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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.

Aiming at the problems of difficulty in verifying the accuracy of the interpolation model of single-beam bathymetric data, a method based on high-precision UAV data to verify the accuracy of the interpolation model was proposed by using the intertidal tidal law. At low tide, UAV photogrammetry was used to construct a high-precision digital surface model (DSM) of the intertidal zone, and at high tide, the single-beam bathymetry data was obtained and the three-dimensional coordinates of the intertidal topographic points were calculated by combining the global navigation satellite system (GNSS) technology, and constructed an intertidal digital elevation model (DEM) by using the following 4 interpolation methods: Kriging, inverse distance weight, completely regularized spline and natural neighborhood interpolation method. Based on UAV data, the accuracy analysis of intertidal zone DEM was carried out. The results show that: (1) PPK technology-assisted UAV photogrammetry can construct high-precision intertidal zone DSM. (2) In the intertidal zone, UAV data can be used as an evaluation criterion for the accuracy of single-beam bathymetry data. (3) When the seabed topography is relatively flat, the completely regularized spline method has higher accuracy than the other three methods, and the coarse difference rate is 12.5%.

Abyssal peridotite is widely distributed in tectonic environments such as mid-ocean ridges, subduction zones, and continental margins, and typically undergoes subsequent alterations, among which serpentinization is the most significant type. Serpentinization refers to the chemical process wherein ferromagnesium-rich minerals in peridotite, such as olivine and pyroxene, are replaced by a series of secondary minerals like serpentine, magnetite, and brucite. The conditions of serpentinization are closely linked with hydrothermal circulation and the migration of mineral-forming substances, bearing significant implications for indicating hydrothermal mineralization. Traditional methods of petrology and geochemistry exhibit polysemic interpretations and uncertainties when reflecting serpentinization conditions, with different minerals or chemical indicators possibly suggesting different outcomes. Oxygen isotopes are ubiquitous in nature and the oxygen isotope tracing method, due to its wide applicability, ease of comparison, and support for in-situ micro-zone analysis, can clearly reflect the reaction conditions and processes of the mineral or rock-fluid system. This study primarily provides an overview of the principles of oxygen isotope thermometry, the process of abyssal peridotite serpentinization, application cases of oxygen isotope thermometry in the serpentinization of abyssal peridotite, factors influencing the oxygen isotope compositions of serpentinites, as well as the advantages and limitations of oxygen isotope thermometry. It aims to offer a reference for a more profound understanding of the serpentinization process of abyssal peridotite.

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.

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.

The Indian Ocean Dipole (IOD) is a pivotal climate phenomenon in the Indian Ocean region, exerting a significant impact on the climate change of the surrounding areas and the global climate system. Accurate prediction of IOD is essential for comprehending the dynamics of the global climate, yet traditional forecasting methods are limited in capturing its complexity and nonlinearity, constraining predictive capabilities. This paper begins by outlining the relevant theories of IOD and evaluates the strengths and weaknesses of traditional forecasting methods. It then provides a comprehensive analysis of the application and development of deep learning in the field of IOD prediction, highlights the advantages of deep learning models over traditional methods in terms of automatic feature extraction, nonlinear relationship modeling, and large data processing capabilities. Additionally, the paper discusses the challenges faced by deep learning models in IOD forecasting: including data scarcity, overfitting, and model interpretability issues, and proposes future research directions to promote innovation and progress in the application of deep learning technology in the field of climate prediction.

Deep-sea sediments have attracted much more attention in recent years because of their potential resources for rare earth elements plus Yttrium (REY). However, the host minerals and enrichment mechanism of REY in deep-sea sediments, and the spatial distribution characteristics and metallogenic regularity of the REY-rich sediments are still unclear. The Clarion-Clipperton Fracture Zone (CCZ) in the East Pacific is the most important polymetallic nodule metallogenic belt, and its potential of REY resources has not been well evaluated. In this study, the whole-rock geochemistry (728 groups of major elements and 625 groups of trace elements) of sediments from 125 stations in the west CCZ over an area of 27 800 km² was analyzed. The results show that the sediments in the study area are significantly rich in MnO, P₂O₅ and REY than those from Australian shales and global subducting sediments. Spatially, ∑REY has a positive correlation with P₂O₅, CaO, and Ce negative anomalies, indicating that calcium apatite is the main host minerals of REY. The average value of ∑REY in the sediments over the study area is 470±202 μg/g, some samples meet the criteria of REY-rich sediments (∑REY>700 μg/g), indicating that the study area has a certain potential of REY resources. Spatial interpolation analysis shows that REY-rich sediments are mainly distributed in the northern area characterized by hilly terrain, while they are poorer in the southern basin with flat terrain. The difference of geomorphology in the study area affects the regional deposition rate and the hydrodynamic sorting of calcium apatite, leading to the north-south zoning of REY resources distribution in the study area.

Tidal current energy is the kinetic energy carried in the horizontal movement of tidal water, which has great development prospects. Accurate simulation and characterization of regional tidal currents can help to efficiently evaluate the spatial and temporal distribution of tidal energy resources, which is the key to the development and utilization of tidal current energy resources. In this paper, a high-resolution numerical model of tidal currents is constructed by applying FVCOM ocean model in Zhoushan sea area where has rich tidal current energy, and the reliability of the model is confirmed by tidal level and current verification. According to the simulation results, six waterways with dense tidal current energy resources in the Zhoushan sea area were identified, among which the average energy density of Xihoumen waterway, Cezi waterway and Taohuagang waterway exceeds 2.0 kW/m², and the maximum energy density exceeds 20 kW/m², and the flow speed over 1.0 m/s of the whole month is more than 80%. During tidal current ebb and flow, the reflow is dominant, while the asymmetry and rotation of tidal current are low. The flow stability coefficient is more than 0.98, so it is more suitable for the development and utilization of tidal current energy than other three waterways. The best location for tidal current energy development in these three waterways was then determined by calculating the significant hours and available hours, and the corresponding exploitable tidal current energy resources were evaluated using the Farm method, which were 27.53 MW, 39.96 MW, and 130.26 MW, respectively.

The clay mineral assemblage in abyssal sediments can reflect the paleoclimate and paleoenvironment changes of the provenance area. Predecessors have many studies about the characteristics of clay mineral assemblages in the Pacific sediments west of the Mariana Trench, but the less research on the characteristics of clay mineral assemblages in the east of the Mariana Trench limits a deeper understanding of the relationship between the Asian aeolian dust input and the Pacific deep-sea sedimentary environment. In this study, 31 surface samples and 1 core sample collected in the Malkus-Wake Seamount area of the Western Pacific during the 2019 DY54 cruise were analyzed for clay minerals, combined with the data collected in the Western Pacific and nearby areas, we discussed the characteristics of composition, distribution and the provenance of clay minerals. The results showed that illite was the main clay mineral in surface sediments of the study area, with an average value of 69%; the content of chlorite and kaolinite were the second, with an average of 16% and 11% respectively; that of smectite was the least, with an average of 3%. In the core P04, the assemblage of the clay mineral was basically the same as surface samples, but below 250 cm depth, the content of smectite increased significantly, while the content of illite decreased relatively. Provenance analysis showed that Asian aeolian dust was the main source of illite in the study area, and kaolinite and chlorite might also be from Asian aeolian dust, and the East Asian winter monsoon was the main transport force of these clay minerals. The smectite in sediments could be mainly formed by weathering of submarine volcanic materials. The increase of illite content while the decrease of smectite content in the upper layer of core P04 sediment was responded to the increase of Asian aeolian dust input since the Middle Pleistocene.

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.

Ocean eddies are prevalent oceanic phenomenon that play a crucial role in the global transportation of oceanic materials and energy. Although traditional methods for detecting ocean eddies are widely used, they suffer from significant drawbacks such as excessive reliance on expert-set thresholds, continuous manual intervention, large detection errors, low efficiency, and poor global applicability, making it difficult to adapt to the complex and variable marine environment. Currently, the rapid development of artificial intelligence (AI) presents a promising solution for the intelligent detection of ocean eddies. AI can automatically and rapidly extract deep features from images, effectively address the challenges posed by the high similarity in oceanic phenomenon features and significant geometric variability. This paper provides an overview of AI-based oceanic eddy identification methods based on different deep learning methods, focuses on coder-decoder structure, fully convolutional neural network, multi-scale context method and attention mechanism, and aims to provide valuable insights and references for future ocean eddy research.

Based on the one-year measured wave data in the coastal area of Cangnan, Zhejiang Province, the characteristics of wave parameters were statistically analyzed, the correlation between wave parameters was analyzed by using the least square method, the relationship between the average wave duration and wave height was studied, the wave energy was estimated, and the characteristics of typical typhoon waves during typhoon “Lekima” were analyzed.The results show that the study area is mainly composed of light waves with spectral peak period of 5-9 s, the annual average significant wave height of 1.22 m, the normal wave direction is E, the strong wave direction is ENE.There is a significant linear relationship between the characteristic wave heights, which conforms to the typical Rayleigh Distribution.In typhoon free period and cold wave free period with significant wave height below 2.7 m and typhoon period with significant wave height above 4.1 m, the average duration of wave decreases exponential decays with the increase of wave height, and the attenuation rate of typhoon period with significant wave height above 4.1 m is higher than that of typhoon free period and cold wave free period with significant wave height below 2.7 m.During the impact of typhoon “Lekima”, the maximum wave height, spectral peak period, and spectral peak density show a basically synchronous process of first increasing and then decreasing, with a maximum spectral peak density of 55.10 m²/Hz;the typhoon wave spectrum before and after the impact of the typhoon show a bimodal spectrum, while the wave spectrum during the most significant period of typhoon impact show a unimodal spectrum.

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.

Based on Western Pacific surface seawater as matrix, a series of reactive phosphorus reference materials with the concentration of 0.5-4.0 μmol/L were prepared. By investigating the uniformity and stability of the 4 batches of samples, it was confirmed that the performance met the needs of requirements. For the determination of reactive phosphorus in aqueous sample, segmented continuous flow analysis system with UV detector was used. The experimental conditions were optimized, the limit of detection(LOD) of the method was 0.03 μmol/L, and the relative standard deviation (RSD) of 4 different concentration batches was 0.37%-2.45%. The reliability of the method was verified by testing a CRM. The error was not more than 0.05 μmol/L. The concentration standard value of the series of reference substances was determined by 6 laboratories, and the relative expended uncertainty of characterization was evaluated, which was 2%-10%.

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.

The intertidal zone is a key area connecting terrestrial ecosystems and marine ecosystems, among which muddy tidal flat is an important and easily overlooked CO₂ collection habitat, and the macrobenthos play a central role in the input, transport and preservation of carbon. Macrobenthos community and living organic carbon pools of muddy tidal flat were analyzed in Aiwan Bay, eastern coast of Zhejiang Province in summer. The average abundance of macrobenthos was 105.2 ±37.2 ind/m², and the average biomass was 46.9 ±6.4 g/m². The major taxa components within the habitat were crustaceans and mollusks, and the ecosystem health status was excellent. The organic carbon contents of macrobenthos at Aiwan Bay from highest to lowest were other animals including fish and nemertinea (40.95%), polychaetas (22.98%), crustaceans (17.24%), echinoderms (15.90%), mollusks (10.76%), and estimated the macrobenthos carbon pool was 163.90 Mg, of which crustaceans have the largest contribution rate, accounting for 59.80%. The exploration of macrobenthos community structure and living organic carbon pools size in muddy tidal flat can provide scientific suggestion for constructing the blue carbon survey system and supply fundamental data to further quantify the overall carbon pool size in coastal habitats.

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.

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.

Coastal erosion leads to land loss and seriously threatens people’s life and property safety. It is great significant to identify coastal erosion vulnerability for disaster prevention and mitigation. The evaluation index system was constructed from three aspects: coastal dynamics, coastal morphology and social economy. Using the DSAS model and remote sensing data, the coast was discretized into equally spaced units based on section method, the weight and grade of the evaluation index were determined based on the entropy weight method, the coastal erosion vulnerability in the study area was calculated, and the spatial differentiation and influencing factors of coastal erosion vulnerability were identified by geographic detector. The results showed that the proportions of coastal erosion vulnerability for extremely high vulnerability, high vulnerability, medium vulnerability, low vulnerability and extremely low vulnerability in central coast of Jiangsu were 5.60%, 15.80%, 30.93%, 24.21%, and 23.46%, respectively, that showed a decreasing trend from north to south. The extremely vulnerable areas of coastal erosion were mainly located in the coastal area between the Zhongshan Estuary and the Sheyang Estuary. The spatial differentiation of coastal erosion vulnerability in central Jiangsu was the result of the synergistic effect of multiple factors such as coastal dynamics, coastal morphology, and economic and social activities. Among them, tidal slope, land cover, average tidal range, and coastline change rate were the dominant factors for the spatial differentiation of coastal erosion vulnerability.

An accuracy analysis of wind speed data retrieved from Sentinel-1 synthetic aperture radar (SAR) was conducted based on buoy observations from the National Data Buoy Center (NDBC). A back propagation (BP) neural network was utilized to correct the deviation in the SAR-derived wind speeds. Sensitivity experiments were designed for environmental factors, the number of training samples for BP neural network input, and neural network structure parameters. Finally, the SAR wind field data were converted into u and v vector wind data, and the accuracy analysis and correction were performed separately for u and v wind components. The experiment finds that the SAR-derived wind speed is underestimated compared to the buoy data. After calibration using BP neural network, the accuracy of SAR-derived wind speed data is improved, and the absolute value of bias of wind speed decreases from 0.78 m/s to 0.04 m/s, the RMSE of wind speed decreases from 1.98 m/s to 1.77 m/s. The sensitivity experiments suggest that low quality environmental factors input data will decrease the calibration effect of BP neural network, and increasing the sample size of the training set can improve that. The calibration results of converted u and v vector wind field data also show that the BP neural network has good correction effect.