Changes and trends in maximum, minimum and mean temperature series in Iran

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1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 16: (2015) Published online 26 February 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: /asl2.569 Changes and trends in maximum, minimum and mean temperature series in Iran Ahmad Reza Ghasemi* Water Engineering Department, Agricultural Faculty, Shahrekord University, Iran *Correspondence to: A. R. Ghasemi, Water Engineering Department, Agricultural Faculty, Shahrekord University, Shahrekord, Iran. Received: 8 January 2014 Revised: 1 September 2014 Accepted: 22 January 2015 Abstract The variations of Iran temperature is studied during Mann Kendall tests show a general dominating positive temperature trend over most parts of Iran with the rate of change varying from 0.09 to 0.38 C decade 1. Also, the minimum temperature increase (0.34 C decade 1 ) is greater than maximum temperature (0.15 C decade 1 ). A more definite trend of warming occurs in summer and spring than winter and autumn. Moreover, the results show that the warm climate regions in Iran are warming at a higher rate than cold climate regions. Pettitt s test also suggesting the abrupt upward change in mean annual temperature, occurs in the late 1990s. Keywords: Iran; temperature trends; Pettitt s test; change point 1. Introduction Surface air temperature is one of the most important elements in weather and climate forecasting, so examination of its behavior is important for understanding of climate variability which can vary spatially and temporally at different local, regional and global scales. The International Panel on Climate Change (IPCC) has clearly identified that temperature trends on a global scale show a warming of 0.85 ( ) C, over the period In addition, each of the last three decades has been successively warmer than any preceding decade since The report also emphasizes that global mean surface temperature exhibits decadal and interannual variability. Because of natural variability, trends based on short records are very sensitive to the beginning and end dates. For example, the rate of warming over the period (0.05 C decade 1 ) is smaller than over (0.12 C decade 1 ) (IPCC, 2013). Climate changes usually have strong effects on people and their behavior, as well as on agricultural resources and the availability of water, and is especially important in regions in which its economic activity is depend on agriculture such as most parts of Iran. Many studies have pointed out direction and significance of temporal trends of temperature at various spatial scales from the global to the local (e.g. Vose et al., 2005; Karoly and Stott, 2006; Brown et al., 2008). They all pointed to the fact that there is a general warming trend in the global mean temperature. Global surface temperature change for the end of the 21st century is projected to likely be in the ranges of C relative to (IPCC, 2013). The results of some of the similar notable studies conducted in the Middle East are as follows. Bani-Domi (2005) suggested that the mean annual minimum and maximum temperature records in Jordan have a warming non-significant trend over the period Gurevich et al., (2011) found an increase in the monthly mean temperatures in the warm season in all areas of Israel approximately from the mid-1980s to the mid-1990s. Altin et al. (2012) showed a significant increase in temperature of approximately 0.4 Cinthe Central Anatolia region of Turkey. The temperature variability issue is being considered by a few Iranian authorities. Tabari et al. (2012) studied the mean air temperature in 29 synoptic stations in Iran ( ) and reported that the annual mean air temperature trend was C decade 1. Zarenistanak et al. (2014) based on four models indicated that temperature may increase between 1.69 and 6.88 C by 2100 over the south-western part of Iran. Moreover, a few researchers like Ghasemi and Khalili (2006, 2008) investigated the atmospheric circulations (e.g. North Atlantic Oscillation, NAO; Arctic Oscillation, AO; North Caspian Sea Pattern, NCP) as the main forcing factors for controlling the temperature variability of Iran. The IPCC reports highlighted the need for more details about regional patterns of climate change. Iran s climate is diverse due to its complexity of topographical features and large geographical area, and thus in case of a local variability in climate change, it is important to know which area could be more affected by these changes. These results can aid with verification of climate model studies, which in turn assist with contingency planning for agriculture and water supply, especially for the future. Thus, the purpose of this paper is to characterize the most important features of spatial (temperature regimes) and temporal (trend and change point) temperature variability over the period , in order to accurately represent local climate diversities Royal Meteorological Society

2 Changes and trends in temperature series in Iran 367 Figure 1. Geographical location of climate stations used in this study. 2. Data and methodology The monthly maximum, minimum and mean temperature are extracted from the Iranian Meteorology Organization for the 38 stations (Figure 1). This study is done with latest data (2010) published by Iranian Meteorological Organization. Also, for clustering the temperatures timeseries, to identify homogeneous groups of stations and create a timeseries for each cluster, an equal length is considered from 1961 to The climatological seasons, i.e. January to March for winter, April to June for spring, July to September for summer and October to December for autumn, are used in computing seasonal values. To identify homogeneous groups of stations with similar annual thermal regimes, a cluster analysis (agglomerative hierarchical clustering, AHC) with Ward s method is applied. Also linear trends are determined on annual, seasonal and monthly timescales, using Mann Kendall non-parametric tests. Also, the magnitude of trend, if a linear trend is present in a time series, is estimated by using a simple non-parametric procedure developed by Sen (1968). These tests have been widely used in climatologic data analysis, because it is less sensitive to the non-normality of the distribution and less affected by extreme values or outliers in the series. A number of methods can be applied to determine change points of a time series (Radziejewski et al., 2000; Chen and Gupta, 2012). In this study, the change point in temperature timeseries is identified by Pettitt s test (Pettitt, 1979). This test is a non-parametric approach for detecting change points based on the 2015 Royal Meteorological Society Mann Whitney two-sample test. It can detect a monotonic trend or a single abrupt change in timeseries (Shao et al., 2010). This method detects a significant change in the mean of a time series when the exact time of the change is unknown (Gao et al., 2011). It has been demonstrated that the Pettitt s test is a useful technique for examining the occurrence of abrupt changes in climatic records (Aondover and Woo, 1998; Smadi and Zghoul, 2006). The Pettitt s test is performed by using statistical package XLSTAT version that provides p-values using Monte Carlo resampling. 3. Results 3.1. Annual temperature regimes in Iran Clustering technique identifies four categories (Figure 2) with similar temperature regimes that can be adapted for use in different Iran areas. For example, cluster 4, characterized by the highest mean, minimum and maximum temperature (Table 1), is mostly composed by stations located in the Persian Gulf coastal zones. Likewise, cluster 3, characterized by the lowest mean, minimum and maximum temperature (Table 1), is constituted by meteorological stations located in the mountains. Cluster 2, prevalently contains weather stations located in the Alborz and Zagros foothills and valleys (the two main mountain ranges in Iran), characterizes by the moderate minimum and maximum temperature compare to other clusters. Cluster 1 prevalently includes meteorological weather stations sited in inner plains, and also coastal zone of the Caspian Sea (Figure 2). Atmos. Sci. Let. 16: (2015)

3 368 A. R. Ghasemi Figure 2. Groups of meteorological stations (clusters) with similar annual thermal regimes. Table 1. Iran cluster sub-division. Cluster number Number of weather stations weather stations mean annual T min ± σ ( C) weather stations mean annual T max ± σ ( C) weather stations mean annual ± σ ( C) ± ± ± ± ± ± ± ± ± ± ± ± Annual trends The period of is used in Figure 3 in which mean temperature anomalies (deviations from the average) are given on the annual scale for all clusters. As shown in the figure, 5-year moving averages in all clusters indicating that a cool period (negative trend) starts in the mid-1960s and lasting until the mid-1970s. The cool period is only significant at 5% level in clusters 2 and 3. Figure 3 also depicts that all clusters have a positive trend with a long hot period since the early 1990s and ends in All increasing trends are significant at 5% level except for cluster 3. To assess temperature variability in clusters, the analysis is made using the Mann Kendall and Sen Slope methods. The results suggest that there is a statistically significant positive linear trend in mean annual temperature at the 1% level (Table 2) in all clusters (varying from 0.20 to 0.38 C decade 1 ), with exception of cluster 3 which does not have a significant trend (0.09 C decade 1 ). Mashhad in cluster 1 (Figure 1), where the mean annual temperatures warming is the strongest, shows a rate of change of 0.62 C decade 1. On the contrary, Khoramabad (cluster 2) and Shahrekord (cluster 3), which situated in the central Zagros mountainous regions, show a decreasing trend at rate of about 0.26 C decade 1. On the subject of T min, cluster 1 (Figure 4(a)) exhibits the greatest increase (+0.58 C decade 1 ), while cluster 3 turns out to be the most varied from the others, presenting the smallest rate in T min (+0.05 C decade 1 ) which is not significant (Table 2). Looking at mean annual maximum temperatures in Table 2, only clusters 1 and 4 (Figure 4(b)) show positive significant trends with a small increase of about 0.2 C decade 1. Concerning all clusters as a whole, the rate of change in T min is found to be 0.34 C decade 1, while it is only 0.15 C decade 1 for T max. This confirms the results of Folland and Karl (2001), in general, minimum temperature increased about twice as fast as maximum temperature over global land areas Seasonal trends To ascertain whether certain seasons show consistently higher or lower temperature trends than others, the mean annual data series are subdivided into seasons. The results obtained for the seasons are shown in Table 3, which clearly indicate that temperature trends

4 Changes and trends in temperature series in Iran 369 (a) (b) Atmospheric science letters trend of warming occurs in summer and spring, in contrast to the observations of a global temperature increase in winter (Hansen et al., 2010). However, our findings in Iran are similar to those found by Klein Tank and Konnen (2003), Domroes and El-tantawi (2005) and Altın et al. (2012), which have shown that temperature values of warm months increased and that warming trends were smaller during winter months. Moreover, it is found that regional differences in areas with warm and cold climate are striking over Iran. So that most significant trends are observed in the areas with warm climate than cold. Although, the maximum temperature change rates do not show a significant difference between warm and cold climate regions, averaging over regions with warm climate yields a significant warming of 0.61 C decade 1 in T min while it is only 0.39 C decade 1 in regions with cold climate, suggesting that the warm climate regions in Iran are coming warm with a much more rapid rate that of the cold climate regions. (c) (d) Figure 3. Mean annual anomalies, 5-year average and trends in T mean in (a) cluster 1, (b) cluster 2, (c) cluster 3 and (d) cluster 4. over the studied period are not consistent between seasons. Averaged over all clusters, T min shows warming trend with an average rate of 0.32 C decade 1 for winter, and it is found to be 0.42 C decade 1 for spring, 0.36 C decade 1 for summer and 0.32 C decade 1 for autumn. These values for T max are 0.22, 0.21, 0.17 and 0.07 C decade 1 for winters, spring, summer and autumn, respectively, which show a higher increase in T min trends in all seasons than in T max, consistent with those for the annual trends. It is also interesting to note that the most significant trends in all clusters are found in summer and spring rather than winter and autumn. In other words, a definite 3.4. Monthly trends The results obtained from the seasonal analysis suggest that it is worthwhile investigating the trends in finer detail. Over the analyzed period, there is a general increase in monthly mean temperature in most stations. However, the increase in temperature is not even significant over the whole of Iran and is not observed at the same rate for all months and stations. The most significant monthly trends (27 of 38 stations) are found in September, while most stations in January show no significant trend (Figure 5). The numbers of stations with significant trends in warm months (April to October) are more than the cold months (November to March), with trends of 0.26 and 0.20 C decade 1, respectively. It is interesting to note that, in about 80% of studied stations, the first or second highest monthly mean temperature trends happen from April to October which are defined as warm months in Iran, coincide with those for the seasons, where spring and summer are the seasons of maximum temperature trend Changes in temperature trends from 1960 to 2010 To investigate whether there is an abrupt increase or decrease (change point) in mean annual temperature, the time series are subjected to a Pettitt s test. Rodionov (2005) advised that if both a step change and a trend are apparent, the data series must be first be de-trended before application of the Pettitt s test. Therefore, any significant linear trend is removed from the raw time series using the slope calculated by the Sen Approach (Gao et al., 2011). The results of this analysis are shown in Figure 6. The Pettitt s test detects a change-point year in 1997 for all clusters that indicates an abrupt increase from 1997 in mean annual temperatures. In other words, a lower average temperature before change point year is replaced by

5 370 A. R. Ghasemi Table 2. Mean annual temperature trends for all clusters. T min T max T mean Cluster (p-value) Slope ( C10year 1 ) (p-value) Slope ( C10year 1 ) (p-value) Slope ( C10year 1 ) (a) (b) Sen's slope: 0.58 C decade 1 y = 0.057x R2 = y = 0.022x R2 = Sen's slope: 0.2 C decade 1 Figure 4. Mean annual trends of (a) T min in cluster 1 and (b) T max in cluster 4. a higher average temperature after change point year. For example, in cluster 2, the average temperatures for de-trended data are 14.7 C (red dashed line) and Number of stations Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 5. Number of stations with significant mean monthly temperature trend C (green dashed line) in the periods before and after 1997, respectively. These values for original data are 15 and 16 C, respectively. Moreover, the differences in average temperatures between the two periods for both the original temperature time series and its de-trended are statistically significant (at p = 0.01 for original data in all clusters and at p = 0.05 in cluster 1andatp = 0.01 in clusters 2, 3 and 4 for de-trended data). These results are consistent with previous studies. Globally, the last decade of the previous century was shown to be substantially warmer than previous decades (Kruger and Shongwe, 2004). Table 3. Seasonal temperature trends for all clusters. T min T max T mean Season Cluster (p-value) Slope ( C10year 1 ) (p-value) Slope ( C10year 1 ) (p-value) Slope ( C/10 year 1 ) Winter Spring Summer Autumn

6 Changes and trends in temperature series in Iran Cluster 1 Table 4. Correlation coefficients between the NAO, AO and NCP and the clusters mu1=17.2 mu2= Cluster mu1=14.7 mu2= Cluster mu1=10.9 mu2= Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 NAO 0.39 a 0.51 a 0.49 a 0.53 a AO b 0.56 a 0.31 b NCP a 0.58 a 0.39 a a Significant at the 5% level. b Significant at the 1% level. (a) (b) Pre Obs Linear(Pre) Linear(Obs) Cluster Pre Obs Linear(Pre) Linear(Obs) Cluster Figure 7. Time series and trend lines of observed mean annual temperature and mean annual temperature as predicted by NAO for (a) cluster 1 and (b) cluster mu1=24.1 mu2=24.5 Figure 6. Change point in mean annual temperature for all clusters (the dashed lines represent the mean of series before and after change point) The influence of NAO, AO and NCP events on mean annual temperature trends Previous studies have shown that there is strong evidence for a linkage between the NAO, AO and NCP and temperature in Iran (Ghasemi and Khalili, 2006, 2008). Table 4 shows the correlation coefficient between these indices and mean annual temperature in all clusters. To test whether the trends in temperatures are forced partly by these indices, regression analysis is performed using average annual indices and mean annual temperatures. From the linear regressions determined between these indices and mean annual temperature, time series for the clusters can be constructed as predicted by these indices. The results indicate that none of the time series of temperatures predicted by NAO, AO and NCP show a significant trend. Examples are given in Figure 7(a) and (b) for the clusters 1 and 4, respectively. In both cases, it is clear that, although the observed trends are significantly positive, the trends predicted by NAO show only small non-significant positive trends. Therefore, it seems that increases in mean annual temperatures in Iran over the period are not forced by the occurrence of North Atlantic Oscillation. 4. Conclusions Our results lead to the following conclusions: For the climate stations utilized in this study, trends of maximum, minimum and mean annual temperature do not, in all cases, coincide with the general trend of the average global time series. In most cases, they do, showing largely positive trends on average. Positive temperature trends are also found not to be consistent between seasons, with spring and summer being the

7 372 A. R. Ghasemi seasons of highest temperature trends on average and autumn and winter being the seasons of lowest trends. The results of monthly mean trends clearly show temperature increase on an annual scale resulted from a pronounced April to October warming. Another important result is that, stations with warm climate, like interior and eastern regions of Iran, are warming with a higher rate than stations with cold climate, which are mostly to be found in west and northwestern parts. In all clusters, the average temperature from 1996 to 2010 is higher than from 1961 to This suggests that there is a somewhat steeper increase in temperatures after the late 1990s. We also test whether the trends in temperatures are found to be forced partly by NAO, AO and NCP. Although there is a significant correlation between annual NAO, AO and NCP and mean annual temperature over Iran, the indications are that NAO, AO and NCP events do not play a significant role in the observed significant increases in mean annual temperatures. References Altın TB, Barak B, Altın BN Change in precipitation and temperature amounts over three decades in central Anatolia, Turkey. Atmospheric and Climate Sciences 2: Aondover T, Woo M Changes in rainfall characteristics in northern Nigeria. International Journal of Climatology 18: Bani-Domi M analysis of temperatures and precipitation in Jordan. Journal of Educational, Social Sciences & Humanities. 17: Brown SJ, Caesar J, Ferro CAT Global changes in extreme daily temperature since Journal of Geophysical Research, [Atmospheres] 113: Chen J, Gupta AK Parametric Statistical Change Point Analysis. Birkhauser: Boston, MA; 240 pp. Domroes M, El-Tantawi A Recent temporal and spatial temperature changes in Egypt. International Journal of Climatology 25: Folland CK, Karl TR Observed Climate Variability and Change. The Scientific Basis. Cambridge University Press: Cambridge, UK. Gao P, Mu XM, Wang F, Li R Changes in stream flow and sediment discharge and the response to human activities in the middle reaches of the Yellow River. Hydrology and Earth System Sciences 15: Ghasemi AR, Khalili D The influence of the Arctic Oscillation on winter temperatures in Iran. Theoretical and Applied Climatology 85: Ghasemi AR, Khalili D The effect of the North Sea-Caspian pattern (NCP) on winter temperatures in Iran. Theoretical and Applied Climatology 92: Gurevich GY, Hadad A, Ohayon B Statistical analysis of temperature changes in Israel: an application of change point detection and estimation techniques. Global NEST Journal 13: Hansen JR, Ruedy MS, Lo K Global surface temperature change. Reviews of Geophysics 48: RG4004, doi: /2010RG IPCC Climate change 2013: the physical science basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press: Cambridge, UK and New York, NY; 1535 pp. Karoly DJ, Stott PA Anthropogenic warming of central England temperature. Atmospheric Science Letters 7: Klein Tank AMG, Konnen GP s in indices of daily temperature and precipitation extremes in Europe Journal of Climate 22: Kruger AC, Shongwe S Temperature trends in South Africa, International Journal of Climatology 24: Pettitt AN A non-parametric approach to the change point problem. Applied Statistics 28: Radziejewski M, Bardossy A, Kundzewicz ZW Detection of change in river flow using phase randomization. Hydrological Sciences Journal 45: Rodionov S A brief overview of the regime shift detection methods. In Large-Scale Disturbances (Regime Shifts) and Recovery in Aquatic Systems, UNESCO ROSTE/BAS Workshop on Regime Shifts, Varna, Bulgaria, June 2005, pp. Sen PK Estimates of the regression coefficient based on Kendall s tau. Journal of the American Statistical Association 63: Shao Q, Li Z, Xu Z detection in hydrological time series by segment regression with application to Shiyang River Basin. Stochastic Environmental Research and Risk Assessment 24: Smadi M, Zghoul A A sudden change in rainfall characteristics in Amman, Jordan during the mid-1950s. American Journal of Environmental Science 3: Tabari H, Hosseinzadeh P, Ezani A, Shifteh B Shift changes and monotonic trends in auto correlated temperature series over Iran. Theoretical and Applied Climatology 109: Vose RS, Easterling DR, Gleason B Maximum and minimum temperature trends for the globe: an update through Geophysical Research Letters 32: 1 5. Zarenistanak M, Dhorde AG, Kripalani RH Temperature analysis over southwest Iran: trends and projections. Theoretical and Applied Climatology 116: