The nutrients from the East China Sea (ECS) through the Tsushima/Korea Strait (TS) strongly impact the ecosystem of the Japan Sea (JS). The complex origins of the Tsushima Warm Current and the various nutrient sources in the ECS result in complex spatial-temporal variations in nutrients in the TS. Using a physical-biological model with a tracking technique, we studied the effects of nutrient sources from the ECS on the TS. Among all the nutrient sources, the Kuroshio has the highest nutrient concentrations in the TS. Its maximum concentration occurs at the bottom, while those of rivers and atmospheric deposition occur at the surface, and that of the Taiwan Strait occurs in the middle layer. The nutrient transport through the TS exhibits similar seasonal variations, as does the volume transport. The transport of nutrients from the Kuroshio accounts for more than 85% of the total. The transport of nutrients from the Taiwan Strait is greater during autumn and winter. The transport of dissolved inorganic nitrogen (DIN) from both rivers and atmospheric deposition through the TS peak in August. Nutrient transport cannot be equated with volume transport. The DIN in the less saline zone originates not only from rivers but also from atmospheric deposition and the Kuroshio. The transport of nutrients from the Taiwan Strait is not as significant as its volume transport in the TS.

Massive bodies of low-oxygen bottom waters are found in coastal areas worldwide, which are detrimental to coastal ecosystems. In summer 2020, the response of coastal hypoxia to extreme weather events, including a catastrophic flooding, an extreme marine heatwave, and Typhoon Bavi, is investigated based on multiple satellite, four cruises, and mooring observations. The extensive fan-shaped hypoxia zone presents significant northward extension during July−September 2020, and is estimated as large as 13 000 km² with rather low oxygen minimum (0.42 mg/L) during its peak in 28−30 August. This severe hypoxia is attributed to the persistent strong stratification, which is indicated by flood-induced larger amount of riverine freshwater input and subsequent marine heatwave off the Changjiang River Estuary. Moreover, the Typhoon Bavi has limited effect on the marine heatwave and coastal hypoxia in summer 2020.

Marine sediments collected from the Zhujiang (Pearl) River Estuary (ZRE) and South China Sea (SCS) were utilized to study the occurrence and spatial distribution of tetrabromobisphenol A (TBBPA) and hexabromocyclododecane (HBCDD). The levels of TBBPA and HBCDD in sediments ranged from not detected (nd) to 6.14 ng/g dry weight (dw) and nd to 0.42 ng/g dw. TBBPA concentrations in marine sediments were substantially higher than HBCDD. The concentrations of TBBPA and HBCDD in the ZRE sediments were significantly greater than those in the SCS. α-HBCDD (48.7%) and γ-HBCDD (46.2%) were the two main diastereoisomers of HBCDD in sediments from the ZRE, with minor contribution of β-HBCDD (5.1%). HBCDD were only found in one sample from the northern SCS. The enantiomeric fraction of α-HBCDD in sediments from the ZRE was obviously greater than 0.5, indicating an accumulation of (+)-α-HBCDD. The enantiomers of HBCDD were not measured in sediments from the SCS. This work highlighted the environmental behaviors of TBBPA and HBCDD in marine sediments.

Hypoxia off the Changjiang River Estuary has been the subject of much attention, yet systematic observations have been lacking, resulting in a lack of knowledge regarding its long-term change and drivers. By revisiting the repeated surveys of dissolved oxygen (DO) and other relevant hydrographic parameters along the section from the Changjiang River Estuary to the Jeju-do in the summer from 1997 to 2014, rather different trends were revealed for the dual low-DO cores. The nearshore low-DO core, located close to the river mouth and relatively stable, shows that hypoxia has become more severe with the lowest DO descending at a rate of −0.07 mg/(L·a) and the thickness of low-DO zone rising at a rate of 0.43 m/a. The offshore core, centered around 40-m isobath but moving back and forth between 123.5°–125°E, shows large fluctuations in the minimum DO concentration, with the thickness of low-DO zone falling at a rate of −1.55 m/a. The probable factors affecting the minimum DO concentration in the two regions also vary. In the nearshore region, the decreasing minimum DO is driven by the increase in both stratification and primary productivity, with the enhanced extension of the Changjiang River Diluted Water (CDW) strengthening stratification. In the offshore region, the fluctuating trend of the minimum DO concentration indicates that both DO loss and DO supplement are distinct. The DO loss is primarily attributed to bottom apparent oxygen utilization caused by the organic matter decay and is also relevant to the advection of low-DO water from the nearshore region. The DO supplement is primarily due to weakened stratification. Our analysis also shows that the minimum DO concentration in the nearshore region was extremely low in 1998, 2003, 2007 and 2010, related to El Niño signal in these summers.

Highly productive estuaries facilitate intense decomposition of dissolved organic matter (DOM) as a carbon source. However, the specific impacts of typhoons on DOM decomposition in eutrophic bays remain unclear. To address this issue, we investigated the spectral characteristics of DOM before and after Typhoon Ewiniar in Zhanjiang Bay, a eutrophic semi-enclosed bay in the northwestern South China Sea. The results revealed that intense microbial decomposition of DOM occurred during the pre-typhoon period because high nutrient inputs facilitated the mobilization of DOM in the bay. However, the intrusion of external seawater induced by the typhoon diluted the nutrient levels in Zhanjiang Bay, reducing the impact of microbial decomposition on DOM during the post-typhoon period. Nevertheless, the net addition of DOM occurred in Zhanjiang Bay during the post-typhoon period, possibly because of the decomposition of particulate organic matter (POM) and desorption of particulate matter. In addition, an increase in apparent oxygen utilization, a decrease in DO saturation and the reduced level of Chl a indicated that organic matter (OM) decomposition was enhanced and OM decomposition shifted to POM decomposition in Zhanjiang Bay after the typhoon. Overall, our study highlighted the shift in the intense OM decomposition from DOM to POM decomposition before and after typhoons in eutrophic bays, providing new insights into the response of typhoons to biogeochemistry.

Except for conventional mesoscale eddies, there are also abundant warm cyclonic eddies (WCEs) and cold anticyclonic eddies (CAEs) in the global ocean. Based on the global mesoscale eddy trajectory atlas product, satellite altimetric and remote sensing datasets, and three-dimensional temperature/salinity dataset, spatiotemporal features of WCEs and CAEs are compared with traditional cold cyclonic eddies and warm anticyclonic eddies in the Kuroshio Extension (KE; 28°−43°N, 140°−170°E) region. Characteristics of abnormal eddies like radius, amplitude, eddy kinetic energy, and proportion in all eddies behave in significant asymmetry on the north and south sides of the KE jet. Unlike eddies in the general sense, temporal feature analysis reveals that it is more favorable to the formation and maintenance of WCEs and CAEs in summer and autumn, while winter is the opposite. The spatiotemporal variation of abnormal eddies is likely because the marine environment varying with time and space. Statistically, proportion of abnormal eddies increases rapidly in decaying stage during the whole eddy lifespan, resulting in smaller average radius, amplitude, sea surface temperature anomaly and sea surface height anomaly compared to normal ones. The three-dimensional composite structures for four types of eddies expose that the difference between abnormal and conventional eddies is not just limited to the sea surface, but also exists within the water below the sea surface. Vertical structures also indicate that the anomalous temperature signal is confined in the water from the sea surface to layers at about 30 m in the KE region.

Sea ice surface roughness (SIR) affects the energy transfer between the atmosphere and the ocean, and it is also an important indicator for sea ice characteristics. To obtain a small-scale SIR with high spatial resolution, a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar (SAR) images, utilizing an ensemble learning method. Firstly, the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice, including the scale and direction of ice patterns. Secondly, a model is developed using the Adaboost Regression model to establish a relationship among SIR, radar backscatter and the spatial information of sea ice. The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper (ATM) in the summer Beaufort Sea. The determination of coefficient, mean absolute error, root-mean-square error and mean absolute percentage error of the testing data are 0.91, 1.71 cm, 2.82 cm, and 36.37%, respectively, which are reasonable. Moreover, K-fold cross-validation and learning curves are analyzed, which also demonstrate the method’s applicability in retrieving SIR from SAR images.

The ocean conditions beneath the ice cover play a key role in understanding the sea ice mass balance in the polar regions. An integrated high-frequency ice-ocean observation system, including Acoustic Doppler Velocimeter, Conductivity-Temperature-Depth Sensor, and Sea Ice Mass Balance Array (SIMBA), was deployed in the landfast ice region close to the Chinese Zhongshan Station in Antarctica. A sudden ocean warming of 0.14℃ (p < 0.01) was observed beneath early-frozen landfast ice, from (−1.60 ± 0.03)℃ during April 16–19 to (−1.46 ± 0.07)℃ during April 20–23, 2021, which is the only significant warming event in the nearly 8-month records. The sudden ocean warming brought a double rise in oceanic heat flux, from (21.7 ± 11.1) W/m² during April 16–19 to (44.8 ± 21.3) W/m² during April 20–23, 2021, which shifted the original growth phase at the ice bottom, leading to a 2 cm melting, as shown from SIMBA and borehole observations. Simultaneously, the slowdown of ice bottom freezing decreased salt rejection, and the daily trend of observed ocean salinity changed from +0.02 d⁻¹ during April 16–19, 2021 to +0.003 d⁻¹ during April 20–23, 2021. The potential reasons are increased air temperature due to the transit cyclones and the weakened vertical ocean mixing due to the tide phase transformation from semi-diurnal to diurnal. The high-frequency observations within the ice-ocean boundary layer enhance the comprehensive investigation of the ocean’s influence on ice evolution at a daily scale.

The mesoscale eddy (ME) has a significant influence on the convergence effect in deep-sea acoustic propagation. This paper use statistical approaches to express quantitative relationships between the ME conditions and convergence zone (CZ) characteristics. Based on the Gaussian vortex model, we construct various sound propagation scenarios under different eddy conditions, and carry out sound propagation experiments to obtain simulation samples. With a large number of samples, we first adopt the unified regression to set up analytic relationships between eddy conditions and CZ parameters. The sensitivity of eddy indicators to the CZ is quantitatively analyzed. Then, we adopt the machine learning (ML) algorithms to establish prediction models of CZ parameters by exploring the nonlinear relationships between multiple ME indicators and CZ parameters. Through the research, we can express the influence of ME on the CZ quantitatively, and achieve the rapid prediction of CZ parameters in ocean eddies. The prediction accuracy (R) of the CZ distance (mean R: 0.9815) is obviously better than that of the CZ width (mean R: 0.8728). Among the three ML algorithms, Gradient Boosting Decision Tree has the best prediction ability (root mean square error (RMSE): 0.136), followed by Random Forest (RMSE: 0.441) and Extreme Learning Machine (RMSE: 0.518).

The Stokes production coefficient (E₆) constitutes a critical parameter within the Mellor-Yamada type (MY-type) Langmuir turbulence (LT) parameterization schemes, significantly affecting the simulation of turbulent kinetic energy, turbulent length scale, and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean. However, the accurate determination of its value remains a pressing scientific challenge. This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E₆. Through the integration of the information of the turbulent length scale equation into a physical-informed neural network (PINN), we achieved an accurate and physically meaningful inference of E₆. Multiple cases were examined to assess the feasibility of PINN in this task, revealing that under optimal settings, the average mean squared error of the E₆ inference was only 0.01, attesting to the effectiveness of PINN. The optimal hyperparameter combination was identified using the Tanh activation function, along with a spatiotemporal sampling interval of 1 s and 0.1 m. This resulted in a substantial reduction in the average bias of the E₆ inference, ranging from O(10¹) to O(10²) times compared with other combinations. This study underscores the potential application of PINN in intricate marine environments, offering a novel and efficient method for optimizing MY-type LT parameterization schemes.