Feature article

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.

Histopathological indexes can be used to assess health of organism, but their application faces challenges such as low efficiency, high cost and strong subjectivity. Introducing artificial intelligence (AI) technology into histopathological analysis of biological tissues can leverage its high-throughput image analysis capabilities, overcoming the limitations in assessing and monitoring marine organism health. Based on our review on health assessment indicators of marine organisms, the application of AI in image analysis, and the use of AI for histopathological image processing, a deep learning-based histopathological image analysis approach was proposed using gill tissues of marine mussels as representative. Through a series of processes such as training, validation, and prediction of histopathology images, it was determined that the Res-UNet deep learning model can efficiently and accurately quantify histopathological damage in mussels’ gills. An automated, high-throughput, and less subjective workflow based on deep learning was finally established, offering new ideas and techniques for marine organism health assessment and marine monitoring.

Based on multi-platform observed data, an unexpected response of a warm mesoscale eddy to bypassed typhoon Megi in the South China Sea in 2010 was observed and investigated. During the passage of typhoon Megi, the SLA maximum of the warm eddy increased from 30 to 36 cm, the radius increased from 78 to 116 km, the eddy kinetic energy increased from 166 to 303 m²/s², and the amplitude increased from 3 to 9 cm. On the right side of the typhoon, the thermocline water at Argo station on the edge of the warm eddy sank by 20 to 40 m. Diagnosis of the wind stress curl alone indicates that the warm eddy should be weaken and the thermocline should be raised, which are inconsistent with the observation results. Diagnosis based on the reanalysis sea surface velocity indicates that during the passage of typhoon Megi, the water diverges below the typhoon path, while the water converges on the right side of the path in the warm eddy region, and the SLA maximum as well as the amplitude of warm eddy are positively correlated with the convergence intensity. Estimation based on the reanalysis sea surface velocity also indicates that the water at Argo station will sink 29 m. Both the warm eddy characteristics and the thermocline depression are consistent with the observation. The case study shows that the response of mesoscale eddy on the edge of typhoon influence to typhoon is constrained not only by wind stress curl but also by the oceanic background conditions, and further attentions are required to explore the corresponding response and mechanism of upper ocean to typhoon.

Combined with the Holland wind fields and the ERA5 wind fields, the mixed wind fields was set up by introducing a weight coefficient varying with the radius of wind speed, and a typhoon wave model in Zhejiang sea area was established using MIKE21 SW. Then, the Holland, the ERA5 and the mixed wind fields were used as the input wind fields to simulate the wind speed and the significant wave height during No.1918 typhoon Mitag, respectively. The verification shows that the simulated results obtained using the Holland wind fields and the ERA5 wind fields cannot agree accurately with the observed data, while the mixed wind fields proposed in this study can improve the simulation accuracy. In order to study whether the above conclusion is universal in Zhejiang sea area, five typical typhoons that have the most serious impact on Zhejiang sea area in the recent 5 years were selected for typhoon wave numerical simulations and the error statistical analysis. The results indicate the wind speed around the typhoon center is relatively good using the Holland wind fields and the average relative errors of the maximum wind speed are 8.62%-10.19%, but the average relative errors of the wind speed below 10 m/s is relatively bigger, reaching 29.76%-44.29%. However, the wind speed around the typhoon center using the ERA5 wind fields is smaller than the observed data, and the average relative errors of the maximum wind speed are 17.64%-25.77%, but the average relative errors of wind speed below 10 m/s are smaller than that using the Holland wind fields, which are 19.64%-32.00%. During the five typhoon processes, the average values of the average relative errors of the significant wave height driven by Holland, the ERA5 and the mixed wind fields are 29.92%, 25.62% and 22.82%, respectively. Correspondingly, the average root mean square errors are 0.46 m, 0.42 m and 0.39 m and the consistency indexes are 0.94, 0.95 and 0.96. The above results shows that the mixed wind fields proposed in this study is universal in Zhejiang sea area and can improve the simulation accuracy of typhoon waves.

Based on Global Navigation Satellite System (GNSS) dynamic precision point positioning technology (PPP), the influence factors of precipitable water vapor (PWV) detection over the ocean were studied. The sampling interval, satellite masking angle, PPP solution method (fixed solution or floating point solution), and the influence of Beidou satellite system combination on ocean PWV retrieval were mainly analyzed. In the marine observation environment, the results show that the accuracy of PWV inversion is the highest when the sampling interval is 30 s. When the number of available satellites is small, the accuracy of PWV inversion is better when the satellite masking angle is set to 5°-10°, and the accuracy decreases gradually with the increase of the satellite masking angle. Whether the PPP solution is fixed or not, it has little effect on the accuracy of PWV inversion. On the basis of GPS/GLONASS system combination, adding Beidou observation value will improve the redundancy of observation and improve the accuracy of PWV inversion.

A new method for determining the foot point of the continental slope (FOS) was proposed for the delineation of the continental shelf in a complex geological context. This method calculated the location of the foot of slope based on the mean gradient of water depth and optimized it by combining the contrary evidence and the principles of convexity, segmentation and continuity. Using the southern continental margin of Mozambique as the study area, the method was applied to extract the most critical basis—FOS for continental shelf delineation using high-precision multibeam topographic data measured in 2021, and the result was confirmed by comparison with those extracted by the Geocap software which is used by the United Nations Commission on the Limits of the Continental shelf, proving the effectiveness and accuracy of this method.

Erythrobacteraceae is widely distributed in marine environments and can synthesize various pigments such as carotenoids. Carotenoids have photoprotective and antioxidant abilities, and they can play a crucial role in the adaptation of Erythrobacteraceae to marine ecosystems. In this study, we obtained the genomes of 107 marine Erythrobacteriaceae strains, analyzed the distribution characteristics of carotenoid biosynthesis genes including crtEBIGYZW in their genomes, constructed phylogenetic trees based on the amino acid sequences of the carotenoid biosynthesis genes, and elucidated the presence and distribution of carotenoid biosynthesis genes in Erythrobacteriaceae from deep-sea and shallow-sea sources. The results suggested that all of marine Erythrobacteriaceae strains contained the crtEIGZ genes, 98.1% of strains contained the crtBY genes, and 43.9% of them contained the crtW gene, which differed among strains but no any specificities found among the deep-sea or shallow-sea sources. Additionally, in the comparison of the phylogenetic topology, it was found that the crtBYZW genes were vertically transferred from their ancestor, while the crtEIG genes were mainly horizontal gene transferred. Our study aids in the understanding of the carotenoid biosynthesis gene evolution in the family Erythrobacteriaceae and also serves as a scientific basis for the study of carotenoid synthesis pathways and genes in other marine bacteria, enables a thorough understanding of marine carotenoid-synthesizing strains.

The waters surrounding South Georgia Island are one of the highest primary productivity regions in the Southern Ocean with enormous carbon sequestration potential. However, the strength of the biological pump efficiency in this area is still uncertain due to the lack of continuous upper ocean observation data.In this study, the hydrological and biogeochemical parameters obtained from the Biogeochemical Argo (BGC-Argo) floats deployed in the South Georgia Island vicinity during the period of 2017-2020 were utilized to investigate the impacts of physical processes on biogeochemical processes and to estimate the carbon export flux in the Antarctic summer. Results indicated that both upstream (northeast of the Antarctic Peninsula) and downstream (Georgia Basin) regions of South Georgia Island exhibited strong seasonal characteristics in Chl-a, with the latter area having a 4-month sustained period of phytoplankton bloom, suggesting a stable and continuous supply of iron. Using the temporal variability of the seasonal particulate organic carbon (POC) export, the summer POC export fluxes of the upstream and downstream regions were estimated to be 7.12±3.90 mmol·m^-2·d^-1 and 45.29±5.40 mmol·m^-2·d^-1, respectively, indicating that the difference might be due to enhanced downward export of organic carbon after the deepening of the mixed layer. The study found that the region maintained a high biological pump efficiency, contrary to the previous conclusion that the Georgia Basin had “high productivity low export efficiency”, which might have been caused by the limited “real-time” representation of the entire seasonal characteristics during ship-based surveys. BGC-Argo provides high spatiotemporal resolution of multi-parameter observation data, and this study demonstrates that it can more accurately quantify and evaluate marine biogeochemical processes and carbon sequestration potential.

Near-inertial waves (NIWs) play an important role in the response of ocean to typhoon. Their frequency varies with the depth and is the main factor in determining the propagation rate of near-inertial energy to the ocean interior. Based on the observation data from mooring, the factors affecting the blue-shift frequency of NIWs excited by typhoon were investigated in northwestern South China Sea. By analyzing the vorticity effect and Doppler effect caused by background currents, this study suggests that the Doppler effect of background currents was the main factor in the blue-shift frequency of NIWs. As depth increased, inertial wave frequencies increased. Quantitative calculations further demonstrated that within the upper 200 meters, the Doppler effect of the background currents was negative, approaching zero in depth around 200 meters. However, in the depth range of 230 to 400 meters, the Doppler effect became positive. This depth range exhibited the maximum strength of the background currents, with their direction aligned with the propagation direction of inertial waves. Consequently, the positive Doppler shift induced by the background currents was most pronounced. The results of this study are important for improving the understanding of the ocean response to typhoons, especially the propagation of near-inertial waves in areas with complex background current structure (e.g., the western boundary current region).

Based on the mesoscale atmospheric model WRF and the regional ocean model ROMS, a two-way coupled WRF-ROMS air-sea model was constructed to simulate the super typhoon Mangkhut in 2018. The results showed that the simulation results of the coupled air-sea model were better than those of the only atmospheric or ocean model, and the error of the typhoon track obtained from the coupled model was within 60 km, which was in good agreement with the best track. Compared with the observation results, the simulation results of wind speed and sea level pressure in the coupled model were better than others model. Based on the simulation results of the coupled air-sea model, the spatial and temporal distribution of the wind field, pressure field, sea surface flow field, and storm surge under the super typhoon Mangkhut were further analyzed. The results showed that: (1) In terms of spatial distribution, after the typhoon entered the South China Sea, the radius of the seven-level wind circle was larger behind the right side of the typhoon; the cyclonic flow field showed a significant Ekman effect with the typhoon wind field, and the flow direction was 45° from the wind direction. The wind field, pressure field, wind-generated flow field and water gain distribution all had obvious asymmetry, and the typhoon intensity, flow velocity and water gain were greater on the right side of the typhoon path than on the left side. (2) In terms of time distribution, the distribution of the wind field and the pressure field were similar and synchronized with the typhoon center, while the wind-driven flow field and storm surge were three hours behind the typhoon track.

Nitrate is the main nitrogen form available for phytoplankton life activities in the ocean, and its nitracline depth (Z_N) directly affects the vertical transport of nitrate and the ocean primary productivity, and then further influences the carbon cycle. With the advancement of ocean observation technologies, the profile data of nitrate have been collected in diversified ways, such as ship-based CTD observations and BGC-Argo automatic observations. The vertical sampling resolution of these techniques varies significantly (the vertical sampling resolution of CTD is lower than that of BGC-Argo). In view of different sampling data, it is urgent to conduct systematic and quantitative comparative analysis and study on the computing methods of Z_N. In this study, three different methods: difference method, gradient method and threshold method, are adopted to compute the corresponding Z_N by using the historical ship-based CTD data and BGC-Argo buoy data in the Northwest Pacific Ocean. The results show that in the case of single nitrate profile, based on BGC-Argo data, the difference between observed Z_N and the Z_N calculated by difference method is only 0.2 m, followed by threshold method is 20.0 m and gradient method is 202.8 m at most. Based on CTD data, the difference between observed Z_N and Z_N calculated by difference method is 2.0 m, the threshold method is 49.0 m, and the gradient method is 155.0 m. Compared with the gradient method and threshold method, the difference between the Z_N calculated by the difference method and the observed Z_N is the smallest. According to the results of statistical error analysis, it is found that the Z_N calculated by the three methods based on BGC-Argo data show a good correlation with the observed Z_N. Among them, the error of difference method is the smallest (R²=0.77, RMSE=28.48 m). The R² and RMSE of threshold method are 0.64 and 34.85 m, and the R² and RMSE of gradient method are 0.52 and 53.80 m. For CTD data, due to its low vertical sampling resolution, the Z_N calculated by the three methods is quite different from the observed Z_N. However, compared with the gradient method and threshold method, the error of the difference method is still the smallest (R²=0.81, RMSE =16.13 m). The R² and RMSE of threshold method are 0.47 and 27.65 m, and the R² and RMSE of gradient method are 0.42 and 36.41 m. The applicability of each method is preliminarily explored through comparing and analyzing the characteristics and differences of them so as to provide some scientific reference for the in-depth research on the vertical distribution characteristics and upward transport process of nitrate.

Based on the high frequency data of sea-air interface buoys, the variation pattern and driving factors of sea-air partial pressure of carbon dioxide (pCO₂) were analyzed and the sea-air CO₂ flux in the coastal waters of Qingdao in spring was estimated. During the observation period, the sea area changed from a carbon sink of atmospheric CO₂ to a carbon source, which was mainly caused by the continuous increase of sea surface pCO₂. By analyzing the controlling factors of pCO₂, it was found that temperature was the main driving factor of pCO₂ growth, and biological processes played a certain inhibiting role. The sea surface pCO₂ showed a diurnal variation. The effects of temperature and biological factors on the diurnal variation of pCO₂ were related to solar radiation, but they had opposite effects. In addition, the analysis showed that different sampling frequencies of buoys affected the estimation of sea-air CO₂ flux and shortening the sampling interval could effectively reduce the deviation of CO₂ flux estimation and improve the accuracy of estimation.

The spatiotemporal variation of surface eddy kinetic energy (EKE) in the South Australian Basin was studied using sea level anomaly during 1993-2019. The results show that in spatial scale there are two regions of high EKE: one to the west, and one to the east. On the seasonal scale, surface EKE is the strongest in austral winter with a maximum (57±9 cm²/s²) in July and the weakest in autumn with a minimum (40±5 cm²/s²) in March. On the interannual scale, surface EKE is related to El Niño-Southern Oscillation (ENSO) and Southern Annular Mode (SAM). Partial correlation analysis indicates that surface EKE shows negative correlations with ENSO, lagging the Niño3.4 index by 9 months, and EKE is significantly weakened (strengthened) in the decaying year of El Niño (La Niña). Meanwhile, surface EKE shows positive correlations with SAM, lagging SAM index by 14 months, and EKE is significantly strengthened (weakened) in the next year of the positive (negative) SAM phases.

Mangroves, coastal salt marshes and seagrass beds, as the typical coastal blue carbon ecosystems, have been widely recognized for their remarkable capacity in carbon storage. Vegetation carbon pool and sediment (or soil) carbon pool were considered to be the major carbon pools within the coastal blue ecosystems and their variations determined the overall carbon sequestration of the ecosystems. From a perspective of carbon pool interactions, this study summarized the previous research work based on literature review, including the interactions within various vegetation carbon pools and within various sediment carbon pools, as well as the interactions between vegetation and sediment carbon pools. Interspecific competition, allochthonous carbon input and biogeomorphology were found to be the key to understand the carbon pool interactions. Finally, a perspective on the current state-of-the-art of blue carbon pool study is offered, with challenges and suggestions for future directions.

Costal ocean receives a bunch of carbon materials and nutrients from terrestrial sources, relates a lot of carbon-involving interactions. Meanwhile, it is normal that sedimentary reservoir-cap systems with good trap conditions beneath coastal ocean, these entrapments have potentials to storage CO₂. This review focuses on the coastal ocean as the research object, and introduces the carbon cycle processes in coastal ocean, their factors which could influence CO₂ fluxes in the carbon cycle processes, and the potential carbon storage mechanisms of the coastal marine sedimentary basins. From the perspective of “carbon peaking and carbon neutrality”, the significance of coastal oceans for “Ocean Negative Carbon Emission (ONCE)”, its potential promotion paths, carbon storage potentials in sedimentary basins and the problems faced by coastal oceans are discussed. Overall, the costal ocean is one of the important blue carbon sink areas. In the coastal marine seawater system, improving the reaction efficiency of microbial carbon pump and carbonate carbon pump have positive significance for CO₂ negative emissions; The suitable reservoir-cap systems for CO₂ storage beneath coastal ocean can not only provide extra spaces, but also guarantee the safety for CO₂ storage. In the future, the main research directions should be to inhibit the conversion process of carbon materials to CO₂ in coastal oceans and ensure the safety of CO₂ storage in sedimentary reservoirs, these could provide theoretical basis and technical guarantee for CO₂ negative emissions.