The position of the coastal waterline-the interface between land and sea-varies over time due to sedimentary and hydrodynamic processes influenced by climate variability. While previous studies have mainly focused on local morphodynamic or storm-driven shoreline changes, the large-scale climatic controls on global waterline variability remain poorly understood. This study addresses this gap by analyzing how climate-driven environmental factors govern coastal waterline dynamics across seasonal to interannual timescales. Using a globally consistent, satellite-derived data set of coastal waterlines from 1999 to 2019, combined with reanalysis-based estimates of sea-level anomalies, wave setup and freshwater runoff, we use an empirical mode decomposition framework to disentangle the contributions of these drivers. This approach isolates the dominant temporal components of waterline variability and identifies regional relationships with environmental forcing. Our results reveal two main regimes of climate-driven waterline variability: (a) a seasonal pattern that intensifies with latitude and is primarily linked to storm-driven wave energy flux and sea-level oscillations; and (b) an interannual pattern that peaks in the tropics and is strongly modulated by large-scale climate oscillations, such as ENSO, and by variability in fluvial discharge. We demonstrate that regional sea-level and freshwater anomalies can alter the waterline by amounts comparable to those caused by waves. This challenges the conventional view that wave-driven morphodynamics dominate waterline change. By framing the waterline as a climate-sensitive indicator of coastal change, this study establishes the first global-scale, data-driven framework for diagnosing and predicting waterline variability. The results provide a basis for developing climate-informed waterline forecasts and improving assessments of coastal vulnerability in the context of ongoing sea-level rise.

Future changes in extreme still water levels (ESWL) will play a critical role in shaping coastal flood risk across western Europe. Yet most large-scale assessments, including recent IPCC reports, estimate ESWL changes using static approaches that account only for long-term sea-level rise while treating other sea-level components as stationary (static approach). Here, we use a regional 3D ocean model to dynamically downscale four CMIP6 GCMs to quantify how changes in sea-level variability - in tides, storm surges, the seasonal cycle, and dynamic sea-level anomalies - modify ESWL projections (dynamic approach) through the 21st century under SSP1-2.6 and SSP5-8.5. Using a transformed-stationary extreme value analysis, we evaluate changes in future ESWL return levels and the contributions of individual sea-level components to these changes. Across western Europe, dynamically simulated changes in the 10-year ESWL average to 39 cm (SSP1-2.6) and 57 cm (SSP5-8.5), but regional deviations reach ±20 cm. Dynamic estimates can locally amplify ESWL by 30–40% relative to static ones, particularly in the southern North Sea, northern Irish Sea, and western Mediterranean, with similar impacts for the 100-year event. In many regions, differences relative to static estimates result from compensation effects between changes across sea-level components. Changes in dynamic sea level anomalies and the seasonal-cycle dominate dynamic contributions to ESWL changes in the Mediterranean and Atlantic façade south of 47°N. Tidal changes dominate in the English Channel and UK/Irish coasts, while storm surges dominate in the southeastern North Sea. However, the reported contributions exhibit a large inter-model spread and cannot be readily attributed to either forced or internal variability. Our results show that future ESWL changes are shaped by multiple still water level variability drivers in addition to long-term trends, underscoring the limitations of simplistic static approaches. They further reveal strong regional differences in dominant drivers and compensations among them, which can only be comprehensively resolved using 3D ocean models that represent coastal processes.

Understanding future changes in extreme stormsurges (ESSs) is critical for coastal risk assessment andadaptation. However, existing projections in Europe are of-ten based on computationally expensive dynamical models,limiting ensemble sizes and thus confidence in projectedchanges. In this study, we develop a cost-effective statisticaldownscaling model (SDM) trained to replicate dynamicallydownscaled storm surges, enabling the generation of a pan-European ensemble of ESS projections based on 17 globalclimate models (GCMs) - substantially expanding previousefforts. The SDM is trained on a storm surge hindcast and demon-strates stable skill across historical and future climates,broadly capturing projected changes in the 10-year returnlevel given by dynamical simulations. Skill degrades forhigher extremes and hence ensemble projections focus on the10-year return level. Results also show overall lower skill forthe eastern Mediterranean and Baltic Seas. Ensemble projec-tions reveal robust multi-model mean changes in the 10-yearreturn level of ESSs by 2100. Negative multi-model meanchanges are identified in the Mediterranean Sea (-7 %), Mo-roccan Atlantic coast (-10 %), and Danish Straits (-6 %),while positive changes of around+6 % are projected for theCeltic and Irish Seas, western Denmark, and the Gulf of Fin-land. Despite these robust signals, inter-model spread is sub-stantial, with likely ranges (17th-83rd percentiles) extend-ing from-25 % to+17 % across Europe, and changes ofup to +/- 35 % in individual models. The southern North Seaand northern Baltic Sea emerge as low-confidence regions,marked by particularly strong inter-model spread. Our results underscore the importance of extended ensem-bles in projecting ESSs in Europe and demonstrate the valueof cost-effective statistical models to complement dynamicaldownscaling in applications that demand extensive simula-tions, such as large-ensemble projections. They also revealthat more sophisticated, extreme-targeted statistical methodsare required to project ESSs in the eastern Mediterranean andBaltic Sea, and overall for higher return periods.

Coastal zones face increasing climate-driven hazards such as sea-level rise, storm surges, flooding and erosion, threatening infrastructure and ecosystems. Addressing the need for actionable climate information in adaptation planning, we developed an integrated Coastal Climate Core Services platform for Europe that consolidates sea-level projections, flood and erosion maps, exposure data, damage estimates, and adaptation options into a unified, cloud-based service. Co-designed with national authorities, municipal planners, and infrastructure owners, the platform offers an interactive web viewer for non-experts and a collaborative coding environment for advanced users. It delivers localized, FAIR(Findable, Accessible, Interoperable, Reusable)compliant data supporting evidence-based adaptation planning and compliance with European frameworks. By bridging scientific data and operational climate services, this platform (https://platform.coclicoservices.eu) enhances climate resilience and informs strategic coastal risk management across Europe and shows how a broad-scale coastal climate service can be delivered.

Sea-level rise is one of the most hazardous climate-change impacts and is projected to trigger dramatic increases of coastal flooding frequency in Europe in the current century and beyond. As such, adaptation-related effective decision making relies on the availability of authoritative and locally relevant information on future coastal sea-levels and their extremes, which include uncertainty quantification. However, current available sea-level projections are typically limited by either too low spatial resolution and therefore missing physical processes relevant at the coast, they account for only part of the sea-level signal (e.g. storm surges), and/or are typically limited to the downscaling of a single atmospheric model and therefore offer no quantification of the potentially significant inter-model uncertainty. In response to this knowledge gap, we present a novel extreme sea-level (ESL) projection dataset which focuses on the North-east Atlantic region. The dataset consists of a CMIP6-forced multi-model ensemble of downscaled projections until the end of the century, generated with a regional 3-dimensional ocean model at ~7km resolution. As such, the model captures not only storm-surge and tide induced ESLs, typically captured in barotropic 2-dimensional models, but also accounts for the contribution of circulation and density-driven modulations to extremes. Therefore, the ensemble dataset offers an excellent opportunity to explore ESL drivers at different spatio-temporal scales, their projected future changes, and associated uncertainties.This dataset will help to advance scientific knowledge on climate-change induced coastal flood risk changes, but also to increase confidence in quantitative assessments of impacts of sea-level rise through its contribution to the Coastal Climate Core Service (CoCliCo), a decision-oriented platform which will inform users on present-day and future coastal risks, and which is currently under development as part of a European Union’s Horizon 2020 project.The CoCliCo project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101003598

Ocean Reanalyses Workshop of the European Copernicus Marine Service What: Gather together ocean reanalyses users and producers to identify users' needs of ocean reanalyses and design the strategy to improve ocean reanalyses to fulfill users' needs When: 10-12 October 2023 Where: Toulouse, France, and online

Extreme sea levels (ESLs) are a major threat for low-lying coastal zones. Climate-change-induced sea level rise (SLR) will increase the frequency of ESLs. In this study, ocean and wind-wave regional simulations are used to produce dynamic projections of ESLs along the western European coastlines. Through a consistent modelling approach, the different contributions to ESLs, such as tides, storm surges, waves, and regionalized mean SLR, as well as most of their non-linear interactions, are included. This study aims at assessing the impact of dynamically simulating future changes in ESL drivers compared to a static approach that does not consider the impact of climate change on ESL distribution. Projected changes in ESLs are analysed using non-stationary extreme value analyses over the whole 1970-2100 period under the SSP5-8.5 and SSP1-2.6 scenarios. The impact of simulating dynamic changes in extremes is found to be statistically significant in the Mediterranean Sea, with differences in the decennial return level of up to +20 % compared to the static approach. This is attributed to the refined mean SLR simulated by the regional ocean general circulation model. In other parts of our region, we observed compensating projected changes between coastal ESL drivers, along with differences in timing among these drivers. This results in future changes in ESLs being primarily driven by mean SLR from the global climate model used as boundary conditions, with coastal contributions having a second-order effect, in line with previous research.

AbstractGlobal hydrological reanalyses are modelled datasets providing information on river discharge evolution everywhere in the world. With multi‐decadal daily timeseries, they provide long‐term context to identify extreme hydrological events such as floods and droughts. By covering the majority of the world's land masses, they can fill the many gaps in river discharge in‐situ observational data, especially in the global South. These gaps impede knowledge of both hydrological status and future evolution and hamper the development of reliable early warning systems for hydrological‐related disaster reduction. River discharge is a natural integrator of the water cycle over land. Global hydrological reanalysis datasets offer an understanding of its spatio‐temporal variability and are therefore critical for addressing the water–energy–food–environment nexus. This paper describes how global hydrological reanalyses can fill the lack of ground measurements by using earth system or hydrological models to provide river discharge time series. Following an inventory of alternative sources of river discharge datasets, reviewing their advantages and limitations, the paper introduces the Copernicus Emergency Management Service (CEMS) Global Flood Awareness System (GloFAS) modelling chain and its reanalysis dataset as an example of a global hydrological reanalysis dataset. It then reviews examples of downstream applications for global hydrological reanalyses, including monitoring of land water resources and ocean dynamics, understanding large‐scale hydrological extreme fluctuations, early warning systems, earth system model diagnostics and the calibration and training of models, with examples from three Copernicus Services (Emergency Management, Marine and Climate Change).

Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a “predicted ocean based on a shared and coordinated global effort” and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categories - production, validation, and data dissemination - the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.

Abstract. The availability of numerical simulations for ocean past estimates or future forecast worldwide at multiple scales is opening new challenges in assessing their realism and predictive capacity through an intercomparison exercise. This requires a huge effort in designing and implementing a proper assessment of models’ performances, as already demonstrated by the atmospheric community that was pioneering in that sense. Historically, the ocean community launched only in the recent period dedicated actions aimed at identifying robust patterns in eddy-permitting simulations: it required definition of modelling configurations, execution of dedicated experiments that deal also with the storing of the outputs and the implementation of evaluation frameworks. Starting from this baseline, numerous initiatives like CLIVAR for climate research and GODAE for operational systems have raised and are actively promoting best practices through specific intercomparison tasks, aimed at demonstrating the efficient use of the Global Ocean Observing System and the operational capabilities, sharing expertise and increase the scientific quality of the numerical systems. Examples, like the ORA-IP, or the Class 4 near real time GODAE intercomparison are introduced and commented, discussing also on the ways forward on making this kind of analysis more systematic for addressing monitoring of ocean state in operations.

Abstract. Coastal services are fundamental for society, with approximately 60 % of the world’s population living within 60 km of the coast. Thus, predicting ocean variables with high accuracy is a challenge that requires numerical models able to simulate from mesoscale to submesoscale processes, to capture shallow water dynamics influenced by wetting-drying and resolve the ocean variables in very high-resolution spatial domains. This paper introduceskey aspects of coastal modelling, such as vertical structure of the mixed layer depth, parameterization of bottom roughness and the dissipation of kinetic energy in coastal areas. It stresses the need for models to account forthe nonlinear interactions between tidal currents, wind waves, and small-scale weather patterns, emphasizing their significance in refining coastal predictions. In addition, observational advancements, such as high-frequency (HF) radar and satellite missions like SWOT, provide unique opportunities to observe coastal dynamics. This integration enhances our ability to model physical and dynamical peculiarities in coastal waters, estuaries, and ports. Coastal models not only benefit from such high-resolution observations but also contribute to evolving observational systems, creating feedback loops that refine monitoring and prediction capabilities. Modeling strategies are also examined, including downscaling and upscaling approaches, and numerical challenges like implementing robust data assimilation schemes to refine estimations of coastal ocean states are addressed. Emerging techniques, such as advanced turbulence closure models and dynamic vegetation drag parameterization, are highlighted for their role in enhancing the realism of modeled coastal processes. Furthermore, the integration of atmospheric forcing, tidal asymmetries, and estuarine dynamics underlines the necessity for models that span the complexities of the coastal continuum. It also demonstrates the critical importance of accurately modeling coastal and estuarine systems to capture interactions between mesoscale and submesoscale processes, their connections to broader oceanic systems, and their implications for sustainable coastal management and climate resilience. This work underscores the potential of advancing coastal forecasting systems through interdisciplinary innovation, paving the way for enhanced scientific understanding and practical applications.

We synthesize sea-level science developments, priorities and practitioner needs at the end of the 10-year World Climate Research Program Grand Challenge 'Regional Sea-Level Change and Coastal Impacts'. Sea-level science and associated climate services have progressed but are unevenly distributed. There remains deep uncertainty concerning high-end and long-term sea-level projections due to indeterminate emissions, the ice sheet response and other climate tipping points. These are priorities for sea-level science. At the same time practitioners need climate services that provide localized information including median and curated high-end sea-level projections for long-term planning, together with information to address near-term pressures, including extreme sea level-related hazards and land subsidence, which can greatly exceed current rates of climate-induced sea-level rise in some populous coastal settlements. To maximise the impact of scientific knowledge, ongoing co-production between science and practitioner communities is essential. Here we report on recent progress and ways forward for the next decade.

We contend that ocean turbulent fluxes should be included in the list of Essential Ocean Variables (EOVs) created by the Global Ocean Observing System. This list aims to identify variables that are essential to observe to inform policy and maintain a healthy and resilient ocean. Diapycnal turbulent fluxes quantify the rates of exchange of tracers (such as temperature, salinity, density or nutrients, all of which are already EOVs) across a density layer. Measuring them is necessary to close the tracer concentration budgets of these quantities. Measuring turbulent fluxes of buoyancy (Jb), heat (Jq), salinity (JS) or any other tracer requires either synchronous microscale (a few centimeters) measurements of both the vector velocity and the scalar (e.g., temperature) to produce time series of the highly correlated perturbations of the two variables, or microscale measurements of turbulent dissipation rates of kinetic energy (ϵ) and of thermal/salinity/tracer variance (χ), from which fluxes can be derived. Unlike isopycnal turbulent fluxes, which are dominated by the mesoscale (tens of kilometers), microscale diapycnal fluxes cannot be derived as the product of existing EOVs, but rather require observations at the appropriate scales. The instrumentation, standardization of measurement practices, and data coordination of turbulence observations have advanced greatly in the past decade and are becoming increasingly robust. With more routine measurements, we can begin to unravel the relationships between physical mixing processes and ecosystem health. In addition to laying out the scientific relevance of the turbulent diapycnal fluxes, this review also compiles the current developments steering the community toward such routine measurements, strengthening the case for registering the turbulent diapycnal fluxes as an pilot Essential Ocean Variable.

Abstract. Forecasting the sea level is crucial for supporting coastal management through early warning systems and for adopting adaptation strategies to climate changes impacts. Such objectives can be achieved by using advanced numerical models that are based on shallow water equations used to simulate storm surge generation and propagation due to atmospheric pressure and winds, or with ocean general circulation, baroclinic models. We provide here an overview on models commonly used for sea level forecasting, that can be based on storm surge models or ocean circulation ones, integrated on structured or unstructured grids, including an outlook on new approaches based on ensemble methods.

Abstract. Sea level rise (SLR) is a global concern for low-lying coastal areas, including many European coasts. The European Knowledge Hub on Sea Level Rise (KH-SLR), a collaborative effort by the Joint Programming Initiatives for “Connecting Climate Knowledge for Europe” (JPI Climate) and for “Healthy and Productive Seas and Oceans” (JPI Oceans), has developed the 1st Assessment Report (SLRE1) to address the challenges posed by SLR in Europe. The report's target audience includes national and subnational bodies focused on research and policy advice for coastal management and climate adaptation, as well as European experts who contribute to shaping policy frameworks and collecting information at a pan-European scale. This report, preceded by a series of targeted surveys and workshops with researchers and stakeholders (e.g. coastal decision-makers), has synthesized the current scientific knowledge on SLR drivers, impacts, and policies at local, national, and European basin scales. It provides in-depth and basin-specific analyses on local sea level changes, compared with relevant global assessments of the Intergovernmental Panel on Climate Change (IPCC). In addition, it identified critical knowledge gaps needed to support the development of actionable information. The Summary for Policymakers (SPM) distils the key findings of the SLRE1, presenting information specific to the six European basins: Mediterranean Sea, Black Sea, North Sea, Baltic Sea, Atlantic, and Arctic. The SPM highlights basin-specific trends, vulnerabilities, and potential impacts, while also orienting future requirements.​​​​​​​

Abstract. Sea level rise has major impacts in Europe which vary from place to place and in time depending on the source of the impacts. Flooding, erosion and saltwater intrusion lead via different pathways to various consequences in coastal regions across Europe. Flooding leads via overflow, overtopping and breaching to damage to assets, environment and people. Erosion leads via cliff failure along a different pathway also to damage and saltwater intrusion affects ecosystems and surface waters and salinizes existing fresh water resources diminishing fresh water availability causing salt damage to crops and health issues to people. This paper provides an overview of the various impacts in Europe.

Wind waves and swells are major drivers of coastal environment changes and coastal hazards such as coastal flooding and erosion. Wave characteristics are sensitive to changes in water depth in shallow and intermediate waters. However, wave models used for historical simulations and projections typically do not account for sea level changes whether from tides, storm surges, or long-term sea level rise. In this study, the sensitivity of projected changes in wave characteristics to the sea level changes is investigated along the Atlantic European coastline. For this purpose, a global wave model is dynamically downscaled over the northeastern Atlantic for the 1970-2100 period under the SSP5-8.5 climate change scenario. Twin experiments are performed with or without the inclusion of hourly sea level variations from regional 3D ocean simulations in the regional wave model. The largest impact of sea level changes on waves is located on the wide continental shelf where shallow-water dynamics prevail, especially in macro-tidal areas. For instance, in the Bay of Mont-Saint-Michel in France, due to an average tidal range of 10 m, extreme historical wave heights were found to be up to 1 m higher (+30 %) when sea level variations are included. At the end of the 21st century, extreme significant wave heights are larger by up to +40 % (+60 cm), mainly due to the effect of tides and mean sea level rise. The estimates provided in this study only partially represent the processes responsible for the sea-level-wave non-linear interactions due to model limitations in terms of resolution and the processes included.