Position variability of the Kuroshio Extension sea surface temperature front

Transcription

1 Acta Oceanol. Sin., 2016, Vol. 35, No. 7, P DOI: /s Position variability of the Kuroshio Extension sea surface temperature front WANG Yanxin 1, YANG Xiaoyi 1 *, HU Jianyu 1 1 State Key Laboratory of Marine Environmental Science, College of Ocean and Earth Sciences, Xiamen University, Xiamen , China Received 11 January 2016; accepted 3 March 2016 The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2016 Abstract High spatial resolution sea surface temperature (SST) data from 1993 to 2013 are used to detect the position of the Kuroshio Extension sea surface temperature front (KEF) from 141 E to 158 E, and the seasonal, monthly and interannual-to-decadal variations of the KEF position are investigated. The latitudinal position of the KEF varies with longitudes: the westernmost part of the KEF from 141 E to 144 E is relatively stable, whereas the easternmost part from 153 E to 158 E exhibits the largest amplitude of its north-south displacement. In the light of the magnitudes of the standard deviations at longitudes, then the KEF is divided into three sections: western part of the KEF (KEFw, E), central part of the KEF (KEFc, E) and eastern part of the KEF (KEFe, E). Further analysis reveals that the KEFw position is dominated by the decadal variability, while the KEFc and KEFe positions change significantly both on interannual and decadal time scales. In addition, the KEFw position is well correlated with the KEF path length. The possible mode leading to the decadal oscillation of the KEFw is further discussed. The KEFw position exhibits significant connections with the Pacific decadal oscillation (PDO) index and the north Pacific gyre oscillation (NPGO) index with a time lag of 40 and 33 months, respectively. Key words: Kuroshio Extension, Kuroshio Extension front, sea surface temperature Citation: Wang Yanxin, Yang Xiaoyi, Hu Jianyu Position variability of the Kuroshio Extension sea surface temperature front. Acta Oceanologica Sinica, 35(7): 30 35, doi: /s Introduction The Kuroshio is the western boundary current in the North Pacific Ocean. The warm, northward-flowing water of the Kuroshio seperates from the coast of Japan (35 N, 140 E) to flow eastward in the North Pacific Ocean. In the region E, this eastward-flowing current is called the Kuroshio Extension (KE). Being an extension of the western boundary current, the KE system has large-amplitude meanders and energetic pinched-off eddies (Mizuno and White, 1983; Qiu and Chen, 2005; Kelly et al., 2010). The KE system has an evident decadal modulation between a stable and an unstable state (Qiu and Chen, 2005; Taguchi et al., 2007). In its stable state, the eastward KE jet is strong; the position moves northward; the southern recirculation gyre strengthens; the eddy kinetic energy decreases accordingly and vice versa (Qiu and Chen, 2005). The transition between the two dynamic states of the KE system is induced by wind-stress curl anomalies in the eastern North Pacific Ocean (Seager et al., 2001; Qiu, 2003; Sasaki et al., 2013). Qiu (2003) and Qiu and Chen (2010) indicated that the Pacific wind-stress curl anomalies are primarily dictated by the Pacific decadal oscillation (PDO) and north Pacific gyre oscillation (NPGO) modes. During the positive PDO (or negative NPGO) phase, the intensified Aleutian Low induces negative sea surface height anomalies (SSHA) through an anomalous cyclonic wind-stress curl. This negative SSHA can stimulate the westward propagating baroclinic Rossby waves, which leads to the weakening and southward shift of the zonal KE jet. The Kuroshio-Oyashio Extension (KOE) is the transition region between subtropical and subpolar gyres, accommodating the most remarkable SST decadal variability in the North Pacific Ocean (Yasuda, 2003). There are two major SST fronts in the KOE region, one is the KEF along the KE, and the other is the subarctic front associated with the Oyashio. Nakamura et al. (1997) and Nakamura and Kazmin (2003) reported the decadal variability of the sea surface temperature (SST) around the two frontal zones. After the 1990s, with the availability of high spatial resolution data, more and more attention was paid to the KEF position. Chen (2008) explored annual mean and seasonal mean positions of the KEF from 1997 to 2008 using satellite SST data. Ma and Xu (2012) investigated the variability of the KEF position in spring and found it is primarily dominated by interannual and decadal variations. Guo (2012) proposed that the amplitude of the winter KEF position variation on an intraseasonal time scale is smaller than that on an interannual time scale. However, rare research deals with monthly variability of the KEF position. In this study, high spatial resolution SST data from 1993 to 2013 are applied to examining the monthly, seasonal and interannual-to-decadal variations of the KEF position both temporally and spatially, and its connections with the KEF path length, the PDO and the NP- GO are also discussed. Foundation item: The National Basic Research Program of China under contract Nos 2015CB and 2012CB417402; the National Natural Science Foundation of China under contract Nos and U *Corresponding author, xyyang@xmu.edu.cn

2 2 Data and methods The primary data sets used in this study include: (1) Daily optimum interpolation sea surface temperature (OISST) data produced by the National Oceanic and Atmospheric Administration (NOAA), with its spatial resolution of and a time period ranging from January 1, 1993 to December 31, 2013 (Reynolds et al., 2007). (2) Maps of absolute dynamic topography (MADT) data available in April 2014 derived from the archiving, validation and interpretation of satellite oceanography (AVISO) of France, with its spatial resolution of and a time period ranging from January 1, 1993 to December 31, 2013 (Rio et al., 2014). The method to detect the KEF is based on Chen (2008), but we make a little modification. The purpose is to find the position of the maximum SST gradient near the KE axis. It is worth mentioning that the KEF detected by this method is a front on the north side of the KE axis. Owing to the more stable KEF path from the coast of Japan (141 E) to the Shatsky Rise (158 E), the KEF detected within E is more reliable. So we choose our study region of N and E. The method to detect the KEF mainly has three steps. (1) Calculate the magnitude of the SST gradient ( T ): jrtj Fig. 1. Mean T (color shading) and MADT (white contours, unit: m) in December Thick white line represents the KE axis: the MADT is 0.9 m. Red line with circles indicates the KEF position. ; where T is the SST; x is the east-west coordinate; and y is the north-south coordinate. (2) Use the KE axis as the initial position of the KEF. Qiu and Chen (2011) defined the 1.0 m SSH contour to be the KE axis, which is consistently located near the SSH gradient maximum. Here, we use the new version of MADT data instead of SSH data. Considering the MADT gradient and the effect of detection, we choose MADT being 0.9 m as the KE axis. (3) Adjust the initial KEF position towards where T reaches a local maximum. Specifically, we establish a natural coordinate system at every point along the initial position of the KEF. At every point, we obtain two points on the normal from each side of the initial KEF with the distance of 20 km. We compare the T of the two points and find the larger one. After sweeping through all points along the initial KEF, we connect all the points with larger T. Then a three-point running mean is applied and we get an updated KEF position. We consider this updated KEF position to be the new initial KEF position and continue to find points of larger T from two sides of it. Then we take the three-point running mean and update the KEF position for the second time. Using this procedure, the KEF position is updated for five times and the final KEF position is obtained (Fig. 1). The KEF intensity is defined to be the T at the final KEF position. Fig. 2. Seasonal variability of the climatological KEF positions and annual mean during E, the seasonal vacillation of the KEF latitudinal position can be as large as 2, with the KEF shifting north in summer and autumn and south in spring and winter. The seasonal variability of these standard deviations of the KEF position from 1993 to 2013 is shown in Fig. 3. For each season, we use its seasonal mean data from 1993 to 2013 to calculate its standard deviation. Generally, the standard deviations increase with longitudes from 141 E to 158 E. It is noteworthy that the maximum standard deviation is located around 145 E. As the KEF position corresponds well with the KE position, the large standard deviations of the KEF around 145 E may be explained 3 Results 3.1 Seasonal variability of the KEF position In this paper, spring is defined as March May, summer June August, autumn September November, and winter December February. The seasonal variability of the climatological KEF positions is shown in Fig. 2. We can see that the mean KEF position is featured by a bimodal pattern. The two crests are located near 143 E and 149 E, respectively (i.e., the KEF meanders northward around there) and the one trough is located near 146 E (i.e., the KEF meanders southward around there). The KEF positions in various seasons almost superpose to the west of 144 E, but whereafter bifurcate with longitudes. To the east of Fig. 3. The seasonal variability of standard deviations of the KEF position from 1993 to 2013.

3 32 by a lee-wave meander induced by flow of the Kuroshio over the Izu Ridge (Mizuno and White, 1983). 3.2 Monthly variability of the KEF position Monthly data from 1993 to 2013 are used to calculate the mean KEF position for each month, and the monthly variability of the mean KEF position is shown in Fig. 4. The KEF position is more northward to the west of 153 E, while it is more southward to the east of 153 E. Specifically, the KEF position around 143 E and 150 E is the northernmost, reaching the north of 36 N. The KEF position from 156 E to 158 E is the southernmost, reaching the south of 34 N. The KEF position is more southward in September than that in other months from 142 E to 145 E, while from 148 E to 151 E the KEF position during February May is more southward than that in other months. Except for E and E, the KEF position has almost the same monthly variability at longitudes, with more southward KEF position during January August and more northward KEF position during September December. It can be noticed that E and E are the locations of the two crests of the annual mean KEF position (as shown in Fig. 2). Fig. 5. The monthly variability of standard deviations of the KEF position from 1993 to Fig. 4. The monthly variability of the mean KEF position from 1993 to Figure 5 shows the monthly variability of standard deviations of the KEF position from 1993 to For each month, we use its monthly mean data from 1993 to 2013 to calculate its standard deviation. In the whole year, the minimum standard deviations of the KEF position exist from 141 E to 144 E, while the maximum standard deviations of the KEF position exist from 153 E to 158 E. Compared with other months, the standard deviations of the KEF position are smaller during April June and August November from 141 E to 144 E. From 152 E to 156 E, the standard deviations of the KEF position during April June are smaller than those in other months. However, for the longitudes E, the standard deviations of the KEF position during April December are mostly larger than those in other months. 3.3 Interannual-to-decadal variability of the KEF position Figure 6a shows the time series of the mean KEF position averaged from 141 E to 158 E. This time series reveals a clear decadal variation: The overall mean KEF shifts southward in , northward in , southward again in , and northward again in The power density spectrum of the monthly time series from 1993 to 2013 can be seen in Fig. 7a and its annual and decadal periodic components both have confidence levels higher than 95%. We can conclude that the mean KEF position averaged from 141 E to 158 E has distinct interannual and decadal variations. Fig. 6. Time series of the overall mean KEF position and mean KEF position in different parts. a. KEF, b. KEFw, c. KEFc and d. KEFe. Blue lines represent the monthly mean time series. Red lines show the one-year running mean time series. Gray lines denote the mean values during According to the different standard deviations at longitudes, we divide the KEF ( E) into three parts: western part of the KEF (KEFw, E), central part of the KEF (KEFc, E) and eastern part of the KEF (KEFe, E). The

4 33 Fig. 7. Power density spectra of monthly mean time series of the overall KEF position and KEF positions in different parts. a. KEF, b. KEFw, c. KEFc and d. KEFe. Blue solid lines indicate the power spectral density. Red dashed lines denote the 95% confidence levels. time series of the KEF position in these three parts are shown in Figs 6b d. We can see that their decadal variations are similar to those of the overall KEF. On the other hand, the amplitude of variation on an interannual time scale increases with longitudes. Figures 7b d are the power density spectra of the monthly mean time series of the KEF position in different parts. The results show that: for the KEFw there is only a decadal periodic component with a confidence level higher than 95%, so the KEFw position variability is dominated by the decadal variability. For the KEFc and the KEFe, we can see from the spectra that the annual and decadal periodic components both exceed 95% confidence level. Thus the KEFc and KEFe positions vary both on annual and decadal time scales. Fig. 8. Comparison of the seasonal mean KEF positions in spring (green), summer (red), autumn (pink) and winter (blue) derived from this study (solid lines) to that from Chen (2008) (dashed lines). 4 Discussion 4.1 Comparison with the previous results Our statistical result of the seasonal mean KEF position is mostly consistent with Chen (2008). The discrepancy lies in that the mean autumn and winter KEF positions calculated in our study are more northward than those in Chen (2008) as shown in Fig. 8. The possible reason may be the difference in time coverages of data sets. In our study, the time period is , spring is defined as March May, summer June August, autumn September November and winter December February. However, Chen (2008) used the data from Spring was defined as January March, summer April June, autumn July September, and winter October December. 4.2 Variability of the KEF state The variation of the KEF path length indicates the variation of its state. The KEF paths can be divided into straight zonal paths (in stable state) and convoluted paths (in unstable state), just like the KE (Qiu and Chen, 2005). It is clear that the KEF in the unstable state has a longer path, while the KEF in the stable state has a shorter path. We plot in Fig. 9 time series of the KEF path length. It shows that the stable state of the KEF exists in , 1998, and , and the unstable state of the KEF exists in , and Fig. 9. Time series of the KEF path length. Blue line represents the time series of the monthly mean KEF path length. Red line denotes the one-year running mean time series. Gray line indicates the mean value during Comparing the time series of the mean KEFw position with KEF path length (Fig. 10), we can see that the northward KEFw position corresponds to the shorter KEF path length, while the southward KEFw position indicates the longer KEF path length.

5 34 Fig. 10. Time series of the KEFw position (blue line) and KEF path length (red line). Both of them are one-year running mean time series. Gray line denotes the mean value of either time series during The correlation coefficient is This phenomenon can be explained by the KE path and the topography of the Izu-Ogasawara Ridge (140 E). Being the front along the KE, more northward KEFw means the more northward KE from 141 E to 144 E, indicating the Kuroshio passes over the northern Izu-Ogasawara Ridge with a deep channel, thus the KE path is straight (Sugimoto and Hanawa, 2012), and the KEF path length is shorter. With the more southward KEFw and KE from 141 E to 144 E, the Kuroshio goes through a shallow channel of the southern Izu-Ogasawara Ridge, so the KE path is convoluted, and the KEF path length is longer. 4.3 Relationship with PDO and NPGO The PDO and the NPGO are important indicators for largescale climate variability in the North Pacific. The PDO is the first empirical orthogonal function (EOF) of the SSTA in the region (25 62 N, W) (Mantua et al., 1997). NPGO is the second EOF of the SSHA in the same region. It is independent of the PDO and characterized by a dipole structure in the SSHA (Di Lorenzo et al., 2008). We plot in Fig. 11 lead-lag correlation coefficients between the KEFw position (shown in Fig. 6b) and the PDO and NPGO indices. It is evident that there is a negative correlation between the KEFw position and the PDO. The correlation coefficient reaches 0.67 when the KEFw lags the PDO by 40 months. On the other hand, there is a positive correlation between the KEFw position and the NPGO. The maximum correlation coefficient can be as large as 0.80 when the KEFw lags the NPGO by 33 months. Figure 12 shows overlaying time series of the PDO (NPGO) and the KEFw position. The KEFw time axis is shifted 40 (33) months forward in time to match the time of the maximum correlation coefficient in Fig. 11. Remarkable out-ofphase (in-phase) relationship between the KEFw position and the PDO (NPGO) on a decadal time scale is readily detected. Previous studies have discussed influence of the PDO and the NPGO on the SSHA in the KE region. Ceballos et al. (2009) indicated that the Rossby waves propagate the NPGO signature in the SSH field from the central North Pacific into the KE region and trigger the variations of the SSHA with a lag of years. Qiu and Chen (2010) calculated the correlation coefficients between the SSH anomalies in the KE region of E and the PDO (NPGO). The maximum correlation coefficients (r=0.6) were found with the PDO (NPGO) leading the KE SSH anomalies 3.5 (3.0) years. This difference of lagging time may to some extent be attributed to the phase difference between the PDO and the NP- Fig. 11. Lead-lag correlation between respective one-year running mean time series of the KEFw position and the PDO/NPGO index. Positive lag corresponds to the KEFw position lagging. Fig. 12. The series of the PDO (NPGO) and the KEFw position. a. Time series of the KEFw position (blue line) and PDO index (red line). Gray line denotes the mean value of either time series during The KEFw time axis is shifted 40 months forward in time to match the time of the maximum correlation coefficient in Fig. 11a. b. Time series of the KEFw position (blue line) and NPGO index (red line). The KEFw time axis is shifted 33 months forward in time to match the time of the maximum correlation coefficient in Fig. 11b. All time series are one-year running mean time series. GO. Our results are consistent with the previous studies and further propose the response of the KEF position to the PDO and the NPGO. Therefore, the PDO or NPGO index could be used to forecast the variability of the KEFw position on a decadal time scale. 5 Conclusions Using a high spatial resolution OISST, we have investigated the multiscale temporal variation of the KEF position from 1993 to The seasonal mean KEF position has a bimodal pattern with a weak seasonal variation, migrating northward during summer and autumn and southward during spring and winter. For

6 35 the monthly climatology, the KEF position around the two-crest of the mean KEF position ( E, E) has different variability than at other longitudes. Generally, the standard deviation of the seasonal mean KEF position on an interannual time scale increases with longitudes, but there exists the maximum around 145 E, which may be induced by the flow of the Kuroshio over the Izu Ridge. The minimum standard deviations of the monthly mean KEF position on an interannual time scale exist from 141 E to 144 E, while the maximum standard deviations exist from 153 E to 158 E. From the above, we note that the standard deviations of the westernmost part of the KEF ( E) are small, whereas those of the easternmost part ( E) are relatively large. According to the magnitudes of the standard deviations at longitude, we divide the KEF into three sections: KEFw ( E), KEFc ( E) and KEFe ( E). The power density spectra show that the KEFw position is dominated by decadal variability, while the KEFc and KEFe positions change significantly both on decadal and on annual time scales. The KE- Fw position is well correlated with the KEF path length, which can be explained by the KE path and the topography of the Izu- Ogasawara Ridge. In addition, the KEFw position exhibits significant connections with the PDO mode and NPGO mode on decadal time scale. So, it can be inferred that these two modes may lead to the decadal variability of the KEFw position. In this study, we have discussed the multiscale temporal variation of the KEF position. Especially, the result of monthly variability of the KEF position is a pioneering work. However, the underlying mechanism of the monthly variability remains to be explored and the difference in the variation periods of the KEF position at longitudes is not explained. Meanwhile, our study is limited to the KEF position, but has not mentioned the variability of the KEF intensity here. All these deserve a follow-up study. Acknowledgements The NOAA high resolution SST data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their web site at The altimeter products were produced by Ssalto/Duacs and distributed by Aviso, with support from Cnes ( References Ceballos L I, Di Lorenzo E, Hoyos C D North Pacific gyre oscillation synchronizes climate fluctuations in the eastern and western boundary systems. Journal of Climate, 22(19): Chen Shuiming The Kuroshio Extension front from satellite sea surface temperature measurements. Journal of Oceanography, 64(6): Di Lorenzo E, Schneider N, Cobb K M, et al North Pacific gyre oscillation links ocean climate and ecosystem change. 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