Sea ice leads in the Arctic, accounting for only 1%−10% area of the whole ice area, play a crucial role in the exchange of energy and moisture between the ocean and the atmosphere. Currently, the analysis of the numerical simulation of the leads mainly focuses on the spatial distribution of the occurrence frequency and the spatio-temporal variations of the lead area proportion within the cell, while few analysis concerns the simulated lead morphology (length, width and orientation). This article is based on the high-resolution (2 km) ice sea coupling model using visual plastic rheologies to simulate sea ice thickness to extract leads, and the lead morphology is compared to three MODIS lead products respectly. The results show that the spatial distribution of simulated leads occurrence frequency in Beaufort Sea is basically consistent with WH2015 and H2019 products. The number density and total length of leads with a width greater than 6 km follow the power law distribution as presented in remote sensing products, while that of the narrow (2−4 km) leads are underestimated due to limited model’s resolution. The correlation between the total length of simulated leads and remote sensed products is high in January and March, but the model fails to reproduce the trends in February and April shown in the remote sensing products. The overall orientation of the simulated leads aligns with the remote sensing products, both show that leads along the north of the Canadian archipelago and the southeast of Beaufort sea are almost parallel to the coastline and the ice drift direction, while the orientation of the simulated leads is more restricted by the continent than that of remote sensing products, and the location of the lead and ice speed turning is not consistent in the middle of the Beaufort Sea. This study highlights the capability of the state-of-the-art high-resolution sea ice-ocean coupled models in simulating various morphological characteristics of sea ice lead, and provides insights for further model improvements.

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.

Antarctic sea ice is a crucial component of the polar climate system, with profound implications for global climate. Sea ice thickness, as one of the key properties of sea ice, holds significant importance for understanding and predicting the influences of climate change by revealing its spatial and temporal distribution patterns and variation trends. However, current monitoring of Antarctic sea ice thickness is constrained by limited ground observations with restricted spatial and temporal coverage or short-term satellite observations, long-term sea ice thickness data remains elusive. To address this issue, this study utilized the continuous satellite radar altimetry data from Envisat and CryoSat-2 and constructed a consistent dataset of radar freeboard of Antarctic sea ice. Then, the penetration depth of radar signals through the snow covers over Antarctic sea ice was quantitatively estimated, and a method applicable to various sea ice-snow scenarios for estimating Antarctic sea ice thickness was developed. The estimated sea ice thickness shows an average absolute bias of approximately 0.28 m compared to in situ measurements from the Australian Antarctic Data Centre, and an average absolute bias of approximately 0.65 m compared to ICESat laser altimeter with a high correlation coefficient of 0.71. Analysis of the spatiotemporal variations of Antarctic sea ice thickness from 2002 to 2023 reveals that thick ice is predominantly concentrated in the western Weddell Sea and Bellingshuan/Amundsen Seas, while ice in other sea sectors is relatively thin. Antarctic sea ice thickness exhibited a slight decreasing trend before 2011, followed by an accelerated decline after 2011 (−0.03 m/a). The distribution and trends of Antarctic sea ice thickness exhibit distinct seasonal and regional characteristics.

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.

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.

This study uses the data of the China’s new generation geostationary-orbit meteorological satellite FY-4B to observe the internal solitary wave (ISW) and study the propagation speed of ISW in the northern South China Sea (NSCS). Firstly, the observable ISW region of the FY-4B satellite was discussed by using ARGI data of 500 m resolution, and the NSCS was confirmed as the research area. Then, the MTI method (Multitemporal Image Comparison method) was used to calculate the propagation velocity of ISWs in NSCS using GHI data of 250 m resolution, and the average propagation velocity of ISW in NSCS is about 1.78 m/s. The average velocity in the eastern deep sea was 3.02 m/s, and it decreased to 1.90 m/s after westward transmission near Dongsha Islands. After the division of Dongsha Islands, the velocity of ISW in the south was faster than the north, with 2.08 m/s and 1.54 m/s, respectively. Finally, the velocity decreases to 0.42 m/s after westward to the nearshore region, then dissipates. Compared with the results of the MTI method and theoretical propagation velocity calculated by the eKdV (extended Korteweg-de Vries) equation in the two-layer mode, and the correlation coefficient between them reached 0.89. Prove the feasibility of using the eKdV theoretical equation to invert the propagation velocity of ISW in the NSCS. Finally, the results of calculating the propagation velocity of ISW by combining measured data with remote sensing images were compared with the results of MTI method. The correlation between the two reached 0.93. This article verifies the high temporal resolution characteristics of geostationary-orbit optical remote sensing data, which have significant advantages in calculating the propagation speed of ISW. It has certain significance for research work such as inversion of ISW parameters in the NSCS.

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.

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.

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.

In order to explore the differences in the structural characteristics of macrobenthos communities in different artificial reefs in the Yellow Sea and Bohai Sea and to evaluate the conservation effect of artificial reefs on biological resources, a simultaneous survey of macrobenthos in four artificial reefs in the Yellow Sea and Bohai Sea was carried out in autumn 2021. A total of 61 macrobenthos species were collected, among which annelids accounted for the most (47.54%). The biomass of macrobenthos in each reef area was higher than that in the control area, and the conservation effect of macrobenthos resources in the old reef area was the best, and the average biomass was 4.19 times that of the control area. The Shannon-Wiener index H' and richness index d of each reef region were higher than those of the control region, and the long reef age region was higher than the short reef age region. The ABC curve shows that the stability of the community in the long-aged reefs is better than that in the short-aged reefs. There are different dominant species in each artificial reef area, but polychaetes are the dominant species in all artificial reef areas. Reef survey sites were roughly divided into 3 groups by cluster analysis and non-metric multidimensional scale ranking (NMDS). Similarity analysis test (ANOSIM) showed that there were significant differences in community structure among reefs (R = 0.651, p < 0.01). The results of SIMPER (percentage of similarity analysis) show that the average percentage of dissimilarity in each reef interval is above 77%. The results show that the artificial reef habitat plays a certain role in the conservation of macrobenthos, and the macrobenthos community varies in different reef regions. This study can provide basic data and basis for the evaluation of artificial reef construction.