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Convection-resolving models allow to explicitly resolve deep convection at horizontal grid spacings of O(1 km). On current supercomputers, refining the grid spacing to the kilometer scale is computationally still extremely demanding, and therefore, climate simulations at this resolution have so far largely been limited to subcontinental computational domains. However, new supercomputers that mix conventional multicore CPUs and accelerators possess properties beneficial for climate codes. Exploiting these capabilities allows expansion of the size of the computational domains to continental scales. Here we present such a convection-resolving climate simulation, using a version of the COSMO model, capable of exploiting GPU accelerators. The simulation has a grid spacing of 2.2 km, 1536 × 1536 × 60 grid points, covers the period 1999–2008, and is driven by the ERA-Interim reanalysis. An assessment of the 10-year-long simulation is conducted using a wide range of data sets, including several rain gauge networks, energy balance stations, and a remotely sensed lightning data set. Substantial improvements are found for the 2 km simulation in terms of the diurnal cycles of precipitation. This confirms results found in studies using smaller computational domains. However, the continental-scale simulations also reveal deficiencies such as substantial performance differences between regions with and without strong orographic forcing. Analysis of the statistical distribution of updrafts and downdrafts shows an increase of the amplitude in seasons with convection and a pronounced asymmetry between updrafts and downdrafts. Furthermore, the analysis of lightning data shows that the convection-resolving simulation is able to reproduce important features of the annual cycle of deep convection in Europe. Demonstration of convection-resolving climate simulations on a continental-scale computational domainRobust improvements in the representation of summer convection

Weather forecasts and climate projections are of tremendous importance for economical and societal reasons. Software implementing weather and climate models is complex to develop and hard to maintain, and requires a large range of different competencies, ranging from environmental sciences, numerical methods, to low level programming. In order to manage this complexity we developed GridTools, a set of software libraries targeted at weather and climate model developers. By separating the model description (front-end) from its efficient implementation on the target platform (back-end), GridTools allows the implementation of performance-portable simulations on a variety of platforms, such as multicore and GPU-accelerated systems. We discuss the application of GridTools to the regional weather and climate model COSMO and show performance results on simple benchmarks as well as on COSMO.