Abstract

The uncertainties in current global and regional climate model integrations are partly related to the representation of clouds, moist convection, and complex topography, thus motivating the use of convection-resolving models. On climate time scales, convection-resolving methods have been used for process studies, but application to long-term scenario simulations has been very limited. Here we present a convection-resolving simulation for a 10 yearlong period (1998–2007) integrated with the Consortium for Small-Scale Modeling in Climate Mode model. Two one-way nested grids are used with horizontal resolutions of 2.2 km for a convection-resolving model (CRM2) on an extended Alpine domain (1100 km × 1100 km) and 12 km for a convection-parametrizing model (CPM12) covering Europe. CPM12 is driven by lateral boundary conditions from the ERA-Interim reanalysis. Validation is conducted against high-resolution surface data. The CRM2 model strongly improves the simulation of the diurnal cycles of precipitation and temperature, despite an enhanced warm bias and a tendency for the overestimation of precipitation over the Alps. The CPM12 model has a poor diurnal cycle associated with the use of parameterized convection. The assessment of extreme precipitation events reveals that both models adequately represent the frequency-intensity distributions for daylong events in summer, but large differences occur for hourly precipitation. The CPM12 model underestimates the frequency of heavy hourly events, while CRM2 shows good agreement with observations in the summer season. We also present results on the scaling of precipitation extremes with local daily mean temperatures. In accordance with observations, CRM2 exhibits adiabatic scaling for intermediate hourly events (90th percentile) and superadiabatic scaling for extreme hourly events (99th and 99.9th percentiles) during the summer season. The CPM12 model partly reproduces this scaling as well. The excellent performance of CRM2 in representing hourly precipitation events in terms of intensity and scaling is highly encouraging, as this addresses a previously untested (and untuned) model capability.

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