Submesoscale activity in the upper ocean has received intense studies through simulations and observations in the last decade, but in the eddy-active South China Sea (SCS) the fine-scale dynamical processes of submesoscale behaviors and their potential impacts have not been well understood. This study focuses on the elongated filaments of an eddy field in the northern SCS and investigates submesoscale-enhanced vertical motions and the underlying mechanism using satellite-derived observations and a high-resolution (~500 m) simulation. The satellite images show that the elongated highly productive stripes with a typical lateral scale of ~25 km and associated filaments are frequently observed at the periphery of mesoscale eddies. The diagnostic results based on the 500 m-resolution realistic simulation indicate that these submesoscale filaments are characterized by cross-filament vertical secondary circulations with an increased vertical velocity reaching O(100 m/d) due to submesoscale instabilities. The vertical advections of secondary circulations drive a restratified vertical buoyancy flux along filament zones and induce a vertical heat flux up to 110 W/m². This result implies a significant submesoscale-enhanced vertical exchange between the ocean surface and interior in the filaments. Frontogenesis that acts to sharpen the lateral buoyancy gradients is detected to be conducive to driving submesoscale instabilities and enhancing secondary circulations through increasing the filament baroclinicity. The further analysis indicates that the filament frontogenesis detected in this study is not only derived from mesoscale straining of the eddy, but also effectively induced by the subsequent submesoscale straining due to ageostrophic convergence. In this context, these submesoscale filaments and associated frontogenetic processes can provide a potential interpretation for the vertical nutrient supply for phytoplankton growth in the high-productive stripes within the mesoscale eddy, as well as enhanced vertical heat transport.

Ship detection using synthetic aperture radar (SAR) plays an important role in marine applications. The existing methods are capable of quickly obtaining many candidate targets, but numerous non-ship objects may be wrongly detected in complex backgrounds. These non-ship false alarms can be excluded by training discriminators, and the desired accuracy is obtained with enough verified samples. However, the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation, which is difficult and time consuming. To address this issue, a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets. Specifically, various features commonly used in SAR image target discrimination are extracted, and several acknowledged classification models and their classic variants are investigated. Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model. Then, the performance of all the discriminators is tested, and better discriminators are selected for implementing the semisupervised training process. These strategies enhance the diversity and reliability of the discriminators, and their heterogeneous ensemble makes more correct judgments on candidate targets, which facilitates further positive training. Experimental results demonstrate that the proposed method outperforms traditional tri-training.

Submesoscale processes in marginal seas usually have complex generating mechanisms, highly dependent on the local background flow and forcing. This numerical study investigates the spatial and seasonal differences of submesoscale activities in the upper ocean of the South China Sea (SCS) and the different dynamical regimes for sub-regions. The spatial and seasonal variations of vertical vorticity, horizontal convergence, lateral buoyancy gradient, and strain rate are analyzed to compare the submesoscale phenomenon within four sub-regions, the northern region near the Luzon Strait (R1), the middle ocean basin (R2), the western SCS (R3), and the southern SCS (R4). The results suggest that the SCS submesoscale processes are highly heterogeneous in space, with different seasonalities in each sub-region. The submesoscale activities in the northern sub-regions (R1, R2) are active in winter but weak in summer, while there appears an almost seasonal anti-phase in the western region (R3) compared to R1 and R2. Interestingly, no clear seasonality of submesoscale features is shown in the southern region (R4). Further analysis of Ertel potential vorticity reveals different generating mechanisms of submesoscale processes in different sub-regions. Correlation analyses also show the vertical extent of vertical velocity and the role of monsoon in generating submesoscale activities in the upper ocean of sub-regions. All these results suggest that the sub-regions have different regimes for submesoscale processes, e.g., Kuroshio intrusion (R1), monsoon modulation (R2), frontal effects (R3), topography wakes (R4).

Reasonably understanding of the long-term wave characteristics is very crucial for the ocean engineering. A feedforward neural network is operated for interpolating ERA5 wave reanalysis in this study, which embodies a detailed record from 1950 onwards. The spatiotemporal variability of wave parameters in the Bohai Sea, especially the significant wave height (SWH), is presented in terms of combined wave, wind wave and swell by employing the 71 years (1950–2020) of interpolated ERA5 reanalysis. Annual mean SWH decreases at −0.12 cm/a estimated by Theil-Sen estimator and 95th percentile SWH reflecting serve sea states decreases at −0.20 cm/a. Inter-seasonal analysis shows SWH of wind wave has steeper decreasing trend with higher slopes than that of swell, especially in summer and winter, showing the major decrease may attribute to the weakening of monsoon. The inner Bohai Sea reveals a general decreasing trend while the intersection connecting with the Yellow Sea has the lower significance derived by Mann-Kendall test. Meanwhile, 95th percentile SWH decreases at a higher rate while with a lower significance in comparison with the mean state. The frequencies of mean wave directions in sub-sector are statistically calculated to find the seasonal prevailing directions. Generally, the dominant directions in summer and winter are south and north. A similar variation concerning to SWH, the trend of the mean wave period is provided, which also shows a decrease for decades.

Sea ice growth and consolidation play a significant role in heat and momentum exchange between the atmosphere and the ocean. However, few in situ observations of sea ice kinematics have been reported owing to difficulties of deployment of buoys in the marginal ice zone (MIZ). To investigate the characteristics of sea ice kinematics from MIZ to packed ice zone (PIZ), eight drifting buoys designed by Taiyuan University of Technology were deployed in the open water at the ice edge of the Canadian Basin. Sea ice near the buoy constantly increased as the buoy drifted, and the kinematics of the buoy changed as the buoy was frozen into the ice. This process can be determined using sea ice concentration, sea skin temperature, and drift speed of buoy together. Sea ice concentration data showed that buoys entered the PIZ in mid-October as the ice grew and consolidated around the buoys, with high amplitude, high frequency buoy motions almost ceasing. Our results confirmed that good correlation coefficient in monthly scale between buoy drift and the wind only happened in the ice zone. The correlation coefficient between buoys and wind was below 0.3 while the buoys were in open water. As buoys entered the ice zone, the buoy speed was normally distributed at wind speeds above 6 m/s. The buoy drifted mainly to the right of the wind within 45° at wind speeds above 8 m/s. During further consolidation of the ice in MIZ, the direct forcing on the ice through winds will be lessened. The correlation coefficient value increased to 0.9 in November, and gradually decreased to 0.7 in April.

In this study, a moored array optimization tool (MAOT) was developed and applied to the South China Sea (SCS) with a focus on three-dimensional temperature and salinity observations. Application of the MAOT involves two steps: (1) deriving a set of optimal arrays that are independent of each other for different variables at different depths based on an empirical orthogonal function method, and (2) consolidating these arrays using a K-center clustering algorithm. Compared with the assumed initial array consisting of 17 mooring sites located on a 3°×3° horizontal grid, the consolidated array improved the observing ability for three-dimensional temperature and salinity in the SCS with optimization efficiencies of 19.03% and 21.38%, respectively. Experiments with an increased number of moored sites showed that the most cost-effective option is a total of 20 moorings, improving the observing ability with optimization efficiencies up to 26.54% for temperature and 27.25% for salinity. The design of an objective array relies on the ocean phenomenon of interest and its spatial and temporal scales. In this study, we focus on basin-scale variations in temperature and salinity in the SCS, and thus our consolidated array may not well resolve mesoscale processes. The MAOT can be extended to include other variables and multi-scale variability and can be applied to other regions.

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents. Multispectral images have the advantages of high spatial resolution, short revisit period, and wide imaging width, which is suitable for large-scale oil spill monitoring. However, in wide remote sensing images, the number of oil spill samples is generally far less than that of seawater samples. Moreover, the sea surface state tends to be heterogeneous over a large area, which makes the identification of oil spills more difficult because of various sea conditions and sunglint. To address this problem, we used the F-Score as a measure of the distance between forecast value and true value, proposed the Class-Balanced F loss function (CBF loss function) that comprehensively considers the precision and recall, and rebalances the loss according to the actual sample numbers of various classes. Using the CBF loss function, we constructed convolution neural networks (CBF-CNN) for oil spill detection. Based on the image acquired by the Coastal Zone Imager (CZI) of the Haiyang-1C (HY-1C) satellite in the Andaman Sea (study area 1), we carried out parameter adjustment experiments. In contrast to experiments of different loss functions, the F1-Score of the detection result of oil emulsions is 0.87, which is 0.03–0.07 higher than cross-entropy, hinge, and focal loss functions, and the F1-Score of the detection result of oil slicks is 0.94, which is 0.01–0.09 higher than those three loss functions. In comparison with the experiment of different methods, the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks, supports vector machine and random forests models, and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods. To verify the applicability of the CBF-CNN model in different observation scenes, we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments, which include two studies areas (study area 2 and study area 3). The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88, which is 0.16–0.24 higher than that of other methods, and the F1-Score of the detection result of oil slicks is 0.96–0.97, which is 0.06–0.23 higher than that of other methods. Based on all the above experiments, we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills, and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

Eutrophication in coastal area has become more and more serious and mariculture potential is a main cause. Although there are some quantitative research on nutrient loads in national and global perspective, the calculation method problems make the results controversial. In this paper, the farming activities are divided into fed culture types (include cage culture and pond culture) and extractive culture types (e.g. seaweed, filter-feeding shellfish culture). Based on the annual yield of China in 2019 and feed coefficient of fed culture types and carbon (C), nitrogen (N), and phosphorus (P) content of extractive culture types, the annual nutrient loads was estimated. The results showed that to coastal region of China (1) annual nutrient released by fed culture types were about 58 451 t of N, 9 081 t of P, and annual nutrient removed by harvest of extractive culture types were 109 245 t of N, 11 980 t of P and 1.86×10⁶ t of C. Overall, the net amount of nutrient removed annually by mariculture industry were 50 794 t of N and 2 901 t of P. (2) The nutrient released from mariculture industry influenced nutrient stoichiometry. Pond farming and seaweed farming had the potential of increasing the molar concentration ratio of N and P (N:P), while cage farming and bivalve farming decreased the N:P. (3) Due to different mariculture types and layouts in the coastal regions in China, N and P loading were regional different. Among the coastal regions in China, net release of nutrient from mariculture occurred only in Hainan and Guangxi regions, while in the other regions, N and P were completely removed by harvest. We suggest decrease the amount of fed culture types and increase the amount of integrated culture with extractive culture types. This study will help to adjust mariculture structure and layout at the national level to reduce the environmental impact.

Complete mitochondrial genomes (mitogenomes) can indicate phylogenetic relationships, as well as useful information for gene rearrangement mechanisms and molecular evolution. Currently, the phylogenetic location of the genus Varuna (Brachyura: Varunidae) has not been well resolved mainly because of limited representatives (only two extant species). Here, we determined a new mitogenome of this genus (Varuna litterata) and added the published mitogenomes to reconstruct the phylogeny of Varunidae. The 16 368-bp mitogenome contains the entire set of 37 genes and a putative control region. The characteristics of this newly sequenced mitogenome were described and compared with the other 15 Varunidae mitogenomes. All 16 analyzed mitogenomes have identical gene order and similar molecular features. The sliding window and genetic distance analyses demonstrate highly variable nucleotide diversity, with comparatively low variability of COI and COII, and high variability of ND6. The nonsynonymous/synonymous substitution rates (dN/dS ratio) analysis shows that all 13 PCGs are under purifying selection and ATP8 gene evolves under the least selective pressure. Twelve tRNA genes, two rRNAs, one PCG, and the putative control region are found to be rearranged with respect to the pancrustacean ground pattern gene order. Tandem duplication/random loss model is adopted to explain the large-scale gene rearrangement events occurring in Varunidae mitogenomes. Phylogenetic analyses show that all Varunidae species are placed into one group, and form a sister clade with Macrophthalmidae. Nevertheless, the phylogenetic relationships within Varunidae are not completely consistent based on the two different datasets used in this study. These findings will contribute to a better understanding of gene rearrangement and molecular evolution in Varunidae mitogenomes, as well as provide insights into the phylogenetic studies of Brachyura.