Balearic Islands Coastal Observing and Forecasting System

Abstract. Rip currents are hazardous, fast-moving seaward flows and remain one of the leading causes of rescues and drownings on surf beaches, yet their automated detection remains a significant challenge due to their amorphous, dynamic morphology and the environmental complexity of the surf zone. This study introduces a novel platform-agnostic deep learning–based framework for automated rip current detection from beach imaging platforms, integrating three core contributions: a diverse new dataset, a rigorous architectural benchmark, and a deployable operational tool. We first present RipAID, a comprehensive dataset enriched with multi-platform imagery and multiple viewing angles to ensure scale-invariant learning. Building on this resource, a systematic evaluation of state-of-the-art architectures demonstrates that geometric fidelity is critical; specifically Oriented Bounding Boxes (OBB) significantly outperform standard axis-aligned methods. Our optimized YOLOv11n-OBB model achieves robust performance (mAP50: 0.927), with inference speeds from 2.4 to 60 FPS on hardware ranging from edge devices to GPU workstations. To bridge the gap between research and practice, and ensure that the results are reusable and reproducible, the framework and model weights have been released as an open-source, containerized module (socib-rip-currents-detection), providing the coastal safety community with a scalable, ready-to-use and standardized tool for continuous, automated rip current monitoring.

CoastPredict UN Ocean Decade Programme represents a cutting-edge initiative that integrates scientific advancements in coastal ocean observing and predicting with the aim of developing applications to support coastal resilience. CoastPredict will support multi-hazard early warning systems, climate risk assessment, and digital twins of the coastal ocean for ecosystems and environmental management and climate adaptation, by integrating observational data from satellites, existing in situ networks, Internet of Things (IoT) sensors, and citizen science into coastal models, and using Artificial Intelligence (AI) and Machine Learning (ML) tools to underpin risk mitigation and resilience strategies.The development of a flexible and interoperable 'GlobalCoast' distributed cloud infrastructure, will link a network of regional and Pilot Site data spaces and existing repositories—such as EMODnet, Copernicus —through a federated system with orchestrated workflows that will provide a cloud-based platform for high-resolution data sharing and analytic tools to ultimately bridge the end-to-end value chain from oceanographic data to user-focused applications. This system will enhance interoperability and real-time access to diverse data including from satellites and cost-effective in situ instruments, and will provide cloud-based computing capacity. The infrastructure will allow for scalable, region-specific cloud platforms, each tailored with customised models and high-resolution simulations for local needs. Furthermore it will support sharing and adaptation of existing prototypes for other regions, for example climate downscaling for risk assessment in the Adriatic Sea, multi-risk early warning systems for extreme events in the Savannah region, and digital twins for nature-based solutions in Italy’s Emilia-Romagna region. Utilising Application Programming Interfaces (APIs), AI and ML driven services, CoastPredict’s cloud framework will enable adaptive, localised modelling, such as storm surge predictions and marine heat wave tracking, offering end users—including researchers, environmental managers, and coastal communities—actionable insights in response to evolving climate risks. *This abstract is proposed for IOC Session 1 - From science to shoreline: bridging knowledge, policy and action for coastal resilience.

The Horizon Europe FOCCUS project (Forecasting and Observing the Open-to-Coastal Ocean for Copernicus Users, foccus-project.eu), is enhancing the Copernicus Marine Environment Monitoring Service (CMEMS) for coastal zones. In collaboration with European Member State Coastal Systems (MSCS) and users, FOCCUS aims to improve and advance the coastal dimension of CMEMS by enhancing existing capability, developing innovative coastal products focusing on three key pillars: i) improving new coastal observations by fusing high-resolution remote sensing and in-situ data and implementing new technologies and approaches, including the use of Artificial Intelligence (AI) methods to improve accuracy; ii) developing advanced hydrology and coastal models including a pan-European hydrological ensemble for improved river discharge predictions, and establishing a unified coastal system by testing new methodologies in MSCS production chains while taking advantage of stochastic simulation, ensemble approaches, and AI technology; and iii) demonstrating innovative products and improved co-produced services that address both Environmental and Societal Challenges, enhancing the performance and societal relevance of coastal ocean forecasting systems in and downstream of CMEMS. As an endorsed action under the UN Ocean Decade’s CoastPredict program, FOCCUS aligns its efforts with international initiatives, fostering collaboration to address the complex challenges of coastal regions worldwide. FOCCUS collaborates with relevant international initiatives, such as the COSS-TT (Coastal Ocean and Shelf Seas Task Team under OceanPredict) and UN Decade programmes (such as CoastPredict, DITTO, and the Decadal Collaborative Center on Coastal Resilience (DCC-CR)). The project emphasizes co-production and collaboration with end-users, policy-makers, and local communities. Through stakeholder engagement and the development of new science-policy interfaces, FOCCUS enhances the societal relevance of coastal forecasting systems, supporting decision-making processes for sustainable coastal management and climate adaptation strategies. Ultimately, FOCCUS provides high-quality, trusted marine knowledge needed for evidence-based management, supporting a sustainable blue economy, and building climate resilience for European and global communities. The consortium consists of 19 partners from 11 countries bringing together a diverse range of expertise in oceanography, observational science, advanced modeling, and technology development, exemplifying the power of international collaboration in advancing ocean sciences and technologies.FOCCUS is funded by the European Union (Grant Agreement No. 101133911). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them.

<strong class="journal-contentHeaderColor">Abstract.</strong> Among the well-known greenhouse gases (GHG), nitrous oxide (N₂O) is the third most impactful, possessing a global warming potential approximately 300 times greater than carbon dioxide (CO₂) over a century. The distribution of N₂O from aquatic environments exhibits notable spatial and temporal variations, and emissions still remain inadequately constrained and underrepresented in global N₂O emission inventories, particularly from coastal zones. This study focuses on the N₂O levels and air-sea fluxes in the Balearic Islands Archipelago coastal waters in the Western Mediterranean Basin. Data were gathered between 2018 and 2023 at three coastal monitoring stations: two in the highly inhabited island of Mallorca and the third in the well-preserved National Park of the Cabrera Archipelago. Seawater N₂O concentrations varied from 6.5 to 9.9 nM, with no significant differences detected across the sites. The average air-sea fluxes were estimated to range from -0.3 to 0.6 &mu;mol m⁻&sup2; d⁻&sup1;, indicating that the study areas generally functioned as weak N₂O sources. A consistent seasonal pattern was noted over the study period. Machine learning analysis indicated that seawater temperature was the primary factor influencing N₂O concentrations, with lesser contributions from chlorophyll levels and salinity.

"Gathering users and developers to shape together the next-generation ocean reanalyses: Ocean reanalyses workshop of the European Copernicus Marine Service" published on 01 Apr 2024 by American Meteorological Society.

<strong class="journal-contentHeaderColor">Abstract.</strong> The northern Red Sea (NRS) is where Red Sea Outflow Water and, occasionally, Red Sea Deep Water, are formed. Glider observations are used to describe the formation mechanisms and pathways of the intermediate waters in the NRS in late winter from 31 January to 18 April 2019. Utilizing glider observations, atmospheric reanalysis products, and satellite datasets, we evaluated the mesoscale activity and the atmospheric conditions that contribute to outflow water formation. The cyclonic circulation in the region surfaces dense water, which exposes it to the atmosphere, ventilating the water column and contributing to phytoplankton growth (enhancement of chlorophyll concentration) due to the nutrients upwelled into the euphotic zone. Subduction of this water in the 3-dimensional cyclonic circulation transported oxygenated, elevated chlorophyll water to depths between 150 m and 250 m along the 28.2 kg/m3 isopycnal. Unlike previous observations, in late February, a strong anticyclonic circulation blocked the inflow of warmer, fresher water into the region. It was accompanied by a negative heat flux and an uplifting of dense water to the surface. Net cooling through mid-March cooled the incoming surface waters from the south. At the end of the observational period, the intrusion of warmer, fresher waters from the south coincided with the re-establishment of cyclonic circulation and capped the dense surface water that had formed during March. These observations demonstrate that multiple processes contribute to Red Sea Outflow Water formation - convective mixing, cyclonic uplifting of dense water, subduction, and meso(submeso-)scale processes.

The sensitivity of a 2DH coastal area (XBeach) and a reduced-complexity (Q2Dmorfo) morphodynamic models to using different forcing sources is studied. The models are tested by simulating the morphodynamic response of an embayed beach in the NW Mediterranean over a 6-month period. Wave and sea level forcing from in-situ data, propagated buoy measurements, hindcasts as well as combinations of these different data sources are used and the outputs are compared to in-situ bathymetric measurements. Results show that when the two models are calibrated with in-situ measurements, they accurately reproduce the morphodynamic evolution with a "Good" BSS (Brier Skill Score). The wave data propagated from the buoy also produces reliable morphodynamic simulations but with a slight decrease in BSS. Conversely, when the models are forced with hindcast wave data the mismatch between the modelled and observed beach evolution increases. This is attributed to a large extent to biased mean directions in hindcast waves. Interestingly, in this small tide site the accuracy of the simulations did not depend on the sea-level data source, and using filtered or non-filtered tides also yielded similar results. These results have implications for long-term morphodynamic studies, like those needed to validate models for climate change projections, emphasizing the need of using accurate forcing sources such as those obtained by propagating buoy data.

Abstract. The sensitivity of a 2DH coastal area (XBeach) and a reduced-complexity (Q2Dmorfo) morphodynamic models to using different forcing sources is studied. The models are tested by simulating the morphodynamic response of an embayed beach in the NW Mediterranean over a 6-month period. Wave and sea level forcing from in-situ data, propagated buoy measurements, hindcasts as well as combinations of these different data sources are used and the outputs are compared to in-situ bathymetric measurements. Results show that when the two models are calibrated with in-situ measurements, they accurately reproduce the morphodynamic evolution with a "Good" BSS (Brier Skill Score). The wave data propagated from the buoy also produces reliable morphodynamic simulations but with a slight decrease in BSS. Conversely, when the models are forced with hindcast wave data the mismatch between the modelled and observed beach evolution increases. This is attributed to a large extent to biased mean directions in hindcast waves. Interestingly, in this small tide site the accuracy of the simulations did not depend on the sea-level data source, and using filtered or non-filtered tides also yielded similar results. These results have implications for long-term morphodynamic studies, like those needed to validate models for climate change projections, emphasizing the need of using accurate forcing sources such as those obtained by propagating buoy data.

Understanding the pathways of floating material at the surface ocean is important to improve our knowledge on surface circulation and for its ecological and environmental impacts. Virtual particle simulations are a common method to simulate the dispersion of floating material. To advect the particles, ocean models’ velocities are usually used, but only recent ones include tidal forcing. Our research question is: What is the effect of tidal forcing on virtual particle dispersion and accumulation at the ocean surface? As inputs we use velocity outputs from eNATL60, a twin simulation with and without tidal forcing. We focus on the Açores Islands region and we find: 1) Surface particles have a larger displacement, but a lower distance travelled with than without tidal forcing 2) Surface accumulation seasonal differences depend on the spatial scale of the ocean structures 3) A greater variability in surface accumulation is present with tidal forcing.