Satellite observations are often incomplete due to cloud cover, resulting in missing ocean data. To address this, we developed T-DINEOF (Data Interpolating Empirical Orthogonal Function), a reconstruction method that simultaneously estimates sea surface temperature, chlorophyll concentration, and wind conditions by learning relationships among variables. Results show that T-DINEOF improves reconstruction accuracy, especially in regions with sparse data or weak correlations, providing more reliable ocean information for environmental monitoring.

Strong motions caused by surface waves can set the material at the bottom in motion. How strong the wave motions need to be depends on the bottom type, for example mud or sand. We estimated how often waves can lift particles from the bottom by comparing wave model results to sea floor samples in the laboratory. We included the effect of seasonal biological activity, which was found to be significant. 

This study reveals the structure and alteration of a narrow freshwater-driven ocean low-density filament and its evolution through tidal modulationa an dlocale filament kinematics. Using multi-platform in situ observations, it is shown that these submesoscale features can rapidly being altered and reshape in near-surface waters, influencing how energy and heat alter just below the ocean-atmosphere interface.

We introduce TS (Temperature-Salinity)-Cast, a novel deep neural network that reconstructs subsurface thermohaline structures from satellite observations. Validated against independent time-series data, TS-Cast achieves root mean squared errors of < 1 °C and < 0.1 psu in the upper 500 m of the Kuroshio Extension, comparable or surpassing data-assimilated numerical models. Critically, we demonstrate that the physical limitations of the input satellite data fundamentally constrain the model's predictive skill.

Ocean bottom pressure (p_b) is a key variable for monitoring the ocean circulation, yet global space gravimetry observations offer limited coverage in space and time. Our work examines how to improve estimates of p_b by optimally combining information in available data with an ocean circulation model. Results indicate that gravimetry data contain information complementary to that available in other ocean datasets and are thus important for determining variations in p_b and related circulations.

This work examines how large-scale climate modes affect waves and storm surges on the northeast coast of England. We combine weather types with a multivariate analysis of sea states. We find that the North Atlantic Oscillation and Scandinavian pattern influence local waves and surges by altering the occurrence probabilities of synoptic conditions associated with specific winds, storm locations, and pressure systems. Our results improve the understanding of climate impacts on coastal conditions.

The study use Conductivity, Temperature and Depth (CTD) measurement (including seals-borne CTD) to describe the seasonality of the stratification and mixing of one of the southernmost Fjords of Patagonia: The Almirantazgo Fjord. The region, typically tidal was indicated as the most important forcing for mixing the inner-sea of Tierra del Fuego, here the study shows that the persistent an episodic strong along-fjord winds could perturb the pynoclyne and rivals to the estuarine circulation.