There are many unsolved questions about the future of climate, and most of them are due to lack of knowledgeabout the complex system of atmosphere, but still there are models that produce relatively realistic projectionsof the future although there are uncertainties in the presentation of them, and that's where statistical methodscould be of help. Here a short introduction is given to the projection of future climate with GCM ensembles andthe uncertainties about them, the emerging probabilistic approach, as well as the REA (Reliability EnsembleAverage) method for measuring the reliability of the model projections. In order to have an impression of theresults of the GCM ensemble results and their uncertainties the results of the weather forecast over a time periodof one year in three dierent cities of Sweden is studied as well.

Title: Uncertainties in regional climate models outputs Author: RNDr. Eva Holtanová Supervisor: doc. RNDr. Jaroslava Kalvová, CSc. Department: Dept. of Meteorology and Environment Protection Faculty of Mathematics and Physics Charles University in Prague Present doctoral thesis focuses on the analysis of uncertainties in regional climate model outputs in the area of the Czech Republic. Generally, the uncertainties in model outputs come from inaccuracies of initial and boundary conditions, further from the necessity to parameterize the small scale processes, and the structure of the model, e.g. the choice of numerical schemes or spatial resolution. In case of the simulations of future climate, another source of uncertainty arises. It is the unknown development of forcings that influence the climate system. The analysis in this work focuses on two multi-model ensembles, that come from two international European projects PRUDENCE and ENSEMBLES. The simulated 30-year mean seasonal air temperature and precipitation amounts are used, for the reference period 1961- 1990, and several future time periods. Two techniques were employed to assess the uncertainties. The first one was aimed at dividing the variance of a multi-model ensemble into contributions of regional model, driving global model and emission...