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Interannual and interdecadal variations of sea surface temperature in the East Asian Marginal Seas
Progress in Oceanography 47 (2000) 191–204 www.elsevier.com/locate/pocean
Interannual and interdecadal variations of sea surface temperature in the East Asian Marginal Seas Won-Sun Park, Im Sang Oh
*
Department of Oceanography and Research Institute of Oceanography, Seoul National University, Seoul 151-742, South Korea
Abstract Interannual and interdecadal variability of sea surface temperature (SST) in the East Asian Marginal Seas (EAMS) from 1951 to 1996 are investigated, and the teleconnection of the EAMS to the equatorial ocean is studied using empirical orthogonal function analysis and coherency analysis. The EAMS have significant coherency with the Nin˜o 3.4 SST at 2- to 3-year periods with a phase lag of 5–9 months in the SST anomaly (SSTA). The EAMS also showed a higher coherency with a 6-year oscillation with a phase lag of 18–22 months. For the 6-year variability, the SSTA of the Nin˜o 3.4 is highly coherent with the latent and sensible heat flux anomaly of the southern East Sea, indicating that the variability of the southern East Sea is related to that in the Nin˜o 3.4 region through the ocean-atmosphere interactions. The southern East Sea also has a higher coherency with the East China Sea. Cooling in the southern East Sea and sudden warming in the northern East Sea were found to have occurred around 1965/1966 in the records of interdecadal variability. The decadal and interdecadal variabilities of the EAMS were found to be teleconnected to the western equatorial Pacific, whereas the interannual variability was highly correlated to the central equatorial Pacific, which is represented by Nin˜o 3.4 SST. 2000 Published by Elsevier Science Ltd.
Contents 1.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
* Corresponding author. Tel.: +82-2-880-6752; fax: +82-2-872-0311. E-mail address: [email protected] (I.S. Oh). 0079-6611/00/$ - see front matter 2000 Published by Elsevier Science Ltd. PII: S 0 0 7 9 - 6 6 1 1 ( 0 0 ) 0 0 0 3 6 - 7
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Data and method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
3. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.1. EOF analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.2. Spectral and coherency analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 4.
Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
1. Introduction It is known that the ocean-atmosphere system has variability on three distinct timescales: interannual (3–7 years), decadal (10–11 years) and interdecadal scale (16–18 years) (Mann & Park, 1996). The interannual variations of sea surface temperature (SST) in the central and eastern equatorial Pacific, known as the El Nin˜o– Southern Oscillation (ENSO) (Rasmusson & Carpenter, 1982), have been linked to regional and global atmospheric anomalies (Horel & Wallace, 1981; Nakamura, Lin & Yamagata, 1997). ENSO has an effect on the atmospheric circulation at midlatitudes and the Asian monsoon (Kawamura 1984, 1998). The air temperature and precipitation in Korea are correlated to those in the equatorial region with a time lag of a few months (Kang & Jeong, 1996; Ahn, Ryu, Cho, Park & Ryoo, 1997; Kang, 1998; Cha, Jhun & Chung, 1999). Like the Pacific Ocean, the East Asian Marginal Seas (EAMS), which include the Yellow Sea, the East China Sea and the East/Japan Sea (hereafter East Sea), show strong spectral peaks of SST anomalies (SSTA) at decadal and interdecadal timescales as well as interannual timescale, as shown later. Therefore it is necessary to describe the spatial and temporal variability of SST and identify the teleconnection between the Pacific Ocean and EAMS if the variability depending on these timescales is to be understood. Researches on the climatology over the EAMS are rather difficult because of the complicated ocean and atmospheric dynamics associated with the Kuroshio and the Asian monsoon. The present paper investigates the variability of SST in the EAMS associated with the Pacific basin scale variability, and compares the SST anomalies in the equatorial region to those in the marginal seas in order to understand what is happening in the marginal seas at other timescales.
2. Data and method The present analysis uses monthly mean SST in 2° latitude × 2° longitude retrieved from the Comprehensive Ocean–Atmosphere Data Set (COADS) as described in Woodruff, Lubker, Wolter, Worley and Elms (1993), for the period from January
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1951 to December 1996. The missing data in 2°×2° grids were linearly interpolated from the adjacent points (see Zhang, Sheng & Shabbar, 1998). If there were no available data around the missing data points, the values for adjacent months, i.e. data for preceding and following months for the same grid point were used to estimate the missing data. Monthly mean values for each grid point are removed to get the monthly anomaly of SST. The analyses used were empirical orthogonal function (EOF) analysis (see Wallace & Dickinson, 1972) and the multitaper method (MTM) (Thomson, 1982). MTM reduces power leakage in the frequency domain, estimates uncertainties by using a small set of tapers rather than a single data taper or a spectral window, and gives satisfactory results in the presence of red noise. The tapers are the discrete set of eigenfunctions, which solve the variational problem of minimizing leakage outside of a frequency band of half bandwidth pfn, where p is an integer, fn(1/(Ndt)) is the Rayleigh frequency. N is number of total data points and dt is sampling interval. We used the schemes for the spectrum determination developed by Mann and Lees (1996) and for the coherency analysis by Mann and Park (1993). For MTM, the number of tapers of three with p=2 is used.
3. Results and discussions 3.1. EOF analyses Characteristics of SST variability in the North Pacific are well documented according to their timescales (Tanimoto, Iwasaka, Hanawa & Toba, 1993; Tanimoto, Iwasaka & Hanawa, 1997). However the EAMS were not examined in detail in their studies. Herein the interannual, decadal and interdecadal variabilities of SST in the EAMS are described. To determine the spatially and temporally dominant oscillations over the EAMS, we performed EOF analyses. In order to separate interannual, decadal and interdecadal timescales, we used three band-pass filters (1.5–5, 5–9, 9–13 years) and one lowpass filter (13 years). These choices of band or low-pass filters are generally consistent with the global analysis by Lau and Weng (1999). For the filter scheme, we use Fourier transforming filter without data taper. Fig. 1a is the first mode of band-pass filtered (1.5–5 years) SSTA; it represents 27.7% of variance. High values were apparent near the east coasts of Korea and in the Yellow Sea, whereas in the Kuroshio region the values were relatively small. The time coefficient exhibited a dominant timescale of 2–3 years. The first mode (47.2%) of 5- to 9-year band-pass filtered SSTA mainly represented 5- to 7-year variability (Fig. 1b). The eigenvectors derived were high in the southwestern East Sea and the Yellow Sea and moderate in the East China Sea but weak in the Kuroshio region. Time coefficients of these interannual modes look like interdecadal modulation pattern of interannual variability (Minobe & Mantua, 1999). In the 5- to 7year variability, large amplitudes occurred around the early 1960s and the mid-1980s. The variability of 6–7 years was dominant from 1953 to 1973, and 5- to 6-year
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variability was dominant from 1973 to 1995. A consistent amplitude modulation pattern with a large amplitude after the mid-1950s and maximum in the early 1960s was also found in the coastal sea surface temperature data from along the east coast of the East Sea (Watanabe, Hanawa & Toba, 1986). Beside this amplification during the early 1960s, our analyses also revealed amplifications during the mid-1980s with a rather shorter period than that in the 1960s. On decadal timescales, an approximately 9-year periodicity was dominant in the first EOF (47.3%), and higher values of eigenvector were found near the polar front in the East Sea (i.e. the front between Liman Cold Current and Tsushima Warm Current, Fig. 1c). The maximum eigenvectors were found in the northeastern part of the polar front region. This result may be consistent with the results of Nakamura et al. (1997). They showed that, the strongest decadal variability in the North Pacific occurs around the subarctic front; this is the most intense front in the basin extending zonally at 42°N, where cool water associated with the subpolar gyre including the Oyashio adjoins warm water in the Kuroshio extension. On the interdecadal timescale, the first mode (52.0%) represented long-term variability in which there was rapid cooling in the southern East Sea but warming in the northern East Sea and the Kuroshio region around 1965/66 (Fig. 1d). Watanabe et al. (1986) pointed out that this cooling in the southern East Sea reflected the changes in large-scale oceanic conditions of the western North Pacific in the 1960s. It is noteworthy that the phase reversal, in which there was warming in the equatorial ocean but cooling in the central North Pacific, occurred in the mid-1970s (Tanimoto et al., 1993). However, the phase reversal in the EAMS occurred in the early 1960s. 3.2. Spectral and coherency analyses In the above EOF analyses we investigated the temporal and spatial characteristics of the SST only in the EAMS. In order to understand how the variability in the EAMS may be correlated to the Pacific Ocean, especially to the equatorial region, we determined the characteristics of energy spectra of each marginal sea and coherency of SSTA with reference to the equatorial ocean. We selected 8 sub-regions for the analyses in accordance with their regional and physical importance, with emphasis being placed on the northwestern Pacific (Fig. 2). For the equatorial region, we used the Nin˜o 3.4 region (5°S–5°N, 120°–170°W) as typifying the central equatorial Pacific Ocean, and the western equatorial Pacific which includes the Warm Pool region. Trenberth (1997) suggested that for a quantitative definition of El Nin˜o and La Nin˜a the use of 5-month running means of SSTA in the Nin˜o 3.4 is acceptable. As for the Pacific Ocean, spectra of SSTA in the EAMS have peaks at interannual, decadal and interdecadal timescales (Fig. 3). At interannual timescales, spectral peaks were found at the periods of 2.0, 3.7 and 7–8 years for the East China Sea (Fig. Fig. 1. The first modes of the EOF analyses of the filtered SST anomalies: (a) 1.5- to 5-year, (b) 5- to 9-year, (c) 9- to 13-year band-pass filtered, and (d) 13-year low-pass filtered. The first modes represent 27.7, 47.2, 47.3, and 52.0% of the variance of each filtered data, respectively. The contour interval is 0.1°C. Results have been scaled by each principal component’s standard deviation.
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Fig. 2. Selected sub-regions for spectral and coherency analyses: Nino 3.4, the Nin˜o 3.4 region; WEP, the western equatorial Pacific; KUR, the Kuroshio region; SCS, the South China Sea; ECS, the East China Sea; YES, the Yellow Sea; ESS, the southern East Sea; ESN, the northern East Sea.
3a), 3.3 and 3.7 years for the Yellow Sea (Fig. 3b), 2.3, 3.3, 6.1, and 7.3 years for the southern East Sea (Fig. 3c), and 5.2 and 6.3 years for the northern East Sea (Fig. 3d). In contrast to the East China Sea and the Yellow Sea, the 5- to 7-year variability was much more dominant in the southern part of the East Sea. This is consistent with the results of Miita and Tawara (1984), which showed that a 6- to 7-year period oscillation is the predominant year-to-year variation in water temperature at the east Korea Strait. Interannual spectral peaks were found at 2.3 and 3.3 years for the South China Sea (Fig. 3e), and 2.6 and 3.6 years also for the Kuroshio region (Fig. 3f). However, at the period of 6–7 years the spectral energy was not significant in the Kuroshio region. In the equatorial ocean, spectra between 3- to 4-year period are common and more spectral peaks were found at periodicities of 2.2, 3.3, 4.2 and 5.6 years for the Nin˜o 3.4 (Fig. 3g) and 3.5, 5.7, 6.7, and 7.6 years for the western equatorial Pacific (Fig. 3h). It is noteworthy that the dominant periodicities were 2–6 years in the Nin˜o 3.4 but 5–8 years in the western equatorial Pacific. Significant energy at decadal and interdecadal timescales were found in the EAMS and the western equatorial Pacific, but not in the Nin˜o 3.4 or the South China Sea, where interannual variability is much stronger than decadal or interdecadal variability. To investigate how the variability of SST in the EAMS is correlated to the Pacific Ocean especially to the equatorial Pacific, coherency analyses are performed in reference to SSTA of the Nin˜o 3.4 and the western equatorial Pacific Ocean. For the 2- to 3-year variability the EAMS have high coherency to the Nin˜o 3.4 with a phase lag of 70–90 degrees, which means that SSTA in the Nin˜o 3.4 region leads the change of SST in the EAMS by 5–9 months (Fig. 4a–d). The confidence
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Fig. 3. Spectra of SSTA of (a) East China Sea, (b) Yellow Sea, (c) southern East Sea, (d) northern East Sea, (e) South China Sea, (f) Kuroshio, (g) Nin˜o 3.4, and (h) western equatorial Pacific.
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Fig. 4. Coherency and phase lag of SSTA of (a) East China Sea, (b) Yellow Sea, (c) southern East Sea, (d) northern East Sea, (e) South China Sea, and (f) Kuroshio in reference to SSTA of the Nin˜o 3.4 with confidence level 50, 90, 95 and 99% from the analysis of multitaper method. Positive phase lag indicates that the SSTA of the Nin˜o 3.4 leads that of the selected sub-region.
level is above 95%. The South China Sea also has high coherency at 2- to 3-year period with a phase lag of about 70-degree with the Nin˜o 3.4 region (Fig. 4e), but the Kuroshio has low coherency on this timescale (Fig. 4f). Since atmospheric circulation can act as a link between SST changes in different part of the world’s ocean (Lau, 1997) and SSTA in the South China Sea should be closely related to the Asian monsoon, the coherency at this timescale might be explained by the effect of the atmospheric forcing. Zhang, Sumi and Kimoto (1996) showed that the effects of El Nin˜o on the East Asian monsoon are felt through the variation in convective activities over the western equatorial Pacific. For the period longer than 5 years, all of the EAMS variability was coherent with the Nin˜o 3.4 particularly at about an 8-year period (Fig. 4a–d). This coherence seems to have no physical meaning, because the 8-year variability in the spectra of the EAMS except for the East China Sea has a little energy (see Fig. 3).
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In the interannual variability with about a 6-year periodicity, which appears as a significant spectral peak in the southern East Sea, the southern East Sea has significant coherence with the Nin˜o 3.4 (Fig. 4c). This phase lag at this 6-year period is 110 degrees, which corresponds with a lag of about 22 months. This lag is similar to that of air temperature; mean air-temperature over Korea and of ENSO are coherent at about a 6-year periodicity but subject to a time lag of 1–1.5 years (Kang, 1998). To understand how the variability of SSTA (especially the 6-year variability in the southern East Sea) is correlated to the equatorial Pacific Ocean, we have examined heat flux variables of the southern East Sea. Fig. 5a–b shows the coherence between the SSTA of the Nin˜o 3.4 and the latent and sensible heat flux anomalies of the southern East Sea. The latent and sensible heat fluxes were derived from COADS. Significant coherence exists at 6- to 7-year period in the latent heat flux, and near both 5 years and 6–7 years in the sensible heat flux. This suggests that the ocean–atmosphere exchange of heat in the southern East Sea at about 6-year period seem to be correlated to the equatorial regions. In the southern East Sea, air temperature anomaly is highly coherent with SSTA at around a 6-year period (Fig. 5c). There could be lots of combinations for coherence between the sub-regions of the EAMS. Here we focus on the interannual variability in relation to the southern East Sea, since about 6-year variability of the sea is significant. Its coherence with the East China Sea is also shown, because of its relationship with the Kuroshio. The East China Sea has significant coherence with the Kuroshio at periods longer
Fig. 5. Coherency and phase lag of (a) latent heat flux anomaly and (b) sensible heat flux anomaly of the southern East Sea in reference to SSTA of the Nin˜o 3.4. Positive phase lag indicates the SSTA of the Nin˜o 3.4 leads outgoing heat fluxes of the southern East Sea. (c) Coherency and phase lag of air temperature anomaly in reference to SSTA, in the southern East Sea.
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than 5 years but with a phase lag of about 30–40 degrees (Fig. 6a). The East China Sea also has high coherence with the Yellow Sea at 3- to 4-year and about 16-year periodicities without any phase lag (Fig. 6b), so SSTA of the East China Sea and the Yellow Sea vary synchronously. There is high coherence between the East China Sea and the southern East Sea at a periodicity of 5 to 6 years (Fig. 6c) and this is also seen in the results of the EOF analysis (see Fig. 1b). However, there was no significant coherence between the East China Sea and the northern East Sea (Fig. 6d). There was high coherence between the Kuroshio and the southern East Sea at about 5- to 6-year periodicity (Fig. 6e), but there was no significant coherence between the Kuroshio and the northern East Sea (Fig. 6f). The high coherence between the Kuroshio and the southern East Sea seems to be unimportant physically because the spectral energy in 6-year period of the Kuroshio is too weak (Fig. 3d). This suggests that the 6-year variability
Fig. 6. Coherency and phase lag of SSTA of (a) Kuroshio, (b) Yellow Sea, (c) southern East Sea, and (d) northern East Sea in reference to the East China Sea, and those of (e) southern East Sea and (f) northern East Sea in reference to the Kuroshio.
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of the southern East Sea is linked not only to the Nin˜o 3.4 via the atmosphere but also to the East China Sea. This is consistent with the results of Watanabe et al. (1986), who suggested that the year-to-year variations in the heat transport of the Kuroshio and the strength of the outbreaks of cold air mass in the winter season are responsible for the year-to-year variations in sea conditions, especially for the 6-year variability in the East Sea. Now we will return to the results of the filtered EOF in order to compare them with those of the coherency analyses. Coherence of the first modes of EOF of the filtered SSTA in the EAMS with reference to the Nin˜o 3.4 (Fig. 7a–d) and to the western equatorial Pacific (Fig. 7e–h) are presented. A significant coherence for the 2- to 3-year variability is found between the SSTA of Nin˜o 3.4 and the first mode of 1.5- to 5-year band-passed EOF with a phase lag about 90 degrees (Fig. 7a), which is consistent with previous coherency analyses. There is also high coherence at 3- to 4-year periodicity. The SSTA in EAMS shows no significant coherence with the western equatorial Pacific at either the 2- to 3-year or the 3- to 4-year variability (Fig. 7e). The interannual variability at these periods in the EAMS data seems to be teleconnected to the central equatorial Pacific. At around 6 years the variability of SSTA as shown by the first mode of EOF in the EAMS is coherent with the Nin˜o 3.4 with a phase lag of 110 degrees (Fig. 7b). This higher coherence at a 6-year periodicity is also found between the Nin˜o 3.4 region and the southern East Sea sub-region (Fig. 4c). In the case of decadal and interdecadal variability, the EAMS has higher coherence with the western equatorial Pacific (Fig. 7g,h) rather than with the Nin˜o 3.4 region (Fig. 7c,d). This high coherence in the EOF results suggests that the sudden cooling in the southern East Sea and the warming in the northern East Sea that occurred in the early 1960s in the EAMS was associated with events in the western equatorial Pacific. Indeed, the low-pass filtered SSTA over the western equatorial Pacific exhibits a consistent warming in the early 1960s (not shown here).
4. Summary and conclusions In this study, we have investigated the characteristics of the interannual, decadal, and interdecadal variabilities of the EAMS and their connectivity to the equatorial Pacific by using SST from COADS for the period from January 1951 to December 1996. High spectral energy was found in the interannual timescales in the EAMS, at around 3–4 years in the East China Sea and 5–7 years in the southern and northern East Sea. In the equatorial ocean, the dominant spectral energy was at 2–6 years in the Nin˜o 3.4 region and 5–8 years in the western equatorial Pacific. There was a synchronous 3- to 4-year periodicity between the two regions. Significant spectral energy on the decadal or interdecadal timescales was seen in both the EAMS and the western equatorial Pacific Ocean. At these timescales the spectral energy was found to be very low in the Nin˜o 3.4 region and the South China Sea. In the EOF analysis of the EAMS, the interannual mode seems to be modulated
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Fig. 7. Coherency and phase lag of the first EOF modes of the filtered SSTA in reference to the SSTA of Nin˜o 3.4 (a–d) and to that of the western equatorial Pacific (e–h). (a) and (e) 1.5- to 5-year band-pass filtered SSTA, (b) and (f) 5- to 9-year band-pass filtered SSTA, (c) and (g) 9- to 13-year band-pass filtered SSTA , (d) and (h) 13-year low-pass filtered SSTA.
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by an interdecadal variability, and the strongest eigenvector was located in the southern East Sea. The decadal mode is distinct in the polar front region in the East Sea. At the interdecadal mode, however, there was a remarkable pattern of sudden cooling in the southern East Sea and sudden warming in the northern East Sea around 1965/1966, a mode which was highly coherent with the SSTA in the western equatorial Pacific. The 2- to 3-year variability of SSTA over the EAMS is coherent with that of the SSTA in the Nin˜o 3.4 with a phase lag of 70–90 degrees (i.e. 5–9 months). There is high coherence between the East China Sea and the southern East Sea at a periodicity of 5–6 years indicating a linkage between physical processes in the two seas. The SSTA of the Nin˜o 3.4 region is highly coherent with the latent and sensible heat fluxes in the southern East Sea. These observations imply that events in the southern East Sea are correlated with those in the central equatorial Pacific and to the East China Sea at a 6-year period. Decadal and interdecadal variabilities of SSTA in the EAMS are highly coherent with the western equatorial Pacific rather than with the central equatorial Pacific.
Acknowledgements The authors thank Prof. S. Minobe for his helpful comments on the manuscript. We also thank two reviewers for their comments and suggestions. This research was partly supported by the Natural Hazards Prevention Research Project, one of the Critical Technology-21 Programs, funded by the Ministry of Science and Technology of Korea. W. Park was supported by the Brain Korea 21 project.
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Interannual and interdecadal variations of sea surface temperature in the East Asian Marginal Seas Won-Sun Park, Im Sang Oh
*
Department of Oceanography and Research Institute of Oceanography, Seoul National University, Seoul 151-742, South Korea
Abstract Interannual and interdecadal variability of sea surface temperature (SST) in the East Asian Marginal Seas (EAMS) from 1951 to 1996 are investigated, and the teleconnection of the EAMS to the equatorial ocean is studied using empirical orthogonal function analysis and coherency analysis. The EAMS have significant coherency with the Nin˜o 3.4 SST at 2- to 3-year periods with a phase lag of 5–9 months in the SST anomaly (SSTA). The EAMS also showed a higher coherency with a 6-year oscillation with a phase lag of 18–22 months. For the 6-year variability, the SSTA of the Nin˜o 3.4 is highly coherent with the latent and sensible heat flux anomaly of the southern East Sea, indicating that the variability of the southern East Sea is related to that in the Nin˜o 3.4 region through the ocean-atmosphere interactions. The southern East Sea also has a higher coherency with the East China Sea. Cooling in the southern East Sea and sudden warming in the northern East Sea were found to have occurred around 1965/1966 in the records of interdecadal variability. The decadal and interdecadal variabilities of the EAMS were found to be teleconnected to the western equatorial Pacific, whereas the interannual variability was highly correlated to the central equatorial Pacific, which is represented by Nin˜o 3.4 SST. 2000 Published by Elsevier Science Ltd.
Contents 1.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
* Corresponding author. Tel.: +82-2-880-6752; fax: +82-2-872-0311. E-mail address: [email protected] (I.S. Oh). 0079-6611/00/$ - see front matter 2000 Published by Elsevier Science Ltd. PII: S 0 0 7 9 - 6 6 1 1 ( 0 0 ) 0 0 0 3 6 - 7
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Data and method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
3. Results and discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.1. EOF analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 3.2. Spectral and coherency analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 4.
Summary and conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
1. Introduction It is known that the ocean-atmosphere system has variability on three distinct timescales: interannual (3–7 years), decadal (10–11 years) and interdecadal scale (16–18 years) (Mann & Park, 1996). The interannual variations of sea surface temperature (SST) in the central and eastern equatorial Pacific, known as the El Nin˜o– Southern Oscillation (ENSO) (Rasmusson & Carpenter, 1982), have been linked to regional and global atmospheric anomalies (Horel & Wallace, 1981; Nakamura, Lin & Yamagata, 1997). ENSO has an effect on the atmospheric circulation at midlatitudes and the Asian monsoon (Kawamura 1984, 1998). The air temperature and precipitation in Korea are correlated to those in the equatorial region with a time lag of a few months (Kang & Jeong, 1996; Ahn, Ryu, Cho, Park & Ryoo, 1997; Kang, 1998; Cha, Jhun & Chung, 1999). Like the Pacific Ocean, the East Asian Marginal Seas (EAMS), which include the Yellow Sea, the East China Sea and the East/Japan Sea (hereafter East Sea), show strong spectral peaks of SST anomalies (SSTA) at decadal and interdecadal timescales as well as interannual timescale, as shown later. Therefore it is necessary to describe the spatial and temporal variability of SST and identify the teleconnection between the Pacific Ocean and EAMS if the variability depending on these timescales is to be understood. Researches on the climatology over the EAMS are rather difficult because of the complicated ocean and atmospheric dynamics associated with the Kuroshio and the Asian monsoon. The present paper investigates the variability of SST in the EAMS associated with the Pacific basin scale variability, and compares the SST anomalies in the equatorial region to those in the marginal seas in order to understand what is happening in the marginal seas at other timescales.
2. Data and method The present analysis uses monthly mean SST in 2° latitude × 2° longitude retrieved from the Comprehensive Ocean–Atmosphere Data Set (COADS) as described in Woodruff, Lubker, Wolter, Worley and Elms (1993), for the period from January
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1951 to December 1996. The missing data in 2°×2° grids were linearly interpolated from the adjacent points (see Zhang, Sheng & Shabbar, 1998). If there were no available data around the missing data points, the values for adjacent months, i.e. data for preceding and following months for the same grid point were used to estimate the missing data. Monthly mean values for each grid point are removed to get the monthly anomaly of SST. The analyses used were empirical orthogonal function (EOF) analysis (see Wallace & Dickinson, 1972) and the multitaper method (MTM) (Thomson, 1982). MTM reduces power leakage in the frequency domain, estimates uncertainties by using a small set of tapers rather than a single data taper or a spectral window, and gives satisfactory results in the presence of red noise. The tapers are the discrete set of eigenfunctions, which solve the variational problem of minimizing leakage outside of a frequency band of half bandwidth pfn, where p is an integer, fn(1/(Ndt)) is the Rayleigh frequency. N is number of total data points and dt is sampling interval. We used the schemes for the spectrum determination developed by Mann and Lees (1996) and for the coherency analysis by Mann and Park (1993). For MTM, the number of tapers of three with p=2 is used.
3. Results and discussions 3.1. EOF analyses Characteristics of SST variability in the North Pacific are well documented according to their timescales (Tanimoto, Iwasaka, Hanawa & Toba, 1993; Tanimoto, Iwasaka & Hanawa, 1997). However the EAMS were not examined in detail in their studies. Herein the interannual, decadal and interdecadal variabilities of SST in the EAMS are described. To determine the spatially and temporally dominant oscillations over the EAMS, we performed EOF analyses. In order to separate interannual, decadal and interdecadal timescales, we used three band-pass filters (1.5–5, 5–9, 9–13 years) and one lowpass filter (13 years). These choices of band or low-pass filters are generally consistent with the global analysis by Lau and Weng (1999). For the filter scheme, we use Fourier transforming filter without data taper. Fig. 1a is the first mode of band-pass filtered (1.5–5 years) SSTA; it represents 27.7% of variance. High values were apparent near the east coasts of Korea and in the Yellow Sea, whereas in the Kuroshio region the values were relatively small. The time coefficient exhibited a dominant timescale of 2–3 years. The first mode (47.2%) of 5- to 9-year band-pass filtered SSTA mainly represented 5- to 7-year variability (Fig. 1b). The eigenvectors derived were high in the southwestern East Sea and the Yellow Sea and moderate in the East China Sea but weak in the Kuroshio region. Time coefficients of these interannual modes look like interdecadal modulation pattern of interannual variability (Minobe & Mantua, 1999). In the 5- to 7year variability, large amplitudes occurred around the early 1960s and the mid-1980s. The variability of 6–7 years was dominant from 1953 to 1973, and 5- to 6-year
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variability was dominant from 1973 to 1995. A consistent amplitude modulation pattern with a large amplitude after the mid-1950s and maximum in the early 1960s was also found in the coastal sea surface temperature data from along the east coast of the East Sea (Watanabe, Hanawa & Toba, 1986). Beside this amplification during the early 1960s, our analyses also revealed amplifications during the mid-1980s with a rather shorter period than that in the 1960s. On decadal timescales, an approximately 9-year periodicity was dominant in the first EOF (47.3%), and higher values of eigenvector were found near the polar front in the East Sea (i.e. the front between Liman Cold Current and Tsushima Warm Current, Fig. 1c). The maximum eigenvectors were found in the northeastern part of the polar front region. This result may be consistent with the results of Nakamura et al. (1997). They showed that, the strongest decadal variability in the North Pacific occurs around the subarctic front; this is the most intense front in the basin extending zonally at 42°N, where cool water associated with the subpolar gyre including the Oyashio adjoins warm water in the Kuroshio extension. On the interdecadal timescale, the first mode (52.0%) represented long-term variability in which there was rapid cooling in the southern East Sea but warming in the northern East Sea and the Kuroshio region around 1965/66 (Fig. 1d). Watanabe et al. (1986) pointed out that this cooling in the southern East Sea reflected the changes in large-scale oceanic conditions of the western North Pacific in the 1960s. It is noteworthy that the phase reversal, in which there was warming in the equatorial ocean but cooling in the central North Pacific, occurred in the mid-1970s (Tanimoto et al., 1993). However, the phase reversal in the EAMS occurred in the early 1960s. 3.2. Spectral and coherency analyses In the above EOF analyses we investigated the temporal and spatial characteristics of the SST only in the EAMS. In order to understand how the variability in the EAMS may be correlated to the Pacific Ocean, especially to the equatorial region, we determined the characteristics of energy spectra of each marginal sea and coherency of SSTA with reference to the equatorial ocean. We selected 8 sub-regions for the analyses in accordance with their regional and physical importance, with emphasis being placed on the northwestern Pacific (Fig. 2). For the equatorial region, we used the Nin˜o 3.4 region (5°S–5°N, 120°–170°W) as typifying the central equatorial Pacific Ocean, and the western equatorial Pacific which includes the Warm Pool region. Trenberth (1997) suggested that for a quantitative definition of El Nin˜o and La Nin˜a the use of 5-month running means of SSTA in the Nin˜o 3.4 is acceptable. As for the Pacific Ocean, spectra of SSTA in the EAMS have peaks at interannual, decadal and interdecadal timescales (Fig. 3). At interannual timescales, spectral peaks were found at the periods of 2.0, 3.7 and 7–8 years for the East China Sea (Fig. Fig. 1. The first modes of the EOF analyses of the filtered SST anomalies: (a) 1.5- to 5-year, (b) 5- to 9-year, (c) 9- to 13-year band-pass filtered, and (d) 13-year low-pass filtered. The first modes represent 27.7, 47.2, 47.3, and 52.0% of the variance of each filtered data, respectively. The contour interval is 0.1°C. Results have been scaled by each principal component’s standard deviation.
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Fig. 2. Selected sub-regions for spectral and coherency analyses: Nino 3.4, the Nin˜o 3.4 region; WEP, the western equatorial Pacific; KUR, the Kuroshio region; SCS, the South China Sea; ECS, the East China Sea; YES, the Yellow Sea; ESS, the southern East Sea; ESN, the northern East Sea.
3a), 3.3 and 3.7 years for the Yellow Sea (Fig. 3b), 2.3, 3.3, 6.1, and 7.3 years for the southern East Sea (Fig. 3c), and 5.2 and 6.3 years for the northern East Sea (Fig. 3d). In contrast to the East China Sea and the Yellow Sea, the 5- to 7-year variability was much more dominant in the southern part of the East Sea. This is consistent with the results of Miita and Tawara (1984), which showed that a 6- to 7-year period oscillation is the predominant year-to-year variation in water temperature at the east Korea Strait. Interannual spectral peaks were found at 2.3 and 3.3 years for the South China Sea (Fig. 3e), and 2.6 and 3.6 years also for the Kuroshio region (Fig. 3f). However, at the period of 6–7 years the spectral energy was not significant in the Kuroshio region. In the equatorial ocean, spectra between 3- to 4-year period are common and more spectral peaks were found at periodicities of 2.2, 3.3, 4.2 and 5.6 years for the Nin˜o 3.4 (Fig. 3g) and 3.5, 5.7, 6.7, and 7.6 years for the western equatorial Pacific (Fig. 3h). It is noteworthy that the dominant periodicities were 2–6 years in the Nin˜o 3.4 but 5–8 years in the western equatorial Pacific. Significant energy at decadal and interdecadal timescales were found in the EAMS and the western equatorial Pacific, but not in the Nin˜o 3.4 or the South China Sea, where interannual variability is much stronger than decadal or interdecadal variability. To investigate how the variability of SST in the EAMS is correlated to the Pacific Ocean especially to the equatorial Pacific, coherency analyses are performed in reference to SSTA of the Nin˜o 3.4 and the western equatorial Pacific Ocean. For the 2- to 3-year variability the EAMS have high coherency to the Nin˜o 3.4 with a phase lag of 70–90 degrees, which means that SSTA in the Nin˜o 3.4 region leads the change of SST in the EAMS by 5–9 months (Fig. 4a–d). The confidence
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Fig. 3. Spectra of SSTA of (a) East China Sea, (b) Yellow Sea, (c) southern East Sea, (d) northern East Sea, (e) South China Sea, (f) Kuroshio, (g) Nin˜o 3.4, and (h) western equatorial Pacific.
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Fig. 4. Coherency and phase lag of SSTA of (a) East China Sea, (b) Yellow Sea, (c) southern East Sea, (d) northern East Sea, (e) South China Sea, and (f) Kuroshio in reference to SSTA of the Nin˜o 3.4 with confidence level 50, 90, 95 and 99% from the analysis of multitaper method. Positive phase lag indicates that the SSTA of the Nin˜o 3.4 leads that of the selected sub-region.
level is above 95%. The South China Sea also has high coherency at 2- to 3-year period with a phase lag of about 70-degree with the Nin˜o 3.4 region (Fig. 4e), but the Kuroshio has low coherency on this timescale (Fig. 4f). Since atmospheric circulation can act as a link between SST changes in different part of the world’s ocean (Lau, 1997) and SSTA in the South China Sea should be closely related to the Asian monsoon, the coherency at this timescale might be explained by the effect of the atmospheric forcing. Zhang, Sumi and Kimoto (1996) showed that the effects of El Nin˜o on the East Asian monsoon are felt through the variation in convective activities over the western equatorial Pacific. For the period longer than 5 years, all of the EAMS variability was coherent with the Nin˜o 3.4 particularly at about an 8-year period (Fig. 4a–d). This coherence seems to have no physical meaning, because the 8-year variability in the spectra of the EAMS except for the East China Sea has a little energy (see Fig. 3).
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In the interannual variability with about a 6-year periodicity, which appears as a significant spectral peak in the southern East Sea, the southern East Sea has significant coherence with the Nin˜o 3.4 (Fig. 4c). This phase lag at this 6-year period is 110 degrees, which corresponds with a lag of about 22 months. This lag is similar to that of air temperature; mean air-temperature over Korea and of ENSO are coherent at about a 6-year periodicity but subject to a time lag of 1–1.5 years (Kang, 1998). To understand how the variability of SSTA (especially the 6-year variability in the southern East Sea) is correlated to the equatorial Pacific Ocean, we have examined heat flux variables of the southern East Sea. Fig. 5a–b shows the coherence between the SSTA of the Nin˜o 3.4 and the latent and sensible heat flux anomalies of the southern East Sea. The latent and sensible heat fluxes were derived from COADS. Significant coherence exists at 6- to 7-year period in the latent heat flux, and near both 5 years and 6–7 years in the sensible heat flux. This suggests that the ocean–atmosphere exchange of heat in the southern East Sea at about 6-year period seem to be correlated to the equatorial regions. In the southern East Sea, air temperature anomaly is highly coherent with SSTA at around a 6-year period (Fig. 5c). There could be lots of combinations for coherence between the sub-regions of the EAMS. Here we focus on the interannual variability in relation to the southern East Sea, since about 6-year variability of the sea is significant. Its coherence with the East China Sea is also shown, because of its relationship with the Kuroshio. The East China Sea has significant coherence with the Kuroshio at periods longer
Fig. 5. Coherency and phase lag of (a) latent heat flux anomaly and (b) sensible heat flux anomaly of the southern East Sea in reference to SSTA of the Nin˜o 3.4. Positive phase lag indicates the SSTA of the Nin˜o 3.4 leads outgoing heat fluxes of the southern East Sea. (c) Coherency and phase lag of air temperature anomaly in reference to SSTA, in the southern East Sea.
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than 5 years but with a phase lag of about 30–40 degrees (Fig. 6a). The East China Sea also has high coherence with the Yellow Sea at 3- to 4-year and about 16-year periodicities without any phase lag (Fig. 6b), so SSTA of the East China Sea and the Yellow Sea vary synchronously. There is high coherence between the East China Sea and the southern East Sea at a periodicity of 5 to 6 years (Fig. 6c) and this is also seen in the results of the EOF analysis (see Fig. 1b). However, there was no significant coherence between the East China Sea and the northern East Sea (Fig. 6d). There was high coherence between the Kuroshio and the southern East Sea at about 5- to 6-year periodicity (Fig. 6e), but there was no significant coherence between the Kuroshio and the northern East Sea (Fig. 6f). The high coherence between the Kuroshio and the southern East Sea seems to be unimportant physically because the spectral energy in 6-year period of the Kuroshio is too weak (Fig. 3d). This suggests that the 6-year variability
Fig. 6. Coherency and phase lag of SSTA of (a) Kuroshio, (b) Yellow Sea, (c) southern East Sea, and (d) northern East Sea in reference to the East China Sea, and those of (e) southern East Sea and (f) northern East Sea in reference to the Kuroshio.
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of the southern East Sea is linked not only to the Nin˜o 3.4 via the atmosphere but also to the East China Sea. This is consistent with the results of Watanabe et al. (1986), who suggested that the year-to-year variations in the heat transport of the Kuroshio and the strength of the outbreaks of cold air mass in the winter season are responsible for the year-to-year variations in sea conditions, especially for the 6-year variability in the East Sea. Now we will return to the results of the filtered EOF in order to compare them with those of the coherency analyses. Coherence of the first modes of EOF of the filtered SSTA in the EAMS with reference to the Nin˜o 3.4 (Fig. 7a–d) and to the western equatorial Pacific (Fig. 7e–h) are presented. A significant coherence for the 2- to 3-year variability is found between the SSTA of Nin˜o 3.4 and the first mode of 1.5- to 5-year band-passed EOF with a phase lag about 90 degrees (Fig. 7a), which is consistent with previous coherency analyses. There is also high coherence at 3- to 4-year periodicity. The SSTA in EAMS shows no significant coherence with the western equatorial Pacific at either the 2- to 3-year or the 3- to 4-year variability (Fig. 7e). The interannual variability at these periods in the EAMS data seems to be teleconnected to the central equatorial Pacific. At around 6 years the variability of SSTA as shown by the first mode of EOF in the EAMS is coherent with the Nin˜o 3.4 with a phase lag of 110 degrees (Fig. 7b). This higher coherence at a 6-year periodicity is also found between the Nin˜o 3.4 region and the southern East Sea sub-region (Fig. 4c). In the case of decadal and interdecadal variability, the EAMS has higher coherence with the western equatorial Pacific (Fig. 7g,h) rather than with the Nin˜o 3.4 region (Fig. 7c,d). This high coherence in the EOF results suggests that the sudden cooling in the southern East Sea and the warming in the northern East Sea that occurred in the early 1960s in the EAMS was associated with events in the western equatorial Pacific. Indeed, the low-pass filtered SSTA over the western equatorial Pacific exhibits a consistent warming in the early 1960s (not shown here).
4. Summary and conclusions In this study, we have investigated the characteristics of the interannual, decadal, and interdecadal variabilities of the EAMS and their connectivity to the equatorial Pacific by using SST from COADS for the period from January 1951 to December 1996. High spectral energy was found in the interannual timescales in the EAMS, at around 3–4 years in the East China Sea and 5–7 years in the southern and northern East Sea. In the equatorial ocean, the dominant spectral energy was at 2–6 years in the Nin˜o 3.4 region and 5–8 years in the western equatorial Pacific. There was a synchronous 3- to 4-year periodicity between the two regions. Significant spectral energy on the decadal or interdecadal timescales was seen in both the EAMS and the western equatorial Pacific Ocean. At these timescales the spectral energy was found to be very low in the Nin˜o 3.4 region and the South China Sea. In the EOF analysis of the EAMS, the interannual mode seems to be modulated
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Fig. 7. Coherency and phase lag of the first EOF modes of the filtered SSTA in reference to the SSTA of Nin˜o 3.4 (a–d) and to that of the western equatorial Pacific (e–h). (a) and (e) 1.5- to 5-year band-pass filtered SSTA, (b) and (f) 5- to 9-year band-pass filtered SSTA, (c) and (g) 9- to 13-year band-pass filtered SSTA , (d) and (h) 13-year low-pass filtered SSTA.
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by an interdecadal variability, and the strongest eigenvector was located in the southern East Sea. The decadal mode is distinct in the polar front region in the East Sea. At the interdecadal mode, however, there was a remarkable pattern of sudden cooling in the southern East Sea and sudden warming in the northern East Sea around 1965/1966, a mode which was highly coherent with the SSTA in the western equatorial Pacific. The 2- to 3-year variability of SSTA over the EAMS is coherent with that of the SSTA in the Nin˜o 3.4 with a phase lag of 70–90 degrees (i.e. 5–9 months). There is high coherence between the East China Sea and the southern East Sea at a periodicity of 5–6 years indicating a linkage between physical processes in the two seas. The SSTA of the Nin˜o 3.4 region is highly coherent with the latent and sensible heat fluxes in the southern East Sea. These observations imply that events in the southern East Sea are correlated with those in the central equatorial Pacific and to the East China Sea at a 6-year period. Decadal and interdecadal variabilities of SSTA in the EAMS are highly coherent with the western equatorial Pacific rather than with the central equatorial Pacific.
Acknowledgements The authors thank Prof. S. Minobe for his helpful comments on the manuscript. We also thank two reviewers for their comments and suggestions. This research was partly supported by the Natural Hazards Prevention Research Project, one of the Critical Technology-21 Programs, funded by the Ministry of Science and Technology of Korea. W. Park was supported by the Brain Korea 21 project.
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