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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 sub-continental computational domains. However, new supercomputers that mix conventional multi-core 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.

[1] Regional climate models (RCMs) from the ENSEMBLES project are analyzed to assess projected changes in 21st century heavy and extreme precipitation events over Europe. A set of 10 RCMs with horizontal grid spacing of 25 km is considered, which are driven by six GCMs under an A1B greenhouse gas scenario. The diagnostics include basic precipitation indices (including mean, wet-day frequency, intensity, and percentile exceedance) and application of generalized extreme value theory for return periods up to 100 years. Changes in precipitation climate between present (1970–1999) and future (2070–2099) conditions are presented on a European scale and in more detail for 11 European regions (mostly in supplemental figures). On the European scale, projections show increases (decreases) in mean amounts and wet-day frequency in northern (southern) Europe. This pattern is oscillating with the seasonal cycle. Changes in extremes exhibit a similar pattern, but increases in heavy events reach much further south. For instance, during spring and fall, much of the Mediterranean is projected to experience decreases in mean precipitation but increases in heavy events. Thus, projected changes in mean and extremes may show different signals. The inter-model spread is partly attributable to a GCM-dependent clustering of the climate change signal, but also affected by RCM uncertainties, in particular in summer. Despite these uncertainties, many of the projected changes are statistically significant and consistent across models. For instance, for the Alps, all models project an intensification of heavy events during fall, and these changes are statistically significant for a majority of the models considered.

Projections of precipitation and its extremes over the European continent are analyzed in an extensive multimodel ensemble of 12 and 50 km resolution EURO-CORDEX Regional Climate Models (RCMs) forced by RCP2.6, RCP4.5, and RCP8.5 (Representative Concentration Pathway) aerosol and greenhouse gas emission scenarios. A systematic intercomparison with ENSEMBLES RCMs is carried out, such that in total information is provided for an unprecedentedly large data set of 100 RCM simulations. An evaluation finds very reasonable skill for the EURO-CORDEX models in simulating temporal and geographical variations of (mean and heavy) precipitation at both horizontal resolutions. Heavy and extreme precipitation events are projected to intensify across most of Europe throughout the whole year. All considered models agree on a distinct intensification of extremes by often more than +20% in winter and fall and over central and northern Europe. A reduction of rainy days and mean precipitation in summer is simulated by a large majority of models in the Mediterranean area, but intermodel spread between the simulations is large. In central Europe and France during summer, models project decreases in precipitation but more intense heavy and extreme rainfalls. Comparison to previous RCM projections from ENSEMBLES reveals consistency but slight differences in summer, where reductions in southern European precipitation are not as pronounced as previously projected. The projected changes of the European hydrological cycle may have substantial impact on environmental and anthropogenic systems. In particular, the simulations indicate a rising probability of summertime drought in southern Europe and more frequent and intense heavy rainfall across all of Europe.

[1] Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20–50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

[1] The Greenland Ice Sheet (GrIS) is one of the most dominant orographic obstacles for atmospheric flow in the Northern Hemisphere. Within an idealized framework, we investigate the potential impact of a reduced Greenland topography upon precipitation, snow accumulation, and atmospheric circulation over the GrIS. Using the global atmospheric model ECHAM5-HAM at about 1° spatial resolution (T106) and with present-day climatological mean conditions, we perform four 16-year sensitivity experiments that are identical except for the height of the GrIS topography: one control simulation with the present-day Greenland topography, and three simulations with topographies reduced to 75%, 50%, and 25% of the present-day height. This simple reduction of the GrIS topography (as compared to realistic melt dynamics) leads to an overall increase in annual total precipitation and snow accumulation, composed of significant increases in eastern, northern, and central Greenland and decreases on the western slopes. In principle, this gain in snow accumulation raises the possibility of a negative feedback that would stabilize the height of the GrIS. However, this feedback is likely overcompensated by enhanced ablation (positive feedback), as surface air temperatures strongly increase with reduced topographic height. The analysis of changes in circulation patterns indicates that flatter topographies allow the atmospheric flow to penetrate farther inland, enabling precipitation in regions that are presently desert-like. Prominent circulation features change, in particular the all-season Greenland Anticyclone and wintertime Icelandic Low become weaker with lower topography.

[1] This paper assesses the impacts of horizontal resolution, snow physics, and atmospheric forcing in snow cover simulations by the European Centre for Medium-Range Weather Forecasts (ECWMF) land surface model Hydrology Tiled ECMWF Scheme for Surface Exchanges (HTESSEL). Off-line simulations are carried out forced by the ECMWF deterministic short-term weather forecasts (WFC) with a resolution of 25 km from March 2006 to June 2010. The horizontal-resolution impact on snow cover is addressed by performing simulations at 25, 80, and 200 km forced by WFC. The impact of atmospheric forcing on snow cover is assessed by forcing the model additionally with the ECMWF Era-Interim (ERAI) reanalysis, at 80 km resolution. Snow physics effects are analyzed by performing an extra simulation forced by WFC using a different snow scheme. The simulations are validated against four independent observational data sets: (1) snow water equivalent (SWE) over Switzerland; (2) snow cover duration in Europe (SNOWCLIM); (3) interactive multisensor snow and ice-mapping system (IMS) snow cover; and (4) Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo. ERAI forced simulations show a systematic underestimation of SWE and snow cover fraction, which is due to an underprediction of snowfall by ERAI. The snow physics experiment highlights the sensitivity of the model to the partitioning between rainfall and snowfall when rainfall interception in the snowpack is neglected. The horizontal resolution has a crucial role in characterizing the snow cover over complex terrain (e.g., orographic areas, coastal and lakes regions). However, improved snow physical parameterizations and meteorological forcing are shown to be the key elements to achieve more accurate simulations of the snowpack and of the snow-atmosphere interactions, also over flat terrain.