Summarising changes in air temperature over Central Europe by quantile regression and clustering

Abstract

Abstract. The analysis of trends in air temperature observations is one of the most common activities in climate change studies. This work examines the changes in daily mean air temperature over Central Europe using quantile regression, which allows the estimation of trends, not only in the mean but in all parts of the data distribution. A bootstrap procedure is applied for assessing uncertainty on the derived slopes and the resulting distributions are summarised via clustering. The results show considerable spatial diversity over the central European region. A distinct behaviour is found for lower (5%) and upper (95%) quantiles, with higher trends around 0.15 °C decade−1 at the 5% quantile and around 0.20 °C decade−1 at the 95% quantile, the largest trends (>0.2 °C decade−1) occurring in the Alps.