Modelling extreme precipitation over the Dinaric Alps: An evaluation of the CNRM-ALADIN regional climate model
Ivusic, Sarah ; Güttler, Ivan ; Somot, Samuel ; Guérémy, Jean-François ; Horvath, Kristian ; Alias, Antoinette
One of the Mediterranean hotspots for extreme precipitation is the coastal mountainous eastern Adriatic and Dinaric Alps regions, which are often affected by heavy precipitation events (HPEs) that can cause severe damage. Representing these events at different time scales and projecting their future evolution using regional climate models (RCMs) remains a key modelling challenge. This study evaluates the impact of model configuration on the representation of extreme daily precipitation in an RCM at climatological (1979-2012) and event scales (HPEs). Additionally, the impact of the spectral nudging (SN) technique is analysed. We compare two CNRM-ALADIN model configurations, and perform several sensitivity tests on specific parameters within a configuration. All simulations are driven by the ERA-Interim re-analysis over the Med-CORDEX domain at 0.11° horizontal resolution. On all examined time scales, model configuration shows a considerable impact on the mean and extreme daily precipitation. The new physical parameterizations of moist processes show improvement at the climatological (precipitation intensity, extreme precipitation and frequency of light precipitation) and event (the occurrence, spatial pattern and structure of HPEs) scales. Extreme precipitation shows limited sensitivity to specific parameters and is highly dependent on HPE. The use of SN improves the temporal variability at climatological scales and the location and occurrence of HPEs. We conclude that extreme precipitation representation in CNRM-ALADIN is more sensitive to change in the model configuration, particularly in the physical parameterizations, than to the application of SN. This study shows that the development and advancement of physical parameterizations can improve the model representation of extreme precipitation at several time scales and can therefore be considered as a means to reduce uncertainties in future climate projections.</p>
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