Nonseasonal sea level variations in the Japan//East Sea ... · Nonseasonal sea level variations in the Japan//East Sea from satellite altimeter data Byoung-Ju Choi and Dale B. Haidvogel

Nonseasonal sea level variations in the Japan//East Sea

from satellite altimeter data

Byoung-Ju Choi and Dale B. HaidvogelInstitute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey, USA

Yang-Ki ChoDepartment of Oceanography, Chonnam National University, Kwangju, Korea

Received 16 March 2004; revised 4 October 2004; accepted 19 October 2004; published 24 December 2004.

[1] A merged TOPEX/POSEIDON and ERS-1/2 altimeter data set is used to estimatenonseasonal variations of sea surface height (SSH) in the Japan/East Sea (JES) fromOctober 1992 through February 2002. The altimeter data have a good correlation (0.95)with tide gauge data in the western JES. The nonseasonal variability of the SSH is higherin the southern warm region than in the northern cold region. The root-mean square valueof the nonseasonal SSH is 5–10 cm in the southern area, whereas it is about 4 cm inthe northern area. Empirical orthogonal function analysis of the nonseasonal SSH showsthe first mode to be related to intraseasonal oscillations over the entire JES and the secondmode to interannual path variations of the Tsushima Warm Current (TWC). The pathchange of the TWC is responsible for the high variability of SSH in the southernregion. INDEX TERMS: 4223 Oceanography: General: Descriptive and regional oceanography; 4243

Oceanography: General: Marginal and semienclosed seas; 4275 Oceanography: General: Remote sensing and

electromagnetic processes (0689); 4556 Oceanography: Physical: Sea level variations; 4572 Oceanography:

Physical: Upper ocean processes; KEYWORDS: sea level, nonseasonal variations, Japan/East Sea, altimeter

data, empirical orthogonal function analysis

Citation: Choi, B.-J., D. B. Haidvogel, and Y.-K. Cho (2004), Nonseasonal sea level variations in the Japan/East Sea from satellite

altimeter data, J. Geophys. Res., 109, C12028, doi:10.1029/2004JC002387.

1. Introduction

[2] The Japan/East Sea (JES) is a marginal sea located inthe northwest Pacific Ocean. It comprises the Ulleung Basinand the Yamato Basin in the south, and the Japan Basin inthe north (Figure 1a). The Yamato Rise is located in themiddle. Water is exchanged through narrow channels withsill depths not exceeding 200 m while the maximum depthof the JES is about 3900 m. Twelve ground tracks ofTOPEX/POSEIDON (T/P) pass over the JES with a dis-tance between the tracks of about 250 km (Figure 1b) andan orbit repeat period of 9.9 days. ERS-1/2 have 41 groundtracks, a spacing of about 70 km, and a repeat cycle every35 days.[3] The Tsushima Warm Current (TWC) enters the JES

through the Korea (Tsushima) Strait (Figure 2a). WarmTWC water is confined to the upper few hundred metersin the JES owing to the shallow sill depths. After the TWCenters the JES, it generally divides into three branches: theNearshore Branch (NB) along the Japanese coast, the EastKorean Warm Current (EKWC) along the east coast ofKorea, and the Offshore Branch (OB) into the south of theUlleung Basin [Katoh, 1994; Hase et al., 1999]. Thethickness of the warm water decreases with latitude untilthe permanent thermocline outcrops along the polar front.

The front along 38�N to 40�N separates the northern coldwater region and the southern warm water region [Prellerand Hogan, 1998; Tomczak and Godfrey, 1994].[4] Sea surface steric height, which is calculated relative

to 500 dbar using temperature and salinity climatologicaldata from the Japan Oceanographic Data Center (JODC)and the Korea Oceanographic Data Center (KODC), isshown in Figure 2b. Geostrophic surface currents flow fromthe east coast of Korea to the Tsugaru and Soya straits withsome meandering along the way. The NB is not identified inthis figure because it flows along an isobath shallower than200 m along the Japanese coast [Hase et al., 1999]. TheEKWC and the polar front are smeared because the stericheight is computed from long-term mean, 1� � 1� griddedhydrographic data.[5] Instantaneous composite dynamic sea level (DSL)

will be estimated by combining the mean sea surface stericheight calculated from the long-term mean hydrographicdata and sea surface anomalies derived from the altimeters[Korotaev et al., 2003]. Composite DSL is the steric heightrelative to 500 dbar in this paper. Horizontal maps ofcomposite DSL have the same physical meaning as thoseof dynamic height for the analysis of surface circulation.The composite DSL can be estimated by combining a meansea surface height calculated by a three-dimensional numer-ical model and the sea surface anomalies from the altimeterdata [Morimoto and Yanagi, 2001]. Although gradients ofsea level across the polar front are in principle better

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, C12028, doi:10.1029/2004JC002387, 2004

Copyright 2004 by the American Geophysical Union.0148-0227/04/2004JC002387$09.00

C12028 1 of 12

maintained in a mean field from a numerical model thanin historical climatological data, the locations of the frontnonetheless vary depending on the details of the modelsimulation such as model used and parameters chosen.For this reason, we choose to use the mean field fromlong-term obervational data rather than from a numericalmodel.[6] Hirose and Ostrovskii [2000] found quasi-biennial

variability in the southern Yamato Basin from along-trackT/P data from 1992 to 1997. Since the along-track T/Paltimeter data have a limited spatial sampling interval(250 km), the spatial structure of the quasi-biennial vari-ability and mesoscale surface circulations could not be wellresolved. Morimoto and Yanagi [2001] used 3.5 years ofERS-2 data and carried out empirical orthogonal function(EOF) analysis of composite DSL. Because seasonal vari-ation of the mean surface circulation was dominant (93%)in the first two modes, other intraseasonal and interannualoceanic processes were buried and indiscernible. Analysisof 14-year-long tide gauge and hydrographic data setsreveals migration of the polar front, and identifies an eddyassociated with intraseasonal and interannual variabilitywith 43% of the total variance in the western JES [Kim etal., 2002]. This analysis was limited to the western JESbecause the hydrographic data did not have uniform datadistribution in time and space.[7] This study aims to estimate nonseasonal variability of

sea surface height in the JES using a merged T/P and ERS-

1/2 altimeter data set, and to find connections among theinterannual variations at different locations. The mergedaltimeter data are described and validated against tide gaugedata in section 2. Nonseasonal variabilities are examined insection 3. Major factors which induce sea level variabilityare discussed in section 4. Finally, we summarize our resultswith remarks in section 5.

2. Validation of Altimeter Data

[8] The merged T/P and ERS-1/2 altimeter data at 1/3degree spatial intervals and with a repeat cycle of 7 dayswere obtained from the Space Oceanography Division ofCollecte Localisation Satellites (CLS) in Toulouse, France[Le Traon et al., 1998; Ducet et al., 2000]. Data fromOctober 1992 through February 2002 are used in this study.T/P data are available for the whole period, but there are nomerged T/P and ERS fields between January 1994 andMarch 1995 (ERS-1 geodetic phase).[9] Typically, tidal correction of altimeter data is car-

ried out with the use of global tidal models such asCenter for Space Research (CSR), Goddard Ocean Tide(GOT), and finite element simulation (FES) models [Eanesand Bettadpur, 1995; Ray, 1999; Le Provost et al., 1994].However, it is difficult to remove the tidal signals completelyfrom altimeter data in the marginal seas using the global tidalmodels because the global models do not have enoughresolution in the marginal seas [Morimoto et al., 2000].

Figure 1. Bathymetry and satellite ground tracks in the Japan/East Sea. (a) Dashed line denotes the200 m isobath and solid lines the 1000, 2000, and 3000 m isobaths. JB, YB, UB, UI, YR, KS, TS, andSS stand for the Japan Basin, Yamato Basin, Ulleung Basin, Ulleung Island, Yamato Rise, Korea Strait,Tsugaru Strait, and Soya Strait, respectively. (b) The ground tracks of TOPEX/POSEIDON (ERS-1/2)are shown as thick (thin) lines. Numbers from 1 to 7 along the coast indicate location of tide gauges atSokcho, Mukho, Pohang, Izuhara, Hamada, Toyama, and Fukaura, respectively.

C12028 CHOI ET AL.: SEA SURFACE HEIGHT IN THE JAPAN/EAST SEA

2 of 12

C12028

Because the semidiurnal and diurnal periods of tides aremuch shorter than the sampling interval of any satellitealtimeter, these tidal signals can appear in altimeter SSHdata as aliased signals at periods much longer than the periodof the original tides [Schlax and Chelton, 1994; Chen andEzraty, 1997].[10] The merged altimeter data were compared with tide

gauge data at Ulleung Island. The altimeter data wereobtained from the closest grid point to the tide gaugestation. We expect the tide gauge data to properly reflectoceanic sea level variations because the island is small andthe depth of the surrounding sea is over 1000 m. Atmo-spheric pressure effects were eliminated from daily meansea level from the tide gauge using a barometric factor of1 cm/mbar. To eliminate the signals shorter than a month inperiod, a lowpass filter (half-power period of 30 day) wasapplied. The merged altimeter sea level every 7 days(circles) is well correlated with the tide gauge data (line)in Figure 3a. The correlation coefficient between the altim-eter and tide gauge data is 0.95, with a root-mean square(RMS) value of the difference of 3.0 cm. These results arecomparable to or better than those of TOPEX in the tropicalPacific, where a correlation of 0.66 and an RMS differenceof 4.3 cm are obtained [Mitchum, 1994]. Since seasonalvariation of SSH is large at Ulleung Island, the meanseasonal cycle has been removed in Figure 3b. The meanseasonal cycle is defined by combining the time series of theannual and semiannual constituents obtained by harmonicanalysis. The correlation coefficient between nonseasonalSSH from the altimeter and that from tide gauge is 0.93,with an RMS value of the difference of 2.7 cm. Simple

averaging of the T/P data in time and space can reduce errorestimates to the order of 2 cm [Cheney et al., 1994].[11] In semienclosed marginal seas such as the JES and

the Mediterranean Sea, sea level does not respond inverse-barometrically to the atmospheric pressure at high frequen-cies (2–20 days) because the straits limit the waterexchanges at high frequencies. Simple analytical models[Candela, 1991; Lyu et al., 2002], which take into accountof friction in the straits, removes these atmospheric pres-sure-driven fluctuations better than the standard inversebarometer method. In the Mediterranean Sea, the RMSdifference between the model correction and inverse ba-rometer correction is about 2 cm [Le Traon and Gauzelin,1997]. In the JES, the two corrections have an RMSdifference of 2–3 cm [Nam et al., 2004]. Because themerged altimeter data set was processed using the inversebarometer method, the altimeter data set may contain about2–3 cm of error from aliasing of the high-frequencyfluctuations in the JES.

3. Results

3.1. Variability of Spatial-Mean Sea Level

[12] The spatial mean of SSH over the JES has beencalculated every 7 days with a latitude-dependent weightingfunction to ensure an equal-area average. The spatiallyaveraged SSH variations reflect changes in total watervolume in the JES. The time series is decomposed into alinear trend, a mean seasonal cycle, and nonseasonal com-ponents (Figure 4). The linear trend is obtained by leastsquares fitting of a straight line to the data. The three

Figure 2. (a) Schematic surface currents in summer and (b) mean sea surface steric height relative to500 dbar. TWC, NB, OB, EKWC, NKCC and LC in Figure 2a stand for the Tsushima Warm Current,nearshore branch, offshore branch, East Korean Warm Current, North Korean Cold Current, and LimanCurrent, respectively. Mean surface currents flow northeastward and the contour interval is 2 cm inFigure 2b.


3 of 12

C12028

Figure 3. Comparison of tide gauge data (line) and altimeter data (circles) at Ulleung Island. (a) SSHand (b) nonseasonal SSH. The daily sea levels are nontidal residuals that have been low-pass filtered toeliminate periods shorter than 30 days.

Figure 4. Spatial mean of sea surface height from October 1992 to February 2002. (a) Mean SSH withthe linear trend, (b) mean seasonal cycle, and (c) nonseasonal component. Note that the vertical scalevaries from Figure 4a to Figure 4b to Figure 4c.


4 of 12

C12028

components account for 1.4%, 68.1% and 30.5% of the totalvariance, respectively.[13] SSH increases gradually from October 1992 to

February 2002 with a slope of 0.58 cm/year, and is lowestin February 1996 and highest in October 1999. The range ofthe mean seasonal cycle is 13.8 cm and that of thenonseasonal component is 16.5 cm. The nonseasonal com-ponent shows both intraseasonal and interannual variabilityof total water volume. The time series of nonseasonal SSHis dominated by 50 to 200 day period fluctuations. Variance-preserving spectra of the nonseasonal SSH will be presentedin section 4.1.

3.2. Spatial Structure of Nonseasonal Variability

[14] SSH variability is not spatially uniform in the JESbecause of mesoscale activity such as the meandering of theTWC and migration of eddies. The spatial structure ofthe nonseasonal component is obtained by removing boththe linear trend and the mean seasonal cycle from thealtimeter data at every grid point.[15] A linear trend exists at most points and is presumably

related to long-period oceanic processes. The mean seasonalsignal mostly reflects seasonal heating and cooling of thesurface layer. However, the mean seasonal cycle mayinclude a seasonal portion of mesocale variability, whichis enhanced or diminished by seasonal changes in stratifi-cation. As a result, when we remove the mean seasonalcycle from the data, the seasonal portion of the mesoscalevariability may be lost. The nonseasonal component con-tains intraseasonal and interannual variations which arerelated to basin-wide oscillations, north-south migration ofthe polar front, meandering of the TWC, and eddy activities.[16] RMS values of the nonseasonal component are 5–

10 cm south of 40�N and southwest of the Tsugaru Strait

(Figure 5a). The largest variability, 10 cm RMS, occurs inthe southern Yamato Basin (135�E, 38�N) where energeticquasi-biennial variability has been reported by Hirose andOstrovskii [2000]. The Ulleung Basin, Yamato Basin andsouthwest Japan Basin (131.5�E, 39.5�N) also experiencehigh nonseasonal variability. The shallow region betweenthe Ulleung Basin and Yamato Basin has smaller variabilitythan the surrounding deep sea. The nonseasonal componentaccounts for more than 60% of the total variance in thewestern interior, the central part along 39�N, west of theTsugaru Strait, and offshore of Siberia (Figure 5b).

3.3. EOF Analysis of Nonseasonal Variability

[17] To examine statistically the temporal and spatialvariability of the nonseasonal component of SSH, an EOFanalysis was performed on the 488 10-day cycles of 1/3degree gridded nonseasonal SSH data. The data wereweighted with a weighting function

ffiffiffiffiffiffiffiffiffiffi

cos qp

to ensure anequal-area treatment in the EOF analysis where q is latitude[North et al., 1982; Hendricks et al., 1996]. The EOFs wereevaluated using singular value decomposition [Kelly, 1988],which separates the nonseasonal component of SSH into488 orthogonal spatial modes and the associated amplitudetime series. The orthogonal spatial modes were renormal-ized by the latitude-dependent weighting factor. We analyzehere only the first two dominant modes, which are statisti-cally independent and significant according to the criteria ofKelly [1988].[18] Figure 6 shows the spatial patterns of the EOFs; the

associated amplitude time series are plotted in Figure 7. Theamplitude time series are normalized by their respectivestandard deviations. The spatial field plots correspond to anormalized amplitude time series value of +1, with units ofcentimeters. The SSH contribution of an EOF mode at any

Figure 5. (a) The root-mean square (RMS) values of nonseasonal SSH and (b) percent of total variancein the nonseasonal variation. Contour interval is 1 cm in Figure 5a and 10% in Figure 5b. Gray colorindicates values larger than 60% in Figure 5b.


5 of 12

C12028

given time is therefore found by scaling the spatial-fieldmap by the appropriate amplitude time series value. Eachdot in the amplitude time series represents a 7-day estimate.The amplitude time series of the first mode has intraseasonal

and interannual variations while that of the second modeshows primarily interannual variation.[19] The first mode accounts for 38% of the total

nonseasonal variance. Since the spatial values of the first

Figure 6. The two empirical orthogonal functions (EOFs) describing the nonseasonal component ofSSH. (a) Higher than 4 cm of spatial values are stippled in the first mode; (b) negative values are shadedin the second mode.

Figure 7. The amplitude time series of the first two EOF modes. The time series have been normalizedby their standard deviations and each dot represents a 7-day estimate.


6 of 12

C12028

EOF are positive over the entire domain, the first modeextracts simultaneous sea level variation and representstotal water volume change within the JES. Backgroundvalues of the first EOF are about 3 cm. High spatialvalues of the first EOF are distributed over the transitionzone between the cold and the warm water regions. Someregions have the first EOF larger than 4 cm (dotted areain Figure 6a): the Ulleung Basin, the southwest JapanBasin (132�E, 39.5�N), the southwestern Yamato Basin,and northern Yamato Basin. Thus, if the total volume ofwater increases (diminishes) within the JES, a largeportion of the changed volume is added to (is subtractedfrom) these regions. The amplitude of the first mode(Figure 7a) is a normalized value of the spatially aver-aged nonseasonal SSH, so that there are intraseasonalvariabilities (50 to 200 day periods) and interannualvariations (300 to 700 day periods) in the time seriesas found in the time series (Figure 4c) and the spectra ofspatial-mean SSH.[20] The second mode represents 8% of the total nonsea-

sonal variance. The EOF of the second mode in Figure 6bshows a succession of three minima (gray areas) and fourmaxima (white areas) along 39�N. High values of this EOFare distributed only between 36�N and 40�N. The amplitudetime series of the second mode (Figure 7b) has negativeminima in July 1993, September 1995, September 1999 andOctober 2001, with positive maxima in October 1994,November 1996, September 1997 and September 2000.The magnitude of the extreme values increases from 1993to 2000.

4. Discussion

[21] Physical interpretations of the two dominant modesare sought in this section using additional data includingsea level from the coastal tide gauges and subsurfacetemperatures from concurrent observations. Detailed driv-ing mechanisms are not pursued. Because interannualvariations in the first mode are smaller than eitherinterannual fluctuations in the second mode or intrasea-

sonal variations in the first mode (Figure 7), the intra-seasonal signals in the first mode and the interannualvariations in the second are the focus of the followingsections.

4.1. Intraseasonal Oscillations in the Japan//East Sea

[22] Rapidly propagating barotropic long waves cansimultaneously change sea level over a semienclosed basinof the dimensions of the JES. The intraseasonal variationsof SSH in the first mode may thus be related to basin-wideoscillations of sea surface height. Such oscillations occurthroughout the data record as shown in the amplitude timeseries of the first mode (Figure 7a).[23] As an example, there are intraseasonal oscillations

from August 1995 to January 1996. During this period, theamplitude time series reaches a minimum in September anda maximum in October. Figure 8 shows the composite DSLdistribution in these two months; sea level is uniformlyraised about 11 cm from September to October with onlyslight changes of surface circulation. As the amplitude timeseries hits a minimum in November and a maximum inDecember, the sea level of the basin uniformly goes down11 cm in November and up again 7 cm in December.[24] As we show next, tide gauges along the coast of the

JES also record these intraseasonal variations. Tides whoseperiods are shorter than a month were removed from thehourly sea level data by harmonic analysis, and the inverse-barometric component was eliminated. To eliminate signalsshorter than a month in period, a lowpass filter with a half-power period of 30 day was applied. Lastly, the meanseasonal cycle was subtracted from the time series. Eachof the resulting time series is successively offset by 10 cm inFigure 9 to emphasize that the variations are nearly uniformin phase and amplitude. The time series represent nontidaland nonseasonal SSH, and their amplitudes are as large as10 cm. Note that the nontidal and nonseasonal SSH timeseries at a given tidal station can differ from those at otherstations because of local coastal effects such as wind setupand set-down, river runoff, or adjacent surface currentvariations.

Figure 8. Composite dynamic sea level (DSL) on (a) 6 September and (b) 4 October 1995. Sea level israised about 11 cm from Figure 8a to Figure 8b over the entire Japan/East Sea.


7 of 12

C12028

[25] Estimates of coherence and phase, computed betweenpairs of tide gauge data sets, are shown in Figure 10.Coherence measures the linear time-invariant relationshipbetween two time series and phase indicates whetherone time series leads or lags the other. Sea level atSokcho, Izuhara and Toyama are selected to representthe Korean coast, the Korea Strait, and the Japanesecoast, respectively. For periods above 60 days, coher-ences between the time series are found to be higherthan the 95% confidence level and the phase differencesare near zero.

[26] Since the amplitude of the first mode is a normal-ized measure of the spatially averaged nonseasonal SSH,we have compared the spatially averaged nonseasonal SSHfrom the altimeters with sea level from tide gauges in thestraits. The nonseasonal component of spatial-mean SSHwithin the JES is correlated with sea level from a tidegauge (129.3�E, 34.2�N) in the middle of the Korea Straitwith a correlation coefficient of 0.86 (Figure 11). Thedaily sea level data from the tide gauge were adjusted withdaily atmospheric pressure and the adjusted sea level wasfiltered with a lowpass filter (half-power period of 30 day).

Figure 9. Nonseasonal SSH from tide gauges along the coast of the Japan/East Sea. Locations of thetide gauges are Sokcho, Mukho, Pohang, Izuhara, Hamada, Toyama, and Fukaura (see Figure 1b) fromtop to bottom.

Figure 10. (a) Coherences and (b) phases between the nonseasonal SSH time series from tide gauges atSokcho, Izuhara, and Toyama. Phase is omitted if coherence is lower than the significance level.


8 of 12

C12028

Coherence analysis shows that they are highly coherentwith near-zero phase difference above a 60-day period(Figure 11). By comparison, the nonseasonal componentof sea level from tide gauges in the Tsugaru Strait hasrelatively less correlation (0.42) with spatial-mean SSH.The high coherence between sea level in the Korea Straitand spatial-mean SSH within the JES indicates that thebarotropic intraseasonal variability of SSH may enterthrough the Korea Strait and modulate sea surface overthe JES. Both power spectra of nonseasonal SSH atIzuhara (dashed line) and of the spatial-mean SSH withinthe JES (solid line) have dominant energy in the intra-seasonal band from 50 to 200 day periods (Figure 12).There are three major peaks around 155, 100 and 80 daysin both tide gauge and altimeter data.[27] Wind, atmospheric pressure, and Rossby or Kelvin

waves have been known to cause intraseasonal variability ofsea level in the Pacific and Indian Oceans [Enfield, 1987;Qiu et al., 1999]. The spatial-mean SSH within the JES isinfluenced by differences between the inflow and outflowtransports through the straits. Hence the driving mecha-nisms of the spatial-mean SSH changes within the sea areclosely related to those of the water exchanges through thestraits. The possibility of barotropic oscillations induced byatmospheric pressure fluctuations of subinertial period (3 to5 days) in the JES was proposed by Lyu et al. [2002]. Usinga simple analytical barotropic model, Lyu [2003] suggestedthat monthly and interannual variations of the inflow

through the Korea Strait and the mean sea level within theJES may be induced by sea level changes outside the Korea,Tsugaru and Soya Straits. Numerical model studies arepresently underway to explore the effects of wind stress in

Figure 11. (a) Nonseasonal components of SSH at Izuhara (thin line) and of spatial-mean SSH withinthe Japan/East Sea (thick line). (b) Coherence and (c) phase between the SSH at Izuhara and the spatial-mean SSH within the Japan/East Sea. The nonseasonal component is departure from the linear trend andthe mean seasonal cycle.

Figure 12. Power spectra of the nonseasonal SSH atIzuhara (dashed line) and the spatial-mean SSH within theJapan/East Sea (solid line) in variance-preserving form. Thespectra of the nonseasonal SSH at Izuhara (dashed line) israised by 5 cm2 for comparison with the other. PSD standsfor power spectral density.


9 of 12

C12028

the straits and sea level changes outside the straits onintraseasonal basin-wide variability.

4.2. Path Selection of the Main Tsushima WarmCurrent

[28] Mean sea surface height from long-term hydro-graphic data has a north-south background gradient, i.e.,SSH is high in the south and low in the north, so that themean geostrophic surface current flows from the Korea Straitto the Tsugaru and Soya Straits (Figure 2b). As the normal-ized amplitude time series of the second mode approaches apositive maximum, the north-south gradient of SSHincreases across the southern zero contour line in the secondEOF by increasing SSH in the south and decreasing SSH inthe middle, thus enhancing the north-south backgroundgradient. As a consequence, a majority of the TWC flowsalong the southern zero contour line and warm water isgenerally confined to the southern region (southern whitearea of Figure 6b). As a result of the surface flow pattern,

most of the warm water accumulates in the southern YamatoBasin and forms a large warm eddy. When the EOFamplitude approaches a negative minimum, the backgroundnorth-south gradient of SSH diminishes across the southernzero contour line and warm water spreads over the region ofnegative EOF (gray area of Figure 6b) and forms severaleddies in this region. In this case, the TWC meanders amongthe warm and cold eddies from the east coast of Korea tothe Tsugaru Strait, and cold water fills the southernYamato Basin to form a cold eddy or tongue (Figure 8).[29] From each instantaneous composite DSL map, the

strongest horizontal gradient of DSL (strongest surfacecurrent) is traced by a line when the amplitude time seriesof the second mode has either a maximum (Figure 13a) or aminimum (Figure 13b). In most cases, the line coincideswith a branch of the TWC and the submerged polar front.When the amplitude time series reaches a positive maxi-mum, the strong surface current flows northeastward fromthe Korea Strait to the Tsugaru Strait. When the amplitude

Figure 13. Flow patterns of the TWC with (a) amplitude maximum of the second mode and (b) withamplitude minimum. Solid, dashed and dotted lines are strongest surface currents derived from compositeDSL on 6 November 1996, 17 September 1997 and 27 September 2000 for the positive maxima(13 September 1995, 22 September 1999, and 17 October 2001 for the negative minima). (c and d)The contour interval is 2.5�C and small dots represent temperature observation stations.


10 of 12

C12028

time series hits a minimum, the surface current flowsnorthward along the Korean coast, reaches about 40�N inthe western JES, meanders to circumvent the southernYamato Basin, and flows toward the Tsugaru Strait.[30] The amplitude time series of the second mode is a

maximum in October 2000 and the main TWC flows alongthe dotted line in Figure 13a. Temperature observed at100 m depth from September to October 2000 shows thatwarm water from the Korea Strait accumulates in thesouthern Yamato Basin. The temperature of the warm wateris higher than 15�C in the southern Yamato Basin, and thecold water (<5�C) occupies the Ulleung Basin (Figure 13c).Temperature data were obtained from JODC and KODC.The amplitude time series is a minimum in October 1999and the main TWC flows along the dashed line inFigure 13b. The subsurface temperature from September toOctober 1999 shows that warm water fills the Ulleung Basin(131�E, 37�N) and a cold eddy (tongue) intrudes from thenorth into the southern Yamato Basin (Figure 13d). Henceinterannual path variations of the TWC mimic the subsur-face temperature distribution and determine the location ofthe submerged polar front, which is the moving boundarybetween the cold and the warm water regions.[31] Hirose and Ostrovskii [2000] found strong interan-

nual variation of SSH within the southern Yamato Basin andnamed it a quasi-biennial variation. They related the SSHvariation to subsurface water density change using hydro-graphic data. Cold and fresh water appeared in July 1993and in October 1995 while a warm and saline eddydeveloped in October 1996 in the southern Yamato Basin.They suggested that a weak summer monsoon wind stresscurl field can excite the quasi-biennial variation. They alsoassimilated T/P along track data into a reduced gravitymodel and showed interactions between the path of theTWC and sea level anomalies of the southern Yamato Basinin October 1995 and October 1996. The events are wellcorrelated with the amplitude time series of the secondmode, i.e., the second mode of our analysis is consistentwith their assimilated model results.[32] Our EOF analysis shows that the quasi-biennial

variation is, in reality, related to path variations of theTWC: the TWC selects its main path, either the EKWCor the OB. When the main path of the TWC selects the OB,warm water accumulates in the southern Yamato Basin.Otherwise, the EKWC gains strength and warm waterspreads over the western part of the sea. This interannualprocess occurs without accompanying changes in totalwater volume in the JES, so that it does not appear in thetime series of spatial-mean SSH.[33] When the EKWC is strong and separates from the

coast at about 38�N, the polar front aligns along 39�N to40�N, the TWC meanders widely and its associated warmand cold eddies become evident in the western part of thesea (Figures 8 and 13b). The wavelength of the meander-ing is about 260 km estimated from the second EOF inFigure 6b. This is comparable to the magnitude (300 km)estimated from hydrographic data [Moriyasu, 1972].

5. Summary and Concluding Remarks

[34] Recent studies of sea level in the JES have foundstrong quasi-biennial variability in the southern Yamato

Basin and significant interannual variation at UlleungIsland. The previous studies emphasized local processesand sampling intervals of the sea level were limited in timeand space. Assimilation of T/P along track data into a reducedgravity model improved our understanding of connectionsamong the interannual variations at different locations.[35] The merged T/P and ERS-1/2 altimeter data sets are

appropriate to study mesoscale surface ocean circulationbecause of their small temporal (7 day) and spatial (1/3�)sampling intervals. We used the merged altimeter data toexamine variability of sea level and surface circulation fromOctober 1992 to February 2002. The altimeter data corre-lates well (0.95) with concurrent tide gauge data at UlleungIsland, with an RMS difference of about 3 cm.[36] The nonseasonal component in spatial-mean SSH

within the JES accounts for 30.5% of the total varianceand has a range of 16.5 cm. RMS values of the nonseasonalSSH are about 4 cm in the northern cold water region andare 5–10 cm in the southern warm water region. Thenonseasonal variation is dominant in the western side ofthe JES, into the eastern side along 39�N, and off theTsugaru Strait.[37] EOF analysis has been used to decompose the

nonseasonal SSH into 488 modes. The first two modesaccount for 38% and 8% of the total variance, respectively.The first mode isolates intraseasonal oscillations over theentire JES. Basin-wide oscillations of sea surface arethought to be responsible for the intraseasonal variations.The oscillations within the JES are correlated with sea levelchange in the Korea Strait. The second mode captures theinterannual path selection of the TWC, which determinesthe strength of the EKWC, the location of the polar front,and the accumulation of warm water in the southern YamatoBasin.[38] Temporal and spatial variability of sea level in the

JES have been quantified in this paper. However, thedriving mechanisms of both intraseasonal oscillations andinterannual path selection of the main TWC need furtherinvestigation in the future. Potential forcing processesinclude sea level change in the western Pacific Ocean,and interannual variation of monsoonal wind over the JES.

[39] Acknowledgments. We appreciate support from the Office ofNaval Research (ONR-N000140010230), the National Science Foundation(NSF-0196458), and the Institute of Marine and Coastal Sciences, RutgersUniversity. Y.-K. Cho was supported by the Meteorological ResearchInstitute, Korea Meteorological Administration (ARGO program). TheCLS Space Oceanography Division supplied the merged T/P and ERS-1/2data. JODC and KODC provided temperature and salinity data. Air pressuredata were obtained from the Korea Meteorological Administration and theNOAA-CIRES Climate Data Center.

ReferencesCandela, J. (1991), The Gibraltar Strait and its role in the dynamics of theMediterranean Sea, Dyn. Atmos. Oceans, 15, 267–299.

Chen, G., and R. Ezraty (1997), Non-tidal aliasing in seasonal sea-levelvariability and annual Rossby waves as observed by satellite altimetry,Annal. Geophys., 15, 1478–1488.

Cheney, R., L. Miller, R. Agreen, N. Doyle, and J. Lillibridge (1994),TOPEX/Poseidon: The 2-cm solution, J. Geophys. Res., 99, 24,555–24,563.

Ducet, N., P. Y. Le Traon, and G. Reverdin (2000), Global high-resolutionmapping of ocean circulation from the combination of T/P and ERS-1/2,J. Geophys. Res., 105, 19,477–19,498.

Eanes, R., and S. Bettadpur (1995), The CSR 3.0 global ocean tidemodel, Tech. Memo CSR-TM-95-06, Cent. for Space Res., Univ. ofTex., Austin.


11 of 12

C12028

Enfield, D. B. (1987), The intraseasonal oscillation in eastern Pacific sealevels: How is it forced?, J. Phys. Oceanogr., 17, 1860–1876.

Hase, H., J.-H. Yoon, and W. Koterayama (1999), The current structure ofthe Tsushima Current along the Japanese coast, J. Oceanogr., 55, 217–235.

Hendricks, J. R., R. R. Leben, and G. H. Born (1996), Empirical orthogonalfunction analysis of global TOPEX/POSEIDON altimeter data andimplications for detection of global sea level rise, J. Geophys. Res.,101, 14,131–14,145.

Hirose, N., and A. G. Ostrovskii (2000), Quasi-biennial variability in theJapan Sea, J. Geophys. Res., 105, 14,011–14,027.

Katoh, O. (1994), Structure of the Tsushima Current in the southwesternJapan Sea, J. Oceanogr., 50, 317–338.

Kelly, K. A. (1988), Comment on ‘‘Empirical orthogonal function analysisof advanced very high resolution radiometer surface temperature patternsin Santa Barbara Channel’’ by G. S. E. Lagerloef and R. L. Bernstein,J. Geophys. Res., 93, 15,753–15,754.

Kim, K., Y.-K. Cho, B.-J. Choi, Y.-G. Kim, and R. C. Beardsley (2002), Sealevel variability at Ulleung Island in the East (Japan) Sea, J. Geophys.Res., 107(C3), 3015, doi:10.1029/2001JC000895.

Korotaev, G., T. Oguz, A. Nikiforov, and C. Koblinsky (2003), Seasonal,interannual, and mesoscale variability of the Black Sea upper layer cir-culation derived from altimeter data, J. Geophys. Res., 108(C4), 3122,doi:10.1029/2002JC001508.

Le Provost, C., M. L. Genco, F. Lyard, P. Vincent, and P. Canceil (1994),Tidal spectroscopy of the World Ocean tides from a finite element hydro-dynamic model, J. Geophys. Res., 99, 24,777–24,797.

Le Traon, P. Y., and P. Gauzelin (1997), Response of the Mediterraneanmean sea level to atmospheric pressure forcing, J. Geophys. Res., 102,973–984.

Le Traon, P. Y., F. Nadal, and N. Ducet (1998), An improved mappingmethod of multisatellite altimeter data, J. Atmos. Oceanic Technol., 15,522–533.

Lyu, S. J. (2003), Temporal variation of the transport from cable voltageacross the Korea Strait and its mechanism, Ph.D. thesis, 136 pp., SeoulNatl. Univ., Seoul.

Lyu, S. J., K. Kim, and H. T. Perkins (2002), Atmospheric pressure-forcedsubinertial variations in the transport through the Korea Strait, Geophys.Res. Lett., 29(9), 1294, doi:10.1029/2001GL014366.

Mitchum, G. T. (1994), Comparison of TOPEX sea surface heights and tidegauge sea levels, J. Geophys. Res., 99, 24,541–24,553.

Morimoto, A., and T. Yanagi (2001), Variability of sea surface circulation inthe Japan Sea, J. Oceanogr., 57, 1–13.

Morimoto, A., T. Yanagi, and A. Kaneko (2000), Tidal correction ofaltimeter data in the Japan Sea, J. Oceanogr., 56, 31–41.

Moriyasu, S. (1972), The Tsushima current, in Kuroshio: Its PhysicalAspects, edited by H. Stommel and K. Yoshida, pp. 353–369, Univ.of Tokyo Press, Tokyo.

Nam, S. H., S. J. Lyu, Y. H. Kim, K. Kim, J.-H. Park, and D. R. Watts(2004), Correction of TOPEX/POSEIDON altimeter data for nonisostaticsea level response to atmospheric pressure in the Japan/East Sea, Geo-phys. Res. Lett., 31, L02304, doi:10.1029/2003GL018487.

North, G. R., T. L. Bell, and R. F. Cahalan (1982), Sampling errors in theestimation of empirical orthogonal functions, Mon. Weather Rev., 110,699–706.

Preller, R. H., and P. J. Hogan (1998), Oceanography of the Sea of Okhotskand Japan/East Sea, Coastal segment (11,S), in The Sea, vol. 11, edited byA. L. Robinson and K. H. Brink, pp. 429–481, John Wiley, Hoboken,N.J.

Qiu, B., M. Mao, and Y. Kashino (1999), Intraseasonal variability in theIndo-Pacific Throughflow and the regions surrounding the IndonesianSeas, J. Phys. Oceanogr., 29, 1599–1618.

Ray, R. (1999), A global ocean tide model from TOPEX/Poseidon Altime-try: GOT99.2, NASA Tech. Memo NASA/TM-1999-209478, GoddardSpace Flight Cent., NASA, Greenbelt, Md.

Schlax, M. G., and D. B. Chelton (1994), Aliased tidal errors in TOPEX/POSEIDON sea surface height data, J. Geophys. Res., 99, 24,761–24,775.

Tomczak, M., and J. S. Godfrey (1994), Regional Oceanography: AnIntroduction, 442 pp., Elsevier, New York.

��Y.-K. Cho, Department of Oceanography, Chonnam National University,

Kwangju 500-757, Korea.B.-J. Choi and D. B. Haidvogel, Institute of Marine and Coastal Sciences,

Rutgers University, New Brunswick, NJ 08901-8521, USA. ([email protected])


12 of 12

C12028

Documents

Nonseasonal sea level variations in the Japan//East Sea ... · Nonseasonal sea level variations in the Japan//East Sea from satellite altimeter data Byoung-Ju Choi and Dale B. Haidvogel