The intensity of deep‐ocean mixing critically shapes the global overturning circulation, but the energy cascade participating in the elevated mixing above rough topography remains poorly understood. Using 350‐day moored observations, the energy cascade triggered by eddy‐topography interactions is for the first time revealed in the deep South China Sea. The deep‐ocean observations show bottom‐intensified near‐inertial wave (NIW) pulses during periods of eddy occurrences. Eddy‐induced NIWs appear to catalyze nonlinear wave‐wave interactions, which further cascade energies from NIWs and internal tides to higher‐frequency internal waves (HFIWs) above fk₁ frequency. Corresponding to the HFIW enhancement, the kinetic energy spectrum at 3–9 cycles per day increases by 2–9 times, and the spectral slope becomes ~ −1 rather than ~ −2, as predicted by the Garrett‐Munk oceanic internal wave spectrum. A cascade of internal waves above rough topography potentially promotes extracting energy from eddies and tides and dissipating this energy to mix deep waters.

Internal solitary waves (ISWs) with features such as large amplitude, short period, and fast speed have great influence on underwater thermohaline structure, nutrient transport, and acoustic signal propagation. The characteristics of ISWs in hotspot areas have been revealed by satellite images combined with mooring observation. However, the ISWs in the Timor Sea, which is located in the outflow of the ITF, have not been studied yet and the characteristics are unrevealed. In this study, by employing the Synthetic Aperture Radar (SAR) images taken by the Sentinel-1 satellite from 2017 to 2022, the temporal and spatial distribution characteristics of ISWs in the Timor Sea are analyzed. The results show that most of ISWs appear in Bonaparte basin and its vicinity. The average wavelength of the ISWs is 248 m, and most of the wave lengths are less than 400 m. The peak line of ISWs is longer in deeper water. The underwater structures of two typical ISWs are reconstructed based on the Korteweg–de Vries (KdV) equation combined with mooring observation. This shows that, compared with the two-layer model, the continuous layered model is more suitable for reconstructing the underwater structures of ISWs. Further analysis shows that both the rough topography and the spring-neap tides contribute to the generation of ISWs in the Timor Sea. This study fills a gap in knowledge of ISWs in regional seas, such as the Timor Sea.

Symmetric instability (SI) is credited with one of the important submesoscale instabilities. However, due to its small scales, it is challenging to capture using current observational measurements and ocean models. Estimates of SI activity are useful for assessing whether SI should be parameterized. Based on a high-resolution ocean model, we use a criterion to assess the spatio-temporal features of SI activity without directly solving SI in the Xisha–Zhongsha waters. An Ertel potential vorticity (PV) analysis is performed, and the negative PV injection and frontal tendency are calculated to analyze the generation mechanisms. The results show that the activity of SI is strongly seasonal. In comparison, SI is active in winter, but it is inactive in summer. In addition, it is mainly found within the ocean surface mixed layer (SML), and it almost disappears in the base of the surface mixed layer (BML). Moreover, the vertical component of the Ertel PV leads to the vertical spatial difference of SI activity, and both the vertical component of the Ertel PV and the sea surface buoyancy flux play an important role in the seasonality of SI activity. The stronger frontogenesis around the Xisha Islands partially accounts for the horizontal distribution difference of SI. This work implies that the parameterization of SI may have potential value in practical application in this region in winter due to the high probability of SI activity.

Higher-accuracy long-term ocean temperature prediction plays a critical role in ocean-related research fields and climate forecasting (e.g., oceanic internal waves and mesoscale eddies). The essential component of traditional physics-based numerical models for ocean temperature prediction is solving partial differential equations (PDEs), which has immense challenges in terms of parameterization, initial values, and boundary conditions setting. Moreover, the existing machine learning models for ocean temperature prediction have “black box” problems, and the influence of external dynamic factors is not considered. Moreover, it is hard to judge whether the model satisfies certain physical laws. In this paper, we propose a physics-guided spatio-temporal data analysis model based on the widely used ConvLSTM model to achieve long-term ocean temperature prediction and adopt two schemes to train the model in vector output and multiple parallel input and multi-step output. Meanwhile, considering the spatio-temporal correlation, physical information such as oceanic stable stratification is introduced to guide the model training. We evaluate our proposed approach on several popular deep learning models in different timesteps and data volumes in the northern coast of the South China Sea, where the frequent occurrence of internal waves leads to an intensity trend of a local transformation of sea temperature. The results show higher prediction accuracy compared with the traditional LSTM, and ConvLSTM models, and the introduction of physical laws can improve data utilization while enhancing the physical consistency of the model.