The seasonal variations of zooplankton communities in the nearby seas of the Island was investigated to know the relationship between the structure of zooplankton communities and environmental factors in the sea areas around Weizhou Island in Guangxi. Collecting samples of zooplankton communities were conducted in January (winter), April (spring), August (summer), and November (autumn) of 2022. The study examines the variations in zooplankton species composition, abundance, biomass, and dominant species across the four seasons, while also investigating their primary environmental influencing factors. A total of 224 species (classes) of zooplankton were identified throughout the four seasons of the year, including 25 species (classes) planktonic larvae, belonging to 18 groups. Copepods were the most abundant (78 species), followed by hydrozoan jellyfish (35 species). The number of zooplankton species shows a seasonal variation pattern of spring > autumn > winter > summer, and the composition of species shows a seasonal variation pattern of spring > winter > autumn > summer. The dominant species group exhibits a seasonal replacement phenomenon of copepods, red tide organisms, and glial organisms, as well as hairy jawbones and copepods. The seasonal variation characteristics of the zooplankton community structure are obvious. The annual average abundance and biomass of zooplankton are (246.50 ± 158.75) ind./m³ and (126.08 ± 192.27) mg/m³, respectively. The abundance and biomass in spring are the highest (712.80 ± 630.28) ind./m³ and (367.79 ± 264.33) mg/m³, while the abundance in winter is the lowest (62.09 ± 29.61) ind./m³ and the biomass in summer is the lowest (35.48 ± 19.88) mg/m³. The results of redundancy analysis indicate that water temperature, salinity, nutrients and chlorophyll a are the main environmental factors affecting the seasonal changes of zooplankton community structure in the sea area of Weizhou Island, Guangxi. The seasonal changes in water masses cause by the ocean monsoon drive the succession of dominant species of zooplankton. The seasonal variation characteristics of the abundance of copepods, an important group of zooplankton in the sea area, are consistent with the migration patterns of the Balaenoptera edeni in the waters of Weizhou Island, indicating that the basic feed organisms of the fishing industry affect the migration activities of the B. edeni. In addition, the ecological impact of the decreased dominant group of copepods caused by the proliferation and growth of protozoa and glial organisms in spring on the waters of Weizhou Island deserves attention and exploration.

In order to investigate the impact of climate change on the catch of bigeye tuna, we utilized the annual Pacific bigeye tuna catch data from 1960 to 2021, which was statistically compiled by the Western and Central Pacific Fisheries Commission. We also employed corresponding monthly climate indices, including Niño1+2, Niño3, Niño4, Niño3.4, Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), North Pacific Index (NPI), and global sea-air temperature anomaly (dT). By using a BP neural network and variable sensitivity analysis, we examined the relationship between these low-frequency climate factors and bigeye tuna catch. Our findings revealed that Niño1+2, SOI, NAO, PDO, NPI, and dT are relatively independent climate factors that have an impact on bigeye tuna catch. The optimal lag orders for these climate factors were determined to be 8 years for Niño1+2, 2 years for SOI, 9 years for NAO, 0 years for PDO, 9 years for NPI, and 3 years for dT. Among these factors, Niño1+2, SOI, and NAO were identified as the key climate factors influencing bigeye tuna catch. We constructed an optimal BP neural network model with a structure of 6-8-1, and the ratio of the difference between the predicted and actual bigeye tuna catch to the actual catch has been maintained within 15% since 1971. Additionally, the trend of the predicted and actual catch was found to be basically consistent, indicating a satisfactory level of model fit.

Based on analyses of solid-phase and porewater chemistry of sediment cores at four sites collected from Laizhou Bay of the Bohai Sea, we revealed diagenetic cycles of iron, manganese and sulfur and their responses to terrestrial inputs and anthropogenic perturbations. Results suggest that water eutrophication of the bay has not given rise to organic carbon (OC) enrichment in the sediments. Actually, contents and lability of sediment OC are generally low, largely due to the inputs of terrestrial refractory OC and intense sediment resuspension induced by natural processes and anthropogenic perturbations in the river-dominated area. This feature greatly dampens sulfate reduction, resulting in low accumulation of total reduced inorganic sulfide (0.28−88 μmol/g). Porewater Mn²⁺ is mainly from reductive dissolution of amorphous and poorly crystalline Mn oxides, while precipitation of MnCO₃ is mainly responsible for Mn²⁺ consumption in sediment below 10 cm depth. Intense sediment resuspension and refractory nature of sediment OC encourage dissimilatory iron reduction, with relative contribution of this pathway to total anaerobic OC mineralization of about 51%, on average. At the site (S6) heavily influenced by the Huanghe River input, dynamic depositional regime facilitates reductive dissolution of manganese oxides, but dampens reduction of iron oxides and sulfate to some extent. Upward diffusive fluxes of porewater Mn²⁺ and Fe²⁺ in the sediments are at the lower end for sediments of other areas dominated by major river inputs, which is attributable to overall low lability of sediment OC.

Oil spill is one of the critical target of marine environmental monitoring. Synthetic Aperture Radar (SAR), thermal infrared remote sensing, and optical remote sensing for monitoring of marine oil spills have been elucidated, and it is crucial for marine environmental protection to utilize the features and advantages of multi-source remote sensing to achieve accurate monitoring and quantitative assessment of marine oil spills. On April 27, 2021, the collision between the Panamanian vessel Sea Justice and the Liberian oil tanker A Symphony resulted in an estimated 9400 t of cargo oil seeping into the yellow sea. Here, we used multi-source satellite remote sensing data to monitor and analyze the coverage of oil spill and the distribution of emulsified oil in this accident. Based on the response mechanism and characteristics of oil spill multi-source remote sensing, the processing of multi-source data is optimized to realize the identification of oil spills and the classification of multiple oil types. The findings indicate that from May 1 to May 22, 2021, the cumulative pixel area of oil spills from A Symphony tanker was 2368.7 km², of which the emulsified oil pixel area was 1019.3 km², accounting for 43.0%. The maximum daily oil spill pixel area reached 734 km². The results of multi-remote sensing monitoring validated each other, and optical remote sensing is more capable of identifying different oil types, in which the emulsified oil represents the key of pollution hazards. It improves the accuracy of monitoring and assessment of marine oil spills, and provides reliable technical and methodological references for the hazard assessment and refined monitoring of oil pollution events.

Recognition of reef-building corals is important for protecting and monitoring coral reef ecosystems. Deep learning, as an advanced technology in image recognition, has been increasingly applied in coral recognition. However, its performance is still challenged by several issues, such as the imbalance of samples among different coral categories within a dataset and the limitation of data diversity. The former makes the deep learning model more likely to extract features from classes with a large number of samples and, therefore, decreases its ability to recognize small-sample-size corals, which often refer to endangered ones needing to be protected. The latter further reduces the performance of deep learning in recognizing corals with different appearances and are captured in variant environments. To solve these two problems, this study develops a reef-building coral recognition method by integrating a category-quantity adaptive deep data augmentation algorithm and transfer learning. To address the first problem, a category-quantity adaptive deep data augmentation algorithm named DeepSMOTE-F₁ is proposed. This algorithm improves the existing DeepSMOTE by introducing a sample-size determination stagey using an F1-score based evaluation metric. It can adaptively augment the number of samples of each category of corals according to its recognition performance so that the deep learning model can fully learn features from each class of corals. For the second problem, transfer learning is used to further enhance the model's ability to extract features. The experimental results on three widely used public coral recognition datasets, RSMAS, EILAT, and EILAT2 show that the recognition accuracy of the proposed DeepSMOTE-F₁ is improved by 2.88%, 0.39%, and 1.54%, respectively, compared with the traditional DeepSMOTE; and the accuracy of the integrated method is improved by 0.76%, 1.40% and 1.30% compared with the existing deep learning methods for coral recognition.

It prevails southwesterly monsoon in summer in the northern South China Sea. Consequently, the seasonal coastal upwellings are frequently driven up in summer off the southeastern coast of the Hainan Island, the eastern coast of the Leizhou Peninsula and the eastern coast of Guangdong Province, and these upwellings have attracted common research attention. The present paper revisits the historic hydrologic survey data in the northern South China Sea. It is found that, besides the coastal upwellings frequently reaching the sea surface in the above-mentioned zones, there are more subsurface upwelling patterns in the northern South China Sea: namely, (1) the subsurface upwelling throughout the eastern coastal zones of Hainan Province and all the western and eastern coastal zones of Guangdong Province under strong overlying southwesterly monsoon; (2) the subsurface one over the wide continental shelf off the western coast of Guangdong Province; (3) the subsurface one driven by the diluted Pearl River runoff off the river mouth; and (4) the bottom continental water invasion into the bays of eastern Guangdong Province and the cyclonic eddies in the bays. These four upwelling patterns have not been reported in the existing research papers, or little attention has been paid to them. Besides the Ekman’s transport due to strong summer southwesterly monsoon and Ekman’s pumping due to overlying wind stress curl, the bottom Ekman’s effect due to the basin-scale South China Sea circulation is possibly the non-negligible driving mechanism for the subsurface upwelling throughout all the coastal zones. In the light of the simultaneous atmospheric circulation over the northern South China Sea, Ekman’s pumping due to positive wind stress curl seems to be the major driving force for the subsurface upwelling over the continental shelf off western Guangdong coast. The positive wind stress curl as well as the diluted Pearl River runoff drives a cyclonic circulation off western Guangdong coast. The seawater divengence in the cyclonic circulation is favorable to the formation and maintenance of the subsurface upwelling. The vertical circulations driven by southwesterly monsoon and horizontal cyclonic ones in the coastal bays of eastern Guangdong Province are the major driving forces for the bottom continental water intruding into the bays. In the Daya Bay, the thermal plume due to the unclear power plants is favorable to strengthen the horizontal cyclonic circulations. The western and eastern capes of the Honghai Bay are favorable to the formation of horizontal cyclonic circulations in the bay. So we need attach more importance to the other driving mechanisms of the upwelling but to surface Ekman’s effect in the northern South China Sea.

Using temperature-salinity profiles and current measurements, satellite data and reanalysis data in April 2018, this study analyses the acoustic field characteristics and effects of an abnormal anticyclonic eddy (AAE) on acoustic propagation on the continental slope area in the northwestern South China Sea (SCS). The results show that the AAE has a lens-shaped structure with a surface cold core, a shallower mixed layer, and subsurface intensified velocities. Unlike the concave sound-speed contours in the normal anticyclonic eddy (NAE), the sound-speed distribution in the abnormal anticyclonic eddy (AAE) exhibits a lens-shaped structure with an upward convexity and downward concavity. The surface sound speed within the eddy is lower than that outside, showing a negative anomaly (<−2 m/s). Conversely, the sound speed in the subsurface layer of the eddy is higher than that outside, showing a positive anomaly (>11 m/s). This results in the thickness of the original double thermocline extending up and down by a total of 47 meters in the presence of the eddy. As the sound propagates from the eddy outside on the shelf to the deep sea, the surface sound channel disappears as the propagation distances decreasing in the AAE, contrast to the increased distance in the NAE. As the sound propagates from the eddy outside in the deep ocean to the shelf, the location of the sound energy convergence zone moves backward and downward in the AAE, with the maximum distance exceeding 24 km and 0.3 km, respectively. This is similar to the situation in the NAE. As the sound propagates from the eddy core to outside in the deep sea, the turning depth of the sound deepens and the distance between the sound energy convergence zones doubles in the AAE, while no changes in the NAE.

Based on the Acoustic Doppler Current Profiler carried by deep-sea moorings, we investigated the diel vertical migration of the acoustic scattering layer and the impact of internal solitary waves on it in the northern South China Sea. The observational results reveal that the acoustic scattering layer, influenced by zooplankton, ascends to shallower depth within approximately an hour after sunset, remains there throughout the night, then migrates to the deeper depth within about an hour before sunrise. The average migration velocities are 4.7 cm/s (upward) and 5.8 cm/s (downward). Additionally, internal solitary waves observed in the region induce a pair of downward and upward currents, with maximum vertical velocities exceeding 50 cm/s, leading to fluctuations of tens to hundreds of meters in the acoustic scattering layer. The acoustic backscattering strength of the ocean’s upper layer reaches its maximum value at the troughs of internal solitary waves. Further research indicates that the daytime internal solitary waves exhibit a stronger correlation between the vertical velocity and the depth-averaged backscattering strength variation compared to the nighttime internal solitary waves. When the vertical velocities induced by both types of waves are equal, the depth-averaged backscattering strength variations during the day are typically greater than those at night.

The analysis of the changes in the path of the Kuroshio south of Japan has always been a hot topic. Previous studies have pointed out that the changes in the Kuroshio path south of Japan are influenced by various factors, such as upstream transport, mesoscale eddies, climate signals etc. However, the causal relationship between these influencing factors is not fully understood. The paper first obtains the time series of the Kuroshio path south of Japan based on the 50 year (1958−2007) China Ocean Reanalysis dataset (CORA) and 14 year (2008−2021) satellite altimeter data, and uses the Complex Empirical Orthogonal Function (CEOF) analysis method to analyze its spatiotemporal characteristics. The results show that the first two main modes obtained by CEOF analysis can describe the main characteristics of the space-time variation of the Kuroshio path in the south of Japan and represent the related eastward and westward signals, respectively. Furthermore, the causal analysis results based on information flow theory indicate that: on the one hand, PDO affects the eddy kinetic energy in the subtropical countercurrent (STCC) region through changes in wind stress, thus affecting the changes of Kuroshio transport in the Tokara Strait, and then has a direct impact on the eastward signal, and finally affects the changes of the Kuroshio path in the southern region of Japan. On the other hand, the eddy kinetic energy of the Kuroshio extension is influenced by the NPGO signal, which affects the westward movement of the mesoscale eddies in the region, thereby directly affecting the westward signal and ultimately affecting the Kuroshio path changes in the region south of Japan. In addition, the experimental results also indicate that the relative vorticity and recirculation gyre strength in the southern region of Japan are responses to the changes in the Kuroshio path, rather than factors affecting the changes in the Kuroshio path.

Tides act an important role in the transfer of ocean energy and mixing, and provide the main energy to maintain the global thermohaline circulation and influence the global ocean circulation. Previous work has explored the sensitivity of ocean circulation states to tidal forcing within an individual ocean model at a low resolution. To further investigate the influence of tidal forcing on ocean circulation and climate states, it is imperative to incorporate the tidal forcing into a coupled climate model. In this paper, the eight major equilibrium constituents are included into the coupled climate model FGOALS-g3 explicitly, and we evaluate its ability to simulate global ocean tides, which lays the basic for the further research on the influence of tidal forcing on large-scale circulation and climate states.We apply tidal harmonic analysis on the sea surface height data to obtain the harmonic constants of each constituent, and compare the model results with the global tidal models TPXO9 and FES2014, and the open ocean tide dataset from st102. The results show that the coupled model FGOALS-g3 can effectively simulate the barotropic tides in the global ocean, with relatively small errors compared to the global tidal models and the observation dataset. Compared with these two global tidal models, the mean square error is relatively small, and the errors are mostly distributed in the region of larger amplitudes. And compared with st102 dataset, the average amplitude relative errors of the eight major equilibrium constituents simulated by FGOALS-g3 are all less than 10%, and the total mean square errors are all less than 10 cm.