In order to address the issue of large errors in predicting SST along the coast using numerical models, this study adopts LSTM, a deep learning method, to develop optimal SST prediction models. The Xiaomaidao Station is selected as an example, and then the method is then extended to 14 coastal stations along the Bohai Sea and the Yellow Sea. The results show that the SST prediction model based on LSTM effectively improves forecast accuracy. The mean absolute errors for 1–3-day SST forecasts of the optimal model at Xiaomaidao Station are 0.20 °C, 0.27 °C, and 0.31 °C, and the root mean square errors are 0.28 °C, 0.36 °C, and 0.41 °C, respectively, representing an average reduction of 78% compared to those of the numerical model. Extending this approach to other forecasting sites along the Bohai Sea and the Yellow Sea results in an average 61% reduction in forecast error when compared with the numerical model. Furthermore, it is found that utilizing an LSTM model can significantly save computational resources and improve the forecasting efficiency.

The safety of winter activities in the Bohai Sea requires more detailed information on ice characteristics and a more refined ice zone division. In the present study, 1/12°-resolution sea ice characteristic data were obtained based on the NEMO-LIM2 ice–ocean coupling model that assimilated MODIS satellite sea ice observations from the years of 2005 to 2022 to acquire new sea ice hindcasting data. On this basis, the ice period, ice thickness, ice concentration, ice temperature, ice salinity, and design ice thickness for different return periods in the 1/4°-resolution refined zoning were analyzed, which were then compared with the sea ice characteristics in the previous 21-ice-zone standard. The distribution of ice temperature and ice salinity was closely related to the distribution of ice thickness. The results of ice period, ice thickness, and ice concentration, as well as design ice thickness for different return periods, and the comparison with the previous 21-ice-zone standards, showed that the ice condition on the west coast of the Bohai Sea has significantly reduced.

During the melt season, surface melting, bottom melting, and lateral melting co-occur in natural ice floes. The bottom melting rate is larger than the lateral melting rate, followed by the surface melting rate, and the smaller the size of an ice floe, the higher the lateral melting rate. To add the scale index of small-scale ice to the melting parametrization scheme, experiments on the melting process of sea ice and artificial fresh-water ice samples in the shape of a disc were carried out in a low-temperature laboratory, under conditions of no radiation, current, or wind, with controlled air and water temperatures. The variations of diameter, thickness, and mass of the ice discs were measured through the experiments. According to the experimental data, a new indicator was created using the ratio of the diameter to the thickness of an ice sample. Based on physical and statistical analyses, the relationships between the surface/bottom melting rates and temperature gradient were formulated. Additionally, the relationships among the lateral melting rate, temperature difference, and the ratio of the diameter to the thickness were also quantified. The equations can be applied to the melting parametrization scheme of ice for a range of diameters up to 100 m, which covers simulations of the energy and mass balance values of the Arctic sea ice and coastal freshwater ice during the summer melt season.

To provide basic drift-ice parameters for new-energy structures in ice-infested nearshore waters, an estimation method of the design drift-ice concentration and floe area was established. A total of 700 satellite images in winters from 2010 to 2021 were used to obtain the drift-ice concentration and floe area from 26 sites in the Bohai Sea. The floe area with 90% cumulative probability was selected as the characteristic floe area in each satellite image. Furthermore, the water temperature threshold ranging from −1.6 °C to −0.5 °C was adopted by considering the drift-ice freezing/melting states because of different salinity values in nearshore waters. The upper limit values of the ice concentration and characteristic floe area at the lowest water temperature of −1.6 °C were finally used as the design drift-ice indexes. The maximum design ice concentrations (floe area) in the Liaodong Bay, Bohai Bay, and Laizhou Bay were 96% (0.43 km²), 91% (0.39 km²), and 87% (0.29 km²), respectively. The design ice concentration at the bay bottom was higher than that near the mouth. The design floe area in the Liaodong Bay and Bohai Bay increased with latitudes, but the trend was opposite in the Laizhou Bay. The method developed in the current study has universal applicability, which can be extended to other ice-cover waters and specific structure designs by adjusting the cumulative probability threshold and the water temperature threshold.

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.