Using the direct observation data of the MSS90L turbulence profiler obtained in the summer cruise of the Changjiang River Estuary scientific investigation and experimental study (NORC2019-03-02) in July 2019, in this paper, the distribution of the turbulent kinetic energy dissipation rate ε and the vertical turbulent diffusion coefficient Kz are calculated and analyzed. The turbulent kinetic energy dissipation rate is 1.72×10⁻¹⁰ W/kg to 2.95×10⁻⁵ W/kg, and the vertical turbulent diffusion coefficient is 3.24×10⁻⁷ m²/s to 4.55×10⁻² m²/s. The distribution of the turbulent kinetic energy dissipation rate and the vertical turbulent diffusion coefficient are similar, both of which are the strongest in the upper layer, the next in the bottom layer and the weakest in the middle layer. In the upper layer, due to wind stress, the turbulent kinetic energy dissipation rate and vertical turbulent diffusion coefficient are larger; the stratification is stronger at the thermocline, which suppresses the dissipation of turbulent kinetic energy and vertical turbulent mixing. The secondary circulation of the salinity front will promote the separation of the low salt water mass, and the vertical circulation caused by the front will enhance the turbulent mixing of the ocean. The energy exchange between the low salt water mass and the outside world is less, and the turbulent kinetic energy dissipation rate is weaker. There are obvious upwelling and downwelling in the Changjiang River Estuary, which are produced by the secondary circulation of the front; the existence of upwelling and downwelling promotes the dissipation of turbulent kinetic energy and turbulent mixing.

In this study, we quantify the mixed-layer salinity (MLS) budget in the Indian Ocean using the salinity budget equation along with Argo salinity, SODA ocean current, evaporation, and precipitation data. The results show that the distribution and seasonal variability for the terms of freshwater flux, advection and entrainment in the Indian Ocean, and their contributions to the variations of MLS. Regarding the climatological annual mean, the negative contribution (15.14%) of the horizontal advection term to MLS is much larger than its positive contribution (9.89%). This implies that the horizontal advection transports low-salinity water to the salty regions, resulting in a decrease in MLS there. The distribution and seasonal variability of the freshwater flux term are generally consistent with those of precipitation, with a positive contribution (13.70%) higher than negative contribution (7.81%). It suggests that the freshwater flux term increases MLS in the Indian Ocean because of the evaporation overwhelming the precipitation. Analysis on the regions with significant seasonal variations of MLS indicates that from June to November, the Southwest Monsoon Current transports low-salinity water from the western equatorial Indian Ocean to the western Arabian Sea, causing salinity in that region to decrease. The advection of high-salinity water from the southern and central to the northern of Bengal Bay is the main reason for the increase in salinity there.

China-France Oceanography Satellite scatterometer (CSCAT) is the first rotating fan beam scatterometer internationally, which was flown onboard China-France Oceanography Satellite (CFOSAT) on October 2018. Based on the maximum likelihood estimation wind inversion algorithm, the residual characteristics of the CSCAT sea surface wind inversion cost function in detail, focuses on the influence of the new observation geometry on the wind inversion residual and wind quality is analyzed in this article, we establish the likelihood probability model function of the ambiguous solutions. The results show that the residual characteristic of the CSCAT wind inversion varies with the position of the wind vector cell (WVC) across the swath. The exponential distribution of the ambiguous solution likelihood probability model function is between −0.4 and −1.8. The results provide an important reference for the quality control of CSCAT and the refinement adjustment of the two dimensional variational ambiguity removal algorithm.

The geochemistry, occurrence phase and enrichment mechanism of REE and PGE were studied in cobalt-rich crusts from the Seamount Xufu in the Pacific by ICP-OES, ICP-MS and phase analysis methods. The results showed that, REE and PGE were enriched in cobalt-rich crusts, REE contents were 1 842−2854 μg/g, and PGE contents were 144−1180 ng/g. REE and PGE contents in the old layers were higher than that in the new layers, and the phosphatization in the old layers might play an active role in the REE and PGE enrichment. Moreover, LREE contents were more than HREE contents, and REE diagrams showed the positive Ce anomalies and no Eu anomalies, so Ce was enriched in cobalt-rich crusts. Meanwhile, there was an obvious contrast between PGE, and PPGE contents were more than IPGE contents. PGE diagrams showed the positive Pt anomalies and negative Pd anomalies, so Pt was enriched and Pd was poor in cobalt-rich crusts. In addition, REE in the new layers were mainly enriched in the iron oxide phase, and the enrichment ratios were 65.40%−70.05%. While REE in the old layers were mainly enriched in the residual phase, and the enrichment ratios were 62.27%−65.77%, because it might be related to the phosphate. Meanwhile, PGE in the new or old layers were mainly enriched in the iron oxide phase, and the enrichment ratios were 63.66%−69.51%, and also the residual phase ranged from 29.20% to 34.68% had an impact on PGE enrichment. Therefore, REE and PGE enrichment were presumed to the iron oxide colloid particles adsorption. In the marine oxidative environment, the soluble Ce³⁺ and Pt²⁺ were oxidized to insoluble Ce⁴⁺ and Pt⁴⁺ respectively, and the precipitates were adsorbed into the iron oxide phase, so REE and PGE were enriched in cobalt-rich crusts.

Sea fog is a kind of disastrous weather phenomenon which occurs on the sea surface. Mastering the distribution and dynamic changes of sea fog can effectively reduce the disasters caused by sea fog. Satellite remote sensing observation has the characteristics of near real time, wide coverage, continuous observation and so on. Especially the geostationary satellite remote sensing observation with high time resolution, which can continuously and dynamically track the occurrence, development and extinction of sea fog. The sea fog events in the Yellow Sea and Bohai Sea are taken from 2018 to 2019 as examples in this paper. Based on the analysis of the multi-channel bright temperature radiation characteristics of sea fog in the Yellow Sea and Bohai Sea by using Himawari-8 (H-8) geostationary satellite data, the separation index of sea fog and cloud, the separation index of sea fog and water, the slope index of multi-band brightness temperature difference and texture index of mid-infrared bright temperature are defined through the difference and ratio combination of different bands, and the night sea fog monitoring algorithm based on multi-exponential probability distribution is proposed to realize the automatic identification of sea fog at night. The algorithm is applied to H-8 and GEO-KOMPSAT2A (GK-2A) geostationary satellite data respectively. The position distribution and coverage area of sea fog identify by multi-time satellite observations of six sea fog events from February to June 2020 are compared to achieve mutual verification. The results show that the algorithm proposed in this paper can effectively recognize sea fog at night. The monitoring results every 10 minutes of continuous observations of H-8 and GK-2A at night on April 29, 2020 are selected to follow up and analyze the area where sea fog occurred, it shows the occurrence, development and evolution of the sea fog event clearly. It indicates that the algorithm can monitor the distribution of sea fog and track the development and change of fog. It can provide scientific basis and decision-making basis for the prevention and mitigation of sea fog.

Based on the outputs of eight earth system models involved in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study assessed the simulation skill of the Southeast Indian subantarctic mode water (SEISAMW) of these models by comparing with observations. Moreover, this study investigated the projected long-term trends in subduction rate, volume and properties of the SEISAMW under medium and high greenhouse gas emission scenarios (i.e., SSP245, SSP585). The results show that the CMIP6 models generally have produced artificially greater mixed layer depth and smaller upper layer potential density in comparison with those of the Argo observation. Consequently, the simulated SEISAMW in the CMIP6 models are generally with larger subduction rate and smaller potential density. Meanwhile, the subduction regions of the SEISAMWs show significant differences among the analyzed CMIP6 models, which are attribute to lateral induction in the mixed layer. Furthermore, in the historical, SSP245 and SSP585 outputs, the SEISAMWs show consistent decreasing trends in subduction rate and volume, increasing trend in temperature, and decreasing trends in salinity and potential density. The long-term trends of the SEISAMWs are largest under SSP585 scenario, followed by the SSP245 scenario and historical simulation. The projected trends of SEISAMW can be explained by the following mechanism: the temperature and freshwater flux in the southeastern Indian Ocean upper layer tend to increase under enhanced radioactive forcing, resulting in shoaling in mixed layer and flattening of the mixed layer gradient. As a result, the trends of SEISAMWs in subduction rate, volume and water properties show larger values in accordance with stronger radioactive forcing.

Tropical cyclone disaster is one of the most serious natural disasters, and its impact mainly depends on the center location and the intensity. Monitoring the location and the intensity of tropical cyclones is of great significance for improving the accuracy of tropical cyclone forecast and for reducing the impact of tropical cyclone disasters. In this paper, the characteristics of the HY-2B scatterometer wind field, as well as its divergence and curl, are analyzed. It is found that the divergence or curl of the wind field near the cyclone center shows remarkable signatures, such that a new method is proposed to identify the cyclone center, and then the results are compared with the conventional method. Moreover, a method for estimating the size of the tropical cyclone is introduced to evaluate the cyclone intensity. Finally, the remote sensing data of Typhoon Francisco and Typhoon Bualoi are used to verify the proposed methods. The results show that the difference between the cyclone center position determined by the scatterometer and the one of the optimal path is generally less than 20 km. Meanwhile, the determined wind radii correspond well with the development of the tropical cyclone.

The storage pressure and reconstruction of the mesostructure of gas-bearing sediments are key factors in the study of shallow gas geohazards. Using an industrial CT scanning test system, vertical rotational (SR) scanning is used, with the position of the microfocus X-ray source fixed and the sample rotated at a constant speed of 360° along the XY plane, with a set rotation step of 0.3° per second, the gas-bearing samples in the reactor are pressurized to 2 MPa, 4 MPa, and 6 MPa, taking into account the best resolution of the sample imaging, the best detection range and other. The results show that the slices and reconstructed images obtained from the CT scan have good experimental results; the greyscale values of small bubbles increase when pressurized to 2 MPa; the overall greyscale values of gas increase significantly when pressurized to 6 MPa; the number of bubbles decrease with increasing bubble radius during the pressurization process; the pressurization process lead to local changes in the solid-liquid-gas phase, which show that the volume of pore gas and pore water changes more than that of the soil skeleton. The overall change is greater than that of the soil skeleton, and the microscopic local location will have a greater rise or decrease. When the gas content at different locations rises and dominates, it will drive the reduction of pore water and the movement of the soil skeleton.

Data measured by ultrasonic anemometer moored at a fixed platform near Maoming City, Guangdong Province from February 15 to May 8, 2012 has been used to analyze turbulence within wave boundary layer. The results confirmed that under swell conditions, there are areas obviously affected by swell in the cospectrum and Ogive curves. Furthermore, the sign change of wave-induced Reynolds stress is related to wave age. This paper presents the statistical distribution of the sign reversal of the Reynolds stress caused by the waves in the along-wind and cross-wind directions with wave age. In addition, this paper extracts the wave-induced Reynolds stress τ_swell based on cospectrum method, and compares it with the wave-induced Reynolds stress τ_*swell calculated by traditional method. The results show that the value of τ_swell is 2−3 orders of magnitude larger than that of τ_*swell, which indicates that the traditional method significantly underestimates the wave-induced Reynolds stress, and the traditional method needs to be improved.

The accurate classification of sea ice is of great significance for mastering the growth and development of sea ice and ensuring the safety of navigation. Due to the influence of data sources and classification methods, the improvement of sea ice classification accuracy is limited. In this paper, for high spatial resolution optical remote sensing images, an accurate sea ice classification method based on texture features and normalized difference vegetation index (NDVI) was proposed, and a random forest classifier was used to construct a sea ice classification method. Taking Jiaozhou Bay of Qingdao as the experimental area and GF-2 as the experimental data, the sea ice types were extracted and compared with other classification methods. The results show that for GF-2 high-resolution optical remote sensing data, compared with the traditional random forest, support vector machine, automatic classification and regression tree methods and maximum likelihood classification method of combining texture features, the overall classification accuracy was improved by 13.70%, 11.60%, 19.22% and 29.37%, respectively. The Kappa coefficient was increased by 0.16, 0.13, 0.22 and 0.44, respectively. Compared with the random forest method based on texture features and normalized difference water index, the overall classification accuracy was improved by 9.67% and Kappa coefficient was increased by 0.09. It shows that the sea ice classification method constructed in this paper can effectively improve the accuracy of sea ice classification, and provide an effective technical means for the accurate classification of sea ice.