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.

Abstract. This paper presents three indices for evaluation of hydrometeorological extremes, considering them as areal precipitation events and trans-basin floods. In contrast to common precipitation indices, the weather extremity index (WEI) reflects not only the highest precipitation amounts at individual gauges but also the rarity of the amounts, the size of the affected area, and the duration of the event. Furthermore, the aspect of precipitation seasonality was considered when defining the weather abnormality index (WAI), which enables the detection of precipitation extremes throughout the year. The precipitation indices are complemented with the flood extremity index (FEI) employing peak discharge data. A unified design of the three indices, based on return periods of station data, enables one to compare easily interannual and seasonal distributions of precipitation extremes and large floods.The indices were employed in evaluation of 50 hydrometeorological extremes of each type (extreme precipitation events, seasonally abnormal precipitation events, and large floods) during the period 1961-2010 in the Czech Republic. A preliminary study of discrepancies among historic values of the indices indicated that variations in the frequency and/or magnitude of floods can generally be due not only to variations in the magnitude of precipitation events but also to variations in their seasonal distribution and other factors, primarily the antecedent saturation.

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.

We used the Köppen-Trewartha classification on the CMIP5 family of global climate model (GCM) simulations and global Climatic Research Unit (CRU) data for comparison. This evaluation provides preliminary insight on GCM performance and errors. For the overall model intercomparison and evaluation, we used 2 simple statistical characteristics: normalized error, which assesses the total relative difference of the area classified by the individual model with respect to the area resulting from CRU data, and overlap, calculating relative area of matching grid boxes in model results and CRU data. With the additional analysis of the classification on world maps, we show that there are some common features in the model results. Many models have problems capturing the rainforest climate type Ar, mainly in Amazonia. The desert climate type BW is underestimated by as many as half of the models, with Australia being a typical example of a region where the BW is not well represented. The boreal climate type E is overestimated by many models, mostly spreading over to the areas of observed tundra type Ft. All applied metrics indicate that with the current generation of GCMs, there is no clear tendency for models to improve the representation of climate types with higher spatial resolution.

RCM simulations captured the basic seasonal dependence of the T‐P relationship over Europe. The simulated T‐P relationship pattern is more strongly influenced by an RCM than a driving GCM. The simulations of the CCLM4‐8‐17 RCM demonstrated noticeably better agreement with observed T‐P relationship fields than the RCA4 RCM simulations. The T‐P relationship simulated by RCA4 tends to shift to positive values in comparison to E‐OBS. These findings were found for both TPI and T‐P correlation.