Projected impacts of climate change are assessed with impact models driven by climate projections. However, impact models can have a high computational cost. Here, we design statistical emulators of impact models based on automatic equation discovery. To our knowledge, this approach is a promising innovation that could pave the way to develop simple and interpretable emulators for various climate impact studies, but also to validate existing knowledge and to understand novel links.

Through a machine learning technique based on seasonal cycles of sea-ice concentration from satellite data over the last 4 decades, our research shows that four regions are sufficient to best regionalize the Arctic. These regions are mainly organized into latitudinal bands and evolve in time and space. The descriptor proposed to monitor Arctic sea-ice changes is the probability to belong to each region. The probability to belong to the permanent sea-ice regions has decreased by 3.1 % per decade.

The circulation of nearshore water is of primary importance for the health of coastal ecosystems and the coastal hazards, such as erosion. The present study focuses on the role played by bottom friction, which is particularly important in rocky or coral reef areas. Using field observations and numerical simulations, we show that the waves are able to increase the bottom friction and therefore affect the whole circulation and water level dynamics.

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.