The analysis of climate patterns can be performed separately for each climatic variable or the data can be aggregated, for example, by using a climate classification. These classifications usually correspond to vegetation distribution, in the sense that each climate type is dominated by one vegetation zone or eco-region. Thus, climatic classifications also represent a convenient tool for the validation of climate models and for the analysis of simulated future climate changes. Basic concepts are presented by applying climate classification to the global Climate Research Unit (CRU) TS 3.1 global dataset. We focus on definitions of climate types according to the Köppen-Trewartha climate classification (KTC) with special attention given to the distinction between wet and dry climates. The distribution of KTC types is compared with the original Köp-pen classification (KCC) for the period 1961−1990. In addition, we provide an analysis of the time development of the distribution of KTC types throughout the 20th century. There are observable changes identified in some subtypes, especially semi-arid, savanna and tundra.

Climate classifications can provide an effective tool for integrated assessment of climate model results. We present an analysis of future global climate projections performed in the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5) project by means of Köppen-Trewartha classification. Maps of future climate type distributions were created along with the analysis of the ensemble spread. The simulations under scenarios with representative concentration pathway (RCP) 4.5 and RCP8.5 showed a substantial decline in ice cap, tundra, and boreal climate in the warming world, accompanied by an expansion of temperate climates, dry climates, and savanna, nearly unanimous within the CMIP5 ensemble. Results for the subtropical climate types were generally not conclusive. Changes in climate zones were also analyzed in comparison with the individual model performance for the historical period 1961−1990. The magnitude of change was higher than model errors only for tundra, boreal, and temperate continental climate types. For other types, the response was mostly smaller than model error, or there was considerable disagreement among the ensemble members. Altogether, around 14% of the continental area is expected to change climate types by the end of the 21st century under the projected RCP4.5 forcing and 20% under the RCP8.5 scenario.

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,

Temperature variability in the Czech Republic is analysed by means of wavelet transforms. This advanced time-frequency analysis provides information about the nature and time-frequency localization of present oscillations. The data set comprises four mean monthly temperature series from Prague-Klementinum, Brno, Mt Milešovka and the gridded temperature series for the Czech Republic. The results of their wavelet transforms are presented as (a) trend analysis (computed by the inverse wavelet transform) and (b) periodicities and oscillations (distinguishable from the series wavelet power spectra and global wavelet spectra). The results show considerable similarity among individual series. Examples are the parts of the wavelet power spectra in the time period between 1930 and 2001 expressed in all series studied, the pronounced oscillations of about 8 and 12-14 years in all series, and the noticeable increase of temperatures in all cases. As a supplement to this study, wavelet transforms were performed on the variance-adjusted version of combined land and marine temperature anomalies for the Northern Hemisphere from the Hadley Centre. Only some results of this analysis are mentioned and discussed.

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