In the Western Alboran Gyre, waters from the Atlantic and Mediterranean meet, creating density differences that cause some water to sink, affecting ocean ventilation and nutrient cycles. We collected data showing patches of water with higher oxygen and chlorophyll levels moving towards the gyre's center, with active mixing at their edges. This mixing diluted the patches, and other factors like water density and light penetration likely played a role in these dynamics.

This paper describes underwater robotic measurements in an energetic strait. The data show how energy is transferred from winds and tides to turbulent processes. Boundary layers of strong turbulence affected the water from surface to seafloor across an unusually deep extent, except when fresher or warmer waters moved into the region. Numerical models revealed that turbulent energy transport allowed boundary layers to interact. This phenomenon may impact the biological structure of coastal seas.

This study explores ocean currents around Curaçao and how land-derived substances like pollutants and nutrients travel in the water. Most substances move northwest, following the main current, but at times, ocean eddies spread them in other directions. This movement may link polluted areas to pristine coral reefs, impacting marine ecosystems. Understanding these patterns helps inform conservation and pollution management around Curaçao.

The distance-weighted averaging method was used to calculate the along-orbit sea surface height (SSH) wavenumber spectra of four satellites and to evaluate the along-track resolution capability of the four satellites. The results show that the resolution of Surface Water and Ocean Topography (SWOT) in the Kuroshio region is 25 km, which is twice the resolution of conventional satellites. A parameter was defined using the cross-power-spectrum approach and used to analyse the global ocean.

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.