J. Ocean Univ. China (Oceanic and Coastal Sea Research) DOI 10.1007/s11802-009-0327-y ISSN 1672-5182, 2009 8 (4): 327-334 http://www.ouc.edu.cn/xbywb/ E-mail:xbywb@ouc.edu.cn

Long Term Sea Level Change and Water Mass Balance in the South China Sea

RONG Zengrui, LIU Yuguang*, ZONG Haibo, and XIU Peng

Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, P. R. China

(Received March 25, 2009; revised May 20, 2009; accepted June 12, 2009)

Abstract Sea level anomalies observed by altimeter during the 1993–2006 period, thermosteric sea level anomalies estimated by using subsurface temperature data produced by Ishii and SODA reanalysis data, tide gauge records and HOAPS freshwater flux data were analyzed to investigate the long term sea level change and the water mass balance in the South China Sea. The altime-ter-observed sea level showed a rising rate of (3.5±0.9) mm yr-1 during the period 1993–2006, but this figure was considered to have been highly distorted by the relatively short time interval and the large inter-decadal variability, which apparently exists in both the thermosteric sea level and the observed sea level. Long term thermosteric sea level from 1945 to 2004 gave a rising rate of 0.15±0.06

mm yr-1. Tide gauge data revealed this discrepancy and the regional distributions of the sea-level trends. Both the ‘real’ and the ther-mosteric sea level showed a good correspondence to ENSO: decreasing during El Niño years and increasing during La Niña years. Amplitude and phase differences between the ‘real’ sea level and the thermosteic sea level were substantially revealed on both sea-sonal and interannual time scales. As one of the possible factors, the freshwater flux might play an important role in balancing the water mass.

Key words sea level change; South China Sea; thermosteric sea level; mass exchange

1 Introduction The South China Sea (SCS), the largest marginal sea in

Southeast Asia, is a semi-closed basin surrounded by China, the Philippines, Borneo Island, and the Indo China Peninsula (Fig.1). Water exchange takes place between it and the East China Sea, the western Pacific and the Indian Ocean through the Taiwan Strait, the Luzon Strait and the Malacca Strait, respectively. As is well known, the SCS is located on the pathway of the East Asian monsoon system, where the sea level is largely influenced by the seasonal reversal of the monsoon winds, which are northeasterly in winter and southwesterly in summer. Some seasonal and interannual features of the SCS have been identified from altimeter observations (e. g., Ho et al., 2000a, b; Hu et al., 2001; Liu et al., 2001; Li et al., 2003; Rong et al., 2007). However, few analyses have been conducted to study the thermosteric contributions. As will be shown in Section 3.2, there is a large discrepancy existing between the ob-served sea level and the thermosteric sea level. Neither the seasonal variation nor the long term sea level change can be explained by thermal expansion.

Sea level change can be expected for two reasons. First, the ocean volume changes whenever a change in the heat content of ocean water takes place; and second, ocean mass

* Corresponding author. Tel: 0086-532-66781629

E-mail: yugliu@ouc.edu.cn

changes due to changes in the mass flow between the ocean and other reservoirs of the hydrologic cycle. How-ever, so far, no analyses have been carried out on the mass balance in the SCS, mainly due to the lack of open sea data, especially the freshwater flux across the ocean surface. Satellite altimetry, operating since the beginning of the 1990s, appears as a new tool for monitoring water volume and mass change of the oceans in response to climate change. More importantly, there has been signifi-cant progress in the development of remote sensing methods to determine atmospheric and oceanic parame-ters from data of the Special Sensor Microwave/Imager (SSM/I) and the Advanced Very High Resolution Radi-ometer (AVHRR) that are of sufficient accuracy for the parameterization of energy and freshwater fluxes across the sea surface. The operational application of recently developed retrieval schemes to data of these two radi-ometers results in a new atlas called Hamburg Ocean At-mosphere Parameters and Fluxes from Satellite data (HOAPS) (Fennig et al., 2006). In view of this present situation, a detailed monitoring of the sea level variability is possible.

The objective of this study was to estimate the long term mean sea level change in the SCS and to assess the thermosteric contribution to sea level change. Different types of data were used: altimeter data, tidal gauge data, objectively analyzed subsurface temperature data and Simple Ocean Data Assimilation (SODA) reanalysis data. We anticipated the longer thermosteric sea level series

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would lead to some new insight into the altimeter-ob-

served sea level rise. The HOAPS freshwater flux data was also analyzed to study the mass balance of the SCS.

In this paper, we shall show some primary relationships between observed sea level and water mass redistribu-tions between the oceans and the atmosphere.

Fig.1 The bottom topography of the South China Sea and the straits between the SCS and the adjacent oceans. The dots denote the tidal gauge stations used in Table 1.

2 Data and Computation 2.1 Satellite Altimeter Observations

A newly-released merged and gridded product of MSLA-DT ref (Maps of Sea Level Anomaly-DT ‘ref’) produced by AVISO based on TOPEX/Poseidon, Jason 1, and ERS-1 and ERS-2 data (Ducet et al., 2000) was used in this study. This product provides sea level anomalies relative to a 7-year mean from 1993 through 1999. Sea level anomalies are presented on 0.25˚×0.25˚grids, every 7 days for the period October 1992 to August 2006. Tidal effects, including those of ocean tides, solid Earth tides, and pole tides, have been removed in the altimeter data processing with the inverted barometer (IB) correction being also made (see the AVISO Handbook for details). Because the maps contain data from multiple altimeters, they are able to resolve variability on scales as small as 150–200 km with an accuracy of a few centimeters over most of the globe (Ducet et al., 2000). These data were used to construct the time series of the monthly mean sea

level anomalies.

2.2 Thermosteric Sea Level The Ishii monthly objectively analyzed subsurface

temperature (version 6.2) is the most recently available dataset produced by Ishii et al. (2003, 2006). The fields, which cover the period 1945 to 2005, are given on 1˚×1˚ grids, and include 16 layers from the surface to 700 m depth. The analysis was based on the World Ocean Data-base 2001 (WOD01) (Boyer et al., 2001), the Global Tem-

perature-Salinity in the tropical Pacific provided by IRD (L’institut de recherché pour le development, France), and the Centennial in situ Observation Based Estimates (COBE) sea surface temperature (Ishii et al., 2005). ARGO- pro-filing buoy data and the latest Global Temperature- Salin-ity Profile Program (GTSPP) data have also been used in the past several years. A bug associated with the mixed layer depth analysis has been fixed too (Ishii et al., 2006).

Thermosteric sea level at any given grid can be com-puted from the density changes of seawater. It is neces-sary to first convert the gridded temperature anomalies to

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density anomalies at each depth using the classical equa-tion of state (Gill, 1982). The thermosteric sea level is further obtained by vertically integrating density anoma-lies at each grid point and each time step according to (e.g. Lombard et al., 2005):

0 0steric

0

( , , ) ( , , , )( , , ) d

( , , )H

x y z x y z th x y t z

x y zρ ρ

ρ−

−=∫ , (1)

where ρ0(x, y, z) is the reference density, which is a func-tion of reference temperature T0 (0 degrees Celsius), ref-erence salinity S0 (Levitus climatology) and depth z. Here ρ(x, y, z, t) is a non-linear function of temperature and salinity (Gill, 1982). The Levitus climatology data was used to provide the reference salinity at each depth for Ishii temperature dataset. Since the climatological salinity was used in Eq. (1), any salinity effect on steric sea level change was not considered.

2.3 Fresh Water Flux A knowledge of precipitation and evaporation and the

resulting freshwater flux across the ocean surface is im-portant for understanding the climate system, the direct forcing of ocean circulation models, the hydrological cy-cle on a global and regional scale, etc. In the past, the major difficulty was the scarcity of data over large re-gions such as the South China Sea. More recently several satellite-derived products based on passive satellite mi-crowave measurements (SSM/I) combined with SSTs from infrared observations (AVHRR) have become available. Based on carefully selected and validated algo-rithms, a new atlas called the Hamburg Ocean Atmos-

phere Parameters and Fluxes from Satellite Data (HOAPS

-II) provides global fields of surface precipitation and evaporation and all basic state variables needed for the derivation of these fluxes (Fennig et al., 2006). In our analysis, the HOAPS-II fresh water flux, i.e. precipitation minus evaporation, was used. The dataset was given as monthly averages of 0.5˚ latitude and 0.5˚ longitude resolution covering the period 1988 to 2002. Typically satellite data sets have a much better spatial as well as temporal resolution than ship data, which is quite impor-tant for computing monthly averages.

3 Results and Comparisons 3.1 The SCS Mean Sea Level Change

Fig.2a shows the altimeter-observed mean sea level anomalies over the SCS during October 1992–August 2006, which contain a superimposed long term sea level trend. The seasonal and interannual sea level variations are shown in Figs.2b and 2c, respectively. A positive sea level trend is present during the period, which reveals a bulk rising rate of (3.5±0.9) mm yr-1, where the error bar of ±0.9 mm yr-1 represents the 95% confidence interval. The annual amplitude of altimeter-measured SCS sea level change is about 65 mm with the maximum occurring around November and the minimum around May. We note that while the annual signal is clearly present during 1993–1996 and 1999–2006, it has been significantly at-tenuated during 1997–1998. After the seasonal signal and the long term trend are removed, one can see that some interannual fluctuations clearly exist. The mean sea level

Fig.2 (a) Time series of the altimeter-observed mean level anomalies over the SCS with trend fit denoted by straight line; (b) Annual fits of the mean sea level anomalies; (c) Residual anomalies after removal of the seasonal (annual and semi-annual) signals denoted by solid line and superimposed SOI (South Oscillation Index) denoted by dashed line.

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anomalies over the SCS are strongly corresponding to ENSO events. The interannual signal shows an extreme low during the 1997/1998 El Niño and two extreme highs during the 1998/1999 and the 1999/2000 La Niña. As shown in Fig.2c, sea level anomalies and the SOI vary coherently: negative values of the SOI (i.e. El Niño event) correlating with negative sea level anomalies and vice versa. The correlation between them reaches 0.57, with the SOI leading the mean sea level anomalies by 4 months. More information about the sea level response in the SCS to ENSO was given in the paper of Rong et al. (2007). In addition to the interannual variability, some decadal variability also seems to exist over the SCS. Sea level rose from 1993 through 2001, even though a mini-mum occurred in 1997/1998, and it fell from 2001 through 2006. Analysis based on long term thermosteric sea level series demonstrates that decadal variability does exist over the SCS (see Section 3.2).

3.2 The SCS Thermosteric Sea Level Change As shown in Fig.3a, during the period of 1945–2005,

the thermal expansion of seawater in the 0–700 m layer of the SCS contributed approximately (0.15±0.06) mm yr-1 to the SCS sea level rise. We also computed the trend only using data covering the same time span as that of altime-ter observations (1993–2005), and the long term rate was found to be about (1.6±0.6) mm yr-1, much larger than the (0.15±0.06) mm yr-1 during 1945–2005. However, it can only account for about half of the altimeter-ob served sea level change rate (i.e. (3.5±0.9) mm yr-1). This could be associated with other effects such as freshwater flux, which might have a significant effect on the trend, espe-

cially when calculating the trend in a certain interval of the total record. The seasonal and interannual sea level variations are shown in Fig.3b and Fig.3c, respectively. Fig.3c shows the thermosteric sea level record over the SCS after detrending and removing semiannual and an-nual terms by the method of least squares. It can be seen that an apparent inter-decadal variability is present. Ther-mosteric sea level fell during the 1955–1965 and 1980–

1990 periods and rose during the 1965–1975 and 1990–

2000 periods. The apparent sea level rise is found around 2000 and such high values have also been reached in the past, e.g. 1955 and 1975. These results can be particularly useful in interpreting altimeter-observed SCS mean sea level changes during the same period. For example, the relatively larger mean sea level rising rate observed by altimeter would not represent the long term sea level rising rate and is likely to be associated with strong decadal variations. Following the tendency of sea level variation, we are apt to expect a sea level fall during the following 5–10 years.

The SODA (Simple Ocean Data Assimilation) global reanalysis datasets (Carton and Giese, 2008) support the findings obtained from the satellite SSH and the Ishii thermosteric sea level. These datasets are provided on a 0.5˚×0.5˚ horizontal resolution with 40 levels from 5 m to 5347 m, over the time period from 1958 to 2004. The variables in the datasets include temperature, salinity, zonal and meridian velocities, wind stress and sea surface height. In our study the temperature was used to calculate the thermosteric sea level of the SCS based on Eq. (1). The reference salinity was determined by time averaging over the whole period for each level.

Fig.3 (a) Time series of the Ishii thermosteric sea level anomalies over the SCS, the trend fit during the whole period being denoted by thin straight line, and the trend fit during 1993–2005 being shown by thick line. (b) An-nual fits of the thermosteric sea level anomalies. (c) Residual after removal of the seasonal signals shown by black curve with the 5-year low-pass filtered time series shown by gray curve being superimposed.

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Fig.4 (a) Four time series from top to bottom, i.e. the al-timeter-observed sea level, SODA-simulated sea level, SODA thermosteric sea level and the Ishii thermosteric sea level. The time series have been shifted vertically to facilitate comparison. (b) The time series of the SOI. Thin curve denotes the original data and thick curve denotes low-pass filtered time series after removal of signals with time scales shorter than 5 years.

Fig.4a shows the four time series from top to bottom, i.e. the altimeter-observed sea level, SODA-simulated sea level, SODA thermosteric sea level and the Ishii ther-mosteric sea level. Thin curves denote the sea level re-sidual after the removal of seasonal signals and thick

curves show the low-pass filtered time series after the removal of signals with time scales shorter than 5 years. As shown in Fig.4a, both ‘real’ sea level and thermosteric sea level show large variability over interannual and de-cadal time scales. Sea level falls during the 1955–1965 and 1980–1990 periods and rises during the 1965–1975, 1990–2000 periods are revealed both by SODA ‘real’ sea level and thermosteric sea level. A falling tendency after 2000 is also evident in all of the datasets. Note that the ‘real’ sea level has a larger variation than the thermosteric sea level; other factors such as river runoff, precipitation and water mass exchange with the adjacent seas should account for this (Rong et al., 2007). These factors might also explain why the thermosteric sea level rising rate is lower than the observed rate during the overlapping period (compare (1.6±0.6) mm yr-1 with (3.5±0.9) mm yr-1). As shown in Fig.4b, which presents the time series of the SOI, the sea level variability keeps a notable correspon-dence to ENSO (SOI): sea level rises when the SOI is higher (La Niña) and falls when the SOI is lower, which is consistent with the conclusion of Rong et al. (2007).

3.3 Sea Level Change Obtained from Tidal Gauge Both interannual and decadal variability of sea level

may distort the real rate of sea level rise. Some longer sea level records are available from tidal gauges. With the great diversity in climatic environments, vertical land movements and ocean variability, one might expect that the relative sea level trends measured at different stations over different time periods may also vary. This is indeed the case and is demonstrated in Table 1, which contains the relative sea level trends from tidal gauge stations around the SCS. The sea level trends estimated from tidal gauge stations around the SCS show a relatively large range from (6.46±2.15) mm yr-1 at Puetro to (0.26±0.34) mm yr-1 at Macau. In a previous study of Rong et al. (2007), they showed that the sea level trends in the SCS are not homogeneous. Here the large discrepancies be-tween different gauges are mainly due to different loca-tions and time periods. Another reason might be that tidal gauges measure sea level relative to a point attached to

Table 1 Sea level trends estimated from tidal gauge stations with error bars representing 95% confidence interval †

Tidal gauge Time interval Trend/Error bar (mm yr-1)

Zhapo (21˚35΄N, 115˚50΄E) 1959–2004 2.21±0.42 Kaohsiung (22˚32΄N, 120˚19΄E) 1974–1996 0.27±1.05 Xiamen (24˚27΄N, 118˚4΄E) 1954–2002 1.01±0.43 Shanwei (22˚45΄N, 115˚21΄E) 1975–2004 2.66±0.69 Macau (22˚12΄N, 113˚33΄E) 1925–1982 0.26±0.34 Quarrybay (22˚18΄N, 114˚13΄E) 1950–2004 0.81±0.38 Haikou (20˚1΄N, 110˚17΄E) 1976–2004 5.81±0.60 Dongfang (19˚6΄N, 108˚37΄E) 1983–1994 3.35±1.86 Beihai (21˚29΄N, 109˚5΄E) 1975–2004 2.32±0.53 Hondau (20˚40΄N, 106˚48΄E) 1957–2001 5.13±0.48 Dadang (16˚6΄N, 108˚13΄E) 1978–2001 2.50±1.07 Vungtau (10˚20΄N, 107˚04΄E) 1979–2001 5.74±1.09 Bintulu (3˚16΄N, 113˚04΄E) 1993–2003 3.95±2.28 Kota (5˚59΄N, 116˚04΄E) 1988–2003 5.01±1.58 Puetro (9˚45΄N, 118˚44΄E) 1990–2002 6.46±2.15

Note: † The locations of the tidal gauge stations are shown in Fig.1.

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the land which can move vertically (Cazenave and Nerem, 2004), thus creating sea level change unrelated to climate variations.

To further investigate the distorting effect of the inter-annual and decadal variability on the estimation of the sea level trends, three groups of curves from top to bottom denoting the sea level anomalies at Zhapo, Quarrybay and Macau are shown in Fig.5, with thin curves denoting the sea level residuals after the removal of seasonal signals and thick curves denoting low-pass filtered time series after the removal of signals with time scales shorter than 5 years. As shown in Fig.5, the sea level anomalies are not dominated by a straightforward trend but rather by interannual fluctuations. One can see that sea level rose during the 1935–1955 and 1990–2000 periods and fell during the 1955–1965 period. Being consistent with Fig.3, there is a falling tendency from 2000, and we might ex-pect a sea level fall during the following 5–10 years. Long term series should be needed to examine this ten-dency in the future.

Fig.5 Sea level anomalies at Zhapo, Quarrybay and Macau, respectively. The time series have been shifted vertically to facilitate comparison.

3.4 Water Mass Balance While altimeter-observed sea level reached its maxi-

mum in November and minimum in May (Fig.2b), the thermosteric sea level reached its maximum in August and minimum in February (Fig.3b), the two being out of phase with each other. The sea level variation due to ocean mass change is the difference between the altime-ter-observed sea level and the thermosteric contribution. As shown in Fig.6, the mass exchange denoted by their difference has a strong annual signal with an amplitude of 70 mm and a maximum around October. This variation should result from ocean mass exchange between the at-mosphere and the continent via precipitation, evaporation and river runoff. Since the SCS is a semi-closed basin, the mass exchange between the SCS and the adjacent oceans

may also play a role. To further understand the mass exchange in the SCS,

we investigated one of the potential factors: the freshwa-ter flux, i.e. precipitation minus evaporation over the SCS. Fig.7a shows the time series of the freshwater flux based on HOAPS data. There is no apparent trend of the fresh-water flux and a strong annual signal exists. As shown in Fig.7b, the freshwater flux is positive from June to No-vember and changes to negative during the remainder of the cycle, which might account for part of the seasonal signal of the mass exchange (Fig.6). The annual freshwa-ter flux over the SCS may be interpreted in terms of equivalent sea level increase from June to November and equivalent sea level decrease from December to May. In an annual cycle, the evaporation almost balances the pre-cipitation. The interannual variability of the freshwater flux is shown in Fig.7c, which peaks in winter with nega-tive values in El Niño years such as 1994 and 1997 and positive values in La Niña years (1998 and 1999), i.e. the freshwater flux decreases during El Niño years and in-creases during La Niña years.

As shown in Figs.2 and 4, there are good correlations between the altimeter observations and the thermosteric sea level variations. The thermosteric contribution to in-terannual sea level change appears positively correlated with altimeter observations. However, as noted by Rong et al. (2007), the interannual thermosteric sea level reached its peaks three months before the altimeter-observed sea level. Fresh water flux plays a major role in balancing this difference. During El Niño, the atmospheric convective activity in the equatorial Pacific shifts eastward and an anomalous descent is formed over the tropical Indo- west-ern Pacific (Klein et al., 1999; Wang, 2002; Huang et al., 2004). Corresponding to the anomalous descent are anom- alous anticyclones above the off-equatorial western Pa-cific during and after the mature phase of El Niño (Weis-berg and Wang, 1997; Wang et al., 1999). The SCS is located near the region of anomalous descent motion. It is unsurprising that the reduced convection decreases the cloud cover and eventually reduces the freshwater flux during El Niño years (Fig.7). The decreased precipitation plays a significant role for the continuous negative sea level anomalies, which will account for about 60 mm (2 mm d-1×30 d) sea level fall during El Niño years. A de-crease in freshwater input might also affect the sea level change via the halosteric sea level changes. Cheng and Qi (2007) found that the halosteric contribution to sea level change was quite small during 1993–2000 but reached about 14% during 2001–2005. A decrease in freshwater input during El Nino years would also increase the salin-ity and thus leads to a drop of halosteric sea level. How-ever, as noted in Cheng and Qi (2007), the contribution of halosteric sea level might not be reliable due to data ac-curacy. The halosteric effect needs to be further discussed in the future.

Previous studies have shown that the interannual vari-ability of the mass exchange between the SCS and the adjacent seas show a good correspondence to ENSO events (e.g., Qu 2004; Rong et al. 2007; Chen and Qi,

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Fig.6 Time series of the mass exchange, i.e., difference between altimeter-observed sea level and the thermosteric contribution (black curve): Annual fits are superimposed (gray curve).

Fig.7 (a) The HOAPS freshwater flux (precipitation minus evaporation) rate over the SCS; (b) annual fits of the freshwa-ter flux rate; (c) residual of freshwater flux rate after the removal of seasonal signals (i.e. annual and semiannual signals).

2007). Then an often raised question is: how does the mass exchange correspond to the observed decadal vari-ability? We examined the mass exchange between the SCS and the adjacent seas based on SODA global re-analysis datasets (see Rong et al., 2007 for details). There are no apparent decadal variabilities in the mass exchange between the SCS and the adjacent seas (figure not shown). However, one should be careful when illustrating this result. Since the resolution of SODA is only of 0.5˚×0.5˚, which might be not high enough to resolve the strait to-pography/dynamics between the SCS and the adjacent seas, especially when estimating the decadal signals that are much smaller than the seasonal signals.

4 Conclusions Based on altimeter-observed sea level data, Ishii and

SODA subsurface temperature data, tidal gauge data and

freshwater flux data, this study provided a detailed de-scription of the long term sea level change and the water mass balance of the SCS. The conclusions are summa-rized as follows.

The altimeter-observed sea level of the SCS had a ris-ing rate of (3.5±0.9) mm yr-1 during the period October 1992–August 2006. However, this rate might have been highly distorted by the relatively short time interval. Fur-ther analyses based on long term thermosteric sea level and SODA reanalysis dataset showed that there were strong interannual and decadal fluctuations in both the ‘real’ and the thermosteric sea level. There were sea level falls during the 1955–1965 and 1980–1990 periods and rises during the 1965–1975 and 1990–2000 periods.

Sea level time series from long term tide gauge obser-vations demonstrated large decadal variability of sea level and non-homogeneous sea level trends over the SCS. The sea level trends estimated from tide gauges around the

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SCS showed a relatively large range from (6.46±2.15)

mm yr-1 to (0.26±0.34) mm yr-1 at different stations. A falling tendency after 2000 was revealed by all datasets. We might expect a sea level fall during the following 5–10 years. Long term series is needed to examine this tendency in the future.

There were substantial amplitude and phase differences between the observed sea level and the thermosteic sea level on both seasonal and interannual time scales, indi-cating a mass exchange between the SCS and the atmos-phere, the continent and the adjacent seas. One of the potential factors, the freshwater flux, was also investi-gated. The freshwater flux was positive from June to No-vember and changed to negative during the remainder of the cycle. The annual freshwater flux across the SCS might be interpreted in terms of equivalent sea level in-crease from June to November and equivalent sea level decrease from December to May. The freshwater flux also showed a good correspondence to ENSO: decreasing during El Niño years and increasing during La Niña years. The anomalous atmosphere descent motion over the SCS during El Niño years might decrease the cloud cover and eventually reduce the freshwater flux during El Niño years. The decreased freshwater flux played a significant role for the continuous negative sea level anomalies, which would account for about 60 mm sea level fall dur-ing El Niño years.

Acknowledgements This study was supported by the National Basic Re-

search Program of China through Grant No. 973-2007CB-

411807.

References Boyer, T. P., Conkright, M. E., Antonov, J. I., Baranova, O. K.,

Garcia, Gelfed, H., R., et al., 2001. Temporal Distribution of Bathythermograph Profiles. In: World Ocean Database 2001. Vol. 2. NOAA Atlas NESDIS 43. 119pp, CD-ROMs, U.S. Government Printing Office, Washington, D.C.

Carton, J. A., and Giese, B. S., 2008. A reanalysis of ocean cli-mate using simple ocean data assimilation (SODA). Mon. Weather Rev., 136 (8): 2999-3017.

Cazenave, A., and Nerem, R. S., 2004. Present day sea level change: observations and causes. Rev. Geophys., 42, RG3001, doi: 10.1029/2003RG000139.

Cheng, X., and Qi, Y., 2007. Trends of sea level variations in the South China Sea from merged altim-etry data. Glob. Planet. Change, 57: 371-382.

Ducet, N., Le Traon, P. Y., and Reverdin, G., 2000. Global high resolution mapping of ocean circulation from TOPEX/Po-seidon and ERS-1 and -2. J. Geophys. Res., 105 (C8): 19 477-

19 478. Fennig, K, Bakan, S., Grassl, H., Klepp, C. P., and Schulz, J.,

2006. Hamburg Ocean Atmosphere Parameters and Fluxes

from Satellite Data -HOAPS II - monthly mean. electronic pub- lication, WDCC, doi:10.1594/WDCC/HOAPS2_MONTHL Y.

Gill, A. E., 1982. Atmosphere-Ocean Dynamics. Academic Press, San Diego, 662pp.

Ho, C. R., Kuo, N. J., Zheng, Q., and Soong, Y. S., 2000b. Dy-namically active areas in the South China Sea detected from TOPEX/Poseidon satellite altimeter data. Remote Sens. Envi-ron., 71: 320-328.

Ho, C. R., Zheng, Q., Soong, Y. S., Kuo, N. J., and Hu, J. H., 2000a. Seansonal variability of sea surface height in the South China Sea observed with TOPEX/Poseidon altimeter data. J. Geophys. Res., 105 (C6): 13 981-13 990.

Hu, J., Kawamura, H., Hong, Kobashi, H., F., and Wang, D., 2001. 3–6 months variation of sea surface height in the South China Sea and its adjacent ocean. J. Oceanogr., 57 (1): 69-78.

Huang, R., Chen, W., Yang, B., and Zhang, R., 2004. Recent advances in studies of the interaction between the East Asian Winter and Summer Monsoons and ENSO cycle. Adv. Atmos. Sci., 21 (3): 407-424.

Ishii, M., Kimoto, M., and Kachi, M., 2003. Historical ocean subsurface temperature analysis with error estimates. Mon. Weather Rev., 131: 51-73.

Ishii, M., Kimoto, M., Sakamoto, K., and Iwasaki, S. I., 2006. Steric sea level changes estimated from historical ocean sub-surface temperature and salinity analyses. J. Oceanogr., 62 (2): 155-170.

Ishii, M., Shouji, A., Sugimoto, S., and Matsumoto, T., 2005. Objective analyses of SST and marine meteorological vari-ables for the 20th century using COADS and the Kobe Cor-rection. Int. J. Climatol., 25: 865-879.

Klein, S. A., Soden, B. J., and Lau, N. C., 1999. Remote sea surface temperature variations during ENSO: Evidence for a tropical Atmospheric bridge. J. Clim., 12: 917-932.

Li, L., Xu, J., Jing, C., Wu, R., and Guo, X., 2003. Annual variation of sea surface height, dynamics topography and circulation in the South China Sea. Sci. China (Series D), 46 (2): 127-138.

Liu, Q., Jia, Y., Wang, X., and Yang, H., 2001. On the annual cycle characteristics of the sea surface height in South China Sea. Adv. Atmos. Sci., 18 (4): 613-622.

Lombard, A., Cazenave, A., Le Traon, P. Y., and Ishii, M., 2005. Contribution of thermal expansion to present-day sea-level change revisited. Glob. Planet. Change, 47: 1-16.

Qu, T., Kim, Y. Y., Yaremchuk, M., Tozuka, T., Ishida, A., and Yamagata, T., 2004. Can Luzon strait transport play a role in conveying the impact of ENSO to the South China Sea? J. Clim., 17: 3644-3657.

Rong Z., Liu, Y., Zong, H., and Cheng, Y., 2007. Interannual sea level variability in the South China Sea and its response to ENSO. Glob. Planet. Change, 55 (4): 257-272.

Wang, C., 2002. Atmospheric circulation cells associated with the El Niño -South Oscillation. J. Clim., 15: 399-419.

Wang, C., Weisberg, R. H., and Virmani, J. I., 1999. Western Pacific interannual variability associated with the El Niño -Southern Oscillation. J. Geophys. Res., 104: 5131-5149.

Weisberg, R. H., and Wang, C., 1997. A western Pacific oscilla-tor paradigm for the El Niño -Southern Oscillation. Geophys. Res. Lett., 24: 779-782.

(Edited by Xie Jun)