Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at $\mathrm{1}/\mathrm{100}$ ° spatial resolution starting from $\mathrm{1}/\mathrm{16}$ ° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data.

Leveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea.

With the goal of determining strategies to maximise drug delivery to a specific site in the body, we developed a mathematical model for the transport of drug nanocarriers (nanoparticles) in the bloodstream under the influence of an external magnetic field. Under the assumption of long (compared to the radius) blood vessels the Navier-Stokes equations are reduced, to a simpler model consistently with lubrication theory. Under these assumptions, analytical results are compared for Newtonian, power-law, Carreau and Ellis fluids, and these clearly demonstrate the importance of shear thinning effects when modelling blood flow. Incorporating nanoparticles and a magnetic field to the model we develop a numerical scheme and study the particle motion for different field strengths. We demonstrate the importance of the non-Newtonian behaviour: for the flow regimes investigated in this work, consistent with those in blood micro vessels, we find that the field strength needed to absorb a certain amount of particles in a non-Newtonian fluid has to be larger than the one needed in a Newtonian fluid. Specifically, for one case examined, a two times larger magnetic force had to be applied in the Ellis fluid than in the Newtonian fluid for the same number of particles to be absorbed through the vessel wall. Consequently, models based on a Newtonian fluid can drastically overestimate the effect of a magnetic field. Finally, we evaluate the particle concentration at the vessel wall and compute the evolution of the particle flux through the wall for different permeability values, as that is important when assessing the efficacy of drug delivery applications. The insights from our work bring us a step closer to successfully transferring magnetic nanoparticle drug delivery to the clinic.

Here, the Copernicus Ocean State Report offers detailed scientific analysis of the ocean under climate change, ocean variability, and ocean extremes. This evidence-based reporting is based on a set of key ocean indicators such as sea surface temperature, sea level rise, ocean heat content, ocean acidification, and sea ice extent. Moreover, key indicators for ocean variability such as the El Niño–Southern Oscillation and major ocean current systems such as the Atlantic Meridional Overturning Circulation are tackled. Major results show that the global ocean's sea surface temperature continues to steadily increase, particularly in the Northern Hemisphere, with a global warming rate of 0.13 ± 0.01 °C per decade from 1982 to 2023. Since around the 1970s, the ocean warming trend has intensified, doubling its rate over the past 2 decades. Concurrently, global mean sea level has risen significantly at intensifying rates from 2.1 mm yr⁻¹ in the 1990s to 4.3 mm yr⁻¹ in recent years, with regional disparities. The Arctic Ocean has faced unprecedented sea ice loss and warming, while Antarctic sea ice has reached record lows. Ocean acidification has progressed, decreasing pH at a rate of −0.017 per decade. Marine heatwaves have become more frequent, intense, and extensive, affecting up to 80 % of the global ocean surface annually. Despite significant variability, extreme ocean surface wind speeds have been prevalent, particularly in the North Atlantic, North Pacific, and Southern Ocean. The Atlantic Meridional Overturning Circulation shows no significant decline but varies substantially. In 2023, La Niña conditions have transitioned to El Niño conditions in the Pacific Ocean.

Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. Retrieving consistent high-resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints, and degradations introduced by the interpolation algorithms used to obtain gap-free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. Building upon recent findings, we conduct an Observing System Simulation Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), and we present a strategy for further refinements. Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics-informed loss functions. The neural networks are thus trained using OSSE data and subsequently applied to the Copernicus Marine Service satellite-derived ADTs and SSTs, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. This study suggests that CNNs are beneficial for improving standard altimetry mapping: they generally sharpen the ADT gradients, with consequent correction of the surface currents direction and intensities with respect to the altimeter-derived products. Our investigation is focused on the Mediterranean Sea, quite a challenging region due to its small Rossby deformation radius (around 10 km).