Bohai Sea ice creates obstacles for maritime navigation and offshore activities. A better understanding of ice conditions is valuable for sea-ice management. The evolution of 67 years of seasonal ice thickness in a coastal region (Yingkou) in the Northeast Bohai Sea was simulated by using a snow/ice thermodynamic model, using local weather-station data. The model was first validated by using seasonal ice observations from field campaigns and a coastal radar (the season of 2017/2018). The model simulated seasonal ice evolution well, particularly ice growth. We found that the winter seasonal mean air temperature in Yingkou increased by 0.33 °C/decade slightly higher than air temperature increase (0.27 °C/decade) around Bohai Sea. The decreasing wind-speed trend (0.05 m/s perdecade) was a lot weaker than that averaged (0.3 m/s per decade) between the early 1970s and 2010s around the entire Bohai Sea. The multi-decadal ice-mass balance revealed decreasing trends of the maximum and average ice thickness of 2.6 and 0.8 cm/decade, respectively. The length of the ice season was shortened by 3.7 days/decade, and ice breakup dates were advanced by 2.3 days/decade. All trends were statistically significant. The modeled seasonal maximum ice thickness is highly correlated (0.83, p < 0.001) with the Bohai Sea Ice Index (BoSI) used to quantify the severity of the Bohai Sea ice condition. The freezing-up date, however, showed a large interannual variation without a clear trend. The simulations indicated that Bohai ice thickness has grown continuously thinner since 1951/1952. The time to reach 0.15 m level ice was delayed from 3 January to 21 January, and the ending time advanced from 6 March to 19 February. There was a significant weakening of ice conditions in the 1990s, followed by some recovery in 2000s. The relationship between large-scale climate indices and ice condition suggested that the AO and NAO are strongly correlated with interannual changes in sea-ice thickness in the Yingkou region.

Sea ice disasters seriously threaten the structural safety of oil platforms in the Bohai Sea. Therefore, it is necessary to carry out a risk assessment of sea ice disasters on oil platforms in the Bohai Sea. In this study, a risk assessment of sea ice disasters on fixed jacket platforms in Liaodong Bay, in the Bohai Sea, was performed in five steps. Firstly, the formation mechanisms of sea ice disasters were analyzed and the sources and modes of sea ice risks were summarized. Secondly, according to the calculation formulas of extreme ice force, dynamic ice force, and accumulated force, several ice indices such as thickness, motion, strength, period, and concentration were proposed as the hazard indices, and corresponding values were then assigned to the proposed indices based on ice conditions in the Bohai Sea. Thirdly, based on four structural failure modes – structural overturning by extreme ice force (Mode 1), structural fracture failure caused by dynamic ice force (Mode 2), the damage of facilities caused by dynamic ice force (Mode 3), and structural function failure caused by accumulated ice (Mode 4) – the structural vulnerability index, overturning index, dynamic index, ice-induced vibration index, and function index were proposed, and corresponding values were assigned to the structural vulnerability index of fixed jacket platforms in Liaodong Bay. Fourthly, the weight of each risk index was determined according to previously recorded sea ice disasters and accidents, and the sea ice risk was then calculated with the weighted synthetic index method. Finally, with the above index system and risk assessment methods, the risk assessment of sea ice disasters on 10 jacket platforms in three sea areas in Liaodong Bay was carried out. The analysis results showed that efficient sea ice prevention strategies could largely mitigate the sea ice-induced vibration-related risks of jacket platforms in Liaodong Bay. If steady-state vibration occurs (usually in front of the vertical legged structure) or the structural fundamental frequency is high, the structural vulnerability is significantly increased and the calculated risk levels are high. The sea ice risk assessment method can be applied in the design, operation, and management of other engineering structures in sea ice areas.

Sea ice concentration is an important observation element and a necessary input parameter for the numerical prediction of sea ice. In this study, a new method is developed for the inversion of sea ice concentration (SIC) in Liaodong Bay from MODIS data based on the Normalized Difference Water Index (NDWI). By comparing the inverted results of the NDWI method and the WU’s methods, as well as observations from six shore-based observation points on the east coast of Liaodong Bay in the winter of 2016/2017 and Landsat 8 OLI data, the method based on NDWI is shown to be a useful tool to estimate the SIC. The SIC obtained by the NDWI method is in good agreement with the survey data. The mean absolute error between the SIC obtained by the NDWI method and visual observation is about 10%. In the comparison data, 92% of the absolute error values of SIC are less than 20%. The average value of the error of the SIC obtained by the NDWI method is 1%, and that of the WU’s method is about −10%, which means the accuracy of SIC obtained by the NDWI method is better. The mean deviation of the error of the SIC obtained by the NDWI method and the WU’s method is about 9% and 13%, which shows a certain improvement in precision. The validation results with the Landsat 8 OLI data show the precision of the SIC obtained by the NDWI method is better than the WU’s method, which is consistent with the validation result of the survey data.

During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related to the thickness of sea ice. For DIT, the sea ice thickness gradually decreases as the temperature increases, and the freezing rate a is 1.48 cm/(°C·d)^1/2. For CIT, when the temperature is −12 °C, the maximum growth rate of ice thickness decreases from 3.5 cm/d to 1.5 cm/d as the ice thickness increases from 0 to 20 cm. The residual method was applied to calculate the oceanic heat flux, which is an important parameter of ice modeling, and both the analytic model (Stefan’s law) and numerical model (high-resolution thermodynamic snow-and-ice model) were utilized in this work. It was found that the accuracy of the simulation results was high when the growth coefficient of the analytic mode was 2.3 cm/(°C·d)^1/2. With an oceanic heat flux of 2 W·m⁻², the maximum error of the numerical model approached 60% in 2010 and 3.7% in 2021. However, using the oceanic heat flux calculated in this work, the maximum error can be significantly reduced to 4.2% in the winter of 2009/2010 and 1.5% in 2020/2021. Additionally, the oceanic heat flux in Liaodong Bay showed a decreasing trend with the increase in ice thickness and air temperature.