Abstract
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.
Highlights
Long-term changes of the global climate system have been observed [1]
According to long-term changes in the 2 m temperature as well as in precipitation sum, GB0028 is similar to the coastDat II forcing
This shows the comparison of GB0028 and coastDat II with the other data sets of the norddeutscher-klimamonitor.de mentioned in Chapter 2.3 and the station observation at Cuxhaven, Hamburg, and Schwerin
Summary
Long-term changes of the global climate system have been observed [1]. They include both natural and anthropogenic variations. Climatic long-term variability and trends are very important at the regional scale. The derived estimates of long-term climate change are merely approximations. Several existing studies deal with long-term variability and trends of station measurements, which are good estimates of long-term changes for a single point of measurement [2,3]. The behavior of the variables remains unclear between and in the surrounding regions of weather stations. This is especially true for heterogeneous meteorological variables. For the region of Hamburg, a detailed documentation of the scientific knowledge of regional climate change is given by Meinke et al [4]
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