Extreme precipitation events in the Mediterranean: Spatiotemporal characteristics and connection to large‐scale atmospheric flow patterns

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.

Homogeneous regions for rainfall distribution in the city of Rio de Janeiro associated with the risk of natural disasters

“…Clustering result showed relevance to the physiographic aspects that characterize the rainfall dynamics in CRJ. Local orography is a key modulator of the spatiotemporal connections and substantially enhances the probability of cooccurrence of extreme precipitation events even for distant locations (Mastrantonas et al 2021). The dendrogram shows groups that were formed, where each group gathers rainfall stations that have the greatest similarity in the rainfall distribution behavior.…”

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“…ECMWF will aim to provide detailed Earth system simulations of the past, present and future with a particular focus on extreme events for several weeks ahead, for example: skilful predictions of extreme temperature anomalies and hydrological impacts such as droughts, air quality, fires, floods, as well as outputs which monitor the environment (i.e., anthropogenic CO2 emissions). In addition, forecasts of natural hazards beyond two weeks require improved understanding of the sources of predictability and teleconnections (Mastrantonas et al 2020). In this context, it is necessary to focus more strongly on a user-oriented evaluation over multiple temporal scales, variables and coupled components which will inform the optimal pathway of model development.…”

Overview towards improved understanding of the mechanisms leading to heavy precipitation in the western Mediterranean: lessons learned from HyMeX

“…Vries (2020) presented for the first time a global and systematic climatological analysis of Rossby wave breaking (RWB), intense moisture transport, and their linkage to extreme precipitation events (EPEs), with the findings of this study contributing to an improved understanding of the atmospheric processes that lead to EPEs. Mastrantonas et al (2021) demonstrated that a clustering combination of sea level pressure (SLP) and geopotential height at 500 hPa (Z500) increases the conditional probability of EPEs by more than a factor of 3, which could be critical for extended-range forecasts. Grazzini et al (2021) further investigated the relation between EPEs and Rossby wave packets (RWPs), showing the evolution and properties of precursor RWPs to be key for the categorization of EPEs.…”