TRENDS IN SURFACE AIR TEMPERATURE DATA OVER TURKEY

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, (1997) TRENDS IN SURFACE AIR TEMPERATURE DATA OVER TURKEY MİKDAT KADIOĞLU Department of Meteorology, Istanbul Technical University, Maslak, 80626, Istanbul, Turkey Received 27 July 1995 Revised 20 June1996 Accepted 11 July 1996 ABSTRACT Eighteen stations are considered for the study of climatic records over Turkey in search for possible trends. Mean, unfiltered, seasonal and annual maximum and minimum temperatures are analysed using the Mann Kendall rank statistic to demonstrate any existence of possible trends. The analyses indicate that the mean annual temperature records in Turkey have a warming trend over the 1939 to 1989 period, but a cooling trend from 1955 to These trends in mean annual temperatures, however, are not statistically significant. Comparatively greater warming effects have occurred in spring and winter minimum rather than the maximum temperature records. A regional increase in the mean minimum temperature around 1955 is attributed to the urban heat island effect. In general, general circulation models (GCM) predictions are consistent with the sign of the trends only in Turkish climate records during the entire 1939 to 1989 period # 1977 by the Royal Meteorological Society. Int. J. Climatol. 17: (1997). (No. of Figs: 4. No. of Tables: 5. No. of Refs: 27.) KEY WORDS: Turkey; run test; Mann Kendall test; trend analysis; temperature maxima; temperature minima INTRODUCTION Because human activities significantly affect the chemical composition of the atmosphere, the artificially induced climatic trends are becoming one of the most widely speculated aspects of climate (Mitchell et al., 1966; Karl, 1988; Dickinson, 1989; Balling, 1992). Because of many uncertainties and difficulties in obtaining homogeneous climatological time series, the trends are also one of the most uncertain aspects of meteorological research (Conrad and Pollak, 1950). Various trend-detection studies have been carried out in different parts of the world, mostly for identification of climate change, if any. Some of these cases have shown significant trend components, especially during the last 40-year period (Karl et al., 1993). Different techniques, such as parametric and non-parametric tests, are used for testing whether there have been statistically significant trends. However, the physical interpretations have been related, at times, to the greenhouse effect, global warming, urban heat islands and to aerosols that exert cooling effects on our climate (Balling, 1992). The effects of urbanization on temperatures and precipitation also have been discussed widely in the literature (Changnon, 1992). In general, previous studies have analyzed instrumental data for locating climate trends, mostly through parametric statistical methods, such as the moving average (Jones and Jiusto, 1980) and linear regression methods, as well as two-phase regression models (Solow, 1987). Parametric methods require the assumption of a normal distribution. However, in a non-parametric test, such as the Mann Kendall rank statistic, the departure from a Gaussian normal frequency distribution is not a concern (Mitchell et al., 1966; Sneyers, 1990). In this paper, therefore, a sequential version of the Mann Kendall rank statistic is preferred for application to monthly mean minimum and maximum temperature records at 18 stations scattered throughout Turkey in order to detect possible local and regional trends. CCC /97/ $17.50 # 1997 by the Royal Meteorological Society

2 512 M. KADIOĞLU DATA Turkey is located at the eastern corner of southern Europe, and is one of the five regions selected by the Intergovernmental Panel on Climate Change (IPCC) for particular study. Actually, in the Eastern Mediterranean, Near and Middle East areas, there are a few studies concerned with recent climatic change (Arseni and Maheras, 1991; IPCC, 1991; Tegart et al., 1990; Kadioğlu, 1993; Toros, 1993; Türkes; et al., 1995). Natural variability of the climate system could be as large as the changes observed to date, but as in the case of Turkey, there are usually insufficient data to estimate its magnitude or sign of natural variability (Wigley and Barnett, 1991). It is, therefore, realized that these series are too short to define a definite long-term climatic trend. Thus, in this study, the recent climatic trends in the available climatological series will be analysed without singling out their cause as being due to climatic change. The basic data are the mean seasonal and annual minimum and maximum temperatures of shelter-level air. Eighteen weather observing stations, as shown in Figure 1, are chosen for the study of climatic or artificially induced trends in Turkey. Relevant information concerning these stations are given in Table I in the order from north to south. The selection of the stations was based on the length of record and the reliability of data. Table I also indicates the period used for calculating mean country averages for Turkey, in addition to the city populations as in In addition, none of these stations had more than 5 per cent of its data replaced with estimates from nearby stations because of missing observations. These stations are located mostly in small towns, but some stations are within or near to large urban areas, which may be influenced by the urban heat island. For instance, Göztepe meteorological station İstanbul falls within the suburban area and therefore it is influenced by the heat island effect due to the high population concentration. It is well known that the urban heat island often tends to manifest itself strongest during night-time hours (Landsberg, 1981). The Swed and Eisenhart (1943) run test is a non-parametric procedure used in determining the homogeneity of a time series. It can adequately detect, in general, inhomogeneities as well as other problems, such as change of instrument, relocation of stations, etc. However, after detection the inhomogeneity, in order to identify what is the major cause, needs further detailed physical and=or meteorological studies. Vining and Griffiths (1985) have used this technique to study the climatic variability at 10 stations in the USA. Figure 1. Station locations

3 Station Longitude ( E) Table I. Station locations, population and temperature data Position TEMPERATURE TRENDS OVER TURKEY 513 Latitude ( N) Elevation (m) Population Record duration Edirne , (62) Zonguldak ,073, (52) Samsun ,158, (62) Trabzon , (61) Göztepe ,309, (62) Kars , (61) Çanakkale , (61) Ankara ,236, (65) Sivas , (61) Kütahya , (61) Elaziğ , (52) Van , (53) İzmir ,694, (53) Konya ,750, (62) Diyarbaki : r ,094, (62) S;. Urfa ,001, (62) Adana ,934, (62) Antalya ,132, (61) Average (51) The test procedure depends on the truncation of the time series concerned at the median level, giving rise to data values greater or smaller than the median. Any uninterrupted sequence of greater (smaller) values preceded and succeeded by at least one smaller (greater) value is referred to as a run. A succession of greater (smaller) values constitute a positive (negative) run. In general, the number of positive runs is equal to negative runs plus or minus 1. For the time series concerned to be homogeneous, the number of positive (negative) runs should be confined within the upper and lower confidence limits at a given significance level (usually 5 or 10 per cent) (Swed and Eisenharth, 1943). The calculation of confidence limits is based on the assumption of a normal distribution. According to this test, if the number of runs falls between confidence limits then the data set considered is homogeneous (Thom, 1966). This test indicated no noticeable heterogeneity in the seasonal and annual temperature data used, except for the temperature series of Antalya along the Mediterranean coast in the southern part of Turkey, with around 111 million population. The temperature records of Antalya, therefore, are excluded from further analysis in this paper. The warming trends detected in this study are broadly consistent with the prediction of GCMs that have been run and which have included Turkey. In fact three high-resolution models, namely, the Canadian Climate Center (CCC), Geophysical Fluid Dynamics Laboratory (GFDL) and United Kingdom Meteorological Office (UKMO) models have been applied over Turkey (Cubasch and Cess, 1990). The averages of these three models are considered for comparison purposes in this study. TREND ANALYSIS Among others, Sneyers (1990) has discussed the problems of statistical methods for detecting climate change. The parametric techniques are based on the assumption of independent observations, and therefore they cannot be valid for correlated sequences. However, non-parametric tests, such as those based on the ranks rather than the magnitude of the observations, are not dependent on the correlation structure. Besides, the seasonal data used in this paper have significant serial correlation structure. Therefore, in this study, a non-parametric statistical test in trend analysis is used.

4 514 M. KADIOĞLU Olaniran (1991) defined a climatic trend as a monotonic increase or decrease in the average value between the beginning and end of an available time series. However, as pointed out by Mitchell et al. (1966), trends in climatological time series are often spurious. Even if the trend is linear, the linear regression method is not the correct tool to use to locate the start of that trend. A better method could be use of multiple-phase regression models, as suggested by Vincent (1990) for testing the homogeneity of monthly temperature series. The data are analysed in order to identify meaningful long-term trends by making use of the sequential version of the Mann Kendall rank statistics, the effective application of which includes the following steps in sequence: (i) The values x i of the original series are replaced by their ranks y i, arranged in ascending order. (ii) The magnitudes of y i,(iˆ1,..., N) are compared with y j,(jˆ1,..., i71). At each comparison, the number of cases y i > y j is counted and denoted by n i. (iii) A statistic t i is, therefore, defined as follows t i ˆ Pi 1 n i 1 (iv) The distribution of the test statistic t i has a mean and a variance as E t ˆi iÿ1 i 2 4 and i i ÿ 1 2i 5 var t i ˆ 3 72 (v) The sequential values of the statistic u(t i ) are then computed as u t ˆ t iÿe t i Š i p 4 var ti Herein, u(t i ) is a standardized variable that has zero mean and unit standard deviation. Therefore, its sequential behaviour fluctuates around zero level. Furthermore, u(t i ) is a Gaussian normal variate. (vi) Similarly, the values of u 0 (t i ) are computed backward starting from the end of the series. Sequences of u(t) and u 0 (t) are shown at lower parts of Figures 2 to 4, as fluctuating sold and dashed lines, respectively. Horizontal dashed lines are drawn by using standard normal distribution and they correspond to the 5 per cent significance level confidence limits as Although the Mann Kendall test can successfully locate the trends, it is not able to provide a measure of the amount of change involved during a defined period. However, the linear changes are estimated by the leastsquares regression analysis. RESULTS Seasonal and annual trends in minimum and maximum temperatures for the period indicated using the Mann Kendall rank statistic, are summarized in Tables II and III, respectively. These tables include station locations according to sequence based on the degrees latitude from north to south. This point enables us to make spatial interpretations along the meridional direction. Herein, seasons are defined as 3-month periods, namely spring (March May); summer (June August); autumn (September November) and winter (December February). The trends in temperature data do not show distinctive meridional patterns across Turkey, but temporal patterns are relatively more obvious. The non-appearance of meridional trends is due to heterogeneous topographic effects as well as surface features such as forests, different types of rock outcrops, different sizes of urbanization centres and climate regions. Because the records do not have a distinctive meridional pattern, it is not necessary to draw spatial maps. Besides, the number of stations are insufficient for such a spatial representation. A close inspection of the results on minimum temperatures indicates that almost all the stations have strong warming trends at night during spring seasons. It is clear from Table II that during spring months, the linear trends are statistically significant at the 1 per cent and 5 per cent levels. In other words, we can say with 99 per

5 TEMPERATURE TRENDS OVER TURKEY 515 Figure 2. The time series of Turkish mean annual minimum temperature an the sequential version of the Mann Kendall test for the period cent confidence that there has been a warming trend during the temperature records, especially at locations between 37 N and 41 N, of mean seasonal minimum temperatures in spring. One of the major reasons for such as trend is the increase of daylight as a result of which the effects of long warming durations during daytime is embedded more and more into the night-time as the spring season evolves. Another significant factor that might play role in the spring night-time temperature increase is the sulphur dioxide pollution due to the use of fuel for heating in the cold part of the years. Accumulations of sulphur gases in the lower atmosphere cause an increase in brightness of clouds and generation of new clouds and hence cooling power during the day and heating power at night (Wigley, 1991). This is supported by the fact that all over Turkey fossil fuel in the form of coal is used for household heating or power generation in thermal power stations. An unprecedented population increase in cities as a result of within-country migration to urban areas, led to the consumption of coal for heating almost abruptly replacing the use of the local biomass in 1950s. The quality of coal used in Turkey emits excessive sulphur dioxide into the lower atmosphere. Table IV also shows that, except for autumn, overall mean seasonal and annual minimum temperatures for the period have increasing trends in Turkey. The computed mean annual minimum temperatures for the 51-year period present a C increase per year (see Figure 2). However, it is not significant at the 0195 level of confidence. The largest portion of this warming occurs in the spring season and the least occurs in the autumn months (see Tables II and V). The major meteorological reason for such a steady increase over the years might be interpreted as the greenhouse effect leading to global climate change over Turkey. Figure 2 shows averaged minimum temperatures for the 18 stations from 1939 to 1989, along with a leastsquares (dashed) line showing the linear trends over the whole period of record. The annual minimum

6 516 M. KADIOĞLU Figure 3. The time series of Turkish mean annual maximum temperature and the sequential version of the Mann Kendall test for the period temperatures for Turkey appear to decrease from 1939 to 1949, and subsequently, start to increase sharply in 1950 up to the 1970s, then again increase in the 1980s. However, the slope of the overall line is C per year, representing a 50-year long linear increase in Turkey s mean annual minimum temperature by an amount of 0163 C. Given the scattered observations around the trend line, there exists a statistical uncertainty regarding the actual value of the trend over the last 51 years. The underlying trend could be higher or lower than the computed average value of 0163 C. On the other hand, greater scatter around the trend line would lead to an increased uncertainty about the actual value of the underlying trend in the data. Although there may be no trend in the period considered, one point (at the beginning or the end) may produce a slope (van Loon and Williams, 1976; Gordon, 1991). The time series might have a non-linear trend, which can also be detected by the Mann Kendall test. According to this test, the trend in mean annual minimum temperatures is significant at the 95 per cent statistical confidence level and the trend started from the early 1950s (Fig. 2, bottom). The trends during the warmest time of day are shown by the records of maximum temperature in Table III. In contrast to the minimum temperatures, the seasonal and annual mean of maximum temperature show mostly decreasing non-linear cooling trends at the majority of stations, although most of them are not statistically significant. Table IV shows that during the winter and spring seasons the mean maximum temperatures for Turkey increased on average by 0136 C and 0166 C (but none of them is significant at either the 0190 or 0195 confidence level), respectively. The rest of the year during summer and autumn, maximum temperatures decrease

7 TEMPERATURE TRENDS OVER TURKEY 517 Figure 4. The time series of Turkish mean annual temperature and the sequential version of the Mann Kendall test for the period TABLE II. Seasonal and annual trends in minimum temperature Station Winter Spring Summer Autumn Annual Edirne ÿ1189 ÿ2107* 0111 Zonguldak ** 2128* Samsun ÿ Trabzon ÿ ÿ2102* ÿ3120** ÿ1146 Göztepe ** 2116* ÿ ** Kars 3117** 3177** ** Çanakkale ** 2144** ÿ ** Ankara ÿ Sivas 2126* 4152** 5144** 2162** 4145** Kütahya ** 1165 ÿ * Elaziğ 1193* 2112* ÿ0159 ÿ * Van ** 1143 ÿ * İzmir ** ** Konya 2105* 3126** 2135* ** Diyarbaki : r ** 1196* ** S;. Urfa ** ** Adana 2146** 4134** 5136** 3192** 5190** Average ** 1178 ÿ * *Significant at the 5 per cent level. **Significant at the 1 per cent level.

8 518 M. KADIOĞLU TABLE III. Seasonal and annual trends in maximum temperature Station Winter Spring Summer Autumn Annual Edirne ÿ0140 ÿ Zonguldak ÿ ÿ0199 ÿ1163 ÿ0160 Samsum 0112 ÿ0189 ÿ2137* ÿ2179** ÿ2102* Trabzon ÿ0145 ÿ1194* 0136 Göztepe ÿ1173 ÿ1183 ÿ0198 Kars ÿ Çanakkale ÿ0176 ÿ1118 ÿ0104 Ankara 0170 ÿ0139 ÿ1166 ÿ1185 ÿ0139 Sivas ÿ Kütahya ** 1144 ÿ Elaziğ ÿ0102 ÿ Van ÿ2135* ÿ2155** ÿ0194 İzmir ÿ ÿ0181 ÿ Konya 0110 ÿ0101 ÿ1153 ÿ1132 ÿ0185 Diyarbaki : r 0102 ÿ0195 ÿ0145 ÿ1146 ÿ0184 S;. Urfa ÿ2121* ÿ2140* ÿ1114 Adana ÿ1143 ÿ Average ÿ0109 ÿ *Significant at the 5 per cent level. **Significant at the 1 per cent level. insignificantly by 020 C and 0171 C, respectively. A plot of the mean annual maximum temperatures (Figure 3), therefore, shows no trend for the period of On average the decreasing trends during the cool period from December to May are almost equal to that of the warm period from June to November. Therefore, records of mean annual maximum temperature over Turkey have a negligible increase, which is actually not much different from zero according to the significance level adopted in all the tables. In order to test which season has the most warming effect on the basis of the available records, the trends in seasonal mean temperatures are also computed and their slopes are included in Table IV. It is obvious from this table that during the summer season, mean temperatures for Turkey increased by 011 C, although during the spring season the temperature increase is almost equal to 110 C. It implies that the steady temperature rise is the most in the spring season, whereas in the summer such an increase is almost reduced by tenfold. A plot of the mean annual temperatures for the period (Figure 4) shows an increasing trend that started around However, this linear trend in mean annual temperature is not statistically significant, and with a rate of increase of C per year, the total increase is 0132 C. With the minimum temperatures increasing faster than the maximum temperatures, one would expect a reduction in diurnal temperature range (the difference between the observed maximum and minimum temperatures). This is consistent with the predictions of some of the model simulations (Rind et al., 1989). TABLE IV. Average linear change in temperatures ( ) Temperature ( C) Minimum Maximum Mean Winter Spring 11326** * Summer ÿ Autumn ÿ01255 ÿ01714 ÿ01459 Annual 01632* *Significant at the 5 per cent level. **Significant at the 1 per cent level.

9 TEMPERATURE TRENDS OVER TURKEY 519 TABLE V. Annual trends in minimum, maximum and mean temperatures for the period Station Minimum Maximum Mean Edirne ÿ ÿ0131 Zonguldak ÿ1162 ÿ2137* ÿ1196* Samsun ÿ2153** 2186** ÿ2178** Trabzon ÿ2149** ÿ2153** * Göztepe 1180 ÿ Kars 2114* Çanakkale 1123 ÿ3102** ÿ1117 Ankara ÿ0123 ÿ0138 ÿ0108 Sivas 1124 ÿ Kütahya 0100 ÿ Elaziğ 1173 ÿ Van İzmir ÿ0112 ÿ0161 ÿ0125 Konya 0194 ÿ0174 ÿ0149 Diyarbaki : r ÿ0178 ÿ1189* ÿ1136 S;. Urfa 1173 ÿ Adana 3139** ÿ Average ÿ0104 ÿ1133 ÿ0181 *Significant at the 5 per cent level. **Significant at the 1 per cent level. Similar to Figure 3, Figure 4 also illustrates a jump upward near This suggests that some nationwide changes had occurred around Because the warming trend in the mean annual temperature comes from the minimum temperatures, this is a change due to a regional increase in minimum temperatures. On the basis of the Turkish State Statistical Institute data, the population of Turkey has almost doubled between 1927 and Parallel to the high birth rate during this period (45 per cent) and industrial development in the early 1930s, Turkey has started to experience a rapid urbanization movement. With all these, the significant warming in minimum temperature is a sign of the local urbanization impact. This also can be verified by comparing our results to those of Türkes; et al. (1995). They mostly analysed annual mean air temperature records from rural areas, inherently with many data problems. Most of their stations show cooling trends. A trend analysis of our mean temperature data from 1955 to 1989 also presents a cooling trend at many stations, but some of them are statistically significant, as shown in Table V. On the other hand, regionalization of trend analysis could not be extended all over Turkey because of mostly unknown homogeneous climatic regions in Turkey. However, Türkes; et al. (1995), have regionalized their climatological data according to the geographical regions of Turkey. In fact, the climatological regions should not be based on geographical regions but on climatological data themselves. In this way, they have assumed the geographical regions of Turkey as identical to the climatological regions. As summarized in Table V our analysis of the mean temperatures at most of the stations across Turkey generally show cooling trends. Only those at the Black Sea coast, however, are statistically significant for the period CONCLUSIONS Analyses of about 60-year unsmoothed temperature records at 18 Turkish sites have been carried out. After the implementation of non-parametric run tests, it is concluded that most of the data are homogeneous. The data are then analysed to identify meaningful trends by making use of a sequential version of the Mann Kendall rank statistic. Evidence from 17 stations suggests that night-time temperatures have a comparatively significant increase while daytime temperatures remain unchanged over Turkey for the period This reduction in the diurnal temperature range is somewhat consistent with the predictions of some of the 26CO 2 GCM simulations.

10 520 M. KADIOĞLU The amount of warming for the period 1939 to 1989 is estimated as 011 C in summer mean and 017 C in winter mean temperatures. Overall, the linear trend in annual mean temperature is 0132 C over the 1939 to 1989 period. However, these are not statistically significant at the 0195 confidence level. If the possibilities of heat island, overgrazing desertification and other signals that exist are considered in the data, then these warming trends could be explained substantially. Although the surface air temperature data in Turkey are inconclusive for determining the existence and amount of long-term climatic trends, it can be said that the warming observed from our empirical analysis is broadly consistent (at least in direction) with the predictions of the GCM models. The mean minimum temperature series show a regional increase around This increase is attributed to local influence of increasing urbanization in Turkey. Alternative analyses of mean temperature records are, therefore, conducted for the period of These analyses, however, indicate overall cooling trends across Turkey, but only those at the Black Sea coast are statistically significant. ACKNOWLEDGEMENTS I would like to thank the Turkish State Meteorological Services for providing data, my colleagues for valuable discussions, and Ms Sevim Yertürk and Mr Hüseyin Toros for drafting figures. I would also like to thank the reviewers for their valuable suggestions. REFERENCES Arseni, P. A. and Maheras, P Some statistical characteristics of air temperature variations at four Mediterranean stations, Theor. Appl. Climatol., 43, Balling, R. C., Jr The Heated Debate: Greenhouse Predictions Versus Climate Reality, Pacific Research Institute for Public Policy, San Francisco, 1955 pp. Changnon, S. A Inadvertent weather modification in urban areas: lessons for global climate change, Bull. Am. Meteorol. 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