Quantifying uncertainties related to observational datasets used as reference for regional climate model evaluation over complex topography — a case study for the wettest year 2010 in the Carpathian region

Abstract Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations. However, uncertainty observations affects evaluation. This work introduces a novel method to quantify uncertainties in and how these affect RCM Besides precipitation temperature, our uses geographic variables (e.g. elevation, variability effect station), which considered as sources. To assess uncertainties, complex analysis based on various statistical tools, e.g. correlation permutation test, was carried out. Furthermore, we special metric, reduction error ( RE ) identify where shows improvement compared lateral boundary conditions (LBCs). We focused Carpathian region, because its unique orographic climatic conditions. The is applied two (CarpatClim E-OBS) RegCM simulations 2010, wettest year this region since 1901. results show that CarpatClim wetter than E-OBS, while temperature similar over lowland; however, E-OBS significantly warmer mountains. By has against LBCs mountains areas with dense station network precipitation. Nevertheless, there significant differences depending dataset concerning can be other datasets, different time periods areas. It also suitable find errors, exemplified paper.