This study investigates the long-term variability of the thermal structure and surface pH of the Bay of Bengal and their potential implications for the underwater acoustic environment. Historical hydrographic observations, including temperature sections from the 1963 International Indian Ocean Expedition and the 2019 Sagar Maitri cruise, together with EN4 temperature and salinity fields, were used to examine long-term changes in the upper-ocean structure. The comparison indicates warming in the upper 50 m and contrasting cooling in the 100–200 m layer. The long-term analysis also shows increasing upper-ocean heat content. Variations in the isothermal, mixed, barrier and sonic layer depths, together with changes in the in-layer and below-layer sound-speed gradients, indicate substantial variability in the upper-ocean acoustic structure and surface-duct characteristics. Surface pH observations from the World Ocean Database and the RAMA/BOBOA mooring show a declining tendency in recent decades, concurrent with the increase in atmospheric CO₂. Sensitivity calculations indicate that decreasing pH reduces the calculated sound-absorption coefficient, particularly at frequencies above 1 kHz, while salinity variability also produces appreciable changes in absorption. BELLHOP simulations further show lower transmission loss under the lower-pH condition, with the difference increasing with propagation range under the prescribed acoustic environment. These results demonstrate the potential influence of long-term hydrographic and carbonate-system changes on the underwater acoustic environment of the Bay of Bengal. The acoustic results are model-derived and require field measurements of acoustic absorption and transmission loss for quantitative validation.
This study evaluates Sea Surface Temperature (SST) variability over a 30-year period (1994–2023) and future projections in the Arabian Gulf, with a focus on the coastal waters of Qatar. Analysis of satellite observations at the select stations reveals warming trends of the order of 0.022–0.034 °C/y. The station-based analysis suggests a potential latitudinal gradient, with SST trends showing higher values at the northern stations, including those along the Qatar coast. For the first time, SST from CMIP6 climate models was compared against observations in this region. The multi-model mean demonstrates reasonable agreement at the studied locations. Climate projections indicate that SST may increase by 1.95–2.19 °C and 4.53–5.04 °C by 2100 under the SSP2-4.5 and SSP5-8.5 scenarios, respectively. Multiple-linear regression using air temperature and wind speed as predictors shows a strong positive association between SST and air temperature, while wind speed shows a weaker negative association. The findings of this study show an accelerated increase in the SST, suggesting an elevated risk of thermal stress on the regional marine ecosystem in the Gulf.
This study presents an updated station-based assessment of Lowest Astronomical Tide (LAT) in the Belgian part of the North Sea using 23 years (2001–2023) of high-resolution water-level observations. Data from four tide gauges—Nieuwpoort (NPT), Oostende (OST), Zeebrugge (ZLD), and the offshore Westhinder station (MP7)—were analysed using the UTide harmonic-analysis framework. The duration and quality of the dataset enable, for the first time in Belgian waters, a systematic comparison of four analysis-window and reconstruction strategies and three tidal-constituent selection strategies. First, sensitivity to the analysis period and reconstruction strategy was assessed by comparing a long-record harmonic analysis, separate annual analyses, extended predictions based on annual coefficients, and complex averaging of annual harmonic coefficients. Second, sensitivity to constituent selection was evaluated using the UTide default set, a fixed Dutch constituent set, and an iterative residual-spectrum-guided procedure implemented with UTide routines. The results show substantial spatial and methodological variability in the estimated LAT values, highlighting the importance of station-specific analysis and careful constituent selection. The contribution of this study is therefore a systematic and reproducible assessment for Belgian waters rather than the development of a new theory of harmonic analysis. Existing station values from the Agency for Maritime and Coastal Services (MDK) and the LAT conversion grid are used as operational consistency benchmarks rather than as independent ground truth. Harmonic-parameter uncertainty was propagated using 1000 Gaussian Monte Carlo realisations. The reported spread is conditional on the selected harmonic model and does not represent a complete LAT uncertainty budget or a measure of absolute accuracy. Comparison of otherwise identical (N = 500) and (N = 1000) simulations showed maximum absolute differences of 0.007 m in the Monte Carlo mean, 0.009 m in the conditional parameter standard deviation, and 0.020 m in one percentile-interval bound across Methods M1–M4. For the preferred residual-spectrum-guided configuration, the corresponding maximum differences were 0.003, 0.004, and 0.006 m, respectively. The deterministic LAT is defined as the minimum obtained from the unperturbed harmonic reconstruction, whereas the Monte Carlo mean is the arithmetic mean of the minimum LAT values obtained from the perturbed realisations; these quantities are therefore reported separately. For the preferred 2001–2019 configuration, the deterministic LAT values were − 0.648, − 0.499, − 0.251, and − 0.300 m TAW/DNG at NPT, OST, ZLD, and MP7, respectively, with corresponding conditional parameter standard deviations ranging from 0.0031 to 0.0034 m. Sensitivity to the selected 19-year input window is reported separately.
Mangrove forests are efficient traps for waterborne microplastics (MP) because of their dense root systems and low-energy tidal hydrodynamics, yet MP data from Indian mangroves remain concentrated in a few well-studied systems. This study characterised MP abundance, morphology and polymer composition in sediment and surface water from the Ennore mangrove forest and adjoining coast, Bay of Bengal, India, sampled across four stations in September 2021. Suspected particles were extracted by hydrogen-peroxide digestion and density separation, identified visually under stereomicroscopy, and confirmed using ATR-FTIR and SEM. MP concentration was significantly higher in coastal sediment (6.0–25.6 particles/kg, peaking near the Ennore Thermal Power Station and industrial discharge points) than in mangrove sediment (2.3–6.0 particles/kg), and was greater in mangrove surface water (7 ± 1 particles/L) than in Ennore Creek water (5 ± 2.6 particles/L) (one-way ANOVA, p < 0.0001). Fibres (53
Tropical seagrass meadows play an important role in coastal carbon cycling, yet the controls on air–sea CO₂ exchange across seasonal and diel scales remain poorly understood. This study investigated the key drivers of CO₂ flux variability in seagrass meadows of Jepara and Karimunjawa, Indonesia, through 24-hour field observations conducted during the southeast monsoon (SEM; August 2022) and northwest monsoon (NWM; February 2023). Air–sea CO₂ fluxes, seawater and atmospheric pCO₂, and environmental variables were measured to assess spatial, seasonal, and diel patterns. Both study sites functioned as net sources of atmospheric CO₂ under all observation periods, with positive ΔpCO₂ values indicating higher pCO₂ in seawater than in the atmosphere. Significant spatial and seasonal differences were observed, whereas diel differences were not significant (p = 0.789). CO₂ fluxes were significantly higher in Karimunjawa (NWM: 10.10 ± 0.99 and SEM: 40.30 ± 3.67 mmol m⁻² d⁻¹) than in Jepara (NWM 9.90 ± 2.33 and SEM: 18.0 ± 2.47 mmol m⁻² d⁻¹; p < 0.0001). Although Karimunjawa exhibited higher seagrass density, greater species diversity, biomass, and organic carbon stocks, stronger wind forcing enhanced gas transfer velocity (kwa), resulting in higher CO₂ emissions. CO₂ fluxes were strongly correlated with kwa, (r = 0.894), highlighting the dominant influence of physical processes. Nevertheless, increased seagrass density during the NWM coincided with lower CO₂ fluxes, suggesting that seagrass ecosystems can partially mitigate atmospheric CO₂ release. Overall, pCO₂sea, ΔpCO₂, and wind-driven gas transfer were the primary controls on CO₂ flux variability. These findings improve understanding of carbon dynamics in tropical seagrass ecosystems and provide important insights for blue carbon conservation and coastal carbon management.
The mitigation of tsunami-type flows generated by severe coastal inundation events such as tsunamis or storm surges can be facilitated through the deployment of buffer blocks, a concept derived from their established application in stilling basins downstream of dam spillways. These strategically positioned blocks create vortices that facilitate a reduction in the flow velocity, aiding in energy dissipation, which is vital during high-energy coastal flow conditions. In addition to their above-stated function, the blocks can enhance the beach aesthetics and offer seating facilities for beachgoers seeking relaxation under normal circumstances. The references to multifunctionality are included solely as design considerations and do not imply validated recreational performance. This study evaluates buffer blocks for energy dissipation under tsunami-type flows, emphasizing block height as a key parameter and systematically varying dimensions to quantify efficiency. The placement of these blocks is carefully planned, positioned several meters from the shoreline over flat ground to eliminate the influence of slope on the energy dissipation. Three rows of buffer blocks with varying dimensions were evaluated for their effect on flow characteristics such as depth and velocity of the flow. A comparative analysis reveals that the variation in block dimensions introduces significant changes in the flow patterns, inducing numerous vortices that contribute to energy dissipation. Momentum flux, computed from observed values of flow velocity (v) and flow depth (h), indicates force reduction in the absence of structures. Numerical studies employing the IITM-RANS3D code of Saincher and Sriram (2022a) have been adopted to assess the effectiveness of rectangular buffer blocks and understand their hydrodynamic processes.
Tropical cyclones (TCs) are cyclonic circulations that occur on the sea surface in tropical and subtropical regions. Due to the significant impact of various ocean and atmospheric conditions on their intensity, forecasting the intensity of TCs is challenging. Additionally, the differences in meteorological elements among TC events complicate the extraction of features from cloud maps. To address above problems, we present a novel TC intensity forecasting model consisting of a TC cloud map prediction module and a TC intensity estimation module. The TC cloud map prediction model, namely SE-SimVP, is capable of capturing the important features in cloud maps effectively by adopting SENet. Furthermore, we design a TC estimation model based on residual structure and CNN. This model effectively captures spatial and structural features in satellite cloud maps, enabling accurate estimation of predicted TC cloud maps. Experiments are conducted on the HURDAT2 and HURSAT-B1 datasets from 2000 to 2016. The experimental results demonstrate the superiority of the proposed model over existing deep learning methods for TC intensity forecasting.
In this study, based on the data from 10 tide gauge stations and satellite altimeter data along the South China coast (SCC) from 1980 to 2022, we analyze the characteristics of sea level change in the SCS during this period, with a particular focus on the anomalously high sea level event of 2017 and its underlying mechanisms. The findings indicate that sea levels along the SCC exhibited a fluctuating upward trend from 1980 to 2022, with an average rise rate of 3.43 mm/yr. Notably, sea levels reached their highest recorded value since 1980 in 2017, exceeding the long-term mean by 83.83 mm. The linear estimate of the mean sea level is 46.01 mm in 2017, accounting for about 54.9
In this study, we examined salinity fields as both passive and active tracers to generate high-resolution sea surface salinity fields using a Lagrangian reconstruction technique, integrating satellite-derived data and numerical advection schemes. Specifically, we employed Version 4.0 of NASA’s Soil Moisture Active Passive (SMAP) Level-3 sea surface salinity product, which features an 8-day running mean and approximately 25 km spatial resolution. Geostrophic surface currents at comparable spatial resolution, sourced from satellite altimetry data provided by the Copernicus Marine Environment Monitoring Service (CMEMS), were utilized in this work. By applying backward and forward numerical advection schemes to the SMAP sea surface salinity fields using these altimetry-derived currents, we captured smaller-scale salinity features, generating reconstructed fields on a 4-km output grid. The passive-advection implementation was first verified in the Gulf Stream through comparison with the published reconstruction of Barceló-Llull et al. (2021), after which the framework was applied and further developed for the Bay of Bengal, the primary study region. Utilizing salinity as a passive tracer allowed us to focus exclusively on horizontal advection without accounting for sources, sinks, or mixing. A sensitivity analysis was performed, which determined that the highest feasible resolution using this approach is 4 km, with an optimal advection integration period of 14 days. Preliminary validation against ship-based thermo-salinograph observations from 2015 to 2024 demonstrates that High-Resolution Sea Surface Salinity via Passive Advection (HRSSS-PA) achieves RMSEs of 0.19 to 3.19 PSU with correlations of 0.12 to 0.93, generally outperforming SMAP, which shows higher errors (0.21 to 4.78 PSU) and weaker or inconsistent correlations (-0.19 to 0.96). This highlights the ability of HRSSS-PA to capture both magnitude and fine-scale salinity variability. To address cases where the assumptions of passive advection break down, the framework was further extended to an active advection approach, in which salinity values were dynamically adjusted during transport to account for freshwater input and precipitation, enabling improved representation under conditions characterized by strong freshwater forcing, sharp sea-surface salinity gradients, and river-plume influence.
The significant wave height ( H_s ) is a fundamental parameter governing wave energy assessment, offshore structural design, and marine hazard evaluation. Accurate prediction of H_s remains challenging because numerical wave models incur extremely high computational costs, largely due to the multiscale compound periodicity and strong randomness inherent in ocean wave systems. A hybrid deep learning framework, CBLA-XGBoost, is proposed for the accurate prediction of H_s , leveraging the complementary strengths of deep learning architectures and gradient boosting methods. Incorporating a Convolutional Neural Network (CNN), and a Bidirectional Long and Short-term Memory network (BiLSTM), and Attention Mechanism (AM), the framework is designed to extract deep features from historical data. Furthermore, the Extreme Gradient Boosting (XGBoost) algorithm is integrated via a multi-model fusion strategy based on weighted ensemble techniques. Input features are initially selected using Pearson’s correlation coefficient and the XGBoost feature importance score. Then, the selected features are fed into AM-based CNN-BiLSTM (CBLA) and XGBoost for training to predict H_s , and the outputs of these individual models are subsequently fused to generate the final predictions. The framework is evaluated for H_s forecasting at three stations in the North Pacific with 1-, 2-, 4-, and 6-hour lead times. Results demonstrate that CBLA-XGBoost outperforms the individual CBLA and XGBoost in terms of prediction accuracy and robustness, and significantly exceeds the performance of other benchmark models.
Marine Heatwaves (MHWs) have intensified in frequency, duration, and intensity due to global warming, posing profound threats to marine ecosystems. While satellite-derived Sea Surface Temperature (SST) datasets are the cornerstone of MHW detection, their reliability remains a critical concern. Specifically, multi-source Level-4 (L4) analysis datasets may introduce substantial uncertainties in regional MHW assessments, particularly within climatically sensitive hotspots. However, a systematic evaluation of these uncertainties across diverse oceanographic regimes remains lacking. This study systematically evaluates the consistency of three widely used global L4 SST products—NOAA OISST v2.1, REMSS MWIR v5.1, and UKMO OSTIA. The assessment spans two contrasting environments: the Eastern China Marginal Seas (representing complex coastal dynamics) and the Northeastern Pacific (representing open-ocean conditions). Additionally, the 2013–2016 “The Blob” event is analyzed as a benchmark case. Key findings reveal: (1) While the three products show strong consistency in mean-state MHW metrics, they differ noticeably in long-term trend estimates. In regions with weak trend signals, relative deviations can be large, indicating that trend detection in such areas is subject to substantial uncertainty. (2) Intensity-related metrics show significantly higher uncertainty than frequency-related metrics. Notably, in the Bering Sea, different products yield divergent signals regarding MHW occurrence. (3) During “The Blob,” absolute discrepancies in total duration and cumulative intensity reached up to 150 days and 250 ^∘ C· days , respectively. These differences may influence the assessment of ecological stress, although the magnitude of this impact requires further validation. This study suggests that reliance on a single SST product may introduce uncertainty, particularly for regional or event-based assessments, and highlights the potential benefits of incorporating multi-source ensemble approaches in future MHW analyses. The authors state that this study does not involve clinical trials.
The Arabian Gulf functions as an important regional carbon sink, yet limited knowledge exists regarding the dynamics of particulate organic carbon (POC) and its environmental drivers. This study investigates the spatial and temporal variability of POC and its relationships with sea surface temperature (SST) and chlorophyll-a (Chl-a) using MODIS/Aqua satellite data from 2003 to 2023. Results reveal distinct seasonality, with higher POC concentrations in winter and lower levels in summer. The average maximum trends derived from the two methods indicated decreases of approximately − 13.5 mg m⁻³ yr⁻¹ for POC and − 0.14 mg m⁻³ yr⁻¹ for Chl-a, while SST exhibited an increasing trend of approximately + 0.43 °C yr⁻¹, particularly across the northern and western regions. Correlation analysis shows a positive association between POC and Chl-a and a negative one with SST. Machine learning models identify Chl-a and SST as key predictors of POC, achieving high accuracy (R² = 0.93, RMSE = 2.24 mg/m³). The Geographically Weighted Regression (GWR) model was employed to analyse and map the spatial distribution of these relationships, revealing that POC increases by approximately 107.39 mg/m³ per 1 mg/m³ rise in Chl-a, while a 1 °C rise in SST decreases POC by about 11.75 mg/m³. These results improve understanding of carbon processes in Gulf waters and provide insights for effective regional carbon management.
This study examines the influence of the Kuroshio intrusion into the South China Sea (SCS) on the sea surface temperature cooling (SSTC) induced by tropical cyclones (TCs) through numerical simulations. Two types of the Kuroshio intrusion are investigated, i.e. the looping path characterized by the formation of an anticyclonic eddy in the SCS, and the leaping path characterized by the minimal intrusion into the SCS. For comparison, a no-Kuroshio case with initial horizontally homogeneous stratification is also simulated. Results indicate that both the looping and leaping Kuroshio modify TC-induced SSTC compared to the no-Kuroshio case, through distinct mechanisms. The looping Kuroshio suppresses TC-induced SSTC to both sides of the Luzon Strait (LS). In the anticyclonic eddy induced by the looping Kuroshio to the west side of the LS, the suppression is related to the warmer subsurface water, smaller vertical temperature gradient and larger mixed layer depth, which lead to weaker vertical mixing. Whereas to the east side of the LS, the cause is horizontal advection. The leaping Kuroshio also suppresses TC-induced SSTC to the east side of the LS through strong horizontal advection, but it enhances the SSTC to the west side of the LS through enhanced vertical mixing. Moreover, it is found that TC-induced SSTC spreads downstream along the major axis of the leaping Kuroshio, which is caused by vertical mixing rather than horizontal advection.
The coastal waters of the Northeast Pacific (NEP) region, along the coastlines of Oregon and Washington in the U.S. and British Columbia in Canada, receive substantial freshwater inflows from rivers. In this study, we assess the impact of these discharges on the seasonal and interannual variability in the near-surface salinity in the NEP coastal transition zone (CTZ), an area of the interior open ocean where the ocean dynamics are influenced by coastal processes. The assessment is based on satellite SMAP and SMOS observations, in-situ Argo profiler data, and outputs of a regional ocean circulation model. The model domain spans from southern Mexico to the Alaska Panhandle and includes freshwater inputs from more than 500 riverine sources along the Pacific coast, obtained from the Global Flood Awareness System (GloFAS). For the study period of 2008–2018, the model solution is compared to an earlier benchmark that only included discharges from major sources including the Columbia River and Salish Sea inputs. Adding the full suite of terrestrial discharges results in a more pronounced freshening of the surface waters from northern California to British Columbia coasts. In particular, it helps to improve the sea surface salinity (SSS) bias and variability in the CTZ off Vancouver Island compared to the Argo data. Satellite SSS combined with altimeter-based sea level anomaly maps reveal patterns indicative of eddy-driven transport of terrestrial waters from the shelf into the CTZ. The volume-averaged salinity term balance analysis in the 50-m near-surface layer in the CTZ domain off Vancouver Island shows that the oceanic transport contributes to freshening the layer in summer. The vertical diffusion term is relatively large and positive in most winters, increasing salinity in the surface boundary layer.
The Colombian Caribbean harbors promising offshore exploitation zones, but they are also highly susceptible to oil spills, which lead to impactful environmental, social, and economic consequences. In this context, this study analyses the development of a new ecosystem of climate services for Colombia, including maritime oil spills, based on different observations and the use of NOAA’s General Operational Modelling Environment (GNOME). To showcase its performance, the study focuses on the oil spill event in the Colombian and Venezuelan Caribbean Sea: The Amuay refinery incident of October 31st, 2017. Simulations were conducted using the GNOME model with various atmospheric forcings from the Climate Forecast System version 2 (CFSv2) and the Centre ERS d’Archivage et de Traitement (CERSAT) and oceanic forcings the Copernicus Global Ocean Physics Reanalysis (GLORYS), the Hybrid Coordinate Ocean Model (HYCOM), and the Navy Coastal Ocean Model (NCOM). The results were compared with ground-based data and satellite imagery, incorporating optical and radar satellite data (Planet, Landsat 8, and Sentinel-1). The use of the system in the Colombian-Venezuelan Caribbean effectively replicated the spill’s arrival date on the Colombian coast and the affected areas, providing a trajectory closely aligned with satellite observations. Nevertheless, the simulations efficacy was found to be contingent on the spill’s initial conditions and the utilized forcing data. The results indicate that, at least for cases like the one analyzed in the Colombian-Venezuelan Caribbean Sea, GNOME is a useful analytical tool for immediate decision-making in hydrocarbon spill incidents. It has the potential for extended forecast horizons at sub-seasonal timescales. However, ongoing efforts to enhance prediction accuracy are essential to strengthening the capacity to respond to oil spill emergencies in the future.
Tropical cyclones profoundly modify surf zone ecosystems by altering hydrography, nutrient cycling, stratification, and phytoplankton community composition. The present study evaluated the impact of Extremely Severe Cyclonic Storm (ESCS) Fani, which made landfall on 3rd May 2019 along the south-central Odisha coast, using in situ observations of nutrients, Chl-a, phytoplankton assemblages, and Ocean Moored buoy Network for Northern Indian Ocean (OMNI) buoy-derived vertical temperature and salinity data. Post-cyclone conditions exhibited marked nutrient enrichment driven by intense vertical mixing and upwelling, with nitrate, nitrite, ammonia, phosphate, and silicate increasing by 90.87
Structured-grid ocean models facilitate downscaling in the northeast Pacific, particularly for operational regional modelling and ocean forecasting in coastal British Columbia (BC), Canada. However, the complex oceanography and rugged coastal geography of BC pose well-known challenges for such models. In this study, we apply Adaptive Grid Refinement in Fortran (AGRIF) two-way nesting within the Nucleus for European Modelling of the Ocean (NEMO) framework to simulate a fjord system on northwestern Vancouver Island as a case study. The nested child domain is configured for Quatsino Narrows, a narrow, shallow channel that forms a T-shaped junction with the deeper Holberg–Rupert Inlets and the nearby Marble River. The AGRIF two-way nested model demonstrates improved skill in representing tidal mixing, as indicated by temperature and salinity profiles collected recently within the junction area. Examination of child-domain outputs saved at 15-minute intervals shows that near-surface and subsurface internal waves are generated sequentially during each flood phase along the tidal-jet section slope. Tidal analysis was conducted to quantify barotropic-to-baroclinic energy conversion and baroclinic tidal energy flux, as well as their seasonal variability. Barotropic-to-baroclinic energy conversion for M2 tides is found along the tidal-jet slope and in the shallow waters west of Hankin Point. Estimates of the spatially integrated baroclinic tidal energy terms and fluxes through boundaries suggest that the T-shaped junction region acts as a sink for M2 and a source for M4 baroclinic tidal energy.
In this study, an Internal Wave Viscosity (IWV) parameterization is implemented in a global tidal model forced by the solar and lunar tidal potential. By comparing the model results before and after the application of IWV with TPXO9, IWV not only contributes to the tidal dissipation in the deep ocean significantly, but also reduces the model bias in the shallow-water areas of the M2, S2, K1, and O1 tides by approximately 30
