Assessment of climate change simulations over climate zones of Turkey

Reg Environ Change DOI 10.1007/s10113-012-0335-0

ORIGINAL STUDY

Assessment of climate change simulations over climate zones of Turkey ¨ nol • Yurdanur S. Unal Barıs¸ O

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. ¨ nol (&) Y. S. Unal B. O 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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¨ nol, Y. S. Unal B. O

(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 ¨ nol and Semazzi (2009) present the model validation and O 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 ¨ nol and Semazzi (2009). Here, we concentrate on the in O 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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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)


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 ¨ nol and Semazzi 2009). A second reason might climate (O 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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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

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300

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Fig. 3 Same as in Fig. 2 but for precipitation

500


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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¨ nol, Y. S. Unal B. O 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 ¨ nol the other countries within the EM domain analyzed in O 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 ¨ nol and Semazzi 2009). The circulation simulations (O 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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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

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0 −10 0.4

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0.2

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0 10 0.4

CEA

ESA

0 −10 0.4

AEG

0.2

0 −10

0 −10

20

0 10 0.4

BLS

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0.2

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0 10 0.4

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0 −10

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0 −10

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0.2

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0 10

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CEA

0.2

0

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20

0 10

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40

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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 ¨ nol and Semazzi 2009). It weaker in A2 simulations (O 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 ¨ nol and Semazzi 2009). This flow pattern the region (O enhances moisture availability over the SEA, which may result in the major precipitation increase. In the same

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50 100 150 200 250 300

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SEA (A2)

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0

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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 ¨ zdogan (2011) reveals that projected snow water equivaO lent 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.

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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


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

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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.

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