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Assessment of Climate Change in Europe from an Ensemble of Regional Climate Models by the Use of Koppen–Trewartha¨ Classification

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Assessment of Climate Change in Europe from an Ensemble of Regional Climate Models by the Use of Koppen–Trewartha¨ Classification INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 33: 2157–2166 (2013) Published online 11 September 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3580 Assessment of climate change in Europe from an ensemble of regional climate models by the use of Koppen–Trewartha¨ classification Clemente Gallardo,a* Victoria Gil,b Edit Hagel,b Cesar´ Tejedab and Manuel de Castroa,b a Instituto de Ciencias Ambientales, Universidad de Castilla-La Mancha, Toledo, Spain b Instituto Meteorol´ogico Regional de Castilla-La Mancha, Toledo, Spain ABSTRACT: Through the use of the climatic classification of Koppen–Trewartha¨ (K-T), the ability to reproduce the current climate of Europe has been shown for an ensemble of 15 regional climate model simulations nested in six global climate models. Depending on the simulation, between 55.4 and 81.3% of the grid points are in agreement with observations regarding the location of climate types in current climate simulations (1971–2000). In this respect, the result of the ensemble of 15 simulations is better than that of any individual model, with 83.5% of the grid points in agreement with observations. K-T classification has also been used to analyse the projected climate change over the 21st century under the SRES-A1B emissions scenario. It was found that 22.3% of the grid points in the domain change their climate by the period 2021–2050 compared to current climate and 48.1% change by 2061–2090. The climate shifts affecting the biggest extensions are projected in Central Europe and Fennoscandia, but other smaller areas suffer more intense changes which potentially are more dangerous to vegetation and ecosystems. Generally, these changes occur at a sustained rate throughout the century, reaching speeds of up to 90 × 103 km2 decade−1 in the retreat or expansion of some climates. KEY WORDS climate classification; climate change; regional climate models; ensemble Received 21 February 2012; Revised 15 July 2012; Accepted 30 July 2012 1. Introduction been gathered over the last 100 years or so, and the outputs of the climate models for past, present or future A climate-vegetation scheme, like the Koppen¨ climate periods. classification (Koppen,¨ 1936) or its improvements (Tre- Within the field of the study of climate change, the wartha and Horn, 1980) is a complex system of climates, Koppen¨ climate classification and its variants have been which is based on the two variables most frequently used by several authors. Lohmann et al. (1993) used used in climate studies: precipitation and temperature. the Koppen¨ classification to check whether a GCM The categories or types of these classifications are not was able to reproduce the present day climate and to only related to the different climates that exist on the analyse how the main climate regions could change as a Earth, but they are structurally related to the potential result of global warming. Leemans et al. (1996) analysed vegetation of each zone, and are also indirectly related the global biome distribution by applying the Koppen¨ to the feasible crops and ecosystems. These relationships method to the output of four GCMs. Kleidon et al. (2000) allow us not only to establish a projection of the future estimated the effect of vegetation on the global climate changes in the climate, but also to give a basic estimation by performing several climate model simulations and of the possible effects on the natural vegetation, crops and then applying the Koppen¨ classification to illustrate the ecosystems. differences among them. Jylha¨ et al. (2010) used the AKoppen-like¨ climate classification has two additional traditional Koppen¨ classification to study climate trends advantages. First, it can be applied practically everywhere in Europe with a set of 19 GCMs. Feng et al. (2012) on the planet, as the temperature and precipitation data assessed current and future climate changes in the Arctic are available almost anywhere over the globe. Second, from the output of 16 GCMs. these variables are also part of the standard output of The Koppen¨ classification has also been applied to global climate models (GCMs) and regional climate the output of RCMs in order to evaluate climatic refuge models (RCMs). Owing to these properties, the Koppen¨ for the People’s Republic of China (Baker et al., 2010), methodology can be applied to track past and future assess the possible increase of aridity caused by the late changes in the climate, using the observations that have 21st century climate change in the Mediterranean region (Gao and Giorgi, 2008), quantify the potential impact of climate change on ecosystems of the Barents Sea Region ∗ Correspondence to: C. Gallardo, Instituto de Ciencias Ambientales, Universidad de Castilla-La Mancha, Avda. Carlos III s/n, 45071 Toledo, (Roderfeld et al., 2008) and estimate the climate change Spain. E-mail: [email protected] effects in Europe (Castro et al., 2007). 2012 Royal Meteorological Society 2158 C. GALLARDO et al. The Koppen¨ classification was also used to characterize 2. The observed present day K-T climate the climate of certain regions (Baltas, 2007), or to detect distribution the 20th century climate change in the Arctic region The Koppen¨ climate classification and its variants are the (Wang and Overland, 2004), in the United States (Diaz most widely used climate classification systems. In the and Eischeid, 2007) or in Europe (Gerstengarbe and present study, an improvement of the original system, the Werner, 2009). K-T (Trewartha and Horn, 1980) climate classification In this work, the outputs of 11 high-resolution RCMs (Table I) was used. were used to reproduce the current climate in Europe and The K-T classification was applied to monthly mean the Mediterranean area and to assess the possible magni- temperature and precipitation data derived from the E- tude of future climate change under SRES-A1B emission OBS data set (Haylock et al., 2008) of the European scenario. Regarding the concentrations of equivalent CO2 Climate Assessment & Dataset (ECA&D) project. In the over the 21st century, the A1B scenario is intermediate present study, version 3.0 of E-OBS data, released in for both the SRES scenarios group and the new RCP April 2010, was used on a 0.25° regular latitude/longitude scenarios. The uncertainty associated with the emissions grid for the period 1971–2000. All but four K-T subtypes scenario has not been explored in this work, but the use of (Ar, Aw, Cw and FI) were present in Europe and an extreme scenario has been avoided. The applied RCMs the Mediterranean area (Figure 1(a)). The climate types were driven by six GCMs, resulting in a total of 15 sim- covering the largest part of Europe are DO (temperate ulations. This makes a difference to other RCM-based oceanic) and DC (temperate continental). Subtropical works, where only one GCM is considered, by providing climates (Cs and Cr) can be found mainly south of ° boundary conditions to only one or several RCMs. This 45 N (except for the coastal areas of western France), means that the analysis presented in this study is more while sub-arctic and polar climates (EO, EC and FT) are ° robust, because, in addition to using an ensemble of sev- located approximately north of 60 Naswellasinthe eral RCMs, it also incorporates the uncertainty related to Alps, as there is no separate alpine climate in the K-T classification. the GCMs. Unlike some previous works (Castro et al., When calculating the K-T climate types, a much 2007), in this study the whole period of 1961–2090 was localized feature was seen over the north of Romania considered, which made it possible to analyse the ten- st (not shown). While in the surrounding areas the DC dency of the changes throughout the 21 century. climate type was dominant, in about 50 grid points The study is organized in the following way. In BW and BS types were detected. It was found that Section 2, a brief description of Koppen–Trewartha¨ (K- this behaviour was caused by anomalous data in the T) climate classification scheme and its observed present E-OBS data set for these few grid points. To further day distribution is shown. In Section 3, the climate investigate this issue, the K-T classification was applied simulations are described. In Section 4, the evolution to monthly mean temperature and precipitation data from of the climate in Europe and the Mediterranean area is the CRU data set (on 0.5° and 10 resolution as well; analysed. Finally, some concluding remarks are presented Mitchell et al., 2004). The above-mentioned feature did in Section 5. not appear in the K-T distribution obtained from the Table I. The Koppen–Trewartha¨ climate classification. Climate type Description Classification criteria Ar Tropical humid All months above 18 °C and less than 3 dry monthsa Aw Tropical wet-dry Same as Ar but 3 or more dry months BW Dry arid Annual precipitation P (cm) ≤0.5 Ab BS Dry semiarid Annual precipitation P (cm) > 0.5 A but smaller or equal than A Cs Subtropical summer-dry 8–12 months above 10 °C, annual rainfall <89 cm and dry summerc Cw Subtropical summer wet Same thermal criteria as Cs, but dry winterd Cr Subtropical humid Same as Cw, with no dry season DO Temperate oceanic 4–7 months above 10 °C and coldest months above 0 °C DC Temperate continental 4–7 months above 10 °C and coldest months below 0 °C EO Sub-arctic oceanic Up to 3 months above 10 °C and temperature of the coldest month above −10 °C EC Sub-arctic continental Up to 3 months above 10 °C and temperature of the coldest month ≤−10 °C FT Tundra All months <10 °C FI Ice cap All months below 0 °C a Dry month: <6 cm monthly precipitation.
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