This study presents the development and sensitivity analysis of the sea level operator within the OceanVar software which implements an oceanographic incremental three-dimensional variational data assimilation scheme. In OceanVar, the background error covariance matrix is decomposed into a sequence of physically based linear operators, allowing for individual analysis of specific error matrix components. The key development of OceanVar2.0 is the full integration of both dynamic height and barotropic model formulations as a flexible option for handling sea level covariance. The comparison of the two formulations of the sea level operator which provides correlations between Sea Level Anomaly, temperature and salinity increments is presented. The sensitivity experiments were performed in the Mediterranean Sea and the quality of the analysis assessed by comparing background estimates with observations for the period January-December 2021. The results confirm the methodological advantage of the barotropic model operator, which successfully overcomes the physical and operational limitations associated with choosing an appropriate level-of-no-motion for the dynamic height formulation. Furthermore, we present a method to assimilate along-track satellite altimetry considering a forecasting model with tides.

Accurate simulation and prediction of ocean waves are essential for coastal risk management and climate studies. Deep learning has shown promising results for wave modeling, but most approaches still operate on regular grids and on forecasting time scales, and do not generalize to unstructured discretization or to long time horizons. Here we present WaveGraph, a model based on Graph Neural Networks (GNNs) that emulates basin-scale wave dynamics directly on unstructured meshes with high resolution along the coasts (up to 2-3 km). Trained on bias-corrected simulation data over the Mediterranean Sea, WaveGraph uses a multiscale architecture combining the unstructured model mesh with a uniform graph, allowing simultaneous representation of local coastal interactions and large-scale wave dynamics. The model reconstructs the evolution of significant wave height, mean period, and mean direction, and is applied autoregressively for a continuous 17-year period without reinitialization or drift. Validation against buoy and satellite observations shows skill comparable to the input data set, and ablation experiments indicate that wind forcing drives most of the long-term stability while wave history improves swell-driven and basin-scale dynamics. These results show that GNNs can provide stable and efficient emulators of spectral wave models on unstructured domains, enabling decadal wave reconstructions.

CoastPredict, a UN Ocean Decade Programme, is co-designing and implementing an integrated coastal ocean observing and predicting system that adheres to best practices and international standards, conceived as a global framework and implemented locally through sustained partnerships.Many coastal services remain fragmented: observing assets, models, and downstream applications are often developed in isolation, and operational solutions do not consistently connect real-time data streams, multi-scale predictions, and decision workflows. This limits the capacity to (i) evaluate compound impacts from extreme events to long-term climate trends, (ii) compare performance across regions, and (iii) translate prediction skill into actionable management solutions. GlobalCoast is CoastPredict’s framework for implementation to address these gaps by linking observations, modelling, and stakeholder needs into fit-for-purpose, locally-led coastal resilience services that can be compared, transferred, and improved across diverse environments.A major step forward has been the consolidation of the GlobalCoast Network of Pilot Sites. The first GlobalCoast survey (2023) identified 138 Pilot Sites in over 74 countries, establishing a global foundation for implementation and benchmarking; the Pilot Site submission process has since been reopened to expand geographic coverage and fill thematic gaps. A new GlobalCoast Network Memorandum of Understanting has been set up and signed by more than 50 parnters.Over the last year, CoastPredict/GlobalCoast has strengthened the enabling backbone for scalable, interoperable services. This includes ProtoCoast, the prototype GlobalCoast cloud infrastructure co-designed by CMCC, SOCIB and EGI (with EOSC/Pangeo-aligned approaches and federated providers), supporting shared code, interactive analysis, and reproducible workflows across Pilot Sites. In parallel, the CoastPredict Secretariat, funded by CMCC, has enhanced coordination across projects and technical support for programme development and integration.GlobalCoast is now advancing from operational oceanography toward operational management solutions through a “menu of solutions” approach: multi-hazard early warning services; coastal climate and risk indicators; pollution and marine litter applications; and decision-support tools for planning and adaptation. By deploying comparable building blocks across sites—while accounting for local dynamics, exposure, governance, and capacity—GlobalCoast enables systematic evaluation of what is transferable, what must be tailored, and what standards and best practices accelerate impact.

As computational resources have increased in availability and capability, so has the complexity of the models used to represent biogeochemical (BGC) processes in ocean simulations. To effectively calibrate the increasingly large number of uncertain parameters in these models, efficient parameter estimation methods are needed to ensure that the models can accurately represent the BGC processes under investigation. In this study, we address this challenge using a multistage automatic parameter estimation methodology that sequentially applies global sampling and local optimization to calibrate both the BGC model parameters and the parameters associated with a one-dimensional physical ocean model. We quantitatively compare the accuracy of sequential and simultaneous parameter estimations of moderately complex BGC and physical models at locations corresponding to the Bermuda Atlantic time series and the Hawaii Ocean time series. The results show that the best overall agreement with the observed mean seasonal cycles is obtained when BGC, advection, boundary condition, and turbulent diffusion parameters are estimated simultaneously, rather than sequentially. Simultaneous estimation of all these parameters results in closer agreement with mean seasonal cycles for oxygen and particulate organic nitrogen. Moreover, the agreement is improved in general when the advection, boundary condition, and turbulent diffusion parameters are included in the estimation, as opposed to calibrating the BGC model alone. This study also serves as a demonstration of a meta-algorithm for parameter estimation in high-dimensional models using a truncated global search with local optimizations.

The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report highlights the accelerating rise of global mean sea level (GMSL), with trends surpassing historical rates observed over the past two millennia. The China-Europe Sea Route (CESR), a region of strategic importance for international trade, is particularly vulnerable to sea level changes and extreme events. This study integrates data from satellite altimetry, tide gauge records, and a global hydrodynamic model to assess absolute and relative sea level variations, as well as extreme sea level events, across eight CESR sub-regions over the period 1993-2023.Statistically significant mean sea level trends confirm systematic decadal variability across regions. Notably, the East China Sea, Yellow Sea and Bohai Sea show a decadal trend slowdown in the second (2003-2013) and third decade (2013-2023) with respect to the first one (1993-2003).Signals of enhanced regional mean SLA trends are observed in the North Indian Ocean, while Pacific sub-regions exhibit pronounced decadal variability. Discrepancies between tide gauge and satellite altimetry in specific areas were attributed to land subsidence and inherent limitations of coastal altimetry.A global hydrodynamic model provided estimates of return periods for extreme sea levels, identifying high-risk zones such as the Bay of Bengal and the South China Sea. However, challenges remain in capturing cyclone impacts, emphasizing the need for continued improvements in modeling frameworks for extreme sea level assessment.By highlighting the importance of localized, data-driven approaches and continuous monitoring, the findings contribute to advancing climate resilience and informing risk mitigation strategies in this globally significant region.

Abstract. The northern Adriatic Sea is known to be threatened by extreme sea level events. Venice is exposed to these events, locally known as Acqua Alta, which cause the flooding of the historical city centre. Despite its relevance, a comprehensive analysis of extreme Acqua Alta events that considers all possible compounding drivers, including baroclinic contributions, is, to our knowledge, still missing. In this work, numerical results based on the Copernicus Marine Mediterranean Analysis and Forecasting System are analyzed to better understand the physical mechanisms underlying the 12 November 2019 Acqua Alta event and to assess individual processes playing a role in the event dynamics. The novel finding of this study is the identification of the contribution of baroclinic dynamics. We estimate a net baroclinic contribution of 7 cm (4 %) to the 175 cm sea level peak. We show for the first time that internal seiches along the northern Adriatic slope contribute to the dynamics of the Acqua Alta event. Moreover, we identify a northern Adriatic density front and quantify its contribution to the time-mean positive northward sea level slope.

Understanding the surface heat budget of the Mediterranean Sea is essential for assessing its role in regional climate and ocean circulation. Under the steady-state heat budget closure hypothesis, the Mediterranean should exhibit a net surface heat loss to balance the heat gained through the inflow of warm Atlantic water at the Gibraltar Strait. However, literature estimates of the net heat flux vary widely, raising questions about the accuracy of existing reanalysis products. In this study, we compute the net surface heat flux over the Mediterranean using two atmospheric datasets: high-resolution (0.125 degrees) ECMWF analysis and lower-resolution (0.25 degrees) ERA5 reanalysis. By applying the same sea surface temperature fields and bulk formulas in both cases, we isolate the impact of atmospheric resolution and data quality. We find that the ECMWF analysis yields a basin-averaged net heat flux of -3.6 +/- 1.3 W m-2, consistent with the closure hypothesis, while ERA5 gives a spurious positive flux of +5 +/- 1.2 W m-2. Furthermore, beyond simply assessing the net heat budget, this study delves into the probability distributions of air-sea heat fluxes, aiming to gain a deeper understanding of associated uncertainties and extreme values in turbulent heat fluxes. The probability distributions for turbulent heat flux components exhibit characteristics such as skewness and kurtosis, respectively, varying across the basin. To assess the influence of extremes, we apply the Interquartile Range (IQR) method within statistical models that account for the skewed nature of turbulent heat flux distributions, enabling a consistent treatment of outliers. Our results reveal that extreme negative heat flux events play a critical role in determining the net heat flux direction; excluding these extremes leads to a spurious positive heat budget. Only ECMWF fields are consistent with the heat budget closure hypothesis. Furthermore, we demonstrate that the Mediterranean heat budget closure hypothesis is connected to extreme heat loss events occurring in key regions of the basin, such as the Gulf of Lion, the Adriatic Sea, the Aegean Sea, and the southern Turkish coasts.

Sea Surface Temperature (SST) reconstructions from satellite images affected by cloud gaps have been extensively documented in the past three decades. Here we describe several deep learning models to fill the cloud-occluded areas starting from MODIS Aqua nighttime L3 images in the Italian Seas. To tackle this challenge, after testing different models and methodologies, we employed a type of Convolutional Neural Network model (U-Net) to reconstruct cloud-covered portions of satellite imagery while preserving the integrity of observed values in cloud-free areas. We demonstrate the high precision of U-Net with respect to available products done using OI interpolation algorithms. Our results are promising with respect to some earlier studies while suggesting further investigation for more robust intercomparison.

Coastal inundation threatens both economic assets and human lives, yet accurate flood mapping remains limited by gaps in data availability and model capabilities. In this study, we enhanced the LISFLOOD-FP model to simulate coastal floods by incorporating wave setup, swash dynamics, and interactions with protective infrastructure such as temporary dunes. We applied this approach to Cesenatico, Italy, where seasonal dunes serve as winter coastal defenses, analyzing two contrasting storm events with observational data for validation: the 2015 Saint Agatha Storm, which breached the dunes causing extensive inland flooding, and the 2022 Denise Storm, where intact dunes successfully prevented inundation. Our results demonstrate that dunes effectively mitigate flooding when intact, but failure of even small sections can trigger widespread inundation, highlighting the critical need for optimized design. This work advances the development of coastal digital twins by introducing a computationally efficient representation of essential physical processes - swash-related erosion of dune stability and swash contribution to flood volumes through an overwash efficiency parameter - enabling practical risk assessment and infrastructure planning in vulnerable coastal regions.

Tracking ocean pollution and marine litter is a compelling problem for safeguarding ocean ecosystems and is recognized as a priority under the UN Ocean Decade Vision 2030. This work investigates how the evolution of pollution transport is affected by ocean dynamics across multiple spatial scales, with particular focus on mesoscale and submesoscale flow structures.To achieve this goal, high- and very high-resolution regional ocean simulations were conducted in the Subpolar and Tropical Northern Atlantic, two open ocean regions characterized by a different baroclinic deformation radius and therefore different mesoscale eddy sizes. Secondly, different oil spill simulations were performed to understand how submesoscale filaments influence pollutant concentration patterns. Idealized coastlines are considered to perform a statistical analysis of beached oil distributions and particles first-passage time, enabling a direct link between transport pathways and underlying flow properties.The high-resolution (“child”) ocean fields were obtained by dynamically downscaling the 1/12° (“parent”) Global Ocean Physics Analysis and Forecast product from the Copernicus Marine Service using the SURF platform (v2.0.1), based on NEMO v5.0.1. Horizontal resolutions of 1/36° and 1/108° were achieved, covering the period 1 January–30 June 2025. Each month has been simulated independently to maintain consistency between the parent and child model fields.The MEDSLIK-II v3.0 software has been used to run multiple oil spill simulations in both the high-resolution and coarse fields. One simulation is run every five days in the period covered by the high-resolution simulations. Each simulation covers ten days and is characterized by a punctual continuous release lasting five days.Beached oil concentration and first-passage time probability distribution functions were computed and compared across resolutions and dynamical regimes.The results show that oil concentration distributions associated with highly resolved ocean fields, appear to be characterized by fatter tails and larger concentration extreme values. This indicates that submesoscale activity, better resolved at finer resolutions, enhances surface pollutant aggregation, while coarser simulations tend to underestimate these extremes.

Mediterranean coastal ecosystems are increasingly threatened by multiple anthropogenic pressures and climate change. As a result, these impacts have caused the decline of key endemic habitats such as Posidonia oceanica meadows and coralligenous reefs. Due to the slow natural recovery of these habitats after degradation, restoration actions play a key role in accelerating ecosystem recovery, reestablishing ecological structure and functional processes, and preventing further biodiversity and ecosystem service loss. Given the frequent habitat fragmentation and high levels of endemism, effective restoration efforts require a multidisciplinary, ecosystem-based approach that integrates marine science, engineering, socioeconomics, and policy. This study describes the holistic approach adopted in the RENOVATE project, which established an integrated framework to address the combined impacts of climate change and human pressures on vulnerable ecosystems. The framework employs advanced observational technologies, field data, and numerical modeling within an adaptive management loop, enabling site-specific, evidence-based restoration planning and assessment of ecosystem services recovery. Additionally, the study reports results from the northern Tyrrhenian coast (Latium, Italy), where RENOVATE aims to protect EU priority habitats and species from human pressures and climate-related threats. Although project activities are still in early stages, results from active restoration in the northern Latium coast show initial establishment and survival at pilot sites, highlighting the framework’s potential to guide effective, replicable interventions in coastal ecosystems. Beyond the regional case study, the proposed framework contributes to global marine restoration efforts by providing a transferable methodology for the management of coastal ecosystems.

Recent advancements within the arena of Artificial Intelligence have widened the potential applications of Machine Learning (ML) frameworks in climate prediction and weather forecasting. For any modern forecasting system, a core objective is linked with handling uncertainty and scientists are interested in the accuracy of the forecasts. The time series forecast of air temperature using ML approaches is available in the literature. But for this study, we have selected major atmospheric variables- air temperature, dew point temperature, wind components and mean sea-level pressure (MSL-P) retrieved from the ECMWF analysis system and which are to be used in perturbation of the ocean forecasting system. In our previous approach, we analysed the probability distributions of the selected atmospheric variables. In this study, we intend to forecast those atmospheric variables using machine learning algorithms to compare with the analysis dataset produced by ECMWF. Under the initial approach, a Convolution Neural Network (CNN) approach is built to predict the time series for the atmospheric variables. The predicted results from the forecasts have shown minimal differences in comparison to the observations. Based on the results produced from the CNN, we would like to apply other ML approaches to compare the accuracy and in the process of selecting a better ML model.

The Mid-Atlantic Bight frontal system along the U.S. northeast shelf is rich in biodiversity. In this region, primary production is influenced by a variety of upwelling processes, including internal instabilities of the front, off-shore forcing from Gulf Stream rings, and wind-driven flows. It is noteworthy that the concentrations of chlorophyll-a (Chl-a) in the shelf-break region are not consistently enhanced throughout the year, although local increases of phytoplankton biomass have been observed in some circumstances. In this work, we investigate the frontal dynamics of one of the possible mechanisms affecting primary production: upwelling via detachment of the Bottom Boundary Layer (BBL). The annual variability of the surface Chl-a in the shelf-break region reveals a 5- to 20-day period, which is potentially consistent with nutrient upwelling associated with the BBL detachment. Details of the process are examined using in situ data by quantifying along-isopycnal changes in properties. As frontal isopycnals rise in the water column, nitrate tends to decrease and Chl-a tends to increase, suggesting utilization of upwelled nutrients by phytoplankton. However, significant fluctuations can be attributed to sample size, intrinsic data variability, and the assumption of homogeneity in the along-shelf dimension.

Assessing climate predictability remains a central challenge in modeling and forecasting the climate system. Approaches from nonequilibrium statistical mechanics, particularly stochastic thermodynamics, have provided insights into non-equilibrium properties of stochastic models, which have proven useful in representing patterns of climate variability. In this work, we investigate the potential of entropy production and frenesy as tools for quantifying the predictability of non-equilibrium fluctuations in the climate system. Entropy production, a measure of the irreversibility of the system’s dynamics, is explored as an intrinsic indicator of predictability and its possible connections to the Anomaly Correlation Coefficient (ACC). Frenesy, a lesser-known quantity derived from active matter studies that captures kinetic fluctuations and dynamical activity, is assessed for its potential role in explaining non-equilibrium processes within the climate system. Thus, we aim to better understand the relationships between these thermodynamic quantities and climate oscillations, such as the El Niño-Southern Oscillation and the Madden-Julian Oscillation, with the ultimate goal of defining a new measure of climate predictability and better comprehending non-equilibrium processes in the ocean and the atmosphere.

Located in the central Mediterranean Sea, the Adriatic Sea experiences complex water circulation patterns driven by saltwater inflow from the Ionian Sea through the Strait of Otranto and the outflow through the same strait but richly charged by fresh and dense waters formed in the northern Adriatic Sea. These circulation patterns place the unique hydrodynamics of the Adriatic Sea as the mainframe in shaping its diverse marine ecosystem, making it a primary region in dense water formation within the Mediterranean Sea. However, in recent decades, the region has continually recorded longer and more intense periods of drought. Some studies account for the loss of about 80 billion tons of freshwater during the 2021-2022 drought only at the Po River basin. To explore this matter further, the present work aims to analyse the last decade (2013-2022) of river discharges into the Adriatic Sea and frame the impacts of recent drought events in the current climatological period. To this end, the hydrological data reconstructed with the European Flood Awareness System (EFAS) were analysed for the period 1991-2022, quantifying river discharges separately in the four subregions of the Adriatic Sea: Shallow Northern Adriatic Sea (SNAd), Northern Adriatic Sea (NAd), Central Adriatic Sea (CAd), and Southern Adriatic Sea (SAd). Over the past 32 years, river discharges have shown different trends along the Adriatic Sea subregions, where a delicate balance between dry seasons in some subregions has been slightly balanced by flood seasons in others and vice versa. This delicate balance, combined with the diversity of its river basins, prevents us from estimating a trend with statistical significance for the Adriatic Sea. However, the river discharge trends are forthright when computed individually for each subregion, balancing slightly negative trends in the northern subregions (-0.6% and -1.0% per year in SNAd and NAd) with intriguingly positive trends in the southern subregions (+0.4% and +1.3% per year in CAd and SAd). When the analysis window narrows to the last decade (2013-2022), this balance breaks down, and a strong negative trend emerges across the entire Adriatic Sea, without exception, indicating reductions of -4.2% per year in freshwater input throughout the river basin. As suggested by the Standardised Flow Index (SFI) results, a climate indicator used to estimate the long-term impact of drought and flood periods on river discharges, 2022 was crucial for the last negative decadal trend. During this year, the northern Adriatic experienced the driest period in the last 32 years, while the southern Adriatic experienced river discharge reductions during flood months. Nevertheless, the most worrying element about the extreme drought of 2022 is that this year is part of a drought cycle that has continuously reduced freshwater availability in the Adriatic Sea every 4-5 years since 2008.

The generation and propagation sites of internal tides in the Mediterranean Sea are mapped through a comprehensive high-resolution numerical study. Two ocean general circulation models were used for this: NEMO v3.6, and ICON-O, both hydrostatic ocean models based on primitive equations with Boussinesq approximation, where NEMO is a regional Mediterranean Sea model with an Atlantic box, and ICON a global model. Internal tides are widespread in the Mediterranean Sea. The primary generation sites: the Gibraltar Strait, Sicily Strait/Malta Bank, and Hellenic Arc, are mapped through analysis of the tidal barotropic to baroclinic energy conversion. Semidiurnal internal tides can propagate for hundreds of kilometres from these generation sites into the Algerian Sea, Tyrrhenian Sea, and Ionian Sea respectively. Diurnal internal tides remain trapped along the bathymetry, and are generated in the central Mediterranean Sea and southeastern coasts of the basin. The total energy used for internal tide generation in the Mediterranean Sea is 2.89 GW in NEMO and 1.36 GW in ICON. Wavelengths of the first baroclinic modes of the M2 tide are calculated in various regions of the Mediterranean Sea where internal tides are propagating, comparing model outputs to a theory-based calculation. The models are also intercompared to investigate the differences between them in their representation of internal tides.

Estuarine zones are particularly vulnerable coastal areas as consequence of the changing climate. The river flow decrease, RD, and the sea level rise, SLR, are leading to: (i) salinization of the surface and subsurface catchment waters, (ii) salt-wedge intrusion SWI moving more and more inland, with a non-linear response to the main drivers of the estuarine dynamics. The current study uses a one-dimensional two-layer estuary box model, the so-called CMCC EBM (Verri et al 2020; 2021) which solves the estuarine water exchange by means of two conservation equations for volume and salt fluxes averaged over the diurnal tidal cycle, plus two parametric equations estimating the SWI length and the along-estuary diffusivity. The EBM has been applied to the Po di Goro branch of the Po river delta, which is characterised by a river-dominated estuary flowing into the micro-tidal Northern Adriatic Sea. A strength of the EBM here proposed is the extremely low computational time which makes it particularly suitable for climate purposes by bridging the gap between available hydrology and marine hydrodynamics projections which reach at most the mesoscale with high computational costs and without representing the estuarine transitional areas. Additional assets are the minimal data storage and no need to postprocess the results as the SWI length is among the model outcomes. On the other hand, a proper tuning of the parametric equations is required and this was made possible by an accurate in-situ monitoring and a site specific “learning dataset” built upon the outcomes of a 3D unstructured modelling of the Po delta system. Considering that there are few studies devoted to the impacts of the local SLR on the SWI and the salinity of estuaries in micro-tidal environments, one of the aims of this study is to expand the knowledge on this topic by proving future projections for the selected test-case. Moreover, the increasing salinization of the Po di Goro estuary threats the local economy and the ecosystem health. Thus, the second aim of this study is to evaluate a Nature-Based-Solution, NBS, to mitigate the SWI, i.e. we assess the salt uptake capability of the Atriplex portulacoides within our modelling study.Three climate experiments with the EBM have been carried out over 1991-2100 with a ‘mechanistic’ approach: (i) Exp1 is a full-forcing experiment with the river inflow (volume flux at the estuary head) and the seawater inflow (volume flux, salinity and sea level at the estuary mouth) provided by a regional climate model RCM considering RCP 8.5; (ii) Exp2 is a twin experiment of Exp1 but neglecting the sea level among input forcings of the EBM; (iii) Exp3 is a twin experiment of Exp1 but with a reduced salinity of the seawater inflow by assuming that 20% of the estuary water volume interacts with the halophytes planted along the estuary banks.We propose a discussion on the relative role of the SLR and the RD in determining the future projections of the Po di Goro estuarine dynamics and the potential effect of a site-specific NBS.

The ocean, a dynamic and complex system, is crucial to Earth’s environmental and human life balance, from regulating the climate to supporting biodiversity and livelihoods. Monitoring the ocean has always been essential, but the increasing climate change impacts have made it a critical necessity for safeguarding our future through timely predictions and mitigation measures. The Ocean Decade is an initiative to find transformative solutions to existing and future challenges facing the ocean and thus humankind. Among its 10 challenges, Challenge 6 focuses on enhancing coastal community resilience to ocean-related hazards by adapting advancements in science. This article explores how building community resilience through data integration, community outreach, and policy development can contribute to achieving the Ocean Decade’s vision of ‘A Safe Ocean’.

This work seeks to enhance the physical representation of coastal-ocean dynamics through integrated numerical models, advancing the understanding of intricate Earth system processes. It specifically focuses on two critical aspects within coastal zones: the influence of vegetation on wave dynamics and the morphodynamic processes driven by sediment transport.Modeling the intricate interplay between waves, seagrass, currents, and sediment processes is crucial for developing a comprehensive and realistic digital twin of the ocean. The absence of robust in-situ observational systems can result in insufficient representation of these highly dynamic environments. We aim to integrate numerical simulations with an observational system design, emphasizing the critical importance of continuous data collection and the cohesive application of empirical measurements within numerical models.The augmented wave model, featuring a refined seagrass representation that incorporates flexibility, seasonal growth patterns, and phenotypic traits informed by site-specific measurements, is applied to the case study in the coastal zone of Civitavecchia in the north-eastern Tyrrhenian Sea, Italy. This study examines the restoration of Posidonia oceanica meadows, and their impact on wave attenuation, utilizing insights derived from the numerical model results. The sediment transport is tested in both an idealized tidal inlet scenario and along the coast of Fiumicino, south of Civitavecchia, with the aim of integrating a three-dimensional model capable of accurately capturing bedload transport influenced by local bathymetry and the advection of suspended sediments from the Tiber River mouth. The respective contributions of these factors to seabed evolution are quantified, and a feedback mechanism is further considered within the circulation and wave models.Ultimately, this synergy aims to improve predictive capabilities in dynamic marine environments, advancing the numerical modeling of coastal-ocean processes to better forecast environmental extremes and enhance our understanding of the underlying physics.

The CoastPredict Programme, an endorsed Programme of the Ocean Decade, has established a central framework for coordination and practical implementation called ‘GlobalCoast’. GlobalCoast will coordinate implementation and integration of the science and technology advances from CoastPredict’s six Focus Areas at Pilot Sites in a range of contrasting Regions of the Global Coastal Ocean, using and developing best practice principles in observing, data management, modelling and co-design. The Programme Focus Areas projects address priorities related to coastal resilience including: Integrated observing and modelling for short term coastal forecasting and early warnings; Future Coastal Ocean climates: Earth System observing and modelling; Solutions for integrated coastal management; Coastal information integrated in an open and free international exchange infrastructure; Equitable coastal ocean capacity.GlobalCoast will overcome a number of existing barriers including: the lack of an international network for Global Coastal Ocean innovation and solutions for integrated observing and prediction, and associated fragmentation of knowledge; the particular challenge regarding open and free data access in the Global South; the lack of end-user (coastal managers / communities) involvement and the long timeframe currently required to demonstrate solutions. Through GlobalCoast, CoastPredict will demonstrate (at Pilot Sites) an integrated observing and predicting system for the global coastal ocean and create globally replicable solutions, standards, and applications that enhance coastal resilience. A global digital cloud-based infrastructure will be key to acceleration - the cloud-based computing platform will enable accelerated data collection and open and free data sharing, and advancement of modelling and analysis tools, aligned with best practices.