The mode-1 semidiurnal internal tides that emanate from multiple sources in the Sulu-Sulawesi Seas are investigated using multi-satellite altimeter data from 1993–2020. A practical plane-wave analysis method is used to separately extract multiple coherent internal tides, with the nontidal noise in the internal tide field further removed by a two-dimensional (2-D) spatial band-pass filter. The complex radiation pathways and interference patterns of the internal tides are revealed, showing a spatial contrast between the Sulu Sea and the Sulawesi Sea. The mode-1 semidiurnal internal tides in the Sulawesi Sea are effectively generated from both the Sulu and Sangihe Island chains, forming a spatially inhomogeneous interference pattern in the deep basin. A cylindrical internal tidal wave pattern from the Sibutu passage is confirmed for the first time, which modulates the interference pattern. The interference field can be reproduced by a line source model. A weak reflected internal tidal beam off the Sulawesi slope is revealed. In contrast, the Sulu Island chain is the sole energetic internal tide source in the Sulu Sea, thus featuring a relatively consistent wave and energy flux field in the basin. These energetic semidiurnal internal tidal beams contribute to the frequent occurrence of internal solitary waves (ISWs) in the study area. On the basis of the 28-year consistent satellite measurements, the northward semidiurnal tidal energy flux from the Sulu Island chain is 0.46 GW, about 25% of the southward energy flux. For M₂, the altimetric estimated energy fluxes from the Sulu Island chain are about 80% of those from numerical simulations. The total semidiurnal tidal energy flux from the Sulu and Sangihe Island chains into the Sulawesi Sea is about 2.7 GW.

Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas–Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters.

Accurate prediction of Significant Wave Height (SWH) is vital for marine engineering safety, yet balancing computational efficiency with physical consistency in long-sequence modeling remains a challenge for data-driven approaches. To address this, we propose the Physics-Guided Dynamic Graph Mamba Network (PG-DyMamba). By integrating oceanographic priors such as windwave relations, our Physics-Aware Graph Learner adaptively captures time-varying multivariate dependencies. Concurrently, the Mamba architecture processes long historical sequences with linear complexity. To ensure physical plausibility, the model employs a composite loss function based on energy conservation and fluid smoothness, effectively constraining predictions to adhere to fundamental physical laws. Empirical evaluations on Australia, NDBC, and North Sea datasets confirm that PG-DyMamba outperforms state-of-the-art baselines. Notably, the model achieves a 19.2% MSE reduction in 48-step prediction horizon on the Australia dataset, demonstrating its robustness for operational marine applications.

Using monthly and daily reanalysis data from the NCEP/NCAR and ERA-5 spanning 1980 to 2025, this study investigates the key preceding forcing factors influencing winter temperatures in the Bohai Sea and northern Yellow Sea (BSNYS) region. The analysis reveals that autumn sea ice anomalies in the Barents–Kara Seas serve as an important predictor for winter temperature anomalies in this region. Sea ice anomalies in this region typically persist from autumn to winter, continuously feeding back to the atmosphere and altering the temperature gradient. This induces lower-tropospheric baroclinic instability, which in turn drives atmospheric circulation adjustments through the feedback of transient eddies onto the mean flow, further strengthens the Eurasian (EU)-like teleconnection pattern, and ultimately affects the climate in the BSNYS region. Furthermore, the correlation between autumn Barents–Kara sea ice and winter temperature anomalies in the BSNYS region shifts from insignificant to significant when comparing the longer period (1950–2025) with the study period, likely due to the enhanced interannual variability of autumn sea ice in this region. This study reveals the key physical processes through which autumn Arctic sea ice influences the winter climate in the mid-latitude BSNYS region, and serves as a useful benchmark for seasonal prediction of winter temperatures in this region.

The current standard for sea ice engineering in the Bohai Sea implements a 1/4° grid method, which cannot satisfy the safety of oil and gas activities in the southern Bohai Sea, and therefore more detailed information on ice conditions and a more refined ice zone division are necessary. In the present study, up to 1/12° resolution sea ice characteristic data (period, thickness, concentration, and strength) were obtained based on the NEMO-LIM2 ice–ocean coupling model. On this basis, the design sea ice strength parameters were derived with different return periods from 1 to 100 years. Among the total of 53 grids, the mean ice periods in the southern Bohai Sea from 1951 to 2022 were 2–35 days, the mean ice concentration values were 8.3–64.6%, and the mean ice thicknesses were 2–15 cm. The design uniaxial compressive strengths and shear strengths at almost all grids exceeded 2.00 MPa and 1.00 MPa for return periods over 20 years, respectively. The design flexural strengths for the 100-year return period ranged from 463 to 594 kPa. For the 100-year return period scenario, all grids exhibited design tensile strengths exceeding 200 kPa. Across the southern Bohai Sea, the most severe ice conditions occur in nearshore zones, and the ice conditions display a distinct spatial gradient with Bohai Bay > offshore deep-water areas > Laizhou Bay. The mean ice thickness, concentration, design flexural and tensile strengths derived in this study were lower compared to the ice parameters suggested in the current standard, and design uniaxial compressive and shear strengths derived here were comparable to those suggested in the current standard. The refined grid used here captures more detailed spatial variations in the design strength values of sea ice engineering parameters in the southern Bohai Sea, providing more accurate data support for the anti-ice design of marine structures.