This study explores the utilization of absolute dynamic topography (i.e. sea level variations relative to a common geoid surface) to quantify some key components of the Baltic water budget: the dynamic volume of the Baltic Sea, river runoff, and the Baltic inflow and outflow. The method and results show great potential for quantification, validation, and a better understanding of the dynamics of the Baltic Sea, especially with a changing climate.

The Mediterranean faces more frequent and intense marine heat waves, harming ecosystems and fisheries. Using machine learning, we developed a model to forecast these events up to 7 d in the future, outperforming traditional methods. This approach enables faster, accurate forecasts, helping authorities mitigate impacts and protect marine resources.

A new Voronoi-mesh-based coastal ocean model has been developed as an alternative to triangle-mesh-based models. The finite volume model has been generalized to run simulations on any mesh type, including triangle, Voronoi, Octree, structured, and mixed meshes. Wave-resolving simulations were conducted for coastal developments to resolve small scales in distributions of the Rouse number. The model has been validated using five tidal time series.

Zooplankton diel vertical migration (DVM) shapes ocean biogeochemical cycles. We present a new DVM model that reproduces migration depths observed in the North Atlantic Ocean. We show that chlorophyll shading contributes to reducing zooplankton migration depth and mainly controls its spatial and temporal variability. Thus, high chlorophyll concentrations may limit carbon sequestration caused by zooplankton migration despite the general abundance of zooplankton migration in these environments.

The Osborn relation is widely used to estimate the diapycnal mixing rate, but its accuracy is questioned due to the assumed constant dissipation ratio (Γ) without identifying mixing types. We identify a salt finger and turbulence in the western Pacific and midlatitude Atlantic, finding that Γ is highly variable and related to turbulence parameters, through which we improve mixing rate estimates. Identifying mixing types and refining Γ are necessary to improve mixing parameterization accuracy.

The entanglement of waves and currents in observational data complicates their respective estimation. We propose a data-driven method that provides a reduced set of modes for waves and currents. These sets of modes are correlated with each other, enabling us to perform a coupled estimation of these two physical processes. This methodology is capable of producing estimates from an instantaneous observation of sea surface height and for a strong jet signal.

This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.

A new method improves the accuracy of climate models by adjusting wave spectrum simulations in the Mediterranean Sea. It corrects biases and accounts for changes in wave patterns due to climate change, such as shifts in direction and frequency. This technique was applied to multiple climate models, assessing future wave conditions for mid-century and end-of-century scenarios. The results underline the importance of precise corrections for better predicting how waves may evolve as the climate changes.

The study presents an operational service that forecasts flood impacts during extreme conditions at three beaches in Barcelona, Spain. The architecture is designed for efficient use on standard desktop computers, using data from the Copernicus Marine Environment Monitoring Service, task automation tools, Python scripts, and the XBeach model to deliver timely results. Extensive validation, including field campaigns and video analysis, ensures accuracy and reliability.

The study explores how unresolved motions in the Benguela upwelling region affect diffusivity estimates and the need for full diffusivity tensors in models. Using a scalar for lateral mixing can be inaccurate due to directional mixing. Analysis of buoys and simulations shows that diffusivity from particle pairs is lower than expected and that removing the mean flow improves estimates. The study shows the importance of full diffusivity tensors for better model mixing and reducing warm biases in models.

The Weddell Sea Bottom Water gravity current transports dense water from the continental shelf to the deep sea and is crucial for the formation of new deep-sea water. Building on vertical profiles and time series measured in the northwestern Weddell Sea, we apply three methods to distinguish turbulence caused by internal waves from that by other sources. We find that in the upper part of the gravity current, internal waves are important for the mixing of less dense water down into the current.

This study provides the first assessment of decadal changes in the whole phytoplankton community, addressed by flow cytometry, in the highly productive waters of the Strait of Dover. A significant surface seawater temperature increase of 1°C, associated with an important change in the nutrient concentration and balance, has triggered a change in the phytoplankton communities, characterized by a higher total abundance and an increasing proportion of the smallest cells (picroeukaryotes and picocyanobacteria).