Poseidon altimetry data

Dsri*588*Jayashree*Venkatachala*BG

Deep-Sea Research I 47 (2000) 681}708

Mesoscale variability in the South China Sea from the TOPEX/Poseidon altimetry data Liping Wang!,*, Chester J. Koblinsky", Stephan Howden! !Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20742, USA "Ocean and Ice Branch, Laboratory for Hydrospheric Processes, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA Received 17 February 1998; received in revised form 29 March 1999; accepted 25 June 1999

Abstract Using wavelet transform we studied the mesoscale variability in the South China Sea (SCS) by analyzing 5-yr (October 1992 to August 1997) TOPEX/Poseidon (T/P) altimetry data. Our analysis suggests that mesoscale variability inside the SCS is weaker than that outside the SCS in the Kuroshio. It is found that despite the large temporal variation in the mesoscale variability, there are two narrow bands of signi"cant mesoscale variability north of 103N throughout much of the 5-yr period. The stronger one lies along the western boundary, while the weaker one is oriented in a southwest-to-northeast direction across the central SCS. In the rest of the SCS, the mesoscale variability is much weaker. In light of the numerical simulation by Metzger and Hurlburt (1996, Journal of Geophysical Research, 101, 12,331}12,352) and an XBT section along 153N, the broad characteristic structure of the mesoscale variability indicates that the large-scale mean circulation in the SCS is primarily a cyclonic gyre north of about 103N. In addition to the mesoscale variability, analysis of both the T/P and the XBT data indicates that there also exists signi"cant intra-annual variability within similar geographic locations. The intra-annual variability is found to be primarily a subsurface feature with a very weak surface signature. ( 2000 Elsevier Science Ltd. All rights reserved. Keywords: South China Sea; Mesoscale variability; Large-scale circulation

1. Introduction Although the South China Sea (SCS) is one of the largest marginal seas, it is sparsely sampled. The XBT data discussed by Wang et al. (1999) in the study of

* Corresponding author. NASA Goodard Space Flight Center, Code 971, Greenbelt, MD 20771, USA. Fax: 001-301-614-5644. E-mail address: [email protected] (L. Wang) 0967-0637/00/$ - see front matter ( 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 7 - 0 6 3 7 ( 9 9 ) 0 0 0 6 8 - 0

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interannual variability in the SCS do not have adequate temporal resolution to describe the mesoscale variability, if any, inside the SCS. There have been some very recent studies (Li et al., 1998; Chu et al., 1998) using in situ data to document mesoscale activity (including possible rings) in both the northern SCS near the Luzon Strait and central SCS. Because of the Kuroshio Intrusion (Nitani, 1972; Shaw, 1989; Shaw et al., 1996), there could be signi"cant mesoscale variability in the northeastern SCS near the Luzon Strait, which may either be generated in the Kuroshio outside the SCS and subsequently move westward into the SCS, as demonstrated in the recent study by Li et al. (1998), or be locally generated through instability of the background mean #ow, i.e., the Kuroshio Intrusion. As in the cases with open oceans (e.g., Hurlburt et al., 1996), numerical models are important tools with which we can presumably explore the mesoscale variability in the SCS. So far, numerical simulations have been concentrated mainly on the largescale circulation in the SCS. Shaw and Chao (1994) and Chao et al. (1996) carried out a series of numerical simulations of the circulation in the SCS. But their emphasis was on large-scale circulation, and their models do not have enough spatial resolution to resolve the mesoscale variability inside the SCS. Metzger and Hurlburt (1996) (MH96) also carried out a series of numerical simulations of the large-scale circulation in the SCS and discussed the coupling of large-scale circulation inside the SCS and outside in the western tropical Paci"c Ocean. Again their emphasis is on large-scale circulation, although some of their model runs do resolve mesoscale variability. The MH96 numerical studies predict that there exists a cyclonic gyre and an associated boundary current around the gyre north at about 103N. South of 103N the circulation is much weaker and less organized. In addition, the MH96 simulation indicates that near the southern boundary of the Luzon Strait there is an in#ow from the Philippine Sea to the SCS, while near the northern boundary there is an out#ow from the SCS to the Kuroshio, grossly similar to Nitani's (1972) observation (it is worth pointing out that the MH96 simulation refers to the long-term mean, while Nitani's observation refers more to a synoptic view, which may be heavily a!ected by eddies, as Nitani himself pointed out). The dilemma is that we do not have enough in situ data to `verifya the broad basic characteristics of the large-scale mean circulation simulated by the MH96 general circulation model (GCM). In open oceans, altimetry data from the Geosat and the TOPEX/Poseidon (T/P) altimetry missions have proven to be the basis for advances in understanding of the basic characteristics of mesoscale variability near strong boundary currents (e.g., Qiu, 1995), or the spatial characteristics of the mesoscale variability have been used to infer and discuss the large-scale mean circulation in light of the close relation between large-scale background mean circulation and mesoscale variability (e.g., Gille, 1994). Fu and Cheney (1995) provided a comprehensive review about progress in the study of mesoscale variability that has been made through utilizing the Geosat and T/P altimetry data. Altimetry data have also been used by Strub and James (1995) to study mesoscale variability near the California coast and by Jacobs and Leben (1990) to study mesoscale variability in the Gulf of Mexico. Shaw et al. (1999) studied the large-scale low-frequency variability in the SCS using T/P data. Attempts have also been made to characterize the temporal modulation of the mesoscale variability, e.g.,

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Qiu (1995) and Adamec (1998). Given the inadequate sampling of in situ data, the T/P altimetry data might be the only data set with which we can obtain some basic description about the broad characteristics of the mesoscale activity in the SCS and in turn determine the large-scale mean circulation in the SCS. There are two objectives for the present study. First, by analyzing 5 yr of T/P altimetry data we want to document the broad basic characteristics of the mesoscale variability in the SCS. The "eld observation of Li et al. (1998) and the synoptic AXBT survey of the SCS of Chu et al. (1998) suggested that there is signi"cant mesoscale variability in the SCS. Because of a lack of long-term in situ measurements, our present information about the large-scale mean circulation in the SCS remains rather sketchy as illustrated in the discussion by Huang et al. (1994). The second objective, which is actually the main objective of this study given our currently rather sketchy knowledge about the large-scale mean circulation in the SCS, is to infer, in light of the MH96 GCM simulation and limited XBT data available, the broad basic characteristics of the large-scale mean circulation through the implicit relationship between mesoscale variability and large-scale background mean #ow, (assuming that instability of the large-scale mean circulation is the primary process through which the mesoscale variability is generated (Tai and White, 1990).

2. Data processing 2.1. T/P altimetry data The sea level height data collected by the T/P altimeter from cycle 2 to 179 (October 1992}August 1997) are used in this study. The data are extracted from the Ocean Path"nder Data Set at Goddard Space Flight Center (http://neptune.gsfc.nasa.gov/ ocean.html). Standard environmental corrections are applied (see Koblinsky et al. (1997) for more detailed discussions about the environmental corrections). After all the environmental corrections are made, a mean sea level is computed at each grid point for the 178 repeat cycles. If the number of observations at a point is less than 75, all data at that point are discarded to avoid undersampled mean sea level. The mean sea level is then removed from each observation, leaving the residual sea level as the database for our present study. To preserve the spatial and temporal characteristics we work on the along-track collinear data. Data along tracks 64 and 82, shown in Fig. 1, are chosen to study the mesoscale variability near the mouth of the SCS (Luzon Strait) and its immediate neighborhood in the western North Paci"c. Data along tracks 44, 6, 95, 57, and 19, as shown in Fig. 1, are chosen to study the mesoscale variability in the interior SCS. The mesoscale signal is extracted in the following way: We "rst remove the seasonal cycle by "tting the data with an annual and a semi-annual harmonic and subtracting it (because the seasonal variability is likely mostly related to steric height e!ect driven locally by air}sea heat #ux, it is not dynamically interesting). After removal of the seasonal cycle, a strong signal with a period of about 60 d is still present. As shown by Schlax and Chelton (1994) tidal aliasing can induce strong 60-d oscillation. In

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Fig. 1. The bottom bathymetry (unit: km) of the SCS with the geographical locations of tracks 64 and 82 near the Luzon Strait and tracks 44, 6, 95, 57, and 19 inside the SCS. Locations of the core of the northern and southern bands of the strong mesoscale variability near the western boundary and in the central SCS are shown in the "gure as circles. Also shown in the "gure are the two XBT sections and locations (represented by the big X's) of the four XBT clusters.

addition, atmospheric forcing at the 60-d frequency band can also drive a strong 60-d oscillation. A wavelet analysis, not shown here, of the surface wind near (1153E, 123N) in the central SCS, randomly chosen from NCEP wind reanalysis, reveals the existence of a strong signal at the 60-d frequency band, which itself undergoes large temporal modulation. Lack of a detailed knowledge about how the 60-d atmospheric oscillation drives the 60-d sea level height variability prevents us from cleanly separating the part of the 60-d sea level oscillation that is caused by tidal aliasing from the part that is driven by the 60-d atmospheric oscillation (it is worth noting that the dynamics of the atmospherically driven 60-d oscillation are fundamentally di!erent from those of the mesoscale variability generated through instability). So to be conservative we simply remove the sea level oscillation with a period around and shorter than 60 d with a simple 7-point Hanning "lter (1!J2/2, 1, 1#J2/2, 2, 1#J2/2, 1, 1!J2/2) in this study, that is, we remove both the tidally aliased and the atmospherically driven 60-d oscillation. In the following, `sea level heighta refers to the de-annualized and low-pass "ltered sea level data. The wavelet spectra shown in

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Figs. 2, 4, and 7 suggest that the simple Hanning "lter very e$ciently removes the 60-d oscillation. The low-pass "ltering also helps to suppress the measurement error. Tsaoussi and Koblinsky (1994) estimated that the measurement error for the raw altimetry data with the mean removed is about 3}4 cm (rms). The 7-point low-pass "ltering reduces the measurement error to roughly 2 cm, assuming the measurement errors in di!erent cycles are uncorrelated.

2.2. XBT data To help us infer the main features of the large-scale mean circulation in conjunction with the MH96 GCM simulation and thus explain the broad spatial characteristics of the mesoscale variability, we also analyze the XBT data from 1967 to 1995 (there is much less XBT data, not shown here, in the late 1970s and early 1980s than in the rest of the period) available through NODC. Only those that extend beyond 300 m depth are retained for the present analysis. The general processing of the XBT data can be found in Wang et al. (1999). Limited by the peculiar spatial XBT data distribution, we choose to discuss the mean temperature structure along two XBT sections shown in Fig. 1 (XBT data are much more densely clustered around these two XBT sections than in the rest of the SCS). The "rst one, a meridional section, crosses the Luzon Strait at 120.83E, which is selected to illustrate the main feature of the westward intrusion of the Kuroshio into the SCS. The second one, more or less a zonal section along 153N, is selected to illustrate the prevailing northward interior #ow in the northern SCS, which suggests a well-organized cyclonic gyre in the northern SCS in light of the MH96 GCM simulation. For the meridional XBT section at Luzon Strait, the data are binned into monthly mean series with a bin size of 23]0.53, while for the zonal section at 153N the data are binned into a monthly mean series with a bin size of 0.53]23 (binning the XBT data into a monthly base before computing the mean helps to reduce the potential bias towards a particular month caused by the uneven XBT sampling through the year). Only the 29-yr mean is discussed in this study. To illustrate the vertical structure of the intra-annual variability, we choose three clusters to crudely represent the intra-annual temperature variability in the upper ocean (0}300 m) inside the SCS: Saigon (1103E, 93N) to represent the western basin; Manila (1193E, 14.53N) to represent the eastern basin; and Kaohsiung (119.53E, 20.53N) to represent the northern basin near the mouth of the SCS. We also choose a spot outside the SCS in the Kuroshio at (122.53E, 21.53N) to represent variability there. The locations of the 4 XBT clusters are shown in Fig. 1 as the big X's. Temporally, the XBT data are binned into a monthly mean time series with a bin size of 33]33. Thus, we obtain four depth-versus-time data series to study the intra-annual #uctuation in the upper ocean temperature. Similar to the processing of the altimetry data, the seasonal cycle is removed. Around Saigon, a continuous monthly-mean time series exists from 1970 to 1980. At Manila, there is a continuous monthly-mean time series from 1967 to 1976. Around Kaohsiung, a continuous monthly-mean time series exists from 1967 to 1981. And at Kuroshio, there exists a continuous monthly-mean time series from 1967 to 1979.

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2.3. Wavelet transform To analyze the mesoscale variability, we use the wavelet transform. Since its introduction by Morlet (1981), wavelet transforms have been widely used in di!erent "elds (see Farge, 1992 for a review). The mother wavelet chosen for the present study is the Morlet wavelet (Farge, 1992)

G

H

2p2 t"exp ! t2!i2pt . k2 t For the present calculation we choose k "6 (which basically determines the resolut tion of the wavelet transform). It is found that the wavelet transform is a very e!ective tool to study the mesoscale variability pulsation and modulation in the Kuroshio Extension region (e.g., Wang et al., 1998c). Gu and Philander (1995) presented a very illustrative discussion about how to apply the wavelet transform to analysis of oceanographic time series. Because of the edge e!ect inherent in the wavelet transform, the "rst and last 3.5-months worth of wavelet transform and the corresponding variance calculations are discarded in all the "gures shown in the following discussions. Mesoscale variance is de"ned as the integration of the square of the wavelet spectrum within the frequency band from 2 to 6 cycles/yr, which measures the overall intensity of the mesoscale variability (see Gu and Philander (1995) for a more detailed discussion about computing the variance). Likewise, intra-annual variance is de"ned as the integration of the square of the wavelet spectrum within the frequency band from 1 to 2 cycles/yr.

3. Mesoscale variability at the mouth of the SCS 3.1. Track 82 Shown in Fig. 2(a) are the sea level height #uctuations at (121.43E, 21.73N) and (123.23E, 17.53N), which are marked in Fig. 1, along track 82 at the mouth of the Luzon Strait. The "rst one (solid line) represents the sea level height #uctuation near the northern boundary of the Luzon Strait, while the second one (dot}dashed line) represents the sea level height #uctuation near the western boundary of the Philippine Sea outside the SCS. Shown in Fig. 2(b) is the wavelet transform of the sea level height #uctuation at (121.43E, 21.73N). The "gure readily demonstrates that the sea level #uctuation is dominated by intra-annual variability with a frequency between 1 and 2 cycles/yr during most of the 4.5-yr period, which will be discussed in the appendix. Mesoscale variability (which in this study loosely refers to variability with frequency between 2 and 6 cycles/yr) has large temporal variation. It is weak (less than 4 cm yr/ cycle), except in the few months around March 1996. During those few months, strong mesoscale variability with a dominant frequency around 3 cycle/yr is present (dominant frequency is de"ned as the frequency at which the wavelet coe$cient

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Fig. 2. The sea level height #uctuations near (121.43E, 21.73N) (solid line) and (123.23E, 17.53N) (dashed line) (a), and their corresponding wavelet spectra (b) and (c), respectively. The unit in (b) and (c) is cm yr/cycle.

achieves its local maximum, which corresponds to the frequency at which the maximum growth occurs in a linear instability model). Fig. 2(b) really illustrates that both the intensity and spreading in the frequency band of the mesoscale variability near (121.43E, 21.73N) undergo large temporal variation. As shown in Fig. 2(b), residual 60-d oscillation tends to be rather weak throughout the 4.5 yr, except between December 1993 and June 1994. It suggests that the 60-d oscillation is very e!ectively suppressed by the 7-point Hanning "lter (the residual 60-d oscillation shown in

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Fig. 3. Temporal #uctuation of the mesoscale variance (cm2) along track 82 (a), and track 64, (b) at the mouth of the SCS.

Fig. 2(b) cannot be tidal alias because tidal alias should be a steady oscillation, which would have appeared in the wavelet transform, shown in Fig. 2(b), as a band with constant wavelet coe$cient at the period around 60 d). Fig. 2(c) shows that mesoscale variability near (123.23E, 17.53N) also has a high temporal #uctuation throughout the 4.5-yr period, similar to the situation at (121.43E, 21.73N). The dominant frequency is around 2.7 cycle/yr from June 1995 to June 1996. In addition, a ridge of relative high wavelet coe$cient extends to high-frequency band around July 1994. Fig. 2(b) and (c) essentially shows the variance distribution of the mesoscale variability in the entire mesoscale frequency band at two isolated locations. Although analysis at "xed points can illustrate the frequency modulation of mesoscale variability, it cannot capture the lateral movement of the mesoscale variability packet (part of the temporal #uctuation exhibited in Fig. 2(b) and (c) is caused by the lateral movement of the mesoscale variability packet along the track). To further depict the temporal and especially the spatial variations of the mesoscale variability, we compute along track 82 the total mesoscale variance. It is shown in Fig. 3(a). On average (not shown here), there are three bands of local maximum variance along track 82. The

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strongest one lies around 21.53N near the northern boundary; the second one lies around 17.53N in the Philippine Sea; and the third one lies around 19.73N inside the Luzon Strait. Fig. 3(a) indicates that the three bands are not constant in the 4.5 yr. Instead, there exist large temporal #uctuations in accordance with Fig. 2(b) and (c), especially the southern one in the Philippine Sea and the one around 19.73N inside the Luzon Strait. 3.2. Track 64 Shown in Fig. 4(a) are the sea level height #uctuations at (122.93E, 233N) and (121.73E, 20.13N) along track 64 at the mouth of the Luzon Strait, which are marked in Fig. 1. The "rst point represents the sea level #uctuation outside the SCS near the core of the Kuroshio, while the second one represents the sea level #uctuation near the southern boundary of the Luzon Strait. Fig. 4(b) shows the wavelet transform of the sea level height #uctuation at (122.93E, 233N). The "gure demonstrates that outside the SCS in the Kuroshio, much stronger mesoscale variability than that shown in Fig. 2 spans most of the 4.5 yr, much longer than those at both (121.43E, 21.73N) near the northern boundary of the Luzon Strait and (123.23E, 17.53N) in the Philippine Sea. Similar to that in the western Kuroshio Extension region (Wang et al., 1998c), the mesoscale variability undergoes large temporal #uctuation. The "gure shows that there are three outbursts of very strong mesoscale events in the "rst 3.5 yr (with wavelet coe$cient above 8 cm yr/cycle) with di!erent characteristic frequency. Fig. 4(b) also shows that in addition to the strong mesoscale variability, strong intraannual variability with a period of &8 months is also present. As a matter of fact, the intra-annual variability, in terms of wavelet coe$cient, is even stronger than or at least comparable to the mesoscale variability during part of the 4.5 yr, somewhat similar to the situation near (121.43E, 21.73N). In addition, the "gure indicates that by and large the mesoscale variability and the intra-annual variability are well separated in the frequency band. Compared to that at (122.93E, 23.03N) inside the Kuroshio, mesoscale variability is much weaker at (121.53E, 19.73N) inside the Luzon Strait, as shown in Fig. 4(c). But it is comparable to that near the northern boundary of the Luzon Strait, as shown in Fig. 2(b). During the 4.5 yr, there is only one period in which strong mesoscale variability is present. Unlike near (122.93E, 23.03N), intra-annual variability near (121.53E, 19.73N) is very weak. To further characterize the spatial and temporal modulation of the mesoscale variability along track 64, the total mesoscale variance is computed and shown in Fig. 3(b). On the average (not shown here), there are two bands of local maximum variance along track 64. The much higher one lies around 233N in the Kuroshio, while the weaker one lies around 203N inside the Luzon Strait. Fig. 3(b) indicates that the two bands are not constant in the 4.5 yr, as already suggested by Fig. 4. Instead, they have large temporal modulation. North of 213N there is a band of high mesoscale variability with its core near 233N outside the Luzon Strait inside the Kuroshio. The three outbursts of very strong mesoscale activity (variance higher than 20 cm2), as shown in Fig. 4(b), extend southward to 223N. Unlike north of 213N outside the Luzon Strait,

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Fig. 4. Same as Fig. 2, but at (122.93E, 23.03N) and (121.73E, 20.13N).

local mesoscale variability maximum inside the Luzon Strait is not very prominent during a substantial portion of the 4.5 yr, as already suggested by Fig. 4(c). During the 4.5 yr, there exist two periods in which the mesoscale variability is much stronger than its mean inside the Luzon Strait. Comparison of Fig. 3(a) with Fig. 3(b) suggests that there exist substantial di!erences between the mesoscale variability near the northern boundary of the Luzon

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Strait and that in the southern half. On average, mesoscale variability near the northern boundary is stronger than that in the southern half of the strait, although both are weaker than that near the core of the Kuroshio outside the SCS. The temporal modulation (or the sporadic nature) near the southern boundary is more pronounced than that near the northern boundary. Despite their large temporal #uctuations, the "gure indicates that through much of the 4.5 yr (putting Fig. 3(a) and (b) together), the two bands of local maximum mesoscale variance in the Luzon Strait are present and remain more or less separated from each other.

4. Mesoscale variability inside the SCS In the last section the mesoscale variability at the mouth of and in the small neighborhood outside the SCS is discussed. In this section we discuss the mesoscale variability inside the SCS using the sea level height observations along tracks 44, 6, 95, 57, and 19, shown in Fig. 1. 4.1. Mean mesoscale variability Shown in Fig. 5 is the mean variance of the mesoscale variability along the 5 tracks during the 4.5 yr. The spatial structure of the mesoscale variance shown in Fig. 5 has a few distinctive features. Near the northern boundary of each track (each track is somewhat arbitrarily cut o! at water depth of about 500 m, as illustrated in Fig. 1, because of concern about environmental corrections in shallow water regions), there is a local maximum with a width of &23. On track 44, which lies just west of the Luzon Strait, the northern maximum, &21 cm2, lies at (119.03E, 20.83N). On track 6, the northern maximum, &16 cm2, lies at (116.93E, 19.13N). On track 95, which about equally divides the SCS (regions deeper than 1000 m) into northeast and southwest halves, the northern maximum, &16 cm2, lies at (114.33E, 18.43N). On track 57, the westernmost track along the northern boundary of the SCS, the northern maximum, &7 cm2, lies at (111.63E, 18.03N). The general characteristic pattern is that the mesoscale variability weakens as we move further southwestward inside the SCS along the northern boundary of the SCS. The mesoscale variability, as measured by its variance, is more than 2 times stronger on track 44 near the mouth of the SCS than at the northwest corner of the SCS. Further west on track 19, the northern maximum, &16 cm2, lies at (110.93E, 13.23N) near the western boundary, which is much stronger than that along track 57. A notable common feature of the northern local maximum mesoscale variability is that they all lie in the region deeper than 2000 m as shown in Fig. 1. In addition to the northern band of local maximum mesoscale variability on each of the 5 tracks, there exists a second band of local maximum mesoscale variability in the central SCS. On track 19, the southern maximum, &18 cm2, lies at (111.33E, 11.63N). On track 57, the southern maximum (which spreads rather wide; therefore we choose the middle one), &11 cm2, lies at (113.33E, 13.83N). On track 95, the southern

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Fig. 5. Mean mesoscale variance (cm2) on tracks 19, 57, 95, 6, and 4 inside the SCS. Shown in the lower right corner are the northern local maximum variances (denoted by a w) and southern local maximum variances (denoted by a c). The variance of each track is shifted 10 cm2 from west to east.

maximum, &10 cm2, lies at (116.03E, 14.13N). On track 6, the southern maximum, &10 cm2, lies at (118.23E, 15.83N). The general pattern is that, in contrast to the situation along the northern boundary from track 44 to 57, the mesoscale variability within the southern band tends to decrease eastward from track 19 to 6. The southern band is oriented roughly from southwest to northeast in the central SCS, as shown in Fig. 1. The southern maximum, &21 cm2, on track 44 lies at (120.13E, 18.13N) along the northwest coast of the Philippines. On tracks 44, 6, and 95, the mesoscale variability near the northern boundary tends to be stronger than that within the southern band in the central SCS. In contrast, on the two westernmost tracks, 19 and 57, mesoscale variability within the southern band tends to be stronger than that near the western boundary. As shown in Fig. 5, except for along track 44 and southern tip of track 19, outside the two bands of strong mesoscale variability the mesoscale variability is generally not much higher than 4 cm2, comparable to the measurement noise of the sea level height variability.

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4.2. Variance yuctuation Fig. 5 shows only the mean mesoscale variability over the 4.5 yr. The indication from Fig. 3 about the mesoscale variability along tracks 82 and 64 near the mouth of the SCS is that the mesoscale variability inside the SCS is also likely to undergo large temporal modulation. The purpose of the discussion here is to demonstrate that despite large temporal #uctuation in their variance and signi"cant lateral movement in their core locations, throughout much of the 4.5 yr the two bands are present and well separated from each other, although sometimes one or both disappear, similar to the situation inside the Luzon Strait. Actually, the double-banded structure of the mesoscale variability along each track is better illustrated in the temporal evolution of the mesoscale variability, largely because the locations of the two bands, notably the southern band, have signi"cant lateral movement. As shown in Fig. 6(a), near the northern end around 20.83N on track 44 there is a band of high mesoscale variability (the contour level with value 7.5 cm2 is somewhat arbitrarily chosen to distinguish the regions with high mesoscale variance from regions with low mesoscale variance, considering the measurement noise is about 4 cm2), which is mostly continuous. The mesoscale variability within this band has large temporal modulation. In addition to the variance #uctuation, the center of the band also moves along the track. But as shown in the "gure, the movement of its core tends to be less than 13. Except for in the "rst 6 months, this band is generally con"ned to the north of 203N and extends laterally about 1.53. In a situation similar to that within the northern band, mesoscale variability within the southern band around 18.13N also undergoes signi"cant #uctuation. As shown in Fig. 6(a), the southern band is generally con"ned to south of 18.53N during most of the 4.5 yr. Near the northern end of track 6 around 193N there is a band of high mesoscale variability, as shown in Fig. 6(b), during most of the 4.5 yr. There are two episodes of much stronger (than its mean) mesoscale activity during the last 2.5 yr. In addition to the variance #uctuation, the center of this band also has noticeable movement. The center #uctuates between 18.83N and 20.03N. During most of the 4.5 yr the northern band is con"ned within a latitudinal band about 1.53 wide. Fig. 5 already indicates that the mesoscale activity in the southern band is substantially weaker than in the northern band on track 6. Fig. 6(b) shows that mesoscale variability in the southern band is weaker than that in the northern band during most of the 4.5 yr. The other reason that the mean in the northern band is higher than that in the southern band is that the central location of the southern band has a larger lateral movement. The presence and disappearance of the southern and northern bands of strong mesoscale activity do not appear to have any correlation. As shown in Fig. 6(b), the two bands are well separated throughout much of the 4.5 yr, despite their signi"cant lateral movements. As shown in Fig. 6(c), on track 95 there is a band of high mesoscale activity around 18.43N near the northern end, similar to the situations on both tracks 44 and 6. This band is more or less continuously present during the 4.5 yr. There are several episodes of strong (much stronger than its mean) mesoscale activity during the 4.5 yr. In addition to this variance #uctuation, the center of the northern band also moves,

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Fig. 6. Temporal #uctuation of the mesoscale variance (cm2) along track 44 (a), track 6 (b), track 95 (c), track 57 (d) and track 19 (e), inside the SCS.

though less signi"cantly than the northern bands on track 44 and 6. The southern band is less well organized than the northern band, and its core has a much more signi"cant lateral movement than its northern counterpart, which results in a rather broad mean southern band as shown in Fig. 5. As already suggested in Fig. 5, mesoscale variability near the northern end of track 57 is much weaker than that near the northern ends of the other four tracks. Fig. 6(d) shows that mesoscale activity near the northern boundary of track 57 is rather

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Fig. 6. (continued).

sporadic and weak throughout much of the 4.5 yr. Only in three brief periods can we observe signi"cant mesoscale activity (with variance higher than 7.5 cm2) within the northern band. Even though mesoscale variability within the northern band is weak, its geographic location is very stable during the 4.5-yr period. Fig. 6(d) indicates that the southern band is much broader than the northern band and spans &43, as already suggested by Fig. 5. The southern band is in fact split into two halves, which are always disconnected during the 4.5 yr (the location of the southern band is simply de"ned as the average of the two, shown in Fig. 1). The pattern in which signi"cant mesoscale activity is split into two bands during most of the 4.5 yr continues to track 19 near the western boundary of the SCS, as shown in Fig. 6(e), although the two bands on track 19 are much closer than those on the other four tracks. As shown in Fig. 6(e), the temporal variation of mesoscale variability in the southern band is quite di!erent from that in the northern band. 4.3. Frequency modulation In the last subsection we discussed modulation of the total mesoscale variance, which measures the overall intensity of the mesoscale activity without being concerned about the temporal #uctuation in the frequency composition of the mesoscale

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Fig. 7. Sea level height anomalies at (119.03E, 20.83N) on track 44 (dot}dashed line), (116.93E, 19.13N) on track 6 (solid line), and (111.33E, 11.63N) on track 19 (dot}dot}dot}dashed line) (a), and their respective wavelet transforms (b), (c), and (d), inside the SCS.

variability. In this subsection we wish to discuss brie#y the frequency modulation of the mesoscale variability at three locations. The purpose of this discussion is to demonstrate that frequency modulation of mesoscale variability inside the SCS exhibits general characteristics somewhat similar to those outside the SCS in the

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Kuroshio discussed in Section 3 and in the western Kuroshio Extension region discussed by Wang et al. (1998b), although both the mesoscale variability and the large-scale background mean circulation inside the SCS are much weaker. Shown in Fig. 7(a) (the dot}dashed line) is the sea level height #uctuation at (119.03E, 20.83N) near the center of the northern band on track 44. Its wavelet transform is shown in Fig. 7(b). The wavelet transform reveals a rich variety of frequency modulation near (119.03E, 20.83N). Two interesting features stand out from the wavelet transform. The "rst one is that during brief periods (&6 months) mesoscale variability can extend to a much wider frequency band than during the rest of the 4.5 yr, exhibiting signi"cant intermittent nature, similar to that found in the Kuroshio Extension region (Wang et al., 1998b), where the mesoscale variability is more than an order of magnitude stronger (in terms of variance). The second one, perhaps more obvious, is that although for most of the 4.5-yr period the mesoscale variability is dominated by a single mode (i.e., there is only one local maximum wavelet inside the mesoscale frequency band), during two brief periods (&6 months) the mesoscale variability is dominated by two modes (i.e., there are two local maximum wavelets inside the mesoscale frequency band). Linear instability calculation such as Kontoyannis's (1997) seems to suggest that there should be only one dominant mode in the mesoscale frequency band. The extra mode found near (119.03E, 20.83N) suggests that one of the two modes might be generated remotely. (The Li et al. (1998) study indicates that anticyclonic rings generated in the Kuroshio can move westward to inside the northern part of the SCS.) Shown in Fig. 7(a) as the solid line and the dot}dot}dot}dashed line are the sea level height anomalies at (116.93E, 19.13N) near the center of the northern band on track 6 and (111.33E, 11.63N) near the center of the southern band on track 19 farther inside the SCS. Their wavelet transforms are shown in Fig. 7(c) and (d), respectively. Most notably di!erent from that at (119.03E, 20.83N) on track 44 is that at both (116.93E, 19.13N) and (111.33E, 11.63N) the mesoscale variability is dominated by a single unstable mode throughout the 4.5 yr. But the dominant frequency has large temporal swing, notably at (111.33E, 11.63N). The most notable common feature exhibited at all three locations is that the wavelet coe$cients have large temporal #uctuation. Noting that shown in Fig. 8 is the mesoscale variability at three "xed locations, part of the variation in the wavelet coe$cients is caused by the lateral shifting of the mesoscale packet along the tracks, in light of Fig. 6.

5. Summary and discussion 5.1. Summary In this study, close to 5 yr (spanning October 1992 to August 1997) T/P altimetry data are analyzed to discuss the broad basic features of mesoscale variability inside the SCS and near the Luzon Strait. The wavelet transform enables us to quantify both temporal and spatial variation of mesoscale variability. During the 5-yr T/P period, it

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is found that there is signi"cant mesoscale variability in the SCS, which is grossly consistent with the "eld observations of Li et al. (1998) and Chu et al. (1998), although much weaker than that found in the regions of the Kuroshio Extension and Gulf Stream. The main results may be summarized as follows: (1) On average during the 5 yr, there are two more or less separated bands of signi"cant mesoscale variability at the Luzon Strait, with the stronger and better organized one near the northern boundary of the Luzon Strait, while the weaker and less organized one in the southern part of the strait. The two bands are present during much of the 5 yr, although both bands exhibit large temporal variation. (2) Inside the SCS, throughout much of the 5 yr there are two separated bands of signi"cant mesoscale variability north of 103N. The northern/western band lies near the northern/western boundary of the SCS in regions with water depth exceeding 2 km. The southern band is oriented southwest to northeast across the central SCS from 123N near the western boundary to the southern boundary of the Luzon Strait. In addition, mesoscale variability has signi"cant geographic variation along both the northern and the southern band. Mesoscale variability within each band exhibits signi"cant temporal #uctuation. The centers of the two bands also have signi"cant lateral movement, with that of the southern band bigger than that of the northern band. But throughout much of the 5 yr, the two bands are well separated. (3) South of 103N mesoscale variability is much weaker than that north of 103N. (4) The wavelet analysis also reveals that the dominant frequency of the mesoscale variability also undergoes large temporal #uctuation, including slow modulation with time scale longer than the characteristic time scale of the mesoscale variability, and fast change with time scale comparable to the characteristic time scale of the mesoscale variability. In addition, spreading of the mesoscale variability over the frequency band also has noticeable modulation. During most of the 5-yr period, the mesoscale variability is dominated by a single mode of variability. 5.2. Instability consideration The most obvious question raised by our analysis of mesoscale variability in the SCS from the T/P altimetry data is this: Why are there two separated bands of signi"cant mesoscale variability inside both the Luzon Strait and the SCS north of 103N? Instability theory (Pedlosky, 1987; Tai and White, 1990) suggests that strong currents should be the primary energy source (either through barotropic or baroclinic instability, although the latter has been suggested to be the main one (Tai and White, 1990) for the development and maintenance of mesoscale variability, which has been demonstrated by the close proximity of strong mesoscale variability near strong currents in the open ocean settings through both in situ observations and remote sensing measurement (e.g., Schmitz, 1997; Fu and Cheney, 1995). The existence of the two separated bands of signi"cant mesoscale variability inside the Luzon Strait and the SCS suggests that the large-scale mean circulation is organized in a somewhat similar way.

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Fig. 8. Twenty-nine-year-mean (67}95) temperature pro"les at 120.83E (at the Luzon Strait, shown in Fig. 1) (a), along the XBT line at about 153N (shown in Fig. 1) (b).

We now "rst look at the mean circulation at the Luzon Strait. Shown in Fig. 8(a) is the 29-yr mean temperature pro"le in the upper 300 m determined from currently available XBT data. Shown in Fig. 9 is the 15-yr mean upper layer current from a numerical simulation of the SCS circulation by MH96 (see MH96 for more detail of the model description). Both the observed temperature structure and the GCM simulation indicate that the main feature is the broad westward #ow from the Kuroshio into the SCS over much of the Luzon Strait and the much narrower eastward #ow from the SCS to the Kuroshio near the northern boundary of the Luzon Strait, which has been discussed previously by Nitani (1972) and Shaw (1989), among others. Part of the reason why the westward branch of the intrusion is rather broad, as shown in Fig. 8, is that the westward branch has a signi"cant southeast to northwest tilt as shown in Fig. 9 of the MH96. As shown in Fig. 8, the eastward out#ow is not well resolved by the XBT data: it is narrower near the northern boundary of the Luzon Strait, as suggested by the MH96 simulation. The analysis of the mesoscale variability from the T/P data appears to indicate that the two bands roughly coincide with the westward in#ow in the south and eastward out#ow near the northern boundary in the Luzon strait, suggesting that in#ow and out#ow provide the energy source for the development and maintenance of the mesoscale variability. To test the stability of the mean #ow, we simplify the discussion by considering the

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Fig. 9. The mean upper layer current and sea surface height from a 15-yr model run (with a 1/16 degree spatial resolution, reproduced from Plate 1 (d) of Metzger and Hurlburt (1996) (see HM96 for more detail of the GCM simulation). The arrow at the top gives the reference velocity of 100 cm/s.

simplest model, a two-layer (with equal depth) b-plane channel model. According to Pedlosky (1987), the critical shear for the onset of linear baroclinic instability is 2bF DD; D" , # K2J4F2!K4

(5.1)

where K is the wave number and JF is the inverse of the Rossby radius of deformation. For 1/JF&40 km, DD; D&3.5 cm/s. # Through the thermal wind relation, the shear #ow (related to 300 m) implied by the temperature structure shown in Fig. 8(a) is ag L¹ ; & H , 4)%!3 f Ly 0 where f is the Coriolis parameter, H is the depth, a is the thermal expansion coef0 "cient, and g is the gravity. Lacking the information about the salinity structure, we ignore its e!ect since we only want to obtain an order of magnitude estimate. For

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H"300 m (clearly the intrusion extends beyond a depth of 300 m, as suggested earlier by Nitani's study, and Fig. 8a), f "5.9]10~5 s~1, a"2.1]10~40C~1, 0 L¹/Ly&23C/200 km, and the vertical shear is ; &10.5 cm/s, 4)%!3 suggesting that the westward in#ow is baroclinically unstable. Although the eastward out#ow near the northern boundary of the Luzon Strait is not well resolved by the XBT data, Fig. 8(a), in conjunction with Fig. 9, indicates that the eastward out#ow is narrower than the westward in#ow, thus stronger (since the volume #ux of the in#ow and out#ow should be about the same, assuming that exchange between the SCS and open oceans occurs mainly through Luzon Strait, Shaw et al., 1999). So like the westward in#ow, the eastward out#ow near the northern boundary of the Luzon Strait is also baroclinically unstable. In the GCM simulation, the upper layer current (taken to represent the surface geostrophic current) at the Luzon Strait is above 10 cm/s, which also suggests that the simulated in#ow and out#ow are baroclinically unstable (if both #ows are assumed to be much weaker at the depth of a few hundred meters than at the surface). The baroclinic instability of the westward in#ow in the southern part of the Luzon Strait and eastward out#ow near the northern boundary indicates that westward and eastward #ows associated with the Kuroshio Intrusion provide the major energy source for the development and maintenance of the two separated bands of signi"cant mesoscale variability in the Luzon Strait as revealed by the 5-yr T/P observation. In between the westward in#ow and eastward out#ow is a transitional zone, where the #ow is much weaker and thus so is the mesoscale variability. The analysis of the mesoscale variability indicates that along the western/northern boundary of the SCS there is a more or less continuous (so far as we can tell from the T/P data) band of signi"cant mesoscale variability from the west of the Luzon Strait southward to about 103N through much of the 5yr. The energy source of this band of signi"cant mesoscale variability is the western boundary current. There have been few direct observations of this western boundary current at a few isolated locations (see Wyrtki, 1961; Huang et al., 1994), which tend not to last su$ciently long to average out the mesoscale variability. Shown in Fig. 8(b) is the temperature structure along a more or less zonal section at 153N. The temperature structure suggests a northward interior #ow. To conserve mass, there should be a southward boundary current at the western boundary (which is not resolved by the XBT data). It suggests the existence of a cyclonic gyre circulation in the northern part of the South China Sea. The "eld observations of Li et al. (1998) also suggest the existence of a cyclonic gyre circulation in the northern part of the SCS. In corroborating the observation, the MH96 GCM simulation, as shown in Fig. 9, indicates that a western boundary current exists continuously along the northern/western boundary of the SCS. As shown by Pedlosky (1987), nonzonal #ow (the western boundary current in the SCS is certainly nonzonal as shown in Fig. 9) is always baroclinically unstable as long as there is nonzero baroclinic shear (Shaw and Chao's 1994 calculation indicates that the western boundary current has signi"cant baroclinic shear). Instability of the western boundary

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current, as shown in Fig. 9 in the calculation of MH96, provides the energy source for the development and maintenance of the northern band of signi"cant mesoscale variability. Actually, both the HM96 GCM simulation and Nitani's (1972) "eld observation indicate that the southern and northern branches of the Kuroshio Intrusion in the Luzon Strait have meridional tilting (i.e., they are not purely zonal), although Fig. 8(a) can only show the zonal component of the Kuroshio Intrusion inside the Luzon Strait. In light of the discussion about the instability of the western/northern boundary, this nonzonality of both branches clearly enhances the baroclinic instability of the Kuroshio Intrusion inside the Luzon Strait. Another feature revealed by the T/P altimetry data is that apart from the band of signi"cant mesoscale variability along the northern/western boundary, there is another separated band of signi"cant mesoscale variability in the interior of the SCS, although it tends to spread wider. The natural question regarding its existence is this: What supports this band of signi"cant mesoscale variability? Both the XBT data along the zonal section (see Fig. 1) and the MH96 GCM simulation indicate the existence of signi"cant northward #ow in the interior SCS away from the western boundary. Could this northward interior #ow provide the energy source for the development and maintenance of this band? Again let us discuss the stability of this interior #ow. To simplify the discussion, we consider the linear baroclinic instability of a pure meridional #ow in a b-plane channel, which is oriented meridionally, in a two-layer model. From linear stability analysis (Pedlosky, 1987) one can show that the critical meridional velocity shear for the onset of baroclinic instability is DD; D"0, # that is, any nonzero meridional shear would support baroclinic instability (because any nonzero meridional shear would result in opposite signs in the zonal gradient of the upper layer and lower layer potential vorticity). Obviously, "nite meridional #ow (thus "nite available potential energy) is needed to support "nite mesoscale variability. (The linear instability analysis only tells us at what condition instability can be triggered. It does not tell us how signi"cant the mesoscale variability can become.) Both the temperature structure along 153N shown in Fig. 8(b) and the MH96 GCM simulation indicate the presence of signi"cant meridional #ow in the interior SCS north of 103N. In addition, the temperature section along 153N suggests that this northward #ow has signi"cant baroclinic shear, and thus indicates that it can support the development and maintenance of mesoscale variability in the southern band through baroclinic instability. (Although the XBT section only shows the presence of northward #ow near 153N, the MH96 simulation, shown in Fig. 9, indicates that the northward #ow extends over considerable latitudinal extent. Furthermore, the MH96 simulation indicates that the northward #ow lies increasingly farther north eastward from the western to the eastern basin, grossly similar to the orientation of the southern band of mesoscale variability shown in Fig. 1.) On the other hand, the MH96 GCM simulation suggests that south of 103N, the circulation is much weaker. That is the reason why no signi"cant mesoscale variability south of 103N is found by the T/P observation, at least during the 5 yr, because the mean #ow is too weak (thus so is the

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available potential and kinetic energy) to support any signi"cant mesoscale variability. In addition to the above general agreement regarding the broad basic spatial characteristics of the mesoscale variability and those of the large-scale background mean circulation in the SCS revealed by the XBT data and the GCM simulation, the speci"c geographical variation of the mesoscale variability is also grossly correlated to the geographical variation in the intensity of the large-scale background mean #ow. (In regions where the large-scale #ow is strong, the available energy for the development of mesoscale variability is also high. In the simple two-layer model of linear baroclinic instability, when the mean #ow is strong, the linear growth is high as readily indicated by Eq. (5.1).) As shown in Fig. 9, the western boundary current tends to weaken along the northern boundary from 1203E to 1103E. In a similar way, the mesoscale variability is decreasing from track 44 to 57 (see Fig. 1 for the geographic location). Fig. 9 seems to suggest that there exists a stronger western boundary current along the east coast of Vietnam around 133N than along the southern coast of China. Thus mesoscale variability is expected to be stronger near the northern end of track 19 than near the northern ends of the other four tracks except for track 44. This kind of close correlation between the intensity of the mesoscale variability and the strength of large-scale background mean circulation has long been noticed along the axes of the Kuroshio Extension and Gulf Stream (e.g., Schmitz, 1997). The presence of signi"cant mesoscale variability clearly complicates the observation of the large-scale background mean circulation in the SCS as discussed by Huang et al. (1994). As most of the in situ observations in the SCS do not have long enough temporal coverage to average out the mesoscale activity, the large-scale background circulation determined from in situ measurements, such as that discussed by Huang et al. (1994) and Chu et al. (1998) through their recent AXBT survey, is clearly marked by energetic mesoscale activity. That is the main reason why the mean circulation discussed by Huang et al. (1994), among others, looks very sketchy. Although numerical studies by MH96 and Shaw and Chao (1994) have indicated that the large-scale background circulation in the SCS has signi"cant seasonal variation, the mesoscale variability, as shown in Fig. 6, does not have any signi"cant seasonal variability (it has signi"cant interannual variation, however). It is not clear why the seasonal variation in the large-scale background circulation does not lead to any signi"cant seasonal variation in the mesoscale variability.

Acknowledgements This study was supported by NASA through the TOPEX/Poseidon Science Investigation. We thank B. Beckley for helping us with the T/P data processing. We also wish to thank the reviewers for their comments, which led to a much better presentation.

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Appendix A. Subannual variability There has been a series of studies that aim to describe the intra-annual variability in the middle latitude open ocean and strong boundary current regions. Tai and White (1991) were the "rst to notice the existence of strong intra-annual sea level height variability in the Kuroshio Extension region by analyzing the Geosat data. Lee and Cornillon (1995) investigated the strong intra-annual variability in the Gulf Stream region by analyzing 7-yr AVHRR data. Wang and Koblinsky (1995,1996) and Wang et al. (1998a) discussed intra-annual variability in strong boundary current regions, such as the Kuroshio Extension, Gulf Stream, and Aguhlus current from analyzing the T/P data. Wang et al. (1998b,c) also noticed the presence of signi"cant intra-annual Rossby wave activity in the middle latitude South Paci"c from analyzing T/P data. The purpose of this appendix is to discuss brie#y the intra-annual variability in the area around the SCS. As shown in Fig. 2(b), there is a strong intra-annual sea level height anomaly from May 1993 to May 1997 around (121.43E, 21.73N) near the northern boundary of the Luzon Strait. In fact, the intra-annual wavelet coe$cient is even higher than that of the mesoscale variability throughout the 4.5 yr. The dominant period of the intraannual variability varies around 7 months. Fig. 2(c) indicates that near (123.23E, 17.53N) in the Phillipine Sea, signi"cant intra-annual variability is present from December 1992 to December 1995. During the 3 yr, the intra-annual variability has a dominant period of about 8 months. Fig. 4(b) shows that there also exists signi"cant intra-annual variability near the core of the Kuroshio throughout the 4.5 yr. Similarly signi"cant intra-annual variability can also be found inside the SCS. For example, near (116.93E, 19.13N) (shown in Fig. 7(c)) noticeable intra-annual variability can be found around January 1993 and December 1995, with a dominant period of about 8 months. All these data indicate that roughly in the area where there exists signi"cant mesoscale variability we can generally "nd signi"cant intra-annual variability, which may even be stronger than the mesoscale variability. Similar to the discussion about the total mesoscale variance discussed in Section 4.2, we can compute the total intra-annual variance integrated between 1 and 2 cycle/yr, which is not shown here. The general results can be summarized as: (1) there are two mostly separated bands of signi"cant intra-annual variability along each track with their cores located in about the same places as those of the mesoscale variability; (2) the dominant frequency undergoes signi"cant low-frequency variation. The information o!ered by the sea level height oscillation only gives the surface topographic characteristics of the intra-annual variability in the early 1990s. If the associated barotropic component is much weaker than the sea level height anomaly, then the sea level height anomaly yields the intraannual variability in the total buoyancy content integrated from the surface to the bottom, according to Gill and Niiler's (1973) discussion. To obtain more detailed information about the vertical structure associated with the intraannual variability we have to use other types of data. The XBT data can give us some information about possible vertical structure associated with the intra-annual

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Fig. 10. Vertical structure of the temporal #uctuation of the total intra-annual variance (3C2) at the four clusters of Kuroshio (a), Kaohsiung (b), Saigon (c), Manila (d), respectively. For display purposes, the variance (3C2) is enlarged 100 times.

variability, though not the complete information (which would need salinity data). Shown in Fig. 10 is the total intra-annual variance of the temperature #uctuation in the upper 300 m. The "gure indicates that the intra-annual variation of temperature is

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very weak near Manila throughout the 8 yr from 1968 to 1975 when the XBT data are available. On the other hand, signi"cant intra-annual variation of temperature can be found during various periods at the other three clusters. The most signi"cant feature exhibited at all three clusters is that the intra-annual temperature variability is mostly a subsurface phenomenon, much like the interannual variability discussed by Wang et al. (1999), with very weak surface signature. That is very di!erent from the seasonal cycle (not shown here), which is mostly a surface intensi"ed oscillation. At all three clusters, the maximum intra-annual temperature variability is located near the depth of 100 m. Near Kuroshio and Saigon, strong intra-annual variability sometimes extends beyond the depth of 300 m. On the other hand, strong intra-annual variability near Kaohsiung is mostly con"ned in the layer between 50 and 200 m. Similar to its manifestation in the sea level height, the intensity of the intra-annual temperature #uctuation undergoes large temporal modulation. The above discussions indicate that both the T/P altimetry and XBT data demonstrate the existence of signi"cant intra-annual variability. It is mostly a subsurface phenomenon with very little surface signature, suggesting that an internal ocean dynamic process plays a prominent role in its generation. While it is generally accepted (e.g., Tai and White, 1990) that baroclinic instability of the background #ow is the main generation mechanism for the observed mesoscale variability, it is unclear what internal ocean dynamic process accounts for the existence of the intra-annual variability. The most notable similar feature (so far as the T/P data can reveal) between the mesoscale and the intra-annual variability is that they both lie near the cores of strong current (shown in Fig. 8), which suggests that the baroclinic instability might also be involved in the generation of the intra-annual variability. From the baroclinic instability of the Charney model (Pedlosky, 1987), we know that in addition to the Charney mode, there are also other unstable modes, such as the Green mode. Presence of the intra-annual variability might be a manifestation of the oceanic Green mode in a baroclinically unstable background #ow in the SCS that has sub-maximum growth rate.

References Adamec, D., 1998. Modulation of the seasonal signal of the Kuroshio Extension during 1994 from satellite data. Journal of Geophysical Research 103, 1956}1974. Chao, S., Shaw, P., Wu, S., 1996. Deep water ventilation in the SCS. Deep-Sea Research I 43, 445}466. Chu, P., Fan, C., Lozano, C., Kerlin, J., 1998. An airborne XBT survey of the South China Sea. Journal of Geophysical Research 103, 21637}21652. Farge, M., 1992. Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics 24, 395}457. Fu, L., Cheney, R., 1995. Applications of satellite altimetry to ocean circulation studies: 1987}1994. Review of Geophysics 32 (Suppl), 213}223. Gill, A., Niiler, P., 1973. The theory of the seasonal variability in the ocean. Deep-Sea Research 20, 141}177. Gille, S., 1994. Mean sea surface height of the Antarctic Circumpolar Current from Geosat data: method and application. Journal of Geophysical Research 99, 18255}18273.

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Gu, D., Philander, G., 1995. Secular changes of annual and interannual variability in the tropics during the past century. Journal of Climate 8, 864}876. Huang, Q., Wang, W., Li, Y., Li, C., 1994. Current characteristics of the South China Sea. In: Zhou, D. (Ed.), Oceanology of China Sea, Vol. 1, pp. 39}47. Hurlburt, H., Wallcraft, A., Schmitz, W., Hogan, P., Metzger, J., 1996. Dynamics of the Kuroshio/Oyashio current using eddy-resolving models of the North Paci"c Ocean. Journal of Geophysical Research 101, 941}976. Jacobs, G., Leben, R., 1990. Loop current eddy shedding estimated using Geosat altimeter data. Geophysical Research Letters 17, 2385}2388. Koblinsky, C., Beckley, B., Ray, R., Wang, Y., Tsaoussi, L., Brenner, A., 1997. NASA Ocean Altimeter Path"nder Project Report 1: Data Processing Handbook, unpublished, 50pp. Kontoyiannis, H., 1997. Quasi-geostrophic modeling of mixed instabilities in the Gulf Stream near 733W. Dyn. Atmos. Oceans 26, 133}158. Lee, T., Cornillon, P., 1995. Temporal variation of meandering intensity and domain-wide lateral oscillations of the Gulf Stream. Journal of Geophysical Research 100, 13603}13613. Li, L., Nowlin, W., Su, J., 1998. Anticyclonic rings from the Kuroshio in the South China Sea. Deep-Sea Research I 45, 1469}1482. Metzger, J., Hurlburt, H., 1996. Coupled dynamics of the SCS, the Sulu Sea, and the Paci"c Ocean. Journal of Geophysical Research 101, 12331}12352. Morlet, J., 1981. Sampling theory and wave propagation. Proceedings of the 51st Annu. Meet. Soc. Explor. Geophys., Los Angeles. Nitani, H., 1972. Beginning of the Kuroshio. In: Stommel, H., Yashida, K. (Eds.), Kuroshio, Physical Aspects of the Japan Current. University of Washington, pp. 129}163. Pedlosky, J., 1987. Geophysical Fluid Dynamics. Springer, Berlin, p. 624. Qiu, B., 1995. Variability and energetics of the Kuroshio Extension and its recirculation gyre from the "rst 2-year TOPEX data. Journal of Physical Oceanography 25, 1827}1842. Schlax, M., Chelton, D., 1994. Aliased tidal errord in TOPEX/Poseidon sea surface height data. Journal of Geophysical Research 99, 24761}24775. Schmitz, W., 1997. On the world ocean circulation. Technical Report WHOI-96-08. Shaw, P., 1989. The intrusion of water masses into the sea southwest of Taiwan. Journal of Geophysical Research 94, 19213}19226. Shaw, P., Chao, S., 1994. Surface circulation in the South China Sea. Deep-Sea Research I 41, 1663}1683. Shaw, P., Chao, S., Fu, L., 1999. Sea surface height variations in the South China Sea from satellite altimetry. Oceanolog Acta 22, 1}17. Shaw, P., Chao, S., Liu, K., Pai, S., Liu, C., 1996. Winter upwelling o! Luzon in the northeastern South China Sea. Journal of Geophysical Research 101, 16435}16448. Strub, T., James, C., 1995. The large-scale summer circulation of the California current. Geophysical Research Letters 22, 207}210. Tai, C., White, W., 1990. Eddy variability in the Kuroshio Extension as revealed by satellite altimetry: energy propagation away from the jet, Reynolds stress, and seasonal cycle. Journal of Physical Oceanography 20, 1761}1777. Tsaoussi, L., Koblinsky, C., 1994. An error covariance model for sea level surface topography and velocity derived from TOPEX/Poseidon altimetry. Journal of Geophysical Research 99, 24669}24683. Wang, L., Koblinsky, C., 1995. Low frequency variability in the regions of the Kuroshio extension and the Gulf Stream. Journal of Geophysical Research 100, 18313}18331. Wang, L., Koblinsky, C., 1996. Low frequency variability in the region of the Agulhas Retro#ection. Journal of Geophysical Research 101, 3597}3614. Wang, L., Koblinsky, C., Howden, S., 1998a. Annual and intra-annual variability in the region of the Kuroshio Extension from the TOPEX/Poseidon and Geosat data. Journal of Physical Oceanography 28, 692}711. Wang, L., Koblinsky, C., Howden, S., Beckley, B., 1998b. Large-scale Rossby wave propagation in the mid-latitude South Paci"c from altimetry data. Geophysical Research Letters 25, 179}182.

Dsri*588

708

L. Wang et al. / Deep-Sea Research I 47 (2000) 681}708

Wang, L., Koblinsky, C., Howden, S., Beckley, B., 1998c. Intermittence and modulation of mesoscale variability. Geophysical Research Letters 25, 4365}4368. Wang, L., Koblinsky, C., Howden, S., Huang, N., 1999. Interannual variability in the South China Sea from XBT data. Journal of Geophysical Research, in press. Wyrtki, K., 1961. Scienti"c results of marine investigations of the South China Sea and Gulf of Thailand 1959}1961. Scripps Institution of Oceanography, University of California, San Diego, Naga Report 2, pp, 164}160.