We investigated seasonal variations in the sea level anomaly in the northwestern Pacific subtropical front zone using weekly satellite-derived products and in situ observations from 2003 to 2009. The sea level anomalies had different seasonal cycles in the cold, front and warm zones. In the former two zones, peak values appeared (∼15 cm) in July–August and hollow values (∼−10 cm) in February–March; in the warm zone, sea level anomalies had no apparent seasonal variations. We found that the correlation between sea level anomaly and sea surface temperature reached values of 0.76 in the cold and front zones, but only 0.38 in the warm zone. The steric height anomaly explained 70% variances of sea level anomaly in the former two zones but only 25% in the warm zone. We examined the mechanisms of the different patterns of sea level anomaly variation among the three different zones and concluded that the steric height anomaly induced by air–sea heat exchanges controlled the seasonal variations of sea level anomaly in the former two zones, while the barotropic term accompanied with subtropical countercurrent lead to the obscure seasonal variations in the warm zone. These high frequency variations merit future study.
The subtropical front is a major oceanic feature of the North Pacific, separating the warm saline North Pacific Central Waters in the south from the cool fresh waters to the north. The subtropical front is remotely related to the East Asian (i.e. South China Sea [SCS]) summer monsoon (Wang et al., 2004). Sea surface temperature (SST) front modulates atmospheric conditions such as wind speed and direction (Xie, 2004), temperature, turbulent fluxes (Small et al., 2008) and deep atmospheric responses (Kobashi and Kobokawa, 2012). All of these variables change significantly from one side of the SST front to the other, with an increase (decrease) in wind speed as the wind blows from cold (warm) to warm (cold) water across the front (Friehe et al., 1991).
The subtropical SST front has been suggested to be correlated with sea level anomaly (SLA) variations (White et al., 1978; Qiu and Lukas, 1996; Kobashi and Kubokawa, 2012). Qiu et al. (2014) quantitatively examined the mechanisms of the subtropical SST front in April–September, and reported that the air–sea heat exchanges in the different parts of the SST front zone were different, which resulted in the SST front disappearance. They also found that the geostrophic advection was quite small in the study area (130–160°E, 20–32°N). The SSTs showed a different trend in the different sides of the SST front; however, the variations of SLA are not yet well understood.
In the present study, we try to investigate the seasonal variations of SLA in the different parts of the SST front using a 7-year record of weekly products of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) SSTs, altimeter data, and in situ observations in the northwestern subtropical Pacific Ocean (Figure 1). We also discuss the mechanisms of the seasonal variations of SLA.
The study period was from 1 January 2003 to 31 December 2009. The AMSR-E on the Aqua satellite has provided global observations of SST since 2002. We used AMSR-E Level 2 data. Its temporal resolution is twice a day, and spatial resolution is 0.25°. Data processing followed Qiu and Kawamura (2012).
Merged sea level anomaly (SLA) data came from two satellite missions, TOPEX/Poseidon and ERS, followed by Jason-1 and Envisat. The mapped altimetry dataset includes one map every 7 days with a (1/3)° spatial resolution on a Mercator grid (Ducet et al., 2000).
Daily surface flux products were derived from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis project. The spatial resolution was .
The Global Temperature and Salinity Profile Program (GTSPP) ‘Best Copy’ data include the full-resolution data from XBTs or CTDs from the ships, Argo profiling float data, or fully processed and quality-controlled data from the organizations that provided the real-time low-resolution data to the GTS (Sun et al., 2010).
Divisions of subtropical sea surface temperature front zone
Figure 2a shows the seasonal variation of the SST front based on the magnitude of the SST gradient. At 20–32°N, the SST gradient showed significant seasonal variation, with a large gradient from October to the following June, and a small gradient from June to September.
The mean frontal positions and standard deviations (grey shaded) are shown in Figure 2b. The frontal position was defined as the location where the maximum SST gradient magnitude occurs. The mean SST front (black) was located at around 22–28°N, with a shifting range of about ±2°. We separated the study area into cold, front, and warm zones; that is, the shaded band-like area was defined as the subtropical SST front area, the cold zone was located to the north of front, and the warm zone was to the south of the front.
Comparisons of the sea level anomaly in the three different zones
Figure 3a shows the seasonal variations in SLA. In the cold and front zones, the minimum value (−10 cm) of SLA appeared in February–March and the maximum value (15 cm) in August; in the warm zone, the maximum value (+8 cm) was in June–July and the minimum value (−3 cm) in March. This indicates that the cold, front, and warm zones experienced different variations in SLA.
To know the relationship between SST and SLA, we plot the scatter of SLA and SST in Figure 4. We matched the weekly SLA and AMSR-E SSTs in each grid of 0.125°. The correlations between SLA and SST were 0.76, 0.76, and 0.38 in cold, front, and warm zones, respectively.
We further examined the relationships between SLA and SST after removing the seasonal cycle, using a high-pass (HP) filter (Figures 4g–i). In the cold zone, the filtered SLA (HP-SLA) and SST (HP-SST) displayed a weak linear relationship with a correlation of 0.4. In the front zone, the variation in HP-SSH was smaller than that in the cold and warm zones, but the variation in HP-SST was larger. In the warm zone, the variance of HP-SLA was quite large compared with the homogenous HP-SST, indicating that the disturbances in SLA did not result from those in SST.
Figures 3b and c show the steric height components calculated from Equations (2) and (3). The amplitude of the steric height anomaly was also much larger to the north than that to the south of the SST front. The steric height anomaly calculated from GTSPP data (Equation (2)) represents the true oceanic conditions, and that from NCEP/NCAR might include some errors from the reanalysis process, coarse spatial resolutions, and the loss of the fresh water flux. Some meso structures were omitted to the south of 24°N. Hence, in the following section, we used the steric height calculated from GTSPP as the real steric height. In the following, we discuss the mechanisms of SLA in the different regions.
Cold and front zones
The variance of the steric height anomaly could explain 70% of the SLA. This finding indicated that seasonal cycles of SLA were dominated by steric height (Figure 3) in the cold and front zones. The high relevance to the SST (Figure 4) also provided us the evidence that seasonal cycles of SLA resulted from heating. The air–sea heat exchanges induced by latent heat flux were the main factor causing the seasonal variations of SST (Qiu et al., 2014). Therefore, latent heat fluxes play an important role in the seasonal variations of SLA in the cold and front zones. For higher frequency variations of SLA (HP-SLA), Kuroshio intrusion might be the main oceanic current. The wind-induced sea level variability with spatial scales of 1000 km and timescales of 20 days has been identified in the northwestern Pacific zone (Hu and Liu, 2002). The Kuroshio intrusions into the cold zone were found by Sugimoto and Hanawa (2014). These support our hypothesis that higher frequency variations of SLA resulted from wind and oceanic current; however, this needs quantitative analysis in the future.
The variance of the steric height anomaly could only explain 25% of the SLA, which indicates that the obscure seasonal cycles of SLA were controlled by the barotropic term (the last term in Equation (1)). The warm zone is located in the area of the strong subtropical countercurrent (STCC) field (Kobashi and Kawamura, 2001; Qiu and Kawamura, 2012), along which the SLA has a nominated oscillation with a period of 68–98 days and a wavelength of about 800 km (Hu and Liu, 2002). These findings suggest the seasonal cycles of SLA possibly resulted from the oscillation of the subtropical countercurrents in the warm zone.
Summary and concluding remarks
The seasonal variations of SLA were investigated in different parts of the SST front zone using satellite and in situ GTSPP data. Significant seasonal cycles were found in the cold and front zones but not in the warm zone. The correlation between SLA and SST was 0.76, 0.76 and 0.38 in the cold, front, and warm zones, respectively. This indicates the seasonal cycles of SLA resulted from steric height in the former two zones and from oceanic countercurrent for the latter one.
After removing the seasonal cycle, the amplitudes of SLA were small in the cold and front zones, and large in the warm zone, which may possibly be explained by the eddy or filament. However, the higher-frequency variations of SLA merit further study. The contribution of barotropic term on the SLA variation also needs future study.
We thank the Japan Aerospace Exploration Agency/Earth Observation Research (JAXA) to provide AMSR-E SSTs products, the National Centers for Environmental Prediction/National Center for Atmospheric Research(NCEP/NCAR) for the heat flux, the Archiving, Validation and Interpretation of the Satellite Oceanographic (AVISO) for the SSH data, and the Global Temperature-Salinity Profile Program (GTSPP) for in situ data.
This study was jointly supported by the National Natural Science Foundation of China (41406002, 41406043), Natural Science Foundation of Guangdong Province (No. 2015A030313151), and the Fundamental Research Funds for the Central Universities (15lgpy26).