1. Introduction
In the extratropical North Pacific, vigorous heat related to the turbulent heat flux (THF; the sum of the sensible and latent heat fluxes) is released from the ocean to the atmosphere in winter (Fig. 1a). The THF release in winter is predominantly controlled by surface wind, which has a negative local correlation with sea surface temperature (SST) (Davis 1976; Frankignoul 1985; Iwasaka et al. 1987; Wallace and Jiang 1987; Lau and Nath 1994; Nakamura et al. 1997; Frankignoul and Kestenare 2002; Kushnir et al. 2002).
(a) Winter upward THF climatology (W m−2) calculated from the daily OAFlux dataset (Yu et al. 2008). Rectangles represent EKOCR (black; 36°–40°N, 155°–160°E) and WKOCR (gray; 36°–40°N, 143°–148°E). (b) Distribution of standard deviation of winter THF (W m−2). (c) Distribution of winter THF in 2005 (W m−2). Contours indicate winter SSH climatology, as reconstructed from satellite altimetry SSH anomaly data and mean dynamic topography (see the text for details; contour interval is 10 cm). Thick black line represents the KE axis, which is defined as the 110-cm SSH contour (see the text for details). Blue squares are regions where water profiles for pure Kuroshio and Oyashio water (shown in Fig. 7) were obtained.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter upward THF climatology (W m−2) calculated from the daily OAFlux dataset (Yu et al. 2008). Rectangles represent EKOCR (black; 36°–40°N, 155°–160°E) and WKOCR (gray; 36°–40°N, 143°–148°E). (b) Distribution of standard deviation of winter THF (W m−2). (c) Distribution of winter THF in 2005 (W m−2). Contours indicate winter SSH climatology, as reconstructed from satellite altimetry SSH anomaly data and mean dynamic topography (see the text for details; contour interval is 10 cm). Thick black line represents the KE axis, which is defined as the 110-cm SSH contour (see the text for details). Blue squares are regions where water profiles for pure Kuroshio and Oyashio water (shown in Fig. 7) were obtained.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter upward THF climatology (W m−2) calculated from the daily OAFlux dataset (Yu et al. 2008). Rectangles represent EKOCR (black; 36°–40°N, 155°–160°E) and WKOCR (gray; 36°–40°N, 143°–148°E). (b) Distribution of standard deviation of winter THF (W m−2). (c) Distribution of winter THF in 2005 (W m−2). Contours indicate winter SSH climatology, as reconstructed from satellite altimetry SSH anomaly data and mean dynamic topography (see the text for details; contour interval is 10 cm). Thick black line represents the KE axis, which is defined as the 110-cm SSH contour (see the text for details). Blue squares are regions where water profiles for pure Kuroshio and Oyashio water (shown in Fig. 7) were obtained.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
The THF shows large temporal variance in the Kuroshio–Oyashio confluence (KOC) region (Fig. 1b), which affects atmospheric fields around the KOC region. Positive air temperature anomalies in the marine atmospheric boundary layer, in association with the upward THF (Tokinaga et al. 2009), form a local minimum in sea level pressure anomalies (Tokinaga et al. 2009; Tanimoto et al. 2011) and are associated with local maxima in cloudiness (Tokinaga et al. 2009) and precipitation (Masunaga et al. 2014, manuscript submitted to J. Climate). The THF around the KOC region maintains near-surface atmospheric baroclinicity, which leads to the organization of a storm track aloft (Nakamura et al. 2004; Joyce et al. 2009; Nonaka et al. 2009; Taguchi et al. 2009; Nakamura and Yamane 2010) and furthermore influences a large-scale atmospheric circulation: the Aleutian low in the central North Pacific (Tanimoto et al. 2003; Taguchi et al. 2012).
In the KOC region, in contrast to other extratropical regions, it has been reported that warmer SST enhances upward heat fluxes: a positive local correlation between SST and upward heat release (Hanawa et al. 1995; Tanimoto et al. 2003; Sugimoto and Hanawa 2011). Recently, in the western part of the KOC region (west of 150°E), it has been reported that positive SST anomalies are formed through warm eddies detached northward from the Kuroshio Extension (KE) in the south (Itoh and Yasuda 2010; Sugimoto and Hanawa 2011), resulting in enhanced upward heat release (Sugimoto and Hanawa 2011). On the other hand, the temporal behavior and the cause of the THF in the eastern part of the KOC region (east of 150°E) have not yet been revealed. A heat flux snapshot in the winter of 2005 (Fig. 1c) displays a vigorous heat release (>350 W m−2) in the eastern part of the KOC, despite a small heat release in the western part of the KOC. It is therefore expected that the temporal behavior and cause of the THF are largely differentiated between the eastern and western parts of the KOC.
The purpose of this study is to investigate THF variations in the eastern part of the KOC and to explore the relationship between THF variations in the eastern and western parts of the KOC. We quantitatively assess the contributions of SST to the THF in the eastern part of the KOC, and we investigate a cause of SST variation in the eastern part of the KOC using temperature–salinity profiles and a satellite-derived altimetry dataset. This paper is organized as follows. Section 2 presents an outline of the datasets used in this study. Section 3 investigates a relationship between the THF in the eastern and western parts of the KOC. Section 4 assesses the contribution of SST to the THF. Section 5 discusses possible causes of SST variation in the eastern part of the KOC. Section 6 presents a summary and concluding remarks.
2. Data and methods
We use temperature–salinity profiles from Argo floats (Oka et al. 2007). The profiles are vertically interpolated at 1-dbar intervals (1 dbar = 104 Pa) using the Akima (1970) scheme. Then, we calculate potential temperature (θ) and potential density (σθ). We use the sea surface height (SSH) dataset reconstructed by adding the satellite-derived SSH anomalies complied by the Archiving, Validation, and Interpretation of Satellites Oceanographic data (AVISO; Ducet et al. 2000) to the mean topography (http://www.aviso.oceanobs.com). The SSH data are available from October 1992 onward, with 7-day temporal resolution and ⅓° (latitude) × ⅓° (longitude) spatial resolution. In this study, we define the KE axis as the 110-cm SSH contour (thick line in Fig. 1c) because this is consistently located at or near the position of the maximum north–south gradient of SSH. We also use the AVISO sea surface geostrophic velocity dataset with 7-day temporal resolution and ⅓° (latitude) × ⅓° (longitude) spatial resolution: the product of which is calculated from the SSH dataset (http://www.aviso.oceanobs.com).
In this study, we specifically examine winter months (December–February) because THF values in the KOC region are largest during this time of year. Note that, in the time series figures, winter values are plotted as the calendar year for midwinter conditions (January–February): for example, winter values for December 1999–February 2000 are plotted as the year 2000. Degrees of freedom in a correlation analysis are estimated by dividing the data length by the integral time scale according to the method used by Davis (1976).
3. Relationship between THF in the eastern and western KOC
We investigate a relationship between the THF in the eastern KOC region (EKOCR; 36°–40°N, 155°–160°E; black rectangle in Fig. 1) and the western KOC region (WKOCR; 36°–40°N, 143°–148°E; gray rectangle in Fig. 1). The THF in the EKOCR (solid line in Fig. 2) shows low-frequency variations, with larger values in the early 2000s and smaller values in the late 1990s and late 2000s. The heat release in the early 2000s is up to about 40% greater than that in the late 1990s and late 2000s. In the WKOCR (dashed line in Fig. 2), the THF also shows low-frequency variations with larger values around the year 2000 and smaller values in the mid-2000s, but the temporal behavior is different from that in the EKOCR. There is no significant correlation between THF values in the two regions (R = 0.39; the value of which does not exceed the 5% significance level).
Winter THF time series (W m−2) averaged for the WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Winter THF time series (W m−2) averaged for the WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Winter THF time series (W m−2) averaged for the WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
To confirm the absence of a relationship between the THF in the eastern and western parts of the KOC, we perform a rotated empirical orthogonal function (REOF) analysis using a varimax orthogonal rotation, which is widely accepted as the most accurate analytic algebraic orthogonal rotation (Kaiser 1958, 1959; Richman 1986). This analysis was applied to the first twenty empirical orthogonal function (EOF) modes—which account for more than 95% of the original variance—extracted using a covariance matrix on the THF around the KOC region of 35°–45°N, 141°–175°E (dashed rectangle in Fig. 3). The first mode (Fig. 3a) is dominated by variance in the WKOCR, and the time coefficient is highly correlated with the THF averaged for the WKOCR (R = 0.97). In the second mode (Fig. 3b), larger variances are found in the EKOCR, and the time coefficient is very similar to the THF averaged for the EKOCR (R = 0.93). A correlation coefficient between the time coefficient in the first and second mode is almost zero (R = 0.03). The series of results shows that the temporal behavior of the THF in the EKOCR is distinct from that in the WKOCR. In the following sections, we specifically focus on the THF in the EKOCR.
(a) First and (b) second REOF modes extracted for winter THF around the KOC region [dashed gray rectangle denotes this area at (bottom)]. (top) Time coefficient (black line), the percentage of explained variance in parentheses, and winter THF (red line; W m−2) averaged for WKOCR in (a) or EKOCR in (b). (bottom) Distribution of correlation coefficients between the time coefficient and winter THF field with black lines denoting significant regions exceeding the 5% significance level and blue rectangles showing WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) First and (b) second REOF modes extracted for winter THF around the KOC region [dashed gray rectangle denotes this area at (bottom)]. (top) Time coefficient (black line), the percentage of explained variance in parentheses, and winter THF (red line; W m−2) averaged for WKOCR in (a) or EKOCR in (b). (bottom) Distribution of correlation coefficients between the time coefficient and winter THF field with black lines denoting significant regions exceeding the 5% significance level and blue rectangles showing WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) First and (b) second REOF modes extracted for winter THF around the KOC region [dashed gray rectangle denotes this area at (bottom)]. (top) Time coefficient (black line), the percentage of explained variance in parentheses, and winter THF (red line; W m−2) averaged for WKOCR in (a) or EKOCR in (b). (bottom) Distribution of correlation coefficients between the time coefficient and winter THF field with black lines denoting significant regions exceeding the 5% significance level and blue rectangles showing WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
4. Role of SST anomalies in the THF in the EKOCR
The THF in the KOC region in winter is positively correlated with winter SST (Hanawa et al. 1995; Tanimoto et al. 2003; Sugimoto and Hanawa 2011). In addition, it was shown that the SST controls the THF variation in the WKOCR (Sugimoto and Hanawa 2011). Thus, we expect that the same would be true for the THF in the EKOCR. We quantitatively assess which parameters (i.e., SST, Qa, SAT, and WND) contribute to determining THF by applying a method used by Sugimoto and Hanawa (2011). This approach requires two types of datasets: daily raw data and daily climatological data. The daily climatological data were obtained by applying a 31-day running filter to calendar-day means throughout the analysis period. Using these data, we model the THF based on the dataset combinations shown in Table 1, with each combination designated as a “run.” The SST run (solid line in Fig. 4a) has a significant correlation (R = 0.67; the value of which exceeds the 5% significance level) with the original THF (dashed line in Fig. 4), and the ratio of the variance of the SST run to that of the original THF is 80.1%. Although the WND run (Fig. 4d) is also significantly correlated with the original THF (R = 0.59), it seems the correlation is due to the lower frequency of the two time series. In fact, the ratio of the variance explained by this run is much smaller (18.3%) than that explained by the SST run. Both the Qa run (Fig. 4b) and the SAT run (Fig. 4c) are not significantly correlated with the original THF (R = −0.06 and −0.10, respectively), and the ratios of the variance explained by these runs are much small (9.7% in the Qa run and 10.8% in the SAT run). It is apparent that SST in the EKOCR is primarily responsible for determination of the THF.
Combinations of variables used in the THF calculations in the different model runs.
Winter time series of THF (W m−2) averaged for EKOCR, for the different model runs: (a) SST, (b) Qa, (c) SAT, and (d) WND run. Dashed line represents the original winter THF time series averaged for EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Winter time series of THF (W m−2) averaged for EKOCR, for the different model runs: (a) SST, (b) Qa, (c) SAT, and (d) WND run. Dashed line represents the original winter THF time series averaged for EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Winter time series of THF (W m−2) averaged for EKOCR, for the different model runs: (a) SST, (b) Qa, (c) SAT, and (d) WND run. Dashed line represents the original winter THF time series averaged for EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
5. Influence of Kuroshio bifurcation on SST anomalies in the EKOCR
We investigate the cause of SST variations in the EKOCR (Fig. 5a). Specifically, we investigate which atmospheric or oceanic processes contribute to the generation of SST anomalies. Here, we explore the relationship between the winter NHF and the time rate of change in SST during winter. In the EKOCR (Fig. 5b), significant correlation coefficients between the NHF values and SST changes are not observed. This shows that SST anomalies in the EKOCR are not a result of heat exchange between the ocean and atmosphere, suggesting that the oceanic processes generate SST anomalies.
(a) Winter time series of SST (°C) averaged for EKOCR (red line). Circles represent warm (white) and cold (red) years. Dashed line represents the winter THF averaged for EKOCR. (b) Map of correlation coefficients between winter upward NHF and time rate of change in SST during winter, defined as SST in February minus SST in November of the previous year. Yellow lines indicate regions exceeding the 5% significance level. Black rectangle represents EKOCR. (c) Winter time series of the EKE (m2 s−2) averaged for WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter time series of SST (°C) averaged for EKOCR (red line). Circles represent warm (white) and cold (red) years. Dashed line represents the winter THF averaged for EKOCR. (b) Map of correlation coefficients between winter upward NHF and time rate of change in SST during winter, defined as SST in February minus SST in November of the previous year. Yellow lines indicate regions exceeding the 5% significance level. Black rectangle represents EKOCR. (c) Winter time series of the EKE (m2 s−2) averaged for WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter time series of SST (°C) averaged for EKOCR (red line). Circles represent warm (white) and cold (red) years. Dashed line represents the winter THF averaged for EKOCR. (b) Map of correlation coefficients between winter upward NHF and time rate of change in SST during winter, defined as SST in February minus SST in November of the previous year. Yellow lines indicate regions exceeding the 5% significance level. Black rectangle represents EKOCR. (c) Winter time series of the EKE (m2 s−2) averaged for WKOCR (dashed) and EKOCR (solid).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Regarding oceanic processes, we first examine regional eddy activity, as the SST variations in the WKOCR are caused by warm eddies detached northward from the KE (Itoh and Yasuda 2010; Sugimoto and Hanawa 2011). Figure 5c represents the winter time series of eddy kinetic energy (EKE), calculated from high-pass-filtered sea surface velocity data with time scales shorter than 300 days, based on a method by Qiu and Chen (2005). The EKE in the EKOCR shows no marked time scale, and its variance is much smaller than that in the WKOCR; the ratio of the variance of the EKE in the EKOCR to that in the WKOCR is 21%. Past studies have reported that the warm eddies detached from the KE are frequently observed in the WKOCR (Sugimoto and Hanawa 2011; Oka et al. 2012) and that EKE levels are higher in the upstream region of the KE (e.g., Qiu 2002). We therefore conclude that the SST anomalies in the EKOCR are not related to mesoscale eddy processes.
Meridional cross sections of (a) θ (°C; contour interval is 1°C), (b) S (psu; contour interval is 0.1 psu), and (c) σθ (kg m−3; contour interval is 0.1 kg m−3) for the longitudinal band of 155°–160°E in February of (top) warm and (middle) cold years, or (bottom) the difference between the two periods (shading; contours indicate values in warm years).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Meridional cross sections of (a) θ (°C; contour interval is 1°C), (b) S (psu; contour interval is 0.1 psu), and (c) σθ (kg m−3; contour interval is 0.1 kg m−3) for the longitudinal band of 155°–160°E in February of (top) warm and (middle) cold years, or (bottom) the difference between the two periods (shading; contours indicate values in warm years).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Meridional cross sections of (a) θ (°C; contour interval is 1°C), (b) S (psu; contour interval is 0.1 psu), and (c) σθ (kg m−3; contour interval is 0.1 kg m−3) for the longitudinal band of 155°–160°E in February of (top) warm and (middle) cold years, or (bottom) the difference between the two periods (shading; contours indicate values in warm years).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Meridional distribution of the Kuroshio mixing ratio (%) for mixed layer properties in February of warm (white) and cold years (red).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Meridional distribution of the Kuroshio mixing ratio (%) for mixed layer properties in February of warm (white) and cold years (red).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Meridional distribution of the Kuroshio mixing ratio (%) for mixed layer properties in February of warm (white) and cold years (red).
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
To understand the transport processes of Kuroshio water into the EKOCR, we perform a composite analysis of sea surface current field for 20 winters from 1993 to 2012. Here, we additionally define two categories (warm and cold SST years) based on the regional time series of SST (Fig. 5a); in warm (cold) SST years, SST is greater (less) than the 1993–2012 mean SST. The warm (cold) SST years mostly overlap the warm (cold) years defined in the previous paragraphs, as the 1993–2012 mean SST (14.3°C) is nearly equal to the 2003–12 mean SST (14.4°C). Results show that the eastward flow across the EKOCR in warm SST years (Fig. 8a) bifurcates from the KE at around 150°E. In contrast, in cold SST years (Fig. 8b), the Kuroshio bifurcation is not observed. We checked that the identical results were obtained in warm and cold years. These results indicate that the warm–salty water transported by the occurrence of the Kuroshio bifurcation is responsible for determining upper-ocean conditions in the EKOCR, and this results in large THF release.
Composite map of winter sea surface velocity vectors, smoothed by a Gaussian filter with an e-folding scale of 55 km to reduce the influence of small-scale perturbations: (a) warm (1995, 1999, 2000, 2002, 2003, 2004, 2005, 2010, and 2011) and (b) cold SST years (1993, 1994, 1996, 1997, 1998, 2001, 2006, 2007, 2008, 2009, and 2012). Small velocity vectors (<7 cm s−1) are gray and large velocity vectors (>20 cm s−1) are red and rescaled. Blue rectangle represents EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Composite map of winter sea surface velocity vectors, smoothed by a Gaussian filter with an e-folding scale of 55 km to reduce the influence of small-scale perturbations: (a) warm (1995, 1999, 2000, 2002, 2003, 2004, 2005, 2010, and 2011) and (b) cold SST years (1993, 1994, 1996, 1997, 1998, 2001, 2006, 2007, 2008, 2009, and 2012). Small velocity vectors (<7 cm s−1) are gray and large velocity vectors (>20 cm s−1) are red and rescaled. Blue rectangle represents EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Composite map of winter sea surface velocity vectors, smoothed by a Gaussian filter with an e-folding scale of 55 km to reduce the influence of small-scale perturbations: (a) warm (1995, 1999, 2000, 2002, 2003, 2004, 2005, 2010, and 2011) and (b) cold SST years (1993, 1994, 1996, 1997, 1998, 2001, 2006, 2007, 2008, 2009, and 2012). Small velocity vectors (<7 cm s−1) are gray and large velocity vectors (>20 cm s−1) are red and rescaled. Blue rectangle represents EKOCR.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
6. Summary and concluding remarks
We investigated THF variations in the EKOCR over a period of 27 consecutive winters (December–February) from 1985/86 to 2011/12, using THF data calculated from a bulk formula using daily variables of OAFlux and bulk coefficients obtained from the TOGA COARE bulk flux algorithm 3.0. The THF in the EKOCR had low-frequency variations with larger values in the early 2000s and smaller values in the late 1990s and late 2000s. The heat release in the early 2000s was up to ~40% higher than that in the late 1990s and late 2000s. We concluded that THF variation in the EKOCR was distinct from that in the WKOCR, based on correlation and REOF analyses. The results of this study strongly suggest the importance of dividing the KOC region into eastern and western parts to derive a better understanding of the air–sea coupled system in the western North Pacific.
We quantitatively evaluated the relative contributions among SST, Qa, SAT, and WND to THF in the EKOCR, by performing the experiments using combinations of two types of variables: daily raw data and daily climatological data. Results showed that SST was primarily responsible for determination of the THF, as is the case in the WKOCR (Sugimoto and Hanawa 2011), where large amounts of heat were released during times of positive SST anomalies. During positive SST anomalies in the EKOCR, large positive anomalies in both θ and S were evident, not only at the sea surface but also below 300 dbar, with water properties corresponding to those of Kuroshio water. We found that warm–salty water transported by the occurrence of the Kuroshio bifurcation was responsible for the generation of positive SST anomalies in the EKOCR.
It is speculated that the occurrence/absence of the Kuroshio bifurcation is related to characteristics of the KE, in a region upstream of the bifurcation. Past studies have reported that the KE path has two dominant variations: a decadal-scale meridional movement and a change in path state (represented by a stable state with two quasi meanders and an unstable state with a convoluted path) on quasi-decadal time scales (Qiu and Chen 2005, 2010; Seo et al. 2014). We examine the relationship between the two KE path variations and the SST field in winter (Fig. 9). Interestingly, SST in the WKOCR is associated with the KE pathlength (Fig. 9b) [as pointed out by Sugimoto and Hanawa (2011)], which is a measure of the stability of the KE path, while SST in the EKOCR is related to meridional movements of the KE (Fig. 9c). Actually, the composite map of winter sea surface velocity based on the KE latitudinal position (Fig. 10) shows that the Kuroshio bifurcation tends to occur when the KE path is located farther north. To more completely understand the air–sea coupled system in the western North Pacific, the relationship between the Kuroshio bifurcation and the KE should be further explored, using high-resolution shipboard observations and eddy-resolving general ocean circulation models.
(a) Winter time series of the latitudinal position of the KE axis based on the satellite-derived SSH product zonally averaged between 141° and 155°E (°N; red line) and the KE pathlength from 141° to 155°E (km; black line). (b) Distribution of correlation coefficients between the KE pathlength and the winter SST field. Contours indicate the winter SSH climatology (contour interval is 10 cm); the thick black line represents the KE axis. Yellow lines indicate significant regions exceeding the 10% significance level. Green rectangles represent WKOCR (dashed) and EKOCR (solid). (c) As in (b), but for the latitudinal position of the KE axis.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter time series of the latitudinal position of the KE axis based on the satellite-derived SSH product zonally averaged between 141° and 155°E (°N; red line) and the KE pathlength from 141° to 155°E (km; black line). (b) Distribution of correlation coefficients between the KE pathlength and the winter SST field. Contours indicate the winter SSH climatology (contour interval is 10 cm); the thick black line represents the KE axis. Yellow lines indicate significant regions exceeding the 10% significance level. Green rectangles represent WKOCR (dashed) and EKOCR (solid). (c) As in (b), but for the latitudinal position of the KE axis.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
(a) Winter time series of the latitudinal position of the KE axis based on the satellite-derived SSH product zonally averaged between 141° and 155°E (°N; red line) and the KE pathlength from 141° to 155°E (km; black line). (b) Distribution of correlation coefficients between the KE pathlength and the winter SST field. Contours indicate the winter SSH climatology (contour interval is 10 cm); the thick black line represents the KE axis. Yellow lines indicate significant regions exceeding the 10% significance level. Green rectangles represent WKOCR (dashed) and EKOCR (solid). (c) As in (b), but for the latitudinal position of the KE axis.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
As in Fig. 8, but for velocity vectors in (a) north (1993, 2000, 2001, 2002, 2003, 2004, 2005, 2010, 2011, and 2012) and (b) south KE years (1994, 1995, 1996, 1997, 1998, 1999, 2006, 2007, 2008, and 2009): in north (south) KE years, the KE latitudinal position is greater (less) than the 1993–2012 mean values.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
As in Fig. 8, but for velocity vectors in (a) north (1993, 2000, 2001, 2002, 2003, 2004, 2005, 2010, 2011, and 2012) and (b) south KE years (1994, 1995, 1996, 1997, 1998, 1999, 2006, 2007, 2008, and 2009): in north (south) KE years, the KE latitudinal position is greater (less) than the 1993–2012 mean values.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
As in Fig. 8, but for velocity vectors in (a) north (1993, 2000, 2001, 2002, 2003, 2004, 2005, 2010, 2011, and 2012) and (b) south KE years (1994, 1995, 1996, 1997, 1998, 1999, 2006, 2007, 2008, and 2009): in north (south) KE years, the KE latitudinal position is greater (less) than the 1993–2012 mean values.
Citation: Journal of Climate 27, 24; 10.1175/JCLI-D-14-00195.1
Acknowledgments
The authors thank the members of the Physical Oceanography Group at Tohoku University for useful discussions. Three anonymous reviewers provided useful and constructive comments that helped improve the manuscript. This work was partly supported by a Grant-in-Aid for Scientific Research on Innovative Areas (23106501, “A ‘hot spot’ in the climate system: Extra-tropical air–sea interaction under the East Asian monsoon system”) from the Ministry of Education, Culture, Sports, Science and Technology and a Grant-in-Aid for Young Scientists (B) (23740348) from the Japan Society for the Promotion of Science.
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