Inter-annual and decadal fluctuations of the Kuroshioin East China Sea and connection with surfacefluxes of momentum and heatJia Wang1 and Lie-Yauw Oey1,2

1National Central University, Jhongli City, Taoyuan County, Taiwan, 2Princeton University, Princeton, New Jersey, USA

Abstract Despite attempts in the literature to link large-scale wind to long-term variations of the Kuroshio inEast China Sea (ECS), the driving mechanism(s) are unknown. Here we use satellite altimetry data, wind, surfaceheat fluxes and sea-surface temperatures (SST) to explain the low-frequency fluctuations of Kuroshio path(KP) in ECS. The dominant fluctuations occur northeast of Taiwan. The KP correlates best with the PTO index ofChang and Oey (2012), less with the PDO index and a Kuroshio transport index, and poorly with other climateindices. The forcing are wind stress curl and surface heat flux northeast of Taiwan, which produce a thermoclinetilt along the Kuroshio. Shelf’s SST warms and cools in response to onshore and offshore KP, but prominentchange occurs at a localized coastal zone shoreward of the above dominant KP-fluctuations. Over the past2 decades, the KP has shifted onshore, coincident with a coastal warming trend.

1. Introduction

The Kuroshio in the western North Pacific begins off the coast of Philippines near 12°N–13°N where the NorthEquatorial Current bifurcates [Nitani, 1972]. By the time the current reaches east of Taiwan, a matured westernboundary current, with speeds exceeding 1ms�1, has formed [Johns et al., 2001]. Northeast of Taiwan, asthe Kuroshio enters the East China Sea (ECS), the current makes a sharp right turn and flows east-northeastwardfollowing the continental shelf break toward Japan. Since Nitani’s [1972] overview, there has been significantprogress on understanding the dynamics of short-term and seasonal variability such as Kuroshio intrusions andfrontal eddies, cross-shelf mixing, and “cold dome” northeast of Taiwan [see reviews by Isobe, 2008 andMatsunoet al., 2009; and summaries inOey et al., 2010;Gawarkiewicz et al., 2011; Jan et al., 2011 and Gopalakrishnan et al.,2013]. By contrast, research on longer time-scale variability, which is the main focus of this work, has beenhampered by a lack of long-term observations. As the ocean warms and sea level rises in a changing climate,there is a need to better understand the slow variation of the Kuroshio and connection with large-scalevariability. The process modulates cross-slope exchanges of heat, salt, and nutrients, with potentially importantconsequences to regional ecosystem and climate [Oey et al., 2013, 2014].

Satellite observations since the 1980s and 1990s now make it possible to study interannual and decadalvariability. The nature and causes of long-term variability of Kuroshio in ECS are not well understood. Han andHuang [2008] found a weak (statistically significant at the 95% confidence level) correlation between seasurface height (SSH) in ECS and the Pacific Decadal Oscillations (PDO). Andres et al. [2009] found highcorrelation between a proxy of Kuroshio transport at the PN-line (near 28°N) and PDO, and explained it interms of the barotropic Sverdrup balance driven by the wind stress curl (WSC) due to PDO. The correlationwith a neighboring measurement of the Ryukyu Current transport is low and insignificant at the 90%confidence level, which puts in doubt the interpretations based on the Sverdrup balance as there is no reasonwhy the argument applies only to Kuroshio but not to the Ryukyu transport. Kagimoto and Yamagata, [1997]had earlier refuted the Sverdrup balance as being the cause of Kuroshio transport at the PN-line. Changand Oey [2011, 2012] also questioned the validity of a Sverdrup balance east of Taiwan and proposed insteadan eddy-driven transport mechanism using a new climate index (see below). Hsin et al. [2013, their Figure 9]related PDO to Kuroshio east of Taiwan; neither the correlation nor the significance was reported; instead,they proposed also the eddy-driven mechanism. Soeyanto et al. [2014] revisited the relation between Kuroshiotransport at PN-line and PDO using data-assimilated model outputs, and found low and insignificantcorrelations (at 95% confidence level); they suggested that eddies play a role. In summary, the following quotefrom Han and Huang [2008] aptly describes the current state of research relating PDO to Kuroshio and the

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PUBLICATIONSGeophysical Research Letters

RESEARCH LETTER10.1002/2014GL062118

Key Points:• Kuroshio fluctuations NE Taiwan areforced by wind and surface heat flux

• PTO rather than PDO is a goodpredictor of Kuroshio fluctuations

• Coastal SST warming is closely tied toKuroshio intrusion

Supporting Information:• Readme• Text S1 and Figures S1–S5

Correspondence to:L.-Y. Oey,lyo@princeton.edu

Citation:Wang, J., and L.-Y. Oey (2014), Inter-annualand decadal fluctuations of the Kuroshioin East China Sea and connection withsurface fluxes of momentum and heat,Geophys. Res. Lett., 41, 8538–8546,doi:10.1002/2014GL062118.

Received 3 OCT 2014Accepted 14 NOV 2014Accepted article online 18 NOV 2014Published online 11 DEC 2014

marginal seas of the western Pacific: “… The present analysis indicates some role of PDO in the regional sealevel variability on the interannual and longer time scales, but the exact extent and mechanisms remainunknown. …” In this study, we attempt to fill this gap of knowledge by using observations to analyze longtime-scale fluctuations of the Kuroshio in ECS and explain the driving mechanisms. Section 2 describes themethods, section 3 the results, section 4 explains the mechanisms, and section 5 is conclusion.

2. Methods

SSH anomalies (SSHA) at 1/4° × 1/4° resolution from October 1992 to 2013 are from AVISO [http://www.aviso.oceanobs.com/]; the product was corrected for aliasing of tides. Satellite data and geostrophic currents forthe Kuroshio in ECS have been used and validated by Andres et al. [2009; and references quoted therein], Changand Oey [2011], Gawarkiewicz et al. [2011], and Hsin et al. [2013]. Wind is from CCMP (1987–2011, 1/4° × 1/4°;http://podaac.jpl.nasa.gov/datasetlist?Search=ccmp), and wind stress is calculated using Oey et al.’s [2006]formula. The SST is from GHRSST (1982–2012, 1/4° × 1/4°; https://www.ghrsst.org/) and surface heat flux is fromthe OAFlux project (http://oaflux.whoi.edu/; 1985–2009, 1° × 1°). Various climate indices such as the PDO aredownloaded from public sites. Data are monthly averaged, and seasonal climatology is removed in order toanalyze inter-annual and longer-period variability. Unless otherwise stated, all quoted correlations are abovethe 95% confidence level, calculated as 1� (1� 0.95)2/(F� 1), where F=N/τN is the degree of freedom, N islength of time series, and τN is the dot product of the auto-covariances of the two time series. Compositesbased on a time series are computed for values exceeding ±1 standard deviation of the time series.

Kuroshio paths (KP) are determined as follows. First estimate a mean path based on the 21 year AVISO SSH[Rio et al., 2011]. Local along-jet (s, positive downstream, along-jet geostrophic velocity vs) and cross-jet(n, positive offshore) axes are then used to define cross-jet sections numbered 1 to 48 from south to northalong the mean path (Figure 1a). Geostrophy is approximately valid as assumed in previous works usingaltimetry data (see supporting information). Let surface geostrophic transport per unit depth V= ∫vs dn,where the integral is from n=�100 km to n=+100 km, i.e., the jet’s width is 200 km [Liu and Gan, 2012]. Thendefine< (…)>= V�1 ∫(…) dn, and therefore X=<nvs> is the n-coordinate of the center of jet based on V[Cushman-Roisin, 1986]. Similarly, Vm= ∫vs

m dn, and< (…)>m= Vm�1 ∫(…) dn, where m is a positive integer

≥1, so that more generally Xm=<nvsm>m, e.g., X2 is the n-coordinate of the center of “energy”. We use X3

(“center of power”) to eliminate n-values which at times can be excessively onshore or offshore when theKuroshio jet is skewed with a weak and broad profile on one side but a sharp decay on the other side of themean path. The results are otherwise similar to X1 used by Hsin et al. [2013] (see supporting information FigureSM-1). The X3 is then time averaged to obtain a new mean path, and the process is repeated till convergence.Two iterations are sufficient to obtain the final KP(s,t) = X3 and mean path; the latter generally coincides withthe location of maximum mean geostrophic speed along the jet where the relative vorticity is zero. In thefollowings, “KP” refers to anomalies relative to the mean path; it is positive offshore and negative onshore.

3. Results

Figure 1a shows the mean Kuroshio path and its standard deviation. There are three locations of prominentKP-fluctuations: SE of Taiwan (Station 3), NE of Taiwan (Station 17), and SW of Kyushu (Station 46). Minimumfluctuations occur near station 11 off Taiwan’s north coast, and also at station 39 near the PN-line. Thismanuscript focuses in the region around Station 17 where the dominant mode of empirical orthogonalfunction [EOF; Kutzbach, 1967] will show the largest variability. Fluctuations near stations 3 and 46 appear asthe second mode which has less than half the variance of mode 1; they are not studied in this work.

Monthly climatology of KP (Figure 1b) shows that it has a significant seasonal variation only at stations 16, 17,19, and 20 northeast of Taiwan where the Kuroshio shows both an onshore shift in winter (February or March)and an offshore shift in at least one summer month (June–September). Such seasonal behavior has previouslybeen reported [e.g., Oey et al., 2010]. However, the seasonal variation is insignificant at other stations. At thePN-line, Kagimoto and Yamagata [1997] concluded that seasonal fluctuations of the transport are weak. As willbe shown below, the KP correlates with Kuroshio transport, so that their conclusion is consistent with Figure 1bthat the seasonal variation near station 39 is insignificant. Off the coast of northern Taiwan, near station 13,Chang and Oey [2011] also found weak and barely significant seasonal variation of Kuroshio transport. On theother hand, Hsin et al. [2013] discussed in some details the seasonal fluctuations of the Kuroshio axis and

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transport east of Taiwan from 22°N to 25°N, and concluded that the seasonal variation is 5–10 times strongerthan inter-annual. However, no standard errors were reported, and their results are contradicted by those ofSoeyanto et al. [2014] who concluded that inter-annual amplitudes are significantly stronger than seasonal.Figure 1b shows that the seasonal fluctuations east of Taiwan (stations 1–14) are insignificant.

Hovmoller plot of KP (Figure 1c) shows short-period propagating signals from south to north along theKuroshio at phase speeds of approximately 15 ~ 30 kmday�1 similar to those reported in the literature [e.g.,Sugimoto et al., 1988]. Our main focus is on variations at inter-annual and longer periods which are alsoseen. Near station 17, there appears to be a slow trend of the Kuroshio shifting more onshore in the past 2decades: plot colors in Figure 1c becoming bluer from mid-1990s to the end of the data in 2013. The trend is10 km onshore per 20 years at the 90% confidence level (Figure SM-2).

EOF analysis is used to identify the dominant mode of variability of KP. Mode 1 accounts for 44% of thetotal variance, more than doubled the next largest mode 2 at 18%. Mode 1 eigenvector (EV1) has a peak at

Figure 1. (a) Mean Kuroshio path (black line) and ± standard deviation (dashed envelope lines) plotted on mean seasurface height (SSH) (m) from AVISO (shading). White contours are isobaths (100, 200, and 500m). The “*” shows locationof SSH anomalies (SSHA) as Kuroshio transport index (KT). Inset plots the path standard deviation (km) as function of stationnumber. (b) October 1992–2013 monthly mean deviations of Kuroshio path (km) from its mean path: “+” indicates wheremeans exceed standard errors. (c) Hovmoller plot of Kuroshio path deviation from mean (km), positive when the Kuroshiodeviates offshore from the mean path, and negative when it deviates onshore.

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station 17, and its principal component (PC1) shows intra-seasonal, seasonal, and inter-annual variations(Figures 2a and 2b). This mode accounts for onshore intrusions (when PC1< 0) and offshore migrations(PC1> 0) of the Kuroshio northeast of Taiwan. For simplicity, “intrusion” will mean either onshore or offshore.Onshore (offshore) intrusions occur more often in winter (summer), but this seasonal variation is barelysignificant as seen by the relatively large standard deviations of the 1993–2013 monthly mean PC1(Figure 2c). There are strong inter-annual fluctuations, shown by the thick black curve in Figure 2b. The KP ismore offshore from 1995 to 1998 and 2002 to 2004, onshore from 2000 to 2002 and, because of the long-term trend, also generally more onshore after 2005 (through end of data 2013; dashed line). Because the KP isdefined based on the geostrophic velocity, the long-term onshore shift is mostly free from being caused by ashift due to large-scale sea-level rise in the western North Pacific [Merrifield et al., 2012; Figure SM-3].

To understand what cause the KP-fluctuations, we first examine how PC1 correlates with indices: Kuroshiotransport (KT), Pacific Decadal Oscillation [PDO; Mantua et al., 1997], and Philippines Taiwan Oscillation [PTO;Chang and Oey, 2012], and then identify the forcing and mechanisms (section 4). We follow Gawarkiewiczet al. [2011] and use AVISO SSHA at (23.9°N, 123.2°E) as a proxy for KT northeast of Taiwan. The PDO is definedas the leading EOF of SST anomalies north of 20°N in the Pacific Ocean; it describes large-scale, long-periodoscillatory patterns of SST and wind stress curl. When PDO is positive, the (anomalous) SST is cooler and WSCis positive over a large area north of 38°N, but SST is warm and WSC is negative immediately off thenorthwestern coast of North America, as well as in the central Pacific from 20°N to 38°N. The PDO has beenshown to successfully explain the inter-annual and decadal variability of the Kuroshio jet east of Japan [seeDi Lorenzo et al., 2008 and references therein]. By contrast, attempts to relate PDO with processes in themarginal seas of the western North Pacific are less satisfactory (see Introduction). The correlations often yieldshort leads (~1month) which had been attributed to barotropic responses, e.g., Andres et al. [2009], butsometimes PDO lags [see Table A2 of Chang and Oey, 2012] which seems unreasonable. The PTO is defined asthe difference in WSC east of Philippines and central Pacific east of Taiwan; it was proposed by Chang and Oey[2012] to explain the effects of eddies on inter-annual variations of Kuroshio and Luzon Strait transports.In contrast to other climate indices of the North Pacific, PTO produces distinct patterns of WSC in the westernNorth Pacific [see Figure 8 of Chang and Oey, 2012]; in particular, when PTO is positive, the wind stress curlover China Seas is negative, and the mean northeasterly wind weakens, and vice versa when PTO is negative.The PC1 is best correlated with PTO with correlation coefficient r= 0.65 when PTO leads by 4months(Figures 2f and 2g). The r is 0.48 for PDO leading by 1month, same also for KT but at zero lag (Figures 2d and 2e).The correlations of PC1 with the El Nino Modoki Index [EMI; Ashok et al., 2007], Western Pacific Index[WP; Wallace and Gutzler, 1981], North Pacific Gyre Oscillations [NPGO; Di Lorenzo et al., 2008], and Nino 3.4are all poor (Figure 2g), suggesting that their connections with Kuroshio fluctuations are tenuous. Thecorrelations in Figures 2d–2g are for the case when the long-term trend in PC1 has been removed, and360 day running mean applied to all series. We repeated the calculations when the trend in PC1 was notremoved, and then also successively used shorter running means 180 and 90 days (Figure SM-4). Theresults confirm that in all cases, PTO yields higher correlations with PC1 than PDO. It also yields highercorrelations than KT in all but the shortest (90 days) running mean; at these intra-seasonal time scales, theuse of PTO (or PDO) as an index is probably unsuitable.

Fluctuations of the Kuroshio produce fluxes across the ECS shelfbreak [e.g., Isobe, 2008]. An observablesignature of these fluxes on the shelf is SST, which may be used to check KP. If indeed the dominantfluctuations are northeast of Taiwan, then after removing the seasonal monthly climatology, one can expectwarming (cooling) over the portion of the coast onshore of Station 17 during the period when KP is onshore(offshore), since the Kuroshio is the most likely source of warm water. Composite of SST based on the PC1(Figure 2h) indeed shows a strong imprint of KP on the ECS shelf. For negative PC1, corresponding to onshoreKP, localized warming with SST≈ 0.7°C is seen along the coast just north of the Taiwan Strait, shoreward ofthe shelfbreak location where EV1 shows the largest amplitude. As negative PC1 generally coincides withperiod when the wind anomaly is northeasterly (Figure 2i), the observed warming may also be caused byonshore Ekman transport of Kuroshio water. However, as Guo et al. [2006] have pointed out, since the wind isof a large scale, the contribution to onshore flux by Ekman transport is uniform all along the shelfbreak of theECS. Also, the mean wind in ECS is northeasterly [e.g., Oey et al., 2014], so that when PC1 is negative, windspeed and hence ocean mixing are stronger, which would instead tend to produce cooling. Theseconsiderations suggest that Kuroshio intrusion rather than Ekman transport is the likely cause of the localized

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Figure 2. (a, b, and c): Empirical orthogonal function (EOF) mode 1 of Kuroshio path (KP): (a) EV1, (b) PC1 (blue and black are 3 and 12month runningmean, dashed istrend w/slope and p-value shown) and (c) monthly mean PC1 and ± standard deviation. (d, e, and f): 12month running mean PC1 (black) and Kuroshio transportindex KT (d, cyan), Pacific Decadal Oscillations (PDO) (e, green), and Philippines Taiwan Oscillation (PTO) (f, blue) shifted according to lags of their maximum corre-lations “r”; numbers across bottom are “r”, 95% significance and lag (months) @maximum r. (g): lagged correlations of PC1 with KT, PDO, and PTO, as well as with threeother indices: El Nino Modoki Index (EMI), Western Pacific Index (�WP), and North Pacific Gyre Oscillations (�NPGO) (all shown in grey; for Nino3.4, r ≈ 0.43, notshown); dash-dots indicate insignificant “r” at the 95% confidence level. (h, i, and j): SST (h: 0.7 (red), 0.6°C, ..), wind stress and wind stress curl (i: dark red andblue = ±5 × 10�8 Nm�3), and net surface heat flux (j: dark blue is heat loss =�20 Wm�2) all composited for negative PC1; black line =mean Kuroshio path.

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warming. Coastal warming along the same portion of the coastal ECS was also postulated by Oey et al. [2013]as being caused by intrusions of Kuroshio waters aided by cross-shelf flows due to topographic standingwave north of the Taiwan Strait [Oey et al., 2014]. Their theory depends on the existence of strongernortheasterly wind, which is consistent with the period of negative PC1. A long-term warming trend was alsofound with significant warming occurring after 1998–1999 [Lima and Wethey, 2012]. It is interesting tocompare if the warming coincides with an offshore-onshore transition of PC1 which also has a long-termtrend (Figure 2b). Since the PC1 is a non-stationary time series, we apply the intrinsic mode functiondecomposition method of Huang et al. [1998] in order to determine if the transition from a generally positive(i.e., offshore KP) to a generally negative (onshore KP) PC1 occurs at approximately the same time as the SSTwarming (Figure SM-5). The multi-decadal mode#7 shows that the transition occurred around 1998,coinciding well with the warming transition of the ECS shelf.

4. Mechanisms

The significant correlation between PC1 and PTO suggests a forcing that is related to the WSC, since PTOdescribes the WSC patterns of the western North Pacific andmarginal seas. Figure 2i shows the wind stress andWSC composited for negative PC1, corresponding to the onshore KP; the case for positive PC1 is similar butpositive and negative regions generally are reversed. The WSC is positive east and northeast of Taiwan, but itbecomes negative further north along the Kuroshio; we define this pattern of along-jet, wind curl dipole asbeing positive. For positive PC1, corresponding to the offshore KP, the dipole becomes negative.

We can explain the connection between Kuroshio path and wind curl dipole using the two-layer planetarygeostrophic model [Salmon, 1992]. Reader not interested in the mathematical developments of the physicalmechanisms can skip this paragraph. The mass conservation and vorticity equations are [Oey et al., 2010]:

Dψ h1ð Þ=Dt ¼ h1=Hð ÞuIb:∇Hþ ∇: κRo2∇h1� �þ QþW (1a)

U:∇ f=Hð Þ ¼ g’=2ð Þk : ∇ h12� ��∇ H�1

� �� � þ k :∇� τo=Hð Þ � ∇: rH�1∇ψ� �

(1b)

where U= (u1h1 + u2h2, v1h1 + v2h2) = k×∇ψ is the total volume transport vector (per unit length), k is the z-directed unit vector, ψ the transport stream function, subscripts 1 and 2 denote upper and lower layers, u andv are zonal (x) and meridional (y) velocities, h the layer depth, f the Coriolis parameter, H = h1 + h2 the totalwater depth, g′ the reduced gravity, τo the wind stress divided by water density, κ the friction coefficient,Dψ/Dt≡ ∂/∂t� (ψy/H)∂/∂x + (ψx/H)∂/∂y denotes the rate change of a parcel moving with the depth-averagedflow, uIb =�ψy/H +g′(h1

2)y/(2fH) +ψx/H� g′(h12)x/(2fH) is the bottom velocity without the Ekman (friction)

contribution, and Ro2 = g′h1h2/(f

2H) is the squared Rossby radius. The “Q” is cooling (heating) term, andW=�k .[(h2/H)∇× (τo/f )] is the surface pumping due to the wind stress; both tend to decrease (increase) h1 ifnegative: cooling for Q and cyclonic WSC for W. The second term on the RHS of (1a) is due to friction whichtends to flatten the h1-anomalies irrespective of onshore or offshore shift of KP. The first term is due toupwelling or downwelling produced by bottom flow moving up or down topography. If onshore (offshore)shift of the Kuroshio corresponds to negative (positive) Q or W, which we will see is the case, then theupwelling (downwelling) term has the same sign as, and therefore reinforces the effects of, Q or W. Toestimate the advective term on the LHS of (1a), we note that it changes sign (because of ∇h1) as the Kuroshiogoes through an onshore or offshore migration phase; the net effects will be assumed negligible. In (1b),since ∇(f/H) points toward shallower water, onshore across the ECS continental slope, U.∇(f/H) is negative(positive) if U points offshore (onshore), corresponding to an offshore (onshore) KP. The sign of each of thethree RHS terms of (1b) then determines how the corresponding physical process contributes to KP. Thefirst term is JEBAR, which is nonzero when h1 varies along isobath, and therefore depends on how h1 changesaccording to (1a). A positive wind curl dipole contributes to W <0, produces surface divergence and hencethinner h1 northeast of Taiwan, and W >0, convergence and thicker h1 further north, so that h1

2 increasesalong the Kuroshio which approximately follows the isobath. The vector ∇(h12) points down-jet, andJEBAR >0 contributing to onshore shift of the Kuroshio (KP <0; Figure 2d). In contrast, a negative wind curldipole produces convergence northeast of Taiwan and divergence further north along the Kuroshio, andJEBAR <0 contributing to offshore shift of the Kuroshio (KP >0). By the same argument, as is clear from (1a),surface cooling northeast of Taiwan (Q <0 there) would also produce a down-jet ∇(h12), i.e., h12 increasesalong the Kuroshio, contributing to JEBAR >0 and onshore shift of KP. This mechanism was proposed by

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Oey et al. [2010] to explain the winter-time onshore intrusion of the Kuroshio. It indeed applies also atinterannual time scales, as is clear from the composite of surface heat flux for negative PC1 in Figure 2j, whichshows a strong localized cooling northeast of Taiwan. For the second term, ∇× (τo/H) =H�1∇× τo +∇H�1 × τo,the first is positive when wind curl dipole>0; the second is also positive, because τo is northeasterly (Figure 2i).Therefore, ∇× (τo/H) >0 (<0) contributing to onshore (offshore) shift of the Kuroshio for positive (negative)wind curl dipole, i.e., negative (positive) PC1. For the third term in (1), define τb = κk×∇ψ= κU, then�∇.(κH�1∇ψ) =�∇× (τb/H) =�κ∇× uA, where uA =U/H is the depth-averaged velocity. The ∇× uAdepends primarily on the cross-slope shear of the along-jet velocity. On one hand, Kuroshio transportdecreases for negative PC1, but as northeasterly monsoon wind strengthens, the (northward) shelftransport also weakens; the two effects tend to cancel, and we assume that the contribution of frictionto the shift of the Kuroshio is correspondingly weak.

In summary, the dominant inter-annual variability of the Kuroshio is closely linked with changes in the surfacefluxes of momentum and heat northeast of Taiwan. During years of anomalous positive wind stress curl,northeasterly wind stress, and cooling, which occur approximately 4–7months after the peak of a negative

Figure 3. (a and b): Wind stress and wind stress curl (range = ±5 × 10�8 Nm�3), and (c and d): net surface heat flux(range = ±20Wm�2) composited for negative PDO (left column, a and c) and negative PTO (right column, b and d);black line =mean Kuroshio path and white contours in Figures 3c and 3d are zero contours.

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PTO, the upper layer depth h1 of the Kuroshio tends to shallow as the current enters the ECS, and h1 deepensalong the jet. As the Kuroshio flows approximately along isobaths, the resulting gradient of h1 alongisobaths is consistent, through JEBAR, to an onshore shift in KP, i.e., PC1< 0, as shown in Figures 2i and 2j.The situation is reversed during years of PC1> 0. A schematic of the h1-tilt and JEBAR is given in Figure 2 ofOey et al. [2010]. Guo et al. [2006] was perhaps the first to demonstrate the singular dominance of JEBAR inexplaining Kuroshio intrusion across the shelfbreak of the ECS, while recognizing that JEBAR only provides aconnection between cross-isobath motion and along-isobath gradient of density (h1). Here we identify theexternal forcing (Figures 2i and 2j) that give rise to JEBAR, and demonstrate how they explain the dominantmode of inter-annual variability of the Kuroshio in ECS.

We can now understand why PTO is more closely correlated with KP than PDO, and what the physical factorsare which give rise to their difference. We composite WSC and Q using PDO and PTO (Figure 3). For WSC, thetwo composites are similar, showing dipole structures northeast of Taiwan. For Q, the sign northeast ofTaiwan for PDO is heating instead of cooling, while the PTO-composite shows a structure similar to Figure 2jfor the composite based on the PC1. These comparisons not only show why PTO is physically a more relevantindex than PDO in explaining the KP, but also provide evidence of forcing by WSC and Q. We leave it for afuture study the detailed analyses of regional differences in climate patterns between PTO and PDO.

5. Conclusions

This study uses 21 year (October 1992–2013) altimetry data from AVISO to analyze the fluctuations ofKuroshio path along the continental slope of the ECS from eastern Taiwan to southern Kyushu. Thedominant spatial structure based on EOF is of one sign, and it has a concentrated amplitude at (123°E, 26°N),some 160 km northeast of Taiwan. We confirm the existence of a seasonal intrusion northeast of Taiwan:onshore in winter from February to March and offshore in summer from June to September; but theseasonal variation is barely significant. At the inter-annual periods, Kuroshio path is best correlated with thePTO index, less so with PDO and Kuroshio transport, and is poorly correlated with other climate indices: Nino3.4, �WP, EMI, and NPGO. The dominant mode is related to strong wind stress curl and surface heat fluxnortheast of Taiwan, which give rise to thermocline see-saw along the Kuroshio at inter-annual time scales.The Kuroshio path has an onshore decadal trend, with largest shift northeast of Taiwan. Kuroshio path andintrusion have imprints on waters of the ECS, which warm and cool in concert with the inter-annuallyvarying strengths of the intrusion. The SST variations impact the overlying winds, which respond in acoupled way [Oey et al., 2013]. Also, SST changes can alter the ecosystem of ECS over long periods, which weplan to study in the future.

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AcknowledgmentsWe thank the two anonymous reviewersfor their comments, E. Chang forproviding the PTO index, B. Johns forPCM-1 data, R. Chang & Y. Lin for sharingtheir codes, and L. Chung for help withthe graphics. L.Y.O. is grateful for theaward from the Taiwan Foundation forthe Advancement of OutstandingScholarship. We acknowledge partialsupport from the National ScienceCouncil of Taiwan. All data aredownloadable from links given in text.

The Editor thanks two anonymousreviewers for their assistance inevaluating this paper.?

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