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This study presents the development and testing of a global navigation satellite system (GNSS) buoy designed for measuring the sea surface elevation and tide level. The precision-point-positioning (PPP) technology is adopted for precise observation. The design of the buoy body is optimized by stability and hydrodynamic calculations. A high-performance embedded data acquisition system with big storage and high-frequency sampling is developed for long-term observation. The GNSS buoy is deployed in a wind farm approximately 70 km offshore of China, and undergoes a 60-day ocean test. A comparison of the sea level elevations obtained from the GNSS buoy and the pressure sensor shows that there is a strong correlation between them, with a correlation coefficient of 0.99. A harmonic analysis is applied to derive the harmonic constants for four key tidal components (M2, S2, O1, and K1). The amplitude differences are −1.2 cm, 1.4 cm, −0.6 cm, and −1.2 cm, respectively, and the phase differences are 1.8°, 2.2°, −1.3°, and −2.9°, respectively. The strong correlation between the measurements of the GNSS buoy and the pressure sensor and the relatively small differences of the amplitude and phase of the main tidal components indicate that the compact GNSS buoy demonstrates a capability to continuously measure the sea surface elevation and tide level with an elevation reference in the open sea.

Air–sea heat flux is one of the most important factors that affects ocean circulation, weather, and climate. Satellite remote sensing could serve as an important supplement to the sparse in situ observations for heat flux estimations. In this study, we analyze the uncertainty of the turbulent heat fluxes derived from wind speed measured by the Cyclone Global Navigation Satellite System (CYGNSS) over the global tropical oceans at different time scales. In terms of spatial distribution, there is large uncertainty (approximately 50 to 85 W·m−2 in the RMSE) near the equator in the western Pacific Ocean, the Arabian Sea, the Bay of Bengal, and near the Gulf of Guinea. The turbulent heat fluxes are in agreement with the buoys in representing the intraseasonal and seasonal variability, but more specific regional validations are needed for revealing the synoptic and sub-synoptic phenomena and the diurnal cycle. The uncertainty of the CYGNSS wind speed contributes approximately 50–57% to the uncertainty of the estimation of turbulent heat fluxes at the frequency band with a typical period of 3–7 days. In addition, the input sea surface temperature, rather than the wind speed, results in differences in the estimation of the monthly mean turbulent heat fluxes in the tropical Atlantic Ocean based on the COARE 3.5 algorithm. In conclusion, although the CYGNSS-derived turbulent heat fluxes are basically in good agreement with the in situ observations, our analysis highlights the importance of considering the limitations of these datasets, particularly in high wind speed conditions and for higher-frequency variations, including at synoptic, sub-synoptic, and diurnal time scales.

The accurate estimation of ocean tide loading displacements is essential and necessary for geodesy, oceanic and geophysical studies. It is common knowledge that K1 and K2 tidal constituents estimated from Global Positioning System (GPS) observations are unsatisfactory because their tidal periods are nearly same to the revisit cycle or orbital period of GPS constellation. To date, this troublesome problem is not fully solved. In this paper, we revisit this important issue and develop a novel method based on the unique characteristic of tidal waves to separate GPS-system errors from astronomical K1/K2 tides. The well-known credo of smoothness indicates that tidal admittances of astronomical constituents in a narrow band can be expressed as smooth functions of tidal frequencies, while the interference of GPS-system errors seriously damages the smooth nature of observed tidal admittances. Via quadratic fitting, smooth functions of tidal frequencies for tidal admittances can be determined, thus, astronomical K1 and K2 tides can be interpolated using fitted quadratic functions. Three GPS stations are selected to demonstrate our method because of their typicality in terms of poor estimates of K1/K2 tidal parameters related to GPS-system errors. After removing GPS-systematical contributions based on our method, corrected K1/K2 tides at three GPS stations are much closer to the modeled K1/K2 tides from FES2014, which is one of the most accurate tide models. Furthermore, the proposed method can be easily applied to other areas to correct GPS-system errors because their smooth nature is valid for global tidal signals.

Ship type identification is an important basis for ship management and monitoring. The paper proposed a new method of ship type identification by combining characteristic parameters from the energy difference between high and low frequencies and the sensitive IMF variance mean value based on the modal decomposition of the underwater radiated noise signals using the Ensemble Empirical Mode Decomposition (EEMD) method. The comparison shows that the characteristic parameters of different types of ship, underwater radiated noises are different, whereas those of the same types of ship, underwater radiated noises fall in close range. Validation experiments based on randomly selected ship underwater radiated noise samples manifest that the method is of good separability for the four types of ship underwater radiated noises in the Deepship dataset. It has a higher identification rate than other methods within the distance range of ship underwater radiated noise detection in the dataset. The accuracy of this method tends to decrease with distance in the classification experiments of the ship underwater radiated noises at different distances.

Exploring multi-timescale tidal variability is fundamental and necessary for numerous practical purposes, such as flood protection, marine cultivation, and ocean transport. It is well known that tides show significant seasonal, inter-annual, and 18.61-year nodal variability. Less known and less discussed is the subseasonal tidal variability (i.e., ter-annual, quarter-annual, and penta-annual cycles) in the coastal ocean. In this study, we explore subseasonal tidal modulations in the Gulf of Tonkin via the combination of four tide gauges and 27-year multi-satellite altimeter observations. Both tide gauges and satellite altimeters indicate that tidal subseasonality is significant in the Gulf of Tokin, although the amplitudes of subseasonal variations are much smaller than those of seasonal variations. Compared to spatially limited tide gauges, satellite altimeters successfully derive the basin-scale tidal subseasonality in the Gulf of Tonkin. The largest amplitude of subseasonal tidal constituents originated from the subseasonality of main tidal constituents, and can reach as high as 31.8 mm. It is suggested that subseasonal variations in ocean environments (e.g., sea levels and ocean stratification) induce tidal subseasonality through changing tidal propagation and dissipation. Although powerful, satellite altimeters also have some defects. Due to tidal aliasing related to long-period sampling intervals, some subseasonal tidal constituents are indistinguishable in satellite altimeter records.

Understanding nodal tidal characteristics is essential for accurate long-term tidal prediction. Observational nodal evolution of tides is mainly based on tide gauge records in coastal areas which are limited in time and space, thus impeding coherent determinations of basin-wide patterns of tidal variability. In this paper, we indicate the potential of satellite altimeter data to investigate 18.61-year nodal modulations of main constituents in the Gulf of Tonkin. Three tide gauges and multi-source satellite altimeter observations (TOPEX/Poseidon, Jason1, Jason2, and Jason3) revealed that 18.61-year nodal cycles in tidal amplitudes have noticeable deviations from the equilibrium tidal theory in the Gulf of Tonkin. In general, M2 and N2 nodal modulations are anomalously larger than theoretical values while K2, K1, and O1 nodal modulations are noticeably smaller than theoretical values. Compared to point-based tide gauges, satellite altimeter records can provide basin-wide features of nodal modulations of main constituents. Although overlapping geographical blocks are applied to eliminate the effect of tidal alias originated from long-period sampling intervals, the estimation of nodal cycles of minor constituents are still questionable. Nevertheless, the methods described here provide a strong foundation for future research on time-varying tidal dynamics using the combination of tide gauges and satellite altimeter data.

As a ubiquitous movement in the ocean, tides are vital for marine life and numerous marine activities such as fishing and ocean engineering. Tidal dynamics are complicated in the East Asian marginal seas (EAMS) due to changing complex topography and coastlines related to human activities (e.g., land reclamation and channel deepening) and natural variability (e.g., seasonal variations of ocean stratification and river flow). As an important tool, numerical models are widely used because they can provide basin-scale patterns of tidal dynamics compared to point-based tide gauges. This paper aims to overview the development history of the numerical simulation of tides in the EAMS, including the Bohai Sea, the Yellow Sea, the East China Sea, the East/Japan Sea, and the South China Sea, provide comprehensive understanding of tidal dynamics, and address contemporary research challenges. The basic features of major tidal constituents obtained by tidal models are reviewed, and the progress in the inversion of spatially and temporally changing model parameters via the adjoint method are presented. We review numerical research on how a changing ocean environment induces tidal evolution and how tides and tidal mixing influence ocean environment in turn. The generation, propagation, and dissipation of internal tides in the EAMS are also reviewed. Although remarkable progresses in tidal dynamics have been made, nonstationary tidal variations are not fully explained yet, and further efforts are needed. In addition, tidal influences on ocean environment still receive limited attention, which deserves special attention.

Exploring the relationship between evaporation (E)-minus-precipitation (P) and sea surface salinity (SSS) is vital for understanding global hydrological cycle changes and investigating the salinity budget. This study quantifies the uncertainty in the relationship between E−P and SSS based on satellite data over the 50°S–50°N ocean from 2012 to 2017 in 140 sets of combinations of E, P and SSS. We find that the uncertainty (10%) in the variability of freshwater flux (FWF) over 2012–2017 is smaller than that in SSS (15%). The difference in the combination of sets of “E-P-SSS” products can lead to the 10% difference in RMSD and 25% difference in area-weighted mean correlation coefficients between SSS tendency and FWF. There is a 24.1~58% area over the global ocean with a significant (p value < 0.05) positive correlation between the FWF and SSS tendency derived from satellite products. The seasonal EMP and SSS tendencies show larger correlation coefficients and lower RMSDs over most sets compared with those on nonseasonal time scales. Large uncertainty in the FWF-SSS tendency relation associated with spread among products prevents the use of one combination of E, P and SSS from satellite-based products for salinity budget analysis.

We report the direct and quantitative measurement of five significant near-inertial waves (NIWs) events observed by Lander at water depths of 600 m to 1100 m at 119°17′ E and 22°06′ N in the northern South China Sea from July to November 2017. We found that these five NIWs events lead to strong shearing, which plays an important role in deep water mixing. Each event corresponds to several different NIWs generation mechanisms. The results show that the NIWs events generated by typhoons were the most regular. This was caused by dispersive NIWs propagation over long periods of time and over long distances. NIWs formed by spontaneous generation do not have this feature. The strongest NIWs events during the observation period were caused by a combination of shelf wave attenuation and monsoon. This time, the signal was transmitted to the seabed, and the upward signal reflected in the meridional direction was found. The reflected signal was anisotropically affected by the seabed topography. A horizontally propagated NIWs event with relatively weak dispersion was also found in this study. Based on the topography, we suspect it was formed by the Lee wave, but we cannot provide any more useful evidence.

Marine heatwaves (MHWs) are prolonged discrete events of anomalously warm seawater observed around the world. The occurrence of MHWs in the Japan/East Sea (JES), a middle latitude marginal sea with abundant aquatic resources, has severe impacts on marine ecosystems and human society. We used satellite remote sensing products spanning 1982–2020 to investigate the mean states and variations of JES MHWs. The results show that the JES MHWs occurred twice per year. More than 40% of the MHWs were shorter than the mean duration of 12.6 days and weaker than the mean intensity of 2.4 °C. Frequent MHWs were observed in August, while high mean intensities were observed in winter and spring in the western JES. In recent years, the area’s averaged yearly sum of MHW intensity in the JES has increased dramatically, with a positive trend of 29.62 °C days per decade—over twice the average global trend (12.37 °C days per decade), which is further confirmed by its first empirical orthogonal function mode. The strengthening trend in MHWs may be explained by oceanic dynamics rather than atmospheric forcing, and may result in increased surface latent heat flux from the ocean to the atmosphere.

The southern coast of Java is known as one of the most productive fishing grounds for tuna, feeding by nutrient-rich water along the coast caused by the subsurface water upwelling. This primary productivity can be evidenced by the high sea surface chlorophyll-a concentration (SSC). Based on satellite remote sensing products, we investigate the multi-scale variability in SSC along the Sumatra-Java coast. The results show that seasonal variability of SSCs is primarily due to monsoon-driven upwelling and rainfall in the Indian Ocean and Indonesian seas sides of the Sumatra and Java Islands, respectively. Local Ekman pumping plays a secondary role, while rainfall input to the ocean has little effect. Coastally trapped Kelvin waves and mesoscale eddies are responsible for the intraseasonal SSC anomalies in regions along the south coast of Java and off the Sunda and Lombok Straits, respectively. The interannual variability in SSC is caused by the anomalous upwelling related to the Indian Ocean Dipole. There was a weak increasing trend of ~0.1–0.2 mg/m3 per decade, above the global averaged trend, which may be related to enhanced local Ekman pumping. These analyses provide an overall description of SSC variations based on satellite observations; however, further investigations based on in situ observations are needed to achieve better quantification.

The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data.