Dynamics of Atmospheres and Oceans Upper Ocean Response Of

Dynamics of Atmospheres and Oceans 47 (2009) 165–175

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Upper ocean response of the South China Sea to Typhoon Krovanh (2003)

Jiang Xiaoping a, Zhong Zhong a,b,∗, Jiang Jing b a Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101, China b Department of Atmospheric Sciences, Nanjing University, Nanjing 210093, China article info abstract

Article history: To quantitatively investigate the dynamic and thermal responses of Available online 22 October 2008 the South China Sea (SCS) during and subsequent to the passage of a real typhoon, a three-dimensional, regional coupled air–sea model is developed to study the upper ocean response of the Keywords: SCS to Typhoon Krovanh (2003). Owing to the scarcity of ocean Coupled model observations, the three-dimensional numerical modeling with high South China Sea resolution, as a powerful tool, offers a valuable opportunity to Typhoon investigate how the air–sea process proceeds under such extreme Response conditions. The amplitude and distribution of the cold path produced by the coupled model compare reasonably well with the TRMM/TMI-derived data. The maximum SST cooling is 5.3 ◦C, about 80 km to the right of the typhoon track, which is consistent with the well-documented rightward bias in the SST response to typhoons. In correspondence to the SST cooling, the mixed layer depth exhibits an increase; the increases in the mixed layer depth on the right of typhoon track are significantly higher than those on the left, with maxima of 58 m. This correspondence indicates that the SST cooling is caused mainly by entrainment. Under the influence of typhoon, a cyclonic, near-surface current field is generated in the upper ocean layer, which moves with the typhoon. The typhoon-induced horizontal currents in the wake of the storm have strong near-inertial oscillation, which gradually propagates downward. The unique features of the SCS response to Typhoon Krovanh are also discussed, such as Kuroshio intrusion and coastal subsurface jets. © 2008 Published by Elsevier B.V.

∗ Corresponding author at: Institute of Meteorology, PLA University of Science and Technology, Zhong Hua Men Wai, Nanjing 211101, Jiangsu, China. Tel.: +86 25 80830255; fax: +86 25 80830255. E-mail address: [email protected] (Z. Zhong).

1. Introduction

Strong atmospheric forcing events such as tropical cyclones and the upper ocean response represent an extreme case of mesoscale air–sea interactions (Polonsky et al., 1992). When a tropical cyclone propagates over the ocean surface, significant exchange of momentum, heat, and mass occurs at the air–sea interface, inducing an intense response of the ocean. The effects of tropical cyclones on the ocean are large, the horizontal extent may span several hundred kilometers, while the vertical depth may extend to 1000 m (Kwon and Riser, 2003). Observational and numerical studies on ocean responses to tropical cyclones have been conducted during the past decades. However, in light of the scarcity of ocean observations, numerical studies offer valuable opportunities to investigate the evolution of air–sea processes under such extreme conditions. Undoubtedly, providing accurate wind stress forcing and thermal fluxes is vital to proper simulation. Zedler et al. (2002) simulated the temperature evolution during the passage of Hurri- cane Felix (1995) using four one-dimensional mixed layer models: Price–Weller–Pinkel (PWP), K Profile Parameterization (KPP), Mellor–Yamada (MY) 2.5, and a modified version of MY 2.5 (MY2). The surface heat flux and wind-forcing time series were constructed using mooring measurements of wind speed and direction. In the numerical experiment of Jacob and Shay (2003), constant air temperature and humidity were assumed to estimate the surface heat fluxes. Rao et al. (2004) studied the response of a coastal ocean excited by a tropical cyclone in the Bay of Bengal using a three- dimensional numerical model. In their study, the model was used to understand the dynamic and thermal responses of the ocean to different cyclonic systems approaching from different directions, but ignoring the effect of air–sea heat exchange on the tropical cyclone-induced sea surface temperature (SST) cooling. Wada (2005) simulated the ocean response to Typhoon Rex (1998) using a mixed layer model. As the atmospheric forcing field, the global analysis (GANAL) data implanted a typhoon-like vortex was used. Sheng et al. (2006) adopted a nested-grid ocean circulation modeling system to assess the upper ocean response of the Scotian Shelf and adjacent slope to Hurricane Juan (2003). A simple Rankine vortex was used to represent the wind stress associated with the hurricane. A mesoscale air–sea coupled model is a useful tool for the study of tropical cyclone–ocean interactions. Coupled models describe the feedback between the tropical cyclone and ocean, which is important both for storm development and the ocean response. For example, the effects of atmo- spherically forced sea state variation on the atmospheric boundary layer and the effects of oceanically forced atmospheric variation on the upper ocean can be addressed. However, most numerical studies on tropical cyclone–ocean interactions have focused on the effects of the SST cooling on the tropical cyclone (Bender and Ginis, 2000; Hong et al., 2000; Chan et al., 2001; Zhu et al., 2004). Few numerical studies have been conducted on the upper ocean response to a tropical cyclone using a real-case forcing field provided by a coupled atmospheric model. The South China Sea (SCS) is the largest marginal sea in the Northwest Pacific. Because of its semien- closed nature, the SCS is subject to high spatial and temporal variability from external forcing factors. One significant source of variability is the tropical cyclones that routinely affect the region. On average, 10.3 tropical cyclones pass through the SCS annually (Wang et al., 2007). However, due to the difficulty of making shipboard measurements during and even after the passage of tropical cyclones and the sparseness of moored instrumentation, observational studies on the upper ocean response of SCS to tropical cyclones are rare. Up to the present, numerical simulations of the upper ocean response to tropical cyclones in the SCS are very few in number. Chu et al. (2000b) used the Princeton Ocean Model (POM) to study the response of the SCS to Typhoon Ernie (1996). A tropical cyclone wind profile model was used to simulate the wind stress caused by Ernie and to force POM. To examine the dynamic and thermal responses of the SCS to a real typhoon, we developed a mesoscale coupled air–sea model based on the nonhydrostatic mesoscale model MM5 (Pennsylvania State University-National Center for Atmospheric Research fifth-generation Mesoscale Model) and the regional ocean model POM, and the upper ocean response to Typhoon Krovanh (2003) was investigated. The outline of this paper is as follows: Section 2 briefly describes the development of the coupled model. The thermal response to Typhoon Krovanh is discussed in Section 3, while the dynamic response is analyzed in Section 4. Summary and conclusions are presented in Section 5. X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175 167

2. The coupled model

2.1. Brief description of Krovanh (2003) and the models

Typhoon Krovanh (2003) was selected for the case study. Krovanh originally developed as a tropical depression on August 17. It intensified into a tropical storm on August 20 and reached typhoon strength on August 22. Adopting a west-northwest track, Krovanh entered SCS on August 23 and then moved across the northern part of SCS. After skirting the northeastern part of Hainan Province, China, Krovanh entered Beibu Wan on August 25 and weakened into a severe tropical storm on August 26 after making landfall over northern Vietnam. It further weakened into a tropical storm and dissipated inland. In this study, POM is implemented in the domain of 0◦Nto30◦N and 99◦Eto130◦E, covering all of the SCS and the western portion of the Pacific. A uniform horizontal resolution of 0.25◦ × 0.25◦ is used. In the vertical, there are 21 sigma layers with a finer resolution over the upper ocean. The external time step is taken to be 60 s, and internal time step 1800 s. The western boundary is fixed and the other three boundaries are open. The monthly velocity, elevation, salinity, and temperature fields from the Simple Ocean Data Assimilation (SODA) data are interpolated to the model grid as dynamic forcing at the open boundaries, with the in- and out-flux being balanced (Alfonso et al., 2000; Ezer and Mellor, 2000). The gravity wave radiation condition is used for the velocity component perpendicular to the open boundaries. An upwind-advection scheme is applied to the temperature, salinity, and velocity components parallel to the boundary so that in case of inflow, the boundaries conditions derived from the SODA data are imported by inward velocities. MM5 uses one nested domain with a grid spacing of 15 km and 29 terrain-following coordinate surfaces in vertical. The time step is 50 s. The model initial conditions and lateral boundary conditions are obtained from the NCEP 1◦ × 1◦ reanalysis data. Because the NCEP analysis contains a vortex weaker than the observed, an observation-based vortex is implanted into the model initial conditions (Davis and Lownam, 2001). The model physics include the Betts–Miller cumulus parameterization scheme (implicit) and the Reisner II (explicit) microphysics scheme. Besides these physics process schemes, the shallow convection scheme, the Blackadar planetary boundary layer scheme and the CCM2 radiation scheme are also utilized. Both the atmospheric model and the oceanic model were initialized at 0000 UTC August 23, 2003, and were simultaneously run forward in time for 102 h.

2.2. Ocean model initialization

It is very important to simulate the ocean response in the coupled typhoon–ocean model with a realistic ocean initialization (Bender and Ginis, 2000). In this study, the initialization procedure included three steps. First, POM was integrated from the calm state of the ocean while the initial temperature and salinity were provided by the Levitus dataset for August, and the wind stress was obtained from COADS. They were both horizontally and vertically interpolated to each grid of POM. After 2 model years, the model reached a quasi-equilibrium state. This step generated a monthly model climatology, which was followed by a second procedure to adjust the upper ocean structure to achieve a more realistic prestorm condition at the start of the typhoon simulation. During this integration, POM was forced by the wind stress from the NCEP reanalysis data, net heat fluxes, and shortwave radiation from the Southampton Oceanography Centre (SOC). In the third procedure, the daily-averaged SST on August 22, 2003, was obtained from the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and assimilated into the model (Bender and Ginis, 2000). The initialization involved replacement of the SST field in the upper ocean mixed layer by the TRMM/TMI SST field and prognostic model integration for another 10 days for dynamical adjustment. During this integration, the SST field at the surface was kept constant.

2.3. The method of coupling

After POM initialization, during the period of one POM internal time step, MM5 is integrated using the SST field from POM initialization. The surface wind speed, latent heat fluxes, and sensible heat fluxes computed by MM5 are passed to POM, which is then integrated over one internal step and a 168 X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175

Fig. 1. Comparison of typhoon tracks and maximum surface winds (m s−1) of the coupled model simulations and the observations. The positions of Krovanh are plotted every 6 h. new SST field is obtained, then, the updated SST is used in the ensuing time steps of MM5. Owing to a latitude–longitude grid is used for POM and an irregular grid in Mercator projection plane is used for MM5, the transfer of the surface wind speed and heat fluxes from MM5 to POM as well as the transfer of the SST field from POM to MM5 were accomplished through the Cressman interpolation scheme, which conserves sensible heat, latent heat through the air–sea interface. In this way, MM5 is driven using the variations in SST simulated by POM, while POM is forced by the surface wind speed and heat fluxes from MM5, which reflect a realistic simulation of the typhoon and ocean.

2.4. The simulated track and intensity of Krovanh

Fig. 1 shows the simulated and observed tracks and maximum surface winds of Krovanh. The simulated typhoon takes a more northward track than that observed during the ﬁrst 48 h. Thereafter, the track is simulated reasonably well, with an averaged error of less than 30 km. The model also reproduces the typhoon intensity very well, with the maximum error of 6 m s−1. The small increase in surface winds immediately after Typhoon Krovanh entered Beibu Wan on August 25 matches the observations.

3. The thermal response

SST data from remotely sensed infrared measurements such as the Geostationary Operational Environmental System (GOES), Advanced Very High Resolution Radiometer (AVHRR), and moderate- resolution imaging spectroradiometer (MODIS) have missing data over the extensive cloud regions associated with these tropical storms. However, satellite microwave measurement systems such as the TRMM/TMI are capable of accurately measuring the SST through clouds (Wentz et al., 2000). Therefore, the TMI-observed SST field on August 25, 2003, the day Krovanh left SCS, is used to verify the model- simulated SST field (Fig. 2). Although the TMI can measure the SST underneath clouds, there are still some missing data near the storm center because of the contamination by heavy rainfall. Fig. 2b shows the TMI-observed Krovanh-induced cold patch. It can be seen that the minimal SST of 23.9 ◦C is cen- teredat(115.1◦E, 19.2◦N), about 60 km to the right of the track. In comparison with the pre-typhoon conditions (29.4 ◦C), the SST has dropped by as much as 5.5 ◦C. The model-simulated SST shows a similar cooling pattern with observed one over SCS, and it is pronounced over the northwestern SCS, with a significant rightward bias of SST cooling with respect to the track. The maximum SST cooling appears at 20.0◦N and 115.2◦E, which is north to the center of TRMM/TMI data, due to the simulated X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175 169

Fig. 2. SST (◦C) distribution in the SCS on August 25, 2003, influenced by Typhoon Krovanh: (a) simulation (the track simulated by the coupled model is shown); (b) TRMM/TMI observation (the observed track is shown, the gap represents missing data in TRMM/TMI-observed SST field). storm takes a more northward track than the observed one during the first 48 h. The maximum SST cooling, dropping from 29.4 ◦Cto24.1◦C, is biased to about 80 km to the right of the track, about 1.5 times the radius of maximum winds (Rmax). The magnitude of maximum SST cooling is in agreement with the observed maxima of 5.5 ◦C. Bender et al. (1993) distinguished three categories of SST cooling observed after the passage of 16 tropical cyclones. These SST decreases were grouped according to slow (<5 m s−1), medium (5–15 m s−1), and fast (>15 m s−1) moving storms as suggested by Bender et al. (1993), with average cooling for the three groups of 5.3 ◦C, 3.5 ◦C, and 1.8 ◦C. In this study, the average moving speed of Krovanh is 6.3 m s−1. However, the Krovanh-generated SST cooling is larger than the criterion for slow-moving storms in the study of Bender et al. (1993). This may be attributed to the shallow mixed layer depth (MLD) in the northwestern SCS (Chu et al., 2000a). The rightward bias in the SST cooling is well established from previous observations and numerical studies. Black (1983) stratified 10 years of Airborne Expendable Bathy Thermograph (AXBT), Airborne Infrared Thermometer (AIRT), and buoy observations using various storms and oceanic parameters to document the ocean thermal response. He suggested that for fast-moving storms, the SST decreases have a crescent-shaped pattern, with maximum cooling located in the right-rear quadrant between 1 and 2Rmax. Shay and Chang (1990) investigated the SST cooling generated by Hurricane Frederic (1979) using a 17-level primitive equation model with a free surface and found that the maximum SST cooling was biased to about 2Rmax to the right of the track. Chu et al. (2000b) showed that the maximum SST ◦ cooling induced by Typhoon Ernie (1996) was 1.5 C at 80 km from the track, which is about 1.5Rmax. The characteristics of the distribution of model-simulated SST deviation induced by Krovanh in different periods are shown in Fig. 3. From 0600 UTC 23 to 0600 UTC 24, the SST cooling center is formed in the right-rear quadrant of the typhoon center, respectively, following the movement of typhoon. At 0600 UTC 23 (Fig. 3a), the maximum SST cooling is 1.7 ◦C, about 148 km from typhoon center. Subsequently, the SST cooling becomes increasingly more significant and the maximum SST cooling center comes closer to the typhoon center, and the maximum SST cooling becomes 3.1 ◦C, about 140 km away from the typhoon center, at 1800 UTC 23 (Fig. 3b). At 0600 UTC 24 (Fig. 3c), the maximum SST cooling center is around 115.2◦E and 20.0◦N, with a value of 5.3 ◦C, 170 km away from the typhoon center. Thereafter, the position of the maximum SST cooling hardly changes, although the typhoon continues to move forward (Fig. 3d). In correspondence to the SST cooling, the MLD exhibits an increase (Fig. 4). The increases in the MLD on the right side of the typhoon track are significantly greater than those on the left side. The prestorm MLD of a nearly uniform 20–40 m on the right side of the track increases by about 20–60 m, while on the left side, the MLD increases by about 15–30 m. 170 X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175

Fig. 3. Horizontal distribution of model-simulated SST (◦C) deviation from the initial time at 0000 UTC 23. (a) At 0600 UTC 23, (b) at 1800 UTC 23, (c) at 0600 UTC 24, and (d) at 1800 UTC 24. Here, the typhoon center is indicated by the typhoon symbol, respectively.

Fig. 4. Difference in ocean MLD (m) between 0600 UTC 25 and 0000 UTC 23. X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175 171

Fig. 5. MM5-simulated sea surface wind vectors (m s−1) and Ekman pumping velocity greater than 2 × 10−3 ms−1 estimated from the wind ﬁelds showing upwelling induced by Krovanh (shaded). (a) 1800 UTC 23; (b) 0600 UTC 24.

Fig. 6. Surface currents (m s−1) in SCS: (a) at 1800 UTC 23; (b) at 0600 UTC 24. Here, the typhoon center is indicated by the typhoon symbol.

Upwelling and underlying cold water entrainment are the widely accepted mechanisms of SST cooling. Price (1981) showed that entrainment is the primary mechanism that lowers the SST. Upwelling causes a signiﬁcant enhancement of the SST response to a slowly moving hurricane but only a negligi- ble one for rapidly moving hurricanes. While upwelling tends to reduce the oceanic MLD, entrainment tends to increase the oceanic MLD. In this study, the distribution of the SST cooling and the MLD increase indicate that entrainment plays a dominant role in lowering SST. This is further substantiated by the following analysis. Fig. 5a and b shows the MM5-simulated sea surface wind ﬁeld and the Ekman pumping velocity estimated from it at 1200 UTC 23 and 0000 UTC 24, respectively. Compared with Fig. 2, it can be seen that the location, scale, and the peak value of the upwelling are not consistent with the cold patch, either in Fig. 5aorinFig. 5b. This means that upwelling plays a minor role in lowering the SST.

4. The dynamic response

The surface current at 1800 UTC 23 and 0600 UTC 24 is shown in Fig. 6a and b, respectively. As seen in the ﬁgures, the dynamic response of the SCS to Krovanh is characterized as a cyclonic, near-surface 172 X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175

Fig. 7. Temporal variation of u (m s−1) and v (m s−1) at the maximum SST cooling (20.0◦N, 115.2◦E) at depths of (a) 30 m and (b) 125 m. current ﬁeld, following the movement of typhoon, which is consistent with previous observational and numerical studies (Price, 1981; Shay and Chang, 1990). Consistent with the rightward-biased distribution of the SST cooling, although not a one-to-one correspondence, the response of the current velocity is also strongly biased toward the right of the typhoon track. Previous studies have identiﬁed the linkage between the storm translation speed and the maximum current speed U in the mixed layer:

U = AT Vt where Vt is the storm translation speed and AT is a measure of the ratio of the along-track advection terms to the local acceleration terms. In the study of Chang and Anthes (1978) and Greatbatch (1983), AT is set to 0.54 and 0.34, respectively. By virtue of 6.3 m s−1 translation speed of Krovanh, the maximum current speed in the mixed layer should be 3.14 m s−1 according to Greatbatch (1983) but 2.15 m s−1 according to Chang and Anthes (1978). Our model-simulated maximum current speed is 2.3 m s−1, which is closer to the estimation by Chang and Anthes (1978). Storm-generated near-inertial currents have been the subject of many observational and numerical studies. It was shown that a strong, near-inertially rotating current was excited by the passage of Hurricane Gilbert (1988) with maxima of about 1–1.4 m s−1 (Shay et al., 1992). In Dickey et al.’s (1998) observational study, large inertial currents were generated within the upper layer by Hurricane Felix (1995). Here, we present the time series of the current components (u and v) at the maximum SST cooling center (20.0◦N, 115.2◦E) for 30 and 125 m, representing the mixed layer and thermocline, respectively (Fig. 7). About 18 h after Krovanh enters the SCS, there is a strong inertial oscillation at 30 m, which propagates downward gradually. Several hours later, inertial oscillation is also exhibited at 125 m, and the amplitudes of u and v in the time series at 125 m is smaller than that at 30 m. The typical period of the inertial oscillation produced by the model is about 33 h, which is very close to the local inertial period at 20◦N (35.1 h). It can also be seen in Fig. 7 that the thermocline currents are 180◦ out of phase with the mixed layer currents. This phase reversal indicates that the mixed layer is forced by the wind stress, whereas the thermocline processes are driven by pressure gradient effects, as pointed out by Price (1983). As discussed by Chang and Anthes (1978), Price (1981), Dickey and Simpson (1983) and Sheng et al. (2006). The rightward bias of the intense inertial currents can be explained as follows: As the storm passes by, the wind stress veers anticyclonically at a ﬁxed point on the right side of the storm track, while the wind stress veers cyclonically on the left side of the storm track. The Coriolis force (on the Northern Hemisphere) turns the ocean currents in the same direction as the wind stress on the right side of the storm track, leading to an efﬁcient transfer of energy from the storm to the ocean currents. X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175 173

Fig. 8. Temporal variation of (a) sea surface wind, (b) mixed layer current speed (shaded circles), and SST (open circles) at Point A (20.0◦N, 115.2◦E).

By contrast, on the left side of the storm track, the ocean currents are turned in the opposite direction to the wind stress, resulting in a weak current. The rightward bias of the near-inertial currents behind the storm leads to stronger entrainment on the right side of the storm track, which, in turn, is mainly responsible for the rightward bias of the SST cooling. To further illustrate the rightward-biased response of the SST cooling and mixed layer currents, two points – A (20◦N, 115.2◦N) and B (19.0◦N, 114.0◦E) – are chosen. Point A is the maximum SST cooling center, and Point B is located to the left of the track. The time evolutions of the sea surface winds, currents speeds, and SSTs at these two points are shown in Figs. 8 and 9, respectively. At Point A, the evolution of the current speeds is strongly dependent on the sea surface winds. In the first 24 h, with the enhancement of the sea surface winds from 14 m s−1 to34ms−1, the current speed correspondingly increases from 0.2 m s−1 to2.0ms−1. The current intensifies the entrainment, and hence, the SST decreases rapidly from 17 h to 38 h (Fig. 8). By contrast, at Point B, the evolution of the current speeds shows little correlation with the winds. The magnitudes of the current speeds at Point B are much smaller than those at Point A, hence, the SST cooling is also much smaller (Fig. 9). Observational studies show an evident intrusion of Kuroshio water into the SCS through the Luzon Strait (Shaw, 1991; Farris and Wimbush, 1996). The influence of Krovanh on the Kuroshio intrusion is simulated as a SST increase of 1 ◦C across the Luzon Strait (Fig. 2). This region appears to be a

Fig. 9. Same as Fig. 8, except for at Point B (19.0◦N, 114.0◦E). 174 X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175

Fig. 10. Latitudinal cross-section along 18◦N of simulated (a) v (m s−1) and (b) w (m s−1) at 2200 UTC 23 (the vertical velocity is magniﬁed by 103, contour intervals is 0.2 m s−1 for v and 0.2 × 10−3 ms−1 for w, respectively).

convergence zone between a northward-flowing coastal current along the western coast of Luzon and the inflow of a branch of the warm Kuroshio Current through the Luzon Strait. Such a convergence produces downwelling and raises the SST by about 1 ◦C and decreases the MLD by 10–20 m (Fig. 4). This phenomenon was also found in the simulation of the response of the SCS to Typhoon Ernie (1996) (Chu et al., 2000b) In the study of Chu et al. (2000b), storm-generated coastal jets (∼40 cm s−1) were found during the passage of Typhoon Ernie. This phenomenon is also found in our study. As seen in Fig. 10, coastal interactions under the storm forcing appear to have produced a subsurface jet (∼60 cm s−1) flowing northward and parallel to the coast of Luzon, which, according to Chu et al. (2000b), is related to the oscillating currents. When the surface currents are onshore to the east and southeast, coastal downwelling occurs and a strong northwesterly subsurface jet is produced. When the currents reverse and become offshore to the west and northwest, coastal upwelling occurs and the subsurface jet reverses to the southeast and weakens slightly.

5. Summary and conclusions

While there are many numerical studies about the upper ocean response to a tropical cyclone, few of them use a real-case forcing field provided by a coupled atmospheric model. Moreover, numerical simulations of the upper ocean response to tropical cyclones in the SCS are very few. In the present study, the nonhydrostatic mesoscale model MM5 was coupled with POM to investigate the dynamic and thermal responses of the SCS to a real typhoon. The use of the coupled model provided more accurate wind stress forcing and thermal fluxes. The simulation demonstrates that SST pattern is in good agreement with those obtained from TRMM/TMI-derived data, with the maximum SST cooling of ◦ 5.3 C, located about 1.5Rmax to the right of the typhoon track. In correspondence to the SST cooling, the MLD increases, exhibiting a strong asymmetry that skewed the response toward the right of the track. This correspondence indicates that the SST cooling is caused mainly by entrainment. Furthermore, the simulation shows several unique features of SCS response to Typhoon Krovanh. The coupled model successfully simulates the SST warming across the Luzon Strait from southwest Taiwan to northwest Luzon, which is generated by the convergence between a northward-flowing coastal current and the intrusion flow from Kuroshio Current through the Luzon Strait.

Acknowledgments

This study was sponsored by the National Natural Science Foundation of China (40675065) and the National Key Basic Research Development Project (2007CB411805 and 2006CB403600). The authors X. Jiang et al. / Dynamics of Atmospheres and Oceans 47 (2009) 165–175 175 give our thankfulness to two anonymous reviewers for their helpful comments and suggestions, which is important in improving the quality of this paper.

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