Download Assessment of climate change in Europe from an ensemble of

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 Köppen–Trewartha
classification
Clemente Gallardo,a * Victoria Gil,b Edit Hagel,b César Tejedab and Manuel de Castroa,b
a
Instituto de Ciencias Ambientales, Universidad de Castilla-La Mancha, Toledo, Spain
b Instituto Meteorológico Regional de Castilla-La Mancha, Toledo, Spain
ABSTRACT: Through the use of the climatic classification of Köppen–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
A climate-vegetation scheme, like the Köppen climate
classification (Köppen, 1936) or its improvements (Trewartha and Horn, 1980) is a complex system of climates,
which is based on the two variables most frequently
used in climate studies: precipitation and temperature.
The categories or types of these classifications are not
only related to the different climates that exist on the
Earth, but they are structurally related to the potential
vegetation of each zone, and are also indirectly related
to the feasible crops and ecosystems. These relationships
allow us not only to establish a projection of the future
changes in the climate, but also to give a basic estimation
of the possible effects on the natural vegetation, crops and
ecosystems.
A Köppen-like climate classification has two additional
advantages. First, it can be applied practically everywhere
on the planet, as the temperature and precipitation data
are available almost anywhere over the globe. Second,
these variables are also part of the standard output of
global climate models (GCMs) and regional climate
models (RCMs). Owing to these properties, the Köppen
methodology can be applied to track past and future
changes in the climate, using the observations that have
∗ Correspondence to: C. Gallardo, Instituto de Ciencias Ambientales,
Universidad de Castilla-La Mancha, Avda. Carlos III s/n, 45071 Toledo,
Spain. E-mail: [email protected]
 2012 Royal Meteorological Society
been gathered over the last 100 years or so, and the
outputs of the climate models for past, present or future
periods.
Within the field of the study of climate change, the
Köppen climate classification and its variants have been
used by several authors. Lohmann et al. (1993) used
the Köppen classification to check whether a GCM
was able to reproduce the present day climate and to
analyse how the main climate regions could change as a
result of global warming. Leemans et al. (1996) analysed
the global biome distribution by applying the Köppen
method to the output of four GCMs. Kleidon et al. (2000)
estimated the effect of vegetation on the global climate
by performing several climate model simulations and
then applying the Köppen classification to illustrate the
differences among them. Jylhä et al. (2010) used the
traditional Köppen classification to study climate trends
in Europe with a set of 19 GCMs. Feng et al. (2012)
assessed current and future climate changes in the Arctic
from the output of 16 GCMs.
The Köppen classification has also been applied to
the output of RCMs in order to evaluate climatic refuge
for the People’s Republic of China (Baker et al., 2010),
assess the possible increase of aridity caused by the late
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
(Roderfeld et al., 2008) and estimate the climate change
effects in Europe (Castro et al., 2007).
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C. GALLARDO et al.
The Köppen classification was also used to characterize
the climate of certain regions (Baltas, 2007), or to detect
the 20th century climate change in the Arctic region
(Wang and Overland, 2004), in the United States (Diaz
and Eischeid, 2007) or in Europe (Gerstengarbe and
Werner, 2009).
In this work, the outputs of 11 high-resolution RCMs
were used to reproduce the current climate in Europe and
the Mediterranean area and to assess the possible magnitude of future climate change under SRES-A1B emission
scenario. Regarding the concentrations of equivalent CO2
over the 21st century, the A1B scenario is intermediate
for both the SRES scenarios group and the new RCP
scenarios. The uncertainty associated with the emissions
scenario has not been explored in this work, but the use of
an extreme scenario has been avoided. The applied RCMs
were driven by six GCMs, resulting in a total of 15 simulations. This makes a difference to other RCM-based
works, where only one GCM is considered, by providing
boundary conditions to only one or several RCMs. This
means that the analysis presented in this study is more
robust, because, in addition to using an ensemble of several RCMs, it also incorporates the uncertainty related to
the GCMs. Unlike some previous works (Castro et al.,
2007), in this study the whole period of 1961–2090 was
considered, which made it possible to analyse the tendency of the changes throughout the 21st century.
The study is organized in the following way. In
Section 2, a brief description of Köppen–Trewartha (KT) climate classification scheme and its observed present
day distribution is shown. In Section 3, the climate
simulations are described. In Section 4, the evolution
of the climate in Europe and the Mediterranean area is
analysed. Finally, some concluding remarks are presented
in Section 5.
2. The observed present day K-T climate
distribution
The Köppen climate classification and its variants are the
most widely used climate classification systems. In the
present study, an improvement of the original system, the
K-T (Trewartha and Horn, 1980) climate classification
(Table I) was used.
The K-T classification was applied to monthly mean
temperature and precipitation data derived from the EOBS data set (Haylock et al., 2008) of the European
Climate Assessment & Dataset (ECA&D) project. In the
present study, version 3.0 of E-OBS data, released in
April 2010, was used on a 0.25° regular latitude/longitude
grid for the period 1971–2000. All but four K-T subtypes
(Ar, Aw, Cw and FI) were present in Europe and
the Mediterranean area (Figure 1(a)). The climate types
covering the largest part of Europe are DO (temperate
oceanic) and DC (temperate continental). Subtropical
climates (Cs and Cr) can be found mainly south of
45° N (except for the coastal areas of western France),
while sub-arctic and polar climates (EO, EC and FT) are
located approximately north of 60° N as well as in the
Alps, as there is no separate alpine climate in the K-T
classification.
When calculating the K-T climate types, a much
localized feature was seen over the north of Romania
(not shown). While in the surrounding areas the DC
climate type was dominant, in about 50 grid points
BW and BS types were detected. It was found that
this behaviour was caused by anomalous data in the
E-OBS data set for these few grid points. To further
investigate this issue, the K-T classification was applied
to monthly mean temperature and precipitation data from
the CRU data set (on 0.5° and 10 resolution as well;
Mitchell et al., 2004). The above-mentioned feature did
not appear in the K-T distribution obtained from the
Table I. The Köppen–Trewartha climate classification.
Climate type
Ar
Aw
BW
BS
Cs
Cw
Cr
DO
DC
EO
EC
FT
FI
Description
Classification criteria
Tropical humid
Tropical wet-dry
Dry arid
Dry semiarid
Subtropical summer-dry
Subtropical summer wet
Subtropical humid
Temperate oceanic
Temperate continental
Sub-arctic oceanic
Sub-arctic continental
Tundra
Ice cap
All months above 18 ° C and less than 3 dry monthsa
Same as Ar but 3 or more dry months
Annual precipitation P (cm) ≤0.5 Ab
Annual precipitation P (cm) > 0.5 A but smaller or equal than A
8–12 months above 10 ° C, annual rainfall <89 cm and dry summerc
Same thermal criteria as Cs, but dry winterd
Same as Cw, with no dry season
4–7 months above 10 ° C and coldest months above 0 ° C
4–7 months above 10 ° C and coldest months below 0 ° C
Up to 3 months above 10 ° C and temperature of the coldest month above −10 ° C
Up to 3 months above 10 ° C and temperature of the coldest month ≤ −10 ° C
All months <10 ° C
All months below 0 ° C
a
Dry month: <6 cm monthly precipitation.
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.
b
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USE OF A KÖPPEN CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE
(a)
(b)
Figure 1. Köppen–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
change the climate type of the grid points in question
from BW/BS to DC (the climate subtype of these grid
points obtained from the CRU data set) in the E-OBSbased K-T distribution, and use this modified data for any
further analysis.
3. Climate model simulations
For the following analysis, data from the ENSEMBLES
project (Hewitt and Griggs, 2004) were used. The applied
RCMs were driven by a variety of GCMs under the
SRES-A1B emission scenario (Nakićenović and Swart,
2000). A short overview of these models is given below,
followed by a brief description of the experiments. The
respective institutions, model names and acronyms are
listed in Table II.
When selecting the RCM simulations to be used, two
main issues had to be considered: spatial and time coverage. As each of the RCMs covers a slightly different area,
the largest possible common domain (hereafter DOM 1)
was defined. Only those points that are inside the common domain were taken into account. As the common
domain excludes the north of Scandinavia, a larger additional area was also selected (hereafter DOM 2). In this
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
maximum common domain (DOM 1) in some analysis
more RCMs could be included (15 simulations), with the
consequent increase in the robustness of the results. The
broader domain (DOM 2) allows us to analyse the evolution of climate in a region like northern Fennoscandia,
which is very sensitive to climate change. The other criterion was the time period covered. As one of the main
goals was to analyse the tendency of the changes in the
K-T distribution throughout the 21st century, only those
simulations covering the entire 1961–2090 time period
were considered; the rest were excluded. The period
1961–2090 was a compromise solution, instead of the
Table II. Regional climate models (RCMs) from which data have been analysed.
Acronyms
C4I
CNRM
DMI
ETHZ
ICTP
KNMI
HC-Q0
HC-Q3
HC-Q16
MPI
SMHI
Institute
Met Eireann, Ireland
Météo-France
Danish Meteorological Institute
Swiss Institute of Technology
The Abdus Salam International
Centre for Theoretical Physics
The Royal Netherlands
Meteorological Institute
UK Met Office, Hadley Centre
Max Planck Institute for
Meteorology
Swedish Meteorological and
Hydrological Institute
 2012 Royal Meteorological Society
Model
Driving GCM (see Table III)
Source
RCA3
RM4.5
HIRHAM5
CLM
RegCM3
HadCM3Q16
ARPEGE
ECHAM5-r3, BCM, ARPEGE
HadCM3Q0
ECHAM5-r3
Kjellström et al. (2005)
Radu et al. (2008)
Christensen et al. (2006)
Böhm et al. (2006)
Giorgi and Mearns (1999)
RACMO2
ECHAM5-r3
van Meijgaard et al. (2008)
HadRM3Q0
HadRM3Q3
HadRM3Q16
REMO
HadCM3Q0
HadCM3Q3
HadCM3Q16
ECHAM5-r3
Collins et al. (2011)
Jacob (2001)
RCA3.0
ECHAM5-r3, BCM, HadCM3Q3
Kjellström et al. (2005)
Int. J. Climatol. 33: 2157–2166 (2013)
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Table III. GCMs that drive the simulations of the RCMs.
Acronyms
HadCM3a
ARPEGE
ECHAM5-r3
BCM
Institute
Source
UK Met Office, Hadley
Centre
Météo-France
Max Planck Institute for
Meteorology
University of Bergen,
Norway
Gordon et al.
(2000)
Gibelin and
Déqué (2003)
Roeckner et al.
(2003)
Furevik et al.
(2003)
a
For this GCM, three different versions (Q0, Q3 and Q16, see Table II)
were run with differing climate sensitivities.
longer 1951–2100 interval, as some of the simulations
started/finished a few years later/earlier.
The GCMs driving the RCMs simulations are listed
in Table III. The three HadCM3 simulations were based
on the same model but with different parameter setting,
in order to obtain different climate sensitivities (Murphy
et al., 2007).
The K-T climate classification was applied to the
above-mentioned climate simulations, both for the present
day climate (1971–2000) and for the future over 30 year
periods overlapped for 20 years. The K-T climate types
were calculated for both domains (DOM 1 and DOM 2).
All the tables presented in the article are based on
the results of the common domain (DOM 1 with a
15 member ensemble), while the figures are composites of the results obtained for the two areas (except
for Figures 3 and 4, which are based on the results of
DOM 1).
The modelled K-T distribution for the reference period
was compared over land points to the one derived from
the E-OBS data to check how the RCMs simulate present
day climate. On the other hand results for the future
were compared to that of the reference period to detect
possible future changes in the climate distribution over
Europe and the Mediterranean area. Results of this study
are presented in Section 4.
4. Results
4.1. Evaluation of the RCM runs for the reference
period
The monthly values of 2 m temperature and precipitation
were averaged over the control period (1971–2000)
and over the whole set of simulations. Then, the KT climate subtypes corresponding to these mean fields
were determined on each grid point (Figure 1(b)). These
fields of average K-T subtypes are the ensemble mean of
reference (EMR). K-T subtypes were also calculated for
the observational database E-OBS for the same period
(Figure 1(a)).
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 matrices. These matrices show the correspondences between
K-T subtypes of the E-OBS climatology and every simulation (not shown) or the EMR (Table IV) for the period
1971–2000. A total of 15 847 grid points with data provided by E-OBS were analysed in the DOM 1 domain.
For a better interpretation of the co-occurrence matrices
the following points should be remembered:
• The main diagonal of each matrix indicates the number
of grid points where the K-T subtype according to
the E-OBS database matches that generated from a
simulation or the EMR.
• A location outside the main diagonal of the matrix
indicates lack of coincidence. A larger separation from
the diagonal indicates larger differences between the
climatology and the simulations.
• Nonzero elements below the main diagonal indicate
that the simulations are warmer or drier than E-OBS.
• Conversely, nonzero elements above the main diagonal
indicate that the simulations are cooler or wetter than
E-OBS.
RCM simulations reproduce the K-T subtypes of EOBS quite well in most of the cases. The percentage of
coincidences of subtypes ranges from 55.4 to 81.3% and
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)
E-OBS (1971–2000)
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
13
84
3
0
0
0
0
0
0
2
69
109
1
2
0
0
0
0
1
59
713
8
11
0
0
0
0
0
10
127
79
22
1
0
0
0
0
62
212
63
3487
647
0
0
0
0
0
0
0
194
5655
8
0
0
0
0
0
0
46
251
878
21
6
0
0
0
0
0
87
279
2038
0
0
0
0
0
0
0
107
190
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.
 2012 Royal Meteorological Society
Int. J. Climatol. 33: 2157–2166 (2013)
USE OF A KÖPPEN CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE
2161
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
C4I
CNRM
KNMI
SMHI
MPI
ETHZ
HC
DMI
ICTP
HadQ0
HadQ3
HadQ16
BCM
ARPEGE
75.75
79.98
80.41
80.29
73.61
68.26
80.58
81.32
69.38
74.88
79.72
65.32
69
(Table V). The EMR has 83.5% of the grids in the main
diagonal of the matrix of co-occurrence, more than any
of the simulations used for its formation. The EMR has
10.7% of the points above the main diagonal and 5.8%
below; this means that it has a slight cold bias. These
figures are in line with those reported by Castro et al.
(2007) and indicate that the EMR performs better than
all the models that constitute it. The explanation for this
result is not obvious and would need further research to
clarify it.
A large part of the differences in K-T subtypes between
EMR and E-OBS is due to a significant number of grids
that belong to the DC subtype for E-OBS and correspond
to the subtype DO for the EMR (Table IV). This result
was already observed by Castro et al. (2007), but this
time the border between DC and DO from north to
south across Central Europe is fairly well outlined by the
RCMs (Figure 1) and differences focus on the eastern
side of the Danube basin and the Crimean Peninsula.
This tendency of the models to establish as DO some
grids that E-OBS classify as DC is the most striking, but
the relative error of other simulated subtypes is larger.
For instance, the number of grid points with the subtype
FT for the EMR is 94.5% more abundant than in E-OBS.
This increase comes at the expense of the decrease in
(a)
55.41
72.52
grid points with EC and EO subtypes in the mountainous
areas of Norway and Sweden and the Kola Peninsula
in Russia. This is caused by the tendency of models to
produce cooler summers in this area which is the key
factor to distinguish between types E and F of K-T.
It is also noteworthy that EMR shows 32% less grid
points for the subtype Cs, owing to the appearance of
DO subtype in the central area of the Iberian Peninsula
and Cr and BS subtypes in different Mediterranean
coastal areas. This assignment of DO instead of Cs is
due to mismatches in the simulation of the temperature
in the transitional seasons, while Cr and BS cases are
attributable to differences in precipitation.
4.2.
Climate change scenario simulations
The K-T climate subtypes were calculated for consecutive
and overlapping 30 year periods between 1961 and
2090 (1961–1990, 1971–2000, 1981–2010 and so on
to 2061–2090) for the individual RCM simulations and
for the ensemble mean. Calculations were performed
using the monthly averages of 2 m temperature and
precipitation over land points only. Because of the large
number of models, only the results obtained with the
ensemble mean are shown in this part of the analysis, for
two intervals: 2021–2050 (Figure 2(a)) and 2061–2090
(b)
Figure 2. Köppen–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.
 2012 Royal Meteorological Society
Int. J. Climatol. 33: 2157–2166 (2013)
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C. GALLARDO et al.
(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
areas where the climate simulations project changes with
respect to the reference period (1971–2000), in Figure 3
only these grid points are plotted. Comparing these
figures with that of the reference period (Figure 1(b)) the
following important changes can be observed:
Northeastward shift of the boundary between DO and
DC climate types. By the end of the 21st century the
DC type withdraws drastically to the northeast. The
DO type is extending in eastern Europe, but at the
same time is loosing territory in western Europe.
Climate types EO, EC and FT in Fennoscandia withdraw drastically to the north. By 2021–2050 the area
covered by EC and FT types is significantly reduced,
and by 2061–2090 the EC and FT types almost completely disappear from northern Europe.
Cs and Cr gain more area in southern and western
Europe, especially by the end of the 21st century.
The BS type gains area in southeast Spain, Italy,
Greece, Turkey and the coastal zones of northern
Africa.
Other figures similar to Figure 3, but for 1981–2010
and 1991–2020 (not shown), reveal that the first clear
climate changes projected are the retreat of the EC
climate in Finland, Sweden and western Russia and
the DC subtype in central Europe. These projections fit
reasonably well with observed climate transitions from
the period 1950–1978 to the period 1979–2006 (Jylhä
et al., 2010). In the following two periods (2001–2030
and 2011–2040) the most striking projected transitions
occur to the east of the Kola Peninsula in Russia (FT-EC),
western France (DO-Cr), in southern Finland (EO-DC)
and north of Black Sea (DC-DO).
For a deeper analysis, Tables VI and VII contain
the co-occurrence matrices (domain DOM 1) for the
ensemble mean of the climate scenario runs. These
matrices express the point-by-point agreement between
the climate types of the reference period (1971–2000)
and the periods 2021–2050 (Table VI) and 2061–2090
(Table VII).
The portion of land area shifting from the current KT climate type to a warmer or drier one is 21.3% for
2021–2050 and 45.2% for 2061–2090. This means that
by the end of the 21st century almost half of the studied
area will undergo a climate type change. It is important to
notice that there are no entries above the main diagonal.
Some unexpected differences between the values in
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)
Ensemble mean (1971–2000)
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
1170.9
80.4
0
0
0
0
0
0
0
0
276.9
77.0
13.2
29.0
0
0
0
0
0
0
792.9
46.0
151.4
0
0
0
0
0
0
0
204.0
222.5
0
0
0
0
0
0
0
0
2295.9
785.7
31.6
0
0
0
0
0
0
0
2119.3
200.4
117.1
0
0
0
0
0
0
0
304.3
230.2
65.2
0
0
0
0
0
0
0
457.4
44.4
0
0
0
0
0
0
0
0
122.8
Areas in 103 km2 .
 2012 Royal Meteorological Society
Int. J. Climatol. 33: 2157–2166 (2013)
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USE OF A KÖPPEN CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE
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)
Ensemble mean (1971–2000)
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
BW
BS
Cs
Cr
DO
DC
EO
EC
FT
1170.9
202.2
0
0
0
0
0
0
0
0
155.0
215.9
30.4
78.8
0.6
0
0
0
0
0
654.0
78.3
376.9
0
0
0
0
0
0
0
154.5
617.1
0
0
0
0
0
0
0
0
1625.9
1478.8
85.2
0
0.3
0
0
0
0
0
1425.6
327.4
353.1
2.1
0
0
0
0
0
0
123.7
424.8
146.4
0
0
0
0
0
0
0
26.7
28.7
0
0
0
0
0
0
0
0
54.8
Areas in 103 km2 .
E-OBS data do not cover the entire area simulated by
the RCM. This is especially evident for BW subtype, as
E-OBS does not provide data in 1 678 grid points in
North Africa where such a climate is simulated by the
models.
The largest changes can be observed in the current
climate types DO, DC and EC.
The DO type is gaining territory mainly from DC, and
to a much smaller extent from EO. On the other hand,
DO is loosing area to BS, Cs and Cr types, among
which DO-Cr is the most dominant shift, followed by
DO-Cs.
The net results of the changes in the DC and EC
covered areas are both negative. The DC subtype
is loosing territory to DO, but also gaining (though
considerable less) from EO and EC. The EC subtype
gains some area from FT, but looses to DC and EO.
It is also important to mention that the BW and BS
subtypes are both predicted to increase their area with
respect to the control period. The BW subtype (dry arid
or desert) appears over a much localized area in the
southeast of Spain, while the BS subtype (dry semiarid or
steppe) gains more area in Spain, Italy, Greece, Turkey
and northern Africa.
Figure 4 shows a schematic plot of these projected
transfers between the different K-T subtypes, also
indicating the sign of the net change of area of the given
subtype. It can be observed that FT, EC and DC are projected to undergo a net loss of area, while the net result of
the changes in the remaining subtypes (EO, DO, Cr, Cs,
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
natural ecosystems and crops.
It is also interesting to see how many grid points
undergo a change from one main climate group (A, B,
C, D, E and F) to another. Compared to the reference
period of 1971–2000, 9.7% of the total area experiences
such a change by 2021–2050. By 2061–2090 the area
affected by this change increases to 23.0%. The grid
points with main climate group change (i.e. the areas with
potentially more dangerous ecosystem changes) can be
found in western France, Alps, Scotland, Fennoscandia,
northwest of Russia, Iceland, several regions of southern
Europe and coastal areas of Wales, Ireland and southern
England.
Figure 5 shows the evolution of K-T climates over
time. It can be seen that, in general, the climates DC,
EC and FT, that is three of the four colder climates,
lose area progressively. BW, BS, Cs, Cr and DO (the
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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Int. J. Climatol. 33: 2157–2166 (2013)
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C. GALLARDO et al.
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
gain for the ensemble in the second half of the 21st
century which is bigger than in any RCM. This is because
the threshold criterion between EO and EC refers to a
temperature of the coldest month above or below −10 ° C
(see Table I). As the minimum temperatures in some grid
points do not coincide in the same month for all the RCM
simulations, the ensemble could have its coldest month
above −10 ° C although most RCMs do not. Therefore,
in the border areas between these two subtypes there are
some grid points in the ensemble that are EO, while in
most simulations they are EC. As a result, at the end of
studied period, EC climate in the ensemble has decreased
more than in any of the RCMs.
It is also remarkable that for the subtype Cr the
ensemble shows more increases than most of the RCMs
(Figure 5). This is mainly because, owing to the criteria that define these climates, in the peripheral regions
of subtype Cs one or a few simulations that are excessively wet in summer or dry in winter can lead to an
ensemble mean with subtype Cr, even while a majority
of simulations show a Cs climate.
 2012 Royal Meteorological Society
The two climates with the largest areas (DO and DC)
are also those with the widest band in Figure 5 and
therefore with the highest degree of spread in square
kilometers between the different models. However, if we
relate this spread in the period 2061–2090 to the extent
of these climates in the EMR, it represents only 38%
for DO and 22% for DC. The Cr subtype also has a
large spread in square kilometers despite not occupying
a large area; this implies a relative spread of 192%.
The difference in square kilometers is moderate for
intermediate extension climates (EC, EO and Cs) and
small for the rest of the climates that are not widespread
(FT, BS and BW). The relative difference for the subtype
EO stands out from the rest of the last six subtypes as
it reaches 72%, while for the others it is always below
40%. For all subtypes, the largest spread occurs in the
last periods.
Looking at the temporal evolution of the ensemble
(Figure 5) six out of nine existing subtypes show a
roughly constant growth (BW, BS and Cs) or reduction
(DC, EC and FT). On the other hand, Cr has a tendency
to accelerate the increase in expanse. The general trend
Int. J. Climatol. 33: 2157–2166 (2013)
USE OF A KÖPPEN CLASSIFICATION TO ASSESS THE CLIMATE CHANGE IN EUROPE
of the ensemble to a monotonous evolution is not always
found in all simulations. Examples of this can be easily
seen (Figure 5) in the graphs of the subtypes DO, DC
and EO, but also exist for other climates.
Another important aspect is the rate at which the
different subtypes of climate expand or contract. The
two coldest subtypes in the studied domain (FT and EC)
decrease their joint area, on average and in net value, at
a rate slightly over 100 × 103 km2 decade−1 . Subtropical
climates (Cs and Cr) will expand with an average
net rate of over 80 × 103 km2 decade−1 . Dry climates
(BW and BS) will increase its area at an average net
rate of approximately 36 × 103 km2 decade−1 . Among
the two temperate and more widespread climates, the
subtype DO will expand at a rate of about 55 × 103 km2
decade−1 , while the DC will shrink at a rate of about
90 × 103 km2 decade−1 . These rates of change in climate
could be large enough that some types of vegetation
and ecosystems would be unable to adapt or migrate to
survive.
5. Concluding remarks
The projected climate change has been analysed for
Europe and northern Africa by applying the K-T climate classification to a set of 15 RCM simulations
nested in six GCMs for the A1B emissions scenario. The
ensemble of simulations reproduces well the observed
climate in the late 20th century (83.5% of matches
with the observations) and better than any of the simulations used in its formation. The most significant
climate spatial transitions observed in this period in
Fennoscandia and Central Europe are simulated with
good approximation.
The ensemble projects for the 21st century a climate
type change for large areas of the domain. Taking as
reference the subtypes of the period 1971–2000, 21.3%
of the total area changes its K-T climate by 2021–2050
and 45.2% by 2061–2090. In addition, rates of net
change are steadily high throughout the 21st century,
ranging from a decrease of 90 × 103 km2 decade−1 of
the area covered by DC climate, to an increase of
55 × 103 km2 decade−1 for Cr and DO climates. Climatetype changes are likely to occur at a fairly sustained rate
throughout the 21st century, which could pose a threat to
many ecosystems starting from the first decades of the
century.
The largest changes from the point of view of spatial displacement of climates could occur in Central
Europe, where the border between subtypes DO and DC
is shifted far to the northeast; in Fennoscandia, where
EC and FT climates almost disappear; in southern and
western Europe, where Cs and Cr subtropical climates
significantly advance; and in the Mediterranean basin,
where BW and BS dry climates also extend. Meanwhile,
the areas where changes are more intense, and potentially more dangerous, are western France, Alps, Scotland, Fennoscandia, northwest of Russia, Iceland, several
 2012 Royal Meteorological Society
2165
regions of southern Europe and coastal areas of Wales,
Ireland and southern England.
Acknowledgements
The ENSEMBLES model data used in this work was
funded by the EU FP6 Integrated Project ENSEMBLES (Contract GOCE-CT-2003-505539) whose support is gratefully acknowledged. We also acknowledge
the E-OBS data set from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data
providers in the ECA&D project (http://eca.knmi.nl). The
high-resolution climate data set available through the Climatic Research Unit and the Tyndall Centre was also very
useful to complete this work.
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