, following Gouriou and Toole (1993). Separately, we averaged both the surface temperature and the depth of an isothermal “mixed” layer and then linearly interpolate between this mixed layer and the ocean interior from the isothermal averages. The averaged fields are then projected back to a 5-m depth grid for analysis. We found that by averaging in temperature space we avoided biases in the resulting mapped fields of up to 0.5°C around the thermocline. An average temperature value was produced 50 times per year (∼weekly). Dynamic height was also calculated from the gridded data using a seasonal temperature salinity relation based on the regional climatology produced by Ridgway et al. (2002). Wind stresses used in this study are derived from the National Centers for Environmental Prediction (NCEP) reanalysis described by Kalnay et al. (1996) as downloaded from the Climate Diagnostic Center in Boulder, Colorado. The seasonal cycle is removed using a least squares fit to sinusoids (three harmonics), and a 4-month low-pass filter is applied. Altimetric sea level data from the Archiving, Validation, and Interpretation of Satellite Oceanographic Data service (AVISO) are also used (Le Traon et al. 1998).

3. Temperature variability

In this and the next section we describe the temperature variability in detail: where it is largest and how time scales change with depth and region. Total mapped temperature variance along the sections is related to the mean background vertical temperature gradient (Figs. 3–5, which show the square root of the variance) in that the largest variability of temperature on depth surfaces occur where the vertical temperature gradient is strongest: in the shallow thin thermoclines at the northern end of IX1 and in the thermoclines along PX2 and IX22. Large amplitude temperature changes also occur where seasonal thermoclines form (winter cooling and deep mixed layers progressing to summer warming and shallow mixed layers): in the southern half of IX1 and the eastern half of PX2. Last, large variance also occurs where the sections cross or impinge near shelf breaks. There is a maximum in variance centered on 124°E along PX2 occurs where the section approaches the shelf break near the chain of islands between Flores and Alor (Fig. 1) and off Mindanao (near 5°N) along IX22 where the XBT line alternately samples the inshore and offshore sides of the strong Mindanao Current.

The distribution of residual variance (unmapped variance, Figs. 3c–5c) representing phenomena with scales shorter than the mapping scales is largely correlated to background stratification, indicating that much could be explained by high-frequency low-mode tidal effects and/or drop-rate variability among the XBT probes. However, some maxima may be associated with the generation of higher-mode vertical structures and/or time scales outside the mapped range.

Examples of the mapped data from the IX1 and PX2 sections are shown in Figs. 6 and 7. Off the Australian coast the yearly formation of the seasonal thermocline dominates variability in the upper 150 m (Fig. 6b). Below this depth, eddy variability is superimposed on a low frequency modulation of the depth and thickness of the main thermocline. During ENSO years (1986/87, 1991–94, 1997/98) the thermocline is shallower and thicker, and surface temperatures are cooler.

Off Java the thermocline is much thinner and shallower than to the south (Fig. 6a). Here temperature below 150 m undergoes strong semiannual variations that feature upward vertical phase propagation (Fig. 6a). Remotely wind-driven by the “Wyrtki jets” (Clarke and Liu 1993), the semiannual variability attenuates toward the surface where interannual variability becomes stronger. A seasonal thermocline is almost entirely absent and, instead, the warm surface layer is punctuated by intense upwelling events, the largest of which occur in late 1988, 1989, 1991, 1994, and 1997.

At the western end of PX2 (Fig. 7a), at the Java Sea shelf break, a thin shallow thermocline also exists. Variability in the surface layer is weak, lacking the dramatic variations observed south of Java and in contrast is dominantly annual, while both semiannual and lower frequency variations occur at depth. Off the Arafura shelf west of Darwin (Fig. 7b), a very strong seasonal thermocline forms, below which the annual period continues to dominate but with striking upward vertical phase propagation.

4. Spectral content of temperature variability

To reveal the spatial characteristics of temperature variability, total variance was binned into four broad spectral bands: for periods less than 0.4 yr (intraseasonal/mesoscale), periods between 0.4 and 1.1 yr (seasonal), periods between 1.1 and 3.2 yr [quasi-biennial (QB) band], and for periods greater than 3.2 yr (ENSO band). The variance in each spectral band is then shown as a percentage of the total mapped variance in Figs. 8–10.

Eddy energy is only partially suppressed by the mapping scales used to average the data, and we find that intraseasonal energy still accounts for over 20% of the total mapped temperature variance in two regions along IX1 (Fig. 8a): off the coast of western Australia where the IX1 line intersects the eddy field associated with the Leeuwin Current and between 13° and 8°S along the axis of the South Equatorial Current, where strong intraseasonal eddies develop as a result of an instability of the current at its seasonal maximum (Feng and Wijffels 2002). The seasonal band (Fig. 8b), which includes both the annual and semiannual frequencies, dominates SST and variability in the seasonal thermocline, the depth penetration of which clearly increases to the south along with the strength of the seasonal heating/cooling cycle. Dominant seasonal variation is also evident throughout the water column between 12° and 18°S (Fig. 8b): this is the forced annual Rossby wave described by Masumoto and Meyers (1998), which is driven primarily by local wind-stress curl and associated Ekman pumping at the longitude of IX1.

Off the coast of Java, the relative importance of the seasonal cycle in temperature variability only becomes large below 100 m, where a narrow region of high seasonal variability is trapped at the coast and extends to below 750 m. Responsible are the semiannual Kelvin waves excited in the equatorial Indian Ocean. Interestingly, coastal SST and temperature variability above 100 m off Java largely occurs in the QB and ENSO bands (Figs. 8c–d). Variance in the QB band occurs in the Java mixed layer and upper thermocline and at depth extends farther offshore than the seasonal variability (Fig. 8c). Off Australia, the ENSO band accounts for between 40% and 70% of the total variability throughout the thermocline.

Along PX2, the intraseasonal band has little energy because of the longer averaging times (e-folding scale of 30 days) needed to grid this more sparsely sampled section. Seasonal variations dominate temperature variability above and below the main thermocline and at the Arafura shelf break (Fig. 9b), while variability in the ENSO band dominates within the thermocline (Fig. 9d). Weak QB variability is also found in the thermocline in the western part of the PX2 section (Fig. 9c).

IX22 features a rich meridional structure in spectral energy distributions (Fig. 10). As observed above, the amplitude of the seasonal thermocline grows poleward. Seasonal energy also dominates in the thermocline in a distinct region between the latitude of the southern tip of Timor (11°S) and 7°S, where the XBT line passes through the Savu Sea (Fig. 1). Both to the north and south of these latitudes, lower-frequency variability (ENSO band) dominates temperature variability in the thermocline, suggestive of a shadow effect on westward propagating energy by the island of Timor, or the effect of higher frequency variability arriving from the Indian Ocean to the west.

5. Relation to remote wind forcing

As was shown by Meyers (1996) the interannual variability along IX1 is strongly related to the large-scale wind stress fields over the Pacific and Indian Oceans. The nature of the relation is qualitatively accounted for by linear wave theory that predicts that equatorial Kelvin and Rossby waves excited by variation in large-scale wind stresses excite coastal waves, which carry the signal to higher latitudes. Here we examine the details of the relationship between these remote wind forcing and sea level and temperature variability in the region at seasonal and longer time scales.

As recognized by Clarke and Liu (1994), zonal wind stress along the equator of the Pacific and Indian Oceans is significantly correlated in the ENSO band, such that during ENSO the weakened Pacific trades are often accompanied by easterly wind anomalies along the Indian Ocean equator. Figure 11a shows the 4-month low-passed anomaly from the mean seasonal cycle of equatorial wind stress in the two basins, revealing the 180° out of phase relation (the correlation is −0.6). However, while Pacific winds are dominated by the ENSO band, the Indian Ocean winds have much more energy at higher frequencies with spectral peaks near periods of 3 yr and between 1 and 2 yr, which is evident in the energy-preserving spectra in Fig. 11b. This higher-frequency “signature” of equatorial Indian Ocean winds versus the lower-frequency energy of the Pacific winds is crucial in distinguishing the impact of these two sources of remote energy on the ocean.

To identify the subsurface thermal variations as a response to wind forcing, a lagged partial regression is performed at each point along the XBT lines, such that
i1520-0485-34-5-1232-e1
where the 4-month low-passed seasonal temperature anomaly T, at time t, is expressed as the sum of three wind indices τ̂i at lag Li with coefficients ai. The wind indices used are the zonal wind averaged along the Pacific equator (note the reverse sign so that positive wind anomalies give positive temperature anomalies), the along path winds on a path extending along the coast from Sunda Strait to the equator and then along the equator to Africa, and a local wind index. These indices (Pacific, Indian, and local) are denoted by i = P, I, and L, respectively. Each wind index had the seasonal cycle removed, was low-pass filtered at 4 months, and normalized before the partial regression was performed. Thus coefficients in (1) can be interpreted as the temperature change associated with a single standard deviation change in the wind index.

All possible lags within ±18 months were searched to find the combination that accounted for the maximum variance at each grid point. Choice of a single “local” wind index for each XBT line was ambiguous, but several were experimented with, such as the total offshore Ekman transport between Fremantle and the equator, the averaged wind stress curl between the XBT line and the coast, and the average zonal wind over the Banda Sea. We found that none of the choices of local wind index absorbed much of the observed variability. The total amount of low-passed variance accounted for by the lagged partial regression is quite high along the sections (Fig. 12): around 60%–80% throughout the thermocline and around 40%–50% at depth and within the seasonal thermocline. The lagged partial regression also accounts for 60%–80% of surface dynamic height variability along each section (upper panels in Fig. 12).

To test the skill of the lagged partial regressions, several simulations were performed using synthetic datasets consisting of the indices mentioned above combined at various amplitudes and lags, and with random noise added to them. We generally found that fitting (1) retrieves the correct lags and signal amplitudes where the individual signals are 25% or more of the total variance.

Figures 13–15 show the temperature coefficients and lags from (1) along the IX1, PX2, and IX22 sections, respectively. Dynamic height relative to 750 m from each of the XBT lines was also analyzed using (1) and the results plotted above each section. A similar calculation was also performed on altimetric sea level for the period 1992–2001. A mean seasonal cycle was removed from sea level anomalies at grid points over the Pacific and Indian Oceans before the resulting series were low-pass filtered as for XBT temperature. The percent of variance fitted, altimeter height coefficients, and lags capturing maximum variance using (1) are plotted in Figs. 16–18.

Since the wind indices used in (1) are normalized, the coefficients plotted are in real physical units (e.g., degrees Celsius for Figs. 13–15, centimeters for Figs. 17–18). Hence, the response during a peak in the wind index (normalized amplitude of 3) is then given by 3 times the coefficient plotted.

6. Southeast Indian Ocean: IX1

Along IX1, Pacific coefficients are positive and high in the main thermocline (above 250 m) across the section except near Sunda Strait and a distinct region below 120 m near 15°S (Fig. 13). The latter is occupied by a very strong lateral salinity front between South Indian Subtropical Water and Indonesian Throughflow water (Fig. 12, salinity is overcontoured in white). As this front undergoes meridional excursions (Wijffels et al. 2002), one would expect that in its vicinity temperature will be a poor proxy for density, which in turn would degrade our ability to measure the dynamic signals. Indeed, we find that the partial lagged regression as a whole performs poorly in the region of this front (Fig. 12).

Partial lag coefficients are largest and positive near either end of the IX1 section. The sense of the partial regression is that a large warming and high sea levels off the Western Australian coast are associated with easterly wind anomalies along the Pacific equator, a result obtained previously by Meyers (1996). Downwelling equatorial Rossby waves excited by the wind anomalies in the central and western Pacific propagate westward and excite coastally trapped waves off Papua New Guinea, which then propagate anticlockwise around Australia.

The lags that we find associated with the warming off Western Australia have a distinct and coherent spatial structure (Fig. 13b): they increase with distance offshore (and northward) to 15°S and then decrease north of 15°S. Small lags are found where the ship tracks touch the Western Australian shelf break (∼24.5° and 32°S) where fast coastally trapped waves have propagated the signal from the western equatorial Pacific.

Two competing effects are at work to form the pattern of lags along IX1: free Rossby wave speeds increase equatorward while the zonal distance between the IX1 line and the coast is larger going equatorward. The effect of distance from the coast produces the increasing lags with latitude until around 15°S where the faster wave speed with decreasing latitude effect dominates to produce a decrease in lags toward the equator. Using the zonal distance to the coast and the deduced lags from Fig. 13, we have calculated the implied phase speed as a function of latitude for the remotely driven Pacific Rossby wave (Fig. 19). For comparison, we calculated the free long Rossby wave speed as
i1520-0485-34-5-1232-eq1
where the gravity wave speed c was taken from Chelton et al. (1998), f is the Coriolis parameter, and β is its meridional derivative. While the implied speeds are slightly smaller than theory predicts, they are in rough agreement and have the same tendency to increase toward the equator.

Equatorial Indian Ocean interannual wind anomalies only produce a significant response along IX1 off Sunda Strait (Fig. 13c), as one would expect based on the excitation of equatorial Kelvin waves and their transmission into coastally trapped waves off Sumatra and Java, as hypothesized by Clarke and Liu (1994). This signal is shallow—only reaching down to 250 m. Free Kelvin waves disperse eastward along vertical ray paths with slopes σ/N while long Rossby wave energy dives westward with slopes of (2l + 1)σ/N, where N is the Brunt–Väisälä frequency, σ is the wave frequency, and l is the meridional mode number (McCreary 1984). Hence, high-frequency energy dives more steeply than low-frequency energy and interannual signals are confined to near the main thermocline (∼100 m depth).

The vertical ray theory also explains the results shown in Figs. 7 and 8 where annual and semiannual energy dominates subthermocline depths off Sunda Strait, while ENSO-band variability is confined to the upper thermocline. Simple ray-tracing theory (following Kessler and McCreary 1993) along the equatorial and coastal waveguides, starting at the thermocline depth of 100 m and 60°E (mid-Indian basin), suggests semiannual energy will dive a farther 300 m (relative to the starting depth) off Sunda, only 200 m for annual frequencies and barely 20 m for the ENSO band (5 yr). Waves originating at 170°W in the equatorial Pacific can dive to well below 1000 m for the semiannual, 300 m for the annual, and barely 30 m for the ENSO band. Thus the upward propagating annual wave seen off the Arafura shelf break (Fig. 7b) is undoubtedly remotely forced in the central Pacific. However, a discrepancy with the linear ray theory is found in the thermocline response off the western Australian coastline, which extends to 300-m depth, much deeper than the ray paths would suggest was possible. Likely, the energy is following the thermocline as it deepens westward and poleward along the ray path from the central Pacific.

The local wind index used in this case is the cross-shelf Ekman flux between Fremantle and the equator along the Western Australian coast. Positive correlations between temperature and this coastal flux are found between 20° and 30°S in the seasonal thermocline at zero lag (Figs. 13e–f), suggesting that either a coastal upwelling signal is generated or simple wind cooling (stronger southerlies gives cooling) may be operating along the southern coast of Western Australia.

7. Banda Sea: PX2

Equatorial Pacific wind energy accounts for much of the thermocline variability in the Banda Sea (Fig. 14a), as noted by Ffield et al. (2002), with the largest response off the Arafura shelf break, consonant with the coastal-trapped wave pathway discussed above. Lags are consonant with Rossby wave radiation westward across the Banda Sea, though they are noisier along this line in comparison with IX1, likely because of poorer time sampling (monthly vs fortnightly), larger internal tide noise, and the complex topography along the line (Fig. 1).

Indian Ocean equatorial wind energy also clearly penetrates through Lombok Strait into the Banda Sea (Fig. 14c), dominating thermocline variability at the Java Sea shelf break. The near zero lag (Fig. 14d) confirms that fast coastally trapped waves are the propagation mechanism here also. Our results show that the event documented by Sprintall et al. (2000) can be generalized to lower frequencies (periods > 4 months) and that Indian Ocean equatorial wind energy routinely propagates into the internal seas. A second zone of Indian Ocean wind energy occurs along the PX2 line near 122°–127°E where the XBT line passes by the second major strait into the Banda Sea, Ombai Strait, confirming propagation across Lombok Strait to the Savu Sea and eastward to Ombai Strait.

Two local wind indices were tried along this section: zonal wind stress averaged over the Banda Sea and upwelling winds off Arafura. Neither of these could account for much of the temperature variance (Fig. 14e).

8. Western Australia to Pacific equator: IX22

The response to equatorial Pacific winds along IX22 is largest north of 6°S, with two lobes of maximum response centred near 4°S and 5°N (Fig. 15a) with lags between 0 and 1 months across most of the thermocline, consonant with the arrival time off Western Australia of between 1 and 2 months in the thermocline. Here we can see that the depth of the Pacific wind signal follows the thermocline down towards the south. Interestingly, lags along this line suggest a lead relation between mixed layer temperatures and zonally averaged equatorial Pacific winds (Fig. 15b). This lead relation applies north of 4°S, a region where the partial regression accounts for up to 70% of SST variations. Indeed, a simple correlation of SST at the equator along IX22 shows that it leads the Pacific zonal equatorial wind stress by 3 months with a correlation of 0.75 (where the 95% significance level is 0.21). This leading behavior penetrates into the thermocline off Mindanao.

Indian Ocean wind coefficients along IX22 are only large in a small region between 10° and 7°S where the XBT line passes through the Savu Sea (Fig. 1), confirming the results from PX2 that some of the Java coastally trapped waves must pass by Lombok and reach Ombai via the Savu Sea. Interestingly, the “Pacific” signal is weaker in this region, suggesting that the island of Timor blocks the Pacific waves from reaching the Savu Sea.

9. Regional sea level response

While the XBT lines reveal the response to the wind changes at depth, a larger spatial context can be derived from measurements of sea level from satellite altimeters. Using (1) we can account for up to 90% of 4-month low-passed seasonal anomalies of sea level height since late 1992 throughout the throughflow region and over large parts of the Pacific and Indian Oceans (Fig. 16). Again, we find that various estimates of “local” winds are unable to capture much variance (and hence are not shown).

The coefficient and lag of sea level for the Pacific wind index (Fig. 17) shows high sea levels occur in the western Pacific in response to easterly wind anomalies, and much of this signal “leaks” along the New Guinea– Irian Jaya–Australian waveguide down to Fremantle, with some suggestion of offshore radiation of energy north of 22°S. Part of this signal also continues around the southwest tip of Australia and along the south coast. Lags off Western Australia are between 0 and 2 months as suggested by the XBT data. The pattern of lags off northwest Australia agrees somewhat with the dynamic height from the XBT data, which was due to Rossby wave radiation from the coast. However, in sea level, the maximum lags occur near 18°S, not 15°S as found on IX1. This difference is likely due to variability occurring below the deepest reach of the XBT lines or the shorter length of the altimetric dataset. In the subtropics, the Pacific wind response dies out near 100°E, in agreement with Masumoto and Meyers (1998) and Potemra (2001) who found that, near this longitude, regional Ekman pumping along Rossby wave characteristics begins to dominate over that generated at the eastern boundary.

Interestingly, there is also a strong response to the Pacific winds northeast of Madagascar with lags that suggest a southward propagating response, either due to local wind changes that correlate with ENSO or expressions of later arrival of Rossby waves at higher latitudes. However, the relatively short satellite altimetric record is likely dominated by the very large 1997/ 98 ENSO event and strong Indian Ocean “Dipole” during the same years (Saji et al. 1999). Westward Rossby wave propagation is also evident in the Coral Sea.

The response in sea level to Indian Ocean winds (Fig. 18) agrees with the XBT results: westerly wind anomalies giving high sea levels in the eastern Indian Ocean equatorial and coastal waveguides. This signal also penetrates into the western portion of the Banda Sea, Savu Sea, and Makassar Strait at near-zero lag. The propagation of the equatorially excited waves around the Bay of Bengal is also evident in this plot, as is the radiation of energy westward from the eastern boundary off Sumatra and Java. Off Java, the lags again have a Rossby wave structure, with faster phase speed at lower latitude (Fig. 19). The western Indian Ocean response to the equatorial zonal wind anomalies is also evident with lower sea level along 8°S at near-zero lag.

10. Summary and discussion

Upper-ocean temperature variability within the Indonesian seas and southeast Indian Ocean has been described based on repeat high-quality XBT data collected since 1983. Besides strong annual variability in the seasonal thermocline and in the thermocline off the Arafura and Java shelf breaks, interannual variability dominates much of the thermocline variability elsewhere.

A lagged partial regression technique reveals that interannual temperature and sea level variability in the southeast Indian Ocean and Indonesia seas is largely driven by remote equatorial winds, as predicted by Clarke and Liu (1994). While the dominating effect of ENSO, through its modulation of Pacific equatorial winds and associated equatorial Rossby wave response, has been proposed and recognized before (Clarke and Liu 1994; Meyers 1996; Potemra 2001; Ffield et al. 2000), we have unambiguously identified their presence in ocean observations, both in the coastal waveguides and as free Rossby waves propagating westward into the southeast Indian Ocean. We also clearly demonstrate that interannual equatorial Indian Ocean wind variability contains different time scales than that found in the Pacific. Indian Ocean wind energy penetrates along the Nusa Tenggara island arc and into the internal Indonesian seas, controlling both sea level and thermocline depth along the eastern Java Sea shelf break and within Makassar Strait. The deduced wave pathways are shown in Fig. 20.

Our results suggest that Indian, and not Pacific, winds may have affected transport changes and thermocline variability observed in Makassar Strait during the Arlindo mooring deployments (Gordon et al. 1999). During the deployment, from November 1996 to July 1998, both the Pacific and Indian equatorial winds were varying in phase over low frequencies but not at higher frequencies (such as the semiannual). Sprintall et al. (2000) clearly show that a higher frequency event (semiannual time scale) can reverse currents and affect temperature at Makassar Strait, and our results show that this can be generalized to lower frequencies and is routine. Since the 1997/98 event was one in which both remote wind sources varied in phase at lower frequency (see Fig. 11a), a longer time series of direct transport estimates in Makassar Strait is needed to determine what controls transport variability. Along with the Molcard et al. (2001) and Hautala et al. (2001) results, it is clear that some Kelvin wave energy can “jump” straits such as Lombok to continue into the Savu Sea and to reach Ombai Strait.

The Pacific-driven coastally trapped waves propagating down the Western Australian coast excite free westward-propagating Rossby waves, which are detectable out to near 100°E near 15°S, where we believe that local wind forcing has grown large enough to “swamp” the eastern boundary signal (Masumoto and Meyers 1998; Birol and Morrow 2001). Potemra (2001) finds a similar mechanism working at annual time scales. This Pacific “wave” travels quite far along the Australian coast and can be detected in altimetric sea level as far as the south coast of Australia to the southern tip of Tasmania, confirming the Pariwono et al. (1986) result gleaned from coastal sea level. Our results also suggest an intriguing lead relation between temperature near the equator in the Celebes Sea and off Mindanao and zonal Pacific equatorial winds. The effect of these remote wind energies on the transport through the Indonesian seas as determined from these XBT lines is explored in an upcoming paper.

Acknowledgments

We thank Anne Gronell for maintaining the XBT archive and quality controlling the data and Rick Bailey, Peter Jackson, and Lisa Cowen at the CSIRO/Bureau of Meteorology Joint Australian Facility for Ocean Observing Systems for managing the XBT network. The work by volunteer observers on merchant ships is gratefully acknowledged.

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Fig. 1.
Fig. 1.

(a) Locations of XBT profiles used in the study. The names of the primary lines and ports are indicated. Water depths shallower than 200 m are shaded gray. Inset shows place names in the Indonesian archipelago

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 2.
Fig. 2.

Data distribution along the three XBT lines as a function of position and time: (a) IX1, (b) PX2, and (c) IX22.

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 3.
Fig. 3.

(a) The mean temperature along the IX1 section, (b) the standard deviation of the mapped temperature on depth surfaces along IX1, and (c) the standard deviation of the residuals of the mapping procedure

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 4.
Fig. 4.

As for Fig. 3 but for the PX2 section.

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 5.
Fig. 5.

As for Fig. 3 but for the IX22 section.

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 6.
Fig. 6.

Temperature as a function of time and depth along IX1 at two latitudes: (a) off the coast of Java at the Sunda Strait and (b) off the coast of Western Australia near 25°S

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 7.
Fig. 7.

Temperature as a function of time and depth along PX2 at two longitudes: (a) off the Java shelf break near 116°E and (b) at the Arafura Shelf break near 133°E

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 8.
Fig. 8.

The percentage of mapped temperature variance in four spectral bands along IX1: (a) intraseasonal (periods less than 0.4 yr), (b) seasonal (periods between 0.4 and 1.1 yr), (c) quasi-biennial band (periods between 1.1 and 3.2 yr), and (d) interannual (periods longer than 3.2 yr)

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 9.
Fig. 9.

As for Fig. 8 but for PX2

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 10.
Fig. 10.

As for Fig. 8 but for IX22

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 11.
Fig. 11.

(a) Time series of interannual anomalies of along path wind averaged along the equatorial and coastal waveguides in the Indian and Pacific Oceans (see text). (b) Variance-preserving spectra of the wind series in (a). The line frequencies plotted are, from left to right, for 10, 5, 2, 1, and 0.5 yr

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 12.
Fig. 12.

The percentage of total variance accounted for by lagged partial regression. Overplotted in white is the mean salinity along the lines with a contour interval of 0.1. Above each plot is the fitted percent variance of dynamic height relative to 700 m. (a), (b), and (c) are for IX1, PX2, and IX22, respectively

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 13.
Fig. 13.

(a), (c), (e) Fitted temperature coefficients (°C) and (b), (d), (f) lags in months for the wind indices along IX1: (a), (b) Pacific wind index; (c), (d) Indian wind index, and (e), (f) the local wind index, here the cross-shelf Ekman flux between Fremantle and the equator. Blank areas in the top panels reflect the presence of topography while blank areas in the plots on the bottom are where the total variance accounted for is less than 30%. Above the main panels is the coefficient in meters or lag in months of surface dynamic height relative to 700 m based on the XBT data

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 14.
Fig. 14.

As for Fig. 13 but for PX2. The local wind index used is the zonal wind stress averaged over the Banda Sea

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 15.
Fig. 15.

As for Fig. 13 but for IX22. The local wind index used is as for Fig. 13

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 16.
Fig. 16.

(a) Standard deviation of low-frequency anomaly of sea surface height (cm), and (b) percent variance captured by the lagged linear partial regression model

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 17.
Fig. 17.

(a) Coefficient (cm) and (b) lag in months of the low-frequency anomaly of SSH for Pacific wind index

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 18.
Fig. 18.

As for Fig. 17 but for the Indian Ocean wind index

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 19.
Fig. 19.

Theoretical zonal Rossby wave speeds based on the atlas of Chelton et al. (1998) (solid line) and the zonal phase speed deduced from the partial regression fits to the Pacific Ocean wind index (see text)

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

Fig. 20.
Fig. 20.

Schematic of remotely forced wave pathways into the throughflow region. Thin broken lines show the waveguide from the equatorial Indian Ocean, with energy spreading into the internal seas through both Lombok and Ombai Straits. Solid black arrows show the pathways for equatorial Pacific wind energy traveling down the Papuan/Australian shelf break and radiating westward-propagating Rossby Waves into the Banda Sea and South Indian Ocean

Citation: Journal of Physical Oceanography 34, 5; 10.1175/1520-0485(2004)034<1232:AIOOWV>2.0.CO;2

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