This study examines how ocean wave conditions around France may change as the climate warms. Using a new statistical approach and multiple climate projections, it shows that extreme waves are likely to become more intense in winter and less severe in summer, especially along the Atlantic coast and the English Channel. The work also proposes a new way to define extreme sea-state conditions over the structure's lifetime, leading to better design.

France aims for major offshore wind growth. We examined future climate impacts on wind, waves, and water levels. Results suggest that mean winds and waves may weaken, but extreme waves and sea levels will increase. These trends are nevertheless accompanied by strong model uncertainties. These findings are necessary for designing durable offshore wind farms in France and ensuring reliable energy production for decades to come.

Old observations are necessary to understand the atmosphere. When direct observations are not available, one can use indirect observations, such as tide gauges, which measure the sea level in port cities. The sea level rises when local air pressure decreases and when wind pushes water towards the coast. Several centuries-long tide gauge records are available. We show that these can be complementary to direct pressure observations for studying storms and anticyclones in the 19th century.

The goal of this paper is to weight several dynamic models in order to improve the representativeness of a system. It is illustrated using a set of versions of an idealized model describing the Atlantic Meridional Overturning Circulation. The low-cost method is based on data-driven forecasts. It enables model performance to be evaluated on their dynamics. Taking into account both model performance and codependency, the derived weights outperform benchmarks in reconstructing a model distribution.

The goal of this paper is to obtain probabilistic predictions of a partially observed dynamical system without knowing the model equations. It is illustrated using the three-dimensional Lorenz system, where only two components are observed. The proposed data-driven procedure is low-cost, is easy to implement, uses linear and Gaussian assumptions and requires only a small amount of data. It is based on an iterative linear Kalman smoother with a state augmentation.