SST variations of the Kuroshio from AVHRR observation *

Transcription

1 Chinese Journal of Oceanology and Limnology Vol. 24 No. 4, P , 2006 SST variations of the Kuroshio from AVHRR observation * ZHANG Caiyun ( 张彩云 ) **, CHEN Ge ( 陈戈 ) (Ocean Remote Sensing Institute, Ocean University of China, Qingdao , China) Received Dec. 12, 2004; revision accepted May 22, 2005 Abstract Using monthly gridded ocean pathfinder Sea Surface Temperature (SST) data with a spatial resolution of 4km from AVHRR, variations of SST over the Kuroshio region northeast of Taiwan Is. during the past two decades ( ) are studied. Some interesting findings are as follows. (1) The climatological SST field shows an expected pattern with southwest-northeast orientated isotherms, and this pattern is mainly dominated by solar irradiance and regional circulation. However, the interannual variation of this pattern is very notable, in particular along Kuroshio path. The most dynamic region is located in the east coast of Taiwan, where cold upwelling is very energetic. (2) Seasonal variation of SST over this region is mainly controlled by see-saw variation of solar irradiance between two hemispheres, but the strong interannual fluctuation of SST is found to be locked to boreal winter (January, February, and last December), and the energetic region is identified along Kuroshio path. This phenomenon seems to closely connect with El Niño s phase locking characteristics. (3) SST anomalies over Kuroshio region have a positive correlation with El Niño-Southern Oscillation (ENSO), which is dramatic due to the weak (strong) North Equatorial Current (NEC) during El Niño (La Niña) events, and the weak (strong) NEC is supposed to induce a same polarity of SST variation along the Kuroshio path. How the interannual variation and seasonal variation interact each other and what is the mechanism between ENSO and the thermal and thermodynamic processes over this region deserve our further analyses. Key words: SST variation; AVHRR; ENSO 1 INTRODUCTION The Kuroshio Current (KC), being the western boundary current in the North Pacific subtropical gyre, is the second strongest current in the world after the Gulf Stream and is famous as a strong and fast flow. KC plays an important role in meridional transports of mass, momentum, heat and fresh water (Macdonald and Wunsch, 1996). KC enters the East China Sea (ECS) on the eastern coast of Taiwan, flows northeastward along 200 m isobath, and leaves the ECS from the southwest of Kyushu (Fig.1), where it splits into two parts with the major branch flowing south of Japan and the minor branch through the Korea and Tsushima Straits as the Tsushima Current. Previous documents about KC have focused on its transport (Akitomo et al., 1991; Ichikawa and Beardsley, 1993; Kawabe, 1995; Yoshinapi et al., 2004), path meander and dynamics (Hurlburt et al., 1996; Guan et al., 1997; Tanaka and Ikeda, 2004), and its predictions (Tanaka et al., 2004). Some investigations suggest that its interannual variation is closely connected to El Niño-Southern Oscillation (ENSO), a strong quasi-periodical signal happened in the eastern tropical Pacific (Hinata, 1996; Imawaki et al., 2001). The study of Akitomo et al. (1996) suggested that the transport of KC increased on or after an El Niño event because of the intensification of trade wind in the Northern Hemisphere. Kawabe (2000, 2001) used a model to indicate that some distinct variations of KC transport are caused by wind stress curl variation that is closely linked to ENSO. SST, being one of the most important geophysical parameters in the ocean, plays an important role in global climate change. More knowledge about SST variation is urgent for studying the thermal and thermodynamic structure over the KC af- * Cosponsored by the NSFC (No ) and the National Basic Research Program of China (No. 2005CB422308). ** Corresponding author: zhangcy@orsi.ouc.edu.cn

2 346 CHIN. J. OCEANOL. LIMNOL., 24(4), 2006 Vol.24 fected region. With increasing power of ocean satellites, remote sensing data have been accumulated for more than a decade, which provides us with more opportunities and convenience to study the variation of KC than just by use of in situ data. Toka and Murakami (1998) investigated unusual behavior of KC system from winter 1996 to summer 1997 by use of SST and ch-a images from Advanced Earth Observing System. Qiu (2000) used TOPEX/Poseidon (T/P) altimetry data to study the interannual variation of Kuroshio Extension (KE) system (KE is a branch of KC after it separates from the Japan coast and enters the open basin of the North Pacific at 35 N, 140 E) and its impact on the wintertime SST field, displaying that SST anomalies associated with large-scale changes of the KE appeared to be independent of the interannual SST changes in the tropical Pacific Ocean. Hwang and Kao (2002) used T/P altimetry data to compute the transport of KC, illustrating that the volume transport of KC northeast of Taiwan has a positive correlation with ENSO with a month lag. Nonaka and Xie (2003) studied the covariations of SST and wind over KC by use of SST data from TRMM and wind speed data from QuikSCAT. Remote sensing data has become more and more popular in scientific community because of its consistence, global coverage, as well as real-time or near-real-time characteristics. The investigations of SST variations over this region may provide some valuable information for its thermaldynamics, transportation, as well as path meander. 2 DATA The NOAA/NASA AVHRR ocean pathfinder SST data set contains monthly averaged SST data and browse images derived from the NOAA AVHRR using the Pathfinder Version 3 algorithm. Data for both ascending pass (daytime) and descending pass (nighttime) are available globally on equal angle grids. These data are provided in Hierarchical Data Format (HDF), and the version 5.0 has been significantly improved from version 4.1. In addition to the increased spatial resolution of 4 km versus 9 km, enhanced ice and land masks were included. Thus version 4.1 contained gridded files of ( ) pixels, version 5.0 contains ( ) pixels. Several other modifications were mode in file structure. In version 5.0, the parameters, SST, quality flags, and number of observations per bin are contained in separate files. Additionally, version 5.0 comes in the HDF-SDS (scientific data set) format while in version 4.1 the data were in the HDF raster format. Data for version 5.0 now span from January 1985 to May For more information on SST data in version 5.0 and detailed information on the pathfinder algorithm please browse ftp://podaac.jpl.nasa.gov or Vazquez (2004). Data are available via anonymous FTP to podaac.jpl. nasa.gov in the pub/sea_surface_temperature/ avhrr/ pathfinder. In this paper, in order to study the SST variation at the beginning region of KC, monthly ascending SST data with a high spatial resolution of 4 km spanning from January 1985 to December 2003 have been compiled and used. 3 CLIMATOLOGY AND VARIATIONS Fig.1 Schematic of the Kuroshio Current Path In this paper, SST data, being firstly publicly released by Physical Oceanography Distributed Active Archive Center (PO DAAC) with so much high spatial resolution of 4 km from Advanced Very High Resolution Radiometer (AVHRR), and spanning about two-decade long from 1985 through 2003, have been compiled to study the regional SST variations over the Kuroshio region northeast of Taiwan. 3.1 Climatology and interannual variation A 19-year climatology of SST over KC region northeast of Taiwan Is. is computed by simply averaging multi-year monthly SST data from January 1985 to December 2003, and the result is plotted in Fig.2a. The standard deviation, being a measure of the total year-to-year variability, is also derived on the basis of annual average from 1985 to 2003 and is plotted in Fig.2b.

3 No.4 ZHANG et al.: SST variations of the Kuroshio from AVHRR observation 347 Fig.2 SST ( ) climatology ( ) derived from AVHRR (a) and standard deviation based on annual mean (b) (The contour interval is 0.5 in a and 0.1 in b) Fig.3 Seasonal geographical distributions of SST ( ) derived from AVHRR a. Spring (March May); b. Summer (June August); c. Autumn (September November); d. Winter (December February) From Fig.2a, we note that the SST mean field in the past two decades over KC region shows a pattern of southwest-northeast oriented isotherms with a negative temperature gradient towards the northwest. Low SSTs are observed along the coast of the ECS and the highs are dominated by KC and Taiwan warm current. This pattern is the result of a joint effect of solar irradiance and surface circulation over

4 348 CHIN. J. OCEANOL. LIMNOL., 24(4), 2006 Vol.24 this region. One cold eddy in the northeast of Taiwan is very notable, which is a typical example of the influences of KC on off-shore environment of China (Sun and Xiu, 1997). As far as the interannual variation of SST is concerned, high values are observed prominently along the KC path, and maximums are found near the east coast of Taiwan Is. (Fig.2b), which mostly is dominated by the low-frequency variation of KC intrusion in the northeast of Taiwan Is. (Tang et al., 2000) or strong year-to-year variation of one of cold eddies located near Hua-lien of Taiwan Is. (Sun and Xiu, 1997). Low values are found in parts of subtropical countercurrent of KC, ECS, and Taiwan Strait, where SST interannual variations are more stable than the region along KC path. 3.2 Seasonal variation and standard deviations Seasonal geographical distributions of SST are plotted in Fig.3a, b, c, and d, respectively. Hereafter, winter is defined as between December and February, spring from March to May, summer from June to August, and autumn from September to November. The corresponding standard deviations based on annual SSTs in spring, summer, autumn, and winter, are computed and plotted in Fig.4a, b, c, and d, respectively. Seasonal variation of SST over KC region is mainly dominated by solar shift between two hemispheres with averaged high (low) values in summer (winter) (Fig.3b, d). In spring, a notable cold tongue along the coast of the ECS has been degraded and isotherms become more horizontally than in winter (Fig.3a). In winter, circulation plays an important role in shaping the SST pattern, and cold coastal water extends from the Changjiang (Yangtze) River mouth to Taiwan Strait (Fig.3d). In summer, the KC path can not be observed from its SST pattern and the autumn pattern is a transitional one from summer to winter with an increasing meridional gradients (Fig.3b, c). The year-to-year variation is very energetic in winter and spring (Fig.4d, a), in particular along KC path, which is easily connected with ENSO as a result of the coupling effect of ENSO and annual cycle in the eastern equatorial Pacific. One of the notable characteristics of ENSO is its tendency to peak toward the end of the calendar year (Rasmusson and Carpenter, 1982). Philander (1983) suggested that the dynamics of this phase locking characteristics is the seasonal movement of the Pacific intertropical convergence zone (ITCZ) and its effect on the atmospheric heating and hence on the coupled ocean-atmosphere instability. Many previous documents have focused on the dynamics of ENSO s phase locking; however, it is still an open question (Tziperman et al., 1995; Galanti and Tziperman, 2000; An and Wang, 2001). Few documents have reported the energetic interannual variation of SST over the KC region which is locked to winter too, although Wallace (1990) described that positive (negative) SST anomalies are often found in winter to be collocated with decreased (increased) speed of prevailing winds. As an extension of the North Equatorial Current (NEC), KC must be closely linked to ENSO considering the relaxation of northeast trade wind in El Nño event. The interannual variations in eastern Asian monsoon, northeast trade wind, heat flux, and active cold eddy over this region must have impacts on the physical mechanism of SST interannual variation along KC path. Detailed investigations about it deserve our further composite studies. 3.3 Anomalies in El Niño and La Niña events To investigate the relationship between SST variability and ENSO, SST anomalies in El Niño and La Niña years were computed, respectively. For the period of the data duration ( ), the years 1986, 1991, 1994, 1997, and 2002 were dominated by El Niños, whereas La Niñas prevailed in 1988,1999, and The averaged SST anomalies for El Niño and La Niña are shown in Fig.5. Positive anomalies were observed in El Niño events and highs were notable along KC path. Most regions were dominated by negative values in La Niña years with the exception of regions near the KC s splitting. The coincidence of high positive anomalies along KC path and eastern Pacific of El Niño indicates a close relationship between them. Guan et al. (1997) gave a possible mechanism for the phenomena in : after the termination of El Niño event, warm SST exited in western tropical Pacific, and local wind stress forced warm water propagating from tropical region to mid-latitude and strengthening the KC transport. However, there is a contradiction. When El Niño happens, because of the relaxation of trade winds and the southward ITCZ, the NEC becomes weak; the width of the Western Pacific Warm Pool (WPWP) narrows and the WPWP migrates eastward

5 No.4 ZHANG et al.: SST variations of the Kuroshio from AVHRR observation 349 Fig.4 Same as Fig.3 except for seasonal standard deviation Fig.5 SST (Unit: o C) anomalies in composite (a) El Niño years (1986, 1991, 1994, 1997, 2002), and (b) La Niña years (1988, 1999, 2000). The thick, thin, and dotted line denote zero, positive, and negative value, respectively

6 350 CHIN. J. OCEANOL. LIMNOL., 24(4), 2006 Vol.24 in the tropical Pacific (Matsuura and Iizuka, 2000), so does the extension of the NEC. Regions dominated by KC should have negative (positive) anomalies in El Niño (La Niña) events especially along KC path. So some interesting problems were raised. What is the role of NEC on the variation of KC northeast of Taiwan Is.? Why KC region especially along KC path has a positive correlation with ENSO? Hwang and Kao (2002) used T/P derived altimeter data to study the KC volume transport, width and axis velocity, showing that KC volume transport has a positive (negative) correlation with ENSO northeast (southeast) of Taiwan Is. (Hwang and Kao, Fig.11, 2002). The negative correlation with ENSO southeast of Taiwan Is. was explained by them that because the NEC is weak in El Niño events and the NEC has the same phase variation as the volume transport of KC southeast of Taiwan Is., so a weak NEC would immediately lead to a weak KC southeast of Taiwan Island. Our result of SST variation northeast of Taiwan Is. is the same as KC volume transport of Hwang and Kao s result. SST variation in KC region northeast of Taiwan Is. has the same phase variation as ENSO and they are in positive correlation, but it is still unclear why ENSO, being a long-wavelength phenomenon, has different effects on KC northeast and southeast of Taiwan Is. On the other hand, the studies of Hwang and Chen (2000a, 2000b) have shown that the South China Sea (SCS) circulation and sea level are also closely correlated with ENSO. Because of the geographical relationship among KC, SCS, and ECS, it is likely that in addition to a direct effect of ENSO, some indirect effects on KC as the response of the SCS and ECS to ENSO may also take place in the northeast of Taiwan Is. Thus, on the interannual time scale of SST variations, the KC-SCS-ECS system is very complicated and needs our further studies by use of in situ data and numerical simulation aside from satellite data. A composite study by use of a joint remote sensing data such as wind, current, sea level at KC region is recommended to put forward an ideal model for the physical mechanism of this complicated KC-SCS-ECS system and its relationship with ENSO. 4 CONCLUSION The public release of AVHRR/SST data with a higher spatial resolution of 4 km and two-decade long records by PO DAAC in 2004 provides us with feasibility and convenience to study regional SST variations based on remote sensing data. The climatological pattern, interannual variation, seasonal distribution of SST over the Kuroshio region have been investigated in this part. Some main conclusions are as follows. (1) The climatological distribution of SST over this region presents southwest-northeast oriented isotherms, and that is a typical pattern dominated by combining effects of solar irradiance and local circulations. (2) The interannual variation of SST is very notable along KC path and the most striking feature is that the highs are found prominently near east coast of Taiwan Is., where cold upwelling is very energetic, suggesting a significant impact of upwelling on the thermodynamic process over that region. (3) The seasonal distribution of SST over this region is an expected pattern. Warm and cold water are found in summer and winter, respectively, accompanying a changing direction in thermal isotherms. (4) A new phenomenon of interannual fluctuation of SST is its phase-locking to boreal winter, suggesting its close relationship to ENSO. Further anomaly patterns in El Niño and La Niña years illustrate the negative polarity between them, which is dramatic due to the weak (strong) NEC during El Niño (La Niña) years. The weak (strong) NEC is supposed to induce a weak (strong) KC; therefore a negative (positive) anomaly pattern should be presented for El Niño (La Niña) events. Oceanographers have used T/P altimeter data to study the KC volume transport, width, and axis velocity, and they derived the same results as SST as far as its interannual variation along KC path is concerned, i.e. the volume transport of KC northeast of Taiwan Is. has a positive correlation with ENSO, which proves the potential application of SST in KC studies. On the other hand, some interesting questions have been raised in this study. What is the role of the NEC on the variation of SST over KC region northeast of Taiwan Island? Why KC region especially along KC path has a positive correlation with ENSO? Why strong interannual variation in SST over this region is phase-locked to boreal winter? What is the relationship between interannual variation and seasonal variation of SST over this region? To answer these questions, we should investigate the complex NEC-KC-SCS-ECS system. A joint view

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