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In recent decades, the Alpine regions have experienced several heavy precipitation events, occasionally accompanied by high wind speeds, causing forest damage and triggering various natural hazards including landslides. These events showed that the interplay of multiple meteorological extremes occurring simultaneously or within a short period of time can lead to more profound ecological and socio-economic consequences than single extremes and overstrain the risk management capacity of affected areas. However, also due to data limitation, few studies addressed compound extremes, especially of precipitation and wind, in the Alpine regions on spatial scales meaningful for the impact and risk analyses. More attention has still been dedicated to single processes or to large-scale evaluations. A better understanding of current likelihood and characteristics of compound extremes on a regional and local level, as well as of skills and limitations of datasets and methods used for their detection, can contribute to improve the assessment of related risks in both current and future climate. This study evaluates a variety of methods and datasets for the detection and characterization of compound wind and precipitation extremes in Trentino South Tyrol region (Eastern Italian Alps) over recent decades. Starting from daily time series of precipitation and maximum wind speed, compound extremes were identified as the concurrent threshold exceedance, based on either percentiles or anomaly levels. The assessment was based on observations collected by nine stations of the regional weather network and two reanalysis datasets, i. e. , the Copernicus Regional Reanalysis for Europe (CERRA, 5. 5 km) and the high-resolution dynamical downscaling of ERA5 reanalysis for Italy (VHRREA-IT, 2. 2 km). Due to the generally limited availability of local observations, reanalyses were investigated as potential alternatives to observations for the analysis of compound extremes. Different combinations of thresholds, temporal and spatial lags were first tested in order to maximize the detection of events while maintaining the overall accuracy. Spatial patterns, seasonality, magnitude and frequency of compound events identified by observations and reanalyses over the common period 1993-2021 will be presented and compared to highlight main differences, advantages and limitations of each dataset. Preliminary results suggest that VHRREA-IT outperforms CERRA in identifying extreme precipitation events, based on comparison with observed values at station level. However, both datasets underestimate local wind speed posing challenges for a robust identification and description of compound precipitation and wind extremes. Despite this limitation, the most intense event of the analysed period, the storm Vaia (October 2018), was detected by both reanalyses with a spatial pattern aligning with observations. The research leading to these results has received funding from Interreg Alpine Space Program 2021-27 under the project number ASP0100101, How to adapt to changing weather eXtremes and associated compound and cascading RISKs in the context of Climate Change (X-RISK-CC).
Maines et al. (Mon,) studied this question.