The continental shelf regime under the United Nations Convention on the Law of the Sea (UNCLOS) marked the first time that the scope of coastal states’ sovereignty rights was extended to the deep-sea areas beyond 200 nautical miles. This provision not only endows coastal states with legal standing for the exploitation of deep-sea resources, but also fosters an institutional linkage between geoscience and international law. To date, a total of 109 submissions have been formally lodged with the Commission on the Limits of the Continental Shelf (CLCS), signifying that global continental shelf delineation has entered a new phase characterized by the high integration of scientific practice and legal procedures. Nevertheless, driven by the rapid advancement of science and technology and the sensitive impacts of geopolitics, continental shelf delineation is confronted with unprecedented challenges, which will exert a major influence on global ocean governance. Based on the 109 submissions received and 44 recommendations issued by the CLCS, this paper systematically sorts out the major progress and challenges in the delineation of continental shelves beyond 200 nautical miles from three dimensions: legal regimes, geoscientific theories, and practices. It aims to reveal how continental shelf delineation has evolved into a crucial driving force reshaping the global marine spatial order within the international deep-sea governance system where scientific evidence and legal institutions intersect, and further looks ahead to the development direction of continental shelf delineation in the context of scientific and technological progress, international cooperation, and global ocean governance.
The sediment source-to-sink system serves as a critical link connecting active carbon pools (e.g., atmosphere, biosphere, hydrosphere) with the stable lithospheric carbon pool, playing a core buffering role in the global carbon cycle. As the core area of marine sedimentary carbon sinks, delta-shelf regions account for over 80% of the global marine sedimentary organic carbon flux while occupying less than 8% of the global ocean area. The processes and mechanisms of carbon burial in these regions are crucial for global carbon balance. This paper systematically reviews the source composition and sedimentary flux characteristics of terrestrial organic carbon in delta-shelf sedimentary systems, focuses on elaborating organic carbon source-to-sink tracing technologies, remineralization processes and their dominant mechanisms, analyzes the impacts of human activities on sedimentary carbon sinks, and discusses marine negative emission and carbon sequestration enhancement schemes based on sediment management. Studies show that the heterogeneity of terrestrial organic carbon, physicochemical conditions of the sedimentary environment, and human disturbance collectively regulate the migration, transformation, and burial efficiency of organic carbon. Currently, the potential of sedimentary carbon sinks has not been fully exploited; thus, it is urgent to promote the integration of sedimentary carbon sinks into the global climate governance system through methodological innovation, mechanism deepening, and technological development, so as to provide scientific support and feasible paths for achieving the temperature control goals of the Paris Agreement.
Deep-water sedimentary processes are key drivers that shape seafloor topography and actively participate in marine material cycles, thereby playing a crucial role in the formation of depositional systems and material cycling along continental margins and within deep-sea basins. The transport and transformation of carbon elements and carbon-containing substances are essential for sustaining organic life and maintaining climate stability. As an important end-member reservoir in this cycle, deep-sea sediments act as efficient sinks for atmospheric greenhouse gases, exerting significant regulatory effects on climate evolution over geological timescales. This study aims to elucidate the coupling mechanisms between distinctive deep-water sedimentary processes and organic carbon burial, providing a theoretical basis for establishing the “Shelf edge-slope-deep sea basin organic matter continuous transport system” and the “Deep-water organic carbon burial pyramid model”. By comprehensively analyzing representative deep-water organic carbon burial systems in global ocean basins, this research demonstrates that turbidity currents and bottom currents are the main dynamic mechanisms enabling the continuous transport of deep-water organic matter. The (micro)biological carbon pump, turbidity current carbon pump, bottom current carbon pump, and deep stratigraphic carbon pump together form the core framework for deep-water sedimentary carbon burial. Furthermore, the factors influencing deep-water organic carbon burial outcomes exhibit hierarchical characteristics. However, current research on deep-water organic carbon burial is still in its early stages, with limited case studies and mechanistic understanding, underscoring the urgent need to strengthen research on carbon burial processes in deep-water environments.
Black carbon (BC), a refractory organic carbon, is produced during the incomplete combustion of biomass and fossil fuels. Globally, an estimated 3%-10% of the annual BC production ultimately buried in marine sediments. As a critical component of the inert carbon pool, its spatiotemporal distribution and source-to-sink processes are essential for understanding global carbon cycling and climate evolution. Based on published BC data from nearly 1 000 marine sediment samples worldwide, this study reveals that BC contents vary widely, from 0.02 to 9.72 mg/g, with averaging 1.06 mg/g and accounting for an average of 15.1% of total sedimentary organic carbon. Spatial patterns are controlled by sediment grain size, organic carbon content, and depositional environments while temporal variations reflect the combined influence of climate change and human activities. Current knowledge of marine sedimentary BC sources predominantly assumes terrestrial dominance, with riverine transport, atmospheric deposition, and coastal erosion as primary input pathways. However, emerging evidence indicates that BC sinking fluxes in mid- to deep-ocean layers substantially exceed known terrestrial supply. This raises the possibility of potential unidentified sources. In addition, BC degradation and recycling processes within the marine systems remain poorly understood. Future research must prioritize source-to-sink dynamics in key areas (e.g., deep-sea environment) by integrating geochemical and organic molecular isotopic techniques to resolve BC cycling mechanisms and address current budget imbalances.
In 1988, the tropical Pacific experienced a strong La Niña event, during which significant equatorial Pacific easterly wind surges were observed. Analysis based on reanalysis data indicates that the intensity of the 1988 surges reached the highest level during 1982-2020. Linear regression results show that the equatorial Pacific SST gradient contributed 70.59% to the surge intensity index in 1988. Further examination of wind field characteristics after removing the influence of the SST gradient reveals that, apart from the enhanced spatial extent of the surge event in late February, both the frequency and magnitude of surges decreased significantly from mid-March onward. To gain deeper insight into the specific causes of the easterly wind surges, a typical case analysis was then conducted to investigate the triggering mechanism of a representative event. Composite analysis confirms that the strong easterly wind surge at the end of March 1988 was closely linked to the convectively active phase of a Madden-Julian Oscillation (MJO) event over the Maritime Continent, which contributed approximately 42.96% to the surge’s formation.
To assess the potential impact of plumes generated by deep-sea mining on the midwater ocean, this study systematically analyzed the flow field characteristics of the intermediate currents at the 1 000 m and 2 000 m in deep-sea mining areas of the Pacific Ocean (Western Pacific: Block C, Block M, Block CW, Block WJ; Eastern Pacific: Block A5, Block KW, Block EK, Block A12678, Block A3, Block A4) and predicted the zonal movement trend of midwater plumes. The analysis was based on the global Argo float trajectory and mid-depth current dataset from the China Argo Real-Time Data Center covering the period from August 1997 to October 2024, combined with data from 3 moored observation stations. The results show that: (1)The currents at the 1 000 m layer in the Pacific mining areas are mainly controlled by zonal currents, with the velocity of eastward jets being greater than that of westward jets. The velocity and direction of currents in the mining areas are sensitive to changes in latitude. (2)The eastward jets that affect the 1 000 m flow field in the Western Pacific mining area have the center located at 14°N (weak) and 18°N (strong). Under their influence, in the southern part of the Block M, the midwater plumes move eastward. In other areas, the midwater plumes move slowly westward.(3) The eastward jets that affect the 1 000 m layer of the Eastern Pacific mining areas have the centers located at 7°N and 9°N. They are stronger in summer and autumn, and weaker in winter and spring. (4)The flow field directional characteristics in the 2 000 m area of Block M, Block A5, Block KW and Block EK are the same as those in the 1 000 m layer, indicating that the depth affected by jets can reach 2 000 m.
Optical survey and evaluation of deep-sea polymetallic nodules face challenges such as low contrast, small object detection, and boundary ambiguity. This study proposes an improved Mask R-CNN model incorporating dynamic sparse convolution (DSConv) and simple parameter-free attention module (SimAM) for nodule image segmentation. SimAM effectively suppresses sediment background interference, while DSConv alleviates boundary blurring. The combined model achieves an accuracy of 91.5%, precision of 78.0%, recall of 75.1%, and IoU of 69.4%. When applying the improved model and the original model to the actual survey lines, it was found that in the identification results of the seabed nodules coverage rate, the proportion of data with an error less than 5%, increased from 57% of the original model to 77% of the improved model. This research can provide a reliable technical solution for the calculation of deep-sea polymetallic nodule coverage rate, and its modular design can also be extended to other fields of target recognition and image segmentation.
This study was conducted to explore the contribution of silicon dissolution from beach sediments to the dissolved silicon budget in the coastal waters. From March to September in 2017, six field surveying cruises were conducted in the Muping offshore area (Yantai, China), the southern North Yellow Sea. By investigating monthly distributions and the averaged values of dissolved inorganic nutrients, monthly accumulation of dissolved silicate anomaly (ΔSi, as defined with the difference between dissolved inorganic nitrogen and silicate concentrations)of 1.5 μmol·L-1 was observed in this offshore area with weak circulation from May to August. Further combining laboratory incubation experiments, theoretical calculation and field data analyses, It was found that the permeable particles in beaches could be dissolved, leaching active silicate to seawater, and increasing the offshore silicate concentration by 0.7~2.0 μmol·L-1 every month, roughly consistent with the monthly accumulation rate of field ΔSi. Extrapolating the beach silicate-leaching flux to the length of the coastline rounding the Yellow Sea, the previously reported imbalance in silicate budget in this coastal sea could roughly be bridged. This study indicated again that the dissolving of permeable particles might contribute significantly to coastal silicate budget.
Tidal flats are influenced by tides, experiencing periodic inundation and exposure, thus the inundation frequency reflects the elevation of tidal flats. This study utilizes time-series SAR satellite remote sensing data to conduct research on the remote sensing inversion method of tidal flat topography based on tidal level complementary cumulative distribution function. The key lies in proposing a new method for inundation frequency correction based on the weighting scale of remote sensing observation counts. And, based on the mathematical definition of inundation frequency, the functional relationship between inundation frequency and tidal flat elevation was explored, leading to the construction of a tidal flat topography inversion model based on the tidal level complementary cumulative distribution function. Then, the validation of the method was conducted in the Yueqing Bay. Based on the time-series Sentinel-1 satellite SAR remote sensing data, the tidal flat topographies for the periods 2019-2020 and 2021-2022 were successfully inverted. The accuracy assessment was conducted based on the corresponding period’s ICESat-2 satellite laser altimetry data. The root mean square errors (RMSE)of the tidal flat topographies for the periods 2019-2020 and 2021-2022 were 0.41 m and 0.51 m, respectively. Additionally, the RMSE of topography for the period of 2019-2020 using in-situ data was 0.48 m. The accuracy assessment suggest that the proposed method in this study can achieve high-precision tidal flat topography without field-measured topographic data. It is expected to be applicable to the monitoring of tidal flat topography in more regions.
Island ecosystems are characterized by resource specificity and ecosystem vulnerability, thus, the scientific assessment of the impact of land use change on the carbon sequestration and other ecosystem service functions of island ecosystems is of great significance to the sustainability management of islands. The Dongtou Islands in Zhejiang Province represents a typical island ecosystem that has undergone developmental utilization such as land reclamation and conservation-restoration initiatives like the Blue Bay Project. It serves as an ideal case study for establishing assessment methods of how land use changes affecting the carbon sequestration service function of island coastal ecosystems and others, and for exploring the effectiveness of management measures. In this study, a land classification model based on XGBoost algorithm was used to obtain land use classification data of the Dongtou Islands in 12 phases (3 years as a phase)from 1988 to 2023 (with accuracy of 91.52%). On this basis, the changes in carbon sequestration amounts of major ecosystems, including woodland, salt marshes, and tidal flats, in the Dongtou Islands were calculated. A coupling coordination degree model of “economic development-land use-carbon sequestration function” was constructed by combining the socio-economic statistical data, and the degree of coupling coordination between the economy and ecosystem of the Dongtou Islands for more than 30 years was explored. The study found that from 1988 to 2023, the total land area of the Dongtou Islands increased by 34.97% due to natural silt deposition and sea reclamation efforts. The cumulative total of ecosystem carbon sequestration amount and net carbon sequestration amount for the main ecosystems amounted to 49.45×104 t and 46.13×104 t, respectively, basically showing an oscillating upward trend. Carbon sequestration mainly resulted from woodland and coastal wetlands (including tidal flats and salt marshes), with cumulative carbon sequestration amount of 25.44×104 t and 24.01×104 t, respectively. The “economic development-land use-carbon sequestration function” coupling coordination degrees of the Dongtou Islands were in a coordinated state from 2006 to 2023. Overall, the coupling coordination degree is greatly affected by the land use changes. Ecological restoration projects can enhance the comprehensive evaluation index of the land use and carbon sequestration function system, and then improve the coupling coordination degree. This study can provide a scientific theories and data foundation for the socio-economic development and ecological environmental protection planning of the Dongtou Islands.
The anaerobic oxidation of methane (AOM) is a pivotal component of elemental cycling within cold seep sediments. This process is usually performed by anaerobic methanotrophic archaea (ANME) and sulfate-reducing bacteria (SRB), which usually exist as symbionts. However, pure cultures of ANME have not yet been obtained, and their slow metabolism hinders further exploration and research into their metabolic characteristics and collaborative mechanisms. In this study, we utilized hybridization chain reaction-fluorescence in situ hybridization (HCR-FISH) technology and high-throughput 16S rRNA gene sequencing to investigate the composition and state of ANME communities at different depths of the sediments in the black microbial mat area of the South China Sea Formosa cold seep. The results showed that ANME-1 and ANME-2 were the dominant groups in the sampled Formosa cold seep sediments. Specifically, ANME-2 was found to form consortia with SRB, while no such associations were detected for ANME-1. This observation suggested that ANME-2 and SRB primarily engage in symbiotic AOM processes, highlighting potential differences in physiological roles and methane metabolism pathways between ANME-1 and ANME-2. Furthermore, in sediment samples of all layers, the diameters of ANME-2/SRB consortia were predominantly concentrated between 3-10 μm. Correlation analysis indicated a significant link between the distribution of consortium diameters and environmental factors such as sulfate concentration in the sediment, underscoring the impact of environmental factors on the growth of ANME/SRB consortia. Additionally, using HCR-FISH, we further discovered the presence of multiple consortium clusters in the Formosa cold seep sediment, characterized by orderly connected and uniform-sized consortium, implying possible connections or cooperative relationships among consortia. This study revealed the presence and distribution patterns of ANME groups and sizes of symbiotic microbial consortia in sediment samples from different depths of the Formosa cold seep, laying the foundation for further understanding methane metabolism mechanisms and ecological functions of different ANME groups in situ cold seep sediments.
Coastal estuaries are influenced by terrestrial inputs and usually act as sources of atmospheric carbon dioxide (CO2), whereas mangrove ecosystems generally serve as sinks of atmospheric CO2. Therefore, accurately measuring the CO2 emissions at mangrove estuaries is of great significance for constructing regional and global carbon budgets. Dongzhai Harbor locates in the northeastern of Hainan Island, and connects to the Qiongzhou Strait outward, surrounding by 5 major small rivers. Mangroves are mainly distributed in the west and south of Dongzhai Harbor. This study conducted four field surveys in Dongzhaigang, the surrounding major rivers and the adjacent sea areas in December 2022 (dry season), December 2023 (dry season), May 2022 (wet season) and August 2023 (wet season) respectively. The results show that the surface water partial pressure of CO2 (pCO2) presents a decreasing trend from rivers to inner and outer harbor. Temperature, river-sea mixing, and biological respiration jointly affect the spatial distributions of pCO2 in the dry and wet seasons. The CO2 flux in wet season (8.8±8.2 mmol·m-2·d-1) is greater than that in dry season (3.4±3.6 mmol·m-2·d-1), and the annual CO2 flux (6.1±6.3 mmol·m-2·d-1) is lower than that in other tropical mangrove estuaries around the world. This study estimates that the estuarine CO2 emission could offset about 10.4%~21.9% of the carbon sequestration by plants in Dongzhai Harbor.
Using eddy-resolving numerical simulation data and historical hydrological observation data, this study investigates the sources, seasonal and interannual variability of two subsurface undercurrents under the Indonesian Throughflow—the Ombai Undercurrent located in the Ombai Strait and the Timor Undercurrent located in the Timor Channel. The results indicate that these two undercurrents exist at depths of approximately 200-800 m, which are a quasi-permanent undercurrent system. The formation of the Ombai Undercurrent is mainly related to the eastward extension of the South Java Undercurrent, while the water source of the Timor Undercurrent is more complex, mostly a mixture of the South Java Undercurrent and the Leeuwin Undercurrent. Both subsurface undercurrents exhibit significant seasonal and interannual variations, with a significant semiannual period at the seasonal scale, typically peaking during the Indian Ocean monsoon transition period (April, May, and October). Combining historical wind, satellite altimeters, and temperature and salinity observation data, it is found that the meridional pressure gradient in the subsurface layer related to local wind and their upwelling is the dominant factor leading to their seasonal changes. At the interannual scale, there is a period of 2-4 years for subsurface undercurrents, which is significantly correlated with the Indian Ocean dipole.
Submarine earthquake is one of the most major factors causing deep-water international submarine cables damage. Understanding the process of submarine cables damage and the mechanism of submarine cables damage caused by turbidity currents after earthquake are of great significance to the security maintenance of international submarine communications. Combined with the lastest research result of global seabed topography and using professional international submarine cables engineering software Makaiplan, the process of plenty of submarine cables damage after Grand Banks Earthquake and Hengchun Earthquake were studied, then the relationship between the pattern of submarine cable damage and the developing process of turbidity currents after earthquake was found, and the mechanism of submarine cables damage caused by turbidity currents after earthquake was summarized. Study result shows that submarine cables break points are located intentively in submarine canyons and trenches. The movement speed of turbidity currents in submarine canyon and submarine trench, which caused submarine cable damage, can reach several ten kilometers to several hundred kilometres per hour. Terrestrial rivers and continental shelf undersea river channels provide materials transportation for the development of turbidity currents. Submarine canyons and trenchs are the pathes of turbidity currents movement then damage plenty of submarine cables. The turbidity currents that developed from upper continental slope in passive continental margin after earthquake can damage submarine cables laid on continental slope, continental rise and abyssal plain. This kind of turbidity currents achieves maximum speed on continental slope, then self-accelerate on abyssal plain. Multiple turbidity currents can develop at different positions of continental slope at the same time in active continental margin, then strike submarine cables which laid on canyons and trenches for multiple times. This kind of turbidity currents achieves maximum speed and self-accelerates in submarine trenches. There are several earthquake-resistance measures: submarine cable routes trying to avoid crossing submarine canyons and trenches which connected to terrestrial rivers or continental shelf channels; using shallow water type submarine cable which has outer armor protection when crossing inevitably; laying submarine cables suspended slightly on the bottom of canyons or trenches with Uraduct protection on them; changing the cross-section shape of submarine cable.
Artificial intelligence in oceanography has demonstrated a great potential with the explosive growth of ocean observation data and numerical model products. This article first reviews the history of ocean big data development, and then introduces in detail the current status of artificial intelligence in oceanography applications including identifying ocean phenomenon, forecasting ocean variables and phenomenon, estimating dynamic parameters, correcting forecast errors, and solving dynamic equations. Specifically, this article elaborates the research on the intelligent identification of ocean eddies, internal waves and sea ice, the intelligent prediction of sea surface temperatures, El Ni?o-Southern Oscillation, storm surges, waves and currents, the intelligent estimation of ocean turbulence parameterization for numerical models, and the intelligent correction of waves and current forecast errors. In addition, it discusses the recent progress of applying physical mechanism fusion and Fourier neural operator for solving ocean dynamic equations. This article is based on the current status of artificial intelligence in oceanography and aims to provide a comprehensive demonstration of the advantages and potential of applying artificial intelligence methods in the field of oceanography. With the two emerging research hotspots: digital twin oceans and artificial intelligence large models, the future development direction of artificial intelligence provides enlightenment and reference for interested scientists and researchers.
Waves are one of the most important phenomena in the ocean. The accurate and quick updated wave forecasting is of crucial significance for ensuring marine activities safety. The development of wave forecast is presented, including the traditional statistical wave forecasting methods, numerical wave prediction models, and the rapidly developing artificial intelligence (AI) wave forecasting methods. Currently, AI wave forecast models have been demonstrated unique advantages in terms of computational efficiency and adaptive forecasting accuracy, and they are gradually being applied in practical wave forecasting operations, transitioning from the research stage. However, they also have limitations, including limited forecasting elements, underestimation of extreme wave conditions, and weak forecasting generalization ability. Based on the characteristics of AI wave prediction, key scientific and technological issues that need to be addressed in current AI wave forecasting are proposed. These include efficient utilization of observational data, incorporation of prior physical knowledge, and enhancement of AI model safety and generalization ability.
