The session focuses on the main Copernicus Marine Service research and development activities on ocean modelling; data assimilation; processing of observations, impact and design of in situ and satellite observing systems; verification, validation, and uncertainty estimates; monitoring and long-term assessment of the ocean physical and biogeochemical states. The session also includes research activities dedicated to the next generation of ocean monitoring and forecasting systems (improved Arctic monitoring, ensemble forecasting, regional ocean climate projections, use of artificial intelligence) and new services for the coastal ocean and for marine biology. The session will also encompass research activities on the development of the European DTO, including the next generation of ocean models combining artificial intelligence and high-performance computing, dedicated infrastructures and platforms as well as protocols and software and the definition of what-if-scenarios.
Presentations are expected from research teams involved in the Copernicus Marine Service, in the European DTO, in the development of in situ and satellite observing systems and of downstream applications and in relevant Horizon Europe projects. Contributions from the international OceanPredict community and from the relevant UN Decade programmes and projects are strongly encouraged.
Since November 2022, and within the framework of the Copernicus Marine Service, Mercator Ocean International has been delivering real-time daily services (weekly analyses and daily 10-day forecasts) with a major release of the global 1/12° high-resolution (eddy-resolving) GLO12 analysis and forecasting system. Ocean observations are assimilated into the model using a reduced-order Kalman filter method (SEEK). Along-track altimeter sea level anomaly, satellite sea surface temperature and sea ice concentration, as well as in situ temperature and salinity vertical profiles, are jointly assimilated to estimate the initial conditions for numerical ocean forecasting. A 3D-VAR scheme is also used to better control slowly evolving large-scale biases in temperature and salinity.
Some interactions with the Hydrography and Oceanography Service of the French Navy have helped to identify several issues, and actions have therefore been taken to resolve the identified problems and further improve the system’s behaviour.
Moreover, the GLO12 system will benefit from several developments throughout 2026, such as the introduction of wave forcing to improve dynamics and system behaviour in the mixed layer depth, daily analyses to improve forecast quality, and the assimilation of SWOT wide-swath observations to improve, in particular, ocean currents and fronts. All these activities have already started in R&D mode and have already produced promising results.
This presentation will describe the components of the system that have been revisited and will show how some identified weaknesses in the system have been, or will be, improved.
How to cite: Lellouche, J.-M., Greiner, E., Ruggiero, G., Bourdallé-Badie, R., Testut, C.-E., Le Galloudec, O., Benkiran, M., and Law Chune, S.: The Copernicus Marine Service global ocean analysis and forecasting 1/12° high-resolution system. Recent changes and future evolution., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3567, https://doi.org/10.5194/egusphere-egu26-3567, 2026.
The Lower and Mid Trophic Levels (LMTL) component of the Spatial Ecosystem And POpulation DYnamic Model (SEAPODYM) estimates, at a global scale, the spatial distribution of biomass densities of meso-zooplankton and micronekton (functional group of organisms with size between 2 and 20 cm). This model is based on a system of advection-diffusion-reaction equations forced by ocean currents, temperature, and Net Primary Productivity (NPP). NPP acts as a biomass source throughout an energy transfer coefficient. Ocean currents control the transport of organisms, and the temperature affects their development and mortality.
Since mid-2019, this model has been used to produce the “Global Ocean low and mid trophic levels biomass content hindcast” product (also known as MICRORYS) of the Copernicus Marine Environment Monitoring Service (CMEMS) catalogue. This product of the green ocean is particularly relevant for studies of ecosystem and fisheries dynamics as micronekton is the food of numerous emblematic and fishery targeted species, respectively such as dolphins and tunas. The MICRORYS product is forced by the CMEMS Global Ocean Physics Reanalysis and the NPP computed from chlorophyll a of the CMEMS Global Ocean Colour multiyear product (satellite observations).
We will present an ensemble run based on different sets of physical and biogeochemical hindcast to quantify the uncertainty of the model with respect to forcings. We will focus this presentation on the micronekton functional group of organisms that perform the largest migrations, between lower meso-pelagic during daytime and epipelagic layers during nighttime, because of its influence on carbon export.
How to cite: Titaud, O., Mérillet, L., and Conchon, A.: Quantifying the uncertainty of the Low and Mid-Trophic Levels hindcasts (MICRORYS) of Copernicus Marine Service catalogue, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1340, https://doi.org/10.5194/egusphere-egu26-1340, 2026.
The DUACS system (Data Unification and Altimeter Combination System), developed as part of the CNES/SALP project, Copernicus Marine Service, and Copernicus Climate Change Service, provides high-quality multi-mission altimetry sea level products for oceanographic applications, climate forecasting centers, and the geophysics and biology communities. These products include user-friendly Level-3 (L3: along-track/swath cross-calibrated sea surface height anomalies [SSHA]) and Level-4 (L4: multi-sensor merged maps or time series) data.
L3 algorithms originally designed for nadir altimeters have been extended to process SWOT swath altimeter data. Enhancements include state-of-the-art Level-2 corrections and models contributed by the research community, a data-driven and statistical approach to identifying and removing spurious or suspicious pixels, a multi-satellite calibration process leveraging the existing nadir altimeter constellation, and a noise-mitigation algorithm powered by a convolutional neural network. The L3 algorithms currently process both resolutions of the Level-2 LR product: 2 km and 250 m. These L3 products are freely accessible via the AVISO+ portal.
This presentation aims to introduce the existing and upcoming L3 KaRIn products that can address coastal applications. Although currently based on LR measurements designed for open‑ocean use, L3 KaRIn products can also be relevant for certain coastal applications. The 250 m data processing enables observations close to the coastline; the availability of several variables allows users to adjust the physical content of the measurement; and a quality flag based on multiple criteria makes it possible to refine data selection. In addition, L3 products benefit from improved calibration that best corrects KaRIn systematic errors and ensures consistency across all altimetric missions. Finally, L3 products are regularly updated to incorporate state‑of‑the‑art processing methods and to adapt the products to users’ specific needs. Thus, a future line of L3 KaRIn products dedicated to coastal needs and possible based on upstream HR may be developed in 2026-2027.
How to cite: Pujol, M.-I., Treboutte, A., Anadon, C., Ballarotta, M., Meis, G., Delepoulle, A., Bonnin, A., Chevrier, R., Vergara, O., and Dibarboure, G.: SWOT-KaRIn Level-3 Products for coastal applications , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9026, https://doi.org/10.5194/egusphere-egu26-9026, 2026.
All stages of marine studies and operations base their success on the accurate knowledge of metocean conditions at the site of interest. In fact, wind and wave phenomena highly affect facility design as well as operation planning in offshore environments. The scarce distribution of offshore measurement stations represents a considerable limit for this purpose. Therefore, reanalysis models, such as widely used Copernicus’ ERA5, are very helpful thanks to their spatial and temporal coverage. Nonetheless, reanalysis data introduce non-negligible uncertainties in all main geophysical variables, affecting the computation of design parameters and wave power estimates.
Several studies have already tested statistical correction methods, in order to reduce the bias between model data and on-site observations in specific locations. Following preliminary assessments previously carried out by the same authors, this study aims at the correction of spatially broad areas beyond the common correction of specific locations, combining re-analysis data with a limited number of observation points within the selected area via a novel a spatially transferable bias-correction framework.
Using the North Sea as a case study, two sub-areas of study are described within the North Sea, containing 6 and 18 measurements points respectively. The buoys provide accurate measurements of wind and wave parameters for a period covering the years 2018 and 2019. In this study, the focus is set on significant wave height (Hs), peak wave period (Tp) and 10-m wind speed (Uw).
For each area, a leave-one-out and leave-two-out (according to measurement availability) spatial cross validation approach is adopted. An already tested bias correction method, based on Quantile Mapping, is used. The technique is calibrated on a subset of the buoys, in order to interpolate the correction factors all over the area. These are used to calibrate ERA5 points contained in the area, with remaining buoys serving for result validation.
The study aims at exploring the applicability of such spatially transferable correction framework within each area, accounting for the different conditions across the ocean and the potential offered by multiple observation points in a relatively restricted area. To assess the impact of this novel framework, the variations in the uncertainty of wave power estimation (using raw and corrected ERA5 data) are analysed.
How to cite: Callea, F., Penalba, M., Giorgi, G., Pasta, E., and Grandi, G.: On the development of a spatially transferable bias-correction framework: Assessing Spatial , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10234, https://doi.org/10.5194/egusphere-egu26-10234, 2026.
The ocean is facing the triple planetary crises of climate change, biodiversity loss, and pollution. Their impacts are monitored by the European Copernicus Marine Service, which integrates ocean observations and models to deliver routine information on ocean physical, ice, and biogeochemical variables. However, variables to monitor changes in marine biodiversity and pollution are not yet monitored directly.
New marine ecosystem variables characterizing the state of the ocean, its marine life, and climate and other human impacts are being developed for the Copernicus Marine Service by the European Horizon NECCTON project. This project has coupled models of ocean life (fish and marine mammals), benthic flora and fauna (e.g., seagrasses, bivalves, crabs), and marine stressors (e.g., metal and plastic pollution in the water column, bottom trawling, compound climate stressors) with the models that are run operationally in the Copernicus Marine Service.
Model outputs for twenty-seven new marine ecosystem variables, computed across all seas monitored by the Copernicus Marine Service, are presented in this contribution. Here, we discuss the insights into marine ecosystem state provided by these new variables. Their readiness level for operational production within Copernicus Marine is assessed. Future uptake by stakeholders is expected to enable new services for marine protected area monitoring and the sustainable management of fisheries.
How to cite: Ciavatta, S. and the NECCTON project team: New capability of monitoring and predicting marine ecosystems: the NECCTON project, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17753, https://doi.org/10.5194/egusphere-egu26-17753, 2026.
The GLOBIO (Bridging Global and Local Scales for Biogeochemical Profile Prediction) project has designed and trained a 1D convolutional neural network (CNN) for global-scale reconstruction of biogeochemical (BGC) Argo profiles of nitrate, chlorophyll-a, and bbp700.
The data for training and evaluation used the Global Data Assembly Center (GDAC) Coriolis dataset. The BGC-Argo profiles obtained from the Coriolis Data Center underwent an additional in-house quality-control procedure to ensure the consistency of the working dataset, with a resulting quality-controlled dataset comprising approximately 229,500 oxygen profiles, 101,000 chlorophyll-a profiles, and 63,000 nitrate profiles, all spanning the period 2010–2024.
To design the CNN, rather than manually designing multiple architectures through trial-and-error, the adopted strategy relied on the automatic discovery and optimization of models using evolutionary algorithms. In particular the DENSER framework, which works by exploring architectural variations across generations (iterations), jointly optimized Mean Absolute Error and model complexity. The evolution produced variable-specific CNNs that were compared to a manually designed architecture previously employed only on the Mediterranean Sea (PPCon). The results showed that this baseline was consistently outperformed, with the following evolved architectural choices:
- Moderate depths (12–19 layers) were most effective for nitrate and chlorophyll, whereas BBP700 reconstruction benefited from deeper networks.
- Larger convolutional kernels were consistently favored, underscoring the importance of capturing broader vertical features of the profiles.
- Evolved architectures tended to be deeper overall but employed fewer parameters per layer, resulting in more efficient networks compared to the PPCon design.
The resulting architecture was firstly trained and further tuned using only 1/50 of the global dataset, before proceeding with training on the full dataset. Spatial-temporal error mapping reveals heterogeneous performance, with larger deviations in ocean regions of sparse sampling or extreme seasonal conditions. A comparison with other machine learning-based methods shows good results of the 1D CNN approach. The next step will provide uncertainty estimation and model localization to generate local models starting from the global ones, moving towards “model-as-a-service”.
How to cite: Cossarini, G., Manzoni, L., Carolina, A., Tonelli, T., and Pietropolli, G.: A Global Machine Learning Modelfor BGC-Argo Profile Prediction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17441, https://doi.org/10.5194/egusphere-egu26-17441, 2026.
The Southern Ocean is a major sink of atmospheric carbon dioxide (CO2) and a key component of the global carbon cycle. Phytoplankton primary production modulates air-sea CO2 exchange, yet its response to ongoing climate-driven changes in storm intensity and storm-track position remains poorly constrained. A major challenge is that most primary production estimates rely on satellite observations restricted to the ocean surface, thereby missing subsurface production and limiting interpretation of storm-driven variability and long-term changes. Here we use the Copernicus Marine Service 3D biogeochemical product derived from in situ and satellite observations to reconstruct depth-resolved primary production over 1998–2023. Weekly three-dimensional fields of phytoplankton biomass and light-related variables are used as inputs to a depth- and phytoplankton-group-resolved bio-optical primary production model. Storm occurrence is characterized using the ERA5 atmospheric reanalysis. This approach allows us to examine how storms influence the vertical distribution of primary production and its partitioning among major phytoplankton groups. We evaluate how these effects vary across Southern Ocean regions and seasons.
How to cite: Mayot, N., Uitz, J., Sauzède, R., Lacour, L., Claustre, H., Cornec, M., and Renosh, P. R.: Assessing storm impacts on Southern Ocean primary production using an observation-based Copernicus Marine Service biogeochemical product, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6740, https://doi.org/10.5194/egusphere-egu26-6740, 2026.
OceanInference is a platform for hosting and operating AI-based ocean forecasting systems. It provides a unified environment for deploying machine-learning models and producing comprehensive ocean forecasts at scale, supporting a variety of applications from research to operational decision-making. The platform integrates advanced post-processing and analysis to generate derived ocean products, transforming model outputs into actionable information. This includes diagnostics and indicators relevant to diverse oceanographic phenomena, providing users with insights beyond standard forecasts. By combining model operation with automated production of higher-level ocean information, OceanInference enables streamlined access to forecasts and derived products within a single, scalable, and flexible framework. Designed to accommodate a wide range of AI models and future developments on the European Digital Twin of the Ocean, OceanInference aims to accelerate the adoption of machine-learning approaches in oceanography while providing reliable and ready-to-use ocean information for scientific, environmental, and operational purposes.
How to cite: Gaudel, Q., El Aouni, A., Aissa-Abdi, Z., Gasperi, J., and Arnaud, A.: OceanInference: a novel platform for ML-based ocean forecast and analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20846, https://doi.org/10.5194/egusphere-egu26-20846, 2026.
Machine Learning solutions for earth system modeling, monitoring and forecasting are growing rapidly. AI-based weather forecasts relying on end-to-end neural schemes [Bi et al., 2023, Lam et al., 2022] reach state-of-the-art performance and are among striking examples of this trend. Recent studies [Garcia et al., 2025, Botvynko et al., 2025, Beauchamp et al., 2025, Martin et al., 2025] support the potential of end-to-end Deep Learning schemes to improve the monitoring and forecasting of the ocean from satellite/in situ observations. In this study we focus on the stochastic extension of the previously developed framework for deterministic short-term neural ocean forecasting workflow [Botvynko et al., 2025]. We define the forecasting task as the training of the 4DVarNet variational neural assimilation scheme adapted to the forecasting of ensemble of ocean states from sparse observations. We present an evaluation framework, and benchmark ensemble 4DVarNet against state-of-the-art assimilation-based and neural forecasts. The results highlight the added value of ensemble formulation of the proposed end-to-end forecasting workflow when compared to its deterministic formulation.
How to cite: Botvynko, D., Haslée, P., Gaultier, L., de Boyer Montégut, C., Chapron, B., el Aouni, A., le Sommer, J., and Fablet, R.: Short-term neural forecasts of ocean dynamics from sparseobservations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19817, https://doi.org/10.5194/egusphere-egu26-19817, 2026.
Marine Biogeochemical models developed in the context of environmental monitoring are becoming increasingly complex, as they are required to represent a growing number of ecosystem indicators. Modern modelling frameworks aim not only to quantify biogeochemical cycles and primary productivity, but also to investigate ecosystem biodiversity, interactions with higher trophic levels, and the impacts of anthropogenic pressures such as fishing. This continuous increase in model complexity poses significant computational challenges and calls for substantial upgrades of existing codes to fully exploit modern high-performance computing (HPC) platforms.
The MedBFM model system is based on the coupled physical–biogeochemical OGSTM-BFM framework. The physical transport component, the OGS Tracer Model (OGSTM), is based on the OPA 8.1 system and is developed at the National Institute of Oceanography and Applied Geophysics (OGS). The biogeochemical component is maintained by the Biogeochemical Flux Model (BFM) Consortium.
MedBFM is operated daily within the Copernicus Marine Service, providing essential information on Mediterranean plankton dynamics to support monitoring of biomass productivity, plankton diversity, carbon sequestration, and ocean acidification.
The OGSTM-BFM model is also employed within the EU Horizon project New Copernicus Capabilities for Trophic Ocean Networks (NECCTON), which unites major European institutes involved in marine forecasting in support of the Copernicus Marine Service.
In addition, the same modelling framework has been used for long-term simulations, including centennial-scale scenario simulations extending up to the year 2100.
In this work, we present performance improvements to OGSTM-BFM achieved through a two-year collaboration within the ESiWACE initiative. ESiWACE3 supports exascale readiness for the European weather and climate modelling community by providing short- and long-term services aimed at improving model performance and facilitating knowledge transfer.
The main activities carried out during the collaboration focused on:
- completing the porting of the OGSTM horizontal and vertical diffusion schemes to NVIDIA GPUs;
- porting the complex carbonate system solver of the BFM to GPUs;
- assessing and optimizing the overall application performance, including the porting of critical code sections, kernels tuning, and improvements to data locality.
The current computational burden of the implementation corresponds to a problem size of approximately 2.6 billion computational elements, accounting for both spatial resolution and biogeochemical complexity. Performance tests conducted on the Leonardo system show a speedup of 7.41 using eight NVIDIA A100 GPUs compared to eight Intel Sapphire Rapids CPUs (112 cores each). This substantial acceleration makes long-term simulations more feasible, while leaving adequate time for data analysis and sensitivity studies. Moreover, enabling GPU support opens the way for efficient deployment of the model on current and future exascale computing platforms, and on-demand simulations within the Digital Twin of the Ocean.
How to cite: Lazzari, P., Lucido, L., Campanella, S., Raffin, E., Bolzon, G., and Salon, S.: Accelerating Marine Biogeochemistry Modelling on GPUs: The OGSTM-BFM Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9169, https://doi.org/10.5194/egusphere-egu26-9169, 2026.
China Sea (CS) consists of South China Sea, East China Sea, Yellow Sea and Bohai Sea. CS connects with terrestrial input from landside and with Western Pacific Ocean (WPC) fluxes from seaside. A healthy, resilient and predictable CS is important to sustainable socioeconomic development in the region and is largely determined by the sustainability of the interlinked spheres that compose the regional earth system (RES), including the lithosphere (land), the hydrosphere (oceans and rivers), the atmosphere, and the biosphere (living things). Based on an unprecedented holistic study of the interactions among natural forcings, human activities, and climate change using an integrated system to create an RES framework (https://earthhk.hkust.edu.hk/), we integrate science, AI and develop a digital twin of the regional earth system that integrates streaming data from observations, an earth simulator of land-ocean-atmosphere and an immersive and interactive visual interface for diagnosis and prognosis in the CS and WPC.
How to cite: Gan, J.: Earth-China: Science and AI-enabled digital twin of regional earth system, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3682, https://doi.org/10.5194/egusphere-egu26-3682, 2026.
Digital twins have the potential to revolutionize marine resource management by providing a virtual framework for assessing human-environment interactions. As a contribution to a sustainable blue economy, this study presents a digital twin concept for the shared use of resources in offshore wind farms in conjunction with low-trophic-level aquaculture. Based on a coupled numerical model of environmental physics and ecology, we extend this framework with a module for the growth of blue mussels (Mytilus edulis). In a specific use case, we demonstrate the co-use of resources by wind energy and aquaculture in the area of an existing offshore wind farm in the German Bight of the North Sea. To address stakeholder concerns about the feasibility of offshore food production, this study uses a series of what-if scenarios to show how different management decisions affect mussel growth, harvest potential, and environmental feedback. For this end, we have developed a pipeline starting from the processing of forcing data downloaded from the Copernicus Marine Services to drive the deterministic model of physics-biogeochemistry-aquaculture, via postprocessing of simulation data, to feeding of simulated scenario bundles into an interactive tool implemented on the Edito Modellab platform. This work demonstrates how simulation-based tools can support sustainable marine spatial planning and adaptive management of the blue economy.
How to cite: Pein, J., Berg, M., Hosseini, S., Jacob, B., Ali, N., and Staneva, J.: Digital Twins of the Ocean for a Sustainable Blue Economy - Demonstrating the Co-use of resources by Offshore Wind Energy and Low-trophic-level Aquaculture, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20786, https://doi.org/10.5194/egusphere-egu26-20786, 2026.
Flooding events constitute a serious risk to human safety and infrastructure in coastal communities. In estuarine environments, extreme sea level events caused by the combined effects of mean sea level anomalies, astronomical tides and storm surge pose significant threats to urban areas as well as critical assets inside the estuary.
To address these challenges, this work presents an Early Warning System for flood risk prevention in estuaries, developed within the COSNORTH project under the framework of the Copernicus National Marine Service Collaboration Programme: EU Coastal Monitoring Demonstrators.
The system is based on the following components:
- An Operational Estuarine Forecast Modelling System nested within the Copernicus Marine Service to provide high-resolution hydrodynamic variables (~50 m spatial resolution) in Santander Bay. A twofold approach has been applied in the estuary, combining deep learning techniques (RNN-LSTM) with dynamic downscaling results based on Delft3D.
- Uncertainty estimates to define confidence intervals for extreme sea level forecasts, derived from historical records by quantifying and sampling error distributions from comparisons between simulated and observed tide and non-tidal residuals during representative extreme events.
- Flooding risk warning levels, categorized into three levels: none, medium, and high. The warning levels are calculated taking into account the coastal and infrastructure flood exposure and the sea level forecasts and confidence intervals previously mentioned.
- A Web App, which allows users to interact with flood risk data, offering detailed insights into affected areas and potential impacts. The platform also allows users to interactively report flooding events, building an observational database that will be used to validate and continuously improve the forecasting system.
As a result, the system provides daily 3-day forecasts of total sea level, astronomical tide and storm surge, as well as the warning levels for floodrisk. The system has been validated using tide gauge data from Puertos del Estado (Spain), showing a high accuracy in modelling storm surge, astronomical tide and total sea level. A comprehensive description of the system implementation and validation will be provided in the presentation, including an intercomparison of sea level simulations derived from AI-based approaches and numerical hydrodynamic modelling, as well as a quantitative uncertainty assessment.
How to cite: Abascal, A. J., Aragón, G., Valle, J., Chaigneau, A. A., Menéndez, M., Rupani, M., Pérez, C. A., García-Alba, J., Manzanas, R., and García, A.: An early warning system for flooding risk prevention in estuaries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20452, https://doi.org/10.5194/egusphere-egu26-20452, 2026.
Anticipating and quantifying the risks of human activities and climate change in coastal zones is essential to support the management of these areas, guaranteeing environmental protection and supporting the blue economy.
CONNECT is a high-resolution, user-driven coastal service that provides continuous insight into the status of coastal waters, seamlessly integrating model-based forecasts and observations to deliver physical and water quality data on Portuguese coastal systems. The coastal service main features are:
i) shelf-to-estuary-to-river, high-resolution, circulation, wave and water quality operational modelling, using SCHISM and downscaling Copernicus Marine Service regional forecasts to produce daily forecasts of physical, biogeochemical and faecal indicator bacteria variables;
ii) real-time physical and biogeochemical data acquisition from in-situ observation networks, including data from the CoastNet monitoring network, and remote sensing data from the Copernicus Marine Service;
iii) AI-based prediction of river flows at the models’ upstream boundaries;
iv) automatic model-observations comparison, enhancing confidence in both;
v) synthesized information through physical and water quality indicators (e.g., statistics) and weekly reports;
vi) on-demand particle tracking simulations allowing users to explore “what-if” scenarios analysis, such as contaminated discharges;
viii) seamless integration with Copernicus Marine Service regional models and Earth Observation data.
Built in collaboration with its users and delivering open-access information through a tailored webGIS portal (https://connect-portal.lnec.pt/connect/), CONNECT supports informed decision-making for different environmental and blue economy challenges, as well as the implementation of the Water Framework Directive (WFD), the Marine Strategy Framework Directive (MSFD), the Floods Directive (FD), the Bathing Water Directive (BWD) and other EU policies (e.g., Green Deal).
The coastal service is under operation for four Portuguese coastal systems, addressing extreme water levels and water quality management (Tagus and Mondego estuaries), shellfish aquaculture management (Ria Formosa) and bathing waters quality management (Albufeira coastal region).
The service’s modular architecture facilitates its extension to other coastal systems, making it a core service toward Digital Twins for coastal areas.
How to cite: Rodrigues, M., Fortunato, A. B., Martins, R., Jesus, G., Brito, A. C., Oliveira, A., Cravo, A., Nahon, A., Costa, J. L., Jacob, J., Alves, E., Mardani, Z., Rodrigues, E., Garel, E., Alexandre, C., Adão, H., and David, L.: CONNECT: high-resolution coastal forecasting and monitoring service for Portugal, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12766, https://doi.org/10.5194/egusphere-egu26-12766, 2026.
Denmark is transitioning toward a more holistic and data-driven approach to marine environmental monitoring. Historically, Denmark relied on ship-based sampling. While thorough, this method limits frequency and spatial coverage and requires considerable resources. Although new technologies, such as Earth Observation (EO), buoy-mounted sensors, and autonomous platforms, are increasingly adopted worldwide, they are not yet systematically integrated into European monitoring programmes in support of e.g., Water Framework Directive (WFD).
A major challenge in assessing ecological health is the reliable tracking of summer chlorophyll-a concentrations and the assessment of oxygen depletion events. These parameters form the basis of the two use-cases within the Coastal DIAMONDS project under the Copernicus Marine Service National Collaboration Programme (NCP). Chlorophyll serves as a critical indicator of eutrophication cf. WFD and oxygen depletion events is fundamental to assess impacts to habitats and ecosystems.
Coastal DIAMONDS represents a shift toward a more integrated solution in a new operational framework designed to support key European directives. The project assimilates Copernicus Marine Service’s real-time datasets, which includes measurements from buoys, bottle sampling, and ferry boxes into regional and then down-scaled national biogeochemical models, complementing national monitoring efforts. Copernicus Marine data is specially featured due to its capabilities of providing open data from all sectors.
This project parallels the “Integrated Marine Monitoring (IMM)” joint initiative between Danish Agency for Green Transition and Aquatic Environment (SGAV) and DHI, with their real time in-situ platform providing model results and sharing monitored data from various sources, complementing the data-sources beyond national data from National Monitoring Programme for the Aquatic Environment and Nature (NOVANA, Nationale Overvågningsprogram for Vandmiljø og Natur).
At the core of the system is a modelling framework built on the DHI MIKE suite. Advanced data assimilation methods, including Optimal Interpolation and the Ensemble Kalman filter, are applied to calibrate environmental variables against observed data. This enables accurate and timely insights into marine ecosystem dynamics. The model complex constitutes several mechanistic models covering Danish water bodies in the North Sea as well as in the Baltic Sea, then downscaling from North Sea and Baltic Sea scale to costal bays and enclosed estuaries.
All typical biogeochemical parameters from the operational models, including chlorophyll-a and oxygen addressing the use-cases, are intended for publication on Denmark’s national environmental data portal. This ensures that researchers and citizens can access up-to-date information and follow national efforts toward improved marine ecosystem health.
How to cite: Wong, X. H., Antonio Arenas, J., Kronborg, M.-B., Erichsen, A., Fenger-Nielsen, R., and Lange, T.: Coastal DIAMONDS (Danish IntegrAted Marine ObservatioN & Data System). Integrating real time data to strengthen monitoring capabilities in Danish marine waters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2309, https://doi.org/10.5194/egusphere-egu26-2309, 2026.
Ostrea edulis, traditionally harvested in Galway Bay for centuries, has seen its populations decline significantly from their former abundance due to multiple stressors. Episodes of low salinity and elevated water temperatures often lead to increased oyster mortality, resulting in economic losses for aquaculture. Dublin Bay, adjacent to Ireland’s largest urban agglomeration, faces additional anthropogenic pressures, further challenging marine biodiversity and water quality.
To address these issues, recent developments funded under the Copernicus Marine Service COP INNO USER Programme and carried out by the Marine Institute (Ireland) and Nologin Oceanic Weather Systems (Spain) have enabled the provision of advanced marine forecasting services to oyster farmers and environmentalists engaged in biodiversity restoration. These developments include marine heatwave forecasting and daily oyster mortality estimation, delivered through an interactive web application called NAUI (biodiver.naui.io). NAUI provides real-time observational data, model forecasts and hindcasts, and tailored products co-developed with end users. Its modelling backbone relies on high-resolution coastal models—ROMS, CROCO-PISCES, and SWAN—downscaled from global and regional Copernicus Marine Service models. Key features include marine condition mapping, low-salinity and heatwave warnings, and indicators of salinity change rates during extreme events, all customizable to stakeholder needs.
This initiative exemplifies the growing effort to transform marine observations and forecasts into actionable information for aquaculture management and biodiversity conservation. Selected as a coastal demonstrator at the Digital Ocean Forum 2024, NAUI is set to integrate with the European Digital Twin of the Ocean (DTO), enabling faster performance and scalability to new regions, thereby enhancing its impact and replicability.
How to cite: Pereiro, D., Dabrowski, T., Garcia-Valdecasas, J. M., and Sotillo, M.: Coastal Monitoring and Forecasting for Galway and Dublin Bays: Supporting Aquaculture, Biodiversity Restoration, and Environmental Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4025, https://doi.org/10.5194/egusphere-egu26-4025, 2026.
Copernicus Marine provides systematic reference information on the physical and biogeochemical ocean state through near-real-time monitoring, forecasts, and multi-decadal records. We present OCEANIDS, a platform and decision-support system (DSS) for climate-informed maritime spatial planning and integrated seascape management, designed to operationalise ocean and coastal data into stakeholder-oriented decision products for ports and coastal authorities.
OCEANIDS is developing an end-to-end digital workflow composed of (i) an EO & Spatial Data Platform that supports data ingestion, cataloguing, visualisation, and APIs, and (ii) the OCEANIDS Decision Support Platform (O-DSP), which exposes decision-support functionality through software services and web interfaces. Within this architecture, OCEANIDS integrates coastal, port, hazard, exposure, and risk layers and makes them available for interactive exploration and downstream processing (e.g., indicator calculation and map-based queries) through platform services.
A central objective is to connect the DSS with the Copernicus ecosystem. OCEANIDS has initiated technical exchanges with Mercator Ocean International (Copernicus Marine Service) and the Copernicus Coastal Hub to integrate relevant services and datasets into the platform while identifying gaps and emerging user needs, ensuring complementarity with existing offerings.
We will present the DSS technical architecture, integration patterns for Copernicus-derived ocean information and DTO-oriented services, and example workflows illustrating how the platform supports reproducible, on-demand decision products at regional-to-coastal scales.
How to cite: Marinou, E., Kontopoulos, C., Wiesner, L., and Charalampopoulou, B.: OCEANIDS Decision Support Platform: End-to-End Workflows for Maritime Spatial Planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20403, https://doi.org/10.5194/egusphere-egu26-20403, 2026.
PredictaMAR is an integrated decision-support platform aimed at improving the sustainability, efficiency, and resilience of artisanal fisheries by reducing spatial, operational, and economic uncertainty in fishing activities. The platform leverages satellite-derived and modelled oceanographic data from the Copernicus Marine Service and combines them with a species-specific weighting framework developed through an extensive scientific literature review. This review draws on peer-reviewed studies, technical reports, and public scientific information, including criteria and oceanographic knowledge aligned with research produced by the Instituto del Mar del Perú (IMARPE).
The methodological core of PredictaMAR integrates key environmental variables—chlorophyll concentration, sea surface temperature, bathymetry, salinity, and ocean circulation—into a multi-criteria weighting table that reflects the relative ecological relevance of each variable for different target species and types of fish aggregations. Rather than producing deterministic predictions, the system estimates a probability of fish presence, translating scientific knowledge into an operational layer that supports risk-aware decision-making for small-scale fishers.
PredictaMAR is being iteratively refined through ongoing field validations conducted in collaboration with artisanal fishers along the Peruvian coast. These validations compare predicted fishing zones with observed fishing outcomes, enabling continuous calibration of weighting parameters and improving model robustness under real operational conditions. Preliminary field tests indicate that the use of targeted predictive zones can significantly reduce exploratory navigation time at sea, which traditionally represents a major source of fuel consumption and economic risk for artisanal fleets.
From an environmental and economic perspective, initial simulations and field observations suggest that optimized route selection enabled by PredictaMAR could reduce navigation distances by approximately 15–30%, depending on species and seasonal conditions. This reduction translates into an estimated decrease in fuel consumption of 20–25% per fishing trip for small vessels using outboard engines, directly lowering operational costs for fishers. Considering average fuel usage patterns in artisanal fisheries, such reductions may correspond to a decrease of several kilograms of CO₂ emissions per trip, contributing cumulatively to meaningful reductions in greenhouse gas emissions at the coastal community scale.
By lowering fuel consumption and time spent at sea, the platform also reduces pressure on marine ecosystems, minimizes unnecessary disturbance, and supports safer fishing practices. These benefits directly contribute to food security by stabilizing fishers’ incomes, improving catch efficiency, and reducing vulnerability to fuel price volatility, which is a critical factor for small-scale fisheries in developing coastal regions.
Conceptually, PredictaMAR aligns with the objectives of the European Digital Twin of the Ocean by demonstrating how Copernicus data, scientific literature, and field validation can be integrated into a practical, scalable tool for sustainable ocean use. The platform illustrates a pathway for translating large-scale Earth observation data into actionable insights that support environmentally responsible fisheries management, climate mitigation efforts, and the long-term resilience of artisanal fishing communities.
How to cite: Warthon, R. and Valenzuela, M.: PredictaMAR: An integrated Copernicus-based decision-support platform for sustainable artisanal fisheries, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22690, https://doi.org/10.5194/egusphere-egu26-22690, 2026.
The Mediterranean Monitoring and Forecasting Center (Med-MFC) of the Copernicus Marine Service provides operational, regular, and systematic reference information on both the blue state (physical oceanography and waves) and the green state (biogeochemistry) of the Mediterranean Sea.
Relying on state-of-the-art modeling and data assimilation systems ingesting in-situ and satellite observations, Med-MFC delivers Near Real Time (NRT) analyses, short-term forecasts up to 10 days, as well as consistent Multi-Year reanalyses and interim extensions at a horizontal resolution of about 4 km.
This contribution presents a detailed description and quality assessment of the most recent modeling upgrades that have been implemented in the latest operational systems since November 2025.
In particular, the major modelling advancements for each system component are as provided hereafter.
The physical NRT operational system has been improved by implementing: (1) updated open lateral boundary conditions in the Dardanelles Strait to enhance the connection with the Black Sea through a high resolution Marmara Sea model; (2) a revised and updated data assimilation model, OceanVar2, which now includes a barotropic model operator for SLA assimilation and a diffuse filter operator; (3) a forecasts initialization with analysis fields on a daily basis, rather than once a week as in the previous version.
The wave NRT operational system has been improved by including wind speed satellite observations in the data assimilation module. In this way, the ECMWF analysis winds used to drive the system, are updated to support the corrections introduced by the assimilation of Significant Wave Height observations (SWH). The complete validation against in-situ and satellite observations included SWH, maximum wave height, and mean wave period, all maintaining the good quality of the previous Med-WAV NRT system version.
The Biogeochemical NRT operational system has been enhanced by: (1) improving the BFM (Biogrochemical Flux Model) with revised carbon-oxygen parameterization and optimization for phytoplankton phenology; (2) using of 6-hours averaged (rather than daily) forcing for transport and ocean physics from the physical NRT system; (3) using Nitrogen and Phosphate air deposition from CAMS and literature instead of a constant value, and (4) using daily terrestrial loads of nutrients and carbon based on freshwater inflow provided by the Copernicus Emergency Service EFAS (European Flood Awareness System) dataset.
The model evolutions have been extensively qualified by comparing model results from a series of sensitivity numerical experiments with respect to best available satellite and in-situ observations in order to provide a reliable validation assessment. Based on the principle of continuous improvement, each evolution has contributed, albeit to different extents, to improve the quality of the delivered variables in the Copernicus Mediterranean NRT products, as evidenced by reduced error and bias relative to the previous versions.
How to cite: Clementi, E., Maicu, F., Cossarini, G., Korres, G., and Drudi, M. and the MED-MFC Team: The Copernicus Mediterranean Forecasting Systems: description and quality assessment of recent evolutions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11302, https://doi.org/10.5194/egusphere-egu26-11302, 2026.
The Mediterranean Sea is frequently impacted by intense wave storms that drive coastal erosion, influence maritime safety, and shape long-term coastal risk. Understanding their long-term variability requires consistent, high-resolution datasets covering multiple decades. In this study, we analyse wave storm characteristics over a 41-year period (1985–2025) using the extended Copernicus Marine Service Mediterranean wave reanalysis. Storm events are identified using a statistically robust threshold-based approach derived from the 99th percentile of significant wave height. Wave storm occurrence, intensity, and duration are evaluated across the Mediterranean and the adjacent Northeast Atlantic, and their long-term trends are examined to assess the evolution of storminess over the last four decades. This work provides an updated basin-wide reference for Mediterranean wave storm behaviour and contributes to improved understanding of marine climate hazards relevant to coastal and offshore applications.
How to cite: Denaxa, D., Oikonomou, C., and Korres, G.: Four decades of Mediterranean wave storms: climatology and trends from Copernicus Marine Service reanalysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22460, https://doi.org/10.5194/egusphere-egu26-22460, 2026.
The Black Sea Monitoring and Forecasting Center (BLK-MFC) is a European reference service within the Copernicus Marine Service (CMEMS), providing routinely ocean analyses, 10-day forecasts, and multi-year reanalyses for the Black Sea basin. The BLK-MFC prediction system is based on state-of-the-art physical (including waves) and biogeochemical models and supports the monitoring and prediction of key ocean processes, as well as the production of Ocean Monitoring Indicators (OMIs). OMIs constitute a core element of the service, providing scientifically robust and policy-relevant information on the state and variability of the Black Sea, including climate change signals and extreme events. Recent OMIs address, among others, heat content, marine heatwaves, circulation, deoxygenation, sea level rise, and extremes.
This contribution presents the main evolutions of the BLK-MFC systems planned for the 2025–2027 period. The proposed roadmap focuses on progressive upgrades of the operational physical, wave, and biogeochemical components, ensuring service continuity while integrating recent scientific and technological advances. Developments for near-real-time (NRT) forecasting include improved data assimilation schemes, updated forcing datasets, exploitation of new satellite missions, and enhanced cross-component consistency.
Key scientific advances include an improved representation of upper-ocean variability, air–sea interactions, wave–current coupling, radiative transfer, and interactions with biogeochemical processes. Wave system upgrades strengthen the characterization of extreme sea states and coastal exposure, while biogeochemical developments enhance ecosystem monitoring, including primary production, water transparency, and deoxygenation, with acidification addressed in future releases. System performance is systematically assessed through the CMEMS Product Quality Dashboard.
Multi-year (MY) reanalyses are comprehensively renewed and extended to provide climate time series starting in 1950, reinforcing the basis for long-term variability and climate assessments. These reanalyses represent a key scientific asset, enabling the extension and consolidation of OMIs and supporting peer-reviewed research on long-term trends in the Black Sea, such as marine heatwaves, ocean salt content, shelf hypoxia and deoxygenation, extreme wave conditions, and applications relevant to the Blue Economy.
Across all components, the BLK-MFC evolution places strong emphasis on harmonization of model grids, forcing, bathymetry, uncertainty assessment and metadata, compliance with the FAIR principles, and systematic validation to ensure scientific robustness and user confidence in the products.
Overall, the 2025–2027 evolution strengthens the BLK-MFC’s capability to deliver reliable, scientifically advanced, and user-oriented ocean monitoring and forecasting services for the Black Sea, serving a broad and growing community of users supporting scientific research, operational decision-making, and climate-oriented applications in a region of high environmental and socio-economic relevance.
How to cite: Palazov, A., Lecci, R., Aydogdu, A., Grégoire, M., and Staneva, J. and the BLK-MFC Team: The Black Sea Monitoring and Forecasting Center Evolution 2025-2027, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4551, https://doi.org/10.5194/egusphere-egu26-4551, 2026.
Since 1997, the multisatellite DUACS system (Data Unification and Altimeter Combination System) has been providing high quality multi-mission altimetry Sea Level products for oceanographic applications, climate forecasting centers, geophysics and biology communities worldwide. They consist in directly usable and easy to manipulate Level 3 (along-track cross-calibrated Sea Level Anomaly SLA) and Level 4 (multiple sensors merged gridded gap-free) products, for global and regional applications. Initiated as part of the CNES/SALP project, this production is now carried out jointly with the Copernicus Marine and Climate Change Services.
This presentation provides an overview of the DUACS products and the planned developments for the coming years.
How to cite: Grassi, K., Meis, G., Treboutte, A., Dupuy, S., Kocha, C., Philipps, S., Ballarota, M., and Pujol, M. I.: DUACS Sea Level altimeter Level-3/4 operational products : overview and future evolutions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11592, https://doi.org/10.5194/egusphere-egu26-11592, 2026.
Amongst the Thematic Assembly Centers (TACs) of the Copernicus Marine Service, the Wind TAC is in charge of providing L3 and L4 sea surface wind and wind stress observation products at a global and regional scale. From 2015, the Wind TAC has been successful in providing data coming from scatterometer observations both Near Real Time (NRT) and reprocessed (Multi Year, MY) data. Since the new phase of the project, starting in 2025, high-resolution (i.e. 1 km) data from C-band Synthetic Aperture Radar (SAR) Satellite have been included in the Wind TAC.
As the Copernicus Marine Service evolution is mainly user-driven, we take this opportunity to announce that L3 daily products of Sentinel-1A winds are operationally processed and delivered in Near Real Time since June 2024. The associated Multi Year database of L3 daily products has also been uploaded to the Copernicus Marine Service and covers data from 2018 to 2025.
To obtain wind data from SAR observations the ocean surface roughness measured by the satellite is transformed into wind speed and direction using an empirical inversion method called the "dual-pol" (Mouche et al. 2017, Mouche et al. 2019) and an a priori wind helps constrain the possible values. To this purpose, the SAR Wind Production Unit uses the L2 files produced by ESA's Mission Performance Center for Sentinel-1 (MPC-SAR) both for NRT and MY processing. Then, the only difference between the two processing chains (NRT and MY) lies in the a priori wind used to constrain the computed wind speed: while the NRT chain uses wind model from the European Center for Medium-range Weather and Forecasting (ECMWF) operational Integrated Forecast System (IFS), the MY chain uses wind model from ECMWF ReAnalysis v5 (ERA5).
To quantify the impact of the input a priori wind, the NRT and MY data are compared over their common period. More generally, the SAR wind data from Wind TAC has been qualified through comparisons against Numerical Weather Forecast (NWP) models, in situ measurements from buoys and co-located measurements from scatterometer. The results of these comparisons are shown here, illustrating some limitations in the methodology and ways to overcome them. Overall, these global comparisons highlight the complementarity of high-resolution SAR wind data with other wind observations.
In the near future, the integration (in 2026 and 2027 respectively) in the SAR Wind TAC of the recently launched Sentinel-1C and Sentinel-1D satellites will allow for better coverage, and the complementarity between observation should be further investigated through triple-collocation between SAR, scatterometer and buoy data.
How to cite: De Carlo, M., Husson, R., Colin, A., Singhoff, H., Cariou, A., Mouche, A., Piollé, J.-F., Cevaer, T., and Berger, H.: SAR measurements’ validation and High-Resolution winds contribution to the CMEMS Wind TAC, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21310, https://doi.org/10.5194/egusphere-egu26-21310, 2026.
Within the Copernicus Marine Service, the Danish Meteorological Institute and the Italian National Research Council (CNR) develop and deliver daily, gap-free (Level 4, L4) sea surface temperature (SST) analyses for the Baltic Sea (BAL) and the Mediterranean Sea (MED), based on infrared (IR) satellite observations. Unlike IR sensors, passive microwave (PMW) radiometers can observe the ocean surface through non-precipitating clouds, providing complementary SST measurements, albeit at coarser spatial resolution (approximately 50 km). This study presents an ongoing assessment of the ingestion of PMW-derived SST into the operational BAL and MED L4 SST production chains. Provided that product accuracy is preserved or improved, the inclusion of PMW data is expected to significantly enhance observational coverage, a critical benefit in cloud-prone regions such as the Baltic Sea. The analysis is based on two years (2020–2021) of daily collated (Level 3C, L3C) SST data from the Advanced Microwave Scanning Radiometer 2 (AMSR2). Following preliminary evaluations of PMW data quality, including validation against in situ measurements, the near-real-time operational chains for both BAL and MED products were run incorporating PMW observations through different ingestion strategies. These strategies were tested to account for the differing spatial resolution and accuracy characteristics of PMW and IR data. The impact of PMW data inclusion on the final L4 SST products was then quantified using statistical metrics such as bias and root mean squared error, which showed an improvement of the analyses accuracy. This work serves as a preparatory step toward the future integration of PMW SST observations from upcoming satellite missions, such as the Copernicus Imaging Microwave Radiometer (CIMR), which will provide microwave SST measurements at an unprecedented spatial resolution of approximately 15 km.
How to cite: Sabatini, M., Karagali, I., Englyst, P., Høyer, J., Fanelli, C., Pisano, A., and Ciani, D.: Impact of Microwave Data into the Copernicus Marine Service SST Products for the Baltic Sea and the Mediterranean Sea , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20709, https://doi.org/10.5194/egusphere-egu26-20709, 2026.
The EU Copernicus Marine Service (CMS) provides a cascade of physical, biogeochemical, and sea-ice state products for the global ocean and European regional seas. Among these, net primary production (NPP) is a key variable representing the process by which phytoplankton convert inorganic carbon into organic matter, thereby forming the base of the food web and regulating biogeochemical cycles in marine ecosystems. Multiple model- and satellite-based NPP products are available through CMS, and understanding to what extent these products agree or disagree is important for linking potential discrepancies to underlying differences in algorithms or model assumptions, guiding product selection, and better interpreting spatiotemporal trends. Here, we intercompare NPP estimates in the Mediterranean Sea from model simulations and satellite observations. Modelled NPP is part of three products provided by CMS, i.e., the nominal modelling products for the Global (GLO), the Atlantic-Iberian Biscay (IBI), and the Mediterranean Sea (MED) biogeochemistry, while two products include NPP based on satellite ocean colour observations, i.e., the Global and the Mediterranean Sea Ocean Colour products. Preliminary results from climatologies calculated over January 1999–December 2022 revealed differences in the timing of NPP peaks. GLO and IBI models (integrated over 200 m) exhibited maxima in February–March, whereas both the MED model (also integrated over 200 m) and satellite products peaked in summer. Monthly NPP magnitudes, however, remained within comparable ranges across all datasets. Consistently, Hovmöller diagrams (models only) clearly showed high NPP in the surface layers during the winter bloom. The GLO hindcast model detected relatively high NPP at the level of the summer deep chlorophyll maxima, while NPP appeared more evenly distributed along the vertical layers across summer months in the MED model. No significant seasonal trends were observed in any of the datasets. Future analyses will include a comparison with the non-Copernicus NPP data from the Carbon, Absorption, and Fluorescence Euphotic-resolving (CAFE) model, as well as a similar intercomparison exercise for CMS chlorophyll products.
How to cite: D'Adamo, F., Di Biagio, V., Cossarini, G., and Teruzzi, A.: An intercomparison of Net Primary Production Copernicus Marine Service products in the Mediterranean Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6686, https://doi.org/10.5194/egusphere-egu26-6686, 2026.
The rising demand for accurate, high-resolution short-term ocean forecasts requires continuous improvements of operational prediction systems. While Copernicus Marine Service Monitoring and Forecasting Centers (MFCs) are increasing their model resolution, the quality of the resulting forecast often remains constrained by inaccuracies in the operational atmospheric forcing fields.
The Copernicus Marine Service Evolution project CERAINE addresses this issue by using data-driven correction techniques leveraging remote-sensing data. CERAINE focuses on developing Artificial Neural Networks (ANNs) to refine operational forcings (surface wind fields and surface currents) within the European North-East Atlantic (NEA) region, with a specific focus on improving surface wind fields critical for wave modeling.
To correct systematic biases on the surface wind fields, a novel approach based on Graph Neural Networks (GNNs) is proposed. This architecture incorporates the spatial dependence of neighboring nodes, thereby inherently accounting for geographical location and context in the prediction. Furthermore, the GNN structure enforces a seamless and physically continuous correction across the entire domain, effectively eliminating blending artifacts often found in other methods. The GNN is trained using a new dataset (IFS_SC) as the target. This dataset is derived from operational wind fields from the ECMWF Integrated Forecasting System (IFS) corrected using scatterometer observations.
Results will demonstrate the developed wind GNN performance, showcasing the benefits of the IFS_SC product over the uncorrected operational forecast. The presentation will specifically highlight how the GNN framework, leveraging the spatial coverage and accuracy of scatterometer data, significantly improves wind prediction consistency and accuracy across the entire NEA domain. Limitations and uncertainties inherent to this methodology will also be discussed.
How to cite: Aquino, V., Makarova, E., Garcia-Valdecasas, J. M., Portabella, M., García-León, M., Aouf, L., Gómez, B., Dalphinet, A., Alonso-Valle, A., Ciliberti, S. A., Aznar, R., Fernández, C., and Sotillo, M.: Graph Neural Networks for Enhanced North-East Atlantic Wind Forecasting using Scatterometer Data , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3510, https://doi.org/10.5194/egusphere-egu26-3510, 2026.
Funded by the Copernicus Marine Service User Engagement Programme, a warning system was developed to monitor marine mine trajectories and their spreading in Romanian marine and coastal areas, as well as in the entire Western Black Sea Basin with a specific tool developed for the Romanian marine and coastal area. With a special focus on the safety navigation sector, the service is focused on simulating the drift of marine mine in the coastal zone, tracking their path and identifying their potential impact on predefined navigation sites/corridors and port areas.
This use case developed by seamod.ro and NIMRD is part of SYROCO 2025 (SYstem for ROmanian COastal monitoring), a suite of high-resolution models coupled with Parcels module and observation tools. It allows users to monitor and simulate the path of dangerous/explosive drifting marine bodies in the Romanian coastal area. The combination of these tools provides reliable estimates of local physical and hydrological/ocean variables at the sea-atmosphere interface. The service is available freely, as an on-demand web platform.
In line with EU environmental priorities such as the EU Marine Strategy Framework Directive, the service contributes to support informed decision-making, including Marine Spatial Planning and Integrated Coastal Zone Management (ICZM) activities.
How to cite: Mateescu, R., Vandenbulcke, L., Niculescu, D., and Vlasceanu, E.: Integrated service for marine mine search and trajectories monitoring in Western Black Sea basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23064, https://doi.org/10.5194/egusphere-egu26-23064, 2026.
This paper estimates the long-term ecological impacts of major oil spills on marine ecosystems by integrating remote sensing data with causal inference methods. Constructing a novel dataset of 14 oil spills worldwide between 2009 and 2011, I employ a synthetic difference-in-differences strategy to analyze satellite-derived measures of marine ecological function. The results reveal that large oil spills produce significant, worsening disruptions in the marine ecological health and food webs. For large oil spills, ten years after a spill event, I find a 26% decrease in phytoplankton carbon biomass, a 16% reduction in chlorophyll-a concentration, and a 20% decline in fish biomass support compared to counterfactual trajectories. Smaller spills, on the other hand, show insignificant impact. The economic damages can reach $1.84 billion for one spill. Smaller spills show no detectable long-term effects. Standard damage assessments thus substantially understate ecological and economic losses and highlight the severe need for a change in policy and assessment methods. I suggest a shift in the focus of disaster response policy towards addressing long-term damages to the ecosystem which involve monitoring and rehabilitating post-spill photosynthetic communities.
How to cite: Can, O. H.: The Long-Term Impact of Oil Spills on Ecosystem Recovery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1505, https://doi.org/10.5194/egusphere-egu26-1505, 2026.
Digital Twins of the Ocean aim to provide integrated, observation-informed representations of ocean systems; however, the performance of models that underpin their potential often degrades in nearshore environments where wave transformation, sediment transport, and morphological change occur at higher resolution spatial and temporal scales. This study presents an integrated observation–modelling framework designed to strengthen coastal components of the European Digital Twin of the Ocean (DTO) by linking Copernicus Marine products with validated nearshore modelling and sustained field observations to support decision making by a municipal authority.
The municipal authority is seeking actionable information on the study area’s coastal dynamics, and a better understanding of the areas dynamism. To aid this understanding, a long-term nearshore wave dataset was developed for the entire Cork coastline using the MIKE 21 SW (Spectral Wave) model. Here, the connection between global and local that is a key element of DTO application has been explored, whereby Copernicus Marine offshore wave reanalysis data were applied as boundary conditions for the period 2000–2023. The model represents key offshore-to-nearshore wave transformation processes, including refraction, shoaling, and directional changes, and provides hourly wave parameters along the 15 m depth contour at 400 m intervals. Model performance was evaluated against significant wave height observations from the Bantry Bay wave buoy, showing good agreement (RMSE < 0.4 m; correlation coefficient > 0.8) and supporting the reliability of the derived nearshore wave dataset.
In parallel, the wave modelling framework is supported by extensive field observations collected through the Atlantic–Arctic Agora (A-A Agora) project and a parallel PhD study on Coastal Vulnerability Assessment, both of which are being delivered in partnership with the local municipal authority. This enables the activity to provide potentially actionable information, while also improving understanding of the shore itself. In parallel, the wave modelling framework is supported by extensive field observations collected through the Atlantic–Arctic Agora (A-A Agora) project and a parallel PhD study on Coastal Vulnerability Assessment, both delivered in partnership with the local municipal authority. This collaboration enables the development of potentially actionable information, while also improving understanding of nearshore and beach-scale processes. Monthly cross-shore beach profile surveys capture short-term morphological variability, while seasonal and post-storm measurements extend the analysis across a range of exposed and sheltered coastal settings. Seasonal sediment grain-size data collected at multiple locations per beach provide additional context for interpreting sediment transport processes and shoreline response.
The combined wave, morphology, and sediment datasets are used to inform and assess local beach-scale morphodynamic models based on the LITPACK modelling suite, enabling scenario-based simulations of sediment transport and shoreline evolution. These models are further applied to explore future coastal evolution scenarios aligned with nationally adopted climate change pathways. By connecting Copernicus offshore wave products with validated nearshore modelling and sustained in situ observations, this work provides practical and transferable insights to guide efforts regarding global-to-coastal model configurations, systematic verification, and scenario-based DTO coastal services that support regional and local authority decision making.
How to cite: karayel, A. N.: From Offshore Copernicus Wave Products to Beach-Scale Digital Twins: Integrating Nearshore Wave Modelling and Field Observations Along the Cork Coastline, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18326, https://doi.org/10.5194/egusphere-egu26-18326, 2026.
Globally, a significant number of ocean models allow the study of various physical processes and large-scale ocean-atmosphere exchanges. In some regions, the current resolutions of available global and regional models remain relatively low and can therefore restrict the feasibility of many studies involving marine circulation and its impact on biogeochemical interactions, as well as some population dynamics, which require both fine spatial and high temporal resolution. Located in the Northeast Atlantic Ocean, Macaronesia is one such region, where the global ocean model and the regional Atlantic-Iberian Biscay Irish (IBI) model, both provided by Copernicus (https://data.marine.copernicus.eu/products?q=ibi) , with resolutions of 1/12° (~8 km) and 1/36° (~3 km) respectively, may remain low to reproduce coastal circulation locally in this area full of archipelagos. This region presents a fairly complex circulation linked to (i) Mesoscale activities and it is part of the (ii) Canary Coastal Upwelling system in the Northwest African coastal zone (between Mauritania and Morocco), among the most productive in the world. Improving the available configurations for this region could allow further investigations into these physical processes, among many others. This study proposes a parameterized 3D configuration using SYMPHONIE Model (https://sirocco.obs-mip.fr/ocean-models/s-model/) in Central East of Macaronesia (which includes the Canary Islands, Madeira and Selvagens archipelagos). The configuration is implemented on a horizontal curvilinear Arakawa C-grid, which integrates very high-resolution bathymetry in coastal areas (~100 m, IHM of Spain and EMODNET) and ~400 m offshore (GEBCO data). This horizontal grid provides a resolution of approximately ~400 m locally in all coastal areas of the domain and a maximum of 2 km offshore. The water column is discretized on 40 vertical levels using the Vanishing Quasi Sigma (VQS) system. The model is forced at its boundaries by daily global ocean conditions (SSH, SST, SSS) from the Copernicus CMEMS global Reanalysis system at a resolution of 1/12° (~8 km) and atmospheric forcings come from daily ECMWF analyses at a resolution of 1/8° (~13.5 km). The tide is taken into account in this configuration, thanks to the FES2014 model at 1/16° resolution (~6.8 km). Simulated over 3 consecutive years (June 2022 – July 2025), this new configuration offers hourly averaged fields of ocean variables (SSH, SST, SSS) and currents at a very high spatial resolution (~400 m to 2 km). The evaluation of this configuration is initially carried out spatially by comparing it with (i) satellite observation data, and then (ii) ponctual stations extracted from the model are compared with observations from ARGO profilers. In order to estimate whether the increased spatio-temporal resolution of this new configuration improves the representation of dynamics in this region, the fields from Symphonie are compared with the global (~8 km) and the regional IBI (~3 km) reanalyses. This increased resolution provided a better representation for regional and coastal circulation patterns and made it possible to study marine connectivity through larval dispersal of several benthic populations predominantly found around those archipelagos.
Keywords : Regional Circulation, Macaronesia, CMEMS Copernicus, Global Ocean - Atlantic-Iberian Biscay Irish Models, Satellite Observations, Argo Profilers, Marine Connectivity, Symphonie.
How to cite: Samou Seujip, M., Duhaut, T., Marsaleix, P., and Guizien, K.: Regional Circulation at the Central-East of Macaronesia from a High Resolved Configuration with Symphonie (3D Hydrodynamic Model), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19002, https://doi.org/10.5194/egusphere-egu26-19002, 2026.
