Download Assessment of climate change simulations over climate zones of

Reg Environ Change
DOI 10.1007/s10113-012-0335-0
ORIGINAL STUDY
Assessment of climate change simulations over climate zones
of Turkey
Barış Önol • Yurdanur S. Unal
Received: 5 October 2011 / Accepted: 15 July 2012
Ó Springer-Verlag 2012
Abstract Projected climate change over Turkey has been
analyzed by using the reference (1961–1990) and future
(2071–2100) climate simulations produced by ICTP-RegCM3. Since examining Turkey as a single region could be
misleading due to the existence of complex topography and
different climatic regions, Turkey has been separated into
seven climatic regions to evaluate the surface temperature
and precipitation changes. Comparison of the reference
simulation with observations was made spatially by using a
monthly gridded data set and area-averaged surface data
compiled from 114 meteorological stations for each climatic region of Turkey. In the future simulation, warming
over Turkey’s climatic regions is in the range of 2–5 °C.
Summer warming over western regions of Turkey is 3 °C
higher than the winter warming. During winter, in the
future simulation, precipitation decreases very significantly
over southeastern Turkey (24 %), which covers most of the
upstream of Euphrates and Tigris river basin. This projected decrease could be a major source of concern for
Turkey and the neighboring countries. Our results indicate
that a significant increase (48 %) in the autumn season
precipitation is simulated over southeastern Turkey, which
may help to offset the winter deficit and therefore reduce
the net change during the annual cycle.
Electronic supplementary material The online version of this
article (doi:10.1007/s10113-012-0335-0) contains supplementary
material, which is available to authorized users.
B. Önol (&) Y. S. Unal
Aeronautics and Astronautics Faculty, Meteorological
Engineering, Istanbul Technical University,
Maslak, Istanbul, Turkey
e-mail: [email protected]
Keywords Climate change Regional climate modeling Climate of Turkey
Introduction
Sensitivity of global mean variables such as temperature
and precipitation to greenhouse forcing shows significant
disagreement between global climate models. However, 15
climate models used in IPCC 4th assessment reports show
remarkable agreement on dry conditions, which will
dominate the eastern Mediterranean region (Wang 2005).
In addition, global future climate projections reveal that
Mediterranean region is one of the most responsive regions
to the global warming (Giorgi 2006).
In Giorgi’s study (2006), a Regional Climate Change
Index (RCCI) has been developed using 20 global climate
models with three IPCC emission scenarios to define the
vulnerability of the regions to climate change. National
Climate Change Index and climate change population
index have also been developed by Diffenbaugh et al.
(2007) using RCCI to describe socio-climatic exposure in
future for all countries. His analysis indicates that Turkey
is more vulnerable to socio-economic affects than the other
Mediterranean countries, especially when its future population is considered. These studies suggest that regional
climate model simulations are necessary over the Eastern
Mediterranean (EM) region, especially concentrating on
Turkey, to provide detailed information about the climate
in the twenty-first century.
Regional changes in climate will most directly affect
human activities, and thus, predictions of regional climate
change are of great practical and scientific interest. We
completed two sets of multi-decadal simulations over the
EM domain with the regional climate model RegCM3
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B. Önol, Y. S. Unal
(Giorgi et al. 1993a, b; Giorgi and Shields 1999) nested
within the NASA Finite Volume General Circulation Model
(fvGCM) for the period of 1961–1990 and for the period of
2071–2100 under SRES A2 emission scenario. Although
Önol and Semazzi (2009) present the model validation and
analysis for the entire domain of this simulation, their study
does not include a detailed analysis for Turkey. However, it
is necessary to do comprehensive analyses of the future
projections for Turkey which has very diverse climatic
regions caused by complex topography and land sea distribution. In this study, we have analyzed regional climate
model outputs produced by RegCM version 3, which is
originally developed by Giorgi et al. (1993a, b) and later
improved by Giorgi and Shields (1999) and Pal et al. (2000,
2007). Relatively high resolution of 30-km is implemented
to better resolve the complex topography of Turkey.
Regional climate modeling studies concerning the EM
region (Krichak et al. 2011, 2007; Hadjinicolaou et al.
2011; Giannakopoulos et al. 2011; Gao and Giorgi 2008;
Alpert et al. 2004) project that the future climate change
over Turkey shows regional variations even though they do
not include the whole of Turkey in their domain. It is noted
that there are significant variations of temperature and
precipitation from south to north and from east to west in
Turkey. Therefore, it is necessary to study the future climate projections regionally in order to analyze the regional
differences and the origin of changes. Recently, projected
changes on water resources in Turkey and the surrounding
regions have been investigated by using RCMs with
Chenoweth et al. (2011) and Hemming et al. (2010). These
studies also pointed out that there are diverse precipitation
regime changes between northern (increase) and southern
(decrease) Turkey for SRES A1B scenario simulated by
HadCM3. However, in both studies, area-averaged calculations have been utilized for all of Turkey to determine
water availability. In the study by Chenoweth et al. (2011),
they used PRECIS model with 25-km horizontal resolution
for the period of 2070–2099. Their results show that projected annual precipitation decreases in the range of
20–100 % over southern and western Turkey and increases
5–15 % over northeastern Turkey. Hemming et al. (2010)
also reaches quite similar results (but of lesser magnitude)
on annual precipitation change using ensemble analysis of
GCM (17 member) and RCM (5 member, 50-km horizontal
resolution) simulations for the 2021–2050 period. In
addition, MM5 simulations (27-km) driven by CCSM3 for
the first and last 5 years of the twenty-first century indicate
more than 100 % precipitation change over southern
(decrease) and northeastern (increase) coasts of Turkey for
all seasons except summer (Evans 2010). Analysis of
super-high-resolution GCM (20-km) simulations by Kitoh
et al. (2008) also agrees with previous studies on annual
precipitation tendencies over Turkey.
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The climate of Turkey is characterized as Mediterranean. The topography and land-use of Turkey are very
diverse, and therefore, large-scale atmospheric motions are
mainly affected by complex surface conditions. The
mountain ranges running parallel to the coasts of the
Mediterranean Sea and the Black Sea result in significant
climate variability between northern and southern parts of
the mountains. Comparison of RegCM simulations with
surface observations in Evans et al. (2004) reveals that the
model produces excessive precipitation over the Eastern
Black Sea Mountains. However, Hahmann et al. (2008)
used WRF and MM5 with higher resolution of 15-km and
simulated the month of January for years between 2001 and
2006 by perfect boundary conditions (NCEP re-analysis).
They found that precipitation field is consistent with the
station observations over the Mediterranean coast. Along
the Aegean Sea coast, mountains are located perpendicular
to the coastline, which permits penetration of the westerly
flow to inland areas. However, climate of the regions close
to the Aegean Sea coastline still differs from the inland
climate due to the elevated topography. During winter,
Turkey is under the influence of airflow with a polar origin.
On the other hand, during autumn and summer, tropical
airflow dominates the region.
The nature of precipitation is an important hydrological
factor, especially over the eastern Anatolia, since the
headwaters of cross-boundary river systems of Turkey are
located within the region. From April through June,
snowmelt contributes around half of the annual runoff. One
of the most significant potential impacts of climate variability and change may be alterations in the regional
hydrological cycle and subsequent changes in river discharge. Change of precipitation regime within this basin
will have significant consequences in the rivers systems of
Euphrates and Tigris, which concern not only Turkey but
also Syria and Iraq. Sustainability of fresh water resources
in the future could be the main driving force for conflicts
among these countries.
Turkey occupies an area of 783,562 km2. It is encircled
by the Aegean Sea to the west, the Black Sea to the north
and the Mediterranean Sea to the south, and the Thrace and
Anatolia parts of Turkey are separated by the Sea of
Marmara. The geographic location of Turkey, as well as
the topographic barriers, shapes the climate of Turkey and
results in very distinct climatic regions. Therefore, it is
necessary to evaluate the climate projections in each distinct region separately.
In this paper, we examine the imprints of reference and
scenario simulations in seven climate zones over Turkey
defined by Unal et al. (2003) using the temperature and
precipitation variability. In Sect. ‘‘Methods and data,’’ we
introduce the methodology of the paper and the data set
used. In Sect. ‘‘Results,’’ temperature and precipitation
Assessment of climate change simulations
simulations for the period 1961–1990 called RF and period
2071–2100 called A2 are presented. The final section
includes conclusion and discussion of our results.
Methods and data
The primary vehicle of our investigation is the RegCM3
regional climate model. The NASA Finite Volume GCM
(fvGCM) archived model simulation data were used to
construct the initial and lateral boundary conditions for two
30-year RegCM3 model simulations; the reference climate
(1961–1990; RF) and the projected climate (2071–2100;
A2). A2 is one of the extreme scenarios of Intergovernmental Panel on Climate Change (IPCC), and we adopted
this scenario because it provides the opportunity to
understand the upper limits of human induced global
warming over the EM region.
Climate models: NASA-fvGCM and RegCM3
The International Centre for Theoretical Physics (ICTP)
Regional Climate Model Version 3 (RegCM3) has been used
for both the RF and A2 simulations. RegCM3 is a threedimensional hydrostatic atmospheric model, and it uses a
sigma-pressure-based vertical coordinate system. The radiation transfer package is based on the NCAR-CCM3 scheme.
Appropriate emission levels based on IPCC SRES were used
in reference and scenario simulations. The atmospheric
component of RegCM3 is coupled to the Biosphere–Atmosphere Transfer Scheme (BATS 1e; Dickinson et al. 1993).
The model includes the SUBEX scheme (Pal et al. 2000) to
calculate large-scale precipitation and has three options for
the convective precipitation scheme to compute cumulus
convection. Grell’s (1993) convective scheme with Arakawa
and Schubert (1974) closure formulation has been adopted in
our simulations. Further descriptions of RegCM3 model are
presented in Pal et al. (2007).
The NASA-fvGCM model outputs have been driven to
generate the initial and lateral boundary conditions for two
30-year RegCM3 model simulations: the reference climate
(1961–1990; RF) and the projected climate (2071–2100;
A2). FvGCM uses a terrain-following Lagrangian control
volume for the vertical coordinate system (Lin 2004). The
horizontal resolution of the fvGCM simulations is considerably high (1° 9 1.25°). Sea surface temperature (SST) in
the RF simulation is derived from observations. For the A2
simulation, monthly SST perturbations (A2 minus RF) are
calculated from corresponding simulations produced by the
Hadley Centre coupled model (HadCM3) and then they are
added to the RF SST values. Detailed explanation of this
method is given by Coppola and Giorgi (2005), which is
the part of the PRUDENCE project.
RF and A2 simulations for the larger model domain of
28°N–50°N, 10°E–50°E has been analyzed and discussed
in Önol and Semazzi (2009). Here, we concentrate on the
smaller domain (36°N–42°N; 26°E–45°E) results of RF
and A2 simulations. In these simulations, horizontal resolution is 30 km and vertical resolution is 18 sigma levels.
Land cover and topography generated by RegCM3 over
Turkey is presented in Fig. 1. Complex structure of Turkey’s topography has been implemented in the model
simulation realistically. Since the topography is a significant factor for the evolution of precipitation over Turkey,
using realistic surface conditions for the simulations are
crucial. Fourteen land cover types are defined by RegCM3
over Turkey. These are also very reasonable considering
the horizontal resolution of 30 km.
Observations
There are two types of observational data sets for surface
temperature and precipitation used in this study. We used
the Climate Research Unit TS 2.1 (CRU) data set (Mitchell
and Jones 2005) for comparing spatial distribution of
model results. The monthly mean gridded CRU data have
resolutions of 0.5° 9 0.5°. Area averages of homogeneous
climate regions of Turkey have been calculated from the
Turkish Meteorological Services data set, which has 114
stations for surface temperature and precipitation for the
time period 1961–1990. Figure 1 illustrates the distribution
of the stations over Turkey.
Results
Simulation results over Turkey have been analyzed over
various climate regions. Because of its large size compared
to the countries over the Eastern Mediterranean region, we
split Turkey into seven homogeneous climate zones. Seasonal surface temperature and precipitation results for
reference and future simulations have been examined based
on these regions. In addition, spatial distributions of surface temperature and precipitation for the time period
1961–1990 have been compared with the CRU observations to check the accuracy of the model results.
Spatial distribution of temperature and precipitation
simulations
In terms of the spatial distribution of the surface temperature, RegCM3 model captures the air pattern quite well
for all seasons (see electronic supplement). Topographic
and continental effects of the inland regions are clearly
distinguished from the temperature distribution. Therefore,
temperature averages decrease gradually toward eastern
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B. Önol, Y. S. Unal
Fig. 1 Over Turkey, the model land cover is derived form Global
Land Cover Characterization (GLCC, upper panel). The model
topography is derived from Global 30 Arc Second Elevation Data (US
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Geological Survey GTOPO30, middle panel). Seven homogeneous
climatic regions of Turkey and location of 114 meteorological
stations (lower panel)
Assessment of climate change simulations
Anatolia where high topography exists. Regional contrast
is high during winter and low during summer due to the
thermal effects. Simulations show that the winter temperatures are overestimated by 2–4 °C over the high altitudes
and inland regions, such as near the eastern border of
Turkey. Summer temperatures are a few degrees warmer
than the observations along seashores and southeastern
Anatolia. However, during transition seasons temperatures
are in good agreement with the observations. RegCM3
simulations have a few degrees of warm bias over Turkey.
One reason for this might be due to the inherited bias of
driving fvGCM. It is known that FvGCM simulation has a
warm bias of about 1 °C relative to CRU for present day
climate (Önol and Semazzi 2009). A second reason might
be the CRU data station locations. It is stated that the
uncertainty associated with the CRU climatology for multidecadal periods are around 0.5–1.3 °C, and it is the largest
over mountainous areas (New et al. 1999, 2000; Giorgi
et al. 2004).
Precipitation distribution for reference simulation demonstrates that RegCM3 realistically reproduces spatial
variability of precipitation for all seasons (see electronic
supplement). However, there are some discrepancies
between simulations and observation on certain seasons,
especially over the coastal and mountainous regions.
RegCM3 produces high precipitation over the Eastern
Black Sea Mountains, Taurus mountains along the Mediterranean coast and eastern Anatolia, except for summer
months. The coastal mountain ranges, which trigger the
precipitation processes, extend almost along the entire
Mediterranean and Black Sea coasts. Since the Eastern
Black Sea region receives great amount of precipitation all
year long, model results show large positive biases around
this region for all seasons. However, along the Aegean and
Mediterranean coasts, positive biases are mainly during
winter and spring months. The existence of very steep
topography over these regions is one of the possible reasons for the overprediction of precipitation. The observational station network does not include the mountainous
areas in this region. Hence, observation systems are likely
to miss orographic precipitation or precipitation from
convective systems with limited spatial extent. Uncertainty
of CRU precipitation data is around 10–25 %, which corresponds to 100-250 mm precipitation annually for the
Eastern Black Sea and Mediterranean regions (New et al.
1999, 2000; Giorgi et al. 2004). Due to abundant moisture
availability originating from the seas along the coastal
regions, the model amplifies precipitation and produces
unrealistic results. It is shown that accurate simulation of
precipitation in the steep topography regions requires the
correct simulation of storm tracks, topographic interactions
and atmospheric stability (Evans et al. 2004).
Table 1 Elevations and number of grids point (RegCM3) and
meteorological stations for seven climatic regions
Regions
Elevations (m)
No. of grid and stations points
RCM
RCM
OBS
OBS
MAR
137
60
36
AEG
467
254
128
22
BLS
CEA
679
1,186
110
971
71
285
11
29
ESA
1,961
1,154
117
8
SEA
1,155
871
142
14
755
137
72
16
MED
14
Climatic regions of Turkey
Turkey is the largest country in the Eastern Mediterranean,
and it is nearly twice and more than three times bigger than
that of Iraq and Romania, respectively. Therefore, we split
Turkey into 7 homogeneous climate regions based on a
previous study by Unal et al. (2003). They analyzed 114
meteorological stations for monthly mean, maximum and
minimum temperatures and for precipitation using the
cluster analysis for the period of 1951–1998. To validate
RF results, the same data set has been used, except that it
was confined to the period of 1961–1990. The only difference in climate zones between our study and Unal et al.
(2003) is that we combined the eastern and western parts of
the Mediterranean region. This is made possible by the
inclusion of the Antalya station which was not used in Unal
et al. (2003) due to homogeneity problem of temperature
data. Omission of the Antalya station in Unal et al. (2003)
study resulted in the extension of the Aegean region toward
the east and confined the Mediterranean region to the
eastern of Antalya Bay. The slightly altered seven climatic
regions for Turkey are the Marmara region (MAR), the
Aegean region (AEG), the Black Sea region (BLS), the
central Anatolia region (CEA), the eastern Anatolia region
(ESA), the southeastern Anatolia region (SEA) and the
Mediterranean region (MED). The location of stations and
the regions are displayed in Fig. 1.
Reference simulation
We have compared reference simulations of Turkey with
the surface area-averaged observations by considering
seven climatic regions as defined in Sect. ‘‘Climate regions
of Turkey.’’ Figure 2 demonstrates the temperature area
averages for each climatic region for all seasons. Table 1
lists the average elevation of the model grid points and
meteorological stations as well as the number of grid points
for all regions.
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B. Önol, Y. S. Unal
Temperature bias in summer is around 1 °C for all the
regions. Other seasons have cold biases in the range of
1–3 °C, which is possibly related to deficiencies over
mountain areas. MAR region temperature biases for winter
and spring seasons are small and less than 0.5 °C. On the
other hand, during summer and autumn seasons, model
results indicate 2 °C warm and cold biases, respectively.
Small differences between observations and simulations for
winter and spring might be due to the relatively low
topography of the region compared to the others. Besides,
the average height of the meteorological stations (60 m) is
very close to the model topography (137 m). The meteorological station network is dense and distributed evenly in
10
Winter
5
Obs
RF
A2
0
Temperature
(a)
−5
Fig. 2 Areal average
comparisons of seasonal surface
temperature (°C) of RegCM3
simulations and meteorological
station data for the seven
climatic regions of Turkey.
a Winter, b spring, c summer
and d autumn
this region as well. In order to calculate the averages, a
total of 36 grid points are used for the simulation, and data
from 14 stations are utilized to calculate observational
averages. The ratio of observation points to model grid
points is highest in this region. It is well known that
RegCM3 has a warm bias over dry areas and dry seasons.
Biases of MED and BLS regions are excellent examples
for revealing observational deficiencies associated with
spatial distribution of the stations. These regions have very
steep coastal topography, and stations over the both regions
are generally located near the coast. Average height of the
grid points within the model for MED region is 755 m,
whereas the average height of stations is only 137 m. It
20
15
10
0
Temperature
Spring
5
(b)
10
20
30
Summer
0
5
Temperature
(c)
5
10
15
20
Autumn
0
Temperature
(d)
MAR
123
AEG
BLS
CEA
ESA
SEA
MED
Winter
300
Spring
Summer
300
Obs
RF
A2
300
Autumn
0
100
Precipitaion
(d)
500
0
100
Precipitaion
(c)
500
0
100
Precipitaion
(b)
500
0
100
300
(a)
Precipitaion
Fig. 3 Same as in Fig. 2 but for
precipitation
500
Assessment of climate change simulations
MAR
indicates that the observational data do not represent high
elevations located mostly northward of the region along the
Mediterranean Sea. Similarly, there is a large difference
between averages of model grid point heights (679 m) and
stations (110 m) in BLS region. Therefore, simulated RF
temperatures for this region are always colder than observations for all seasons, and negative bias is even further
enhanced in winter and autumn.
In the interior, the continental effects dominate seasonal
temperature variability. There is a gradual decrease in
temperature toward eastern Anatolia. In the central Anatolian region, a strong daily contrast exists during winter
and transitional seasons due to high plateau, which has an
AEG
BLS
CEA
ESA
SEA
MED
average height around 1,000 m. Simulated summer temperatures in the interiors of Turkey (CEA) are very consistent with the observations. In general, regional contrasts
decrease during summer in all regions due to continental
effects. In CEA, there is a plateau effect rather than a sharp
topography effect, which results in a more even distribution
of temperatures, and decreases the differences between the
model and observation temperatures. However, in the
transition seasons, the model has a 2 °C cold bias. Even
though ESA region is the largest one represented by 1961
grid points in the model, there are only 8 meteorological
stations to verify the model results with the surface
observations. Also, the average elevation of the stations is
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B. Önol, Y. S. Unal
Fig. 4 Seasonal areal averages
change (A2 minus RF) for the
seven climatic regions of
Turkey. a Temperatures
changes (K), all seasons and
regions are statistically
significant at the 95 % condense
level and b precipitation
changes (%), shaded ones are
statistically significant at the
95 % condense level
(a)
(b)
very low. The difference between average model grid point
elevations and station elevations is around 800 m. As a
result, simulations for all seasons show negative bias
except winter. In general, the regional biases in the simulations for 1961–1990 period are mostly in the range of
±3 °C for temperature. RegCM3 estimates temperatures
colder than observations except for the MAR region.
The ability of RegCM3 to reproduce the precipitation
climatology is impressive as shown in Fig. 3. In particular,
for winter and spring, comparison between the model and
observations are an important source of confidence in
RegCM3 for conducting climate change studies. However,
there are also significant differences between RF and station averages over ESA region in winter and over the BLS
region during spring. Precipitation over ESA region, which
is represented with an average height of 1961 meter in the
model, should be expected to be in the form of snow in
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winter. The RegCM3 large-scale precipitation scheme does
not have ice physics, and we believe that this may account
for much of the discrepancy between the model results and
observed climate during the winter simulation over the
regions with high topography. The high orography around
the Mediterranean Sea and Black Sea regions determines
the areas of cyclogenesis and, in turn, changes the mesoscale structure of synoptic systems (Alpert et al. 1990;
Lionello et al. 2006). Orographic upslope lifting becomes
very effective for ascent of humid air and persistent precipitation, especially along the Mediterranean and Black
Sea coasts. In spring, model bias over BLS could be related
to the steep topography, which was also noted in a previous
study of Evans et al. (2004). During summer months,
almost all of Turkey is under the influence of northeasterly flow. Arabic low dominates southeastern and eastern Turkey. In general, except the BLS region, seasonal
Assessment of climate change simulations
Fig. 5 Schematic
representation of low level flow
change and precipitation change
(A2-RF) over Anatolian Plateau
precipitation in all regions is fairly low. Summer precipitation simulations show that the model under-predicts
precipitation over Turkey.
Our detailed analysis of seasonal precipitation and
temperature patterns reveal that climatology of temperature
and precipitation for the climatic regions over Turkey have
been simulated realistically by RegCM3.
Scenario simulation
Under SRES A2 scenario, temperature change with respect
to 1961–1990 period varies dramatically from region to
region and from season to season. It is estimated that
warming over Turkey’s climatic zones is in the range of
2–5 °C (Fig. 4a).
Summer temperature changes are more dominant in the
A2 simulation. This behavior has also been observed for
the other countries within the EM domain analyzed in Önol
and Semazzi (2009) and over the MAR and AEG regions.
Surface temperature increase over the MAR and AEG
regions is about 5 °C in summer. These changes become
more striking in the area averages than in the spatial pattern-based model results. These two regions are prominent
on tourism and industrial sectors in Turkey, and 5 °C
increase in summer temperature may cause serious economic and social implications. During winter season,
warming over the same regions is just over 2 °C. The
difference between the summer and winter changes is
about 3 °C, and it could play an important role in contributing to temporal shifts of the transition seasons over
these two regions. This seasonal contrast in temperature
may cause an instability problem for the surface wind. In
addition, summer temperatures over MAR and AEG are
more than 1 °C higher than for ESA and SEA. On the other
hand, warming in winter over the ESA and SEA regions,
which have higher altitudes, is nearly 1 °C higher than for
MAR and AEG. This could be due to the snow cover
reduction over regions of higher altitude for winter. Winter
temperature increase for seven regions varies between 2
and 3 °C. Autumn temperature changes for all regions are
affected by the extension of the summer season due to the
global warming. Temperature increase over AEG, CEA,
SEA, SEA and MED is just over the 4 °C and just below
the 4 °C for MAR and BLS.
The precipitation results over the regions for future
simulation are more striking than temperature results
(Fig. 4b). The most significant precipitation changes were
calculated over the MED region in winter and over the
SEA region in autumn. We note a 34 % decrease in MED
winter precipitation, which is statistically significant. It is
related to the change in the atmospheric circulation, which
in turn causes reduced orographic forcing. We believe that
the same circulation changes are also responsible for the
enhanced orographic forcing, especially on the east of
BLS, and results in significant precipitation increase
(15 %).
Meridional component of 850 hPa wind field in A2
winter simulations becomes more northerly than the RF
simulations (Önol and Semazzi 2009). The circulation
change and its relation with the precipitation change for
Turkey is schematically illustrated in Fig. 5. When the
low-level flow anomaly (A2-RF) is perpendicular to the
mountain range, the Black Sea Mountains force moist air to
move upward and causes persistent rainfall on the northern
side of the mountains. However, relatively dry air reaches
to the southern part of the Black Sea Mountains and results
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B. Önol, Y. S. Unal
Fig. 6 Zonally averaged (Black Sea: 30E:40E; Mediterranean: 30E:36E) meridional wind (m/s) and change of cloud mixing ratio (%) for RF
and A2 winter season simulations
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Assessment of climate change simulations
Winter
Frequency
0.4
MAR
0.2
Frequency
0.4
0
20
0
10
20
30
40
0
10
20
20
30
40
0.2
0
10
20
30
40
0
−10
20
30
40
20
30
40
20
30
40
SEA
0.2
0
10
20
0
10
0.4
MED
0.2
20
0
10
0.4
SEA
0
−10
0.4
BLS
ESA
0.2
0.2
0
10
0.4
CEA
ESA
0
−10
0.4
AEG
0.2
0
−10
0
−10
20
0
10
0.4
BLS
Summer
0.4
0.2
0.2
0.2
0.4
MAR
0
10
0.4
AEG
0
−10
Frequency
10
0.2
0.4
Winter
0.4
0.2
0
−10
Frequency
Summer
0.4
MED
0.2
0
10
Temperature (°C)
20
0
10
Temperature (°C)
CEA
0.2
0
10
Temperature (°C)
20
0
10
20
30
40
Temperature (°C)
Fig. 7 Frequency distributions of temperature for seven regions over Turkey for winter and summer months. White and gray histograms
correspond to the reference and future simulations, respectively
in less precipitation. Similarly, flow passes the Anatolian
plateau, ascends along the Taurus mountain range and even
more drying occurs. Zonally averaged (Black Sea:
30E:40E; Mediterranean: 30E:36E) meridional wind (m/s)
for RF and A2 simulations and cloud mixing ratio (%)
change in A2 simulation have been shown in Fig. 6 to
support the argument for winter season precipitation
change. It reveals that meridional flow (northward) in A2
simulation decreases on the upwind side where the cloud
mixing ratio rises up to 10 % over the Black Sea region.
Similarly, meridional flow (southward) in the Mediterranean region intensifies where the cloud mixing ratio
declines up to 25 %. Therefore, on the southern side of the
Taurus mountain range, precipitation decreases in A2
simulations (Fig. 4b). Precipitation reductions over the
AEG and SEA regions are around 20 and 25 % respectively in winter. In general, the precipitation change for the
rest of the regions is under 10 % and not statistically
significant.
Spring season precipitation changes for the regions of
Turkey are not statistically significant except for the AEG
region, which experiences a decrease of 18 %. The existence of a circulation change in spring, similar to winter
circulation, affects the precipitation distribution. However,
the spring season precipitation change is weaker than the
winter change. Precipitation decreases 20 % over the MED
region and increases \5 % over the BLS region; they are
within ±10 % for the rest of regions (Fig. 4b).
In autumn, the precipitation increases over all regions,
but only changes over ESA and SEA regions are found to
be statistically significant. Precipitation over the SEA
region is projected to increase by as much as 48 %. One of
the main reasons for this is that geopotential height at
850 hPa over the southern border of Turkey becomes
weaker in A2 simulations (Önol and Semazzi 2009). It
changes the dominant flow pattern influencing the SEA
region. Flow pattern also extends into Iraq and Syria. This
structure supports the moisture entrainment from the
Mediterranean Sea, the Red Sea and the Persian Gulf into
the region (Önol and Semazzi 2009). This flow pattern
enhances moisture availability over the SEA, which may
result in the major precipitation increase. In the same
123
B. Önol, Y. S. Unal
50 100 150 200 250 300
50 100 150 200 250 300
150
50
0
150
SEA (A2)
50
50 100 150 200 250 300
0
Precipitation (mm/month)
MED (RF)
50 100 150 200 250 300
Precipitation (mm/month)
MED (A2)
0
200
300
400
100
50
0
200
300
Turkey (RF)
0
0
50 100 150 200 250 300
Precipitation (mm/month)
50 100
0
50 100 150 200 250 300
Precipitation (mm/month)
0
Precipitation (mm/month)
50
50
50
0
0
0
100
150
CEA (A2)
100
100
150
CEA (RF)
100
Precipitation (mm/month)
50 100
200
300
Precipitation (mm/month)
Turkey (A2)
150
400
100
300
50
200
0
100
Precipitation (mm/month)
150
0
0
0 10
0 10
50
30
100
50
50 100 150 200 250 300
Precipitation (mm/month)
0
0
150
BLS (A2)
0
100
150
50
50 100 150 200 250 300
Precipitation (mm/month)
BLS (RF)
50
SEA (RF)
0
0
Precipitation (mm/month)
30
50 100 150 200 250 300
Precipitation (mm/month)
100
100
150
AEG (A2)
50
0
0
Precipitation (mm/month)
0
0
50
100
150
AEG (RF)
100
100
0
Precipitation (mm/month)
ESA (A2)
150
50 100 150 200 250 300
0
0
50
50
50
0
0
ESA (RF)
150
150
MAR (A2)
100
100
150
MAR (RF)
0
50 100 150 200 250 300
Precipitation (mm/month)
0
50 100 150 200 250 300
Precipitation (mm/month)
Fig. 8 Frequency distributions of monthly precipitation for seven regions over Turkey. RF and A2 correspond to the reference and future
simulations, respectively
season, we note a 20–25 % increase in the autumn precipitation over the MAR, ESA and MED regions.
Upstream of Euphrates and Tigris river basins are located
in SEA and ESA regions. Turkey’s engaged large integrated
water resources development project includes 22 dams built
over these two river systems and is expected to irrigate
1.7 ha of land. Seasonal change of precipitation is crucial
for freshwater management in the region. A recent study by
Özdogan (2011) reveals that projected snow water equivalent for the second half of the twenty-first century, which is
produced by a hydrological model driven with 13 different
GCMs, declines 10–50 % in ensemble mean of scenario
simulations (A2 and B1) over the Euphrates–Tigris basin.
123
Hence, it is important to determine the winter precipitation
decrease and the autumn precipitation increase regionally to
produce regional and seasonal river basin management
plans. It is expected in A2 scenario that the autumn precipitation increase (48 %) compensates for the lack of
precipitation during winter in SEA. Therefore, the change
of total annual precipitation becomes negligible. However,
consideration of climate change impacts on human security
and social stability may change from one region to another.
The study from Scheffran and Battaglini (2011) indicates
that societal risks are high and adaptive capacity is low in
the SEA region which enhances potential conflicts and
stress on water resources. These consequences are
Assessment of climate change simulations
significant for sustainable management of water resources
within these two river basins and future adaptation studies.
Since the amount of actual precipitation over the entire
country of Turkey in summer season is very small and none
of the regional changes are statistically significant, summer
percent changes over any of the regions are not considered
as important as the other seasons. However, the 20 %
increase over SEA and the 30 % decrease over MAR for
summer precipitation are noteworthy.
The changes of the tails of probability distributions
between reference and future simulations are as important
as the shift of the mean since it determines the degree of
the climate change impact on societies. More detailed
analyses have been discussed related to the change of
probability distribution for climate simulations by Ferro
et al. (2005). We explored probability distributions of
temperature and precipitation in each region. Figure 7
indicates temperature distribution, where the reference
period is shown in white and the future is in gray. In both
seasons and in all regions, the distribution for 2071–2100
has shifted toward higher temperatures from the control
period. It shows a higher probability of mild winters and a
lower probability of cold winters. The wider distribution
with a lower peak indicates an increase in interannual
variability especially in winter season in all regions. The
most dramatic change occurs in ESA, SEA and MED
regions. However, the variability in ESA, SEA and MED
regions for the summer decreases while it slightly increases
for the other regions. Figure 8 illustrates the frequency
distribution of monthly precipitation for the control and the
future in all regions and the whole of Turkey. The distribution characteristics of precipitation for the period of
2071–2100 are different in extreme ends from the control.
It is noteworthy that the number of months with total
precipitation more than 100 mm (200 mm) increases in
MAR, ESA and AEG (in BLS) regions while the number of
months with precipitation above 200 mm decreases in
MED for A2 scenario. There is not a significant change
observed on the other regions.
Conclusions
It is necessary to take preventive measures for adaptation to
the accelerated climate change in the future by considering
the regional needs of Turkey since it covers a large territory
and its surface characteristics vary extremely toward east.
In this study, simulations for reference (RF; 1961–1990)
and for future (A2; 2071–2100) were evaluated by taking
into account the regional differences of Turkey. We
focused on temperature and precipitation change over
Turkey and analyzed the results for seven distinct climatic
regions.
The reference climate simulation over Turkey was
compared with CRU data and the area average of surface
observations over Turkey. RF simulation indicates that
temperature bias is positive only in summer season and it is
around 1 °C for MAR and AEG. However, in general the
model shows a cold bias changing between 1 and 3 °C for
all seasons. Precipitation distribution for the same period
demonstrates that RegCM3 realistically mimics spatial
variability of precipitation for all seasons. However, there
are some inconsistencies between the observations and
simulations, especially over the coastal and mountainous
regions. RegCM3 overestimates precipitation along Eastern Black Sea Mountains, Taurus Mountains and over
Eastern Anatolia, except for the summer season. Very steep
topography over these regions is one of the possible reasons for excessive precipitation estimation.
Besides spatial comparisons, RF simulations were verified by using area averages of surface observations for
each distinct climatic region. We have noted some significant differences between the control run and the observations for the spring season precipitation over the BLS
region in Turkey, and for the temperature for all the seasons of the year. We attribute these differences mainly to
observational deficiencies. However, model shortcomings
may also significantly account for some of the systematic
biases particularly over the BLS region during spring. In
spite of these differences, RegCM3 is able to reproduce the
precipitation climatology reasonably well, especially for
winter and spring seasons. Detailed analysis of seasonal
precipitation and temperature over Turkey’s climatic
regions for reference climate builds confidence on future
climate simulations.
One of the most striking results for the future simulation
is that the projected summer temperature changes of 5 °C
over the MAR and AEG regions are the highest in comparison with the other regions. On the other hand, in the
same regions winter temperature changes (2 °C) are
smaller than all the other regions. The seasonal temperature
difference between winter and summer in the future is 3 °C
more than reference climate in the MAR and AEG regions.
Hence, temperature in the transition seasons could change
rapidly as a result of increased annual cycle amplitude.
Spatial distribution of the projected summer temperature
shows a distinct positive gradient from high elevation
regions (ESA and SEA) to low elevations (MAR and
AEG). On the contrary, the gradient reverses in winter
season.
Precipitation results in future simulation reveal that
precipitation in all seasons and in almost all regions
decreases except for the autumn season. All the major
precipitation changes for the climatic regions are found
statistically significant. The range of decrease is 10–35 %.
The most dramatic precipitation changes are found during
123
B. Önol, Y. S. Unal
winter season in the SEA (-24 %), MED (-34 %) and
BLS (?18 %) regions as a result of low-level circulation
change. It is important to note that these findings were also
consistent with the previous studies (Chenoweth et al.
2011; Hemming et al. 2010; Evans 2010; Kitoh et al. 2008)
that were performed with different regional and global
climate models. In the future simulation, 850 hPa wind
anomalies show a more northerly component in winter. As
it is obvious in schematic representation of Fig. 5, this
circulation changes enhance the orographic forcing of
Black Sea Mountains and consequently increase the precipitation in the BLS region. On the other hand, the same
circulation results in reduced precipitation in the MED
region.
Autumn precipitation change in the SEA region is also
significant as in winter season, which has an increased
precipitation around 48 %. The upstream of the Euphrates
and Tigris river basins is located in the SEA region of
Turkey. These two rivers are the main fresh water sources
of the region. Even though the precipitation decrease during winter creates water stress in SEA, autumn precipitation increase compensates for the winter precipitation
deficit. As a result, it is expected that annual total precipitation might not change significantly in the future.
It is noteworthy that analyzing the climate change
simulations on the climatologically homogeneous regions
is necessary to determine the impact of climate change
over large countries such as Turkey since these results
might lead decision makers to develop adaptation strategies
and plans on a regional basis and interdisciplinary
researchers to conduct impact studies.
Acknowledgments We would like to thank Prof. Fredrick Semazzi
for his encouragements and academic support during this study.
References
Alpert P, Abramsky R, Neeman BU (1990) The prevailing summer
synoptic system in Israel—subtropical high, not Persian trough
Israel. J Earth Sci 39:93–102
Alpert P, Osetinsky I, Zivb B, Shafir H (2004) Semi-objective
classification for daily synoptic systems: application to the Eastern
Mediterranean climate change. Int J Climatol 24:1001–1011
Arakawa A, Schubert WH (1974) Interaction of a cumulus cloud
ensemble with the large-scale environment, Part I. J Atmos Sci
31:674–701
Chenoweth J, Hadjinicolaou P, Bruggeman A, Lelieveld J, Levin Z,
Lange MA, Xoplaki E, Hadjikakou M (2011) Impact of climate
change on the water resources of the eastern Mediterranean and
Middle East region: modeled 21st century changes and implications. Water Res 47:W06506. doi:10.1029/2010WR010269
Coppola E, Giorgi F (2005) Climate change in tropical regions from
high resolution time slice AGCM experiments. Q J R Meteor Soc
131:3123–3145
123
Dickinson RE, Henderson-Sellers A, Kennedy PJ (1993) Biosphereatmosphere transfer scheme (BATS) version 1E as coupled to the
NCAR Community Climate Model, Tech. Rep., TN-387 ? STR.
NCAR, Boulder, Colorado, p 72
Diffenbaugh NS, Giorgi F, Raymond L, Bi X (2007) Indicators of
21st century socioclimatic exposure. PNAS 104(51):20195–
20198
Evans JP (2010) Global warming impact on the dominant precipitation processes in the Middle East. Theor Appl Climatol
99:389–402. doi:10.1007/s00704-009-0151-8
Evans JP, smith RB, Oglesby RJ (2004) Middle East climate
simulation and dominant precipitation processes. Int J Climatol
24:1671–1694
Ferro CAT, Hannachi A, Stephenson DB (2005) Simple nonparametric techniques for exploring changing probability distributions of weather. J Clim 18:4344–4354
Gao XJ, Giorgi F (2008) Increased aridity in the Mediterranean region
under greenhouse gas forcing estimated from high resolution
regional climate model projections. Glob Plan Change 62:195–
209
Giannakopoulos C, Kostopoulou E, Varotsos KV, Tziotziou K,
Plitharas A (2011) An integrated assessment of climate change
impacts for Greece in the near future. Reg Environ Change
11:829–843
Giorgi F (2006) Climate change hot-spots. Geophys Res Lett
33:L087707
Giorgi F, Shields C (1999) Tests of precipitation parameterizations
available in latest version of NCAR regional climate model
(RegCM) over continental United States. J Geophys Res 104
(D6), 6353–6375
Giorgi F, Bates G, Nieman S (1993a) The multi-year surface
climatology of a regional atmospheric model over the western
united states. J Clim 6:75–95
Giorgi F, Marinucci M, Bates G (1993b) Development of a second
generation regional climate model (regcm2): boundary layer and
radiative transfer processes. Mon Weather Rev 121:2794–2813
Giorgi F, Xunquiang B, Pal JS (2004) Mean, interannual variability
and trends in a regional climate change experiment over Europe.
I. Present-day climate (1961–1990). Clim Dyn 22:733–756
Grell GA (1993) Prognostic evaluation of assumptions used by
cumulus parameterizations. Mon Weather Rev 121:764–787
Hadjinicolaou P, Giannakopoulos C, Zerefos C, Lange MA, Pashiardis S, Lelieveld J (2011) Mid-21st century climate and weather
extremes in Cyprus as projected by six regional climate models.
Reg Environ Change 11:441–457
Hahmann AN, Rostkier-Edelstein D, Warner TT, Liu Y, Vandenberghe F, Swerdlin SP (2008) Toward a climate downscaling for the
Eastern Mediterranean at high-resolution. Adv Geosci
12:159–164
Hemming D, Buontempo C, Burke E, Collins M, Kaye N (2010)
2010: How uncertain are climate model projections of water
availability indicators across the Middle East? Philos Trans R
Soc A 368:5117–5135. doi:10.1098/rsta.2010.0174
Kitoh A, Yatagai A, Alpert P (2008) First Super-high-resolution
model projection that the ancient ‘‘Fertile Crescent’’ will
disappear in this century. Hydro Res Lett 2:1–4. doi:10.3178/
HRL.2.1
Krichak SO, Alpert P, Bassat K, Kunin P (2007) The surface
climatology of the eastern Mediterranean region obtained in a
three-member ensemble climate change simulation experiment.
Adv Geosci 12:67–80
Krichak S, Breitgand J, Samuels R, Alpert P (2011) A doubleresolution transient RCM climate change simulation experiment
for near-coastal eastern zone of the Eastern Mediterranean
region. Theor Appl Climatol 103(1–2):167–195
Assessment of climate change simulations
Lin S-J (2004) A vertically Lagrangian finite-volume dynamical core
for global models. Mon Weather Rev 132:2293–2307
Lionello P, Bhend J, Buzzi A, Della-Marta PM, Krichak S, Jansà A,
Maheras P, Sanna A, Trigo IF, Trigo R (2006) Cyclones in the
Mediterranean region: climatology and effects on the environment. In: Lionello P, Malanotte-Rizzoli P, Boscolo R (eds)
Mediterranean climate variability. Elsevier, Amsterdam, pp 324–
272
Mitchell TD, Jones PD (2005) An improved method of constructing a
database of monthly climate observations and associated highresolution grids. Int J Climatol 25:693–712
New M, Hulme M, Jones PD (1999) Representing twentieth century
space-time climate variability. Part 1: development of a 1961–90
mean monthly terrestrial climatology. J Clim 12:829–856
New M, Hulme M, Jones P (2000) Representing twentieth-century
space-time climate variability. Part II: development of 1901–96
monthly grids of terrestrial surface climate. J Clim 13:2217–
2238
Önol B, Semazzi FHM (2009) Regionalization of climate change
simulations over the Eastern Mediterranean. J Clim 22:1944–
1961
Özdoğan M (2011) Climate change impacts on snow water
availability in the Euphrates-Tigris basin. Hydrol Earth Syst
Sci 15:2789–2803
Pal JS, Small EE, Eltahir EAB (2000) Simulation of regional scale
water and energy budgets: influence of a new moist physics
scheme within RegCM. J Geophys Res Lett 105:29579–29594
Pal JS, Giorgi F, Bi X, Elguindi N, Solmon F, Gao X, Rauscher SA,
Francisco R, Zakey A, Winter J, Ashfaq M, Syed FS, Bell JL,
Diffenbaugh NS, Karmacharya J, Konaré A, Martinez D, da
Rocha RP, Sloan LC, Steiner AL (2007) Regional climate
modeling for the developing World: The ICTP RegCM3 and
RegCNET. Bull Am Meteor Soc 88:1395–1409
Scheffran J, Battaglini A (2011) Climate and conflicts: the security
risks of global warming. Reg Environ Change 11:27–39. doi:
10.1007/s10113-010-0175-8
Unal Y, Kindap T, Karaca M (2003) Redefining the climate zones of
Turkey using cluster analysis. Int J Climatol 23:1045–1055
Wang G (2005) Agricultural drought in a future climate: results from
15 global climate models participating in the IPCC 4th
assessment. Clim Dyn 25:739–753
123