Editorial Type:
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Article Type:
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Subsurface Mesoscale Eddies Observed in the Northeastern South China Sea: Dynamic Features and Water Mass Transport

Zhongbin Sun aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory/Sanya Oceanographic Institution, Ocean University of China, Qingdao/Sanya, China
bQingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Zhiwei Zhang aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory/Sanya Oceanographic Institution, Ocean University of China, Qingdao/Sanya, China
bQingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Bo Qiu cDepartment of Oceanography, University of Hawai‘i at Mānoa, Honolulu, Hawaii

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Chun Zhou aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory/Sanya Oceanographic Institution, Ocean University of China, Qingdao/Sanya, China
bQingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Wei Zhao aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory/Sanya Oceanographic Institution, Ocean University of China, Qingdao/Sanya, China
bQingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Jiwei Tian aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory/Sanya Oceanographic Institution, Ocean University of China, Qingdao/Sanya, China
bQingdao National Laboratory for Marine Science and Technology, Qingdao, China

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Online Publication:
22 Apr 2022
Print Publication:
01 May 2022
DOI:
https://doi.org/10.1175/JPO-D-21-0177.1
Page(s):
841–855
Full access

Abstract

A train of subsurface mesoscale eddies (SMEs) consisting of two cyclones and two anticyclones was observed in the northeastern South China Sea (NESCS) in 2015 by a mooring array. In contrast to the widely reported surface-intensified eddies, the SMEs had weak surface signals but showed maximum velocity at ∼370 m with a magnitude of 17.2 cm s−1. The SMEs generally propagated westward with a speed of ∼4.3 cm s−1, which resulted in a distinct ∼120-day-period oscillations in the moored time series. Based on the concurrent velocity, temperature, and salinity from the mooring array, three-dimensional structures of the SMEs were constructed, which were then used to quantify water mass transports induced by them. The results revealed that all these SMEs were vertically tilted with an influence depth exceeding 1000 m. Water mass analysis suggested that the cyclonic and anticyclonic SMEs trapped the northwest Pacific water and the NESCS local water, respectively. The cyclones transported 1.00 ± 0.25 Sv (1 Sv ≡ 106 m3 s−1) North Pacific Intermediate Water westward into the NESCS during the 2-yr observation period, accounting for 61.7% of the observed volume transport through the Luzon Strait between 25.8 and 27.4σ0. Furthermore, it also showed that both the trapping and stirring effects of the SMEs induced an eastward heat transport across the Luzon Strait, but the role of the former was much more important than the latter. The present results suggested that the SMEs near the Luzon Strait may provide a novel route for the intermediate-layer water exchange between the NESCS and Pacific.

© 2022 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: Zhiwei Zhang, zzw330@ouc.edu.cn

Abstract

A train of subsurface mesoscale eddies (SMEs) consisting of two cyclones and two anticyclones was observed in the northeastern South China Sea (NESCS) in 2015 by a mooring array. In contrast to the widely reported surface-intensified eddies, the SMEs had weak surface signals but showed maximum velocity at ∼370 m with a magnitude of 17.2 cm s−1. The SMEs generally propagated westward with a speed of ∼4.3 cm s−1, which resulted in a distinct ∼120-day-period oscillations in the moored time series. Based on the concurrent velocity, temperature, and salinity from the mooring array, three-dimensional structures of the SMEs were constructed, which were then used to quantify water mass transports induced by them. The results revealed that all these SMEs were vertically tilted with an influence depth exceeding 1000 m. Water mass analysis suggested that the cyclonic and anticyclonic SMEs trapped the northwest Pacific water and the NESCS local water, respectively. The cyclones transported 1.00 ± 0.25 Sv (1 Sv ≡ 106 m3 s−1) North Pacific Intermediate Water westward into the NESCS during the 2-yr observation period, accounting for 61.7% of the observed volume transport through the Luzon Strait between 25.8 and 27.4σ0. Furthermore, it also showed that both the trapping and stirring effects of the SMEs induced an eastward heat transport across the Luzon Strait, but the role of the former was much more important than the latter. The present results suggested that the SMEs near the Luzon Strait may provide a novel route for the intermediate-layer water exchange between the NESCS and Pacific.

© 2022 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: Zhiwei Zhang, zzw330@ouc.edu.cn
Keywords: Eddies; Mass fluxes/transport; Mesoscale processes; In situ oceanic observations

1. Introduction

The South China Sea (SCS) is the largest marginal sea in the northwestern Pacific, and it exchanges water masses with the Pacific Ocean mainly through the Luzon Strait. As a semiclosed, deep-water marginal sea, the SCS is abundant with active multiscale dynamic processes with spatial scales ranging from thousands of kilometers to centimeters, including large-scale circulation (e.g., Wang et al. 2011; Gan et al. 2016), mesoscale eddies (e.g., Chen et al. 2011; Z. W. Zhang et al. 2017), submesoscale processes (e.g., Zhong et al. 2017; Zhang et al. 2020), small-scale internal waves (e.g., Alford et al. 2015; Huang et al. 2016), and microscale turbulent mixing (e.g., Tian et al. 2009; Yang et al. 2016). Among these dynamic processes, the mesoscale eddies with spatial and time scales of O(50–300) km and O(20–200) days, respectively, are demonstrated to play significant roles in energy transfer and water mass transport (heat, salt, nutrients, etc.) in the SCS, through which they further modulate the SCS basin-scale circulation and the local climate variability (e.g., Xue et al. 2004; Chen et al. 2012; Chow and Liu, 2012; H. Wang et al. 2012; X. Wang et al. 2012; Zhang et al. 2013, 2016). Given the importance of mesoscale eddies in the SCS, they have drawn increasing attention in recent decades (Wang et al. 2003; Zhang et al. 2016; see a review by Zheng et al. 2017).

Among the different regions of the SCS, the northeastern SCS (NESCS, Fig. 1a) is one of the richest regions of eddy activities (Wang et al. 2003; Chen et al. 2011; Nan et al. 2011; Sun et al. 2016, and references therein). Mesoscale eddies in this region (i.e., NESCS) are suggested to be generated through the following three candidate mechanisms. First, many of the eddies, particularly those southwest of Taiwan in winter, are generated through the Kuroshio Loop eddy shedding and the associated hydrodynamic instabilities (D. Wang et al. 2008; Nan et al. 2015; Z. W. Zhang et al. 2017). Second, some eddies can be directly driven by wind stress curl associated with the orographic winds, for example, the Luzon cold eddy west of the Luzon Island (G. Wang et al. 2008; Wang and Gan, 2014; He et al. 2015). Third, several studies suggested that some eddies west of the Luzon Strait may be originated from the northwestern Pacific through westward propagation (Zheng et al. 2011; Hu et al. 2012). Eulerianly (i.e., observations at a fixed location), propagation of these eddies can result in strong intraseasonal variations in upper-ocean current in the NESCS, which generally show dominant periods of 30–90 days in the kinetic energy spectrum (Cheng et al. 2015; Wang et al. 2020). Although these widely studied mesoscale eddies are demonstrated to be a full-water column phenomenon that can extend to sea bottom, they are generally surface intensified with the maximum velocity occurring at the sea surface (e.g., Zhang et al. 2013, 2016; Shu et al. 2019).

Fig. 1.
Fig. 1.

(a) Locations of the mooring array (stars) and bathymetry of the NESCS and northwestern Pacific. Red and purple stars represent moorings NS1–NS5 at the NS section and EW1–EW5 at the EW section, respectively. Purple vectors denote the mean surface geostrophic currents. Black solid line indicates the 1000-m isobath. (b) The detailed configuration of the mooring array and topography at the NS section in 2014–15. Black stars and circles represent ADCPs and RCMs, respectively. Black dotted lines denote current observations by ADCPs. (c) As in (b), but for the EW section (containing site NS3 in the east).

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

Apart from the surface-intensified mesoscale eddies mentioned above, there is another type of mesoscale eddies that show the maximum velocity in the subsurface layer, which are referred to as subsurface mesoscale eddies (SMEs hereafter) in this study. Generally, SMEs present lens-shaped thermohaline structures in the subsurface but have weak expressions at sea surface (e.g., Richardson et al. 2000; Lin et al. 2015). As a result, it is more difficult for them to be detected by satellite observations. In addition, SMEs can trap a huge amount of water masses from their origin and keep them coherent for a long period (sometimes exceeding one year). Therefore, they are suggested to be an efficient transporter for materials in subsurface layers of the ocean (e.g., Z. G. Zhang et al. 2017a, and references therein). Although SMEs have been widely observed in open oceans (e.g., Wilson et al. 2002; Johnson and McTaggart 2010; Chaigneau et al. 2011; Nan et al. 2017; Barceló-Llull et al. 2017; Dilmahamod et al. 2018; Yang et al. 2019), their relevant literature in the SCS is very rare, and this is in sharp contrast with the extensively studied surface-intensified eddies in this region. To the authors’ best knowledge, there are only a few hydrographic survey-based case studies that have reported the flow and thermohaline properties of SME-like structures in the SCS (Xie et al. 2011; Z. X. Zhang et al. 2014; Lin et al. 2017). The poor observational knowledge of SMEs in the SCS may be ascribed to two reasons. First, because SMEs have very weak surface expressions, it is difficult to conduct target observations without the guidance of satellite-derived surface information. Second, the SCS is abundant with strong internal tides that have comparable spatial scales with mesoscale eddies (Zhao 2014; X. Huang et al. 2018). As a result, it is challenging to identify and cleanly separate SMEs’ signals based on traditional instantaneous hydrographic measurements (Cao et al. 2019). Due to the scarcity of long-term high-resolution in situ observations, our knowledge of SMEs in the SCS, such as their three-dimensional (3D) structure, propagation and evolution, water mass transport, and generation mechanism, remain elusive.

In this study, based on the long-term observations from a mooring array in the NESCS, a train of SMEs consisting of two cyclones and two anticyclones are directly captured. Through analyzing the high-resolution moored data, dynamic features of these SMEs are reported, and their roles in volume and heat transports are discussed. This paper is organized as follows. Section 2 describes the data used in this study. Sections 3 and 4 show the dynamic features and transports of SMEs, respectively. Finally, a summary and discussion are given in section 5.

2. Observational data

a. Moored data

To investigate the multiscale dynamic processes in the SCS, the Ocean University of China has constructed the SCS Mooring Array system since 2009 (see Sun et al. 2020). As a part of this system, a mooring array, consisting of 10 bottom-anchored moorings (Fig. 1, section NS: NS1–NS5, section EW: NS3 and EW1–EW5) was deployed in the NESCS between June 2014 and June 2016. Typically, each mooring is equipped with one upward-looking and one or two downward-looking 75-kHz acoustic Doppler current profiles (ADCPs), temperature chains consisting of temperature loggers and conductivity–temperature–depths (CTDs), and several recording current meters (RCMs) to measure the velocity and temperature–salinity (T–S) data in the nearly full water column. Detailed configurations of the moorings can be found in Tables S1 and S2 in the online supplemental material (refer also to Sun et al. 2020).

The data processing is similar to our previous mooring-based studies (e.g., Zhang et al. 2015a). For velocity and temperature data, all the original data were first hourly averaged and linearly interpolated onto a 5-m vertical interval. Then, to remove the tidal and inertial signals, a 2.5-day fourth-order Butterworth low-pass filter was applied to the hourly data. Finally, the data were daily averaged and the subinertial daily velocity and temperature data were obtained for analysis in this study. For salinity data, because of the coarse resolutions of CTDs, we did not apply the same processing procedure as the velocity and temperature. Instead, we linearly interpolated the hourly salinity data onto isopycnic surfaces, which by nature removed the influence of thermocline heaving caused by waves. Under the influence of energetic diurnal and semidiurnal internal tides in the NESCS (Zhao 2014), the CTDs typically swung at tidal frequencies, providing us one or two salinity profiles in a specific depth range depending on the designed depth of CTD and the extent of mooring swing. Through performing a daily composite for the moored CTD profiles, we obtained daily salinity time series on specific isopycnic surfaces (between 23.0 and 27.6σ0 with an interval of 0.05σ0) for each mooring. Apart from the salinity data, the CTD-derived temperature data were also interpolated onto the isopycnic surfaces. The processed CTD-derived salinity and temperature data were then finally used to analyze the water mass properties and transports of the SMEs.

b. Satellite data

To examine surface information of these SMEs, sea level anomaly (SLA) and absolute geostrophic velocity data from a merged product of Jason-3, Sentinel-3A, HY-2A, SARAL/AltiKa, Cryosat-2, Jason-2, Jason-1, TOPEX/Poseidon, Envisat, GFO, and ERS-1/2 were also used in this study. The data were distributed by the Copernicus Marine Environment Monitoring Service with temporal and spatial resolutions of 1 day and 1/4°, respectively. The SLA and geostrophic velocity data between 2014 and 2016 were downloaded and used in this study.

c. HYCOM product

To help analyze generation processes of the SMEs, the data of the Hybrid Coordinate Ocean Model (HYCOM) Navy Coupled Ocean Data Assimilation were also used in the study. The HYCOM model has a horizontal resolution of 1/12° for both latitude and longitude. Vertically, it has 33 levels with a resolution of 10 m near the surface and 500 m near the bottom of 5500 m. Previous studies demonstrated that because of the data assimilation, the HYCOM product can well reproduce the mesoscale processes in the NESCS (e.g., Park and Farmer 2013; Zhang et al. 2013; Huang et al. 2017). The daily velocity and temperature data in the year 2015 were finally used in the study.

3. Dynamic features of SMEs

a. Observed general features

Figures 2a and 2b present the depth–time distributions of temperature and meridional velocity distributions observed at site EW2, respectively (EW2 is the central mooring of the array). To focus on the mesoscale signals, a 20-day low-pass filter has been applied to the temperature and velocity data here. From Fig. 2a, it is found that strong intraseasonal oscillations with a period of 3–5 months occurred between March and October 2015. The most prominent characteristic is that the oscillations presented lens-like structures that showed opposite isothermal undulations above and beneath ∼350-m depth. Compared with the upper-layer above ∼350 m, the isotherm fluctuations beneath that were much stronger and the maximum undulation exceeded 165 m. Corresponding to the lens-like structures, the temperature anomalies (T′, obtained by subtracting the mean temperature between November 2014 and February 2016) at 200 m showed reversed signs with those at 600 m (Fig. 2c). At the 600-m depth, the T′ changed sign three times (i.e., negative, positive, negative, and positive) between March and October 2015, which indicated the occurrence of two concave lenses and two convex lenses. With respect to the meridional velocity, it was northward (southward) before and southward (northward) after a convex (concave) lens, respectively, which corresponded well with the thermal-wind relation. The meridional velocity anomaly (υ′, calculated using the same approach with the T′) at 400 m showed similar and sometimes larger (between June and August 2015) magnitudes compared with that at 100 m (Fig. 2d), indicating that current velocities of these lens-like structures are not surface-intensified but sometimes subsurface intensified.

Fig. 2.
Fig. 2.

(a) Depth–time plots of 20-day low-pass-filtered temperature between November 2014 and February 2016 at site EW2. The black thin lines are temperature contours with an interval of 1°C, and the two black thick lines denote isolines of 16° and 7°C. (b) As in (a), but for the meridional velocity distributions. The black dashed lines denote zero velocity contours. (c) Time series of T′ at depths of 200 m (blue line) and 600 m (red line). Note that the T′ at 600-m depth has been multiplied by 5. (d) Time series of υ′ at depths of 100 m (blue line) and 400 m (red line). Purple dashed boxes in the figure indicate the occurrence of SMEs between March and October 2015.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

In Figs. 3a and 3b, we show the depth-dependent power spectra of υ′/2 and gρ′/2Nρ0 [i.e., the kinetic energy (KE) and available potential energy (APE) spectra] at mooring EW2, respectively. Here, g is the gravity acceleration, N is the buoyancy frequency, ρ0 = 1030 kg m−3, and ρ′ is the density anomaly calculated based on temperature (see details in Miao et al. 2021). It is found that both the KE and APE spectra show a significant ∼120-day-period energy peak in the subsurface layer. For this energy peak, the largest KE and APE spectral densities occur at 300–400 m and 600–800 m, respectively. The above phenomena coincide well with the features of the lens-associated temperature and velocity variations as seen from Fig. 2. In addition to the ∼120-day-period, the KE (APE) spectrum also shows energy peaks at 25–40 and 70–90 days (70–90 days). However, energy of these shorter-period signals is trapped to the upper 300 m, which may be associated with surface-intensified mesoscale processes (Zhang et al. 2015a). Note that the APE is much larger than the KE and the Burger number estimated using the mean KE divided by the mean APE is 0.38, suggesting that these lens-like structures are mesoscales (or quasigeostrophic) in dynamics (Cushman-Roisin and Jean-Marie 2011). Given that the subsurface lens-like structures had a ∼120-day period, their signals became even clearer after we applied a 90–160-day bandpass filter to the temperature and velocity time series at site EW2 (Fig. 4). Corresponding to the subsurface lens-like structures, the bandpass filtered temperature (Tb′, hereafter) presented two peaks and one trough in vertical with their respective depths of ∼100, ∼500, and ∼360 m, respectively. With respect to the bandpass filtered meridional velocity (υb′) and absolute velocity, their maximums occurred at ∼370 m with the respective maximum magnitude reaching 15.8 and 17.2 cm s−1. Note that significant signals of Tb′ and υb′ associated with the lens-like structures can still be found at 1000 m, suggesting deep penetration of these structures. Furthermore, these lens-like structures were tilted in the vertical with deeper signals leading upper ones, which will be further discussed in section 4.

Fig. 3.
Fig. 3.

Power spectra distributions of (a) υ′/2, denoting kinetic energy spectrum, and (b) gρ′/2Nρ0, denoting available potential energy spectrum, at site EW2 in 2015 in the upper 1000-m depth.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

Fig. 4.
Fig. 4.

(a),(c) As in Figs. 2a and 2b, but after a 90–160-day bandpass filter. The black solid lines in (c) indicate velocity contours of ±6, ±10, and ±14 cm s−1. (b),(d) The mean absolute Tb′ and υb′ averaged between March–October 2015.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

To check whether the lens-like structures had surface expressions, we compared the altimeter SLA and geostrophic velocity anomalies with the moored-derived dynamic height (DH) and υ′ at EW2 (Fig. 5). Here, the DH was calculated using DH(z)=∫Z0z−(ρ′/ρ0)dz, where Z0 = −1000 m is the reference level. For the DH at 350-m depth (DH350 for short), it displayed two positive and two negative phases between March and October 2015 (Fig. 5a), which well coincided with the ∼120-day-period lens-like structures. With respect to the DH at 50 m, it differed greatly from the DH350 in terms of magnitude, period, and phase during the lens period. By contrast, the DH at 50 m was similar to the SLA and the DH integrated from −350 to −50 m, whose variations were dominated by higher-frequency signals (recall Fig. 3). The above comparisons of DHs at different depths also applied to υ′ (Fig. 5b). From March to October 2015, υ′ displayed distinct ∼120-day-period oscillations at 350 m while it showed higher-frequency variations at 100 m and sea surface. After the lens events from November 2015 to February 2016, however, both the DH and υ′ at 350 m showed similar temporal variations with those at sea surface, which corresponded to two anticyclonic and one cyclonic surface-intensified mesoscale eddies that propagated across the mooring EW2 (seen from the altimeter data; figure not shown). The above results demonstrated that the lens-like structures have weak surface expressions, and they are, therefore, more difficult to detect from altimeter data than the surface-intensified mesoscale eddies (Fig. S1).

Fig. 5.
Fig. 5.

(a) Time series of SLA (black line), dynamic height at 50-m depth (red line), 350-m depth (blue line), and the dynamic height integrated from −350 to −50 m (green line) between November 2014 and February 2016 at site EW2. (b) Time series of altimeter surface geostrophic υ′ (black line), mooring-observed υ′ at 100 m (red line) and 350 m (blue line) depths between November 2014 and February 2016 at site EW2. Red boxes indicate the occurrence of SMEs between March and October 2015.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

b. Propagation

In Fig. 6a, we show the longitude–time distribution of Tb′ averaged between 400 and 600 m along the EW section. It shows that the two negative and two positive Tb′ events associated with the lens-like structures were captured by all of the six moorings. The Tb′ in the east led that in the west, indicating that these lens-like structures had a westward propagation tendency. Through combining this propagation direction and positive and negative phases of υb′ observed by the EW2 (same for the other EW moorings), we infer that it was a cyclonic, then an anticyclonic, then another cyclonic, and finally another anticyclonic SME (C-SME1, A-SME1, C-SME2, and A-SME2 hereafter) that crossed each mooring in sequence. In another word, the two concave and two convex lenses were cyclonic and anticyclonic SMEs, respectively, which formed a SME train that propagated across the moorings.

Fig. 6.
Fig. 6.

(a) Tb′ distributions along the EW section averaged between 400- and 600-m depth. Green solid lines denote the westward propagation processes of four SMEs in 2015 indicated by the maximum Tb′. Black triangles are the locations of moorings at the EW section. The distances from site NS3 are marked on the top of the figure. (b) Schematic diagram of the method to calculate the propagation direction angle (α) of the SMEs. The blue circle denotes the SME’s maximum-velocity circle. A and B are the two points where the SME’s maximum-velocity circle crossed a specific mooring. The eddy center, mean velocity vector averaged along the subtense AB, and propagation speed vector of SMEs are indicated by O, Um, and Up, respectively.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

Based on the distance–time plot of Tb′ in Fig. 6a, we infer that on average, it took 57 days for the SMEs to propagate from the east end (20.7°N ,119.9°E) to the west end (21.1°N, 117.9°E) of the EW section. This leads to a propagation speed of 4.3 cm s−1 along the EW section (direction angle of the EW section is 167.5° relative to due east). To estimate the propagation direction angle of the SMEs, we further made the following assumptions: 1) SMEs had a circular shape, 2) the velocity field of the SMEs is nondivergent and axisymmetric, and 3) when crossing a mooring, the SMEs kept a uniform propagation velocity (including speed and direction). Based on these assumptions, we can deduce that the propagation angle of the SMEs (i.e., α in Fig. 6b) equals to the angle of the averaged velocity vector along the subtense AB in Fig. 6b. Here, A and B are the two points where the SME’s maximum-velocity circle crossed a specific mooring. The maximum rotational velocity (swirl velocity hereafter) of the SMEs can be calculated using the 90–160-day bandpass-filtered moored velocity and its mean value for the SMEs reached 15.5 cm s−1 at EW2. By converting the subtense AB to the time between the two maximum rotational velocities observed by a mooring, we calculated the propagation angles of the SMEs at each mooring site based on the bandpass-filtered velocity averaged between 200- and 400-m depth. The mean propagation direction for different moorings and different SMEs was estimated to be 174°, which means that the SMEs nearly propagated toward westward. The close value between the EW direction angle and the SMEs’ propagation angle (i.e., 167.5° versus 174°) suggests that the propagation speed of SMEs was approximately 4.3 cm s−1, i.e., the speed along the EW section. Multiplying the propagation speed (i.e., 4.3 cm s−1) by the observed period of the SME train (i.e., ∼120 days), we estimate that the SME train had a wavelength of 446 km. Accordingly, the mean radius of the SME was about 112 km (i.e., a quarter wavelength), which is larger than the first baroclinic Rossby deformation radius at this latitude (∼55 km; Chelton et al. 1998).

c. Water mass properties

To examine the water mass properties of the SMEs, we show the isopycnic distributions of salinity at the sites EW2 and EW4 in Figs. 7c and 7d, respectively. It is found that during the periods of the two C-SMEs (with negative DH350), salinity in the intermediate layer between 25.8 and 27.4σ0 was smaller than the other times. The minimum salinity even reached 34.2 psu, which is close to the salinity minimum of the North Pacific Intermediate Water (NPIW) east of Luzon Strait (Qu et al. 2000). During the periods of the two A-SMEs (with positive DH350), on the other hand, the intermediate-layer salinity did not show difference with the normal state. The intermediate-layer salinity minimum was about 34.4 psu, which is close to that of the typical NESCS local water. In the upper-layer above 25.8σ0, the C-SMEs and A-SMEs also showed different salinity. It was higher during the C-SMEs periods but lower during the period of A-SME1 and the first half period of A-SME2. During the second half period of A-SME2, the upper-layer salinity was also elevated, which was most possibly associated with the Kuroshio intrusion at that stage rather than the A-SME2 itself (Fig. S1i). Note that the upper and intermediate layers here are defined on isopycnic surfaces according to the water mass properties rather than the traditional definition to estimate the Luzon Strait volume transport (e.g., Tian et al. 2006).

Fig. 7.
Fig. 7.

(a),(b) Time series of dynamic height at 350-m depth at sites EW2 and EW4, respectively. Blue and red shadings indicate occurrence of C-SMEs and A-SMEs. (c),(d) Potential density–time plots of salinity measured by CTDs at sites EW2 and EW4, respectively. The depth of each isopycnal is marked on the left side of (c). Black lines denote the salinity contour of 34.35 psu. Blue dashed boxes indicate the occurrence of the two C-SMEs.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

To see the T–S properties more quantitively, we compare the composite mean T–S diagrams within the C-SMEs and A-SMEs and outside of SMEs in Fig. 8, respectively. Here, the periods of C-SMEs and A-SMEs were chosen as the time when the DH350 is negative and positive, respectively. It shows that the T–S diagram outside the SMEs had a salinity maximum and minimum of 34.62 and 34.4 psu in the upper and intermediate layers, respectively, which represents the typical local water in the NESCS (Qu et al. 2000; Z. W. Zhang et al. 2017; Sun et al. 2020). For the C-SMEs, the mean T–S diagram had a salinity maximum (minimum) of 34.71 psu (34.36 psu), which was 0.09 psu (0.04 psu) higher (lower) than that of the NESCS local water. In the upper and intermediate layers, the T–S properties within the C-SMEs were closer to the North Pacific Tropical Water (NPTW) and the NPIW, respectively. With respect to the A-SMEs, however, its mean T–S diagram was very close to the NESCS local water. The above results suggest that the C-SMEs have trapped the northwest Pacific water and transported it into the NESCS interior. This point is further verified by the fact that the intermediate-layer low-salinity cores moved westward along with the propagation of the two C-SMEs (Fig. 9). The water trapping and transporting effect of the SMEs is theoretically consistent with their strong nonlinearity, i.e., swirl velocity is much larger than propagation speed (Chelton et al. 2011). The water transports caused by these SMEs are examined in detail in the next section.

Fig. 8.
Fig. 8.

Mean T–S diagram of water masses within the two C-SMEs (blue line), two A-SMEs (red line), and outside of the SMEs (green line) obtained from all the moored CTD data except site NS1. Shadings represent the standard deviations of the mean T–S. Black dashed lines denote contours of potential density.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

Fig. 9.
Fig. 9.

Salinity distributions at sites (a) NS3 and (b)–(f) EW1–EW5. Black lines denote salinity contours of 34.35 psu. Purple arrows indicate the westward movement of intermediate-layer low-salinity cores along with the propagation of the two C-SMEs.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

4. Transports of SMEs

a. 3D structures

Transporting processes of mesoscale eddies strongly depend on their 3D velocity and thermohaline structures (Chaigneau et al. 2011; Z. G. Zhang et al. 2014). Therefore, before quantifying the volume and heat transports caused by the SMEs, we first constructed their 3D structures of velocity and temperature anomalies based on the following procedures. First, the SMEs-associated daily temperature and velocity anomalies (i.e., Tb′ and Ub′) were obtained by applying the 90–160-day bandpass filter to the data from the moorings NS1–NS5. Then, through multiplying the time by the 4.3 cm s−1 westward propagation speed, the Tb′ and Ub′ data in the zonal direction were also obtained based on the NS moorings. In this process, we have assumed that the SMEs did not change when they propagated across the NS section. Finally, by interpolating the depth-dependent data onto a 10 km × 10 km horizontal grid, the 3D fields of Tb′ and Ub′ of the SMEs were constructed (Fig. 10). Based on the 3D velocity field of the SMEs, we can infer that the maximum-velocity radius of the SMEs was about 110 km. This result is close to the previously mentioned radius calculated using the propagation speed multiplied by a quarter period and demonstrates the validity of the construction method of 3D structures used here.

Fig. 10.
Fig. 10.

The 3D structures of (a) A-SME1 and (b) C-SME2. Shading and black arrows denote Tb′ and Ub′ of eddies, respectively. Black dotted lines indicate the z axis of the coordinate. Purple dots denote the eddy centers at each layer, which are defined as the zero-velocity points. Purple dotted lines denote the axis of eddy centers and green thick lines at 1000-m depth present the drift of eddy center from the z axis. Green circles at each layer denote the edge of the eddies whose radius is defined as the mean radial distance of the maximum velocity.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

The 3D structures of the A-SME1 and the C-SME2 in Fig. 10 indicate that the SMEs had the following notable characteristics. First, both the anticyclonic and cyclonic SMEs can penetrate deep with their influence depth reaching at least 1000 m. Their velocities were subsurface intensified, and the maximum velocity occurred at ∼370-m depth (Fig. S2). Second, with increasing depth, the eddy center (defined as the zero-velocity point) gradually tilted toward southwest. The tilting distance from 150- to 1000-m depth exceeded 40 km, which is similar to the result for the surface-intensified eddies reported by Zhang et al. (2016). The above tilting direction is opposite against the vertical shear of the mean current in the study region (mean velocity shear is westward, Fig. S3), indicating that the SMEs can extract energy from the background current through baroclinic energy conversion (Pedlosky 1987; Vallis 2006). This is consistent with the observed fact that the SMEs were at their growth stage at the NS section (Fig. 6a). Third, Tb′ of the SMEs presented a dipole and a monopole structure above and beneath the 450-m depth, respectively. Specifically, above the 450-m depth, Tb′ was negative/positive (positive/negative) in the western/eastern side of the A-SME (C-SME). This dipole pattern is caused by the SMEs’ stirring effect exerting on the background temperature gradient (e.g., Qiu and Chen 2005; Hausmann and Czaja 2012), which is set up by the Kuroshio intrusion in the NESCS (Fig. S4a). Here, the stirring effect refers to the horizontal tracer advection induced by the SMEs’ rotational velocity, which includes the contributions of both adiabatic (i.e., isopycnal heaving) and diabatic processes. With respect to the monopole Tb′ structure beneath the 450-m depth, it is attributed to the isopycnal depression and elevation (i.e., vertical advection) caused by the A-SME and C-SME, respectively (e.g., Chaigneau et al. 2011). Note that the core of the monopole Tb′ did not coincide with the eddy center but was located on its northeast side. This phase difference between velocity and temperature anomalies would induce a net down gradient heat transport (from high temperature to low temperature), which will be examined in section 4c.

b. Volume transport

The results in section 3c demonstrated that the C-SMEs had carried the northwest Pacific water into the NESCS. To quantify the volume transport caused by the C-SMEs, we first constructed their 3D structures of isopycnic salinity and temperature anomalies (Si′ and Ti′) using the method in section 4a. Here, Si′ and Ti′ were calculated by subtracting the respective mean values during the periods without eddies and Kuroshio intrusions. As seen from its distributions for the C-SME2 (Fig. 11), Si′ was dominated by positive and negative values above and beneath 25.8σ0 (with a mean depth of ∼230 m), respectively. This isopycnic surface is roughly the interface between the saltier/warmer NPTW in the upper and the fresher/colder NPIW in the intermediate layers (recall Fig. 9). On most of the isopycnic surfaces, largest Si′ occurred near the eddy center, which is a distinct feature of eddy trapping (e.g., Z. G. Zhang et al. 2014; Frenger et al. 2015). Due to the compensation of salinity and temperature on isopycnals, the distributions of Ti′ were very close to Si′ (Fig. S5). With respect to the C-SME1, its Si′ and Ti′ showed similar 3D structures with the C-SME2 except that the magnitudes were smaller and larger in the upper and intermediate layers, respectively (Fig. S6).

Fig. 11.
Fig. 11.

(a)–(i) Si′ distributions on different isopycnic surfaces for the C-SME2. The potential density and corresponding depth are marked on the bottom of each subplot. (j) Vertical profile of area-averaged Si′ within the C-SME2. Gray shading denotes the standard deviation.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

After obtaining the eddies’ 3D structures of Si′ and Ti′ on isopycnic surfaces, we then estimated the volume transport using the similar approach as that in Z. W. Zhang et al. (2017). For the points within the eddy scope, if Si′ on a specific isopycnal is larger than both 0.01 psu and the standard deviation of salinity, the relevant water is thought to be originated from the northwest Pacific that was trapped and transported by the C-SMEs. All the water points that satisfy the above criteria were accumulated to calculate the water trapping area on each isopycnal. Then, the trapped volume was calculated by vertically integrating the trapping area from 27.4 to 23.0σ0. It is revealed that the volume of northwest Pacific water trapped within the C-SME1 and C-SME2 were (3.55 ± 1.00) × 1013 and (3.58 ± 0.69) × 1013 m3, respectively (Table 1). This means that the two C-SMEs transported a total of (7.13 ± 1.69) × 1013 m3 northwest Pacific water into the NESCS. Dividing this volume by the total observational period (i.e., 2 years, recall section 2a) gives rise to an equivalent annual-mean transport of 1.13 ± 0.27 Sv (1 Sv ≡ 106 m3 s−1). Further quantifications suggest that 11.5% and 88.5% of this volume transport (i.e., 0.13 ± 0.02 Sv and 1.00 ± 0.25 Sv) were contributed by the NPTW between 23 and 25.8σ0 and the NPIW between 25.8 and 27.4σ0, respectively. The SMEs-induced NPIW transport accounts for 61.7% of the mean volume transport across the NS section between 25.8 and 27.4σ0 (i.e., 1.62 Sv from Pacific to SCS) calculated directly using the moored velocity data. The above result demonstrates that the C-SMEs play a very important role in the intermediate-layer water exchange between the NESCS and northwest Pacific.

Table 1

The trapping-induced volume and heat transports of C-SMEs.
Table 1

c. Heat transport

In the real ocean, both the trapping effect and the stirring effect of mesoscale eddies can induce heat transports (e.g., Qiu and Chen 2005; Hausmann and Czaja 2012; Dong et al. 2014; Frenger et al. 2015). Here, the trapping heat transport (THT) and stirring heat transport (SHT) caused by the SMEs were quantified and compared. Given that it is the zonal heat transport across the Luzon Strait (roughly represented by the NS section) that matters for the Pacific–SCS heat exchange, we only focused on the SMEs-induced heat transport in the zonal direction here. For THT, only the one associated with the C-SMEs was estimated because the A-SMEs trapped the local water and therefore did not contribute to the Pacific–SCS heat exchange. To estimate THT, we first calculated the available heat content anomalies of the C-SMEs by integrating the ρ0CpTi′ over the trapped volume by the C-SMEs (recall section 4b). Here, Cp = 4200 J kg−1 °C−1 is the specific heat capacity. It showed that corresponding to the trapped NPTW and NPIW, the C-SMEs had a heat content anomaly of (1.18 ± 0.28) × 1019 and (−6.45 ± 1.88) × 1019 J (sum of the two C-SMEs) in the upper and intermediate layers, respectively. Totally, the C-SMEs resulted in a net heat content anomaly of (−5.27 ± 1.60) × 1019 J in the NESCS. Given the westward propagation of the SMEs, this heat content anomaly corresponds to a mean eastward THT of 0.84 ± 0.25 TW (1 TW = 1012 W) if dividing it by the 2-yr time.

To obtain the SHT, we first calculated the stirring-induced heat flux (SHF) using the formula ρ0Cpub′Tb′¯ based on the moored data from the NS section. Here, the overbar denotes a 120-day running mean. Different from the THT, the temperature and velocity anomalies in the above formula were calculated on z levels rather than on isopycnals. It is found that although the SHF changed sign both spatially and temporally, it was dominated by negative and positive values (i.e., westward and eastward) above and beneath ∼320 m, respectively (Figs. 12 and 13). The negative (positive) zonal mean SHF above (beneath) ∼320 m agrees well with the phase difference between Tb′ and Ub′ of the SMEs as seen from their 3D structures (recall Fig. 10 and section 4a). Comparisons between the SHF and zonal gradient of the mean temperature (i.e., ∂T¯/∂x) suggest that the SHF was downgradient both above and beneath ∼320 m (note that in the study region, ∂T¯/∂x is positive above ∼320 m but becomes negative between 320 and 1000 m; Fig. S4b). With respect to the meridional SHF, it was negative over the whole upper 1000-m column (not shown), which was also down gradient according to the mean temperature distributions. The down gradient SHF indicates that the SMEs obtained their energy through baroclinic instability that released the background available potential energy (Bishop 2013), which is theoretically consistent with the vertically tilted structure of the SMEs (Roemmich and Gilson 2001; Vallis 2006).

Fig. 12.
Fig. 12.

The latitude–time plots of zonal stirring heat transport at different depths. Positive (negative) values indicate eastward (westward) heat transport.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

Fig. 13.
Fig. 13.

Vertical profiles of stirring (red line) and trapping (blue line) heat transports induced by SMEs in 2015. Note that the trapping heat transport was linearly interpolated from the isopycnic surfaces onto z levels based on the mean depth of each isopycnal.

Citation: Journal of Physical Oceanography 52, 5; 10.1175/JPO-D-21-0177.1

After integrating the SHF meridionally, we obtained the SHT per unit depth across the NS section. In Fig. 13, we compare the equivalent annual-mean vertical profiles of THT and SHT (per unit depth) caused by the C-SMEs. Here, THT was linearly interpolated from the isopycnic surfaces onto z levels based on the mean depth of each isopycnal. For a fair comparison, the annual-mean SHT associated with the C-SMEs was calculated using time integral of the mean SHT during the C-SMEs periods divided by 2-yr time. It shows that corresponding to the vertically reversed direction of ∂T¯/∂x, both THT and SHT were positive in the upper, but negative in the intermediate, layers. Although SHT showed a larger magnitude than THT in the upper layer, the former was weaker than the latter in the intermediate layer. The vertically integrated SHT was only 0.09 ± 0.03 TW, which was an order of magnitude smaller than THT (i.e., 0.84 ± 0.25 TW). The above results demonstrate that the SMEs’ trapping effect played a more important role than their stirring effect in the heat transport, which is contrary to previous conclusions for surface-intensified mesoscale eddies (e.g., Frenger et al. 2015; Sun et al. 2019).

5. Summary and discussion

Based on a mooring array deployed in the NESCS, a SME train consisting of two cyclones and two anticyclones was observed between March and October 2015. In contrast to the widely reported surface-intensified eddies, the SMEs displayed weak surface expressions but had the maximum current velocity at ∼370 m with a magnitude of 17.2 cm s−1. The cyclonic and anticyclonic SMEs showed concave and convex lens-like thermal structures, respectively, and the vertical centers of the lenses coincided well with the maximum-velocity depth. Based on spatiotemporal variations of the SMEs-related signals along the EW mooring section, we inferred that the SMEs generally propagated westward with a speed of ∼4.3 cm s−1. Corresponding to the propagation of the SMEs, the moored velocity and temperature displayed ∼120-day-period oscillations in their time series and these suggested that the SMEs had a mean radius of 112 km.

Based on concurrent velocity and T–S data from the moorings, 3D structures of the SMEs were constructed. It is revealed that the SMEs can penetrate at least to 1000 m and vertically, they tilted toward southwestward with increasing depth. As a result of the horizontal advection (vertical heaving) effect of the SMEs, their Tb′ presented a dipole (monopole) structure above (beneath) 450 m. Water mass analysis suggested that the C-SMEs and A-SMEs trapped the northwest Pacific water and the NESCS local water, respectively. For the two C-SMEs, they had on average transported 0.13 ± 0.02 Sv NPTW and 1.00 ± 0.25 Sv NPIW into the NESCS during the 2-yr observation period. This westward NPIW transport accounted for 61.7% of the observed mean volume transport across the Luzon Strait between 25.8 and 27.4σ0. Furthermore, the zonal heat transports caused by both the C-SMEs’ trapping and stirring effects were estimated and compared. It showed that the C-SMEs’ trapping effect caused a total of 0.84 ± 0.25 TW eastward heat transport, which was one order of magnitude larger than that caused by their stirring effect (i.e., 0.09 ± 0.03 TW). Overall, the above results have revealed that the SMEs near the Luzon Strait may provide a novel route for the intermediate-layer water exchange between the NESCS and Pacific.

Assuming that the SMEs conserved their water properties after generation, we also examined their possible origins through tracing their core salinity and potential spicity on the isopycnal surfaces (Zhang et al. 2015b; R. X. Huang et al. 2018; Gao et al. 2020). From the climatological salinity and potential spicity distributions on the 26.65σ0 (the core isopycnal of the NPIW) in Fig. S7, we can infer that the C-SMEs and A-SMEs were originated from the regions east and west of the Luzon Strait, respectively. To further investigate generation processes of the SMEs, we have analyzed the 1/12° HYCOM product. Two SMEs that share similar characteristics with the observed ones were well reproduced by the HYCOM product in 2015 (Figs. S8 and S9). From Fig. S9, we can see that the northward-flowing Kuroshio was weakened and even cut off by a strong westward-propagating Pacific cyclonic eddy after the late May (Figs. S9a–d). At the same time, an embryo C-SME was generated east of the Luzon Strait, which successfully propagated westward across the strait in absence of the Kuroshio barrier. Then, the strong Kuroshio recovered from the early July and the C-SME was strengthened west of the Luzon Strait as it interacted with the Kuroshio (Figs. S9e–f). Subsequent to the westward propagation of the C-SME, a trailing A-SME was generated locally northwest of the Luzon Island after the late July (Figs. S9g–i). Generation of this A-SME seems to be associated with interactions among the C-SME, the Kuroshio, and the topography, but the detailed generation mechanism is unclear and needs further investigations. Note that from the early August, a strong anticyclonic eddy appeared east of the Luzon Strait. In contrast to the cyclonic one, this anticyclonic eddy tended to strengthen the northward-flowing Kuroshio, which blocked its westward penetration across the strait. The above results further demonstrate that the C-SME and A-SME were originated from the regions east and west of the Luzon Strait, respectively. It explains why the C-SMEs can trap the Pacific water into the NESCS while the A-SMEs cannot.

Acknowledgments.

This study was jointly supported by the National Natural Science Foundation of China (91958205, 42076004, 91858203), the National Key Research and Development Program of China (2018YFA0605702, 2016YFC1402605), and the Fundamental Research Funds for the Central Universities (202041009, 201861006, 202013028). Z. Z. is also supported by the ‘Taishan’ Talents program (tsqn202103032). The satellite altimeter data and HYCOM products were downloaded from https://resources.marine.copernicus.eu/ and http://www.HYCOM.org/dataserver/glb-analysis, respectively.

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  • <sc>Fig</sc>. 1.
    View in gallery
    Fig. 1.

    (a) Locations of the mooring array (stars) and bathymetry of the NESCS and northwestern Pacific. Red and purple stars represent moorings NS1–NS5 at the NS section and EW1–EW5 at the EW section, respectively. Purple vectors denote the mean surface geostrophic currents. Black solid line indicates the 1000-m isobath. (b) The detailed configuration of the mooring array and topography at the NS section in 2014–15. Black stars and circles represent ADCPs and RCMs, respectively. Black dotted lines denote current observations by ADCPs. (c) As in (b), but for the EW section (containing site NS3 in the east).

  • <sc>Fig</sc>. 2.
    View in gallery
    Fig. 2.

    (a) Depth–time plots of 20-day low-pass-filtered temperature between November 2014 and February 2016 at site EW2. The black thin lines are temperature contours with an interval of 1°C, and the two black thick lines denote isolines of 16° and 7°C. (b) As in (a), but for the meridional velocity distributions. The black dashed lines denote zero velocity contours. (c) Time series of T′ at depths of 200 m (blue line) and 600 m (red line). Note that the T′ at 600-m depth has been multiplied by 5. (d) Time series of υ′ at depths of 100 m (blue line) and 400 m (red line). Purple dashed boxes in the figure indicate the occurrence of SMEs between March and October 2015.

  • <sc>Fig</sc>. 3.
    View in gallery
    Fig. 3.

    Power spectra distributions of (a) υ′/2, denoting kinetic energy spectrum, and (b) gρ′/2Nρ0, denoting available potential energy spectrum, at site EW2 in 2015 in the upper 1000-m depth.

  • <sc>Fig</sc>. 4.
    View in gallery
    Fig. 4.

    (a),(c) As in Figs. 2a and 2b, but after a 90–160-day bandpass filter. The black solid lines in (c) indicate velocity contours of ±6, ±10, and ±14 cm s−1. (b),(d) The mean absolute Tb′ and υb′ averaged between March–October 2015.

  • <sc>Fig</sc>. 5.
    View in gallery
    Fig. 5.

    (a) Time series of SLA (black line), dynamic height at 50-m depth (red line), 350-m depth (blue line), and the dynamic height integrated from −350 to −50 m (green line) between November 2014 and February 2016 at site EW2. (b) Time series of altimeter surface geostrophic υ′ (black line), mooring-observed υ′ at 100 m (red line) and 350 m (blue line) depths between November 2014 and February 2016 at site EW2. Red boxes indicate the occurrence of SMEs between March and October 2015.

  • <sc>Fig</sc>. 6.
    View in gallery
    Fig. 6.

    (a) Tb′ distributions along the EW section averaged between 400- and 600-m depth. Green solid lines denote the westward propagation processes of four SMEs in 2015 indicated by the maximum Tb′. Black triangles are the locations of moorings at the EW section. The distances from site NS3 are marked on the top of the figure. (b) Schematic diagram of the method to calculate the propagation direction angle (α) of the SMEs. The blue circle denotes the SME’s maximum-velocity circle. A and B are the two points where the SME’s maximum-velocity circle crossed a specific mooring. The eddy center, mean velocity vector averaged along the subtense AB, and propagation speed vector of SMEs are indicated by O, Um, and Up, respectively.

  • <sc>Fig</sc>. 7.
    View in gallery
    Fig. 7.

    (a),(b) Time series of dynamic height at 350-m depth at sites EW2 and EW4, respectively. Blue and red shadings indicate occurrence of C-SMEs and A-SMEs. (c),(d) Potential density–time plots of salinity measured by CTDs at sites EW2 and EW4, respectively. The depth of each isopycnal is marked on the left side of (c). Black lines denote the salinity contour of 34.35 psu. Blue dashed boxes indicate the occurrence of the two C-SMEs.

  • <sc>Fig</sc>. 8.
    View in gallery
    Fig. 8.

    Mean T–S diagram of water masses within the two C-SMEs (blue line), two A-SMEs (red line), and outside of the SMEs (green line) obtained from all the moored CTD data except site NS1. Shadings represent the standard deviations of the mean T–S. Black dashed lines denote contours of potential density.

  • <sc>Fig</sc>. 9.
    View in gallery
    Fig. 9.

    Salinity distributions at sites (a) NS3 and (b)–(f) EW1–EW5. Black lines denote salinity contours of 34.35 psu. Purple arrows indicate the westward movement of intermediate-layer low-salinity cores along with the propagation of the two C-SMEs.

  • <sc>Fig</sc>. 10.
    View in gallery
    Fig. 10.

    The 3D structures of (a) A-SME1 and (b) C-SME2. Shading and black arrows denote Tb′ and Ub′ of eddies, respectively. Black dotted lines indicate the z axis of the coordinate. Purple dots denote the eddy centers at each layer, which are defined as the zero-velocity points. Purple dotted lines denote the axis of eddy centers and green thick lines at 1000-m depth present the drift of eddy center from the z axis. Green circles at each layer denote the edge of the eddies whose radius is defined as the mean radial distance of the maximum velocity.

  • <sc>Fig</sc>. 11.
    View in gallery
    Fig. 11.

    (a)–(i) Si′ distributions on different isopycnic surfaces for the C-SME2. The potential density and corresponding depth are marked on the bottom of each subplot. (j) Vertical profile of area-averaged Si′ within the C-SME2. Gray shading denotes the standard deviation.

  • <sc>Fig</sc>. 12.
    View in gallery
    Fig. 12.

    The latitude–time plots of zonal stirring heat transport at different depths. Positive (negative) values indicate eastward (westward) heat transport.

  • <sc>Fig</sc>. 13.
    View in gallery
    Fig. 13.

    Vertical profiles of stirring (red line) and trapping (blue line) heat transports induced by SMEs in 2015. Note that the trapping heat transport was linearly interpolated from the isopycnic surfaces onto z levels based on the mean depth of each isopycnal.

Fig. 1.

(a) Locations of the mooring array (stars) and bathymetry of the NESCS and northwestern Pacific. Red and purple stars represent moorings NS1–NS5 at the NS section and EW1–EW5 at the EW section, respectively. Purple vectors denote the mean surface geostrophic currents. Black solid line indicates the 1000-m isobath. (b) The detailed configuration of the mooring array and topography at the NS section in 2014–15. Black stars and circles represent ADCPs and RCMs, respectively. Black dotted lines denote current observations by ADCPs. (c) As in (b), but for the EW section (containing site NS3 in the east).
Fig. 2.

(a) Depth–time plots of 20-day low-pass-filtered temperature between November 2014 and February 2016 at site EW2. The black thin lines are temperature contours with an interval of 1°C, and the two black thick lines denote isolines of 16° and 7°C. (b) As in (a), but for the meridional velocity distributions. The black dashed lines denote zero velocity contours. (c) Time series of T′ at depths of 200 m (blue line) and 600 m (red line). Note that the T′ at 600-m depth has been multiplied by 5. (d) Time series of υ′ at depths of 100 m (blue line) and 400 m (red line). Purple dashed boxes in the figure indicate the occurrence of SMEs between March and October 2015.
Fig. 3.

Power spectra distributions of (a) υ′/2, denoting kinetic energy spectrum, and (b) gρ′/2Nρ0, denoting available potential energy spectrum, at site EW2 in 2015 in the upper 1000-m depth.
Fig. 4.

(a),(c) As in Figs. 2a and 2b, but after a 90–160-day bandpass filter. The black solid lines in (c) indicate velocity contours of ±6, ±10, and ±14 cm s−1. (b),(d) The mean absolute Tb′ and υb′ averaged between March–October 2015.
Fig. 5.

(a) Time series of SLA (black line), dynamic height at 50-m depth (red line), 350-m depth (blue line), and the dynamic height integrated from −350 to −50 m (green line) between November 2014 and February 2016 at site EW2. (b) Time series of altimeter surface geostrophic υ′ (black line), mooring-observed υ′ at 100 m (red line) and 350 m (blue line) depths between November 2014 and February 2016 at site EW2. Red boxes indicate the occurrence of SMEs between March and October 2015.
Fig. 6.

(a) Tb′ distributions along the EW section averaged between 400- and 600-m depth. Green solid lines denote the westward propagation processes of four SMEs in 2015 indicated by the maximum Tb′. Black triangles are the locations of moorings at the EW section. The distances from site NS3 are marked on the top of the figure. (b) Schematic diagram of the method to calculate the propagation direction angle (α) of the SMEs. The blue circle denotes the SME’s maximum-velocity circle. A and B are the two points where the SME’s maximum-velocity circle crossed a specific mooring. The eddy center, mean velocity vector averaged along the subtense AB, and propagation speed vector of SMEs are indicated by O, Um, and Up, respectively.
Fig. 7.

(a),(b) Time series of dynamic height at 350-m depth at sites EW2 and EW4, respectively. Blue and red shadings indicate occurrence of C-SMEs and A-SMEs. (c),(d) Potential density–time plots of salinity measured by CTDs at sites EW2 and EW4, respectively. The depth of each isopycnal is marked on the left side of (c). Black lines denote the salinity contour of 34.35 psu. Blue dashed boxes indicate the occurrence of the two C-SMEs.
Fig. 8.

Mean T–S diagram of water masses within the two C-SMEs (blue line), two A-SMEs (red line), and outside of the SMEs (green line) obtained from all the moored CTD data except site NS1. Shadings represent the standard deviations of the mean T–S. Black dashed lines denote contours of potential density.
Fig. 9.

Salinity distributions at sites (a) NS3 and (b)–(f) EW1–EW5. Black lines denote salinity contours of 34.35 psu. Purple arrows indicate the westward movement of intermediate-layer low-salinity cores along with the propagation of the two C-SMEs.
Fig. 10.

The 3D structures of (a) A-SME1 and (b) C-SME2. Shading and black arrows denote Tb′ and Ub′ of eddies, respectively. Black dotted lines indicate the z axis of the coordinate. Purple dots denote the eddy centers at each layer, which are defined as the zero-velocity points. Purple dotted lines denote the axis of eddy centers and green thick lines at 1000-m depth present the drift of eddy center from the z axis. Green circles at each layer denote the edge of the eddies whose radius is defined as the mean radial distance of the maximum velocity.
Fig. 11.

(a)–(i) Si′ distributions on different isopycnic surfaces for the C-SME2. The potential density and corresponding depth are marked on the bottom of each subplot. (j) Vertical profile of area-averaged Si′ within the C-SME2. Gray shading denotes the standard deviation.
Fig. 12.

The latitude–time plots of zonal stirring heat transport at different depths. Positive (negative) values indicate eastward (westward) heat transport.
Fig. 13.

Vertical profiles of stirring (red line) and trapping (blue line) heat transports induced by SMEs in 2015. Note that the trapping heat transport was linearly interpolated from the isopycnic surfaces onto z levels based on the mean depth of each isopycnal.
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