Monthly series from 7 Global Climate Models (GCMs) were used to estimate forthcoming changes in global solar radiation, precipitation amount, daily average temperature, and daily temperature range in the Czech region. Scenarios were constructed using the pattern scaling technique: the standardised scenario, which relates the climate variable responses to a 1°C rise in global mean temperature (T G ), was multiplied by the predicted change (ΔT G ). The standardised scenarios were determined from the GCM runs, ΔT G values were calculated by the simple climate model MAGICC. Two groups of uncertainties were analysed: (1) uncertainties in the standardised scenario, with (1a) inter-GCM variability, (1b) internal GCM variability, (1c) uncertainty due to the choice of the site (within the Czech territory), (1d) uncertainty involved in the regression technique; (2) uncertainties in ΔT G , with (2a) choice of the emission scenario, (2b) value of the climate sensitivity factor. In the case of Group 1, (1a) dominated, (1b) was in some cases similar to (1a), and (1c) was nearly negligible; regression uncertainty (1d) indicated that the climate variable changes are often statistically insignificant. In the case of Group 2, uncertainty due to climate sensitivity (2b) dominated for the nearest future, but uncertainty in emission scenarios (2a) attained greater importance later in the 21st century. The mean magnitude of the effect of aerosols on changes in temperature and precipitation was mostly lower than its inter-GCM variability, which was lower than (in the case of the temperature changes) or similar to (in the case of precipitation) the inter-GCM uncertainty in greenhouse gas (GHG) simulations. A stochastic model was developed to assess the combined effect of inter-GCM uncertainty, regression uncertainty, and uncertainty in ΔT G . While the overall uncertainty in the temperature scenarios was dominated by inter-GCM uncertainty and ΔT G uncertainty, the aggregated uncertainty in the precipitation scenarios was dominated by inter-GCM uncertainty only. KEY WORDS: Climate change scenarios · Uncertainty analysis · Global climate models · Pattern scalingResale or republication not permitted without written consent of the publisher Editorial responsibility: Claire Goodess,

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The computer-assisted classification of weather at Prague-Clementinum used the average linkage clustering technique. Since the results of the clustering exhibit the snowballing effect, the usual methods of determining the threshold aggregation level (i.e. the level at which the clustering procedure is to be terminated) appeared to be inapplicable. A new method based on Monte Carlo simulations of the means was developed. Its key idea is the termination of the clustering procedure at different aggregation levels in different parts of the data set. This ensures that the number of resultant clusters is reasonable, while minimizing the numbers of very small clusters and unclustered days.The weather categorization resulted in 44 clusters for 14 winters in the period 1965-1978. Thirty-one of the clusters had sizes of 5 or more days. The Monte Carlo scores, comparing the means and variances of the clusters with those of a large number of subsets chosen randomly, indicate that all the resulting clusters represent meaningful weather types.This study may provide a basis for nucleated clustering, which will enable us to deal with longer data series and to study the long-term trends of the properties of weather types.

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