Spatial Structure of Turbulent Mixing in the ... - AMS Journals

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Spatial Structure of Turbulent Mixing in the Northwestern Pacific Ocean QINGXUAN YANG, WEI ZHAO, MIN LI, AND JIWEI TIAN Physical Oceanography Laboratory, Ocean University of China, Qingdao, China (Manuscript received 26 June 2013, in final form 21 May 2014) ABSTRACT Turbulent mixing in the northwestern Pacific Ocean is estimated using the Gregg–Henyey–Polzin scaling and Thorpe-scale methods. The data sources are the hydrographic observations during October and November 2005. The results reveal clear spatial patterns of turbulent mixing in the study area. High-level diffusivity on the order of 1023 m2 s21 or larger is found within the western boundary region, where the Kuroshio flows northward. The width covered by this prominent diffusivity shows an increase from 128 to 188N. The horizontal distribution of depth-averaged diffusivity in the top 500 m shows enhanced mixing with diffusivity of 6 3 1023 m2 s21 south of 98N where the Mindanao Eddy remains a quasi-permanent feature. These two distinct patterns of diffusivity distribution suggest that the Kuroshio and the Mindanao Eddy are likely responsible for the elevated turbulent mixing in the study area.

1. Introduction The northwestern Pacific Ocean is a region where several water masses converge. Some of these water masses are formed elsewhere and move into the region through different paths (Fine et al. 1994; Kaneko et al. 2001). The dominant water masses in the northwestern Pacific Ocean are the high-salinity North Pacific Tropical Water (NPTW) around 24.0 su in the thermocline and the lowsalinity North Pacific Intermediate Water (NPIW) around 26.8 su in the intermediate layer (Nitani 1972). In addition to these locally formed water masses, the Antarctic Intermediate Water (AAIW) formed in the South Pacific Ocean also extends from the Coral Sea into the northwestern Pacific Ocean (Rochford 1960; Reid 1965). The northwestern Pacific Ocean is also a region where several currents exist. One of the main currents is the westward North Equatorial Current (NEC). It bifurcates near the Philippine coast and feeds the northwardflowing Kuroshio (KC) and the southward-flowing Mindanao Current (MC) (Nitani 1972), forming the so-called

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Corresponding author address: Jiwei Tian, No. 238 Songling Road, Physical Oceanography Laboratory, Ocean University of China, Qingdao, China. E-mail: [email protected] DOI: 10.1175/JPO-D-13-0148.1 Ó 2014 American Meteorological Society

NEC–MC–Kuroshio (NMK) current system (Qiu and Lukas 1996). Both of these western boundary currents eventually turn eastward as the Kuroshio Extension (KE) and part of the North Equatorial Countercurrent (NECC), respectively (Wyrtki 1961). Two undercurrents, named the Luzon Undercurrent (LUC; Qu et al. 1997) and the Mindanao Undercurrent (MUC; Hu et al. 1991), flow southward and northward beneath the KC and the MC, respectively. A schematic map of these currents is shown in Fig. 1b. A key factor that regulates water properties and drives ocean circulations is turbulent mixing (Munk and Wunsch 1998). Koch-Larrouy et al. (2007) examined the effect of internal tidal mixing on the transformation of the Pacific Water into the Indonesian Throughflow Water. The Indonesian Throughflow Water was well simulated when they introduced an average diffusivity of 1.5 3 1024 m2 s21 into a regional ocean general circulation model (OGCM). Furue and Endoh (2005) found the Pacific middepth diffusivity is responsible for strengthening the global meridional overturning circulation (MOC) in their numerical model; when this middepth diffusivity is reduced to the background value of 1025 m2 s21, not only the Pacific circulation is greatly weakened, but also the productions of the North Atlantic Deep Water (NADW) and the Antarctic Bottom Water (AABW) are significantly reduced. Meanwhile, the diffusivity in the deeper part of the Pacific acts similarly as that in the middepth, which was

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FIG. 1. (a) Bathymetry of the study area. The solid squares indicate the LADCP stations, and the solid triangles indicate VMP stations. The CTD measurements are available at each station marked by solid dots, squares, and triangles. (b) A schematic of main currents in the study area. The purple shading indicates the western boundary region discussed in this paper. The North Equatorial Current (NEC), Kuroshio (KC), Mindanao Current (MC), Luzon Undercurrent (LUC), Mindanao Undercurrent (MUC), Mindanao Eddy (ME), and Halmahera Eddy (HE) are all indicated.

proportional to the production of the AABW in their numerical experiments. Endoh and Hibiya (2006) used a slightly larger diffusivity than the predicted value based on the parameterization results of Hibiya et al. (2006) and found there was a significant imbalance of 7.8 Sverdrups (Sv; 1 Sv [ 106 m3 s21) between the upwelling in the so-called mixing hot spots and the deepwater production. They hinted that the magnitude of the MOC in the Pacific Ocean was overestimated by observations and doubted the validity of previous OGCM results, in which diffusivity was assigned ad hoc to attain the magnitude of the flow that was suggested by the current meter moorings and hydrographic surveys. Qu et al. (1999) suggested that the distinct properties of the NPTW and NPIW become less so toward the west due to turbulent mixing. Li and Wang (2012) found an elevated mixing in the MC owing to the significant salinity gradient near the Mindanao Eddy (ME). They revealed that this mixing made a remarkable contribution to the modulation of water properties; for example, salinity decreased quickly along the MC path, with a decreasing rate reaching O(1027) psu s21.

However, turbulent mixing itself is scarcely studied and reported in the northwestern Pacific Ocean. In recent years, Tian et al. (2009) reported that the mixing level in the Pacific Ocean was on the order of 1025 m2 s21, consistent with the background values in the open oceans, while the mixing in the South China Sea is two orders of magnitude larger than that in the Pacific Ocean. Jing and Wu (2010) used the CTD measurements along 1378E to estimate turbulent mixing based on the Thorpescale method and a finescale parameterization. They found that the mean value of diffusivity is on the order of 1024 m2 s21 at 1500 m above the bottom, stronger than that in the midlatitude interior ocean, and that higher values exceeding 1024 m2 s21 exist around rough topography. Their results also suggested turbulent mixing in the upper-ocean displayed distinct seasonal variation, bearing a statistically significant correlation with surface wind forcing. Jing et al. (2011) draw a similar conclusion based on the CTD measurements along three sections in the subtropical northwestern Pacific. In addition, they revealed the important role of anticyclonic eddies in enhancing turbulent mixing at a greater depth. These studies facilitated our understanding of turbulent mixing

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FIG. 2. Measured spectra of vertical strain in the (a) western boundary region, in the (b) Mindanao Eddy region, and in the (c) interior ocean, along with the GM model spectra (green curve).

in the northwestern Pacific Ocean, but they failed to provide a detailed picture in terms of spatial distribution due to limited data. It is, therefore, the intent of this paper to present a spatial structure of turbulent mixing east of the Philippines based on the CTD measurements collected along seven sections during October and November 2005. In the text that follows, the field experiment, including the instruments used and their setups, is described in section 2. An overview of the parameterization is given in section 3, along with data processing routines and methods used to quantify diapycnal diffusivity. The spatial structure of turbulent mixing is presented in section 4. Discussion and summary are given in section 5.

2. Field experiment The field observations of temperature, salinity, and pressure were carried out on board the Research Vessel (R/V) Dong Fang Hong 2 from 25 October through 28 November 2005. A total of 89 stations were sampled along six zonal sections and one meridional section in the northwestern Pacific east of the Philippines, which covers most of the NMK area (Fig. 1a). Near the coast, the distance between two neighboring stations was less than 25 km to capture detailed structures in the western boundary region. The instrument used to measure temperature and salinity profiles was the SBE 911 plus CTD manufactured by Sea-Bird Electronics (SBE), Inc. With the pre/postcruise calibrations, the accuracy of the CTD sensors is about 0.001 psu for salinity, 0.0028C for temperature, and 1 dbar for pressure at a sampling rate of 24 Hz. Most of the profiles cover the upper 2000 m, but some reached 3000 m or deeper. The initial processing of CTD measurements was performed based on several processing modules built in the SBE data

processing software. Subsequently, the spikes of temperature and salinity were removed; this step is critical to derive accurate mixing diffusivity since both strain estimation and overturn detection are sensitive to CTD data quality. To keep the spatial resolution unchanged, these removed values were replaced by linear interpolation of ‘‘good data.’’ In this study, only the downcast data are used, and the data shallower than 10 m were discarded because the CTD deployment began at 10 m and the sensors were not stable before reaching this depth. Velocity profiles measured by lowered ADCP (LADCP) are available at 10 stations along one section. The LADCP was Workhorse Sentinel type from Teledyne RD Instruments with transducer frequency of 300 kHz. The vertical bin size was set to 10 m, the number of layers was set to 13, and the sampling frequency was set to 1 Hz. LADCP collected current velocity relative to itself with an estimated uncertainty of 1 cm s21. The method we used to process the LADCP data is an inverse method; the software is from Martin Visbeck’s release, and the version is 10.8b (Visbeck 2002). Besides these finescale observations, at three stations the microscale sampling was carried out by a vertical microstructure profiler (VMP)-2000 made by Rockland Scientific International, Inc. The VMP system was equipped with two shear probes, which sampled shear at 512 Hz with a maximum deployment depth of 2000 m. In our operation, the guard mode was used, which gave a shear dissipation rate noise level of 10210 W kg21 to our measurements.

3. Parameterization Several methods are available to estimate diapycnal diffusivity K from CTD measurements, such as the Gregg–Henyey–Polzin (GHP) scaling based on internal

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1 Rv (Rv 1 1) ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi , h(Rv ) 5 pffiffiffi p Rv 2 1 6 2 j(f /N) 5

FIG. 3. Bin averages of Rv with respect to squared buoyancy frequency N2 from 147 profile segments. The box size is proportional to the number of data points going into the average (the number is marked to the right of each box). The vertical lines indicate uncertainties based on the standard deviations of Rv .

wave–wave interaction theory (Kunze et al. 2006) and the Thorpe-scale method (Klymak et al. 2011). The GHP scaling was initially developed by Henyey et al. (1986). We use the most recent incarnation by Gregg et al. (2003), which is expressed as K 5 K0

hj2z i2 2 2 GM hjz i

h(Rv )j(f /N) ,

(1)

f arccosh(N/f ) , f30 arccosh(N0 /f30 )

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and

(2)

(3)

where K0 5 5 3 1026 m2 s21; hj2z i represents the finescale internal wave strain variance inferred from observations, and GM hj2z i is the strain variance inferred from the Garrett and Munk (GM) spectrum (Garrett and Munk 1972, 1975). To quantify hj2z i, profiles were broken into half-overlapping 320-m-long segments, starting from the bottom. In the GM model, an open-ocean internal wavefield is constructed at a fixed buoyancy frequency of N0 (5.2 3 1023 s21) at the latitude of 308N. In the above equations, f and N are the Coriolis and buoyancy frequencies. The term Rv represents the shear/strain variance ratio, which is set to 7 in this study, as suggested by Kunze et al. (2006), since for most CTD profiles velocity was not measured. In this study, strain is computed as jz 5 (uz 2 uz )/uz , where uz is the potential temperature gradient, and uz is the mean value based on quadratic fitting to each potential temperature segment. Strain variance was obtained by integrating strain spectra S[jz ](k) from the minimum wavenumber k1 to maximum wavenumber k2. The value k1 is defined as the reciprocal of the record length being analyzed. The term k2 is determined as

FIG. 4. VMP observations and parameterization results at three stations. The thin gray curves indicate the VMP measurements with high spatial resolution, the gray vertical lines indicate the depth-averaged VMP results, and the black vertical lines indicate the parameterization results. These three panels indicate three VMP profiles collected at three stations. The locations of three stations are indicated as solid triangles in Fig. 1.

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FIG. 5. Vertical distribution of diffusivity (m2 s21) along the sampled sections. This set of diffusivity results is inferred from the finescale parameterization. A logarithmic scale is used here for the diffusivity.

Ð k2

k1 S[jz ] dk # 0:1 (Kunze et al. 2006). It was reported that due to strong background stratification in the pycnocline, the strain spectra tend to be red near the lowest resolved wavenumbers (e.g., Kunze et al. 2006; Jing and Wu 2013); as a result, these authors chose the minimum wavenumber k1 to be 0.04 rad m21 to avoid contamination. In our calculation, in order to avoid any contamination from strong stratification, we discarded the data in the pycnocline. As a result, the strain spectral level at the small wavenumbers turns out to be low (Fig. 2). Figure 2 shows the averaged strain spectra binned into the western boundary, the ME, and the interior, along with their corresponding GM spectra. We can see that the internal wave energy level varied from region to region. The most striking feature is that the internal wave energy was extremely elevated in the upper layer in the ME. The elevated energy occurred intensively over large scales (small wavenumbers). Away from the eddy, the spectral level fell to lower levels, which is almost in the same order as the upper

and lower layers in the western boundary region and the interior (Figs. 2a,c). There is no doubt that Rv has a direct influence on the diffusivity estimate via h(Rv ). Therefore, it is necessary to first examine the sensitivity to Rv and check the validity of choosing Rv 5 7 in this study. Referring to previous studies (e.g., Naveira Garabato et al. 2004; Kunze et al. 2006; Thompson et al. 2007; Damerell et al. 2012; Frants et al. 2013), the Rv values mostly range from 7 to 20 in different regions and during different cruises. Values of h(Rv ) varied from 1 at Rv 5 3 for the canonical internal wavefield proposed by Garrett and Munk (1972, 1975) to ;11 at Rv 5 20 for the maximum value in the literature. As a result, using a constant Rv of 7 might lead to uncertainties in derived turbulent diffusivity with a factor of 3–4, at most. Based on the LADCP observations along with the concurrent CTD measurements at 10 stations along Section A, we examined the Rv values (Fig. 3). Altogether, 147 segments were taken into consideration. The results show that Rv values

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FIG. 6. Absolute geostrophic velocities from the SSALTO/ DUACS product on 9 Nov 2005. The black dots indicate the station locations as in Fig. 1.

approximately range from 3 to 20, and most Rv values (116 out of 147) are around 7. Further, the comparison of the two sets of results based on the finescale parameterization and microscale measurements by VMP shows similar overall trends (Fig. 4). Although almost all are within the same order of magnitude, there are quantitative differences between VMP observations and parameterizations, by a factor of 2–4, among individual profiles at different depths. The fact that two different estimates give similar vertical structures suggests that the parameterization results based on Rv 5 7 are reasonable. Frants et al. (2013) suggested that CTD-based strain diffusivity estimates could replicate the real spatial structure derived from direct microstructure observations, which is consistent with our results. However, the difference between estimates and observations reported by Frants et al. (2013) is nearly one order of magnitude at some depths, which is larger than the difference we found in this study. This is likely because the stratification in our study area is relatively stronger, where the finescale parameterization works well.

FIG. 7. Shear variance calculated along Section A based on the Simple Ocean Data Assimilation (SODA, version 2.2.4) product. The shear variance is defined as (›u/›z)2 1 (›y/›z)2 , where u and y are the horizontal velocities. The red curve indicates the value averaged from 1228 to 1248E, which roughly represents the western boundary region, while the blue curve indicates the value averaged from 1268 to 1308E, which represents the interior Pacific Ocean.

Similar results and discussions are also available in Waterman et al. (2013) for the Southern Ocean Finestructure Experiment (SOFine) and in Sheen et al. (2013) for the Diapycnal and Isopycnal Mixing Experiment in the Southern Ocean (DIMES). Under the well-established relation between the Ozmidov scale and Thorpe scale (LT) (Dillon 1982), turbulent kinetic energy dissipation rate « can be related to LT as follows:

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FIG. 8. Horizontal distributions of depth-averaged diffusivity for (a) the upper layer (0–500 m) and (b) the lower layer (500 m to the bottom). A logarithmic scale is used here for the diffusivity.

« 5 0:64L2T N 3 ,

(4)

where LT can be calculated as the root-mean-square displacement of a parcel, and the buoyancy frequency N is evaluated from the gradient of reordered density profile. Here, the displacement is defined as depth difference between a measured density profile and the reordered version of the same profile (Finnigan et al. 2002). The most common model for K is based on Osborn (1980), which is K 5 G«/N 2 ,

(5)

where the mixing efficiency G is set to 0.2. However, care must be taken to avoid interpreting noises in density measurements as genuine overturns when using the Thorpe-scale method. The run length and water mass tests proposed by Galbraith and Kelley (1996) and the two-parameter (Ro, DN) diagnostic proposed by Gargett and Garner (2008) are employed to reject spurious overturns. The disadvantage of the Thorpe-scale method is that overturns could not be easily detected

in the shallow layer if there exists a strong stratification. Therefore, there may be turbulence, although no overturns could be detected in the water column. In this paper, we only use this method to estimate diffusivity in the deeper water column, to avoid an underestimation of « due to not fully resolving overturns in the upper layer.

4. Results Sections of inferred diffusivity K based on the GHP scaling are shown in Fig. 5. The diffusivity values range from the minimum value of less than 1025 m2 s21 to the maximum value of larger than 1022 m2 s21. High-level mixing with diffusivity values around 1023 m2 s21 or larger can be seen in the western boundary region. The width covered by high diffusivity values exhibits an increase from south to north; the width is 1.58 at Section C and is 3.58 at Section A. The western boundary current of the Kuroshio may account for this variability. As shown in surface geostrophic currents from the altimeter data, the Kuroshio flows northward with a speed reaching 1 m s21 (Fig. 6), which can generate vertical

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FIG. 9. Averaged shear variance along Sections E and F (red; within the ME) and along Section D (blue; outside the ME) derived from the SODA product.

shears as large as 5 3 1026 s22 along its path (Fig. 7). While in the interior, the shear variance hardly exceeds 1027 s22 in most of the water column. The high shear in the western boundary region can generate instabilities to furnish elevated mixing. The minimum diffusivity value on the order of 1025 m2 s21 exists in the area far away from the western boundary and the bottom and is in a region with the deepest bathymetry. The horizontal distribution of depth-averaged diffusivity is shown in Fig. 8. Considering the mean depth of the MUC core is around 500 m, we partition the water column into two layers in the vertical: one above 500 m and the other below. The depth-averaged diffusivity in the upper layer presents a prominent spatial variability, which also reveals elevated diffusivity values within the western boundary region along Sections A, B, and C. The diffusivity values in the western boundary region are as high as 1023 m2 s21, which are one or two orders bigger than those far away from the

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boundary. In the western tropical Pacific, zonal equatorial currents and western boundary currents bring in water masses of different origins (Fine et al. 1994). Strongly contrasting properties of these water masses provide a potential for intensive mixing, which is further enhanced by the instabilities associated with strong shears, vigorous mesoscale eddies, elevated tidal energy, and complicated topography in this region (Lukas et al. 1991, 1996). Some highly elevated diffusivity values reaching as high as 6 3 1023 m2 s21 appeared along Sections E and F, which are two orders bigger than those in the northern sections and is a striking feature in Fig. 8a. Here, we ascribe this high-level mixing to the ME, which is a quasi-permanent eddy associated with the turning of the NEC near the coast of the Philippines and its subsequent traveling to the east as part of the NECC, according to Wyrtki (1961). Lukas et al. (1991) pointed out that the center of the ME was located at 78N, 1288E and that the ME had a diameter of around 250 km based on drifting float data. Qu et al. (1999) suggested that the ME be centered at 78N, 1298E based on the isopycnal depth distribution of the historical data. Seen from the surface geostrophic currents from the altimeter data, an evident cyclonic eddy remained at Sections E and F (Fig. 6) during the whole measurement period. This eddy is the ME, which was located around 6.58N, 128.58E with a diameter of about 220 km, consistent with earlier studies. The area occupied by the ME includes Sections E and F and the southernmost part of Section G, where highly elevated diffusivity values were found. There exists a larger shear within the ME, compared to that outside the ME, as shown in Fig. 9. The maximum shear variance within the ME could reach 3 3 1025 s22, which is larger than that outside the ME in the upper 500 m. This high-level shear variance is the most likely candidate for generating the enhanced mixing within the ME region. In some cases, the velocity at the base of the eddy even showed an opposite direction to that within the eddy, where the strengthened shear is prone to be generated. For example, Liang and Thurnherr (2011) presented a large negative correlation between surface and deep velocities when the energetic anticyclonic eddies occurred at the observed site in the East Pacific Rise. From another point of view, internal waves may be trapped by large shear within the ME, which transfer momentum into the deep ocean to facilitate mixing (e.g., Booker and Bretherton 1967). For example, the enhanced bottom mixing that almost reached 1022 m2 s21 at 88N in Section G (Fig. 5g) is possibly related to this process. In the lower layer, the diffusivity values are around 1024 m2 s21 or even 1025 m2 s21, which are one to two

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FIG. 10. (a) An example of an overturn, (b) horizontal distribution of overturn number during the field experiment, (c) the maximum thickness (m) of overturns in each profile, and (d) inferred lower-layer (500 m to the bottom) diffusivity (m2 s21; a logarithmic scale is used for the diffusivity) from the Thorpe-scale method. Here, the maximum cutoff values of the color bars are set to 40 and 30 for (b) and (c), respectively, for better illustration.

orders smaller than those in the upper layer. The vertically averaged diffusivity values in the lower layer show a nearly uniform structure except for some elevated values of 1023 m2 s21 within the western boundary region along Sections A and B. The shallower bathymetry may account for this high-level mixing, which is especially clear in Fig. 5b. The elevated values reaching 1022 m2 s21 exist over a basinlike area with a depth shallower than 1500 m from 1228 to 1248E.

In Fig. 10a, an example of an overturn is given, which starts at 1480 m and ends at 1520 m, with a thickness of 40 m. This overturn can be clearly detected from the original density profile and the sorted density profile. The overturn number and the maximum thickness for these overturns in all the profiles are given in Figs. 10b and 10c. The overturn number for each profile varied, mostly ranging from 20 to 30. A large number of overturns occurred in the western boundary region along

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FIG. 11. Diffusivity profiles at seven stations along Section G, where the sampling depth exceeds 2000 m. The red curves indicate the results from the Thorpe-scale estimate, and the blue lines indicate the finescale parameterization results.

Sections A and E and south of 98N along Section G. The maximum thickness of the detected overturns is mostly between 10 and 20 m. There were limited overturns with a 30-m thickness north of 98N along Section G; while in the southern part of this section, there were some thicker overturns with thickness reaching 100 m. Only the depth-averaged diffusivity in the lower layer is shown in Fig. 10d since the overturn in the upper layer can be hardly detected due to strong stratification. The diffusivity based on Thorpe-scale estimate shows weak mixing in the lower layer, about 1025 m2 s21 in magnitude, while some larger values reaching 1024 m2 s21 occurred within the western boundary region along Sections A and B. This structure shows a comparable picture with that based on the GHP scaling in Fig. 8b, and there are only small quantitative differences between the two estimates. To further examine the difference in diffusivity results based on these two methods, we compare the two sets of results at seven stations along Section G (Fig. 11). At these stations, all the measurement depths exceed 2000 m, below which remarkable overturns exist. We are encouraged that the two sets of results show consistent trends, although there exist slightly discrepancies with a factor ranging from 2 to 4 most of the time. Frants et al. (2013) found the Thorpe-scale method performed well in the Drake Passage, where high mixing level exists, and noted its

failure in the southeastern Pacific, where low mixing level exists. The accuracy of the Thorpe-scale estimate is sensitive to instrument noise, local stratification, and the typical size of true density displacement relative to background noise. In our study area, the stratification is generally stronger than that in the Southern Ocean. Meanwhile, significant overturn size and percentage of large displacements in our study are similar to those found in the Drake Passage. As a result, the diffusivity estimates based on the Thorpe scale could yield useful information in our study, although they differ from the results based on the strain method in quantity.

5. Discussion and summary The results show that large diffusivity values in our estimates are related to mesoscale eddy and boundary current in the upper layer and to bottom bathymetry and tides in the lower layer. The wind is not considered here, since the wind speed during the whole observation period was consistently weak, mostly ranging from 4 to 5 m s21; the wind direction was almost invariable. In the following, we will examine to what degree diffusivity depends on each of the factors and quantify them. We use eddy kinetic energy (EKE), bottom roughness, and semidiurnal (M2) tidal kinetic energy (TKE) to measure the strength of mesoscale eddy, bottom bathymetry, and

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FIG. 12. Spatial distribution of (a) eddy kinetic energy, (b) bottom bathymetry roughness, and (c) M2 tidal kinetic energy. The logarithmic scales are used in (a)–(c). (d)–(f) Corresponding scatters plots between diffusivity and each variable. The red lines are fitted relationship.

tide. The EKE was estimated based on the surface geostrophic current anomalies from the altimeter data produced by the Segment Sol multimissions d’ALTimétrie, d’Orbitographie et de localisation precise/Data Unification and Altimeter Combination System (SSALTO/DUACS) and distributed by the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO), with the support from Centre National d’Études Spatiales (CNES) (http://www.aviso.oceanobs.com/ duacs/). Ship-sounding bathymetry data from Smith and Sandwell (1997) (version 15.1, available at http:// topex.ucsd.edu/marine_topo/) was used to derive bottom roughness, defined here as the variance calculated in 1/ 48 3 1/ 48 area, a reasonable scale for internal tide generation (St. Laurent and Garrett 2002). Integrated barotropic M2 TKE was derived from the Oregon State University (OSU) Tidal Data Inversion (http:// volkov.oce.orst.edu/tides/), following Egbert and Ray (2003). The results show that the EKE is remarkably

higher in the ME area, reaching 1021 m2 s22, which is at least one order bigger than those in the rest of the study area (Fig. 12a). As we expected, the higher bottom roughness at a level of 105 m2 mainly occurred in the Philippine Trench, east of the Philippine Islands, where the topography changes abruptly (Fig. 12b). By contrast, the M2 TKE is at a lower level east of the Philippine Islands, ranging from 3 to 10 J m22 (Fig. 12c). The diffusivity variations with respect to EKE, roughness, and M2 TKE are shown in Figs. 12d, 12e, and 12f, respectively. Here, the diffusivity values in the upper layer are used to construct Fig. 12d, while the diffusivity values in the lower layer are used to construct Figs. 12e and 12f. A linear fitting was used, and the results show a good relationship between each pair. The diffusivity is significantly proportional to the EKE, roughness, and M2 TKE with a p value of 0.05 and their slopes being 0.4, 0.3, and 0.5, respectively. These results confirm that the area occupied by the ME with high EKE has enhanced

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diffusivity. The diffusivity is locally intensified over regions with prominent topographic features, for example, the basinlike area with a depth shallower than 1500 m from 1228 to 1248E along Section B. This enhanced mixing is likely due to local interaction between geostrophic/tidal flows and topography, such as the breaking of locally produced internal tides (Polzin et al. 1997), in combination with the reflection, scattering, and breaking of remotely generated internal waves (Johnston et al. 2003; Tian et al. 2009; Decloedt and Luther 2010). Barotropic tides are one of the energy sources, which generate internal waves when encountering rough topography. In combination with the effect of bottom roughness, M2 TKE is possibly responsible for the elevated mixing level at several western and southern stations along Sections A and G. In this study, highly elevated diffusivity on the order of 1023 m2 s21 is found in the western boundary region and within the ME. Potential mechanisms for these high diffusivity values are briefly discussed. Over the other parts of the study area, the diffusivity is around 1025 m2 s21 on average, which is consistent with the background value for turbulent mixing in the open ocean. In summary, the mixing in the study area does not show a simple uniform structure either vertically or horizontally; instead, the mixing level varies from upper layer to deeper layer and from region to region. As mentioned in the introduction, a constant diffusivity value is often employed across the whole model domain in numerical ocean models (e.g., Furue and Endoh 2005; Koch-Larrouy et al. 2007). An improved mixing scheme is used in Endoh and Hibiya (2006); specifically, they used 2 3 1025 m2 s21 for the Pacific background diffusivity level and 1 3 1024 m2 s21 for prominent topographic features, wind-driven mixing hot spots, and areas adjacent to the northern and western boundaries. However, they did not consider vertical variation. Their assumptions are different from the actual mixing structures based on our estimates, and the inferred results from these numerical simulations should be treated with care. We believe that these mixing patterns unveiled in this study must be well reproduced in the numerical models, in order to simulate realistic ocean state in this area. However, the data used here are limited to 1 yr and only in October and November. Therefore, research on temporal variability of turbulent mixing in this area is needed when more data become available. Acknowledgments. This work is supported by the National Key Basic Research Program of China (Program 973) (Grant 2014CB745003), the Natural Science Foundation of China (Grants 91028008 and 41176008), SIDSSE-201207, and the National High Technology Research and Development Program of China (Grants

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