Sea ice is an indicator of global climate change, and the change of Arctic sea ice is related to global warming and sea level rise. Aiming at the problems such as inaccuracy and slow speed of extracting details from sea ice by traditional semantic segmentation model, an improved DeepLabV3+ sea ice extraction method was constructed. Firstly, we replaced the Xception backbone network with MobileNetV2, which significantly reduces the network’s parameter count and save time while maintaining the accuracy of sea ice extraction. Secondly, we enhanced the ASPP module to DenseASPP, further expanding the receptive field during multi-scale feature extraction for sea ice, resulting in denser features. Lastly, we introduced a coordinate attention mechanism to strengthen the focus on both channel and spatial features, enhancing the extraction of fine edge details in sea ice. The Greenland Sea in the Arctic is selected as the experimental area, and 10 Sentinel-1A dual-polarization SAR images from the winter of 2020 to 2022 in the sea area are processed and labeled to form a data set for the experiment, we compared our method with classic models such as U-Net, PSPNet and DeepLabV3+. The results showed that our method achieved anmIoU of 88.46% and an mPA of 94.16%. Compared to the traditional DeepLabV3+, mIoU increased by 2.35%, mPA increased by 2.90%, and the parameter count and GFLOPs decreased 45.08 M and 106.01 G, respectively. Meanwhile, the training time and sea ice extraction time decreased by 68% and 30%, respectively. Compared to U-Net、PSPNet and other models, the optimal results are also obtained. Compared with other models, the new model constructed in this paper has a stronger learning ability about sea ice characteristics, can obtain more detailed information of sea ice and greatly saves time, and can provide technical support for the study of sea ice degradation monitoring under global warming environment.

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.

When black carbon deposits on snow/ice surface, it can reduce the albedo and increase the absorption of shortwave radiation. The changes in black carbon and their impact on the sea ice melting process are worth investigating. Study of the influence of black carbon in the Arctic Ocean was conducted using the CICE sea ice model. The results indicates that under the impact of black carbon deposition from different sources, from 1980 to 2014, the simulated summer albedo of the Arctic Ocean decreased by 0.82% to 1.71%, ultimately causing a decrease in sea ice extent by 0.97%−1.93%. In the Barents Sea, Kara Sea, and Laptev Sea, the summer sea ice area reduction caused by black carbon is approximately 2–3 times greater than the overall reduction in the Arctic Ocean. The simulation results under different black carbon deposition all show that from 1980 to 1995, the impact of black carbon on albedo in the Arctic exhibited a decreasing trend. However, from 1996 to 2014, the black carbon effect shifted to an increasing trend. In low-latitude regions, due to the retreat of sea ice, the effect of black carbon showed a decreasing trend, while in high-latitude regions, due to the cumulative effect of black carbon in multi-year ice, the radiative impact of black carbon showed an enhancing effect.

Coastal Acoustic Tomography (CAT) is an effective tool to observe the flow field in the large offshore range using high-frequency acoustic signals, of which direct observation range is still limited. The numerical ocean model provides a large-scale ocean background field with simulation errors, and the resolution and accuracy of the flow field results can be improved by assimilating the CAT data with the ocean background results. In this paper, we applied a method to obtain a larger range of two-dimensional ocean flow field results by fitting ocean-mode flow field results using Stream Function and assimilating CAT data using the Ensemble Kalman Filtering algorithm. The assimilation study used the unstructured grid Finite-Volume Community Ocean Model (FVCOM) as the background field, and the four CAT stations experiment conducted in Bali Strait, Indonesia, from 1st to 3rd June 2016 as the observational data. After fitting background field by Stream Function and assimilating CAT data, the two-dimensional flow field in Bali Strait is obtained. The assimilation results were compared with those of the same period of observation and tide level data, which is found that the flow function fitted and assimilated flow field can more accurately describe the high and low tides and flow conditions in the Bali Strait. By introducing the functional relationship between the CAT data and the flow field it can effectively reduce the error of the ocean model and the sparsity of the original observation data.

The crown-of-thorns starfish (CoTS) outbreak has caused severe damage to coral reefs in China and the Indo-Pacific region. Fish predators have been considered as an important factor in controlling the population outbreak, but there is a lack of research on the fish that can prey on crown-of-thorns starfish in China. By collecting coral reef fish from five outbreak areas of crown-of-thorns starfish in the Xisha Islands, CoTS DNA was detectedin the intestinal contents of the reef fish using PCR technology, and which compared with reported CoTS larval predators. The results showed that a total of 62 fish belonging to 23 families, 36 genera and 50 species were captured in this survey. CoTS DNA was detected in the gut contents of four fish species, namely Naso lituratus, Dascyllus reticulatus, Chelilinus trilobatus and Lethrinus erythropterus. Among them, N. lituratus, C. trilobatus and L. erythropterusare reported for the first time as potential predators of CoTS. This study has identified the potential predators of CoTS for the first time in China, providing important references for the development of early prevention and control techniques for crown-of-thorns starfish.

Yellowfin tuna (Thunnus albacares) and skipjack tuna (Katsuwonus pelamis) are pelagic and highly migratory species, serving as primary targets in global pelagic fisheries. Their population distribution and abundance are susceptible to the impacts of climate-induced changes in the marine environment, exhibiting a response lag. In order to explore the influence of climate change on the juvenile populations of yellowfin tuna and skipjack tuna in the western and central Pacific Ocean (WCPO) and the associated lag effects, this study, based on Long Short-Term Memory (LSTM) neural networks, analyzed the impact of the Oceanic Niño index (ONI) on the Catch per Unit Effort (CPUE) of yellowfin tuna and skipjack tuna in the WCPO purse seine fishery from 1982 to 2021. Different time step lengths were employed to simulate the lag effects (0−12 months) of CPUE response to ONI. The results indicate LSTM is a suitable tool for analyzing the lag effects of relationship between the abundance of pelagic species, such as yellowfin tuna and skipjack tuna, and environmental factors like ONI. In the WCPO regions north and south of the equator, there exists a time lag in the response of juvenile yellowfin tuna and skipjack tuna CPUE to ONI, with the optimal lag period being 12 months for each region. The correspondence of the optimal lag period with the age of the harvested population (nearly 1 year) suggests that the reproductive capacity or survival rate of juvenile yellowfin tuna and skipjack tuna is influenced by climate change and the resulting changes in the marine environment. The research methodology and results provide new insights for subsequent studies in analyzing the stock dynamics and distribution of key species in the WCPO.

Typhoons can have serious impacts on tidal flat ecosystems, particularly on the composition and distribution of macrobenthic communities. However, there is a lack of field data during typhoons, and the understanding of how typhoons affect the ecosystem is still limited. Therefore, this study conducted hydrodynamic observations and synchronous sampling of macrobenthic organisms before, during, and after Typhoon “Muifa”in September 2022, along the salt marsh-mudflat transect in the Chongming Dongtan area of the Changjiang River estuary. The study found: (1) During Typhoon “Muifa”, the effective wave height in the salt marshes was 2−4 times that of normal weather, and the combined wave-current shear stress was 10 times higher. (2) Within a week after Typhoon “Muifa”, the species number, abundance, and biomass of macrobenthic organisms in the salt marshes were 1.9, 3.8, and 3.0 times higher than before the typhoon, respectively. The dominant species of the salt marsh (Ilyoplax deschampsi, Assiminea sp., Assiminea violacea, Corbicula fluminea) increased by one (Assiminea violacea) compared with that before the typhoon (Assiminea sp., Ilyoplax deschampsi, Corbicula fluminea), and the primary dominant species shifting from Assiminea sp. to Ilyoplax deschampsi. (3) Within a week after Typhoon “Muifa”, the indicators of species number, abundance, and biomass of macrobenthos in the salt marsh increased, while the abundance of macrobenthic organisms on the mudflats at the forefront of the salt marsh decreased. This is attributed to the macrobenthic organisms (Ilyoplax deschampsi, Assiminea sp., Corbicula fluminea) on the mudflats migrating rapidly to the relatively less hydrodynamically stressed salt marshes during the strong hydrodynamic stress caused by the typhoon. (4) Two weeks after Typhoon “Muifa”, the abundance of macrobenthos in salt marshes recovered. The results of this study indicate that salt marsh vegetation not only provides ecological services such as wave attenuation, flow reduction, and shoreline protection, but also serves as a refuge for macrobenthic organisms during typhoons.

Digital bathymetric models (DBMs) are important basic geographic information data in the fields of offshore engineering construction, resource development, environmental protection and so on. The existing global public DBMs products such as GEBCO (The General Bathymetric Chart of the Oceans), SRTM (The Shuttle Radar Topography Mission) and ETOPO (Earth Topography) have different data types, data sources and product accuracy in different sea areas. In order to reconstruct China’s offshore bathymetric model using global bathymetric data and DBMs products, this paper proposed a weighted fusion reconstruction framework based on bathymetric partition. Firstly, the reliability and applicability of six commonly used DBMs products (GEBCO_2022, SRTM30_PLUS, SRTM15_V2.5.5, TOPO_25.1, DTU10, ETOPO_2022) were compared and analyzed in five dimensions (overall accuracy, different water depths, route profiles, geographical partitions, local details). Then, considering the bathymetric and topographic characteristics, the study area was segmented and partitioned, and the optimal DBMs products in the partition were selected, and the optimal weighted fusion was carried out with the minimum error as the constraint. Finally, the fusion results were processed by measured value recovery, smooth filtering and other post-processing to form a high-precision seamless bathymetric model with 15" resolution in offshore waters around China’s coastline. The results showed that the RMSE of the fusion results was reduced by 27%, 14%, 14% and 13% compared with SRTM30_PLUS, GEBCO_2022, SRTM15_V2.5.5 and ETOPO_2022, and the details of the topograhy were also retained. The feasibility of the fusion framework was proved, which could provide a reference for the fusion reconstruction and timely updating of large-scale seabed topography from multiple datasets.

Dosidicus gigas is an oceanic economic fish in the offshore waters of Peru, and its abundance is greatly affected by environmental factors. This study obtained the abundance of D. gigas through fishing logs from September to December in 2018−2021, combined with environmental factors, including surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and chlorophyll a concentration (Chl a) acquired from satellite remote sensing. Random forest model and ArcGIS were applied to analyze the correlations between the abundance of D. gigas and environmental factors. Results showed that the distribution of the center of gravity of fishing ground was concentrated in the range of 13°−21°S, 76°−87°W during 2018−2021, and the center of gravity shifted from northwest to the southeast from September to December. The results of random forest model analysis showed that, the impact of environmental factors on the abundance and distribution of D. gigas in the offshore waters of Peru varied among different months. The optimal SST ranged from 16.3℃ to 18.5℃, The optimal SSS ranged from 35.1 to 35.4, the optimal SSH ranged from 0.55 m to 0.60 m, the optimal Chla concentration ranged from 0.18 mg/m³ to 0.46 mg/m³. The predicted CPUE values derived from the random forest model were generally consistent with the nominal CPUE distribution, indicating the suitability of the random forest model for analyzing the relationship between D. gigas and environmental factors in the offshore waters of Peru. This study is of great significance for understanding the resource dynamics of D. gigas and guiding its production.