Statistical analysis and temporal trend of annual maximum temperatures of Abadan in Southwestern of Iran


Authors: Yousef Ghavidel Rahimi & Mahmoud Ahmadi _ 

“Arabian Journal of Geosciences” published by ‘Springer’ Website.

Abstract: Human and natural physical caused climate change can affect climate extremes, as well as maximum temperatures. The variations of the interannual and interdecadal hot temperatures have been studied between 1960 and 2012 (53 years) based on Abadan annual maximum temperatures (AAMT) data. The decadal analysis of the time variations of AAMT showed a positive and ascending trend in 60d, 70d and 90d, the neutral trend in 80d, and the descending trend in the last decade of the twentieth century. The positive time variations of AAMT in the 60d and 70d are too severe and more significant thanthatofother decades. The analysis of relationship between the absolute AAMT and the factors such as the global annual mean land-ocean temperature index (GAMLOTI), the annual concentration of atmospheric carbon dioxide (ACD), and the summer June – July – August (JJA) index of North Atlantic Oscillation (NAO) teleconnection pattern in AAMT showed that there is a significant correlation between AAMT and the mentioned factors. Thus, therelationship between the AAMT and the GAMLOTI is significant with a correlation coefficient of 0.602 at the significant level of 0.01. ACD had a negative significant correlation (r =0.60), and the JJA NAO index had a negative significant correlation (r = −0.34) with AAMT. The results of multiple regression analysis showed that the overall effect of the GAMLOTI, the annual concentration of ACD, and the JJA NAO index on the temporal variability of AAMT during a 53-year period is 46.2 %. The relationship between the AAMT and

GAMLOTI and the annual concentration of ACD represents the effects of global warming on the variability of the AAMT.

[Keywords Maximum temperatures . Temporal trend analysis . Climatechange . Teleconnection . Abadan]
High temperature extremes are a serious threat to society, the environment, and the economics of countries. High extreme temperatures are important features of weather events. Extreme weather events are, by definition, rare events that refer to a departure from what is considered the norm (Greenough et al. 2001). The extreme values are defined as
“The highest or lowest values for a meteorological parameter, or an exceptionally large scale between the highest and lowest values for a meteorological parameter, over a specific time period, such as a month, season, year or decade ” (Smith 2006). Extreme temperature anomalies can cause a lot of damages; therefore, their occurrences have been investigated for rather long time. Muller and Kaspar (2014) noted that a small shift in the mean and variance of a climate variable might lead to a strong shift in the frequency of respective weather and climate extremes.
In recent years, the climatology of high extreme temperatures has gained increased scientific and practical importance, and the dimensions and impacts of extreme temperatures, especially the high extreme temperatures, have been considered by a widespread range of climatologists (Beniston and Stephenson 2004;IP C2007; Unkasevic and Tosic 2009; Erlat and Türkeş 2013;Burić et al. 2014). Weisheimer and Palmer (2005) examined changes in extreme seasonal (DJF and JJA) temperatures in 14 models for 3 scenarios. They showed that by the end of twenty-first century, the probability of such extreme warm seasons is projected to rise in many areas including North America. On a global scale, daily extreme temperatures show a considerable upward tendency, in particular towards less cold rather than warmer conditions (Alexanderetal. 2006).There is strong evidence that warming has lent to changes in temperature extremes including heat waves since the mid-twentieth century (IPCC 2013).
Several studies have analyzed observed trends in maximum temperature variability and found a general increasing trend in different climatic zones of Iran as a whole (Shirgholami and Ghahraman 2005; Khoshhal Dastjerdi and Ghavidel Rahimi 2008; Khorshiddoust et al. 2009; Rahimzadeh et al. 2009; Ghavidel Rahimi 2011, 2012; Zarenistanak et al. 2014; Farajzadeh et al. 2014). In addition, several studies have investigated relationships between GAMLOTI and temperature (Alijani and Ghavidel Rahimi 2005) and impacts of ACD doubling (Khorshiddoust and Ghavidel Rahimi 2006; Ghavidel Rahimi 2007)ontempera- ture extremes in Iran. It is very likely that the human-induced increase in greenhouse gases has contributed to the increasein extreme temperatures. Overthe past 50years, there has beena moderate to strong statistical connection between all seasonal maximum and minimum temperatures and greenhouse gases in different regions of Iran (Salehian 2014). Salehian (2014) believes that the observed increase in the frequency of previously rare summertime maximum temperatures is more consistent with the consequences of increasing greenhouse gas concentrations than with the effects of natural climate variability. It is extremely unlikely that the observed increase has happened through chance alone.
Simultaneous changes in spots distributed across a widespread area in the world have long been recorded in meteorology literature (Hurrell et al. 2003). These simultaneous changes are generally called teleconnection, or large scale connection. In addition to this, while temperatures in a particular part of the world are below the seasonal mean from time to time, in an other part of the world, milder conditions prevail. One of the large scale connection patterns that have important impact on the middle and high latitude in the Northern Hemisphere is NAO. The NAO affects are gion from the north eastern costs to the Siberian region and similarly from the arctic region to the subtropical Atlantic region (Hurrell et al. 2001).The NAO is especially influential on the temperature at the regions where continental climate prevails. In contrast, at the regions where and oceanic climate prevails, this effect is either too weak or creates an insignificant (Bozyurt and Ozdemir 2014).
Atmospheric teleconnection patterns and their influence on summertime series of maximum temperature have been not studied widely, and research in this area is relatively low.
Although most studies on the extreme temperatures have focused on changes over time in their frequency and intensity, a few studies have focused on the contribution of climate
Arab J Geosci
change to specific events. In this regard, some studies have examined the effects of teleconnection patterns on occurred extreme temperatures. For example Folland et al. (2009)ex- amined many aspects of the summer NAO, including its temporal evolution and surface impacts. The emphasis in that paper was placed on northwest Europe where the influence of the summer NAO is strongest. They conclude that the summer NAO greatly affects temperature and precipitation in this region, where summers with high NAO indices tend to be warm and dry. They also showed that the summer NAO and its impacts over England can be traced via proxy records all the way back to the eighteenth century. Further more, research showed that there are statistically significant positive correlations between the minimum, maximum, and mean temperatures in the Alps (Lopez Moreno et al. 2011). NAO-positive phases show an enhancement of the Atlantic meridional sea level pressure gradient, which contributes to strength zonal flows. Such streng thening is responsible for the increased moist and warm advections to northeast Europe, producing warm and humid conditions over Scandinavia and part of northern Russia, but losing progressively such effect toward the south and Eastern Europe. None the less, positive NAO phases have been also associated to warmer daytime maximum temperature in the Iberian Peninsula, northern Italy, and the Balkans, but not for the night-time minimum temperature (Trigo et al. 2002).
Regarding the analysis of Wettstein et al. (2002), such variations in the frequency and/or intensity of the NAO–AO would play quite a significant role not only in average temperature response butal so in extreme events via changes in the mean and/or variance of temperature.
The teleconnection between Indian Ocean sea surface temperature anomalies and the frequency of high extreme temperatures across the southern Yangtze River valley was investigated by Hu et al. (2012). The impact of large-scale circulation patterns such as the El Niño – Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Northern Annular Mode (NAM) modes on terrestrial climate on annual to decadal time scales can be profound. In particular, there is considerable evidence that the state of the semodes affects substantially the likelihood of extreme temperature (Kenyon and Hegerl 2010) over North America. Wang et al. (2014) investigates summer high temperature extremes in southeast China and their linkage with the ENSO and atmospheric circulations in the East Asian summer monsoon. The study ’s results showed that the subtropical circulation index is negatively corelated to summer high tempera- tures over Egypt and is associated with large-scale climate indices of the tropical and subtropical Atlantic sector (Hasanean 2005).
Abadan, as one of the most important southwestern cities, has an extremely hot weather during the year, particularly in summer, and, in fact, it is climatologically considered as one

of the thermal poles in Iran and the world. The main objective of this study is to show temporal variations of Abadan annual maximum temperatures (AAMT) associated with the GAMLOTI, the changes in ACD, and the NAO teleconnection pattern.
Data and methods
Abadan is 116 km away from Ahvaz and in Southwestern of Khuzestan Province in the entrance of the Persian Gulf. Abadan, an island, is located in Arvand Rood delta between the rivers of Bahman Shir and Karoon and Arvand. Abadan station is located at geographical coordinates of 30 ° and 22 min north latitude and 48 ° and 15 min east longitude (Fig. 1). The elevation of Abadan station above sea level is at 6.6 mT, and thus, this station is low lying and unalleviated. The port station is placed in the neighborhood of world desert countries such as Iraq, Kuwait, and Saudi Arabia and has a hot and arid climate (BWh class in the Köppen climate classification system).
Fig. 1 The geographical location of Abadan in Khuzestan province
The annual absolute maximum temperature (all occurred in summer) data of Abadan station in a 53-year statistical period between 1960 and 2012 were obtained from Iran Meteorological Organization (IRMO) and use dint his research. In addition to a long-term period of 1960–2012, the data of AAMT was analyzed in several distinct periods including the period of 1990– 2012 and several decadal periods. In this study, in addition to Abadan land station data, the data of the GAMLOTI (obtained from The NASA Goddard Institute for Space Studies (GISS)), the data of seasonal and annual index of the NAO teleconnection pattern produced by James Hurrell, as well as the annual data of ACD concentration (obtainedfromthe website) have been used. The robust sequential nonparametric Mann –Kendall test (SNMKT) is used for the test of trend significance and for detection of mutations in the time series. This method tests if there is a trend in the time series data. It is a nonparametric rank-based procedure, robust to the influence of extremes, and suitable for application with skewed variables. The SNMKT, as well as other nonparametric trend tests, is therefore more suitable for detecting trends in climatological time series,

The geographical location of Abadan in Khuzestan province
The geographical location of Abadan in Khuzestan province

which are usually skewed and may be contaminated with outliers (Hamed 2008). This statistics is highly recommended for general use by the World Meteorological Organization (Mitchell et al. 1966) because it can test trends in a time series without requiring normality or linearity (Wang et al. 2008).




In the above equations, N is the statistical duration or the samplesize. The confluence of Ui and Ui′ in the certainty scope of ±1.96 (5% level) indicate the significant changes in the time series and the behavior of Ui after the confluence determines the descending or ascending state of series. The graphs in which the lines of Ui and Ui′ do not cross each other, or in other words, the lack of confluence of these two lines is an indication of the series to be free of trend (Khoshhal Dastjerdi and Ghavidel Rahimi 2008).
Furthermore, to investigate the overall and cumulative effect of annual ACD concentration, the GAMLOTI and the summer JJA NAO index on AAMT the multiple regression and analysis of variance are used.
The measuresof central tendency, dispersion, and skewnessof the AAMT data both asa whole and indecadal subperiodsare given in Table 1.
The results showed that the average long-term Abadan station temperature in the 53-year time scale is more than 50 °C that is considered a so high temperature not only in Iranbut alsointhe world.Among the decades studied,the last decade of twentieth century is considered the warmest decade in Abadan station with an average AAMT of 50.82 and an absolute maximum of 52 °C. The least AAMT in a 53-year time scale and the decade studied were not <47 °C, and the highest minimum in 80c was over 53 °C. The expansion of AAMT in the53-year long-term period and decades has reached to 50 °C, and in the last decade of the twentieth century has reached to 51 °C. The coefficient of time variations of the AAMT in 70d and the53-year long-term period has reached a maximum level, and in the last decade of the twentieth century, it has reached to its lowest level.
To analyze the time variations, the linear graph and linear trend component of AAMT time series are depicted in Fig. 2. A 3-year moving average is used for the smoothing of the intensive time variation of AAMTand as seen in Fig. 2;the3- year moving average has greatly reduced and leveled the intensity of time variations of AAMT (Fig. 2).

Statistical parameters of the AAMT in the Table 1. long-term and decadal time scale
Table 1: Statistical parameters of the AAMT in the long-term and decadal time scale
Fig. 2 The long-term temporal trend of AAMT time series in the statistical period 1960–2010. The thick line of linear trend and the long line are the 3year moving average


As given in Fig. 2, 34.4% time variations of AAMTcan be explained by the increasing linear trend. In other words, the time trend of AAMT as linear has in the 34.4% variation. To determine the significance of the time variations of AAMT, the SNMKT is used, and as seen in Fig. 3, SNMKT graph shows the collision of two lines of Ui and Ui′ in the certainty scope of 95% (±1.96), which indicates the significant variations of series, and the behavior of Ui after the last confluence is ascending, and thus, the increasing trend of the AAMTwill continue in the future with 95% probability.
The analysis of AAMT trend variations in the decadal time scale shows the adherence of the time variations of 60d to linear model, 70d to the extremely exponential trend, 80d to the neutral or non-specific trend, 90d to the relatively extreme
increasing exponential trend, and the last decade of the twentieth century to the relatively extreme descending trend (Fig. 4). The positive time variations of AAMT in both 60d and 70d are more severe and significant than the temperature changes in other decades.
The global greenhouse warming is considered one of the main causes for increasing the annual maximum temperature in different areas of the world. Climate sciences researches have indicated that increased CO2 concentration in the atmosphere pilots to global warming and intensifies the global distribution of extreme high temperatures. The greenhouse gas of carbon dioxide released in the atmosphere has a major role in global warming, and, accordingly, presence of statistically significant relationship between the ACD concentration


Fig. 3 Abrupt change for AAMT time series in summer as derived from sequential version of the Mann–Kendall test (SNMKT)
Fig. 4 a Decadal trend of the series variations in AAMT
Fig. 4 a Decadal trend of the series variations in AAMT


and climatic elements, particularly the AAMT, can be considered as a sign of impressibility of climate and climatic elements from the global greenhouse warming. In this regard, the calculation of correlation coefficient between AAMT and the
average annual concentration of ACD in the 53-year time scale in 1960– 2012 showed a significant relationship of 0.601, the ratio of which is significant at the level of 0.01. Furthermore, the significant correlation coefficient


Fig. 5 The virtually coordinated and simultaneous variations AAMTand the GAMLOTI


of 0.60 calculated between AAMT and the GAMLOTI is otherwise considered as a sign of compliance of AAMT with the global warming. The virtually coordinated and simultaneous variations of AAMT and the GAMLOTI are significant in the graph in Fig. 5. Inordertohave a co-scale and more clarity in the graph of Fig. 5,both
data of AAMT and GAMLOTI are normalized between 0 and 1.
In addition to the effect of global greenhouse warming on the variability of AAMT recorded during a 53-year period that was determined by the relationship between the CAD released in the atmosphere and the GAMLOTI in annual time scale

Fig. 6 The synchronic variations between AAMTand the summer (JJA) NAO index in 1960–2012
Fig. 6 The synchronic variations between AAMTand the summer (JJA) NAO index in 1960–2012
Table 2. Unusual observations in GAMLOTI and AAMT data series
Table 2. Unusual observations in GAMLOTI and AAMT data series

during the study, there is a significant relationship about
−0.337 between the summer June–July–August (JJA) NAO teleconnection pattern and AAMT, the correlation coefficient of which is significant at the level of 0.05. Given the inverse correlation coefficient calculated, it becomes clear that the negative NAO phase in the summer causes to increase and intensify AAMT, and its positive phase causes to decrease AAMT that are greatly clear in the graph of Fig. 6.Inorderto have a co-scale and more clarity in the graph of Fig. 5,both data of AAMT and data of the summer NAO index are normalized between 0 and 1.
The NAO exhibits considerable interseasonal and interannual variability, and prolonged periods (several months) of both positive and negative phases of the pattern are common. Strong negative phases of the NAO tend to be associated with above-normal temperatures in the Abadan. During the strong negative NAO phases, westerly winds weaken due to atmospheric blocking and becomes meridional. This strong meridional flows advect warm air into the Mediterranean and Middle East regions. Meridional winds were caused by warm air advection during the negative phase. In this time warm air blows from the lower geographical latitude as Arabian Peninsula and North Africa to Abadan and hot temperatures occur.
Given the significant correlations computed between the annual concentrations of ACD, the GAMLOTI, the JJA NAO index, and AAMT, the overall effect of these factors on time variations of AAMT was analyzed using the multiple regression method. Primary multiple regression analysis showed that there are unusual data points (UDP) in GAMLOTI and AAMT time series. The observed UDP are presented in Table 2. The UDP can have a strong influence on the results; therefore, observed UDP were omitted to gain better result. (See Table 3)
Computed correlation coefficients and multiple regression parameters showed optimized results after removing of UDP. The optimized computed correlation coefficients are shown in Table 4


Table 3. Optimized correlation coefficients after removing of UDP
Table 4. The results of multiple regression analysis
Table 4. The results of multiple regression analysis

All computed Pearson correlations are significant at the 0.01 level. The following optimized multiple regression equations and calculations represent the overall role of the sefactors in the variability of AAMT (Table 5): Theregre sionequationis:
AAMT ¼ 42: 7 þ 0 : 0084 GAMLOTI þ 0 :0206 CO2 −0 : 198 JJA NAO The results of the regression model showed that the constant with a value of 42.6 is significant. In addition, the predictors including CO2 (p =0.016) and JJA NAO (p = 0.012) are significant, whereas the GAMLOTI is not significant (Table 4).
The equation and correlation coefficient of multiple regression previously showed that, generally, three factors of the annual concentrations of ACD, the index of GAMLOTI, and the JJA NAO index are capable of explaining 46.2% of time variationsofAAMT.Inother words,46.2% ofthe variationin AAMTcan be explained by the multiple regression model. According to ANOVA results, the relationship between y
(AAMT) and the X (GAMLOTI, ACD, and JJA NAO) variables in the model is statistically significant (F =13.47,
p <0.05).
This study investigated the statistical characteristics and temporal trends of maximum (high) temperature events in Abadan, Iran, using observational data of 53years. Theresults showed that the long-term trend of time variations of AAMT have the positive and ascending slope and is increasing with a positive linear trend. The correlation coefficient of linear trend

Table 5. Analysis of variance (ANOVA) properties
Table 5. Analysis of variance (ANOVA) properties

component shows 34.4% time variation of AAMT recorded. SNMKT showed that the trend of time variation of AAMT was significant, and thus, more increasing and intensification of AAMT will continue in the future with the probability of 95%.
The trend analysis of variations of AAMT in the decadal time scale (inter decadal) shows the adherence of the time variations of 60d to linear model, 70d to the extremely exponential trend, 80d to the neutral or nonspecific trend, 90d to the relatively extreme increasing exponential trend, and the last decade of the twentieth century to the relatively extreme descending trend. The positive time variations of AAMT in both 60d and 70d are more severe and significant than the temperature changes in other decades.
The calculation of correlation coefficients showed that there is a significant relationship between AAMT, the annual concentrations of ACD, the GAMLOTI, and the JJA NAO index, and these agents are the factors affecting the variability of AAMT. Generally, in negative phase years when the NAO index values are negative, there is a negative correlation between the maximum temperature levels at the Abadan station and the NAO index values. In other words, as the NAO index values decrease, the summer time maximum temperatures tend to increase. The reason for that is likely to be the westerly zones that carry great amounts of humid and warm air masses to the Middle East region, and thus, warm and dry air conditions blow from the lower geographical latitude as Arabian Peninsula and North Africa to Abadan and hot temperatures occur.
The results of multiple regression analysis and ANOVA showed that, generally, three factors of the annual concentrations of ACD, the GAMLOTI, and the JJA NAO pattern are capable of explaining 46.2% of time variations of AAMT.
Acknowledgments: The authors are very grateful to the anonymous referees for their encouragement, helpful suggestions, and constructive comments, which resulted in this improved manuscript.

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