Swells are critical concerns regarding safety, marine transportation, and coastal engineering construction of coastal countries along the Gulf of Guinea and have been scientific problems due to the lack of systematic theoretical, numerical, and observational research. In this study, a double nesting numerical model was constructed and validated from the Atlantic Ocean to the Gulf of Guinea based on simulating waves nearshore (SWAN) to explore the swell characteristics and source tracing in the Gulf of Guinea in winter and summer seasons from 2020 to 2021. Simulation results reveal that swells are stronger and deflect more to the west in winter than summer, even though they dominate in both seasons in the Gulf of Guinea in the S-SW directional range. Simulated two-dimensional (2D) wave spectral patterns not only clarify wave composition, variation, and propagation properties from the central South Atlantic Ocean to the Gulf of Guinea, but also distinguish swell strength and directional range in winter and summer. The NW wind events induce swells which spread toward the SSE-ESE direction from the North Atlantic Ocean, big wind source generates sustained and stable S-SW swells from the South Atlantic Ocean, and corresponding swell-influenced areas are discussed. The strongest swell event in the Gulf of Guinea during the simulation was used as a case study to trace its source. A strong clockwise wind vortex within the Roaring Forties induced these large swells in the Gulf of Guinea approximately 5.5 days later, and swell propagation formed a regular isoline of peak period distribution from the South Atlantic Ocean to the Gulf of Guinea in the SSW-SW direction.

A mobile marine seismometer (MMS) is a vertical underwater vehicle that detects ocean seismic waves. One of the critical operational requirements for an MMS is that it remains suspended at a desired depth. This article aimed to propose a fixed-depth suspension control for the MMS with a limited onboard energy supply. The research team established a kinematic model to analyze fluctuations in the vertical motion of the MMS and the delayed response of the system. We ascertained a direct one-to-one correlation between the displacement volume of the mobile ocean seismic instrument and the depth at which it reaches a state of neutral buoyancy (commonly referred to as the hover depth). A fixed-depth control algorithm was introduced, allowing a gradual approach to the necessary displacement volume to reach the desired suspension depth. The study optimized the boundary conditions to reduce unnecessary adjustments and mitigate the time delay caused by the instrument’s inertia, thereby significantly minimizing energy consumption. This method does not require calculating the hydrodynamic parameters or transfer functions of the MMS, thereby considerably reducing the implementation complexity. In the three-month sea trial in the South China Sea, the seismic instrument was set to hover at 800 m, with a permissible fluctuation of ±100 m, operating on a seven-day cycle. The experimental results show that the seismic instrument has an average hover error of 34.6 m, with a vertical drift depth of 29.6 m per cycle, and the buoyancy adjustment system made six adjustments, indicating that our proposed control method performs satisfactorily. In addition, this method provides new insights for the fixed-depth control of other ocean observation devices that rely on buoyancy adjustment.

The correct understanding of fish population structure plays a positive role in their fishery management. The dotted gizzard shad, Konosirus punctatus, is widely distributed in the coastal waters of the northwestern Pacific. With the over-exploitation of economically important fishes, its importance is increasingly prominent. To further examine the population genetic structure of K. punctatus across the northwestern Pacific, the amplified fragment length polymorphism (AFLP) and the inter-simple sequence repeats (ISSRs) were employed to perform genetic variation analysis. The results showed that the combination of polyacrylamide gel electrophoresis and silver staining can effectively detect genetic variation for K. punctatus populations. The average proportions of polymorphic loci were 46.26% and 87.13% for AFLP and ISSR markers, respectively, and the genetic diversity parameters showed no obvious differences among populations. Both analysis molecular variance (AMOVA) and pairwise F _st suggested that there was significant genetic differentiation between Chinese and Japanese populations. All samples also clustered into two clades based on the unweighted pair-group method analysis (UPGMA) tree by two markers, which indicated significant genetic differentiation among populations. Consistent with the previous studies, there are two highly differentiated groups at the nuclear gene level and they were suggested to be treated as two separate genetic management units. The results of the present study could provide the genetic management strategy for this important economic species.

Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species, severely affecting the ecological balance of coastal wetlands. Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora, but they require large datasets and have poor interpretability. A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery. Firstly, to get the high canopy cover and dense community characteristics of Spartina alterniflora, multi-dimensional shallow features are extracted from the imagery. Secondly, to detect different objects from satellite imagery, index features are extracted, and the statistical features of the Gray-Level Co-occurrence Matrix (GLCM) are derived using principal component analysis. Then, ensemble learning methods, including random forest, extreme gradient boosting, and light gradient boosting machine models, are employed for image classification. Meanwhile, Recursive Feature Elimination with Cross-Validation (RFECV) is used to select the best feature subset. Finally, to enhance the interpretability of the models, the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations (SHAP) is combined with these classifications to explain the model prediction process. The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island, it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8% compared with the model without image texture features. Through multiple model comparisons and feature selection via RFECV, the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods, proving that feature reduction can effectively enhance model generalization. Additionally, visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.

Significant wave period is an important parameter in coastal and offshore engineering design. Traditional spectral wave models do not directly calculate this parameter, which means that it needs to be estimated from the spectral periods using empirical formulas. The wave energy period is one of the wave periods directly output by many wave models and is often used in studies of wave energy. This study investigated the relationship between significant wave period and wave energy period using wave data measured at three stations in the coastal waters of China. The observations recorded at these stations in the South China Sea, the East China Sea, and the Bohai Sea covered a wide range of surface wave conditions. Analysis indicated that the ratio of significant wave period to wave energy period is closely related to the Goda peakedness parameter of the wave spectra. Therefore, we proposed an empirical formula in which significant wave period is a function of wave energy period and the Goda peakedness parameter. Evaluation results showed that the performance of this formula is substantially better than that of fitting formulas that use constant coefficients.

Nonlinear internal waves (NLIWs) exhibit robust dynamic submesoscale motions, connecting large-scale tides to small-scale shear instabilities in the ocean. Previous studies have mainly focused on their generation mechanisms and evolution along their paths. Considering their global distribution resulting from the primary origin in tide-topography interaction, there is an increasing cross-disciplinary interest in understanding how these energetic and ubiquitous NLIWs contribute to sediment redistribution in the ocean. This paper presents fundamental theories on NLIWs and comprehensively reviews triggering mechanisms, different types of instability, and sediment responses by summarizing recent theoretical parameterizations, numerical simulations, laboratory experiments, and in-situ observations. We specifically focus on elucidating various types of instability along with their impact on sediment dynamic processes. Finally, we outline several unresolved issues that require further exploration for a quantitative investigation into NLIW-induced sediment transfer in the ocean.

To explore the geochemical characteristics and genesis of the elements in ferromanganese nodules from the Northwest Pacific, this study analyses the mineral composition, elemental content, occurrence phase and genetic mechanisms of samples by X-ray diffraction (XRD), inductively coupled plasma-optical emission spectrometry (ICP-OES), inductively coupled plasma-mass spectrometry (ICP-MS) and phase analysis methods. The results show that ferromanganese nodules are mainly hydrogenetic, and Mn/Fe content ratio ranges from 0.95 to 2.05. The major minerals are vernadite (δ-MnO₂) and amorphous ferric oxyhydroxide (FeOOH), and the secondary minerals include todorokite, birnessite, quartz and plagioclase. Ferromanganese nodules contain high contents of Co (0.24%–0.42%), Cu (0.23%–0.73%), Ni (0.33%–0.86%) and rare earth elements (REEs, 1192–1990 µg/g), which have positive Ce and negative Y anomalies but no Eu anomaly. A cluster analysis suggests that the elements in ferromanganese nodules can be divided into three groups: hydrogenetic components, including Fe, Ti, Zr, P, Pb, Co, Ba, Sr, V and REEs; diagenetic components, including Mn, Ni, Mg, Zn and Cu; and detrital components, including Al, Na, K and Ca. According to chemical leaching, ferromanganese nodules can be divided into four phases: Na, Ca, Mg and Sr are mainly enriched in the carbonate phase; Mn, Co, Ni and Ba are mainly enriched in the Mn-oxide phase; Fe, P, Ti, Cu, Pb, V, Zn, Zr and REEs are mainly enriched in the Fe-oxide phase; and Al and K are mainly enriched in the residual phase. A combination of the two different methods reveal selective enrichment of metal elements from seawater by ferromanganese nodules, featuring multisource mineralization. Moreover, through ion exchange and adsorption, approximately 71.2% of REEs are enriched in the Fe-oxide phase, 15.4% in the Mn-oxide phase and 12.4% in the residual phase, while REE contents in the carbonate phase are relatively low. In addition, under the oxic conditions of seawater, the oxidation of soluble Ce³⁺ to insoluble CeO₂ together with Fe-Mn minerals results in Ce enrichment in ferromanganese nodules. This study provides a reference for the metallogenesis of ferromanganese nodules from the Northwest Pacific.

Merged satellite altimeter products are widely used in ocean-related fields. Currently, the altimeter merged products of archiving validation and interpretation of satellite oceanographic (AVISO) data are widely used internationally. Chinese National Satellite Ocean Application Service also released merged altimeter products (ALT MUL) in 2023. However, there are few studies on the quality assessment of ALT MUL. Based on the data of AVISO merged products, Jason3 satellite, tide gauge and drifter buoy, the quality assessment and effect analysis of ALT MUL merged products were carried out by means of error evaluation index, interpolation along rails, velocity inversion and power spectrum. The result shows that the average sea level anomaly (SLA) of ALT MUL is about 2 cm smaller than that of AVISO. And they are consistent with the large-scale characteristics and spatial distribution. These two SLA products are both in accordance with normal distribution. Results indicate a lesser congruence between ALT MUL and Jason3 satellite compared to AVISO. This difference may be attributed to the fact that AVISO products use Jason3 satellite as cross-calibrated reference satellite during the merged process. Comparing the matching effect of the two merged products with the tide gauge and drifter buoy, ALT MUL merged products are superior to AVISO in general. The energy spectral density was calculated by using Jason3 satellite data along the orbit, and the two merged products were interpolated to the data points along the orbit. The effective resolution of AVISO and ALT MUL merged products was 180 km and 210 km respectively through spectral calculation, indicating that AVISO merged products have higher effective resolution.

Arctic sea ice is an essential component of the climate system and plays an important role in global climate change. This study calculates the volume flux through Fram Strait (FS) and the sea ice volume in the Greenland Sea (GS) from 1979 to 2022, and analyzes trends before and after 2000. In addition, the contributions of advection and local processes to sea ice volume variations in the GS during different seasons are compared. The influence of the surface air temperature (SAT) and the sea surface temperature (SST) on sea ice volume variations is discussed, as well as the impact of atmospheric circulation on sea ice. Results indicate no significant trend in the sea ice volume flux through FS from 1979 to 2022. However, the sea ice volume in the GS exhibited a notable decreasing trend. Compared with the period of 1979–2000, the sea ice volume decreasing trend accelerated significantly during the period of 2001–2022. During winter, ice advection from the central Arctic Ocean exert a strong influence on the sea ice volume variations in the GS, whereas during summer, local processes, including the interactions with the atmosphere and ocean, as well as the dynamic process of sea ice itself, exert a considerable impact. The sea ice volume in the GS declined rapidly after 2000. Furthermore, the effects of local processes on sea ice have intensified, with the SST exerting a stronger influence on the sea ice volume variations in the GS than the SAT. The positive Arctic oscillation and dipole anomaly are important drivers for the transport of Arctic sea ice to the GS. The Winter North Atlantic oscillation intensifies ocean heat content, affecting sea ice in the GS.

Spatio-temporal variation of sound speed, in seafloor geodetic precise positioning, can always be attributed to the time error. Firstly, this paper analyzes the existing error compensation model, i.e., the time ratio model, which is expressed by the recorded time multiplying a ratio coefficient. And then a time split model is proposed by expressing the acoustic ray traveling time as the recorded time pluses a perturbation time error. The theoretical differences between the proposed time bias compensation model and the time ratio model are analyzed. Under the new framework, sound speed perturbation models with optimal single-layer spatial gradient and multi-layer spatial gradients are developed to compensate for sound speed error in the complex cases. Numerical computation shows that the simple time split model keeps the same accuracy as some complicated models while considering the distribution of random error. Furthermore, multi-layer model can improve the positioning accuracy without putting the pressure on parametrization.