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

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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).

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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. b A = 2.3 T − 0.64 Pw + 41, being T , the mean annual temperature (° C) and Pw, the percentage of annual precipitation occurring in the coolest 6 months. c Dry summer: the driest summer month <3 cm precipitation and less than one-third of the amount in the wettest winter month. d Dry winter: precipitation in the wettest summer month higher than 10 times that of the driest winter month.

 2012 Royal Meteorological Society Int. J. Climatol. 33: 2157–2166 (2013) USE OF A KOPPEN¨ CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE 2159

(a) (b)

Figure 1. Koppen–Trewartha¨ climate types distribution for (a) the E-OBS data set and (b) the ensemble mean of the RCM simulations. Both for the reference period (1971–2000). The thick black line shows the boundary of the domain DOM 1. The area of the map with colours other than white deﬁnes the domain DOM 2.

CRU data, and, therefore, it was decided to manually the largest possible common domain (hereafter DOM 1) change the climate type of the grid points in question was deﬁned. Only those points that are inside the com- from BW/BS to DC (the climate subtype of these grid mon domain were taken into account. As the common points obtained from the CRU data set) in the E-OBS- domain excludes the north of Scandinavia, a larger addi- based K-T distribution, and use this modiﬁed data for any tional area was also selected (hereafter DOM 2). In this further analysis. case, the number of simulations available (11) is less than for the other domain (15). The reason why two different domains (Figure 1) are used is that by using the 3. Climate model simulations maximum common domain (DOM 1) in some analysis For the following analysis, data from the ENSEMBLES more RCMs could be included (15 simulations), with the project (Hewitt and Griggs, 2004) were used. The applied consequent increase in the robustness of the results. The RCMs were driven by a variety of GCMs under the broader domain (DOM 2) allows us to analyse the evo- SRES-A1B emission scenario (Nakicenovi´ c´ and Swart, lution of climate in a region like northern Fennoscandia, 2000). A short overview of these models is given below, which is very sensitive to climate change. The other cri- followed by a brief description of the experiments. The terion was the time period covered. As one of the main respective institutions, model names and acronyms are goals was to analyse the tendency of the changes in the listed in Table II. K-T distribution throughout the 21st century, only those When selecting the RCM simulations to be used, two simulations covering the entire 1961–2090 time period main issues had to be considered: spatial and time cover- were considered; the rest were excluded. The period age. As each of the RCMs covers a slightly different area, 1961–2090 was a compromise solution, instead of the

Table II. Regional climate models (RCMs) from which data have been analysed.

Acronyms Institute Model Driving GCM (see Table III) Source

C4I Met Eireann, Ireland RCA3 HadCM3Q16 Kjellstrom¨ et al. (2005) CNRM Met´ eo-France´ RM4.5 ARPEGE Radu et al. (2008) DMI Danish Meteorological Institute HIRHAM5 ECHAM5-r3, BCM, ARPEGE Christensen et al. (2006) ETHZ Swiss Institute of Technology CLM HadCM3Q0 Bohm¨ et al. (2006) ICTP The Abdus Salam International RegCM3 ECHAM5-r3 Giorgi and Mearns (1999) Centre for Theoretical Physics KNMI The Royal Netherlands RACMO2 ECHAM5-r3 van Meijgaard et al. (2008) Meteorological Institute HC-Q0 UK Met Ofﬁce, Hadley Centre HadRM3Q0 HadCM3Q0 Collins et al. (2011) HC-Q3 HadRM3Q3 HadCM3Q3 HC-Q16 HadRM3Q16 HadCM3Q16 MPI Max Planck Institute for REMO ECHAM5-r3 Jacob (2001) Meteorology SMHI Swedish Meteorological and RCA3.0 ECHAM5-r3, BCM, HadCM3Q3 Kjellstrom¨ et al. (2005) Hydrological Institute

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Table III. GCMs that drive the simulations of the RCMs. 4. Results 4.1. Evaluation of the RCM runs for the reference Acronyms Institute Source period HadCM3a UK Met Office, Hadley Gordon et al. The monthly values of 2 m temperature and precipitation Centre (2000) were averaged over the control period (1971–2000) ARPEGE Met´ eo-France´ Gibelin and and over the whole set of simulations. Then, the K- Dequ´ e´ (2003) T climate subtypes corresponding to these mean fields ECHAM5-r3 Max Planck Institute for Roeckner et al. were determined on each grid point (Figure 1(b)). These Meteorology (2003) fields of average K-T subtypes are the ensemble mean of BCM University of Bergen, Furevik et al. Norway (2003) reference (EMR). K-T subtypes were also calculated for the observational database E-OBS for the same period a For this GCM, three different versions (Q0, Q3 and Q16, see Table II) (Figure 1(a)). were run with differing climate sensitivities. A grid to grid comparison of the individual simulations and the EMR with the E-OBS database has been done through the development of co-occurrence matri- longer 1951–2100 interval, as some of the simulations ces. These matrices show the correspondences between started/finished a few years later/earlier. K-T subtypes of the E-OBS climatology and every simu- The GCMs driving the RCMs simulations are listed lation (not shown) or the EMR (Table IV) for the period in Table III. The three HadCM3 simulations were based 1971–2000. A total of 15 847 grid points with data pro- on the same model but with different parameter setting, vided by E-OBS were analysed in the DOM 1 domain. in order to obtain different climate sensitivities (Murphy For a better interpretation of the co-occurrence matrices et al., 2007). the following points should be remembered: The K-T climate classification was applied to the above-mentioned climate simulations, both for the present • The main diagonal of each matrix indicates the number day climate (1971–2000) and for the future over 30 year of grid points where the K-T subtype according to periods overlapped for 20 years. The K-T climate types the E-OBS database matches that generated from a were calculated for both domains (DOM 1 and DOM 2). simulation or the EMR. All the tables presented in the article are based on • A location outside the main diagonal of the matrix the results of the common domain (DOM 1 with a indicates lack of coincidence. A larger separation from 15 member ensemble), while the figures are compos- the diagonal indicates larger differences between the ites of the results obtained for the two areas (except climatology and the simulations. for Figures 3 and 4, which are based on the results of • Nonzero elements below the main diagonal indicate DOM 1). that the simulations are warmer or drier than E-OBS. The modelled K-T distribution for the reference period • Conversely, nonzero elements above the main diagonal was compared over land points to the one derived from indicate that the simulations are cooler or wetter than the E-OBS data to check how the RCMs simulate present E-OBS. day climate. On the other hand results for the future were compared to that of the reference period to detect RCM simulations reproduce the K-T subtypes of E- possible future changes in the climate distribution over OBS quite well in most of the cases. The percentage of Europe and the Mediterranean area. Results of this study coincidences of subtypes ranges from 55.4 to 81.3% and are presented in Section 4. is over 70% in 10 of the 15 RCM simulations analysed

Table IV. Co-occurrence matrix between EMR and E-OBS for DOM 1 domain.

Ensemble mean (1971–2000) BW BS Cs Cr DO DC EO EC FT

E-OBS (1971–2000) BW 13 2 1 0 0 0 0 0 0 BS 84 69 59 10 62 0 0 0 0 Cs 3 109 713 127 212 0 0 0 0 Cr 0 1 8 79 63 0 0 0 0 DO 0 2 11 22 3487 194 46 0 0 DC 0 0 0 1 647 5655 251 87 0 EO 0 0 0 0 0 8 878 279 107 EC 0 0 0 0 0 0 21 2038 190 FT 0 0 0 0 0 0 6 0 302

Columns contain the number of grid points of the land domain that correspond to each of the K-T subtypes according to the EMR. Rows contain the number of grid points for each K-T subtype following the climatology of the E-OBS database.

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Table V. Percentage of the total studied grid points located on the main diagonal of the co-occurrence matrices for each analysed GCM-RCM pair for the domain DOM 1.

GCM RCM ECHAM5 HadQ0 HadQ3 HadQ16 BCM ARPEGE

C4I 75.75 CNRM 79.98 KNMI 80.41 SMHI 80.29 68.26 69.38 MPI 73.61 ETHZ 80.58 HC 81.32 74.88 79.72 DMI 65.32 55.41 72.52 ICTP 69

(Table V). The EMR has 83.5% of the grids in the main grid points with EC and EO subtypes in the mountainous diagonal of the matrix of co-occurrence, more than any areas of Norway and Sweden and the Kola Peninsula of the simulations used for its formation. The EMR has in Russia. This is caused by the tendency of models to 10.7% of the points above the main diagonal and 5.8% produce cooler summers in this area which is the key below; this means that it has a slight cold bias. These factor to distinguish between types E and F of K-T. ﬁgures are in line with those reported by Castro et al. It is also noteworthy that EMR shows 32% less grid (2007) and indicate that the EMR performs better than points for the subtype Cs, owing to the appearance of all the models that constitute it. The explanation for this DO subtype in the central area of the Iberian Peninsula result is not obvious and would need further research to and Cr and BS subtypes in different Mediterranean clarify it. coastal areas. This assignment of DO instead of Cs is A large part of the differences in K-T subtypes between due to mismatches in the simulation of the temperature EMR and E-OBS is due to a signiﬁcant number of grids in the transitional seasons, while Cr and BS cases are that belong to the DC subtype for E-OBS and correspond attributable to differences in precipitation. to the subtype DO for the EMR (Table IV). This result was already observed by Castro et al. (2007), but this 4.2. Climate change scenario simulations time the border between DC and DO from north to The K-T climate subtypes were calculated for consecutive south across Central Europe is fairly well outlined by the and overlapping 30 year periods between 1961 and RCMs (Figure 1) and differences focus on the eastern 2090 (1961–1990, 1971–2000, 1981–2010 and so on side of the Danube basin and the Crimean Peninsula. to 2061–2090) for the individual RCM simulations and This tendency of the models to establish as DO some for the ensemble mean. Calculations were performed grids that E-OBS classify as DC is the most striking, but using the monthly averages of 2 m temperature and the relative error of other simulated subtypes is larger. precipitation over land points only. Because of the large For instance, the number of grid points with the subtype number of models, only the results obtained with the FT for the EMR is 94.5% more abundant than in E-OBS. ensemble mean are shown in this part of the analysis, for This increase comes at the expense of the decrease in two intervals: 2021–2050 (Figure 2(a)) and 2061–2090

(a) (b)

Figure 2. Koppen–Trewartha¨ climate type distribution for the ensemble mean of the RCM simulations for the period (a) 2021–2050 and (b) 2061–2090. The thick black line shows the domain DOM 1. The area with colours other than white deﬁnes the domain DOM 2.

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(a) (b)

Figure 3. Projected climate type transitions for the ensemble mean during (a) the 2021–2050 period and (b) 2061–2090 period. Both ﬁgures with respect to the reference period (1971–2000).

(Figure 2(b)). In order to facilitate the detection of climate changes projected are the retreat of the EC areas where the climate simulations project changes with climate in Finland, Sweden and western Russia and respect to the reference period (1971–2000), in Figure 3 the DC subtype in central Europe. These projections fit only these grid points are plotted. Comparing these reasonably well with observed climate transitions from figures with that of the reference period (Figure 1(b)) the the period 1950–1978 to the period 1979–2006 (Jylha¨ following important changes can be observed: et al., 2010). In the following two periods (2001–2030 and 2011–2040) the most striking projected transitions Northeastward shift of the boundary between DO and occur to the east of the Kola Peninsula in Russia (FT-EC), DC climate types. By the end of the 21st century the western France (DO-Cr), in southern Finland (EO-DC) DC type withdraws drastically to the northeast. The and north of Black Sea (DC-DO). DO type is extending in eastern Europe, but at the For a deeper analysis, Tables VI and VII contain same time is loosing territory in western Europe. the co-occurrence matrices (domain DOM 1) for the Climate types EO, EC and FT in Fennoscandia with- ensemble mean of the climate scenario runs. These draw drastically to the north. By 2021–2050 the area matrices express the point-by-point agreement between covered by EC and FT types is significantly reduced, the climate types of the reference period (1971–2000) and by 2061–2090 the EC and FT types almost com- and the periods 2021–2050 (Table VI) and 2061–2090 pletely disappear from northern Europe. (Table VII). Cs and Cr gain more area in southern and western The portion of land area shifting from the current K- Europe, especially by the end of the 21st century. T climate type to a warmer or drier one is 21.3% for The BS type gains area in southeast Spain, Italy, 2021–2050 and 45.2% for 2061–2090. This means that Greece, Turkey and the coastal zones of northern by the end of the 21st century almost half of the studied Africa. area will undergo a climate type change. It is important to notice that there are no entries above the main diagonal. Other figures similar to Figure 3, but for 1981–2010 Some unexpected differences between the values in and 1991–2020 (not shown), reveal that the first clear Tables VI and VII and in Table IV arise because the

Table VI. Co-occurence matrix for the ensemble mean of the RCM simulations for the time interval 2021–2050 (domain DOM 1) with respect to the reference period (1971–2000).

Ensemble mean (2021–2050) BW BS Cs Cr DO DC EO EC FT

Ensemble mean (1971–2000) BW 1170.9 0 0 0 0 0 0 0 0 BS 80.4 276.9 0 0 0 0 0 0 0 Cs 0 77.0 792.9 0 0 0 0 0 0 Cr 0 13.2 46.0 204.0 0 0 0 0 0 DO 0 29.0 151.4 222.5 2295.9 0 0 0 0 DC 0 0 0 0 785.7 2119.3 0 0 0 EO 0 0 0 0 31.6 200.4 304.3 0 0 EC 0 0 0 0 0 117.1 230.2 457.4 0 FT 0 0 0 0 0 0 65.2 44.4 122.8

Areas in 103 km2.

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Table VII. Co-occurence matrix for the ensemble mean of the RCM simulations for the time interval 2061–2090 (domain DOM 1) with respect to the reference period (1971–2000).

Ensemble mean (2061–2090) BW BS Cs Cr DO DC EO EC FT

Ensemble mean (1971–2000) BW 1170.9 0 0 0 0 0 0 0 0 BS 202.2 155.0 0 0 0 0 0 0 0 Cs 0 215.9 654.0 0 0 0 0 0 0 Cr 0 30.4 78.3 154.5 0 0 0 0 0 DO 0 78.8 376.9 617.1 1625.9 0 0 0 0 DC 0 0.6 0 0 1478.8 1425.6 0 0 0 EO 0 0 0 0 85.2 327.4 123.7 0 0 EC 0 0 0 0 0 353.1 424.8 26.7 0 FT 0 0 0 0 0.3 2.1 146.4 28.7 54.8

Areas in 103 km2.

E-OBS data do not cover the entire area simulated by Figure 4 shows a schematic plot of these projected the RCM. This is especially evident for BW subtype, as transfers between the different K-T subtypes, also E-OBS does not provide data in 1 678 grid points in indicating the sign of the net change of area of the given North Africa where such a climate is simulated by the subtype. It can be observed that FT, EC and DC are pro- models. jected to undergo a net loss of area, while the net result of The largest changes can be observed in the current the changes in the remaining subtypes (EO, DO, Cr, Cs, climate types DO, DC and EC. BS and BW) is projected to be positive. This indicates that the projection points to a decrease in the diversity of climates in Europe, with potential consequences on The DO type is gaining territory mainly from DC, and natural ecosystems and crops. to a much smaller extent from EO. On the other hand, It is also interesting to see how many grid points DO is loosing area to BS, Cs and Cr types, among undergo a change from one main climate group (A, B, which DO-Cr is the most dominant shift, followed by C, D, E and F) to another. Compared to the reference DO-Cs. period of 1971–2000, 9.7% of the total area experiences The net results of the changes in the DC and EC such a change by 2021–2050. By 2061–2090 the area covered areas are both negative. The DC subtype affected by this change increases to 23.0%. The grid is loosing territory to DO, but also gaining (though points with main climate group change (i.e. the areas with considerable less) from EO and EC. The EC subtype potentially more dangerous ecosystem changes) can be gains some area from FT, but looses to DC and EO. found in western France, Alps, Scotland, Fennoscandia, northwest of Russia, Iceland, several regions of southern It is also important to mention that the BW and BS Europe and coastal areas of Wales, Ireland and southern subtypes are both predicted to increase their area with England. respect to the control period. The BW subtype (dry arid Figure 5 shows the evolution of K-T climates over or desert) appears over a much localized area in the time. It can be seen that, in general, the climates DC, southeast of Spain, while the BS subtype (dry semiarid or EC and FT, that is three of the four colder climates, steppe) gains more area in Spain, Italy, Greece, Turkey lose area progressively. BW, BS, Cs, Cr and DO (the and northern Africa. warmest climates in Europe) increase their area. EO

Figure 4. Transfers between the different K-T climate subtypes. The + or − signs (in parentheses) indicate a net gain or loss of area, respectively. The numbers on the arrows give the net area (103 km2) that undergoes the given transition. First number is for the period 2021–2050, while the second number is for 2061–2090.

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Figure 5. Evolution of K-T climatic subtypes through the increase/decrease over the period 1971–2000 of the area occupied by each subtype climate. In the x-axis the 11 time periods analysed are represented, while the y-axis shows the variation of the occupied area in units of 103 km2. The black line represents the evolution of the ensemble, while the grey band represents the spread of the area covered by the models. For obtaining this band the two models with lowest values and the two models with highest values were discarded. The 11 time periods are: (1) 1961–1990, (2) 1971–2000, (3) 1981–2010, (4) 1991–2020, (5) 2001–2030, (6) 2011–2040, (7) 2021–2050, (8) 2031–2060, (9) 2041–2070, (10) 2051–2080 and (11) 2061–2090. subtype has a special behaviour, as EO shows an area The two climates with the largest areas (DO and DC) gain for the ensemble in the second half of the 21st are also those with the widest band in Figure 5 and century which is bigger than in any RCM. This is because therefore with the highest degree of spread in square the threshold criterion between EO and EC refers to a kilometers between the different models. However, if we temperature of the coldest month above or below −10 °C relate this spread in the period 2061–2090 to the extent (see Table I). As the minimum temperatures in some grid of these climates in the EMR, it represents only 38% points do not coincide in the same month for all the RCM for DO and 22% for DC. The Cr subtype also has a simulations, the ensemble could have its coldest month large spread in square kilometers despite not occupying above −10 °C although most RCMs do not. Therefore, a large area; this implies a relative spread of 192%. in the border areas between these two subtypes there are The difference in square kilometers is moderate for some grid points in the ensemble that are EO, while in intermediate extension climates (EC, EO and Cs) and most simulations they are EC. As a result, at the end of small for the rest of the climates that are not widespread studied period, EC climate in the ensemble has decreased (FT, BS and BW). The relative difference for the subtype more than in any of the RCMs. EO stands out from the rest of the last six subtypes as It is also remarkable that for the subtype Cr the it reaches 72%, while for the others it is always below ensemble shows more increases than most of the RCMs 40%. For all subtypes, the largest spread occurs in the (Figure 5). This is mainly because, owing to the crite- last periods. ria that deﬁne these climates, in the peripheral regions Looking at the temporal evolution of the ensemble of subtype Cs one or a few simulations that are exces- (Figure 5) six out of nine existing subtypes show a sively wet in summer or dry in winter can lead to an roughly constant growth (BW, BS and Cs) or reduction ensemble mean with subtype Cr, even while a majority (DC, EC and FT). On the other hand, Cr has a tendency of simulations show a Cs climate. to accelerate the increase in expanse. The general trend

 2012 Royal Meteorological Society Int. J. Climatol. 33: 2157–2166 (2013) USE OF A KOPPEN¨ CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE 2165 of the ensemble to a monotonous evolution is not always regions of southern Europe and coastal areas of Wales, found in all simulations. Examples of this can be easily Ireland and southern England. seen (Figure 5) in the graphs of the subtypes DO, DC and EO, but also exist for other climates. Acknowledgements Another important aspect is the rate at which the different subtypes of climate expand or contract. The The ENSEMBLES model data used in this work was two coldest subtypes in the studied domain (FT and EC) funded by the EU FP6 Integrated Project ENSEM- decrease their joint area, on average and in net value, at BLES (Contract GOCE-CT-2003-505539) whose sup- a rate slightly over 100 × 103 km2 decade−1. Subtropical port is gratefully acknowledged. We also acknowledge climates (Cs and Cr) will expand with an average the E-OBS data set from the EU-FP6 project ENSEM- net rate of over 80 × 103 km2 decade−1. Dry climates BLES (http://ensembles-eu.metoffice.com) and the data (BW and BS) will increase its area at an average net providers in the ECA&D project (http://eca.knmi.nl). The rate of approximately 36 × 103 km2 decade−1. Among high-resolution climate data set available through the Cli- the two temperate and more widespread climates, the matic Research Unit and the Tyndall Centre was also very subtype DO will expand at a rate of about 55 × 103 km2 useful to complete this work. decade−1, while the DC will shrink at a rate of about − 90 × 103 km2 decade 1. These rates of change in climate References could be large enough that some types of vegetation Baker B, Diaz H, Hargrove W, Hoffman F. 2010. 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 2012 Royal Meteorological Society Int. J. Climatol. 33: 2157–2166 (2013)