1. Introduction
The western boundary current (WBC) region of the North Pacific is characterized by vigorous heat release related to turbulent heat flux (THF; sum of sensible and latent heat fluxes) from the ocean to the atmosphere during winter (Fig. 1; Hanawa et al. 1995; Josey et al. 1998; Tanimoto et al. 2003; Tomita and Kubota 2005; Kubota et al. 2008; Bond and Cronin 2008) and one of extremely large THF-release regions in the world’s oceans. To date, numerous authors have shown their interest in variable THF, and they have reported that the heat flux exchanges between the ocean and atmosphere over the WBC region affect various oceanic and atmospheric phenomena such as ocean mixed layer temperature (Suga and Hanawa 1995; Taneda et al. 2000), storm-track variations (Joyce et al. 2009; Taguchi et al. 2009), and cloud distributions (Tokinaga et al. 2009).
Climatologies of upward THF (W m−2) calculated from the daily OAFlux (Yu et al. 2008) dataset: (top to bottom) winter (December–February), spring (March–May), summer (June–August), and autumn (September–November).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Climatologies of upward THF (W m−2) calculated from the daily OAFlux (Yu et al. 2008) dataset: (top to bottom) winter (December–February), spring (March–May), summer (June–August), and autumn (September–November).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Climatologies of upward THF (W m−2) calculated from the daily OAFlux (Yu et al. 2008) dataset: (top to bottom) winter (December–February), spring (March–May), summer (June–August), and autumn (September–November).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
The previous statistical studies have presented the evidence that the oceanic variations are primarily responsible for THF variations in the tropical region (e.g., Hanawa et al. 1995)—more upward heat fluxes on warmer sea surface temperature (SST) areas. On the other hand, generally over the extratropical North Pacific, the wind-induced THF negatively correlates with SST anomalies found in observational studies (Davis 1976; Frankignoul 1985; Wallace and Jiang 1987; Iwasaka et al. 1987; Nakamura et al. 1997; Frankignoul and Kestenare 2002) and numerical studies (Lau and Nath 1994; Kushnir et al. 2002); colder SST is attributable to the oceanic heat release, that is, upward heat fluxes. However, a closer examination of Fig. 6a presented by Hanawa et al. (1995) shows positive correlations between wintertime SST and heat fluxes in the WBC region of the North Pacific—more active upward heat fluxes on the warmer SST area. Tanimoto et al. (2003) pointed out that the winter SST variations in the WBC region can force the overlying atmosphere just as they do in the tropics.
Recently, Yu et al. (2008) developed the objectively analyzed air–sea flux dataset (OAFlux) with a relatively high spatial resolution on 1° × 1°, the products of which are a combination of observed satellite-derived data, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40; Uppala et al. 2005), and National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996; Kanamitsu et al. 2002). In this study, we reconsider the positively correlated relations between the upward THF and SST in the WBC region of the North Pacific, using the daily OAFlux dataset, and we attempt to assess the contributions of SST in determining the THF quantitatively. Additionally, using satellite-derived datasets of SST and sea surface height (SSH) with high spatial and temporal resolutions, we reveal mechanisms inducing SST variations in the WBC region.
The remainder of this paper is organized as follows. Section 2 presents an outline of the datasets used for this study. Section 3 investigates temporal variations of THF and assesses contributions of SST in determining the THF. Section 4 explores causes of SST variations using the satellite-derived datasets with high spatial and temporal resolutions. Section 5 presents our summary.
2. Datasets and method
We use high-resolution SST products based on the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite data (Reynolds et al. 2010). The products have a spatial grid resolution of 0.25° and temporal resolution of 1 day. We also use the SSH reconstructed by adding the satellite-derived SSH anomaly dataset of the Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO; Ducet et al. 2000) to the mean dynamic topography of Rio and Hernandez (2004). A substantial part of the altimeter-measured SSH signals results from the seasonally varying surface buoyancy fluxes, which cause expansion or contraction of the water column (Wang and Koblinsky 1996; Stammer 1997; Vivier et al. 1999). These so-called steric height signals have spatial scales much greater than those of oceanic mesoscale eddies; their effects are largely confined to the seasonal thermocline of the upper ocean (Gill and Niiler 1973). Because the steric height signals are not specifically addressed in this study, they are removed from the SSH anomaly data based on procedures described in Stammer (1997) using the daily heat flux data of the Japanese 25-year Reanalysis (JRA-25; Onogi et al. 2007). The SSH anomaly dataset used for this study has a 7-day temporal resolution and ⅓° × ⅓° spatial resolution.
In this study, we particularly examine winter months [December–February (DJF)] because the THF has largest values in the WBC region (Fig. 1). The analysis period of the present study is 16 winters from December 1992/February 1993 to December 2007/February 2008 because the satellite-derived SSH observations commenced in October 1992.
3. Role of SST variations on the THF
We investigate temporal features of THF in the WBC region. Figure 2 presents the standard deviations of winter mean THFs during the study period, which is superposed by climatological winter mean SSH. The THFs have largest variances over the Kuroshio–Oyashio Confluence Region (KOCR; specifically 142°–150°E, 35°–40°N for this study), the value of which corresponds to 10%–20% of the winter climatology (Fig. 1). Figure 3a displays a time series of THF in the KOCR. In winter mean THF (black circles in Fig. 3a), low-frequency variations are apparent, with larger values around 2000 and smaller values in the early 2000s.
Winter SSH climatology (cm). The contour interval is 5 cm. The thick white line indicates the axis of Kuroshio Extension, which is defined as 210-cm SSH isoline (see details in the text). Shading represents standard deviations of winter mean THF. The black rectangular region represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Winter SSH climatology (cm). The contour interval is 5 cm. The thick white line indicates the axis of Kuroshio Extension, which is defined as 210-cm SSH isoline (see details in the text). Shading represents standard deviations of winter mean THF. The black rectangular region represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Winter SSH climatology (cm). The contour interval is 5 cm. The thick white line indicates the axis of Kuroshio Extension, which is defined as 210-cm SSH isoline (see details in the text). Shading represents standard deviations of winter mean THF. The black rectangular region represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of THF averaged for the KOCR (W m−2). The gray line represents results smoothed by using a 11-day running mean filter. Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c),(d) As in (a), but SST run, SAT run, and WND run, respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of THF averaged for the KOCR (W m−2). The gray line represents results smoothed by using a 11-day running mean filter. Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c),(d) As in (a), but SST run, SAT run, and WND run, respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of THF averaged for the KOCR (W m−2). The gray line represents results smoothed by using a 11-day running mean filter. Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c),(d) As in (a), but SST run, SAT run, and WND run, respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Tanimoto et al. (2003) assessed relative contributions among SST, surface air temperature, and wind speed in determining the THF through linearizing the flux anomalies [their Eqs. (6) and (7)], and they concluded that the SST imparted strong impacts on the THF in the WBC region. However, the linearization might lead to problems from the viewpoint of independence between variables (SST, surface air temperature, and surface wind speed) because a recent study of Xie et al. (2010) reported that the surface air temperature followed SST to first order in the open ocean. We newly attempt to assess quantitatively which parameters—that is, SST, surface air temperature, and surface wind speed—contribute in determining THF, by applying a similar approach used by Tomita and Kubota (2005); two types of datasets are prepared for bulk parameters. One is raw daily data and the other is daily climatological data—daily climatology is defined with a slowly varying seasonal cycle obtained by taking the calendar day mean throughout the study period and then applying a 31-day running mean filter. We calculate THFs using combinations of these parameters shown in Table 1 based on Eqs. (1) and (2). These results are designated as “run” data. The SST run (black circles in Fig. 3b) bears a good resemblance to the original THF variations (black circles in Fig. 3a); actually, a high correlation coefficient is obtained (R = 0.86) and furthermore the ratio of variance of SST run to that of original THF is about 98%. Although the run derived by the surface wind speed (WND run; black circles in Fig. 3d) also correlates positively with the original THF (R = 0.60), the variance explained by the WND run is quite small (15%). Interestingly, the SAT run (black circles in Fig. 3c) negatively correlates with the THF (R = −0.17), which means that the surface air temperature contributes to the decrease in the THF. However, its ratio of variance to the original THF is small (23%) and roughly cancels with the contribution of the surface wind speed. Next, we confirm spatial relationships between THF and SST in winter. Figure 4a displays a correlation coefficient map between winter mean upward THF and winter mean SST at each grid point. Interestingly, the positive correlation coefficients are found north of 35°N, that is, the Kuroshio Extension (KE), and especially the highest correlation coefficients are obtained over the KOCR. The series of results shows that, in the KOCR, the SST is primarily responsible for determination of the THF.
Combinations of variables in the calculation of turbulent heat fluxes.
(a) Correlation coefficient map between winter mean upward THF and mean SST at each grid point. Contour interval is 0.2 and thick contours indicate a region exceeding a 1% significance level. The black rectangular region represents the KOCR. (b) As in (a), but for the winter mean upward net surface heat flux of OAFlux and temporal change rate of SST during winter.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Correlation coefficient map between winter mean upward THF and mean SST at each grid point. Contour interval is 0.2 and thick contours indicate a region exceeding a 1% significance level. The black rectangular region represents the KOCR. (b) As in (a), but for the winter mean upward net surface heat flux of OAFlux and temporal change rate of SST during winter.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Correlation coefficient map between winter mean upward THF and mean SST at each grid point. Contour interval is 0.2 and thick contours indicate a region exceeding a 1% significance level. The black rectangular region represents the KOCR. (b) As in (a), but for the winter mean upward net surface heat flux of OAFlux and temporal change rate of SST during winter.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Which atmospheric processes or oceanic processes contribute to the generation of SST anomalies in the KOCR? To address this question, we investigate the relationships between the winter net surface heat flux of OAFlux (sum of THF, net surface longwave radiation, and net surface shortwave radiation) and the temporal change rate of SST during winter (Fig. 4b)—the temporal change rate is defined as SST in February minus SST in November at the previous year. In the KOCR, the correlation coefficient is slightly positive, although its value is not statistically significant, which implies that the SST tends to increase when heat is released from the ocean to the overlying atmosphere and vice versa. This result shows that the SST anomalies in the KOCR are not formed through heat exchange between the ocean and atmosphere but predominantly through oceanic processes. In the next subsection, we will clarify these oceanic processes.
4. Causes of SST variations in the KOCR
In this section, we attempt to reveal mechanisms causing SST variations in the KOCR. For this purpose, we use satellite-derived SST and SSH datasets with high spatial and temporal resolutions.
Figure 5a displays a time series of daily SST anomalies averaged in the KOCR of 142°–150°E, 35°–40°N, using the satellite-derived SST dataset of Reynolds et al. (2010); daily climatology is defined with a slowly varying seasonal cycle obtained by taking the calendar mean for each day throughout the analysis period and then applying a 31-day running mean filter, and daily anomaly is calculated by subtracting the daily climatology. The time series has a clear low-frequency time scale and the behavior bears a close resemblance to the THF displayed in Fig. 3a—warmer around 2000 and colder in the early 2000s.
(a) Time series of daily satellite-derived SST anomalies averaged for the KOCR (°C). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c) As in (a), but for the EKE levels averaged for the KOCR (m2 s−2) and the KOC-regional SST standard deviation for the daily satellite-derived SST anomalies averaged for the KOCR (°C), respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of daily satellite-derived SST anomalies averaged for the KOCR (°C). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c) As in (a), but for the EKE levels averaged for the KOCR (m2 s−2) and the KOC-regional SST standard deviation for the daily satellite-derived SST anomalies averaged for the KOCR (°C), respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of daily satellite-derived SST anomalies averaged for the KOCR (°C). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b),(c) As in (a), but for the EKE levels averaged for the KOCR (m2 s−2) and the KOC-regional SST standard deviation for the daily satellite-derived SST anomalies averaged for the KOCR (°C), respectively.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
As causes of SST variations, the following three mechanisms are postulated: wind-induced oceanic Rossby waves, meridional movement of the KE axis and mesoscale eddies. We explore influences of these three mechanisms on SST variations in the KOCR.
a. Relation between SST variation and the baroclinic Rossby wave
Numerous authors have suggested that low-frequency SST variations around the KOCR are caused by an upward–downward movement of the main pycnocline depth attributable to propagation of wind-induced Rossby waves (e.g., Seager et al. 2001; Schneider and Miller 2001; Schneider et al. 2002)—positive (negative) SST anomalies are formed as a result of the downward (upward) movement of the main pycnocline depth.
We use the satellite-derived SSH dataset as an indicator of the main pycnocline depth. Winter mean SSH anomalies averaged for the KOCR are closely correlated with the winter mean SST anomalies (black circles in Fig. 5a) (R = 0.85; see Table 2). This relation is consistent with those reported for the past works (e.g., Seager et al. 2001). We examine whether the SSH variations in the KOCR results from the oceanic Rossby wave. Figure 6a displays a longitude–time diagram of SSH anomalies averaged for a zonal band of 35°–40°N, the anomaly of which is smoothed using a Gaussian filter with an e-folding scale of 200 km and 5 months. The following interesting feature becomes apparent: Rossby waves do not seem to reach the KOCR of our interest, even though most signals appear to propagate from the eastern region. We examine whether the SSH variations in the KOCR are related to the Rossby wave propagation by using a correlation analysis. Figure 6b represents a longitude–time diagram of lagged correlation maps of SSH anomalies averaged within a zonal band of 35°–40°N for the SSH averaged within the central North Pacific of 160°W–180°, 35°–40°N, where it is regarded as a formation region of the Rossby waves (e.g., Sugimoto and Hanawa 2009). Results show clearly that the SSH signals in the KOCR are not caused by the Rossby waves formed in the central North Pacific because the negative correlation coefficients in the KOCR are obtained at lags of 2–5 years. That is, Figs. 6a and 6b tell us that the Rossby waves cannot penetrate to the KOCR. We therefore conclude that the SST in the KOCR is not formed through the oceanic Rossby wave.
(a) Longitude–time diagram of the satellite-derived altimetry SSH anomaly averaged within a zonal band of 35°–40°N, the SSH anomaly is smoothed by a Gaussian filter with an e-folding scale of 200 km and 5 months (cm). (b) Longitude–time diagram of lagged correlation maps of SSH anomalies averaged within a zonal band of 35°–40°N for the SSH averaged within the central North Pacific of 160°W–180°, 35°–40°N, where it is estimated as a formation region of the wind-induced Rossby waves (e.g., Sugimoto and Hanawa 2009). Positive lags correspond to the SSH averaged within the central North Pacific leading.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Longitude–time diagram of the satellite-derived altimetry SSH anomaly averaged within a zonal band of 35°–40°N, the SSH anomaly is smoothed by a Gaussian filter with an e-folding scale of 200 km and 5 months (cm). (b) Longitude–time diagram of lagged correlation maps of SSH anomalies averaged within a zonal band of 35°–40°N for the SSH averaged within the central North Pacific of 160°W–180°, 35°–40°N, where it is estimated as a formation region of the wind-induced Rossby waves (e.g., Sugimoto and Hanawa 2009). Positive lags correspond to the SSH averaged within the central North Pacific leading.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Longitude–time diagram of the satellite-derived altimetry SSH anomaly averaged within a zonal band of 35°–40°N, the SSH anomaly is smoothed by a Gaussian filter with an e-folding scale of 200 km and 5 months (cm). (b) Longitude–time diagram of lagged correlation maps of SSH anomalies averaged within a zonal band of 35°–40°N for the SSH averaged within the central North Pacific of 160°W–180°, 35°–40°N, where it is estimated as a formation region of the wind-induced Rossby waves (e.g., Sugimoto and Hanawa 2009). Positive lags correspond to the SSH averaged within the central North Pacific leading.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
b. Relation with the meridional movement of the KE axis
Reportedly, the latitudinal movement of the KE position is related to SST variations around the KOCR (e.g., Qiu et al. 2007; Joyce et al. 2009). We examine a relationship between SST in the KOCR and the latitudinal position of KE. Here, we define the axis of the KE as the 210-cm SSH isoline. As shown by the thick white line in Fig. 2, the 210-cm SSH isoline is consistently located at, or near, the ∂h/∂y (north–south gradient of SSH) maxima. The selection of 210-cm SSH isoline is not sensitive to results in later analyses. Actually, we confirmed that almost identical results were obtained by using other isolines such as the 220-cm SSH isoline.
Figure 7a displays a time series of latitudinal position of KE. It has clear low-frequency variations, as already pointed out by the past research (Qiu and Chen 2005; Taguchi et al. 2007)—northward movement in the early 2000s and southward movement in the late 1990s and the late 2000s. The meridional movement is about 200 km in latitude and the KE is located approximately south of 35°N throughout the study period. That is, the KE is fundamentally situated south of the KOCR. Furthermore, the correlation coefficient between the KE position and SST anomalies (black circles in Fig. 5a) is approximately zero (see Table 2). Therefore, we conclude that the SST anomalies are not caused by the latitudinal movement of the KE axis.
(a) Time series of latitudinal position of KE averaged for 141°–165°E (°N). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b) As in (a), but for the KE pathlength integrated from 141° to 165°E (km).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of latitudinal position of KE averaged for 141°–165°E (°N). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b) As in (a), but for the KE pathlength integrated from 141° to 165°E (km).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
(a) Time series of latitudinal position of KE averaged for 141°–165°E (°N). Black circles represent winter mean values. Horizontal dashed line indicates a mean value of the quantity over the analysis period. (b) As in (a), but for the KE pathlength integrated from 141° to 165°E (km).
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
c. Roles of the anticyclonic eddies related to the meander of the KE
We investigate roles of mesoscale eddy for determination of SST anomalies in the KOCR. Figure 5b represents the eddy kinetic energy (EKE) calculated from high-pass-filtered SSH data with time scales shorter than 300 days. The EKE level presents the following clear low-frequency variation: it (black circles in Fig. 5b) correlates well with the SST anomaly in the KOCR (R = 0.72 for black circles in Fig. 5a; see Table 2).
Correlation coefficients between winter mean SST anomalies in the KOCR (black circles in Fig. 5a), winter mean SSH anomalies in the KOCR, winter mean EKE level in the KOCR (black circles in Fig. 5b), winter mean KOC-regional SST standard deviation (black circles in Fig. 5c), winter mean latitudinal position of KE (black circles in Fig. 7a), and winter mean pathlength of KE (black circles in Fig. 7b).
Next, we investigate the temporal change of the standard deviation in the KOCR for the daily SST anomalies averaged within the KOCR (Fig. 5c). This time series also has a low-frequency variation and the temporal variation closely resembles the SST anomalies (R = 0.76 for black circles in Figs. 5a and 5c). In addition, the temporal change of standard deviation has a high correlation with the EKE level (R = 0.62 for black circles in Figs. 5b and 5c). The series of results shows that the SST anomalies in the KOCR tend to become higher (lower) values during the time of nonhomogeneous (homogeneous) SST distribution in space associated with the higher (lower) EKE level.
The mesoscale eddies are expected to be primarily responsible for determination of the EKE level and SST distribution in space. To confirm these relations visually, we present several snapshots of the SST anomaly and SSH fields (Fig. 8). At the period when the SST anomaly in Fig. 5a reaches a positive peak (Fig. 8a; 20 January 1999), within the KOCR, three anticyclonic eddies are observed and the SST anomaly field becomes inhomogeneous. The higher SST anomalies are located on these eddies. It is natural that these mesoscale anticyclonic eddies are detached from the KE in the south. On the other hand, when the SST anomaly in Fig. 5a reaches a negative peak (Fig. 8b; 28 January 2004), the anticyclonic eddies are not apparent and large-scale negative SST anomalies are distributed—it is a homogeneous SST state. It is therefore found that the positive SST anomaly is formed through anticyclonic (warm) eddy activities; the SST in the KOCR shows positive (negative) anomalies when the EKE level becomes higher (lower) there. Interestingly, the EKE levels have a high correlation with the SSH anomalies averaged for the KOCR, which is regarded as a regional-mean main pycnocline depth (R = 0.81; see Table 2). It can be pointed out that the main pycnocline depth variations associated with the SST changes in the KOCR result from the anticyclonic (warm) eddy activity.
Snap shots taken at (a) 20 Jan 1999 and (b) 28 Jan 2004. (left) Satellite-derived SST anomaly map. The contour interval is 0.5°C. (right) Satellite-derived SSH map. The contour interval is 10 cm. The white rectangular area represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Snap shots taken at (a) 20 Jan 1999 and (b) 28 Jan 2004. (left) Satellite-derived SST anomaly map. The contour interval is 0.5°C. (right) Satellite-derived SSH map. The contour interval is 10 cm. The white rectangular area represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
Snap shots taken at (a) 20 Jan 1999 and (b) 28 Jan 2004. (left) Satellite-derived SST anomaly map. The contour interval is 0.5°C. (right) Satellite-derived SSH map. The contour interval is 10 cm. The white rectangular area represents the KOCR.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4023.1
These anticyclonic eddies are well known to be detached from the KE. It is postulated that the unstable state of the KE is suitable for the generation of anticyclonic (warm) eddies. We investigate temporal variations of the pathlength of the KE as an indicator of stable/unstable conditions of the KE. As shown in Fig. 7b, the pathlength has a low-frequency variation, that is, a stable state in the early 2000s and unstable state around 2000, and its temporal variation is very similar to the EKE level (R = 0.60 for black circles in Figs. 7b and 5b); the EKE level tends to be higher in the KOCR when the KE path is more convoluted, and vice versa.
5. Summary and remarks
We used the THF calculated from the bulk formula using daily variables of OAFlux (i.e., surface wind speed, surface air specific humidity, surface air temperature, and SST) and bulk coefficients obtained from the TOGA COARE bulk flux algorithm 3.0, and we specifically focused on the THF variations during the winter (December–February) season because the DJF months corresponded to the period having peak winter conditions in the atmosphere field. The THF over the KOCR showed the largest temporal variance in the North Pacific. We quantitatively evaluated the relative contributions among SST, surface air temperature, and surface wind speed in determining the THF by performing simple experiments using combinations of the following two types of variables: raw daily data and daily climatological data. Results showed that the SST was primarily responsible for determination of the THF—a huge amount of heat was released in the state of the positive SST anomaly. By using the satellite-derived SST and SSH datasets with high spatial and temporal resolutions, it was found that the SST anomalies in the KOCR were generated predominantly through the anticyclonic (warm) eddy activities detached northward from the KE; the SSTs take positive anomalies when the EKE level becomes higher there, associated with a more convoluted KE path.
We succeeded in reemphasizing importance of SST in determining THF over the KOCR by using dataset with higher spatial resolution compared to dataset used by past works (5° × 5° for Hanawa et al. 1995 and 2° × 2° for Tanimoto et al. 2003). In addition, the assessment approach used to estimate the contributions of SST in determining the THF largely differs from that in past works (Hanawa et al. 1995; Tanimoto et al. 2003), and we succeeded in providing clear evidence that SST was primarily responsible for the THF over the KOCR. The salient contribution of our study is to have shown that the SST anomalies inducing the THF variations were formed through activities of anticyclonic (warm) eddies detached northward from the KE.
Acknowledgments
The authors wish to express their sincere appreciation to the members of Physical Oceanography Group at Tohoku University for their useful discussion. The first author (SS) was partly supported by the 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 by the Grant-in-Aid for Young Scientists (B) (23740348) from the Japan Society for the Promotion of Science. The second author (KH) was financially supported by the Japan Fisheries Agency. Comments from two anonymous reviewers were particularly helpful for improving our paper.
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