We present a method for forecasting water levels at river open boundaries in coastal ocean models, where tidal signals are influenced by variable river discharge. The method uses non-stationary tidal analysis (NS_TIDE) to generate future water levels based on a deterministic tidal signal and extrapolations of upstream hydrological predictors. This allows the river boundary to be placed within the tidal propagation zone without modelling the full tidal extent or coupling a separate river model. Case studies for the Fraser and Saint John rivers demonstrate that the method reproduces water levels and river outflow skilfully under a range of seasonal flow conditions. Forecast accuracy is comparable to gauge-driven configurations in most downstream areas, with root mean square values of model data using gauge data or NS_TIDE for river water level differing by no more than five centimetres. Performance gradually declines upstream. This novel approach in modelling tidal estuaries reduces computational cost, avoids the complexity of coupled river modelling, and is well suited to operational forecasting systems requiring short-range river boundary forcing.
The presence of unusually low-salinity water from the Changjiang River (CR) poses a significant marine environmental threat in the East China Sea (ECS), disturbing aquatic habitats and ecosystems in surrounding countries' waters. Demand for early warning of such threats by the fishing and aquaculture industries has been increasing in recent decades. This article introduces the Data-assimilative Regional Ocean Prediction system (DROPS), an operational forecasting system designed to predict the spatiotemporal behavior of low-salinity plumes in the northern ECS. DROPS is based on a regional ocean model with a four-dimensional variational data assimilation, covering the Yellow Sea and ECS shelves. This operational system runs automatically once a day, including a 72-hour hindcast and a 120-hour forecast, using initial conditions from the previous assimilation run. Predictions were compared with real-time measurements from weather buoys, ocean research stations, and unmanned wave gliders. DROPS successfully captured the low-salinity and high-temperature water observed over the northern ECS during the summer of 2022, showing moderate skill in predicting salinity and temperature variations. Data assimilation decreased temperature and salinity errors by 27% and 22%, respectively, improving forecasting performance. Our prediction system provides essential information to support decision-making for issuing early warnings of low-salinity and high-temperature water events.
Astronomical tides are a primary driver of sea level variability in coastal areas, significantly contributing to high water extremes and coastal flooding. Therefore, accurate tide predictions are essential for analysing extreme sea levels and developing robust coastal sea level predictions. Classical harmonic tidal analyses rely on subjective user choices, such as training period length, temporal separation between training and prediction periods, selection of tidal constituents, and specific harmonic analysis methods (e.g. nodal correction factors). Here we show that these choices significantly impact tidal reconstructions and predictions. For example, the amplitude of the annual and semi-annual components can vary by up to 30% over the historical period when using 5-year moving windows, highlighting the importance of the training period selection. High-frequency tidal constituents' amplitude is less sensitive but still varies by up to 5% over the last 40 years. Furthermore, different choices in harmonic analysis result in differences exceeding 10 cm between tide predictions, a common accuracy benchmark in ports. These findings inform users of harmonic tidal analysis about the strengths and weaknesses of specific sets of parameters chosen for the tidal analysis. Based on our results, we propose a list of recommendations for tidal analysis tailored to different situations.
Many industries rely on wave data to understand the potential for wave energy extraction, or to understand the wave environment for the design of marine structures and to plan operations and maintenance. Three ocean reanalysis datasets, ERA5, WAVEWATCH III and Copernicus Global Ocean Waves Analysis and Forecast, are compared to in-situ wave buoy data collected along the north of Scotland. All reanalysis datasets correlated well with the wave buoy data, with the Copernicus Global Ocean Waves Analysis and Forecast dataset being statistically the closest to the buoy data. However, all three reanalysis datasets underpredict significant wave height during extreme wave events. From comparisons of the wave buoy data at one site, it was found that although extreme events are underpredicted, the WAVEWATCH III reanalysis data performed the best, although still under predicted extreme wave heights. Of the reanalysis models compared against wave buoy data here, it is suggested that for extreme wave analysis the WAVEWATCH III model is recommended, whilst for long term statistics and weather windowing the Copernicus Global Ocean Waves Analysis is a good option. Whilst reanalysis data sets are a valuable resource for marine renewable energy, developers should be aware of the limitations of these datasets, in particular for extreme wave conditions.
Cyanobacteria are bioactive compounds that produce toxins known as harmful algal blooms, posing serious threats to humans and marine life. Consequently, developing a robust monitoring framework to track and forecast the growth of such blooms is critical. The present work aims to understand the key environmental drivers influencing cyanobacterial bloom dynamics along the Southeast coast of India using model-based outputs from the NASA Ocean Biogeochemical model (NOBM) during 2004-2014. A Granger causality network analysis was employed to identify statistically significant causal relationships between environmental variables and cyanobacteria bloom concentrations. The analysis reveals unidirectional causal links from Sea surface temperature (SST) and precipitation to the cyanobacteria blooms. Bidirectional links exist between Nitrate (NO3) and Mixed layer depth (MLD) with the cyanobacterial blooms, which is statistically significant at a 95% confidence interval. SST, NO3 and MLD are the dominant causal drivers that promote the growth of cyanobacterial blooms across the South-East coast of India, evident from the higher values of outdegree for NO3, SST and MLD and higher values of indegree for Cyanobacteria during 2004-2014. Therefore, the present study provides a mitigation measure to monitor and forecast the growth of harmful algal blooms across the coastal areas.
The seasonal and interannual variability of physicochemical (temperature, salinity, dissolved oxygen, and nutrients) and productivity characteristics of the Arabian Sea (AS) are studied using 30 years (1993-2022) of datasets. Chlorophyll-a (Chl-a) variance at specific depths identified four core variability regions: Western AS (WAS), Eastern AS (EAS), Northern AS (NAS), and Central AS (CAS). The highest Chl-a content was observed in the WAS region during the summer monsoon, followed by the NAS region, where the winter monsoon dominated productivity. Nutrient entrainment from the WAS to the open ocean enhances productivity in the CAS, which lags by over a month compared to other regions. It appears that nitrate and phosphate contribute to productivity in all regions. However, silicate has no contribution in the EAS region, but iron does. Parameters like Chl-a, net primary production, nitrate, and phosphate in all regions have decreased, and on the contrary, iron has increased, but increase of silicate in the EAS region. This study also unveils the El-Ni & ntilde;o/Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) effects on biogeochemistry parameters across AS regions. During ENSO/IOD years, Chl-a anomaly reveals a strong correlation between IOD and EAS suggesting more substantial influence on the productivity of this region.
A variety of near-real-time observations are routinely assimilated into operational numerical simulations of the ocean. This analysis focuses on how subsurface profiles from autonomous underwater gliders and Argo profiling floats have varying impact in the operational Navy Coastal Ocean Model (NCOM) for the US East Coast region depending on the proximity of the profiles to the Gulf Stream. Changes in the model's representation of the ocean state from 24-hour-ahead forecasts to the following days' nowcast runs are used to evaluate the impact of observations made available within the final day before the model valid time during 2017. In general, this metric of observation impact decays over a spatial scale of O(100) km, consistent with covariance scales in the data assimilation scheme. However, observations within and near the Gulf Stream are associated with forecast-to-nowcast changes in the model that are about twice as large as for observations far from the Gulf Stream. Moreover, the strongly advective nature of the Gulf Stream leads to elevated downstream impact of observations within the current. For constraining ocean models, these results suggest that autonomous underwater gliders may be most effectively used to target regions with strong gradients, such as are common along oceanic boundaries.
Trajectory forecasting based on geophysical models is a useful tool for contingency and emergency aid at sea. Uncertainties in the used geophysical models, which propagate into the trajectory forecast, can be addressed through ensemble modelling. Here, we evaluate the performance of an operational ensemble prediction system on short-term forecasts for 17 undrogued drifters deployed in the Barents Sea and Fram Strait in 2022. Predicted and observed trajectories were compared by rotary spectra analysis, rank histogram, reliability diagram, and error$/$/spread to determine the ability of the model to reproduce the observed physical processes and their uncertainties. We found that the physical processes dominating the observed spectra at inertial and subinertial frequencies are accounted for in the modelled trajectories, but that the model underestimates the energy content for higher frequencies (> 0.083 cph) with up to two orders of magnitude. Ensemble underdispersion is linked to model error rather than initial and boundary conditions. Reliability is achieved if the main forcings are accurately reproduced by the geophysical models. For highly dynamic regions, such as the Fram Strait, transient small-scale phenomena representation is critical at the uppermost ocean layer for accurate trajectory forecasting.
The French Sea Surface Salinity (SSS) Observation Service is the main provider of thermosalinograph (TSG) observations from ships of opportunity at global scale, in real time (RT) for operational oceanography and in delayed time (DT) for research. We develop here a near real time (NRT) processing chain, which aims at transposing the human-operated DT processing into automatic algorithms to deliver data with optimal quality in less than a week. Quality Control (QC) flags are based on realistic instrumental thresholds, and comparison with a SSS climatology and colocated satellite temperature data. To correct instrumental SSS biases due to fouling, water samples used in DT are replaced by colocated Argo data in NRT. The method includes detection of ship harbour calls, when the largest biases appear. The NRT processing chain is retrospectively applied on a 2014-2019 TSG database, then evaluated and optimised by comparison to the same TSG database processed in DT, in terms of QC flags and corrections. Using a SSS climatology based on Mercator Global Reanalysis, NRT QC flags reach an 88% agreement with DT QC flags. Compared to unprocessed data, the differences with DT processed data are 4-5 times smaller after NRT QC and correction.
Continued greenhouse gas emissions have caused global sea levels to rise, leading to coastal erosion and flooding. However, some regions worldwide are experiencing coastline expansion, while the drivers are often overlooked. In this study, we conducted an intensive analysis of significantly expanding (>1 km) coastlines worldwide based on the Global Long-term Shoreline Evolution (GLSE) dataset between 1986 and 2016. The study employs the interpretation of remote sensing imagery to identify and categorise coastline types comprehensively, focusing on the main drivers of coastline expansion. Coastline types include natural (e.g. rocky, sandy, muddy, and biological) and artificial (e.g. traffic, aquaculture, port, and embankment) coastlines. At the global scale, 2939 km of coastline experienced significant expansion, with aquaculture coastlines dominant at 22%, followed by biological (17%) and muddy coastlines (15%). At the national level, China accounted for 27% of global expansion, driven primarily by artificial factors (43% aquaculture), while Brazil exhibited the highest natural expansion (97% biological coastlines). At the continental scale, Asia's coastline expansion was predominantly artificial (34%), whereas Africa and South America were dominated by natural drivers (88% and 94%, respectively). Among deltas, aquaculture constituted 36% of expansion, with the Yellow River Delta showing the highest mean expansion (3369.7 m) and the Nile Delta reaching a maximum of 9920.6 m. This study not only deepens our understanding of how sea-level rise and human activities affect coastlines but also offers valuable insights into the sustainable management and utilisation of coastal resources.
Thermaikos Gulf, located in the northeastern Mediterranean Sea, faces significant anthropogenic pressures and natural hazards, requiring reliable metocean forecasts for weather, ocean circulation, sea levels, waves, and hazard predictions, including pollutant transport, coastal floods, and freshwater discharges. The Wave4Us operational platform addresses these needs by providing high-resolution and specialised forecasts, accessible to local authorities, researchers, and the public. Additionally, on-demand predictions for marine pollution, coastal inundation, and heatwaves offer real-time insights to emergency responders and coastal authorities during hazardous events. This study presents the platform's structure, modelling advancements, and predictive skill for specific hazards. Forecast efficiency is evaluated against satellite and field observations: (i) the simulated oil spill spreading is verified by satellite data; (ii) the modelled freshwater discharges are validated against field measurements (high correlation, RMSE < 10%); (iii) a pronounced river plume spreading is confirmed by ocean/tracer simulations and satellite imagery; (iv) the prediction of sea level, wave conditions, and coastal flooding under a severe low-pressure system is validated against measurements and documented events; (v) the marine heatwave predictions is confirmed by comparing simulated and satellite sea temperatures (error < 1%). These evaluations demonstrate the platform's reliability in forecasting key environmental risks, aiding decision-making and response efforts in the Thermaikos Gulf region.
Tropical cyclones in the northern Indian Ocean (IO) were examined in the context of basin-specific warming from 1960 to 2022. In the Arabian Sea (AS) the increase in the total number of cyclones on a decadal-time scale closely followed basin-averaged rapid warming and reflected the presence of environmental conditions congenial for cyclogenesis. The increased occurrence of higher category cyclones in the AS after 1995 was closely linked with the accelerated warming and a 5-fold increase in the tropical cyclone heat potential (TCHP). Changes in the dynamic variables such as decreased vertical wind shear and increased relative vorticity, and increase in the thermodynamic variables viz. TCHP and relative humidity acted in tandem to create conditions favourable for generation of a greater number and more intense tropical cyclones after 1995. This was facilitated by the increased occurrence of positive Indian Ocean dipole (IOD) after 1995 which deepened the isothermal layer and modified the environmental parameters through altered Walker circulation in the AS. In contrast, increased occurrence of positive IOD cooled the eastern IO and that resulted in the observed reduced rate of warming in the Bay of Bengal (BoB) after 1995. The lack of trend in the total number of cyclonic systems on decadal-time scale in the BoB was linked to non-supportive dynamic environmental conditions for cyclogenesis. The continued occurrence of higher category cyclones in the BoB, despite the lack of decadal trend, could be explained, in part, by the higher magnitudes of tropical cyclone heat potential and higher atmospheric moisture content.
Sardine growth and variability along the Indian East coast have been less studied compared to the West coast and seldom attempted in numerical models. Both coasts exhibit significant differences in physical and chemical properties as well as biological productivity. This study realistically simulated sardine growth along the Indian East Coast with seasonal and spatial variability by using a sardine bioenergetics model. The model comprises a lower trophic level model (NEMURO) and a fish bioenergetics component, which utilises prey densities and temperature derived from NEMURO. After hatching, sardine weight showed a consistent increase from June to February and reached 50-80 g and 75-100 g in Tamil Nadu and Andhra Pradesh, aligning with observed ranges of landing data. Fish weight declined during summer (25-40 g) and further gained from June. Optimum water temperature and high phytoplankton abundance (June-January) favoured sardine growth, while temperature beyond the preferred range and minimal phytoplankton availability (March-May) resulted in weight loss in all locations. Key model parameters influencing sardine growth and its seasonality are discussed in detail. The outcome of this numerical model shows a promising step towards numerical predictions of the Indian East Coast pelagic fishery.
This paper proposes a new transformed linear simulation model for wave record simulation. The joint distributions of amplitudes and periods of random waves in a short-crested random sea can subsequently be obtained by statistical post processing of the wave record simulated using this proposed new method. For implementing the proposed new simulation method, a transformation expressed in a monotonic exponential function has been constructed so that the first three moments of the original true process match the corresponding moments of the transformed model. The proposed new simulation method has been applied to generate a wave record which is subsequently processed mathematically for forecasting the joint distributions of wave amplitudes and periods of a sea state with the surface elevation data measured at the coast of Yura in the Japan Sea, and the new simulation method's accuracy and efficiency are convincingly validated by using comparisons with the results obtained from a linear simulation method and from a second-order nonlinear simulation method.
Extreme climate events, now more frequent, are defined by the IPCC as occurrences outside typical weather ranges. Extreme rainfall, for instance, can lead to excessive freshwater inflow into saltwater environments, disrupting ecosystems like coastal lagoons. The Mar Menor lagoon in Spain faces issues like eutrophication and habitat loss due to changing water conditions. Monitoring water quality is crucial for managing these risks. While traditional sampling methods are valuable, real-time data from buoys enables quicker responses. This study focuses on data from sampling stations and a smartbuoy in the Mar Menor lagoon following rainfall events on October 6th and 10th, 2022. The goal was to assess how long the ecosystem took to recover. Results showed that the southern part of the lagoon had a delayed recovery compared to the northern region, with significant impacts on salinity, turbidity, and oxygen levels. Immediately after the rainfall, lower surface salinity was observed in the southern lagoon due to freshwater influx, while the northern region remained stable. Freshwater also affected bottom salinity along the western shore. By October 19th, salinity in the lagoon's center had increased but had not returned to pre-rainfall levels, with lower salinity still present in the southern region. Turbidity also increased along the western and southern shores due to runoff carrying nutrients and sediments, potentially disrupting local ecosystems. The continuous data from the smartbuoy offered detailed insights into hydrological changes, salinity, turbidity, and oxygen variations. This real-time data is essential for effective environmental management and conservation efforts in the lagoon ecosystem.
Two sets of simulations for 1993-2005 are carried out with a medium-resolution ocean and sea-ice model covering the North Pacific, Arctic and North Atlantic Oceans. The first set, using the same model parameters and three different atmospheric forcing datasets (DFS5.2, JRA55-do and ERA5), all show too fast melting of Arctic in spring and summer compared with the ice concentration based on satellite remote sensing. The simulation using ERA5 obtains the smallest ice concentration (largest deviation from satellite data) in summer, and the smallest ice thickness in both summer and winter, corresponding to the largest warm bias of surface air temperature over the Arctic sea-ice. In the second set of simulations using ERA5, changing either the snow conductivity (in W m-1 K-1, from the constant value of 0.31 to 0.15 during April -September and 0.5 during October-March) or the albedo of bare puddled ice (from 0.53 to 0.63) leads to an increase in ice concentration in summer, and ice thickness in both summer and winter. The simulation using ERA5 with both parameters altered is from October 1993 to March 2023, and obtains seasonal, interannual and long-term variations of ice area generally consistent with satellite data.
The main aim of this paper is to study the characteristics of sea and swell waves in the Arabian Sea using deep sea moored buoys. This study examines wave measurements from the northern and southern Arabian Sea with a focus on spectral characteristics and seasonal variations over eight years. The analysis reveals a bimodal wave spectrum in January and February, with prominent wind-induced waves. In March, swells start to dominate, leading to a decrease in wind-driven waves. During the Southwest monsoon from June to September, swells subsequently become the dominant wave component, resulting in a single-peaked spectrum. Findings indicate that higher wave heights and extended periods are more frequent in the northern Arabian Sea (NAS), with considerable intra-annual variation in peak spectral energy during the monsoon season. The monthly averaged spectra revealed interannual variability during the SW monsoon, with peak spectral energy of swell waves decreasing in June, July and increasing in August. The seasonal patterns exhibited significant changes over the years, with opposing trends in Hm0 values between July and August for the northern and southern parts of the Arabian Sea. Directional spectra derived from the in-situ data during the two concurrent cyclones, Tropical Cyclone Kyarr and Tropical Cyclone Maha, were analysed, where maximum significant wave heights (Hm0) of 7.38 and 3.29 m were observed. Model-derived wave parameters are compared with measured data, revealing a clear underestimation and overestimation of the significant wave heights during extreme events by the ERA5 model.
Tropical cyclones (TCs) are major natural disasters that can cause significant damage and loss of life in coastal areas. Arabian Sea has experienced many such TCs, with varying degrees of severity. These TCs are associated with heavy rainfall, flash floods, and make significant damage to infrastructure, society, and the environment. A range of factors influence cyclones in the Arabian Sea including sea surface temperatures, ocean currents, eddy activity, the position of the monsoon jet axis, and the Indian Ocean Dipole (IOD). The frequency of these TCs has already increased due to rising sea surface temperatures and changes in wind patterns. Researchers have conducted extensive studies in the region to better understand tropical cyclones and their impacts, focusing on sea surface cooling, air-sea interactions, and the effects of climate change. By studying these TCs over several decades, trends and patterns can be identified, contributing to the development of more accurate forecasting models and early warning systems to better prepare for future events. This paper reviews the history, track patterns, biological and physical impacts, numerical modelling, and future trends of tropical cyclones in the Western Arabian Sea, with the aim of supporting policymakers in developing more effective strategies for mitigating the impacts of future TCs.
Over the last 30 years or so, moored buoy networks have been developed by many countries as part of their operational observing capability. One such network is the UK Met Office's moored buoy network that was largely established in the early 1990s. This article describes the evolution of the network, how the buoy technology has developed, the applications for which the data are used and international coordination on moored buoy activities.
Safe execution of a marine operation (MO) such as a jack-up leg lowering depends on two main design parameters i.e. the characteristic value of an impact velocity and its corresponding operational limit. While the former is lower than the latter, a workable weather window (WOWW) is identified, and the operation can be executed. However, various sources of uncertainty can affect the design parameters and make the operation unsafe. This paper introduces a methodology to assess WOWWs including uncertainties in forecasted directional (2D) wave spectra for various lead times, errors from an efficient machine learning algorithm used to predict dynamic responses, and uncertainties in the actual operational limit. A simple semi-probabilistic response-based load-resistance factor design (LRFD) format is proposed to assess WOWWs. The factors are calibrated using a limit state function and an acceptable MO-dependent failure probability. For a jack-up leg lowering operation, the summer operability decreases from 86 (deterministic) to 23% (semi-probabilistic) when including these sources of uncertainty. This study offers a robust and simple method to find WOWWs and help superintendents make on-board decisions. Findings from this paper can be used as a guide for future improvement of MO design standards.
