@article{gledocs_11858_8413,
author = {Mastrantonas, Nikolaos and Herrera‐Lormendez, Pedro and Magnusson, Linus and Pappenberger, Florian and Matschullat, Jörg},
title = {Extreme precipitation events in the Mediterranean: Spatiotemporal characteristics and connection to large‐scale atmospheric flow patterns},
year = {2021-01-25},
publisher = {John Wiley & Sons, Ltd.},
publisher = {Chichester, UK},

abstract = {The Mediterranean region is strongly affected by extreme precipitation events (EPEs), sometimes leading to severe negative impacts on society, economy, and the environment. Understanding such natural hazards and their drivers is essential to mitigate related risks. Here, EPEs over the Mediterranean between 1979 and 2019 are analysed, using ERA5, the latest reanalysis dataset from ECMWF. EPEs are determined based on the 99th percentile of their daily distribution (P99). The different EPE characteristics are assessed, based on seasonality and spatiotemporal dependencies. To better understand their connection to large‐scale atmospheric flow patterns, Empirical Orthogonal Function analysis and subsequent non‐hierarchical K‐means clustering are used to quantify the importance of weather regimes to EPE frequency. The analysis is performed for different variables, depicting atmospheric variability in the lower and middle troposphere. Results show a clear spatial division in EPE occurrence, with winter and autumn being the seasons of highest EPE frequency for the eastern and western Mediterranean, respectively. There is a high degree of temporal dependencies with 20% of the EPEs (median value based on all studied grid cells), occurring up to 1 week after a preceding P99 event at the same location. Local orography is a key modulator of the spatiotemporal connections and substantially enhances the probability of co‐occurrence of EPEs even for distant locations. The clustering clearly demonstrates the prevalence of distinct synoptic‐scale atmospheric conditions during the occurrence of EPEs for different locations within the region. Results indicate that clustering, based on a combination of sea level pressure (SLP) and geopotential height at 500 hPa (Z500), can increase the conditional probability of EPEs by more than three (3) times (median value for all grid cells) from the nominal probability of 1% for the P99 EPEs. Such strong spatiotemporal dependencies and connections to large‐scale patterns can support extended‐range forecasts.},
note = { \url {http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/8413}},
}