Climatic Trends in the Temperature of Çanakkale City , Turkey

Climate is a dynamic process changing in both temporal and spatial scales. Climate change and global warming has been extensively accepted and commonly described as rising of the temperature. Long-term trends and changes in the series of monthly, seasonal and annual temperature of Çanakkale station (Çanakkale, Turkey) of Turkish State Meteorological Service (TSMS) were analyzed by considering temporal characteristics. Climatic data for temperature encompasses the period of between 1970 and 2012. Temperature data set has been arranged as climatological seasons that spring (March, April, May), summer (June, July, August), autumn (September, October, November), and winter (December, January, February). Non-parametric tests and Box-Jenkins method were used to determine climatic trends. Pettitt change-point analysis was applied for determining the change point of temperature dataset. Results of trend analysis revealed that there was a statistically significant increasing trend in the temperature. Mean annual temperature is predicted to increase 0.02977°C per year and it is forecasted to reach 15.9946°C in 2022. On the other hand, mean seasonal temperatures are predicted to increase 0.0121°C, 0.05877°C, 0.0350°C, and 0.0031°C per year for spring, summer, autumn, and winter, respectively. The increase in temperature trends indicates that global warming is causing to climate change. In light of the results, it is crucial to state that Çanakkale city will be affected by global warming and climate change, and also will have a warmer climate in the future.


Introduction
Climate change related with rise in global temperatures is based on increases in the emissions of greenhouse gases (Arnell, 2003).Climate changes by anthropogenic activities are associated with increases in atmospheric CO2 and other gases (Tabari & Talaee, 2011).These changes in climate because of the increased greenhouse influence are estimated to be a reason for major changes in different climatic variables for instance surface temperature, precipitation, solar radiation, and humidity (Haskett et al., 2000;Yu et al., 2002).Recently, it has been observed that there was an increase of 0.7°C in mean global surface temperature from the initial of the century and it is predicted to increase 3°C in the next century (Ejder et al., 2016b).The Intergovernmental Panel on Climate Change (IPCC, 2007) expected that climate change and global warming will have potential effects on mean temperature, precipitation regime, drought, and sea level rising.
Climatic trends are one of the most common aspects of the climate due to anthropogenic activities affect the atmosphere composition (Kadıoğlu, 1997).Trends are also one of the most unclear aspects of climatological researches as a consequence of difficulties and uncertainness in providing consistent climatological time series.
Trends in global mean surface temperature are a valuable indicator of climate change and variability (Tabari & Talaee, 2011).Therefore, the main purpose of the study was to determine trends in the time series of monthly, seasonal and annual temperature in Çanakkale for the period of 1970-2012.Furthermore, possible climatic changes in temperature will be forecasted.

Study Area and Data
Çanakkale is located in the western part of Turkey.It is surrounded by Aegean Sea, Çanakkale Strait, and Marmara Sea (Figure 1).The climate is typically of the transition climate type that characterized by hot and dry summers, and cold and rainy winters.Mean monthly temperatures of Çanakkale pointed out that July is the warmest month and January is the coldest month with longterm averages of 25°C and 6.4°C, respectively (Cengiz & Akbulak, 2009).

Methods
In this study, change point analysis, trend analysis, and non-parametric tests were applied.
The null hypothesis of the Pettitt's change-point test is that there is no change point exists.KT is the statistic of null hypothesis in Equation (1). where In Equation ( 2), Ut,T, confirms in this formula whether two examples x1,……, xt and xt+1,……,xT are in the same population or not.Associated probability (p) is used in significance calculating.The significance probability of KT is predicted for p ≤ 0.05 with the formula in Equation (3).

Trend Analysis
The most commonly used method for determining the tendency of climatic time series is trend analysis (Hamed & Ramachandra Rao, 1998).Therefore, trend analysis and Box-Jenkins method (Box & Jenkins, 1976) was used in this study for determining the tendency of temperature time series.Box-Jenkins technique is based on linear, discontinuous, and stochastic processes.This method used for forecast and analysis of a time series.An autoregressive integrated moving average (ARIMA) is applied for non-stationary processes whereas autoregressive (AR), moving average (MA), autoregressive-moving average (ARMA) models are applied for stationary processes.The main purpose of these models is to determine the model which fits best to the time series and which contains the fewest parameters (Box & Jenkins, 1976).ARIMA models utilize a linear combination for the prediction of a time series.In the ARIMA model (p, d, q), p designates the number of AR terms, q designates the number of MA terms and d designates the order of differencing.The ARIMA model used in this study is formulated in Equation (4).
Xt is the variable that will be described in t time, c is the constant, Φ is the coefficient of per p parameter, θ is coefficient of per q parameter, and et are the errors in t time.
The ARIMA (1, 0, 1) model was used in trend analyses and autocorrelation analyses were also performed to define the reliability of the results achieved from analyses.It was aimed to suggest practical quantitative results by forecasting the statistical data analysis in the climatic data assessment stage of the study.It is attempted to predict future projections by forecasting a 5-year range applied to the time series.

Mann-Kendall Test
Mann-Kendall test is a regularly used test to determine the trends in a time series (Mann, 1945;Kendall, 1955).Extreme values in the dataset have impacts on the average.Mann-Kendall test is an effective test for determining trends in a time series with extreme values (Durdu, 2010).In this study, a non-parametric Mann-Kendall test (Mann, 1945;Kendall, 1955) was executed to examination of potential trends in the temperature.Kendall's tau and Spearman's rho tests were also applied.Non-parametric Mann-Kendall and Spearman's rho tests provide more reliable and appropriate results than parametric tests.The statistic formula of Mann-Kendall test is explained in Equation (5-8).
Zc and β are given as where Zc is the test statistic.H0 will be rejected while |Zc| > Z1-α/2, wherein Z1-α/2 are the standard normal variables and α is the significance level for the test.The magnitude of the trend is given in Equation (8).

𝛽 =𝑀𝑒𝑑𝑖𝑎𝑛 (
A positive value of β indicates an increasing trend, while a negative value of β indicates a decreasing trend where 1 < j < i < n.

Results
Results of Pettitt change-point analysis specified that the change point for mean annual temperature was 1997 (Table 1).Trend analysis results showed that temperature has an upward trend (Figure 2).Mean annual temperature is predicted to increase 0.02977°C per year and it is forecasted to reach 15.9946°C in 2022 (Table 2).1998, 1993, 1989, and 1993 for spring, summer, autumn, and winter, respectively (Table 1).Trend analysis results displayed that temperature has an upward trend for all seasons (Figure 3).Mean seasonal temperature is expected to increase 0.0121°C, 0.05877°C, 0.0350°C, and 0.0031°C per year for spring, summer, autumn, and winter, respectively (Table 2).1983, 2002, 2001, 1996, 1993, 1994, 1993, 1984, 1981, 1989, 1983, and 1977 for the months from January to December, respectively (Table 1).The results of trend analysis demonstrated that temperature has an increasing trend for all months excepting February (Figure 4).Temperature values are forecasted to decrease for February.Mean monthly temperature values are estimated to increase 0.0012°C, -0.0126°C, 0.0117°C, 0.0044°C, 0.0202°C, 0.0407°C, 0.0641°C, 0.0720°C, 0.0362°C, 0.0371°C, 0.0315°C, and 0.0161°C per year for the months from January to December, respectively (Table 2).

Discussion
It is important that to investigate the climate change impacts and to take into account the outcomes of different climate change projects.One of the most important and fundamental indicators of global climate change is the variation in temperature.Most climate models expect an increase in temperature at the end of the 21st century (García-Ruiz et al., 2011).It is reported that increased temperature could be related to the global warming by (Chen & Xu, 2005).
In this study change points were found in 1990s.Similarly, previous studies on climatic changes in western part of Turkey specified that increasing trends in temperature over the study area were started in 1970s and progressively was clarified during 1990s (Aksoy, 2007;Aksoy et al., 2007;Türkeş & Acar Deniz, 2011).
Non-parametric Mann-Kendall and Spearman's rho tests pointed out that there were statistically significant correlation.Particularly, the correlation of mean annual temperature, mean seasonal temperature for summer and autumn, and mean monthly temperature for June, July, August, and September are significant at the 0.01 level.Also, the correlation of the mean monthly temperature for May is significant at the 0.05 level.
It was reported that the temperature increased in a statistically significant way in the Middle East between 1950 and 2003 (Zhang et al., 2005), in the south of Europe (Alcamo et al., 2007), in Turkey between 1971and 2004(Sensoy et al., 2008), between 1963and 2007(Durdu, 2010) and between 1975 and 2010 (Sütgibi, 2015).Particularly, Ejder et al. (2016aEjder et al. ( , 2016b) ) and Kale et al. (2016aKale et al. ( , 2016b) ) reported that temperature has a statistically significant upward trend in Çanakkale.On the other hand, seasonal and monthly temperature trends were investigated by several authors (Karabulut et al., 2008;Şimşek et al., 2013;Ertaç et al., 2015;Tatlı & Altunay, 2015).Tatlı & Altunay (2015) investigated possible climate change effects in Turkey using monthly temperature dataset and reported that there were increasing trends for all seasons.Ertaç et al. (2015) forecasted monthly mean temperature for İstanbul (Turkey) and indicated that temperature has increased.Karabulut et al. (2008) studied temperature trends in Samsun (Turkey) and found that temperature increased both annually and seasonally.The authors have documented that summer season has a statistically significant increasing trend.Şimşek et al. (2013) examined annual and seasonal trends in meteorological data in Hatay (Turkey) and reported that all seasons have increasing trends excluding the winter season.Turan et al. (2016) investigated changes in sea surface temperature (SST) and possible effects on the biodiversity in the Turkish coasts of the Mediterranean, Marmara and Black Sea.They found that SST for all seas have increasing trends.Also, it is reported that the number of alien fish species in these seas have a highly increasing trend for the last two decades.Authors indicated that biodiversity will have significant change and there will be more invasion of the alien species in the Mediterranean.They highlighted that one of the major reasons of the alien species invasion in these seas is the increase of SST.Therefore, the results of this study are agreed with the results of other published papers and pointed out that temperature is increasing.
Mean annual and seasonal temperature values are estimated to increase year by year.Furthermore, mean monthly temperature values are predicted to increase for all months excluding February.Average values for February is forecasted to decrease between 2018 and 2022.Dixon et al. (2009) put excessive emphasis on some scientists anticipate winter seasons to have more precipitation in global circulation scenarios.On the other hand, February is one of the months of the winter season.Therefore, the decrease in the predicted temperature values for February could become understandable.
Climate projections are generated by using simulations of the climate change.These simulation studies could be inadequate caused by absence or unreliability of the data.Globally projected simulations may possibly not be usable locally.Limitations of this investigation may perhaps be incompleteness of the data or variability of climate systems.Long-term and continuous data have been used to eliminate these limitations in this study.

Conclusion
It was found that there was a statistically significant upward trend in temperature in Çanakkale.Mean annual, seasonal and monthly temperature are predicted to increase in general manner.It is crucial to state that Çanakkale city will be affected by global warming and climate change, and also will have a warmer climate in the future.

Figure 1 .
Figure 1.Study area and location of meteorological station Data for the temperature obtained from Çanakkale station of Turkish State Meteorological Service (TSMS) for the period of 1970-2012 were used in this study.Time series of temperature data set has been arranged as climatological seasons that spring (March, April, May), summer (June, July, August), autumn (September, October, November), and winter (December, January, February).

Figure 2 .
Figure 2. Trend analysis result of mean annual temperature

Figure 3 .
Figure 3. Trend analysis results of mean seasonal temperature.

Table 1 .
Change-point years and results of the non-parametric tests

Table 2 .
Predicted temperature values for 2018-2022 Pettitt's analysis stated that the change points for mean monthly temperature were determined