Spring temperature variability over Turkey since 1800 CE reconstructed from a broad network of tree-ring data

The meteorological observational period in Turkey, which starts ca. 1930 CE, is too short for understanding long-term climatic variability. Tree rings have been used intensively as proxy records to understand summer precipitation history of the region, primarily because they have a dominant precipitation signal. Yet, the historical context of temperature variability is unclear. Here, we used higher-order principle components of a network of 23 tree-ring chronologies to provide a high-resolution spring (March–April) temperature reconstruction over Turkey during the period 1800– 2002. The reconstruction model accounted for 67 % (Adj. R2 = 0.64, p < 0.0001) of the instrumental temperature variance over the full calibration period (1930–2002). The reconstruction is punctuated by a temperature increase during the 20th century; yet extreme cold and warm events during the 19th century seem to eclipse conditions during the 20th century. We found significant correlations between our March–April spring temperature reconstruction and existing gridded spring temperature reconstructions for Europe over Turkey and southeastern Europe. Moreover, the precipitation signal obtained from the tree-ring network (first principle component) showed highly significant correlations with gridded summer drought index reconstruction over Turkey and Mediterranean countries. Our results showed that, beside the dominant precipitation signal, a temperature signal can be extracted from tree-ring series and they can be useful proxies in reconstructing past temperature variability.


Introduction
Long-term meteorological observations in the Mediterranean region allow access to 100 years of instrumental recordings of temperature, precipitation and pressure in most of the region.Moreover, natural archives as well as documentary information provide resources with which to make sensitive climate reconstructions (Luterbacher et al., 2012).An extensive body of literature details climate changes in the Mediterranean region over the last two millennia (Luterbacher et al., 2012).Paleolimnological studies provide evidence that the Medieval Climatic Anomaly (MCA; 900-1300 CE) characterized warm and dry conditions over the Iberian Peninsula, while the Little Ice Age (LIA; 1300-1850 CE) brought opposite climate conditions, forced by interactions between the East Atlantic and North Atlantic Oscillation (NAO; Sanchez-Lopez et al., 2016).In addition, Roberts et al. (2012) highlighted an intriguing spatial dipole NAO pattern between the western and eastern Mediterranean region, which brought antiphased warm (cool) and wet (dry) conditions during the MCA and LIA.The hydro-climate patterns revealed by previous investigations appear to have been forced not only by NAO but other climate modes with nonstationary teleconnections across the region (Roberts et al., 2012).
The climate of Turkey is mainly characterized by a Mediterranean macroclimate (Türkeş, 1996a).Contrary to most countries in the Mediterranean region, Turkey has relatively short meteorological records, which start in the 1930s, for understanding long-term climatic variability.On the other hand, proxy records such as speleothems (Fleitmann et al., 2009;Jex et al., 2010;Göktürk et al., 2010), lake sediments  (YAU, SIA andSIU in Mutlu et al., 2011. TIR in Akkemik et al., 2008.TAN in Köse et al., 2013.KIZ, ESK, TEF, BON, KEL, USA, FIR and TUR in Köse et al., 2011. CAT andINC in Köse et al., 2005).The box (dashed line) represents the area for which the temperature reconstruction was performed.(Wick et al., 2003;Jones et al., 2006;Roberts et al., 2008Roberts et al., , 2012;;Kuzucuoglu et al., 2011;Woodbridge and Roberts, 2011;Ülgen et al., 2012;Dean et al., 2013) and tree rings, have been used to reconstruct long-term hydroclimate conditions over Turkey.Tree rings in particular have shown to provide useful information about the past climate of Turkey and were used intensively during the last decade to reconstruct precipitation in the Aegean (Griggs et al., 2007), Black Sea (Akkemik et al., 2005(Akkemik et al., , 2008;;Martin-Benitto et al., 2016), Mediterranean regions (Touchan et al., 2005a), as well as the Sivas (D' Arrigo and Cullen, 2001), southwestern (Touchan et al., 2003(Touchan et al., , 2007;;Köse et al., 2013), south-central (Akkemik and Aras, 2005) and western Anatolian (Köse et al., 2011) regions of Turkey.These studies used tree rings to reconstruct precipitation because available moisture is often found to be the most important limiting factor that influences radial growth of many tree species in Turkey.These studies revealed past spring-summer precipitation, and described past dry and wet events and their duration.Recently, Cook et al. (2015) presented Old World Drought Atlas (OWDA), which is a set of year-by-year maps of reconstructed Palmer Drought Severity Index from tree-ring chronologies over the Europe and Mediterranean Basin.
Besides detailed information on precipitation history represented by these paleoscientific studies, we still have very limited knowledge on past temperature variability of Turkey.For example, significant decreases in spring diurnal temperature ranges (DTRs) occurred throughout Turkey from 1929 to 1999 (Turkes and Sumer, 2004).This decrease in spring DTRs was characterized by daytime temperatures that remained relatively constant while a significant increase in nighttime temperatures were recorded over western Turkey and were concentrated around urbanized and rapidly urbanizing cities.The historical context of this gradual warming trend in spring temperatures is unclear.Heinrich et al. (2013) provided a winter-to-spring temperature proxy for Turkey from carbon isotopes within the growth rings of Juniperus excelsa M. Bieb.since 1125 CE.Low-frequency temperature trends corresponding to the end of the MCA and the LIA were identified in the record, but the proxy failed to identify the recent warming trend during the 20th century.In this study, we present a tree-ring-based spring temperature recon-struction from Turkey and compare our results to previous reconstructions of temperature and precipitation to provide a more comprehensive understanding of climate conditions during the 19th and 20th centuries.

Climate of the study area
The study area, which spans 36-42 • N and 26-38 • E, was based on the distribution of available tree-ring chronologies.This vast area covers much of western Anatolia and includes the western Black Sea, Marmara and western Mediterranean regions.Much of this area is characterized by a Mediterranean climate that is primarily controlled by polar and tropical air masses (Türkeş, 1996a;Deniz et al., 2011).In winter, polar fronts from the Balkan Peninsula bring cold air that is centered in the Mediterranean.Conversely, the dry, warm conditions in summer are dominated by weak frontal systems and maritime effects.Moreover, the Azores highpressure system in summer and anticyclonic activity from the Siberian high-pressure system often cause below normal precipitation and dry subhumid conditions over the region (Türkeş, 1999;Deniz et al., 2011).In this Mediterranean climate, annual mean temperature and precipitation range from 3.6 to 20.1 • C and from 295 to 2220 mm, respectively, both of which are strongly controlled by elevation (Deniz et al., 2011).

Development of tree-ring chronologies
To investigate past temperature conditions, we used a network of 23 tree-ring site chronologies (Fig. 1).Fifteen chronologies were produced by previous investigations (Mutlu et al., 2011;Akkemik et al., 2008;Köse et al., 2005Köse et al., , 2011Köse et al., , 2013) ) that focused on reconstructing precipitation in the study area.In addition, we sampled eight new study sites and developed tree-ring time series for these areas (Table 1).Increment cores were taken from living Pinus nigra Arnold and Pinus sylvestris L. trees and cross-sections were taken from Abies nordmanniana (Steven) Spach and Picea orientalis (L.) Link trunks.
Samples were processed using standard dendrochronological techniques (Stokes and Smiley, 1968;Orvis and Grissino-Mayer, 2002;Speer, 2010).Tree-ring widths were measured, then visually cross-dated using the list method (Yamaguchi, 1991).We used the computer program COFECHA, which uses segmented time-series correlation techniques, to statistically confirm our visual cross-dating (Holmes, 1983;Grissino-Mayer, 2001).Cross-dated treering time series were then standardized by fitting a 67 % cubic smoothing spline with a 50 % cutoff frequency to remove non-climatic trends related to the age, size and the effects of stand dynamics using the ARSTAN program (Cook, 1985;Cook et al., 1990a).These detrended series were then pre-whitened with low-order autoregressive models to produce time series with a strong common signal and without biological persistence.These series may be more suitable to understand the effect of climate on tree growth, even if any persistence due to climate might be removed by pre-whitening.For each chronology, the individual series were averaged to a single chronology by computing the biweight robust means to reduce the influences of outliers (Cook et al., 1990b).In this research we used residual chronologies obtained from ARSTAN to reconstruct temperature.
The mean sensitivity, which is a metric representing the year-to-year variation in ring width (Fritts, 1976), was calculated for each chronology and compared.The minimum sample depth for each chronology was determined according to expressed population signal (EPS), which we used as a guide for assessing the likely loss of reconstruction accuracy.Although arbitrary, we required the commonly considered threshold of EPS > 0.85 (Wigley et al., 1984;Briffa and Jones, 1990).

Identifying relationships between tree-ring width and climate
We extracted high-resolution monthly temperature and precipitation records from the climate dataset CRU TS 3.23 gridded at 0.5 First, the climate-growth relationships were investigated with response function analysis (RFA; Fritts, 1976) for biological year from the previous October to current October using the DENDROCLIM2002 program (Biondi and Waikul, 2004).This analysis is done to determine the months during which the tree growth is the most responsive to temperature.RFA results showed that precipitation from May to August and temperature in March and April have dominant control on tree-ring formation in the area.Second, we produced correlation maps showing correlation coefficients between tree-ring chronologies and the climate factors most important for tree growth, which are May-August precipitation and March-April temperature, to find the spatial structure of radial growth-climate relationship (St. George, 2014;St. George and Ault, 2014;Hellmann et al., 2016).For each site we used the closest gridded temperature and precipitation values.

Temperature reconstruction
The climate reconstruction is performed by regression based on the principal components (PCs) of the 23 chronologies within the study area.Principle component analysis (PCA) was done over the entire period in common with the treering chronologies.The significant PCs were selected by stepwww.clim-past.net/13/1/2017/Clim.Past, 13, 1-15, 2017  (Wigley et al., 1984).
wise regression.We combined forward selection with backward elimination setting p < 0.05 as entrance tolerance and p < 0.10 as exit tolerance.The final model obtained when the regression reaches a local minimum of the root-meansquared error (RMSE).The order of entry of the PCs into the model was PC 3 , PC 21 , PC 4 , PC 15 , PC 5 , PC 17 , PC 7 , PC 9 , PC 10 .The regression equation is calibrated on the common period  between robust temperature time series and the selected tree-ring series.Third, the final reconstruction is based on bootstrap regression (Till and Guiot, 1990), a method designed to calculate appropriate confidence intervals for reconstructed values and explained variance even in cases of short time series.It consists in randomly resampling the calibration datasets to produce 1000 calibration equations based on a number of slightly different datasets.
The quality of the reconstruction is assessed by a number of standard statistics.The overall quality of fit of reconstruction is evaluated based on the determination coefficient (R 2 ), which expresses the percentage of variance explained by the model and RMSE, which expresses the calibration error.This does not ensure the quality of the extrapolation which needs additional statistics based on independent observations, i.e., observations not used by the calibration (verification data).
They are provided by the observations not resampled by the bootstrap process.The prediction RMSE (called RMSEP), the reduction of error (RE) and the coefficient of efficiency (CE) are calculated on the verification data and enable testing the predictive quality of the calibrated equations (Cook et al., 1994).Traditionally, a positive RE or CE value means a statistically significant reconstruction model, but bootstrap has the advantage of producing confidence intervals for such statistics without theoretical probability distribution; finally, we accept the RE and CE for which the lower confidence margin at 95 % are positive.This is more constraining than just accepting all positive REs and CEs.For additional verification, we also present traditional split-sample procedure results that divided the full period into two subsets of equal length (Meko and Graybill, 1995).
To identify the extreme March-April cold and warm events in the reconstruction, standard deviation (SD) values were used.Years 1 and 2 SD above and below the mean were identified as warm, very warm, cold and very cold years, respectively.As a way to assess the spatial representation of our temperature reconstruction, we conducted a spatial field correlation analysis between reconstructed values and the gridded CRU TS3.23 temperature field (Jones and Harris,  2008) for a broad region of the Mediterranean over the entire instrumental period (ca. 1930-2002).Finally, we compared our temperature reconstruction and also precipitation signal (PC 1 ) against existing gridded temperature and hydroclimate reconstructions for Europe over the period 1800-2002.We performed spatial correlation analysis between (1) our temperature reconstruction and gridded temperature reconstructions for Europe (Xoplaki et al., 2005;Luterbacher et al., 2016) and OWDA (Cook et al., 2015); and (2) PC 1 and summer precipitation reconstruction (Pauling et al., 2006)   OWDA (Cook et al., 2015).To assess the significance of the correlation between our reconstruction (y) and gridded reconstructions (xj , j = 1. ..N ), we have calculated significance thresholds based on a Monte Carlo technique.For each grid point j , we have calculated the correlation between xj and y, but with a random permutation of the values of our reconstruction.This is repeated 1000 times with a different permutation.The 1000 correlation coefficients thus obtained are expected to be zero as the correlation is established on noncorresponding years.The 95th quantile of these 1000 coefficients is assumed to be passed in less than 5 % of the cases.Then a correlation coefficient with a higher value is considered as positive with a 95 % confidence.These thresholds are obtained with a common permutation for all xj so that the spatial structure is conserved in the tests.The + sign is assigned to the xj with a correlation higher than an expected value under the noncorrelation hypothesis.

Tree-ring chronologies
In addition to 15 chronologies developed by previous studies, we produced 6 P. nigra, 1 P. sylvestris and 1 A. nordmanniana/P.orientalis chronologies for this study (Table 2).The Çorum district produced two P. nigra chronologies: one being the longest (KAR; 627 years long) and the other the most sensitive to climate (SAH; mean sensitivity value of 0.25).Previous investigations of climate-tree growth rela-  tionships reported a mean sensitivity range of 0.13-0.25 for P. nigra in Turkey (Köse et al., 2011;Akkemik et al., 2008).The KAR, SAH and ERC chronologies (with mean sensitivity values from 0.22 to 0.25) were classified as very sensitive, and the SAV, HCR and PAY chronologies (mean sensitivity values range 0.17-0.18)contained values characteristic of being sensitive to climate.The lowest mean sensitivity value was obtained for the ART A. nordmanniana/P.orientalis chronology.Nonetheless, this chronology retained a statistically significant temperature signal (p < 0.05).

Tree-ring growth-climate relationship
RFA coefficients of May to August precipitation are positively correlated with most of the tree-ring series (Fig. 2) and among them May and June coefficients are generally significant.The first principal component of the 23 chronologies, which explains 47 % of the tree-growth variance, is highly correlated with May-August total precipitation, statistically (r = 0.65, p < 0.001) and visually (Fig. 3).The high correlation was expected given that numerous studies also found similar results in Turkey (Akkemik, 2000a, b, 2003, Akkemik et al., 2005, 2008;Akkemik and Aras, 2005;Hughes et al., 2001;D'Arrigo and Cullen, 2001;Touchan et al., 2003Touchan et al., , 2005aTouchan et al., , b, 2007;;Köse et al., 2011Köse et al., , 2012Köse et al., , 2013;;Martin-Benitto et al., 2016).The influence of temperature was not as strong as May-August precipitation on radial growth, although generally positive in early spring (March and April; Fig. 2).Conversely, the ART chronology from northeastern Turkey contained a strong temperature signal, which was significantly positive in March.
Correlation maps representing influence of May-August precipitation (Fig. 4a) and March-April temperature (Fig. 4b) also showed that strength of the summer precipitation signal is higher and significant almost all over Turkey.Higher precipitation in summer has a positive effect on tree growth, because of long-lasting dry and warm conditions over Turkey (Türkeş, 1996b;Köse et al., 2012).Spring precipitation signals are generally positive and significant only for four tree-ring sites.The sites located at the upper distributions of the species generally showed higher correlations.The highest correlations obtained for Picea/Abies chronology (ART) from the Caucasus, and for Pinus nigra chronology (HCR) from the upper (about 1900 m) and southeastern distribution of the species.This black pine forest was still partly covered by snow from the previous year during the field work in fall.Higher temperatures in spring maybe cause snowmelt earlier and lead to produce larger annual rings.In addition to these chronologies, we also used the chronologies that revealed the influence of precipitation as well as temperature to reconstruct March-April temperature.

March-April temperature reconstruction
The higher-order PCs of the 23 chronologies are significantly correlated with the March-April temperature and, by nature, are independent of the precipitation signal (Table 3).The best selection for fit temperatures are obtained with the PC 3 , PC 4 , PC 5 , PC 7 , PC 9 , PC 10 , PC 15 , PC 17 , PC 21 , which explains together 25 % of the tree-ring chronologies.So, the temperature signal remains important in the tree-ring chronologies and can be reconstructed.The advantage to separate both signals through orthogonal PCs enable removal of unwanted noise for our temperature reconstruction.Thus, PC 1 was not used as a potential predictor of temperature because it is largely dominated by precipitation (Table 3, Fig. 3).The last two PCs contain a too-small part of the total variance to be used in the regressions.However, even if Jolliffe (1982) and Hadi and Ling (1998) claimed that certain PCs with small eigenvalues (even the last one), which are commonly ignored by principal components regression methodology, may be related to the independent variable, we must be cautious with that because they may be much more dominated by noise than the first ones.So, the contribution of each PC to the regression sum of squares is also important for selection of PCs (Hadi and Ling, 1998).The findings of Jolliffe (1982) and Hadi and Ling (1998) provide a justification for using non-primary PCs, (e.g., of second and higher order) in our regression, given that correlations with temperature may be overpowered by affects from precipitation in our study area (E.R. Cook, personal communication, 2011).
Using this method, the calibration and verification statistics indicated a statistically significant reconstruction (Table 4, Fig. 5).For additional verification, we also present www.clim-past.net/13/1/2017/Clim.Past, 13, 1-15, 2017 split-sample procedure results.Similarly, the bootstrap results, derived calibration and verification tests using this method indicated statistically significant RE and CE values (Table 5).
The regression model accounted for 67 % (Adj.R 2 = 0.64, p < 0.0001) of the actual temperature variance over the calibration period .Also, actual and reconstructed March-April temperature values had nearly identical trends during the period 1930-2002 (Fig. 5).Moreover, the treering chronologies successfully simulated both high frequency and warming trends in the temperature data during this period.The reconstruction was more powerful at classifying warm events rather than cold events.Over the last 73 years, 8 of 10 warm events in the instrumental data were also observed in the reconstruction, while 5 of 9 cold events were captured.Similarly, previous tree-ring-based precipitation reconstructions for Turkey (Köse et al., 2011;Akkemik et al., 2008) were generally more successful in capturing dry years rather than wet years.
Among the warm periods in our reconstruction, conditions during the year 1879 were dry, 1895 wet, and 1901 very wet across the broad region of western Anatolia (Köse et al., 2011).Hence, we defined 1879 as a warm (in March-April) and dry year (in May-June), and 1895 and 1901 were warm and wet years.In the years 1895 and 1901 the com-  bination of a warm early spring and a wet, late springsummer caused enhanced radial growth in Turkey, interpreted as longer growing seasons without drought stress.
Of these event years, 1897 and 1898 were exceptionally cold and 1845, 1872 and 1873 were exceptionally warm.During the last 200 years, our reconstruction suggests that the coldest year was 1898 and the warmest year was 1873.The reconstructed extreme events also coincided with accounts from historical records.Server (2008) recounted the winter of 1898 as characterized by anomalously cold temperatures that persisted late into the spring season.A family that brought their livestock herds up into the plateau region in Kırşehir seeking food and water were suddenly covered in snow on 11 March 1898.This account of a late spring freeze supports the reconstruction record of spring temperatures across Turkey and offers corroboration to the quality of the reconstructed values.Seyf (1985) reported that extreme summer temperature during the year 1873 resulted in widespread crop failure and famine.Historical documents recorded an infamous droughtderived famine that occurred in Anatolia from 1873 to 1874 (Quataert, 1996;Kuniholm, 1990), which claimed the lives of 250 000 people and a large number of cattle and sheep (Faroqhi, 2009).This drought caused widespread mortality of livestock and depopulation of rural areas through human mortality, and migration of people from rural to urban areas.Further, the German traveler Naumann (1893) reported a very dry and hot summer in Turkey during the year 1873 (Heinrich et al., 2013).Conditions worsened when the international stock exchanges crashed in 1873 (Zürcher, 2004).Our temperature record suggests that dry conditions during the early 1870s were possibly exacerbated by warm spring temperatures that likely carried into summer.A similar pattern of intensified drought by warm temperatures was demonstrated recently by Griffin and Anchukaitis (2014) for the current drought in California, USA.
Extreme cold and warm events were usually 1 year long, and the longest extreme cold and warm events were 2 and 3 years long, respectively.These results were similar with durations of extreme wet and dry events in Turkey (Touchan www.clim-past.net/13/1/2017/Clim.Past, 13, 1-15, 2017  (Xoplaki et al., 2005), (b) gridded summer temperature reconstruction for Europe (Luterbacher et al., 2016), (c) Old World Drought Atlas (OWDA; Cook et al., 2015).Panels (d) and (e) show spatial correlations between PC 1 and OWDA (Cook et al., 2015) and gridded European summer precipitation reconstruction (Pauling et al., 2006), respectively.The + sign represents significant correlation coefficients (p < 0.05).et al., 2003, 2005a, b, 2007;Akkemik and Aras, 2005;Akkemik et al., 2005, 2008, Köse et al., 2011;Güner et al., 2016).Moreover, seemingly innocuous short-term warm events, such as the 1807 event, were recorded across the Mediterranean and in high elevations of the European regions.Casty et al. (2005) et al., 2010).However, their temperature trend does agree with trend analyses conducted on meteorological data from Turkey and other areas in the eastern Mediterranean region.The warming trend seen during our reconstruction calibration period  was similar to the data shown by Wahl et al. (2010) across the region and hemisphere.Further, the warming trends seen in our record agrees with data presented by Turkes and Sumer (2004), of which they attributed to increased urbanization in Turkey.Considering long-term changes in spring temperatures, the 19th century was characterized by more high-frequency fluctuations compared to the 20th century, which was defined by more gradual changes and includes the beginning of decreased DTRs in the region (Turkes and Sumer, 2004).

Comparison of instrumental gridded data and spatial reconstructions
Spatial correlation analysis revealed that our network-based temperature reconstruction was representative of conditions across Turkey as well as the broader Mediterranean region (Fig. 7).During the period 1930-2002, estimated temperature values were highly significant (r range 0.5-0.6,p < 0.01) with instrumental conditions recorded from southern Ukraine to the west across Romania, and from northern areas of Libya and Egypt to the east across Iraq.The strength of the reconstruction model is evident in the broad spatial implications demonstrated by the temperature record.Thus, we interpret warm and cold periods and extreme events within the record with high confidence.We compared our tree-ring-based temperature reconstruction with existing gridded temperature reconstructions for Europe (Xoplaki et al., 2005, Fig. 8a;Luterbacher et al., 2016, Fig. 8b) and the OWDA (Cook et al., 2015, Fig. 8c) for further validation of the reconstruction.Spatial correlations over the past 200 years were lower with reconstructed European summer temperature (May to July; Fig. 8b).Yet, we expected this result because of the paucity of Turkey derived proxies in the other reconstructions, as well as the differing seasons involved across the reconstructions.Similarly, our reconstruction showed weak correlations with summer drought index over Turkey.Besides comparing different seasons, perhaps this is because less precipitation begets drought conditions rather than high temperature in the region.The highest and significant (p < 0.05) correlations were found with European spring (March to May) temperature reconstruction over southeastern Europe, which are stronger over Turkey (Fig. 8a).We used the mean of corresponding grid points from European spring temperature reconstruction over the study area (36-42 • N, 26-38 • E) to show how the correlation changed over time (Fig. 9).The correlation cowww.clim-past.net/13/1/2017/Clim.Past, 13, 1-15, 2017 efficient was highly significant (r = 0.76, p < 0.001) during our calibration period .We found lower but still significant correlation (r = 0.35, p < 0.10) for the period of 1901-1929, which climatic records are very few over the region while available data has sufficient quality for most parts of Europe.These results give additional verification for our reconstruction.Moreover, our reconstruction has a weak, insignificant relationship (r = 0.13, p > 0.10) during the 19th century.This may be related to poor reconstructive skill of European spring temperature reconstruction over Turkey, which contains few proxies from the country (Xoplaki et al., 2005;Luterbacher et al., 2004).Nonetheless, these results demonstrate that tree-ring chronologies from Turkey can serve as useful temperature proxies for further spatial temperature reconstructions to fill the gaps in the area.
We also compared the precipitation signal (PC 1 ) obtained from our tree-ring network with OWDA (Cook et al., 2015) and gridded European summer precipitation reconstruction (Pauling et al., 2006) to test the strength of the signal spatially (Fig. 8d and e, respectively).We calculated highly significant positive correlations with summer drought index over Turkey and neighboring European countries such as Greece, Bulgaria, Romania and Italy while significant correlations are lower for the northern Mediterranean countries (Fig. 8d).These results showed that summer precipitation signal represented by PC 1 is very strong not only on instrumental period but also on pre-instrumental period and represents a large spatial coverage.We found low and insignificant correlations over Turkey and Mediterranean countries with European summer precipitation reconstruction (Fig. 8e).Pauling et al. (2006) stated that poor reconstructive skills determined over Turkey because of few instrumental record before the1930s.

Conclusions
In this study, we used a broad network of tree-ring chronologies to provide the first tree-ring-based temperature reconstruction for Turkey and identified extreme cold and warm events during the period 1800-1929 CE.Similar to the precipitation reconstructions against which we compare our air temperature record, extreme cold and warm years were generally short in duration (1 year) and rarely exceeded 2-3 years in duration.The coldest and warmest years over western Anatolia were experienced during the 19th century and the 20th century is marked by a temperature increase.
Reconstructed temperatures for the 19th century suggest that more short-term fluctuations occurred compared to the 20th century.The gradual warming trend shown by our reconstruction calibration period  is coeval with decreases in spring DTRs.Given the results of Turkes and Sumer (2004), the variations in short-and long-term temperature changes between the 19th and 20th centuries might be related to increased urbanization in Turkey.
We highlight that the 20th century warming trend is unprecedented within the context of the past ca.200 years, especially over the past ca.15 years.Correlations with gridded climate fields and other climate reconstructions from the region revealed that our network-based temperature reconstruction was representative of conditions across Turkey, as well as the broader Mediterranean region.Expanding the tree-ring network across Turkey, especially to the east, will improve the spatial implications of future temperature reconstructions.
The study revealed the potential for reconstructing temperature in an area previously thought impossible, especially given the strong precipitation signals displayed by most tree species growing in the dry Mediterranean climate that characterizes broad areas of Turkey.Our reconstruction only spans 205 years due to the shortness of the common interval for the chronologies used in this study, but the possibility exists to extend our temperature reconstruction further back in time by increasing the sample depth with more temperaturesensitive trees, especially from northeastern Turkey.Thus, future research will focus on increasing the number of treering sites across Turkey and maximizing chronology length at existing sites that would ultimately extend the reconstruction back in time.

Figure 1 .
Figure 1.Tree-ring chronology sites in Turkey used to reconstruct temperature.Circles represent the new sampling efforts from this study and the triangles represent previously published chronologies (YAU, SIA and SIU in Mutlu et al., 2011.TIR in Akkemik et al., 2008.TAN in Köse et al., 2013.KIZ, ESK, TEF, BON, KEL, USA, FIR and TUR inKöse et al., 2011.CAT and INC in Köse et al., 2005).The box (dashed line) represents the area for which the temperature reconstruction was performed.

Figure 2 .
Figure 2. Summary of response function results of 23 chronologies.Red color represents negative effects of climate variability on tree ring width; blue color represents positive effects of climate variability on tree ring width; * indicates statistically significant response function coefficient (p < 0.05).Each response function includes 13 weights for average monthly temperatures and 13 monthly precipitations from October of the prior year to October of the current year.

Figure 3 .
Figure 3.The comparison of May-August total precipitation (black) and the first principal component of 23 tree-ring chronologies (gray).Correlation coefficient between two time series is 0.65 (p < 0.001).

Figure 4 .
Figure 4. Maps showing Pearson's correlation coefficients between the sites chronologies and (a) May-August total precipitation and (b) March-April mean temperature for the period 1930-2012.For each site, the closest gridded (0.5 • × 0.5 • ) climate data obtained from CRU dataset were used.Graduated circle size and color correspond to correlation coefficient versus the climate variable.Black lines around circles represent significant correlation coefficients (p < 0.05).

Figure 5 .
Figure 5. Actual (instrumental) and reconstructed March-April temperature ( • C).Dashed gray lines represent actual values and solid black lines represent reconstructed values shown with trend lines.The tendency to warm up at the reconstructed temperature is in good agreement with the trend in instrumental data.

Figure 6 .
Figure 6.March-April temperature reconstruction for Turkey for the period 1800-2002 CE.The horizontal, central white line shows the reconstructed long-term mean and does not include instrumental data; black background denotes Monte Carlo (n = 1000) bootstrapped 95 % confidence limits; and the red line shows 13-year low-pass filter values.

Figure 7 .
Figure 7. Spatial correlation map for the March-April temperature reconstruction.Spatial field correlation map showing statistical relationship between the temperature reconstruction and the gridded temperature field at 0.5 • intervals (CRU TS3.23; Jones and Harris, 2008) during the period 1930-2002 over the Mediterranean region.For each grid, calculated correlation coefficient from 0.20 to 0.60 is significant (p < 0.05).

Figure 8 .
Figure 8. Spatial correlation maps for the March-April temperature reconstruction and precipitation signal (PC 1 ) obtained from the tree-ring dataset during the period 1800-2002 over Europe.Maps demonstrate spatial field correlations between our temperature reconstruction and (a) gridded spring temperature reconstruction for Europe(Xoplaki et al., 2005), (b) gridded summer temperature reconstruction for Europe(Luterbacher et al., 2016), (c) Old World Drought Atlas (OWDA;Cook et al., 2015).Panels (d) and (e) show spatial correlations between PC 1 and OWDA(Cook et al., 2015) and gridded European summer precipitation reconstruction(Pauling et al., 2006), respectively.The + sign represents significant correlation coefficients (p < 0.05).
reported the year 1807 as being one of the warmest alpine summers in the European Alps over the last 500 years.As such, a drought record from Nicault et al. (2008) echoes this finding, as a broad region of the Mediterranean Basin experienced drought conditions.Heinrich et al. (2013) analyzed winter-to-spring (January-May) air temperature variability in Turkey since 1125 CE as revealed from a robust tree-ring carbon isotope record from Juniperus excelsa.Although they offered a long-term perspective of temperature over Turkey, the reconstruction model, which covered the period 1949-2006, explained 27 % of the variance in temperature since the year 1949.In this study, we provided a short-term perspective of temperature fluctuation based on a robust model (calibrated and verified 1930-2002; Adj.R 2 = 0.64; p < 0.0001).Yet, the Heinrich et al. (2013) temperature record did not capture the 20th century warming trend as found elsewhere (Wahl

Table 1 .
Site information for the new chronologies developed by this study in Turkey.

Table 2 .
Summary statistics for the new chronologies developed by this study in Turkey.
* EPS is the expressed population signal

Table 3 .
Principal components analysis statistics for the Turkey temperature reconstruction model.
1 Indicates the PCs that were used in the reconstruction as predictors. 2 Which exceed a value of ±0.2.

Table 4 .
Calibration and verification statistics of the bootstrap method (1000 iterations applied) showing the mean values based on the 95 % confidence interval (CI).Note that RMSE: root mean squared error, R 2 coefficient of determination, RE: reduction of error, CE: coefficient of efficiency, RMSEP: root-mean-squared error prediction.

Table 5 .
Calibration and cross-validation statistics for the Turkey temperature reconstruction model.