Seasonal cycle and interannual variability in coastal circulations in the northern South China Sea (NSCS) are investigated using satellite altimeter data from March 1993 to September 2016. Altimeter-derived velocity anomalies are in good agreement with acoustic Doppler current profilers (ADCP) observations at an adjacent location. Along-shelf volume transport anomalies in the NSCS indicate northeastward transports from mid-spring to summer and southwestward transports from mid-autumn to winter, which are consistent with previous studies in this region. According to convergence and divergence in the target control volumes, cross-shelf volume transports are estimated as the differences between two neighboring along-shelf volume transport anomalies, with the assumption that long-term mean along-shelf volume transports at each cross-sections are identical. The results show onshore transports in mid-autumn and offshore transports in early summer. The comparison between altimeter-derived and ADCP-estimated cross-shelf volume transports is encouraging, especially when the region has relatively low mesoscale activities and a low freshwater input. Reconstructed cross-shelf volume transports through multiple linear regression reveal that seasonal harmonics is the primary force in driving cross-shelf volume transports in the NSCS, while wind and El Niño have secondary effects on controlling cross-shelf volume transports in different regions. The present study helps to quantify the long-term coastal circulations, especially cross-shelf volume transports, based on altimeter data, which has important implications on the dynamics in coastal regions where observational data is limited.

The Pacific Decadal Oscillation (PDO), the dominant pattern of sea surface temperature anomalies in the North Pacific basin, is an important low-frequency climate phenomenon. Leveraging data spanning from 1871 to 2010, we employed machine learning models to predict the PDO based on variations in several climatic indices: the Niño3.4, North Pacific index (NPI), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio Extension (KOE) region (SSH_KOE and Ther_KOE), as well as the Arctic Oscillation (AO) and Atlantic Multi-decadal Oscillation (AMO). A comparative analysis of the temporal and spatial performance of six machine learning models was conducted, revealing that the Gated Recurrent Unit model demonstrated superior predictive capabilities compared to its counterparts, through the temporal and spatial analysis. To better understand the inner workings of the machine learning models, SHapley Additive exPlanations (SHAP) was adopted to present the drivers behind the model’s predictions and dynamics for modeling the PDO. Our findings indicated that the Niño3.4, North Pacific index, and SSH_KOE were the three most pivotal features in predicting the PDO. Furthermore, our analysis also revealed that the Niño3.4, AMO, and Ther_KOE indices were positively associated with the PDO, whereas the NPI, SSH_KOE, and AO indices exhibited negative correlations.

Deep-sea mining has garnered significant global attention, and accurate prediction of ocean currents plays a critical role in optimizing the design of sediment plume monitoring networks associated with mining activities. Using near-seabed mooring data from the Western Pacific M2 block (Beijing Pioneer polymetallic nodule Exploration Area, BPEA), this study trained four machine learning models—LSTM, XGBoost, ARIMA, and SVR—on current velocity to generate 96 h forecasts. Key findings include the following: LSTM and ARIMA models outperformed XGBoost and SVR in near-seabed current prediction. 1 h ahead forecasts substantially improved accuracy over rolling predictions (an iterative process where predicted values are treated as observed values for subsequent prediction steps), reducing zonal current (east–west component) RMSE from 2.395 cm/s to 1.120 cm/s and meridional current (north–south component) RMSE from 2.024 cm/s to 1.224 cm/s. For practical deployment, 3 h ahead forecasts achieved a zonal current RMSE of 1.412 cm/s.