The seasonal variability of the eddy kinetic energy (EKE) along the Kuroshio Current (KC) is examined using outputs from an eddy-resolving (1/10°) ocean model. Using a theoretical framework for climatological monthly mean EKE, the mechanisms governing the seasonal cycle of upper-ocean EKE are investigated. East of Taiwan, the EKE shows two comparable peaks in spring and summer in the surface layer; only the spring one is evident in the subsurface layer. The seasonality is determined by mixed barotropic (BTI) and baroclinic (BCI) instabilities. Northeast of Taiwan, the EKE is also elevated during spring–summer but with a sole peak in summer, which is dominated by the meridional EKE advection by the KC. In the middle part of the KC in the East China Sea, the mesoscale (>150 km) EKE (EKE_MS) is relatively strong during spring–summer, whereas the submesoscale (50–150 km) EKE (EKE_SM) is significantly enhanced during winter–spring. The seasonal cycles of EKE_MS and EKE_SM are primarily controlled by the external forcing and BCI, respectively. In particular, the higher EKE_MS level in summer is mainly due to the increased wind work. West of the Tokara Strait, the EKE exhibits a prominent peak in winter and has its minimum in summer, which is regulated by the BCI. As the submesoscale signals are partially resolved by the model, further studies with higher-resolution simulations and observations are needed for a better understanding of the EKE_SM seasonality and its contribution to the seasonally modulating EKE_MS along the KC.

This paper describes the thermal lag correction for Glider Payload Conductivity Temperature Depth Profiler data with a poor sampling rate. In particular, the thermal lag correction is more vulnerable to the influence of temperature data. According to variations in salinity with depth and the vertical downcast speed of the glider, salinity data are divided into five parts, and a method based on Morison et al. is proposed to determine the correction parameters. At 40–94 dbar and 140–280 dbar, the salinity difference is dominated by the temperature difference. At 94–140 dbar, the salinity difference is immune to the temperature difference and has a greater influence on the thermal lag-induced salinity error correction. After the sectional correction, the accuracy of the typical salinity interval is upgraded from 0.011 to 0.006 psu, which shows the effectiveness of this sectional method on correcting temperature difference.