Summer Anticyclonic Eddies Carrying Kuroshio Waters Observed by a Large CPIES Array West of the Luzon Strait

Ruixiang Zhao aState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
bSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Xiao-Hua Zhu aState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
bSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
cSchool of Oceanography, Shanghai Jiao Tong University, Shanghai, China

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Chuanzheng Zhang aState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
bSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China

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Hua Zheng aState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
cSchool of Oceanography, Shanghai Jiao Tong University, Shanghai, China

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Ze-Nan Zhu aState Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China

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Qiang Ren dKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
eCenter for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China

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Yansong Liu dKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
eCenter for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
fLaboratory for Ocean and Climate Dynamics, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Feng Nan dKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
eCenter for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
fLaboratory for Ocean and Climate Dynamics, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Fei Yu dKey Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
eCenter for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China
fLaboratory for Ocean and Climate Dynamics, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Abstract

The Kuroshio intrusion into the South China Sea (SCS) in summer is weak and has rarely been reported by in situ observations. Here, we describe a new form of Kuroshio water intrusion that is strongest during the summer, the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy originating north of Luzon Island. From early July to mid-September 2018, two NLWEs moving northwestward were captured by a mooring array consisting of 27 current- and pressure-recording inverted echo sounders (CPIESs). The three-dimensional CPIES estimates reveal that the NLWEs carried large amounts of saline Kuroshio waters (S > 34.7 psu) in the subsurface, which was also evidenced by Argo float profiles. The Kuroshio intrusion was confined to waters shallower than the 14.8°C isotherm. Historical data for NLWEs suggest that they occur mostly during the summer but are absent between November and March, which is attributed to seasonal wind stress curl (WSC). However, because the seasonal signal of WSC during summer is small, intraseasonal WSC is directly responsible for the generation of NLWEs.

Significance Statement

This paper describes a new type of Kuroshio water intrusion into the South China Sea (SCS)—the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy generated north of Luzon Island. The eddy mostly occurs during summer when the Kuroshio intrusion is commonly considered the weakest. From observations of a large CPIES array, we provide a cradle-to-grave picture of the NLWE. NLWEs are estimated to contribute almost half of the westward Luzon Strait transport during the summer and, as such, play an important role in the seasonal stratification and circulation in the northeastern SCS.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao-Hua Zhu, xhzhu@sio.org.cn

Abstract

The Kuroshio intrusion into the South China Sea (SCS) in summer is weak and has rarely been reported by in situ observations. Here, we describe a new form of Kuroshio water intrusion that is strongest during the summer, the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy originating north of Luzon Island. From early July to mid-September 2018, two NLWEs moving northwestward were captured by a mooring array consisting of 27 current- and pressure-recording inverted echo sounders (CPIESs). The three-dimensional CPIES estimates reveal that the NLWEs carried large amounts of saline Kuroshio waters (S > 34.7 psu) in the subsurface, which was also evidenced by Argo float profiles. The Kuroshio intrusion was confined to waters shallower than the 14.8°C isotherm. Historical data for NLWEs suggest that they occur mostly during the summer but are absent between November and March, which is attributed to seasonal wind stress curl (WSC). However, because the seasonal signal of WSC during summer is small, intraseasonal WSC is directly responsible for the generation of NLWEs.

Significance Statement

This paper describes a new type of Kuroshio water intrusion into the South China Sea (SCS)—the North Luzon Warm Eddy (NLWE), which is an anticyclonic eddy generated north of Luzon Island. The eddy mostly occurs during summer when the Kuroshio intrusion is commonly considered the weakest. From observations of a large CPIES array, we provide a cradle-to-grave picture of the NLWE. NLWEs are estimated to contribute almost half of the westward Luzon Strait transport during the summer and, as such, play an important role in the seasonal stratification and circulation in the northeastern SCS.

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao-Hua Zhu, xhzhu@sio.org.cn

1. Introduction

The Luzon Strait, which is 300 km wide and located between Taiwan Island and Luzon Island, is a key channel connecting the South China Sea (SCS) and the Pacific Ocean. The warm and saline Kuroshio waters from the Pacific enter the SCS through this gap and are of vital importance to the thermohaline structures and circulation of the SCS. The intrusion of Kuroshio waters into the SCS appears in different patterns and can be classified into direct intrusion branches, loop currents, and detached anticyclonic eddies (Hu et al. 2000; Caruso et al. 2006; Nan et al. 2011a, 2015). The loop currents (or the Kuroshio loops) are not persistent, but occur occasionally (Sun et al. 2020). The presence of a cyclonic eddy often facilitates an anticyclonic eddy shed from these loop currents (Zhang et al. 2017). In rare cases, nonlinear Rossby waves from the Pacific can directly penetrate the SCS (Hu et al. 2012). Winter is the most favorable season for Kuroshio intrusion; in contrast, summer Kuroshio intrusion is rarely reported in the literature (e.g., Yuan et al. 2014; Yang et al. 2019). The seasonality of the Kuroshio intrusion has been attributed to the seasonal variability of winds (Farris and Wimbush 1996; Wu and Hsin 2012) or the strength of the upstream Kuroshio (Sheremet 2001; Sheu et al. 2010; Yuan and Wang 2011).

The west of the Luzon Strait is one of the regions with the most frequent mesoscale eddies in the SCS (Wang et al. 2003; D. Wang et al. 2008; G. Wang et al. 2008; Xiu et al. 2010; Chen et al. 2011; Zhang et al. 2013, 2016, 2017). These eddies can maintain the properties of water in their upper layer and transport them to other regions (Zu et al. 2013; Yang et al. 2019) and have greater impacts than seasonal winds on the thermohaline structure of the water column (Zhao et al. 2017). Strong seasonality is a feature of eddies generated here. For example, in winter, the Luzon Cold Eddy (LCE) occurs northwest of Luzon Island (Yang and Liu 2003), whereas in summer the Luzon Warm Eddy (LWE) is dominant. Their generation is closely related to the wind stress curl (WSC) from orographic wind jets (G. Wang et al. 2008). However, LWEs identified from altimeter maps have only been captured by single hydrographic surveys and a few Argo profiles, with relatively little research attention (Li et al. 1998; Yuan et al. 2007; Chen et al. 2009).

Li et al. (1998) first reported an anticyclonic eddy carrying Kuroshio waters near the Dongsha Islands in the summer of 1994. However, by analyzing merged sea surface heights (SSHs) derived from altimeters, Yuan et al. (2007) argued that this eddy originated northwest of Luzon Island instead of the Kuroshio. They also found that the occurrence of the anticyclonic eddy is seasonal, forced by the southwest monsoon and the negative WSC generated northwest of Luzon Island in summer, and moves toward the continental shelf in fall and winter, leading to current variations. The same authors note, however, that the generation of eddies could not be fully explained by seasonal winds. Chen et al. (2009) demonstrated the seasonal pattern of the LWE from the empirical orthogonal function (EOF) decomposition of merged sea level anomaly data. From the temperature and salinity (T/S) properties of Argo profiles inside an LWE during 2006, they suggested that the LWE incorporated mixed waters from the Kuroshio and the SCS. From a summer hydrographic cruise, Yang et al. (2019) reported an anticyclonic eddy trapping and transporting saline Kuroshio waters into the SCS. Although this eddy was not an LWE, it originated from the central Luzon Strait. This implies that the anticyclonic eddies generated around Luzon Island in summer may be closely related to the Kuroshio intrusion.

To date, studies of LWEs remain limited. Because the LWEs reported by Yuan et al. (2007) and Chen et al. (2009) originated west or northwest of Luzon Island, the waters that they trapped came mostly from the SCS; however, in some cases, LWEs can arrest large amounts of Kuroshio waters when generated in the region north of Luzon Island near the Kuroshio. These eddies are named North Luzon Warm Eddies (NLWEs) to distinguish them from previously reported LWEs. Here, we report two NLWEs that occurred during the summer of 2018. Specifically, we used observational data from an array consisting of 27 current- and pressure-recording inverted echo sounders (CPIESs) to investigate the structures of these NLWEs.

The rest of the paper is organized as follows: section 2 describes the data used and the methods for CPIES data processing and estimation; section 3 describes the observations, including the evolution of the NLWEs, the evidence for Kuroshio waters, and the structure of the NLWEs; section 4 discusses the generation mechanism of the NLWEs; and section 5 provides a summary and conclusions.

2. Data and methods

a. Data

CPIESs are a type of bottom mooring equipped with a mounted instrument measuring acoustic travel time from the sea surface and back (τ), bottom pressure (Pbot), and near-bottom velocities 50 m off the seafloor (v). The body of the CPIES is seated 1 m off the seafloor on a mooring platform with additional iron anchors. One CPIES can estimate the steric and mass-loading components of SSH from τ and Pbot, respectively. Large CPIES arrays are deployed in regions such as the Kuroshio Extension (Donohue et al. 2010), the Japan/East Sea (Mitchell et al. 2005), and the Antarctic Circumpolar Current (Tracey et al. 2013), and they have proved successful in mapping the three-dimensional density and geostrophic current field.

A large CPIES array was deployed in the SCS from June 2018 to August 2019 under the joint efforts of the Second Institute of Oceanography, Ministry of Natural Resources, and Institute of Oceanology, Chinese Academy of Sciences. The array spanning a 230 km × 400 km region west of the Luzon Strait comprised 28 CPIESs (Fig. 1). To the best of our knowledge, the scale of the CPIES mooring array is the largest ever reported for the SCS. One CPIES (C19) failed to recover data for unknown reasons, so data were retrieved from the remaining 27 arrays. CPIESs C21, C23, C29, and C30 were deployed in December 2017 as planned and other deployments were postponed to the following summer because of bad weather conditions. Two tall moorings (M1 and M2; Table 1) consisting of RDI Workhorse acoustic Doppler current profilers (ADCPs; 75 kHz), conductivity–temperature–depth measurers (CTDs; Seabird SBE37SM), current meters (CMs; Nortek, Aquadopp), and temperature chains (Seabird SBE56) were deployed near C21 and C27. Each ADCP observed 74 bins with a size of 8 m. All the mooring data were detided and applied with a fourth-order Butterworth low-pass filter going forward and backward to avoid phase shift with a cutoff period of 72 h, and were subsampled at one-day intervals. A summary of the mooring information is provided in Table 1.

Fig. 1.
Fig. 1.

Location map of the mooring array. White circles filled with black denote recovered CPIES sites, while the empty circle (C19) indicates a CPIES that was not recovered. Red triangles are tall moorings sites (M1 and M2). Ocean bathymetry (m) is shown by the color shades based on ETOPO1. Arrows illustrate the mean sea surface velocities of the Kuroshio from the CMEMS satellite altimetry products. Vectors with a speed less than 0.4 m s−1 are masked.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

Table 1

Information of the tall moorings.

Table 1

We also used observations from the following sources: 6-hourly sea surface wind products from the Cross-Calibrated Multi-Platform (CCMP) with a spatial resolution of 1/4° × 1/4°; daily merged sea surface height anomaly data products from the European Copernicus Marine Environment Monitoring Service (CMEMS) with a spatial resolution of 1/4° × 1/4°; Argo profiles (Liu et al. 2021) provided by the China Argo Real-time Data Center (http://www.argo.org.cn/); and General Digital Environmental Model (GDEM; version 3.0) monthly climatology data with 78 standard depths and a horizontal resolution of 1/4° × 1/4°. The GDEM incorporated temperature and salinity profiles from the Mater Oceanographic Observational Dataset of the Naval Research Laboratory (NRL). The GDEM has been found to perform well in reproducing hydrographic mean fields in the ocean (Qu et al. 2006; Carnes 2009; Wang et al. 2011).

b. Methods

1) Processing of the raw CPIES data

All CPIESs were configured to measure τ, Pbot, and v at intervals of 24 pings per hour, 6 per hour, and hourly, respectively. A sliding event was identified at the C13 and C29 stations, where the Pbot records suffered an abrupt “jump.” This effect was removed by adding an offset (dp) to the Pbot records after the event. The offset was determined by iteration, whereby the detided residuals of the entire time series had a minimum deviation. The τ time series after this jump was also adjusted accordingly by adding the following offset: dτ=2dp/(ρgc), where ρ is mean density, g is gravitational acceleration, and c is the mean speed of sound at the sea floor. The τ time series was processed by windowing, median filtering, and despiking to yield an hourly dataset. The exception to this was the τ record of C16, which was very scattered and, thus, discarded. Eight major tidal constituents (K1, O1, P1, Q1, M2, S2, N2, and K2) were removed from the bottom pressure data using the tidal response method, and the detided Pbot data were dedrifted following Kennelly et al. (2007). The hourly τ, Pbot, and v time series were applied with a fourth-order Butterworth low-pass filter going forward and backward to avoid phase shift with a cutoff period of 72 h, and the hourly data were resampled at one-day intervals to obtain a daily time series. The hourly τ was further deseasoned and converted to a latitude-independent dynamic τ (Donohue et al. 2010). Following Donohue et al. (2010), the seasonal signal τ˜(t) is determined by the following procedure, where t is the date of a year: 1) the residual of a fitted spline curve relating τ0–300 (τ from 0 to 300 dbar) to τ300–1000 (τ from 300 to 1000 dbar) was calculated from historical hydrographic casts; 2) the residual was grouped and averaged in monthly bins, and then it was replicated three times to yield a three-year record, 3) the record was further smoothed using a Butterworth low-pass filter going forward and backward with a cutoff period of 3 months; 4) the record of the middle year was extracted as τ˜(t), and 5) the de-seasoned signal τds(t) is calculated as τds(t)=τ(t)τ˜(t). The hourly τ, Pbot, and υ had standard errors of 0.45 ms, 0.007 dbar, and 1.5 cm s−1, respectively.

2) Estimation of the three-dimensional field

The gravest empirical mode GEM (Fig. 2; see also Figs. S1 and S2 in the online supplemental material) are empirically determined lookup tables used to estimate the temperature, salinity, and specific volume anomaly profiles from CPIES τ measurements (Watts et al. 2001; Park et al. 2005), respectively. To construct the GEM field of the SCS (Fig. 2a; see also Figs. S1a and S2a), a total number of 1269 hydrographic profiles including CTD and Argo casts near the observational sites were used (blue dots in Fig. 3a). The mean rms error of the specific volume anomaly was 1.36 × 10−7 m3 kg−1 for the upper 1000 m (Fig. 2c). We chose a reference level of 1000 dbar and leveled the hourly τ from empirical linear relationships to derive τ1000. Following Kennelly et al. (2008), τ1000 time series at each CPIES location were calibrated by adding offsets (τoffset) using the CTDs taken there throughout the field program:
τoffset=1Ni=1N(τCTD(01000)iτIES(01000)i),
where τCTD(01000)i is the calculated acoustic round trip time from the sea surface and 1000 dbar based on the CTD profile, τIES(01000)i is the CPIES derived τ1000 averaged of 6-hourly samples centered on the time of the CTD, and the superscript i denotes the ith available CTD cast.
Fig. 2.
Fig. 2.

(left) GEM, (center) seasonal correction for the upper layer, and (right) GEM root-mean-square errors for specific volume anomalies in the (top) SCS, (middle) Kuroshio, and (bottom) NLWE. Units of the color scales are indicated above each panel.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

Fig. 3.
Fig. 3.

(a) Locations of hydrocasts from the SCS (blue), the Kuroshio region (red), and the origin of NLWE (green). (b) T/S diagram for the mean profiles of the SCS (blue), the Kuroshio (red), and the origin of NLWE (green). The solid lines are from in situ hydrocasts and the dashed lines are from HYCOM. (c) Scatterplot of τ1000 and the baroclinic SSH for SCS waters (blue) and Kuroshio waters (red). Fitted linear relationships are shown in blue and red, respectively. (d) The mean depth-dependent salinity profiles of the SCS (blue), the Kuroshio (red), and the origin of NLWE (green) from HYCOM. The dashed black line indicates the merged salinity profile with the mixing coefficient b = 0.60.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

The optimal interpolation (OI) technique adapted from Bretherton et al. (1976) was used to map the two-dimensional τ1000 with a correlation length scale of 120 km. The far-field mean τ1000 was converted from the GDEM climatology. The three-dimensional specific volume anomaly field was determined from the GEM lookup table, and the geopotential height was derived by its integration on the vertical. Then, we calculated the baroclinic velocity shears Vbc(p) from the geopotential height estimates by assuming geostrophy. For each CPIES, we obtained velocities at the reference depth (1000 m) of each station by adding baroclinic velocity shear Vbc(Pref) − Vbc(Pbot) between the reference depth and the ocean bottom to the measured near-bottom velocities V(Pbot). The velocity field of the reference depth was further obtained by combining the above velocities and Pbot anomalies using a multivariate OI scheme (Watts et al. 2001; Donohue et al. 2010) with a correlation length of 100 km.

We noted that the two NLWEs in 2018 that passed the CPIES observational region might have carried large volumes of Kuroshio waters. Thus, the SCS GEM could not reproduce the correct temperature and salinity profiles inside the NLWEs, which are characterized by warm and saline (S > 34.7 psu) subsurface waters (Long et al. 2021). To solve this problem, the NLWE GEM (see the lower panels in Fig. 2) was used instead for the regions influenced by the NLWEs while the SCS GEM was used for the other regions. Because there are only 41 profiles available near the origin of NLWEs (green dots in Fig. 3a), which are too few to construct a reliable GEM, the NLWE GEM adopted in our paper is a mixture of the SCS GEM and the Kuroshio GEM determined by the coefficient a:
GEMNLWE=(1a)×GEMSCS+a×GEMKUR,
where GEMSCS, GEMKUR, and GEMNLWE are GEM fields of temperature or salinity for SCS, Kuroshio, and the NLWEs, respectively. The number of hydrographic profiles used by GEMKUR (Fig. 2d; see also Figs. S1d and S2d) is 632 (red dots in Fig. 3a). The coefficient a can be regarded as the mixing portion of Kuroshio water carried by the NLWE. To determine a, the 1/12° Hybrid Coordinate Ocean Model (HYCOM) reanalysis was used. The HYCOM assimilated historical T/S profiles and was reported to have a good performance in simulating mesoscale eddies or Kuroshio intrusion in the South China Sea (Park and Farmer 2013; Zhang et al. 2013, 2017; Huang et al. 2016, 2017). The mean T/S curves from HYCOM within the boxes of the SCS (17°–22°N, 117°–119°E), the Kuroshio (17°–22°N, 121°–123°E, except the origin of NLWE), and the origin of the NLWE (18°–20°N, 121°–122°E) are quite close to those of CTD/Argo profiles within the corresponding boxes (Fig. 3b), suggesting that HYCOM can correctly reproduce the regional T/S characteristics. Because HYCOM can offer numerous T/S profiles to build the GEM field, including in the origin of NLWEs where historical hydrographic casts are quite few, the coefficient a can be determined when the root-mean-square difference (RMSD) between the merged GEM [(1 − a) × GEMSCS + a × GEMKUR] and GEMNLWE reached the minimum. Here GEMSCS, GEMKUR, and GEMNLWE are all constructed by T/S profiles of HYCOM. The optimal a for temperature and salinity GEM are 0.55 and 0.63, respectively, which means the NLWE is mixed with Kuroshio water occupying a portion of 60%. Here we derived this mixing coefficient using another method: if we assume that the salinity of NLWE is linearly mixed by SCS water and Kuroshio water at the same depth:
SNLWE=(1b)×SSCS+b×SKUR.
By searching for the least RMSD, the mixing coefficient b was determined as 0.56 with HYCOM profiles (Fig. 3d), which is close to previously derived term a. Here a is set to 0.60 in (2) to derive the GEMNLWE as a linear combination of GEMSCS and GEMKUR constructed with historical hydrographic casts. We did not use the GEMNLWE of HYCOM directly because HYCOM reanalysis cannot be treated as equal to observations. Because the lower bound of τ1000 of GEMSCS is 1.319 s, GEMNLWE with τ1000 lower than 1.318 s was replaced with GEMKUR, and GEMNLWE with τ1000 ranges between 1.318 and 1.319 s was linearly interpolated. The rms error of the specific volume anomaly for reconstructed GEMNLWE was 1.43 × 10−7 m3 kg−1 for the upper 1000 m. The NLWEs were identified using the Okubo–Weiss method (Okubo 1970; Weiss 1991) from the merged sea level anomaly (MSLA). The area of the NLWEs satisfies W < −0.2 Wσ, where W is the Okubo–Weiss parameter and Wσ is its standard deviation for our observation region. As shown later (Fig. 7), the Kuroshio waters carried by NLWEs only exist shallower than the 14.8°C isotherm, corresponding to a depth of approximately 250 m. Thus, the Kuroshio GEM was only used for these depths. To avoid “gaps” in the estimates introduced by the different GEMs, the estimated temperature and salinity fields near the NLWEs were smoothed horizontally and vertically with a length of 20 km and 50 m, respectively.

3) Estimation of the sea surface height

From the regional historical hydrographic dataset, one CPIES can estimate both the mass loading (barotropic) and steric (baroclinic) components of SSH at its geographic location. The former is related to water mass changes and is derived from Pbot; the latter is related to water density changes in GEMs derived from τ. The barotropic ηBT is given by the following:
ηBT=ηrefηIB=PbotρbgPatmIBρsg,
where the prime indicates the mean-removed anomaly, ρb is the density near the bottom (1030 kg m−3), ρs is the density near the sea surface (1020 kg m−3), g is the gravitational acceleration (9.8 m s−2), and PatmIB is the change in pressure caused by the atmospheric inverse barometer effect, which was derived from atmospheric sea surface pressure data (Patm) from NCEP–NCAR reanalysis, by subtracting the globally mean Patm with water depths greater than 1000 m from the interpolated local Patm (Park et al. 2012). The baroclinic ηBC was obtained as follows:
ηBC=ϕg=ref0δdpg,
which is the specific volume anomaly (δ) integrated from the reference depth and the sea surface. Following convention, this is determined empirically based on a linear relationship with τ1000. Here, the τ fluctuations caused by ηBT changes and seasonal variations were subtracted following Park et al. (2012). The baroclinic ηBC was also derived from the CPIES density estimations and shows little difference. Notably, the linear empirical relationship depends greatly on the chosen dataset (Fig. 3c); the estimated SSHs differ when the hydrocasts from the Kuroshio are used. Thus, the difference between the CPIES-derived and satellite-observed SSHs serves as a good indicator of Kuroshio water intrusion.

3. Results

a. NLWEs identified from altimeters

Two NLWEs influenced our observation region successively during the summer–fall period of 2018; their centers are marked as black stars and triangles in Fig. 4. The first NLWE (herein “eddy A”) was generated northeast of Luzon Island in early July and was accompanied by a strong negative WSC forced from the atmosphere (Fig. 5). We also observed a weak anticyclonic eddy northwest of Luzon Island on 3 July, which was absorbed by eddy A on 7 July to form a larger eddy. The merged new eddy continued to move northwestward and weakened until a weak anticyclonic SSH anomaly was generated northeast of Luzon Island and merged again on 27 July. Eddy A grew extremely large after the merging event and peaked on 8 August. The eddy covered the region southwest of Taiwan Island, spanning 118.5°–119°E and 19.5°–22.2°N. It then moved westward and quickly dissipated after 12 August.

Fig. 4.
Fig. 4.

Evolution of the NLWEs identified by merged sea surface height anomaly (MSLA) data from the CMEMSs. Centers of NLWE A and B are marked as black stars and triangles, receptively, and the edges of NLWEs are shown by black contours. Blue dots indicate the location of an Argo float with the maximum salinity labeled nearby. The contour interval for MSLA is 0.05 m. The dotted line is a section to demonstrate the CPIES estimation results in Fig. 12.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

Fig. 5.
Fig. 5.

Distribution of wind stress curl (N m−3) during the evolution of the NLWEs corresponding to Fig. 4. The centers (black stars or triangles for A and B, respectively) and edges (black contours) of the NLWEs are also shown. Thin gray lines indicate MADT contours of 125 and 130 cm, which roughly outline the Kuroshio path.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

The second NLWE (“eddy B”) formed in early August and originated northeast of Luzon Island when there was a strong negative WSC in the region (Fig. 5). Eddy B remained north of Luzon Island for almost three weeks, and then moved northwestward following a similar path to eddy A from September. Eddy B was much smaller than eddy A, with one likely contributing factor being that it did not absorb other anticyclonic eddies during its evolution. Once reaching southwest Taiwan, eddy B did not turn westward as in the case of eddy A, but continued its heading and moved out of our observational region after 20 September.

The difference between our observed anticyclonic eddies and that reported by Yuan et al. (2007) is that their LWE originated northwest rather than northeast of Luzon Island. Nevertheless, there are many common characteristics of these anticyclonic eddies that are generated in summer to early fall north of Luzon Island. The eddies we observed seemed to be closely related to forcing by the negative WSC, which is most favorable in summer. Because their origins are located closer to the path of the Kuroshio, it is expected that these NLWEs may carry Kuroshio waters to the SCS during their evolution, as discussed in the following section.

b. Evidence of Kuroshio waters intrusion

1) CPIES-estimated SSH

As demonstrated in section 2b, CPIESs can be used to estimate SSH. Because the barotropic components of SSH are an order of magnitude smaller than the baroclinic components, the estimation of SSH essentially involves the linear mapping of τ based on its relationship with different waters. As shown in Fig. 3c, the estimated SSHs from Kuroshio waters (HKur) are higher than from SCS waters (HSCS) with the same τ (τ > 1.321 s). Therefore, we analyzed the SSH estimations with different hydrocast origins and compared them with the merged SSH products derived from satellite altimetry (Hsat).

The Hsat of each CPIES station was interpolated from the merged absolute dynamic topography (MADT) of the CMEMS. It is evident that the estimated HSCS values are generally in good agreement with Hsat, especially for the stations in the SCS interior (Fig. 6). In comparison, the correlations are much weaker near the Luzon Strait, probably because of the strong mixing of different waters. From July to September when our observational region was impacted successively by the two NLWEs, Hsat northeast of the Luzon Strait increased and appeared as bumps or humps. These humps were significantly underestimated by HSCS but were better reproduced by HKur. This strongly suggests that NLWEs carry large portions of Kuroshio waters, while HSCS failed to capture these SSH signals. The disagreement in SSHs is unlikely attributable to Hsat mapping errors, which tend to be random (Le Traon and Morrow 2001). There is no evidence that the disagreement is attributable to internal tides, which may bring considerable errors to the altimeter SSH (Ray and Zaron 2011; Zhao 2014), because the observed internal tidal signals during July to September were not particularly strong (Fig. S3). Because the SCS hydrographic dataset contains Kuroshio waters incorporated during the winter, and the signals are fully captured by the seasonal correction scheme of SSH estimation, HSCS generally matched Hsat during the winter when the Kuroshio intrusion was the strongest.

Fig. 6.
Fig. 6.

Sea surface height (SSH; m) time series for the CPIES sites. Black lines are derived from the merged absolute dynamic topography (MADT) from CMEMS. Blue and red lines show the SSH estimated by the CPIES for SCS and Kuroshio waters, respectively. The shaded area indicates the evolution period of the NLWEs. The subplots are arranged by location except the rightmost column, where stations C13–C19 were in close proximity.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

2) Argo-observed salinity

One Argo profile (WMO: 2901482) entered our observational region and was captured by eddies A and B (Fig. 4). The Argo float (serial number 6875) was deployed under the U.S. Argo project in May 2018, headed north and turned eastward into July, and then headed north again after 23 July. The float drifted around the rim of eddy A and entered its interior between 4 and 12 August. Although it never reached the center of eddy A, the maximum salinity of the profiles increased to values above 34.7 psu, as is adopted in the literature (Hu et al. 2012) as a criterion for Kuroshio water intrusion. The core of the high-salinity waters (Fig. 7) was located at a depth of 140 m with a thickness of less than 40 m. From the T/S scatters of the Argo profiles, it was evident that this small patch of saline water was closer to the mean Kuroshio waters; however, the waters at other depths had T/S properties closer to those of the SCS. Because the profiles only stayed at the rim of the anticyclonic eddy, saline Kuroshio waters were not captured fully and were expected to be concentrated at the center.

Fig. 7.
Fig. 7.

(a) Temperature and (b) salinity profiles for the upper 500 m measured by float (WMO: 2901482) during the passage of the NLWEs. Black dashed lines indicate the 14.8°C isotherm where T/S curves of SCS and Kuroshio waters intersect [shown in (c) and (d)], while the solid lines indicate the 34.7-psu isohaline. The gray contours have intervals of 1°C and 0.1 psu for temperature and salinity, respectively. (c),(d) T/S diagrams of the float when it was captured by eddy A and B, respectively. The mean T/S curves of the SCS (blue) and Kuroshio waters (red) are also plotted.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

When eddy A quickly moved westward after 16 August, the Argo float exited the eddy and the measured maximum salinity dropped below 34.7 psu. It then stayed near 21°N, 120°E west of the Luzon Strait and encountered eddy B on 5 September. In this case, it entered the center of eddy B and recorded a maximum salinity of 34.95 psu (on 5 September). The saline water parcels (S > 34.7) occupied a depth range of 100–200 m. The T/S scatters further confirmed the existence of Kuroshio waters; the Argo float reported warm and saline waters closer to the Kuroshio origins in the upper layer. For depths with temperatures greater than 14.8°C, where the T/S curves of mean SCS waters and Kuroshio waters intersect (Fig. 3b), the waters maintained their SCS properties. In this case, the Argo profile was located at the center of eddy B. Therefore, we assumed that the profile captured the Kuroshio waters to the greatest extent in the vertical direction. Although the deep waters inside the NLWEs originated from the SCS, the CPIES-estimated SSH from Kuroshio-derived profiles (HKur) still showed better agreement with Hsat because SSH is mostly determined by steric changes in the upper layer.

c. NLWEs from CPIES estimation

1) Validation of CPIES estimates

To examine the performance of the CPIES estimates, data in the upper layer (<1000 m) from the two moorings were used for comparison. We considered the vertical motion of the moorings by interpolating the CPIES estimates to the changing depths of the mooring instruments. Note that the temperature chain data constructed using the SBE56 temperature loggers were not used in this comparison because they lacked pressure records.

As shown in Fig. 8, there was good agreement between the temperature estimates from the CPIES (red) and the observations from the ADCP/CTD. The root-mean-square errors (RMSEs) of the ADCPs of M1 and M3 were 0.33° and 0.16°C, respectively. The RMSEs for the CTDs ranged between 0.14° and 0.19°C, which is small compared to the temperature variations of the observation depths. On 28 October 2018, the CPIES overestimated and underestimated temperature by more than 0.5°C at depths shallower and deeper than 500 m, respectively. This disagreement is likely attributed to mixing events. The performance of the temperature estimates was expected because the GEM RMSEs were less than 0.2°C for depths greater than 400 m. Salinity record comparisons (Fig. S4) are not significant because salinity contributes little to density, and salinity variations are generally small in the SCS below 300 m.

Fig. 8.
Fig. 8.

Temperature time series from tall moorings (blue) and CPIES estimates, providing a comparison for (a),(b) M1 and (c)–(g) M2. In each panel, instrument types and nominal depths are shown in the upper-left corner; the root-mean-square difference is shown in the lower-left corner. Black dots denote the date when a strong mixing event likely occurred.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

We also used the Argo profiles that were arrested by NLWEs to check the performance of CPIES temperature and salinity estimates in the upper layer. As shown in Figs. 9a and 9c, the CPIES estimated profiles (red lines) match the Argo observations (blue lines) quite well. We also note that the subsurface saline waters (S > 34.7 psu) were well reproduced by the two-GEM method; the estimated subsurface salinity will be much lower if only the GEM of the SCS is used. Throughout the water column, the mean temperature and salinity differences obtained by CPIES estimates and Argo observations are generally less than 1°C and 0.1 psu, respectively. Exceptions to this are depths near 100 and 200 m, where the temperature difference is much lower and higher, and the salinity difference much higher and lower, respectively. These patterns may reflect the mixing process of different waters given that the Argo-observed temperature and salinity profiles have great deviations. Based on these results, we conclude that the CPIES estimates can accurately reproduce the thermohaline structure of NLWEs that trap Kuroshio waters in the subsurface.

Fig. 9.
Fig. 9.

Comparison of (a) temperature and (c) salinity profiles from CPIES estimates (red) and Argo observations (blue) when the Argo float was arrested by NLWEs. The dashed line in (c) indicates 34.7 psu. (b),(d) Actual temperature and salinity differences (CPIES estimates minus Argo observations; thick lines), respectively, and the mean difference ± one standard deviation, respectively (dashed lines).

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

Figure 10 shows the zonal and meridional velocities of the upper 250 m from the ADCP observations and CPIES estimates for M1 and M2, respectively. A 20-day running average was applied to all time series. According to ADCP observations, M1 had relatively large velocity variations associated with strong events, such as mesoscale eddies or Kuroshio intrusions; M2 had much lower velocity variations and was only affected by some weaker eddies. The CPIES estimates successfully captured these events and matched the ADCP observations quite well. For the zonal components, the RMSE was less than 0.09 and 0.04 m s−1 for M1 and M2, respectively; for the meridional components, the RMSE was less than 0.09 m s−1 in both cases. The errors in the calculated meridional velocities were much larger because there were no CPIES stations to the east of M1 and M2. Based on these comparisons, we found that the CPIES array accurately estimated the three-dimensional temperature (density) and velocity field. The structures of the NLWEs revealed by the CPIES estimates are discussed in the next section.

Fig. 10.
Fig. 10.

Eastward (U) and northward (V) velocity time series from ADCP (blue) and CPIES estimates. The root-mean-square deviations are shown in the upper-left corners of each panel.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

2) Three-dimensional structures of NLWEs

The horizontal distributions of temperature, salinity, and velocity fields from the CPIES estimations during the evolution of the NLWEs are shown in Fig. 11. Because eddy-induced variability was mainly concentrated in the upper layers, we only illustrate the estimates at depths of 0, 150, 300, and 500 m. From July to September 2018, two anticyclonic eddies successively entered the CPIES observational region and propagated northwestward. These maps were consistent with those from the MSLA. With the arrival of NLWEs, high-temperature and salinity cores could be identified in all layers except for the surface high-temperature cores. These cores were not significant for eddy A, for which salinity at 500 m was slightly lower (∼0.02 psu). The NLWEs were also poorly distinguishable from satellite-mapped sea surface temperature (SST) products (data not shown), which might be because of the SCS summer stratification feature. Eddy-induced anomalies were most significant in the subsurface and decreased significantly with depth. With the GDEM monthly mean subtracted, the maximum temperature and salinity increase at 150 m was 3.98°C and 0.31 psu, respectively. The Kuroshio waters carried by the NLWEs were wrapped by the 34.7 psu isohaline at 150 m.

Fig. 11.
Fig. 11.

Distribution of temperature (T) and salinity (S) for depths 0, 150, 300, and 500 m, during the evolution of the NLWEs. Superimposed arrows indicate the CPIES estimated velocities. The 34.7-psu isohaline is shown by thick gray lines.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

To further demonstrate the vertical structures of the NLWEs, we chose a section (the dotted line in Fig. 4) that approximately follows the propagation path. Figure 12 shows the distributions of temperature, salinity, and velocities across this section. It is evident that the NLWEs, characterized by subsurface saline waters (S > 34.7 psu), moved northwestward along this section. When the centers of the NLWEs moved around the section, water cores with salinities higher than 34.8 psu were observed. Anticyclonic velocity structures can also be seen around the center of the eddy. Superimposed on the westward mean flow, the eddy-induced velocity field was not symmetrical but was stronger in the negative components (i.e., southwestward) across the section. This implies that mean flows are important for the evolution of NLWEs.

Fig. 12.
Fig. 12.

Distribution of temperature, salinity, and velocity across the section indicated by the black dotted line in Fig. 4. For the temperature, salinity, and velocity panels, the black line indicates the contours of 20°C, 34.7 psu, and 0 m s−1, respectively, where the contour interval is 1°C, 0.1 psu, and 0.1 m s−1, respectively. Positive velocities occur toward the northeast.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

From the CPIES estimates, we identified that the depth of high-salinity water was approximately 150 m. This is consistent with the estimates from the Argo-observed profiles near the core of eddy B (Fig. 7), indicating the applicability of our two-GEM estimation technique and providing additional evidence of the Kuroshio intrusion. Considering the water within the NLWEs (upper 250 m) is a mixture between the Kuroshio and local water, we estimate the percentage of Kuroshio water from salinity measurements from Argo floats, which were completely trapped within the NLWEs (Fig. 7d). At a depth of z m (0 < z < 250), the percentage of Kuroshio water ai(z) from the ith (i = 1, 2, 3) Argo observation is derived from the formula
SNLWE(z)=[1ai(z)]SSCS(z)+ai(z)SKur(z),
where SNLWE(z), SSCS(z), and SKur(z) are salinity of observation, typical SCS waters, and Kuroshio waters, respectively (Fig. S5). Then, we calculated the volume and salt content using the above yielded
ai(z):Veddy=2500Ddσa(z)dz, and
Seddy=ρ02500Da(z)S(σ,z)dσdz,
respectively, where a(z)=i=13ai(z)/3, ρ0 = 1020 kg m−3, and D is the eddy area. The total volume and salt content of the Kuroshio waters carried by eddy A were estimated to be 9.26 × 1012 m3 and 3.02 × 1014 kg on 8 August, and 4.11 × 1012 m3 and 1.33 × 1014 kg for eddy B on 17 September, respectively. As shown in section 4a, 52 NLWEs were generated and moved westward between 1993 and 2021 (Table S1). Assuming that these eddies trapped the same volume of Kuroshio waters as the average of eddies A and B (i.e., 6.69 × 1012 m3), the annual-mean volume of transport was approximately 0.38 Sv (i.e., 6.69 × 1012 m3 × 1.8 per year; 1 Sv ≡ 106 m3 s−1). This equates to 38% of the 1.0 Sv summer mean westward Luzon Strait transport (LST) estimated by Zhao et al. (2009).

4. Discussion

Based on section 3a, we identified that the generation of the studied NLWEs was accompanied by negative WSC over the northeast of Luzon Island. Yuan et al. (2007) discussed the relationship between NLWEs and monsoons. In their study, the variation of winds northwest of Luzon Island led to local SSH variation by one to two months, and the seasonal negative WSC was quite weak, such that seasonal winds did not seem to be directly responsible for the generation of NLWEs. As is well known, the winds and SSH in the SCS are dominated by seasonal signals, indicating that a strong correlation is not sufficient to interpret their relationship.

To assess the role of winds on the generation of NLWEs, we first identified historical (1993–2021) anticyclonic eddies generated within the blue box (Fig. 13a) north of Luzon Island from MSLA products. An eddy center was defined as the point with the maximum SSH within the area of the eddy. A total of 123 anticyclonic eddies were identified, of which 71 were locally dissipated and only 52 (Table S1) traveled farther to the northwest or west (Fig. 13a). The mean date of eddy generation was 11 July. Their statistics suggest that these eddies can only be generated between April and October, and summer (June–August) is the most favorable season for eddy generation. Composites of WSC during summer (Fig. 13c) and winter (Fig. 13d) show significant differences; a negative WSC core dominates the region north of Luzon Island in summer, while a positive WSC core dominates in winter. The strong positive WSC is likely to drive seasonal LCE (Yang and Liu 2003) and is responsible for the absence of NLWEs in winter. Although the negative WSC (up to −2.8 × 10−7 N m−3) in summer is much weaker than the positive WSC in winter (up to 1.2 × 10−6 N m−3), it provides the condition under which NLWEs can be generated.

Fig. 13.
Fig. 13.

(a) Trajectories of anticyclonic eddies intruding the SCS (west of 119.5°E) generated in the blue box north of the Luzon Island. Triangles indicate the fates of eddies. Red lines are for eddies during the observational period of the CPIES array. (b) Monthly histogram of anticyclonic eddies generated in the blue box in (a). (c),(d) Composite maps of WSC (N m−3) from June to August and from December to February, respectively.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

To further investigate the role of WSC in driving the NLWEs, we extracted the signals of winds and SSH by averaging the WSC and MSLA covering the area 18°–19.5°N, 120°–122°E. We applied a fourth-order Butterworth low-pass filter going forward and backward with a cutoff period of 300 days to the spatially averaged WSC and MSLA time series to obtain the seasonal signals. Because MSLA is a smoothed product that lacks high-frequency signals, a bandpass filter with cutoff periods of 20 and 300 days was applied to both the WSC and MSLA.

Seasonally, WSC and SSH have a maximum correlation coefficient of −0.73 when the WSC leads SSH by 20 days (Fig. 14a). The lag time (20 days) was close to the lag (one month) with the maximum cross-correlation between local WSC and SSH northwest of Luzon calculated by Yuan et al. (2007). The mean annual cycle of regional WSC showed that this reached its minimum on 15 July, while regional SSH reached its maximum on 3 August (19 days later). The seasonal signal of the spatially averaged WSC was only negative a few days around 15 July. Such a weak seasonal WSC is unlikely to directly drive NLWEs. The weak seasonal WSC was also noticed by Yuan et al. (2007); although these authors suggested that seasonal WSC played an important role in generating LWEs, they reported that the detailed generation mechanism of LWEs is complicated and may be related to the momentum imbalance of the North Luzon Current.

Fig. 14.
Fig. 14.

(a) Averaged seasonal signal for WSC (blue line) and SSH (red line) within the box north of Luzon Island (18°–19.5°N, 120°–122°E). Note the time series have no units because they were divided by their standard deviations. Blue and red triangles indicate the dates when WSC and SSH reached their maximum and minimum values, respectively. The largest correlation coefficient r and its corresponding time when SSH lagged WSC is stated at the lower-left corner of each plot, calculated from a time series spanning from 2002 to 2021. (b) As in (a), but for the filtered time series with seasonal signals removed. The correlation coefficient and lag time during the CPIES observational period (June 2018–July 2019) are shown in parentheses.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

On intraseasonal time scales, WSC and SSH have a maximum correlation coefficient of −0.49 when WSC leads SSH by 3 days (Fig. 14b). The WSC and SSH time series span 19 years, from 2002 to 2021. The lag time was consistent with the SSH and WSC distributions shown in Figs. 4 and 5, with positive SSH anomalies being generated to the north of the Luzon Island soon after the prevailing negative WSC. During our observational period, the correlation reached −0.68 when WSC led SSH by 3 days. Moreover, the mean generation date for NLWEs (11 July) was closer to the seasonal WSC minimum (15 July) than the seasonal SSH maximum (3 August). This implies that the generation of NLWEs is more closely linked to negative WSC over intraseasonal time scales than seasonally. In other words, in winter, negative WSC is rare to the north of the Luzon Island because of the strong positive seasonal WSC, while in summer, although the negative seasonal WSC is weak, NLWEs can be triggered by intraseasonal negative WSC to the north of Luzon Island.

5. Summary and conclusions

A large CPIES array provided an unprecedented opportunity to investigate the rich multiscale dynamical processes near the Luzon Strait, where phenomena such as strong eddies and Kuroshio intrusion occur. CPIES estimates proved to be excellent at reproducing the full-depth hydrographic fields in this hotspot and captured two anticyclonic eddies (NLWEs) carrying Kuroshio waters during the summer–fall season of 2018.

Similar to previous studies focusing on LWEs generated west of Luzon Island, the NLWEs generated north of Luzon Island were found to be seasonal anticyclonic eddies. During the CPIES observational period, two NLWEs (A and B) were generated north of Luzon Island accompanied by a strong anticyclonic WSC. Eddy A was much larger in size because it absorbed two small anticyclonic eddies nearby. A strong cyclonic eddy separated eddy A and eddy B, which successively moved northwestward across the CPIES array forming an eddy train. These eddies demised northwest of the observational region near 21.5°N and 118.5°E. Because the NLWEs were generated close to the Kuroshio main path, a large amount of Kuroshio water was entrained, as identified by SSH signatures and Argo T/S profiles. Saline water led to significant changes in temperature and salinity in the upper layer of the NLWEs, particularly around the subsurface layer near 150 m. The water column below the 14.8°C isotherm (at a depth of approximately 200 m) inside the NLWEs was still controlled by the SCS waters. The total volume and salt content of Kuroshio waters carried by eddy A was estimated to be 9.74 × 1012 m3 and 3.38 × 1014 kg on 8 August compared to 4.62 × 1012 m3 and 1.59 × 1014 kg for eddy B, respectively. The annual mean transport for historical NLWEs was estimated to be 0.38 Sv, which accounts for 38% of the westward Luzon Strait volume transport during summer (Zhao et al. 2009).

The seasonal monsoon and its associated negative WSC north of Luzon Island provide favorable conditions for the generation of NLWEs in summer, but the mean WSC is not strong enough to drive anticyclonic eddies. The local mean WSC reached its minimum on 15 July, leading to the local highest SSH after 19 days. Historical statistics of anticyclonic eddies generated north of Luzon Island indicate that NLWEs do not occur between November and March, with a mean generation date of 11 July. With the seasonal signals removed, local WSC is also strongly correlated with local SSH (r = −0.48 overall and r = −0.68 for the CPIES observational period). The short lag (3 days) is more consistent with the observation that anticyclonic eddies always appeared after the prevailing local negative WSC, which explains why the mean generation date of NLWEs (11 July) was closer to the date when WSC was minimal (15 July). Thus, we conclude that the local negative intraseasonal WSC is directly responsible for the generation of NLWEs. In other words, the positive seasonal WSC during winter prevents the generation of NLWEs, which are only generated during other seasons under the influence of local strongly negative WSC events. Intraseasonal WSC may also explain the generation of the LWE reported by Yuan et al. (2007), who suggested that the generation mechanism is complicated and could not be fully driven by seasonal winds.

In previous studies, the Kuroshio intrusion into the SCS was considered to be weakest during the summer. The seasonal NLWEs discussed in this paper (Fig. 15) are a new type of Kuroshio water intrusion that occur in summer and have not been previously documented. Because NLWE-induced volume transport likely accounts for almost half of the LST during summer, NLWEs play an important role in stratification west of the Luzon Strait and may also contribute to SCS seasonal circulation. Moreover, as revealed by previous studies, eddies hitting the SCS continental shelf can excite cross-shelf exchange (Wang et al. 2018) and topographic Rossby waves (Wang et al. 2019; Zheng et al. 2021a,b, 2022a,b), and the latter can induce considerable abyssal velocity variation. Further research is now required to better understand the interannual variability of NLWEs and their impacts on deep circulation in the SCS.

Fig. 15.
Fig. 15.

Schematic diagram showing the “life cycle” of a NLWE.

Citation: Journal of Physical Oceanography 53, 1; 10.1175/JPO-D-22-0019.1

Acknowledgments.

The State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, and the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) contributed to the work equally and should be regarded as co–first institutional affiliations. This study was supported by the Scientific Research Fund of the Second Institute of Oceanography, MNR (Grants QNYC2102 and JZ2001), the National Natural Science Foundation of China (Grants 41920104006, 41906023, and 41906024), the Project of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography (SOEDZZ2106 and SOEDZZ2207), the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University (Project No. SL2021MS021), and the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai (No. 311020004).

Data availability statement.

Bathymetry data were obtained from ETOPO1 (https://doi.org/10.7289/V5C8276M). Surface geostrophic currents and absolute dynamic topography data were obtained from the CMEMS (http://marine.copernicus.eu/). The moored data analyzed here can be obtained from the corresponding author upon reasonable request. Quality-controlled Argo profiles were retrieved from the Observation and Research Station of the Global Ocean Argo System (Hangzhou), MNR. CTD profiles were obtained from the World Ocean Database (https://www.ncei.noaa.gov/access/world-ocean-database-select/dbsearch.html) and the SCS Open Cruise. The CTD profiles from “SCS Open Cruise” are not publicly available and only the cruise participants are allowed to use the data.

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    • Search Google Scholar
    • Export Citation
  • Wang, Q., and Coauthors, 2019: Energetic topographic Rossby waves in the northern South China Sea. J. Phys. Oceanogr., 49, 26972714, https://doi.org/10.1175/JPO-D-18-0247.1.

    • Search Google Scholar
    • Export Citation
  • Watts, D. R., X. Qian, and K. L. Tracey, 2001: Mapping abyssal current and pressure fields under the meandering Gulf Stream. J. Atmos. Oceanic Technol., 18, 10521067, https://doi.org/10.1175/1520-0426(2001)018<1052:MACAPF>2.0.CO;2.

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    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Wu, C.-R., and Y.-C. Hsin, 2012: The forcing mechanism leading to the Kuroshio intrusion into the South China Sea. J. Geophys. Res., 117, C07015, https://doi.org/10.1029/2012JC007968.

    • Search Google Scholar
    • Export Citation
  • Xiu, P., F. Chai, L. Shi, H. J. Xue, and Y. Chao, 2010: A census of eddy activities in the South China Sea during 1993–2007. J. Geophys. Res., 115, C03012, https://doi.org/10.1029/2009JC005657.

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    • Export Citation