All those factors lead to notable differences in the quality of the observational datasets (Tanarhte et al. In addition, some observational datasets (e.g. Other differences may include quality checking and error correction procedures. The observations used for creating the dataset differ from each other in many characteristics, including spatiotemporal resolution, length and homogeneity of the measurement time series. Interpolation tends to introduce other bias, such as the excessive smoothing of spatial variability, and may thus lead to an underestimation of extremes (Hofstra et al. Kyselý and Plavcová ( 2010) highlighted the facts that stations may not be representative for a wider area and insufficient density of information from station observations used for the interpolation lead to bias in E-OBS, which affect the evaluation of RCMs. ( 2009) tested precipitation and temperature from the E-OBS dataset and found inhomogeneities in the underlying station data and underestimation of extremes within the data. Most studies focusing on Europe use the E-OBS dataset, which covers the whole continent. 2009 Kyselý and Plavcová 2010 Palazzi et al. Several studies quantify the observation-related uncertainty by comparing observation-based gridded datasets for specific variables (mostly precipitation or temperature) and different regions (e.g. However, measuring precipitation is challenging because of its high variability in space and time and the existence of measurement errors (Bacchi and Kottegoda, 1995 Frei 2014 Prein and Gobiet 2017 Kotlarski et al. 2019), for which temperature and precipitation are most often used (Perkins et al. Furthermore, the availability of reliable high-quality observational data is important for model evaluation (Kotlarski et al. Observations are used during the model development phase, but model calibration and initialisation also often heavily rely on observational datasets (e.g. It is also suitable to find dataset errors, which is also exemplified in this paper.Ĭlimate researchers use general circulation models (GCMs) and regional climate models (RCMs) to improve our understanding of the climate system. The evaluation method can be applied to other datasets, different time periods and areas. Nevertheless, there are significant differences in the results depending on which observational dataset was used concerning precipitation. By the RE metric, RegCM has improvement against the LBCs over mountains for temperature and areas with dense station network for precipitation. The results show that CarpatClim is wetter than E-OBS, while temperature is similar over the lowland however, E-OBS is significantly warmer than CarpatClim over the mountains.
The method is applied to two observational datasets (CarpatClim and E-OBS) and to RegCM simulations for 2010, the wettest year in this region since 1901. We focused on the Carpathian region, because of its unique orographic and climatic conditions. Furthermore, we used a special metric, the reduction of error ( RE) to identify where the RCM shows improvement compared to the lateral boundary conditions (LBCs). correlation analysis and permutation test, was carried out. To assess these uncertainties, a complex analysis based on various statistical tools, e.g. elevation, variability of elevation, effect of station), which are considered as uncertainty sources. Besides precipitation and temperature, our method uses geographic variables (e.g.
This work introduces a novel method to quantify the uncertainties in the observational datasets and how these uncertainties affect the evaluation of RCM simulations. However, the uncertainty of observations affects the evaluation. Gridded observational datasets are often used for the evaluation of regional climate model (RCM) simulations.
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