The objective of this study is to characterize the observed evolution of compound winter low wind and cold events impacting the French electricity system. The frequency of compound events exhibits a high interannual variability and a decrease over the 1950–2022 period. We further show that the regional atmospheric circulation is an important driver of compound events occurence, but do not strongly contributes to the observed decrease.

The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.

Mistral wind is a renowned phenomenon in the Mediterranean, yet its large-scale controlling mechanisms have not been systematically mapped. Here, using a new mistral database for 1981–2016, the upper-tropospheric flow patterns are classified by a self-organizing map algorithm, resulting in 16 distinct patterns related to Rossby wave life cycles. Each pattern has unique surface impact, having implications to understanding mistral predictability, air–sea interaction and their future projections.

Wind speeds at turbine hub heights are scarcely measured and often replaced by numerical datasets when simulating wind power time series for various impact studies. Can we trust these modelled wind speeds? This work investigates five wind-speed datasets, comparing them to actual wind-speed and wind-power observations in France. They show various skills in terms of bias and hourly variability, leading to more or less realistic wind-power simulations at the local scale.

In a context of climate change, the wind energy sector has seen a very sharp growth requiring accurate forecasts. Air density is a key variable in the wind energy modeling as it can make the power output varies by almost 20 %. In this paper, a numerically low-cost method is evaluated. This method improves the wind energy modeling by more than 15 % and by almost 40 % when the atmospheric conditions are far from the standards atmospheric conditions used to produce the wind turbine power curve.