Spectral nudging is a method that was developed to constrain regional climate models so that they reproduce the development of the large-scale atmospheric state, while permitting the formation of regional-scale details as conditioned by the large scales. Besides keeping the large-scale development in the interior close to a given state, the method also suppresses the emergence of ensemble variability. The method is mostly applied to reconstructions of past weather developments in regions with an extension of typically 1000–8000 km. In this article, the authors examine if spectral nudging is having an effect on simulations with model regions of the size of about 700 km × 500 km at midlatitudes located mainly over flat terrain. First two pairs of simulations are compared, two runs each with and without spectral nudging, and it is found that the four simulations are very similar, without systematic or intermittent phases of divergence. Smooth fields, which are mainly determined by spatial patterns, such as air pressure, show hardly any differences, while small-scale and heterogeneous fields such as precipitation vary strongly, mostly on the gridpoint scale, irrespective if spectral nudging is employed or not. It is concluded that the application of spectral nudging has little effect on the simulation when the model region is relatively small.
Abstract. This study tackles the question: Do very high-resolution convective-permitting regional climate model (RCM) simulations add value compared to coarser RCM runs for certain extreme weather conditions, namely strong wind and storm situations? Ten strong storm cases of the last two decades were selected and dynamically downscaled with the RCM COSMO-CLM (24 and 2.8 km grid point distance). These cyclones crossed the high-resolution model domain, which encompasses the German Bight, Northern Germany, and parts of the Baltic Sea. The multiple storm analysis revealed added value for the high-resolution regional climate simulation for 10 m wind speed, mean sea level pressure, and total cloud cover for most storms which were examined, but the improvements are small. Wind direction and precipitation were already well simulated by the coarser RCM and the higher resolution could often not add any value for these variables. The analysis showed that the added value is more distinct for the synoptic comparisons than for the multiple storm study analyzed with statistical measures like the Brier Skill Score.
Long-term atmospheric changes are a result of complex interactions on various spatial scales. In this study, we examine the long-term variability of the most important meteorological variables in a convection-permitting regional climate model simulation. A consistent, gridded data set from 1948 to 2014 was computed using the regional climate model COSMO-CLM with a very high convection-permitting resolution at a grid distance of 2.8 km, for a region encompassing the German Bight and Northern Germany. This is one of the very first atmospheric model simulations with such high resolution, and covering several decades. Using a very high-resolution hindcast, this study aims to extend knowledge of the significance of regional details for long-term variability and multi-decadal trends of several meteorological variables such as wind, temperature, cloud cover, precipitation, and convective available potential energy (CAPE). This study demonstrates that most variables show merely large decadal variability and no long-term trends. The analysis shows that the most distinct and significant positive trends occur in temperature and in CAPE for annual mean values as well as for extreme events. No clear and no significant trend is detectable for the annual sum of precipitation and for extreme precipitation. However, spatial structures in the trends remain weak.
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