Search Results (362)

High-resolution mooring observations captured diverse upper-ocean responses during typhoon passage, showing strong agreement with satellite-derived sea surface temperature and salinity. Analysis indicates that significant wind-induced mixing drove pronounced near-surface cooling and salinity increases at the mooring site. This mixing enhancement was predominantly governed by rapid intensification of near-inertial shear in the surface layer, revealed by mooring observations. Unlike shear instability, near-inertial horizontal kinetic energy displays a unique vertical distribution, decreasing with depth before rising again. Interestingly, the subsurface peak in diurnal tidal energy coincides vertically with the minimum in near-inertial energy. While both barotropic tidal forcing and stratification changes negligibly influence diurnal tidal energy emergence, significant energy transfer occurs from near-inertial internal waves to the diurnal tide. This finding highlights a critical tide–wave interaction process and demonstrates energy cascading within the oceanic internal wave spectrum. Full article

Halifax Harbour, a major seaport in Nova Scotia that is approximately 100 km southeast of the Bay of Fundy, comprises a deep inner region called Bedford Basin, connected to the adjacent ocean by a shallow channel called The Narrows. A study of sea level and currents reveals the presence of episodic oscillations in The Narrows, with a period of approximately 2 h. The oscillation strength varies from day to day and, to some extent, through the seasons. The median amplitude of the associated sea level variation is $18\%$ that of the de-tided signal, rising to $32\%$ at the 95-th percentile. Values this large may be of concern for the transit of deep-draft vessels through shallow parts of the harbour and for the clearance of tall vessels under the two bridges that span The Narrows. Another concerning issue is the matter of oscillations being superimposed on storm surges. In addition to such direct effects of sea level variation, shear associated with the oscillations may increase the turbulent mixing in the region, affecting the overall state of this estuarine system. We explore the nature of the oscillations as a first step towards the improvement of prediction schemes for sea level and currents in the region. This involves an analysis of the oscillations in the context of seiche and Helmholtz resonance theories and the use of a 2D numerical model to handle realistic bathymetric conditions and other complications that the simpler theories cannot address. We conclude that the predictions of Helmholtz resonance theory are in reasonable agreement with both the observations and the predictions of the numerical model. Full article

Nodal tides, which follow an 18.6-year cycle, influence tidal variations at any given location in the ocean. Conventional nodal tide theory neglects land effects and topological change. Due to the complex seabed topography around Taiwan waters, the purpose of this paper is to use the long-term tidal data of six stations to discuss the effects of perigean and nodal tides on 20 constituents and to compare the results with previous theories. A modulation method is employed to fit the annual amplitude estimated by harmonic analysis (HA). The top four constituents of the fitted and theoretical values of nodal amplitude factor (AF) and phase factor (PF) are O₁, K₁, K₂, and Q₁. We find that perigean tides or second-order nodal tides considered in the fitting contribute to almost identical performance. The linear time change considered in the AF fitting has better fitting than the mean water level involved. Full article

The Upper Carboniferous strata in the eastern Qaidam Basin, comprising several hundred meters of thick, mixed clastic–carbonate successions that have been little reported or explained, provide an excellent geological record of paleoenvironmental and paleo-sea level changes during the Late Carboniferous icehouse period. This tropical carbonate–clastic system offers critical constraints for correlating equatorial sea level responses with high-latitude glacial cycles during the Late Paleozoic Ice Age. Based on detailed outcrop observations and interpretations, five facies assemblages, including fluvial channel, tide-dominated estuary, wave-dominated shoreface, tide-influenced delta, and carbonate-dominated marine, have been identified and organized into cyclical stacking patterns. Correspondingly, four third-order sequences were recognized, each composed of lowstand, transgressive, and highstand system tracts (LST, TST, and HST). LST is generally dominated by fluvial channels as a result of river juvenation when the sea level falls. The TST is characterized by tide-dominated estuaries, followed by retrogradational, carbonated-dominated marine deposits formed during a period of sea level rise. The HST is dominated by aggradational marine deposits, wave-dominated shoreface environments, or tide-influenced deltas, caused by subsequent sea level falls and increased debris supply. The sequence stratigraphic evolution and geochemical records, based on carbon and oxygen isotopes and trace elements, suggest that during the Late Carboniferous period, the eastern Qaidam Basin experienced at least four significant sea level fluctuation events, and an overall long-term sea level rise. These were primarily driven by the Gondwana glacio-eustasy and regionally ascribed to the Paleo-Tethys Ocean expansion induced by the late Hercynian movement. Assessing the history of glacio-eustasy-driven sea level changes in the eastern Qaidam Basin is useful for predicting the distribution and evolution of mixed cyclic succession in and around the Tibetan Plateau. Full article

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the $\tau$-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the $\tau$-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments. Full article

As society continues to develop, the demand for, and dependence on, energy for production and daily life activities are constantly increasing. Driven by environmental awareness and limited land resource, people have begun to reduce their dependence on fossil fuels and turn to the ocean for energy. Oceans contain vast and abundant energy resources, such as waves, tides, temperature differences and salinity gradients, all of which can be used for power generation. These resources are clean, efficient, renewable and inexhaustible, making them reliable “blue energy sources”. In addition, they are also generally not limited by land use areas, meeting the need for sustainable energy development. This article summarizes the technical characteristics of ocean energy, such as wave, tidal curre1nt, tidal, temperature difference and salinity gradient energies, and combs through the technological forms of different ocean energies, respectively. It also summarizes the development status of the ocean energy industry, and analyzes the industrial maturity of wave energy, tidal energy, etc, predicts future ocean energy development trends, and highlights the influence of ocean energy on sustainable development. We hope that this article provides a reference for scholars and institutions that dedicated to the research and development of ocean energy. Full article

This study aimed to accurately simulate the main tidal characteristics in a regional domain featuring four open boundaries, with a primary focus on baroclinic tides. Such understanding is crucial for improving the representation of oceanic energy transfer and mixing processes in numerical models. To this end, the astronomical potential, load tide effects, and a wavelet-based analysis method were implemented in the three-dimensional ROMS model. The inclusion of the astronomical tidal and load tide aimed to enhance the accuracy of tidal simulations, while the wavelet method was employed to analyze the generation and propagation of internal tides from their source regions and to characterize their main features. Twin simulations with and without astronomical potential forcing were conducted to evaluate its influence on tidal elevations and currents. Model performance was assessed through comparison with tide gauge observations. Incorporating the potential forcing improves simulation accuracy, as the model fields successfully reproduced the main features of the barotropic tide and showed good agreement with observed amplitude and phase data. A complex principal component analysis was then applied to a matrix of normalized wavelet coefficients derived from the enhanced model outputs, enabling the characterization of horizontal modal propagation and vertical mode decomposition of both $M_2$ and nonlinear $M_4$ internal tides. Full article

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

Anticyclonic mesoscale eddies are known to trap and modulate near-inertial kinetic energy (NIKE); however, the spatial distribution of NIKE within the eddy core and periphery, as well as the mechanisms driving its energy cascade to smaller scales, remains inadequately understood. This study analyzed the evolution of NIKE in anticyclonic eddies using satellite altimetry and field observations from four mooring arrays. By extracting near-inertial oscillations (NIOs) and subharmonic wave kinetic energy across mooring stations during the same period, we characterized the spatial structure of NIKE within the eddy field. The results revealed that NIKE was concentrated in the eddy core, where strong NIOs (peak velocity ~0.23 m/s) persisted for ~7 days, with energy primarily distributed at depths of 200–400 m and propagating inward from the periphery. Subharmonic waves fD1 generated by interactions between NIOs and diurnal tides highlighted the role of the vertical nonlinear term in energy transfer. A further analysis indicated that under vorticity confinement, NIKE accumulated in the core of the eddy and dissipated through shear instability and nonlinear wave interactions. The migrating anticyclonic eddy thus acted as a localized energy source, driving mixing and energy dissipation in the ocean interior. Full article

Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article

Sea surface temperature (SST) is considered a strong indicator of climate change, being an essential parameter for long-range weather and climate forecasting. Another important indicator of climate change is sea level (SL), which has a longer history of systematic instrumental observations. This paper aims to examine the relationships between low-latitude variations in ocean characteristics (SST and SL) and surface air temperature (SAT) anomalies in the Arctic and mid-latitudes, and discuss the possibility of using SST and SL as predictors to forecast seasonal SAT anomalies. Archives of meteorological observations, atmospheric and oceanic reanalyses, and long-term series of tide gauge data on SL were used in this study. An analysis of relationships between seasonal SAT in different mid-to-high latitude regions and SST made it possible to identify areas in the ocean that have the greatest influence on SAT patterns. The most commonly identified area is located in the tropical North Atlantic. Another area was found in the Indo-Pacific warm pool. The predictive potential of the relationships identified between ocean characteristics (SST and SL) and SAT will be used to build deep learning models aimed at predicting climate variability in mid-to-high latitudes. Full article

Traditional methods for the construction of intertidal digital elevation models (DEMs) require the integration of long-term multi-sensor datasets and struggle to capture the spatiotemporal variation caused by ocean dynamics. The SWOT (surface water and ocean topography) mission, with its wide-swath interferometric altimetry technology, provides instantaneous full-swath elevation data in a single pass, offering a revolutionary data source for high-precision intertidal topographic monitoring. This study presents a framework for SWOT-based intertidal DEM extraction that integrates data preprocessing, topographic slope map construction, and tidal channel masking. The radial sand ridge region along the Jiangsu coast is analyzed using SWOT L2 LR (Low Resolution) unsmoothed data from July 2023 to December 2024. Multisource validation data are used to comprehensively assess the accuracy of sea surface height (SSH) and land elevation derived from LR products. Results show that the root mean square error (RMSE) of SSH at Dafeng, Yanghe, and Gensha tide stations is 0.25 m, 0.19 m, and 0.32 m, respectively. Validation with LiDAR data indicates a land elevation accuracy of ~0.3 m. Additionally, the topographic features captured by LR products are consistent with the patterns observed in the remote sensing imagery. A 16-month time-series analysis reveals significant spatiotemporal variations in the Tiaozini area, particularly concentrated in the tidal channel areas. Furthermore, the Pearson correlation coefficient for the DEMs generated from SWOT data decreased from 0.94 over a one-month interval to 0.84 over sixteen months, reflecting the persistent impact of oceanic dynamic processes on intertidal topography. Full article

The Baltic Sea is an intra-continental marginal sea that is vertically stratified with a strong halocline isolating the saline bottom layer from the brackish surface layer. The surface layer is eutrophic, and abiotic zones lacking oxygen are common in the deeper regions. While freshwater is constantly flowing into the North Sea, oxygen-rich bottom waters can only occasionally enter the Baltic Sea following a special sequence of transient weather conditions. These so-called Major Baltic Inflow events can be monitored via the sea level gradients between the Kattegat and the Western Baltic Sea. Innovative interferometric altimetry from the Surface Water and Ocean Topography (SWOT) mission gave us the first opportunity to directly observe the sea level signal associated with the inflow event in December 2023. Recent high-rate multi-mission nadir altimetry observations support the SWOT findings for scales larger than 50 km. The SWOT observations are compared to the simulations with the regional 3D HBMnoku ocean circulation model operated by the German Federal Maritime and Hydrographic Agency (BSH). The model explains more than 80% of the variance observed by SWOT and up to 90% of the variance observed by the nadir altimeters. However, the north–south gradients of the two datasets differ by about 10% of the overall gradient. Comparisons with tide gauges suggest possible model deficiencies on daily to sub-daily time scales. In addition, the SWOT data have many fine scale structures, such as eddies and fronts, which cannot be adequately modeled. Full article

The Southeastern Tropical Indian Ocean (SETIO) forms part of the global ocean conveyor belt and thermohaline circulation that has a significant influence in controlling the global climate. This region of the ocean has very few observations using surface drifters, and this study presents, for the first time, paths of satellite tracked drifters released in the Timor Sea (123.3° E, 13.8° S). The drifter data were used to identify the ocean dynamics, forcing mechanisms and connectivity in the SETIO region. The data set has high temporal (~5 min) and spatial (~120 m) resolution and were collected over an 8-month period between 17 September 2020 and 25 May 2021. At the end of 250 days, drifters covered a region separated by ~8000 km (83–137° E, 4–21° S) and transited through several forcing mechanisms including semidiurnal and diurnal tides, submesoscale and mesoscale eddies, channel and headland flows, and inertial currents generated by tropical storms. Initially, all the drifters moved as a single cluster, and at 120° E longitude they entered a region of high eddy kinetic energy defined here as the ‘SETIO Mixing Zone’ (SMZ), and their movement was highly variable. All the drifters remained within the SMZ for periods between 3 and 5 months. Exiting the SMZ, drifters followed the major ocean currents in the system (either South Java or South Equatorial Current). Two of the drifters moved north through Lombok and Sape Straits and travelled to the east as far as Aru Islands. The results of this study have many implications for connectivity and transport of buoyant materials (e.g., plastics), as numerical models do not have the ability to resolve many of the fine-scale physical processes that contribute to surface transport and mixing in the ocean. Full article

The marine environment’s complexity poses considerable difficulties for the stability and reliability of communication links. The restricted coverage of onshore base stations in marine areas makes relay technology a critical solution for extending the communication coverage. Here, connectivity analyses help nodes select the optimal forwarding links, reducing transmission failures and improving the network performance. However, the rapid changes in marine wireless channels and the complexity of hydrological conditions make it challenging to acquire precise channel state information (CSI). In particular, dynamic environmental factors like tides, waves, and wind speed lead to substantial variations in the channel parameters over time. In response to these challenges, this paper puts forward a ship-to-shore communication system using relay ships to extend the coverage of terrestrial base stations. A novel channel modeling method is designed to capture the characteristics of marine wireless channels accurately. Additionally, a machine learning (ML)-based approach is introduced to predict the dual-hop link connection probability at future time points by analyzing historical time-series data on oceanic environmental and ship movement parameters. The proposed model consists of a convolutional-layer-based feature extractor and a bidirectional long short-term memory (BiLSTM) estimator. The CNN module extracts effective high-level features from the input data, while the BiLSTM module further explores the dependencies and dynamic patterns along the temporal dimension. The attention mechanism is introduced to distinguish the importance of the information through a weighted approach. The experimental results show that compared to traditional methods and other deep learning approaches, the proposed CNN-BiLSTM-AM model performs better in terms of its prediction accuracy and fitting ability. The model’s mean squared error (MSE) is as low as 0.0126. Full article