RE-EVALUATION OF TRENDS AND CHANGES IN MEAN, MAXIMUM AND MINIMUM TEMPERATURES OF TURKEY FOR THE PERIOD

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. : () Published online in Wiley InterScience ( DOI:./joc. RE-EVALUATION OF TRENDS AND CHANGES IN MEAN, MAXIMUM AND MINIMUM TEMPERATURES OF TURKEY FOR THE PERIOD MURAT TÜRKEŞ,* UTKU M. SÜMER and İSMAİL DEMİR Turkish State Meteorological Service, Ankara, Turkey Received September Revised January Accepted January ABSTRACT Mean, maximum and minimum surface air temperatures recorded at climatological stations in Turkey during the period from to were analysed to reveal spatial and temporal patterns of long-term trends, change points, significant warming (cooling) periods and linear trend rates per decade. Annual, winter and spring mean temperatures have tended to increase, particularly over the southern regions of Turkey, whereas summer and particularly autumn mean temperatures have decreased over the northern and continental inner regions. Annual, winter, spring and summer maximum temperatures have indicated a positive trend at many stations, except those in the Central Anatolia and Black Sea regions and partly in the Eastern Anatolia region. Autumn maximum temperatures, however, have decreased slightly over much of Turkey, except for the Aegean region. The majority of the urbanized and rapidly urbanizing stations in Turkey have been experiencing an apparent night-time warming, especially during the warm and dry period of the year. Minimum temperatures have shown a statistically significant positive trend at stations annually, stations in spring and stations in summer; most are significant at the. level. Winter and autumn minimum temperatures have indicated a general negative trend in some portions of the Marmara, Black Sea and Eastern Anatolia regions, whereas a general positive trend has been seen over much of Turkey along with the significant trends in the Mediterranean region of the country. Summer night-time warming rates are generally larger than in spring and autumn night-time temperatures. On the other hand, the night-time warming rates of spring and summer are generally stronger than those in spring and summer daytime temperatures. By considering the marked increasing trends in spring, summer and annual minimum temperatures of urban stations, we have recognized that this is a clear and significant indication for the existence of a strong night-time urban warming in Turkey. Copyright Royal Meteorological Society. KEY WORDS: Turkey; air temperature; urbanization; non-parametric tests; homogeneity; serial dependence; trend and trend rate; change point. INTRODUCTION Human-induced climate change and changes in climatic variability continue to be major global change issues not only for the present generation but also for future generations. Based on the latest scientific assessment of the Earth s climate system, Folland et al. () have revealed that average global surface temperature has increased by about. ±. C since the late th century. The Northern Hemisphere experienced cooling during the period from to, while the Southern Hemisphere showed warming. They pointed out that the recent warming was largely globally synchronous, but was more pronounced in the Northern Hemisphere continents during winter and spring. Folland et al. (), by assessing a large number of studies, have also indicated that analyses of mean daily maximum and minimum land surface air temperatures continued to support a decrease in the diurnal temperature ranges in many parts of the world. * Correspondence to: Murat Türkeş, Turkish State Meteorological Service, Department of Research, PO Box, Ankara, Turkey; mturkes@meteor.gov.tr Copyright Royal Meteorological Society

2 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR Minimum temperatures increased at nearly twice the rate of maximum temperatures during the period from to. As an expected result of concern over climate change, there has been an increasing number of studies in the last years dealing with long-term surface air temperature variations and trends, and variations and anomalies associated with circulation types across the Mediterranean basin, particularly for the eastern basin and individual countries (Metaxas et al., ; Arseni-Papadimitriou and Maheras, ; Esteban- Parra and Rodrigo, ; Türkeş et al., ; Türkeş et al., ; Kadıoğlu, ; Proedrou et al., ; Tayanç et al., ; Kutiel and Maheras, ; Ben-Gai et al., ; Maheras et al., ; Price et al., ; Quereda Sala et al., ; etc.). The main findings and conclusions from some studies related to the eastern Mediterranean basin can be summarized as follows. Proedrou et al. (), based on annual and seasonal temperatures determined from ground and satellite measurements, detected an overall cooling trend for the majority of Greek stations in winter for the entire period of, and in annual and summer mean temperatures but up to the mid-s. They also showed that summer temperatures had a warming trend roughly after at most stations, and satellite measurements had an insignificant warming trend. Kutiel and Maheras () examined the temperature regime over the Mediterranean basin and the relationship between temperature variations and circulation indices. For seasonal temperature trends, they used the data in grid boxes of for the entire Mediterranean area and at six stations. They found a warming trend, which was more evident in the western Mediterranean than in the eastern Mediterranean. In another detailed study on circulation types over the Mediterranean basin, Maheras et al. () showed the complexity of the Mediterranean climate, especially the atmospheric driving forces of the temperature regime and anomalies. They confirmed the conclusions of the previous studies, that prevailing temperature conditions in the westcentral Mediterranean basin were influenced by circulation over the Atlantic, whilst the eastern part of the basin was subject to varying influences, dependent on circulation over southern Asia, North Africa, eastcentral Europe, and on season. Price et al. () found, for two stations in Cyprus, an increasing trend with a rate of about C/ years in annual mean temperatures. They showed minimum temperatures generally increased at a larger rate than maximum temperatures, resulting in a decrease in long-term diurnal temperature range. Ben-Gai et al. () analysed the maximum and minimum temperatures of stations in Israel for the period. They revealed that both temperatures were characterized by a significant decreasing trend during the cool season and by an increasing trend during the warm season. Based on the results from previous studies (Türkeş,, a; Türkeş et al., ; Kadıoğlu, ; Tayanç et al., ), a general decrease was dominant in annual and seasonal mean surface air temperature series over much of Turkey. In particular, the coastal regions of Turkey were generally characterized by colder than long-term average temperature conditions during the period between the late s and early s. The cooling tendency was particularly marked in summer mean temperatures. A general increasing trend was evident in seasonal minimum temperature series over much of Turkey, and a general decreasing trend in maximum temperature series in all seasons, except spring, over much of Turkey (Türkeş, a; Türkeş et al., ). A general decreasing trend was apparent in mean annual and seasonal global solar radiation data, particularly in annual, summer and autumn series, over most of Turkey during the period (Aksoy, ). Aksoy () attributed the decrease of the solar radiation to deterioration of air quality in Turkey. Nevertheless, this situation has begun to change for about last years in Turkey, particularly during the warm period of the year (Erlat,, ; Türkeş, ). When we made a test study, it was pointed out that previous results with cooling trends in mean and maximum temperature series have been weakening and been less significant. This is due to the increases in the mean, maximum and minimum temperature series of Turkey during the last years or so, particularly in spring and summer seasons. Consequently, we have analysed the re-evaluated and updated data set of Turkish mean, maximum and minimum temperature series of stations for a longer study period, from to. The scope of the paper is: (i) to give detailed information on the updated Turkish temperature data and homogeneity assessments of temperature series; (ii) to assess the rapid urbanization in Turkey; (iii) to reveal the nature and magnitude of the serial dependence and long-term trends, and the change points and significant warming (or cooling) periods in annual and seasonal mean, maximum and minimum temperature series of Turkey by using non-parametric tests; and (iv) to detect the linear trend rates per decade in the same temperature series. Copyright Royal Meteorological Society Int. J. Climatol. : ()

3 TEMPERATURE TRENDS IN TURKEY. DATA AND ASSESSMENT OF HOMOGENEITY This study is based on an updated temperature data set consisting of monthly averages of the daily mean, daily maximum (daytime) and daily minimum (night-time) temperatures. Temperature data have been observed and recorded by the Turkish State Meteorological Service (TSMS) since the late s. We found some errors in the monthly minimum temperature series at the very beginning of the study. These errors were identified directly by checking the daily temperature records in the original climatological registers of the TSMS archive. The errors arose from the fact that negative signs of some daily minimum temperature values, in the months mostly from December to March, were omitted by mistake during the process of inputting daily records into the TSMS database. The errors of sign in minimum temperatures of some stations, which are located over the colder and continental central Anatolia and eastern Anatolia regions of Turkey, were corrected by using the daily records in the original climatological registers. The corrections were also reflected in the TSMS database. Temperature variations and trends for Turkey were re-analysed by using a newly updated data set including monthly averages of daily mean, maximum and minimum surface air temperatures. These temperature series of observations were recorded at stations of the TSMS, during the period. Values for December were used only for computing winter averages in the year of. Selection of the stations was carried out by taking into account the stations used in our previous studies (Türkeş et al.,, ; Türkeş, ). A total of stations, of which are common with Türkeş et al. (), were re-examined with respect to their length of record, geographical distribution over Turkey and homogeneity. For the present study, inhomogeneity means non-climatological strong jumps (step-wise changes) in the mean of the series. Missing values in monthly temperature records were filled in by a simple approach. In the case of a monthly missing value, this was replaced by the year monthly average centred on the missing month. The number of missing values that were filled in is less than % of all monthly values in a station s selected study period. For the study, annual and seasonal average series were calculated from the monthly mean, monthly maximum and monthly minimum series. In order to detect homogeneity in mean annual and seasonal series, first a homogeneity analysis was performed by using the non-parametric Kruskal Wallis (K W) test for homogeneity was carried out of the means and variances (Sneyers, ; Türkeş, b; Türkeş et al., ) of both - and -year subperiods. The analysis was carried out for the means and variances of both and -year sub-periods. This objective analysis was done not only to detect inhomogeneity (inconsistency) in the overall series, but also to examine whether the recent observations of about the last decade (in which increased spring and particularly summer minimum temperatures were dominant at many stations) affected the consistency of the temperature series. Second, the non-parametric Wald Wolfowitz (W W) serial correlation test was applied to the series to examine the nature and magnitude of the serial dependence from year-to-year variations, and/or abrupt changes in the series (Sneyers,, ; Türkeş, b; Türkeş et al., ). The result of the W W test, when supported by information from both a station s history file and statistical and graphical time-series analyses, is very useful for deciding whether a statistically significant inhomogeneity in a series (especially with a probability less than.) arose from a non-climatological jump, from natural low-frequency fluctuations, or a strong persistence. A non-climatological jump in the mean of a series may result either from an abrupt change associated with relocation of a station or from a steep trend (a rapid increase or decrease) in temperature values because of different factors, such as urbanization (i.e. urban heat island effect or urban cooling effect respectively). Third, statistically significant inhomogeneities from the K W test were checked by means of the additional information from our own station s history file and the plotted time-series to some extent. Time-series plots were prepared for both original temperatures with smoothed values and the u(t) and u (t) values derived from the sequential analysis of the Mann Kendall (M K) test. Eighty stations were selected at the beginning for the purposes of homogeneity and the decision-making study. The homogeneity assessments of the mean, maximum and minimum temperature series can be summarized as follows. The K W test has revealed some seasonal variations, particularly for the minimum temperatures. According to the results from the K W test, most annual mean, maximum and minimum temperature series have been found to be homogeneous with respect to the homogeneity of both means and variances of and year Copyright Royal Meteorological Society Int. J. Climatol. : ()

4 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR length sub-periods. Winter mean, maximum and minimum temperature series at all stations are homogeneous. There is neither any significant jump nor any apparent long-period fluctuation in the winter temperature series. Most of the spring and summer mean and maximum temperature series are homogeneous with respect to both non-climatological jumps in the means and changes in the variances. Nevertheless, approximately half of the spring minimum temperature series, most of which are characterized by a significant secular increasing trend, seem to be inhomogeneous, although there is no non-climatological marked jump in these series. Indeed, it is clear that very rapid and significant increasing trends are the prevalent behaviour with respect to long-term variations of the spring minimum temperatures. Time-series plots of the spring minimum temperatures have also proved that series of most stations, even in the different geographical regions of Turkey, are considerably similar to each other in terms of the year-to-year variability, long-period fluctuation and trend patterns (Figures and ). It has been realized, from the non-randomness characteristics of the climatic series, that the summer minimum temperature series are the most striking series in Turkey. According to the statistical tests, summer minimum temperature series are not random either against the homogeneity and persistence or against the secular trend. Most series seem to be inhomogeneous and their year-to-year variability is mostly characterized by a significant positive serial correlation coefficient. In fact, long-term variations in summer minimum temperatures of these stations are explained either with a significant secular increasing trend or with a rapid increase over the last to year period (Figure ). Consequently, we recognize that the large (significant) test statistics from the K W test showing inhomogeneity for the spring and summer minimum temperature series are very much likely associated with rapid and significant increases in the means of those series. Most autumn mean, maximum and minimum temperature series are also random with respect to both non-climatological jumps in the means and changes in the variances. By using all types of climatological assessments mainly based on the statistical analyses and the station s history information, along with numbers of missing values and length of records, ten of the stations were extracted from the study. Three of the ten stations taken out of the study had experienced station relocation from an inland site to the coast or to a site nearer to a coastal area than that of its previous location. These stations, namely Fethiye, Antalya and Anamur, are located in the western and middle sub-regions of Turkey s Mediterranean coastal belt. We found significant inhomogeneities at those relocated stations in terms of the long-term variations and trends in temperature series. In particular, the relocations of the Fethiye and Anamur stations in, from inner parts of the cities to their present sites near the coast and just on the coast respectively, created an artificial cooling effect on temperature measurements throughout the year compared with the observations previous to. For instance, in Fethiye, a non-climatological step-wise decrease was detected in the annual (Figure ) and seasonal (not shown here) mean, maximum and minimum temperatures. This non-natural cooling effect, which appears to be stronger during the warmer part of the year, is very likely to be associated with land and especially sea breezes, which are the dominant local wind circulation in the spring, summer and autumn months on the Mediterranean coast of Turkey (Türkeş, ). The main nonrandomness characteristics of the temperature series observed at a relocated station are that these series show almost the same trend behaviour in all months and seasons, such as very significant decreasing (increasing) trend statistics for almost all months and seasons. In other words, the sign and magnitude of the trends in those series do not depict any natural inter-seasonal differences after the relocation occurred, because the artificial cooling (warming) effect of the station s relocation has suppressed the natural variability and in particular the trend characteristics of the temperature series at that station. Finally, the temperature series from principal climatological stations found to be homogeneous were chosen for re-analysing and re-evaluating the long-term variations and trends in the Turkish temperature series during the period from to. Of the stations, (%) stations have a length of record in the period and (%) stations in the period. Consequently, by taking into account the fact that official climatological observations at many of the Turkish stations started in the late s, an approximately year observation period at % of the selected stations can be accepted as relatively sufficient. The spatial distributions of the geographical regions and the locations of the stations in Turkey are shown in Copyright Royal Meteorological Society Int. J. Climatol. : ()

5 TEMPERATURE TRENDS IN TURKEY a. Mean temperature variations b. Mean temperature trend Relocation of station X X X c. Maximum temperature variations Relocation of station X X X e. Minimum temperature variations d. Maximum temperature trend f. Minimum temperature trend Relocation of station X X X Figure. Variations and trends in annual mean, maximum and minimum temperature series of Fethiye: (a), (c), (e) interannual variations with smoothed line by the Binomial filter ( ) with padded ends, long-term average X ( ), and averages of sub-periods ( ) before (X )andafter(x ) ; (b), (d), (f) trends from sequential values of the statistics u(t) ( )andu (t) ( ž ) of the M K test, with critical value of ±. at the. level of significance ( ) Figure. Stations are well distributed, not only across the country but also across the geographical regions of Turkey; the exceptions are the western Mediterranean coast, Eastern Anatolia region and the inner portion of the eastern Black Sea. This is mainly due to inhomogeneous series related to the relocation of some coastal stations for the Mediterranean region, and a greater number of missing values and shorter record lengths for stations of the Eastern Anatolia region and eastern Black Sea sub-region.. URBANIZATION IN TURKEY Turkey is one of the rapidly urbanizing countries in the developing countries of the world. According to the results of the census (DİE, ), the population of Turkey increased from in to in, with a. annual rate of increase during the period. Of this total population (de facto), settled in cities (centres of provinces and districts), whereas settled in villages. The annual rate of population increase is. for centres of provinces and districts, and. for villages. From the census, the share of the total population in centres of provinces and districts is.%, and in villages it is.%. In the census, these proportions were.% and.% respectively. The city population of Turkey in increased by.% compared with the city population in. Population increased in all geographical regions except around the Black Sea during the Copyright Royal Meteorological Society Int. J. Climatol. : ()

6 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR A E G E A N B L A C K MAR Sea of Marmara BLS CAN EAN AEG SAN MED km SEA Geographical location of the stations Figure. Spatial distribution of Turkey s geographical regions and location of stations with numbers used in the study (BLS: Black Sea; MAR: Marmara; AEG: Aegean; MED: Mediterranean; SAN: Southeastern Anatolia; CAN: Central Anatolia; EAN: Eastern Anatolia) (see Table III for names of the stations) Table I. Populations (de facto) and annual rates of change of Turkey s geographical regions (DİE, ) Region Census population Annual rate of change ( ) ( ) Black Sea. Marmara. Aegean. Mediterranean. Southeastern Anatolia. Central Anatolia. Eastern Anatolia. Total (Turkey). period (Table I). The greatest change occurred in the Marmara region, with a rate of.%; the lowest was in the Black Sea region with a rate of.%. Karl et al. () classified US cities in their study. If the station classification used by Karl et al. () is considered, almost all stations we have selected for the study are located in medium urban and large urban cities. We have slightly modified their original categories by considering city sizes, urban rural characteristics and growth tendencies in Turkey. Our modification divides the medium urban category into two classes (Table II). Based on these considerations, the classification of Turkish stations and the number of stations within these classes are given in Table II. Of the stations, one station is small urban (rural suburban), nine are medium urban (suburban), and and are respectively medium and large urban stations characterized by a high rate of population increase (Table III). The large urban stations constitute about % of the total, which is the same based on both classifications. Most of the stations are very likely to have been affected by rapid urbanization, and thus subject to both urban heat island effects and urban cooling effects. Both the urban warming and the urban cooling, due to the heat island effect and the effect of air pollutants (e.g. Copyright Royal Meteorological Society Int. J. Climatol. : ()

7 TEMPERATURE TRENDS IN TURKEY Table II. Classification of Turkish stations for the study Population size Classification Station number (percentage) Class symbol Urbanization characteristic P< S-I Rural (true rural) () P< S-II Small urban (rural suburban) (.) P< S-I Medium urban (suburban) (.) P< S-II Medium urban (urban) (.) P S Large urban (urban) (.) Table III. Population of provinces and districts where stations we used are located in the census years of and, and annual rate of change during the period from to. Arranged by using the original figures from DİE () Region Station Latitude (N) Population of City Centre (de facto) Classification longitude (E) of cities No Name Annual rate of change ( ) BLS Hopa,. S-I Rize,. S-II Trabzon,. S Giresun,. S-II Samsun,. S Sinop,. S-I İnebolu,. S-II Kastamonu,. S-II Merzifon,. S-I Çorum,. S Zonguldak, -. S Bolu,. S-II MAR Adapazarı,. S İzmit,. S Göztepe (İstanbul),. S Sarıyer,. S Florya (İstanbul),. S Lüleburgaz,. S-II Edirne,. S Tekirdağ,. S Bilecik,. S-I Bursa,. S Çanakkale,. S-II Biga,. S-I Bandırma,. S-II Balıkesir,. S AEG Kütahya,. S Uşak,. S Afyon,. S Dikili,. S-I Akhisar,. S-II (continued overleaf ) Copyright Royal Meteorological Society Int. J. Climatol. : ()

8 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR Table III. (Continued) Region Station Latitude (N) Population of City Centre (de facto) Classification longitude (E) of cities No Name Annual rate of change ( ) Manisa,. S İzmir,. S Aydın,. S Muğla,. S-I Bodrum,. S-I MED Burdur,. S-II Isparta,. S Alanya,. S Silifke,. S-II Mersin,. S Adana,. S İskenderun,. S Antakya,. S Kahramanmaraş,. S SAN Gaziantep,. S Adıyaman,. S Sanlıurfa,. S Diyarbakır,. S Mardin,. S-II Siirt,. S Cizre,. S-II CAN Yozgat,. S-II Sivas,. S Çankırı,. S-II Ankara,. S Eskişehir,. S Kırşehir,. S-II Kayseri,. S Niğde,. S-II Konya,. S EAN Kars,. S-II Iğdır,. S-I Ağrı,. S-II Erzurum,. S Erzincan,. S Malatya,. S Elazığ,. S Van,. S Hakkari,. S-II sulphur dioxide (SO ) and particulate matter, aerosols, etc.) respectively, may have had a positive and negative radiative forcing on the nature and magnitude of the year-to-year variations and the secular trends. An urban cooling effect would show up particularly in daytime (i.e. maximum) temperatures of some stations with low urban air quality. In Turkey, almost all principal and even ordinary climatological stations have been surrounded by the rapidly urbanizing areas of cities since the mid to late s. Urban development in developing countries is Copyright Royal Meteorological Society Int. J. Climatol. : ()

9 TEMPERATURE TRENDS IN TURKEY quite different compared with the developed countries. The high rates of population increase in those countries and mass human migrations towards medium size (i.e. districts, towns) and large cities and metropolitan areas have caused large disorganized legal and illegal settlements in the old cities or newly urbanizing cities. This situation in Turkey has led the city centres and their surrounding urban and suburban areas to grow in a very fast and unplanned manner. These adverse effects of rapid and unplanned urbanization have also led to a significant change in vegetation and other surface land characteristics of the urbanized and/or urbanizing areas of Turkey. Consequently, meteorological observation sites have been affected by changes in surface features, particularly by decreases (increases) in the existing and/or being planted vegetation cover in nearby areas. Man-made buildings, asphalt-covered and dark-coloured streets, and other types of infrastructure activity and services have also affected the weather stations. In some cases, newly planted trees, green belts, parks, etc. near the station can affect the observed values, and thus the natural variations and trends. A large number of studies (Oke,,, ; Landsberg, ; Kukla et al., ; Karl et al., ; Karl and Jones, ; Jones et al., ; etc) addressed the issues of urbanization detection (urban growth and population increase) and urbanization or heat island effect on the urban temperature regime, and long-term temperature variations and trends. Tayanç et al. () examined the effects of urbanization on the Turkish temperatures and the relationship between air pollution and temperature trends and variations in detail.. METHODOLOGY The non-parametric M K rank correlation test (Sneyers, ) was used to detect any possible trend in temperature series, and to test whether or not such trends are statistically significant. A detailed assessment for testing of climatic data unevenly distributed in time and a comparison of methods for estimating the significance level of a trend can be found in a recent study performed by Huth (). The M K test statistic u(t) is a value that indicates direction (or sign) and statistical magnitude of the trend in a series. When the value of u(t) is significant at the % significance level, it can be decided whether it is an increasing or a decreasing trend depending on whether u(t) > oru(t) <. A % level of significance was also taken into consideration. Partial and short-period trends, and a change point or beginning point of a trend in climatic series were investigated by using time-series plot of the u(t i ) and u (t i ) values. In order to obtain such a timeseries plot, sequential values of the statistics u(t) and u (t) were computed from the progressive analysis of the M K test. Following Sneyers (), this procedure is formulated as follows: first, original observations are replaced by their corresponding ranks y i, which are arranged in ascending order. Then, for each term y i, the number n k of terms y j preceding it (i >j) is calculated with (y i >y j ), and the test statistic t i is written as t i = i k= n k The distribution function of the test statistic t i has a mean and a variance derived by E(t i ) = i(i )/ and var(t i ) = [i(i )(i + )]/ Values of the statistic u(t i ) are then computed as u(t i ) = [t i E(t i )]/ var(t i ) Finally, the values of u (t i ) are similarly computed backward, starting from the end of the series. With a trend, intersection of these curves enables the beginning of a trend in the series to be located approximately. Without any trend, a time-series plot of the values u(t i ) and u (t i ) shows curves that overlap several times. An -point Binomial filter was used as a low-pass filter to investigate visually the characteristics of the long-period fluctuations in the series (WMO, ). Copyright Royal Meteorological Society Int. J. Climatol. : ()

10 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR The non-parametric W W serial correlation test was chosen to determine randomness against the serial dependence (persistence) in temperature series (Sneyers, ). Sneyers () advised use of the W W serial correlation test combined with the M K and Spearman rank correlation tests, because changes in climatic series seem, generally, to proceed in an abrupt manner rather than a linear or monotonic trend. The W W statistic u(r) gives objective information about the nature and magnitude of possible persistence in a long time-series. Using a one-sided test of the normal distribution, the null hypothesis of randomness is rejected for large values of the test statistic u(r). The alternatives to randomness may indicate the presence of some forms of fluctuation or of abrupt change. In most cases, for annual and winter precipitation series and summer maximum temperatures for Turkey, a significant positive serial correlation (PSC) coefficient reveals the existence of a low-frequency fluctuation (Türkeş et al., ; Türkeş, ). The least-squares linear regression equations were calculated to detect warming (cooling) rates per decade. In estimating linear regression lines, we have used the simple least-squares approach with time as the independent variable, and temperature values as the dependent variable. The statistical significance of each estimated X (β) coefficient was tested using the Student s t test for significance with (n ) degrees of freedom. In using two-tailed test of the Student s t distribution, the null hypothesis for the absence of any trend is rejected for large values of t. Programs for all computations and statistical analyses were prepared using the FORTRAN programming language. The interpolation method of kriging was used in order to produce the contours shown on the spatial distribution maps. It has been applied to the resultant test statistics u(t) of temperature trends from the M K test by means of a mapping package. Nevertheless, the trend analysis results were not assessed for field significance.. RESULTS OF ANALYSES Only a summary table (Table IV) has been given in this section for the results of the M K rank correlation and the W W serial correlation tests, owing to the large volume of results, although the tables have been arranged for all the resultant test statistics... Trends and changes in temperature series... Trends and changes in annual temperature series. For annual mean temperatures, the year-to-year variations in the series are generally characterized by a PSC coefficient from the W W test. For annual mean temperatures series, this is an indicator of low-frequency fluctuation of various types and levels, but not an abrupt change. The spatial distribution pattern is not complex, even though the resultant test statistics of the M K test give both negative and positive trends. Trends are not significant in most stations, except for warming in a few stations (Table IV). Significant positive trends are evident over the eastern MED sub-region, which is one of the highly urbanized areas of Turkey, namely the Adana Mersin district (Figure (a)). For annual maximum temperatures, significant PSC coefficients characterize the observed low-frequency fluctuations in the series. As with annual mean temperatures, these series show mostly statistically insignificant increasing and decreasing trends over much of Turkey. The slightly increasing trends show up over the western and eastern regions of the country, in which the significant warming is seen over the inner part of the Aegean region and in some small areas of the SAN region (Figure (b)). Decreasing trends are observed mainly over the CAN region, with a small area of significant cooling within that region, and the eastern BLS sub-region. For annual minimum temperatures, the year-to-year variations of stations are characterized by a significant PSC coefficient (Table IV). Minimum temperatures have tended to increase significantly at of the stations in Turkey (Table IV). Minimum temperatures show a well-defined spatial coherence characterized by a significant strong warming (Figure (c)), the probability of which is very much below the. significance level at stations. Stronger warming trends of annual minimum temperatures are mostly observed in the stations that are rapidly urbanizing or which are already urbanized cities, as in spring and summer. The spatial distribution pattern of significant warming trends exhibits an apparent geographical Copyright Royal Meteorological Society Int. J. Climatol. : ()

11 TEMPERATURE TRENDS IN TURKEY Table IV. Number of the stations indicating a significant trend and/or a serial correlation in the mean, maximum and minimum temperature series of stations, at the. level of significance, according to the Mann-Kendall (M K) and Wald-Wolfowitz (W W) tests a Region Winter Spring Summer Autumn Annual M K W W M K W W M K W W M K W W M K W W Mean temperatures BLS MAR AEG MED SAN CAN EAN Total Maximum temperatures BLS MAR AEG MED SAN CAN EAN Total Minimum temperatures BLS MAR AEG MED SAN CAN EAN Total a +: increasing trend from the M K test and PSC coefficient from the W W test; : decreasing trend from the M K test and negative serial correlation coefficient from the W W test. autocorrelation (relation) over Turkey. This spatial pattern is described by the observed maximum warming areas over the western parts of the MAR, BLS and CAN regions, İzmir Manisa district of the AEG region, and eastern EAN, MED and SAN regions. On the other hand, decreasing trends that are mostly insignificant show an apparent spatial coherence over the northern parts of the CAN and EAN regions and eastern BLS sub-region (Figure (c)). Erzurum station, which is in that coherent area, has a jump in the minimum series towards cooler temperatures. The beginning of the secular trends and the periods of significant warming (cooling) are determined by means of the time-series plots of the u(t) and u (t) values from the sequential analysis of the M K test (Figure ). Results from evaluation of these time-series plots are summarized for the selected stations that are representative for the coherent regions with significant trends as follows: A common observed and statistically significant secular warming trend in many stations, with different significant warming periods, such as in the late s to at the Göztepe station, in at Bolu, in the late s to at İzmir, in and the mid-s to at some stations. Copyright Royal Meteorological Society Int. J. Climatol. : ()

12 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR B L A C K Sea of Marmara A E G E A N a. Annual mean temperature trend (u(t)) B L A C K Sea of Marmara A E G E A N b. Annual maximum temperature trend (u(t)) B L A C K Sea of Marmara A E G E A N c. Annual maximum temperature trend (u(t)) Figure. Spatial distribution patterns of annual mean (a), maximum (b) and minimum (c) temperature trends of stations in Turkey from the M K test statistic u(t). Pale shading: significant at the. significance level; dark shading: significant at the. level. Critical values of. and. are taken as. and. respectively in drawing contours of significance limits A common observed and statistically significant secular warming trend and a recent significant warming period at the end of the series at Giresun, Ankara, Kırşehir, Van, Gaziantep and Mersin.... Trends and changes in winter temperature series. For winter mean temperatures, the results of the W W test show they are all statistically random against the serial dependence. Both weak negative and positive trends characterize the winter mean temperature series (Figure (a)). Slightly decreasing trends are evident over the western and eastern BLS region, whereas slightly increasing trends are found over the Copyright Royal Meteorological Society Int. J. Climatol. : ()

13 TEMPERATURE TRENDS IN TURKEY Giresun annual minimum (BLS) Göztepe annual minimum (MAR) Zonguldak annual minimum (BLS) Uşak annual minimum (AEG) Bolu annual minimum (BLS) Ízmir annual minimum (AEG) Mersin annual minimum (MED) Şanliurfa annual minimum (SAN) Erzurum annual minimum (EAN) Adana annual minimum (MED) ) Ankara annual minimum (CAN) Elazig annual minimum (EAN) Kirşehir annual minimum (CAN) Gaziantep annual minimum (SAN) Van annual minimum (EAN) Figure. Temporal patterns of the trends in annual minimum temperature series of selected stations in Turkey from sequential values of the statistics u(t) ( )andu (t) ( ž ) of the M K test, with critical value of ±. at the. level of significance ( ) mid-western and eastern parts of the CAN and western part of the SAN along with the Mersin Adana district. For winter maximum temperatures, most winter maximum series are characterized by a negative serial correlation coefficient that is mostly insignificant. Even though negative coefficients are not statistically significant, high-frequency oscillations are apparent in the series (Figure ). Spatial and temporal patterns of trends in maximum temperatures are very similar to those of mean temperatures. Trends in most series are not significant (Table IV and Figure (b)). A general cooling is dominant over the BLS region and mid-southern parts of the CAN region, whereas a general warming is seen over the western and mid-eastern regions of Turkey. A few of the increasing trends are significant. For winter minimum temperatures, the majority of the series are random against the PSC. A significant PSC coefficient is found only for a few stations located on the Mediterranean coast of the Anatolian Plateau. Winter minimum temperatures show a general increasing trend over much of Turkey, except for some areas particularly in the BLS region (Figure (c)). A coherent region generally characterized by a significant warming is evident at the well-known urbanized stations with a high rate of population increase in the MED region. It is seen that some stations, such as Göztepe, Mersin and Adana, show a systematic warming Copyright Royal Meteorological Society Int. J. Climatol. : ()

14 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR B L A C K Sea of Marmara A E G E A N A E G E A N A E G E A N Sea of Marmara B L A C K Sea of Marmara B L A C K d a. Winter mean temperature trend (u(t)) b. Winter maximum temperature trend (u(t)) c. Winter minimum temperature trend (u(t)) Figure. As in Figure, but for winter trend, if time-series plots of the selected stations are assessed (Figure ). On the other hand, some stations exhibit high year-to-year variations without showing any apparent secular trend. Minimum temperatures of some stations have tended to increase after, which was one of the coldest years in the observation records for most stations in Turkey (Türkeş, ; Türkeş et al., ). Nevertheless, this warming has not reached the significance level on time-series plots of the u(t) and u (t) values (Figure ).... Trends and changes in spring temperature series. For spring mean temperatures, mostseriesare characterized by an insignificant negative serial correlation coefficient. Most of the country, except the CAN Copyright Royal Meteorological Society Int. J. Climatol. : ()

15 TEMPERATURE TRENDS IN TURKEY Giresun winter maximum (BLS) Mersin winter maximum (MED) Şanliurfa winter maximum (SAN) Göztepe winter maximum (MAR) Erzurum winter maximum (EAN) Zonguldak winter maximum (BLS) Adana winter maximum (MED) Ankara winter maximum (CAN) - - Uşak winter maximum (AEG) Elazig winter maximum (EAN) - Gaziantep winter maximum (SAN) Kirşehir winter maximum (CAN) - Bolu winter maximum (BLS) Ízmir winter maximum (AEG) Van winter maximum (EAN) - Figure. Interannual variations in winter maximum temperature series of selected stations in Turkey, with smoothed line by the Binomial filter ( ) with padded ends and long-term average ( ) region, experiences either weak or strong warming (Figure (a)). Warming trends are significant only at eight stations (Table IV). For spring maximum temperatures, insignificant negative serial correlation coefficients describe the high year-to-year variations in the series. Spring maximum temperatures do not show apparent spatial distribution patterns characterized by a significant warming or a cooling. Increasing trends at a weak degree of significance level are seen over the BLS, northern MAR and EAN regions, along with a slight significant warming in the İzmir Dikili district and the SAN region. Decreasing trends indicate a spatial coherence over the middle part of the CAN region (Figure (b)). For spring minimum temperatures, the year-to-year variations are described mostly by a PSC coefficient, of which are statistically significant (Table IV). A considerable number of these significant PSC coefficients coincide with the series that have tended to increase significantly in the mean of the series rather than a low-frequency fluctuation (Figure ). Spring, as in the results from the past study by Türkeş et al. (), has experienced not only a statistically significant but also a very rapid night-time warming over much of Turkey. Minimum temperatures have significantly increased at stations (Table IV), of which are at the. significance level. Coherent regions with a significant night-time warming appear mainly over the most Copyright Royal Meteorological Society Int. J. Climatol. : ()

16 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR Giresun winter minimum (BLS) Zonguldak winter minimum (BLS) Bolu winter minimum (BLS) Göztepe winter minimum (MAR) Uşak winter minimum (AEG) Ízmir winter minimum (AEG) Mersin winter minimum (MED) Adana winter minimum (MED) ) Gaziantep winter minimum (SAN) Şanliurfa winter minimum (SAN) Ankara winter minimum (CAN) Kirşehir winter minimum (CAN) Erzurum winter minimum (EAN) Elazig winter minimum (EAN) Van winter minimum (EAN) Figure. As in Figure, but for winter minimum temperature series urbanized or rapidly urbanizing cities with a high rate of population increase (Figure (c)). These warming areas can be summarized as follows: İstanbul and its large surrounding urban and suburban areas (İstanbul metropolitan area); western parts of the BLS and CAN regions; the İzmir Manisa district; the MED region and western part of the SAN region; a coherent area from the middle BLS region to the MED warming area via the eastern part of the CAN region; and the eastern part of the EAN region. Time-series of variations and trends from an -point Binomial low-pass filter and the u(t) and u (t) values of the M K test both prove that series of most stations, even in the different geographical regions of Turkey, have been rather similar in terms of their year-to-year variability, long-period fluctuation and secular and/or partial trend patterns (Figures and ). This close similarity also includes the peculiarities for the beginning of the trends and periods of significant warming in some series. Temporal characteristics derived from the time-series plots can be summarized as follows: At some stations, the beginning point of the trend is found in the late s and early s. In many of them, the period of significant warming begins immediately after the early s or in the early to mid s. Copyright Royal Meteorological Society Int. J. Climatol. : ()

17 TEMPERATURE TRENDS IN TURKEY B L A C K Sea of Mamara a. Spring mean temperature trend (u(t)) B L A C K Sea of Mamara A E G E A N d b. Spring maximum temperature trend (u(t)) f Sea of Mamara B L A C K A E G E A N A E G E A N c. Spring minimum temperature trend (u(t)) Figure. As in Figure, but for spring At some stations, the beginning point of the trend is located in the late s, in and the mid to late s. In many of these stations, a period of significant warming begins in the late s and early s, and lasts to the end of the series. At some stations, the beginning point of the trend starts in the mid to late s and early s. These stations have a relatively shorter period of significant warming.... Trends and changes in summer temperature series. For summer mean temperatures, theseriesare characterized mostly by a statistically significant PSC coefficient. The PSC coefficients qualify observed lowfrequency fluctuations in the series. Mean temperatures have generally shown a slight increase at many stations Copyright Royal Meteorological Society Int. J. Climatol. : ()

18 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR Samsun spring minimum (BLS) Giresun spring minimum (BLS) Zonguldak spring minimum (BLS) Bolu spring minimum (BLS) Göztepe spring minimum (MAR) Çanakkale spring minimum (MAR) Usak spring minimum (AEG) Afyon spring minimum (AEG) Ízmir spring minimum (AEG) Adana spring minimum (MED) Gaziantep spring minimum (SAN) Sanliurfa spring minimum (SAN) Sivas spring minimum (CAN) - - Ankara spring minimum (CAN) Eskisehir spring minimum (CAN) Kirsehir spring minimum (CAN) Elazig spring minimum (EAN) Van spring minimum (EAN) - Figure. As in Figure, but for spring minimum temperature series Copyright Royal Meteorological Society Int. J. Climatol. : ()

19 TEMPERATURE TRENDS IN TURKEY Samsun spring minimum (BLS) Giresun spring minimum (BLS) Zonguldak spring minimum (BLS) Bolu spring minimum (BLS) Göztepe spring minimum (MAR) Çanakkale spring minimum (MAR) - Usak spring minimum (AEG) Afyon spring minimum (AEG) Ízmir spring minimum (AEG) Adana spring minimum (MED) Gaziantep spring minimum (SAN) Sanliurfa spring minimum (SAN) Sivas spring minimum (CAN) Ankara spring minimum (CAN) Eskisehir spring minimum (CAN) Kirsehir spring minimum (CAN) Elazig spring minimum (EAN) Figure. As in Figure, but for spring minimum temperature series - Van spring minimum (EAN - over the western and southern regions of Turkey, along with a marked increasing trend in the MAR and SAN regions and at around the İzmir Manisa district (Figure (a)). These series have indicated a significant warming trend at nine stations in different regions (Table IV). A general decreasing trend has shown up over the rest of the country, particularly in the central and eastern regions. Copyright Royal Meteorological Society Int. J. Climatol. : ()

20 M. TÜRKEŞ, U. M. SÜMER AND İ. DEMİR B L A C K Sea of Mamara A E G E A N a.summer mean temperature trend (u(t)) B L A C K Sea of Mamara A E G E A N b.summer maximum temperature trend (u(t)) B L A C K Sea of Mamara A E G E A N c.summer minimum temperature trend (u(t)) Figure. As in Figure, but for summer For summer maximum temperatures, the year-to-year variations of stations are described by a statistically significant PSC coefficient (Table IV). Significant PSC coefficients are a statistical indication of observed low-frequency fluctuations in these series rather than an abrupt change (Figure ). Long-term variations of maximum temperature series are characterized now by both positive and negative trends for the study period of. Decreasing trends that are mostly insignificant show a spatial coherence over the central parts of the MAR and CAN regions and the eastern MED sub-region (Figure (b)). On the other hand, summer maximum temperatures have significantly increased at nine stations (Table IV). Coherent areas with increasing trends are seen in the western and eastern regions of Turkey. Significant Copyright Royal Meteorological Society Int. J. Climatol. : ()

21 TEMPERATURE TRENDS IN TURKEY Giresun summer maximum (BLS) Zonguldak summer maximum (BLS) Bolu summer maximum (BLS) Göztepe summer maximum (MAR) Mersin summer maximum (MED) Sanliurfa summer minimum (SAN) Erzurum summer maximum (EAN) Ankara summer maximum (CAN) Usak summer minimum (AEG) Adana summer maximum (MED) Elazig summer maximum (EAN) Ízmir summer maximum (AEG) Gaziantep summer maximum (SAN) Kirsehir summer maximum (CAN) Van summer maximum (EAN) Figure. As in Figure, but for summer maximum temperature series warming trends are found in the AEG and SAN regions and northeastern part of the Anatolian Plateau (Figure (b)). Indeed, this present situation of increasing trends in summer maximum temperatures of some stations is considerably different from the results of our previous studies, which revealed a significant cooling at many stations in Turkey (Türkeş, ; Türkeş et al., ). The period of those studies was. That period included a short but marked period of cooler than normal maximum temperature conditions in the early to mid s and ended at the single marked cooler year of. These decreased temperature conditions controlled the direction of the trend at most stations towards cooling. However, it is seen in the present study that marked increases in maximum temperatures of many stations after have controlled the direction, or nature (sign), of the trend (Figure ). Consequently, it is found that an increasing trend has started to dominate at some stations. As we have already pointed out, this explanation is valid for annual mean and maximum temperature series compared with the previous results for. Copyright Royal Meteorological Society Int. J. Climatol. : ()