High-risk atmospheric circulation patterns for Italian precipitation extremes

Extreme precipitation events (EPEs) are meteorological phenomena that are likely to intensify as a result of climate change. They are a major concern for our society, especially in densely populated areas, as they can have significant economic and environmental impacts. Therefore, identifying large-scale atmospheric circulation that lead to EPEs is crucial for detecting geographical areas at risk and mitigating their adverse impacts. To achieve this objective, we study the circulation patterns associated with EPEs in Italy. Initially, we focus on North-Central Italy and we identify the precipitation extremes in three datasets: ARCIS 3.0, MSWEP, and CERRA LAND. Circulation types associated with the EPEs are obtained by applying Self Organizing Maps (SOMs), an unsupervised artificial neural network widely used in synoptic climatology, to anomalies of geopotential height at 500 hPa and mean sea level pressure. Since ArCIS, the reference dataset, is limited to North-Central Italy, we extend the analysis to the whole of Italy using CERRA-Land. Such choice is based on the fact that it produced the most similar results to ArCIS in North-Central Italy compared to MSWEP. We then generate composites of various variables (all retrieved from ERA5) for each SOM pattern to better understand the circulation patterns and characterize the atmospheric dynamics associated with extreme events. Additionally, we analyze the probability of exceeding the 99th percentile of wet-days to identify the areas impacted by each pattern. Composites for the different circulation types show variations in the synoptic pattern's position within the Mediterranean basin, as well as differences in the direction and intensity of moisture flux. These patterns influence distinct regions and display varying frequencies across seasons. In future works the classification obtained by this study will be applied to climate model simulations, aiming to investigate the role of anthropogenic climate change in the dynamics leading to EPEs in Italy.