- Email: [email protected]
Testing for long-term trends in climatic variables in Iran
Atmospheric Research 100 (2011) 132–140
Contents lists available at ScienceDirect
Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Testing for long-term trends in climatic variables in Iran Hossein Tabari a,⁎, Behzad Shifteh Somee b, Mehdi Rezaeian Zadeh c a b c
Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, 65174, Islamic Republic of Iran Department of Irrigation, Faculty of Agriculture, Zabol University, Zabol, Islamic Republic of Iran Department of Water Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Islamic Republic of Iran
a r t i c l e
i n f o
Article history: Received 8 November 2010 Received in revised form 7 January 2011 Accepted 9 January 2011 Keywords: Climate change Climatic variables Trend analysis Statistical tests Serial correlation
a b s t r a c t Analysis of long-term climatic datasets is currently of unprecedented interest to the scientific community. In this study, the trends of the annual maximum (Tmax), minimum (Tmin) and mean (Tmean) air temperatures and precipitation (P) time series were examined in the west, south and southwest of Iran for the period 1966–2005. The magnitude of the climatic trends was derived from the slopes of the regression lines, and the statistical significance was determined by means of the Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests. Pre-whitening was used to eliminate the influence of serial correlation on the Mann– Kendall test. The results showed a warming trend in annual Tmean, Tmax and Tmin at the majority of the stations which mostly began in the 1970s. On average, the magnitudes of the significant positive trends in annual Tmean, Tmax and Tmin were (+)0.412, (+)0.452 and (+)0.493 °C per decade, respectively. However, the variations of the P series were not uniform over the region and there were various patterns (increasing and decreasing trends). © 2011 Elsevier B.V. All rights reserved.
1. Introduction There is mounting evidence that the global climate is changing in response to human activity and that the increase in temperature recorded during the 20th century cannot be entirely attributed to natural causes. Analysis of air temperature data at global scales with respect to climate change indicates a 0.4 °C to 0.8 °C rise since 1860 (Intergovernmental Panel on Climate Change, 1996a, 1996b). While changes in global mean surface temperature are a useful indicator of climate change and variability, a greater understanding of the causes of some of the changes has been gained over the past few decades by considering related changes in other climatic variables (Rio et al., 2007). The analysis of long-term changes in climatic variables is a fundamental task in studies on climate change detection. Several researchers studied variability and trends in temperature and precipitation across the world (de la Casa and
⁎ Corresponding author. Tel.: +98 911 2528074; fax: +98 811 4227012. E-mail address: [email protected] (H. Tabari). 0169-8095/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2011.01.005
Nasello, 2010; del Rio et al., 2005; Kampata et al., 2008; Krishnakumar et al., 2009; Liu et al., 2008; Matti et al., 2009; Partal and Kahya, 2006; Price et al., 1999; Ventura et al., 2002; Yue and Hashino, 2003; Zhang et al., 2009). Turkes and Sumer (2004) studied the spatial and temporal patterns of trends in maximum and minimum temperatures and diurnal temperature range for the period 1929–1999. Maximum temperature has shown weak warming and cooling in comparison with significant warming of minimum temperature in many regions of Turkey and in most seasons. In addition, diurnal temperature range has significantly decreased at most of urbanized and rapidly urbanizing stations throughout the seasons except partly in winter. Smadi (2006) examined changes in annual and seasonal mean minimum and maximum temperatures in Jordan during the 20th century. He showed a significant warming trend after the years 1957 and 1967 for minimum and maximum temperatures, respectively. Hamdi et al. (2009) analyzed climatic variables at six weather stations in Jordan and found an increasing trend in annual minimum temperature and a decreasing trend in diurnal temperature range. However, no visible trends were found in annual precipitation and maximum temperature.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
In Iran, Raziei et al. (2005) investigated the temporal trends in annual precipitation in the central and east of Iran during 1965–2000. Their results showed that there is no evidence of climate change. Although many stations showed negative trends indicating the decrease in precipitation, this trend was not statistically significant at the 95% confidence level. In addition, they indicated that the southeast corner of Iran has experienced climate change in the form of negative precipitation trend. Ghahraman (2006) analyzed the longterm trend of mean annual temperature at 34 synoptic stations in Iran. The results showed that there was a positive trend in 50% of stations, while 41% of stations had a negative trend. Modarres and da Silva (2007) examined the time series of annual rainfall to assess climate variability in the arid and semi-arid regions of Iran. Their results indicated increasing and decreasing monthly rainfall trends over large continuous areas in the study region. These trends were statistically significant mostly during the winter and spring seasons, suggesting a seasonal movement of rainfall concentration. Tabari and Marofi (2011) investigated temporal variations in
133
pan evaporation (Epan) and the associated changes in climatic variables in western Iran for the period 1982–2003. The results showed a significant increasing trend in Epan in 67% of the selected stations, and the increasing Epan was strongly related to air temperature changes. Tabari et al. (2011) analyzed the trends in annual, seasonal and monthly reference evapotranspiration (ETo) in the western half of Iran during 1966–2005. They showed a positive trend in ETo at the majority of the stations. Additionally, the main cause of the increasing trend in ETo was an increase in air temperature in the study area. Previous studies carried out in Iran provided important findings, although they have focused on only one climatic variable at a time. In this study, more climatic variables were considered and several statistical tests were used for trend detection in order to find out how global warming is impacting the climate of the study area. The primary aim of the present research was to understand the variations in maximum, minimum and mean air temperatures and precipitation by means of the Mann–Kendall, Mann–Whitney,
Fig. 1. Spatial distribution of the synoptic stations in Iran map.
134
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
Table 1 IRIMO synoptic stations considered and their data availability. Station
Latitude (N)
Longitude (E)
Elevation (m a.s.l.)
No. of air temperature data a
No. of precipitation data a
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
25° 26° 27° 28° 29° 31° 31° 32° 34° 35° 36° 35° 33°
57° 54° 56° 50° 52° 52° 48° 50° 47° 48° 46° 47° 48°
5.2 23 10 20 1484 2030 23 2049 1319 1680 1523 1373 1148
35 40 40 39 38 37 37 40 40 40 37 39 38
35 40 40 39 38 39 38 40 40 40 38 39 38
46′ 50′ 22′ 50′ 36′ 40′ 40′ 51′ 09′ 43′ 16′ 00′ 17′
Complete dataset have 40 number of records.
linear regression and Mann–Kendall rank statistic tests in the west, south and southwest of Iran. The secondary objective was to assess the serial correlation effect on statistical testing for the test statistic of the Mann–Kendall test and to determine the starting time of trends with the Mann–Kendall rank statistic test. 2. Materials and methods 2.1. Data
Tmax
0.6 0.4 0.2 0
1 0.8
Tmin
0.6 0.4 0.2 0 nd Ja a s Ba r-Le k nd ng e ar -A h bb Bu as sh eh Sh r ira Ab z ad a A n Sh hw ah az Ke rek rm or an d s H hah am ed Sa an g S he Kh an z a or nd ra m aj -A ba d
lag-1 serial correlation coefficient
1 0.8
Ba
Ba
nd ar Jas Ba -Le k nd ng ar eh -A bb Bu as sh eh r Sh ira Ab z ad a A n Sh hw ah az Ke rek rm ord an s H ha am h ed a Sa n g Sa hez na Kh nd or aj ra m -A ba d
lag-1 serial correlation coefficient
Series of annual maximum (Tmax), minimum (Tmin) and mean (Tmean) air temperatures and precipitation (P) were analyzed in the present study. The data were collected from 13 synoptic stations in the west, south and southwest of Iran for the period 1966–2005 and were provided by the Islamic Republic of Iran Meteorological Office (IRIMO, 2007). These stations were selected taking into account the length and com-
pleteness of records, so that most of the region was covered by the corresponding data. The details of data availability for these 13 stations (Fig. 1) are reported in Table 1. Several strategies have been described in the literature, which have been developed to detect non-homogeneities in the data series (Peterson et al., 1998). In this study, both the double-mass curve (Kohler, 1949) method and the correlation analysis were used to the climatic variables time series of each station. A double-mass curve analysis is a graphical method for identifying or adjusting inconsistencies in a station record by comparing its time trend with those of other relatively stable records of a station, or an average of several nearby surrounding stations. The results of the correlation analysis and the doublemass curve analysis were checked in order to contrast both series and to use them alternatively when a segment of anyone of the series was missing. The first method showed considerable close correlations with average correlation coefficients
Stations
Tmean
0.6 0.4 0.2
nd Ja a s Ba r-Le k nd ng ar eh -A bb Bu as sh eh Sh r ira Ab z ad a A n Sh hw ah az Ke rek rm or an d s H hah am ed Sa an g S he Kh an z or an d ra m aj -A ba d
0
Stations
0.6
P
0.4 0.2 0 -0.2 -0.4 nd Ja a s Ba r-Le k nd ng ar eh -A bb Bu as sh eh r Sh ira Ab z ad a Ah n Sh w ah az Ke rek rm ord an s H hah am ed a Sa n g Sa hez Kh n or and ra m aj -A ba d
lag-1 serial correlation coefficient
1 0.8
Ba
lag-1 serial correlation coefficient
Stations
Ba
a
38′ 32′ 13′ 59′ 32′ 11′ 20′ 17′ 21′ 12′ 15′ 20′ 26′
Stations
Fig. 2. Lag-1 serial correlation coefficient for the climatic variables at the stations.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
between 0.91 and 0.95 for the climatic variables time series. The second method indicated average correlation coefficients between 0.96 and 0.98. Results of the double-mass curves of all stations are almost a straight line, and no any obvious breakpoints are detected in the time series. In the case series with a few missing observations at a station, the recorded values in neighboring stations with high correlation (r greater than 0.8 at the 95% confidence level) were used to complete the air temperature and precipitation records. The estimated amount of missing data was about 3.8% and 3.1% for the air temperature and precipitation series, respectively. 2.2. The used techniques The Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests were applied to detect and describe significant trends in the climatic variables. In addition, the magnitude of the trends was derived from the slope of the regression line. 2.2.1. Mann–Whitney test The Mann–Whitney test is a non-parametric test for assessing the significance of a difference in median or central tendency or mean of two series. The test is often viewed as the non-parametric equivalent of the Student's t-test. The major difference between the Mann–Whitney test and the Student's t-test involves the concept of normal distribution. The Mann– Whitney test does not require sample data to be normal. The null hypothesis for the test is that the two series from which samples have been drawn have equal medians or means. The alternatives are that the series do not have equal medians (Yue and Wang, 2002). 2.2.2. Mann–Kendall test The Mann–Kendall test is one of the widely used nonparametric tests to detect significant trends in hydrological and meteorological time series (e.g., Modarres and da Silva, 2007; Partal and Kahya, 2006; Tabari et al., 2010, 2011). The test compares the relative magnitudes of sample data rather than the data values themselves (Gilbert, 1987). One advantage of this test is that the data need not conform to any
135
particular distribution. The second advantage of the test is its low sensitivity to abrupt breaks due to inhomogeneous time series (Jaagus, 2006).
2.2.3. Mann–Kendall rank statistic test The Mann–Kendall rank statistic test proposed by Sneyers (1990) is used for determining approximate year of beginning of the significant trend (e.g., del Rio et al., 2005; Partal and Kahya, 2006). This test sets up two series, a progressive one and a backward one. If they cross each other, and then diverge and acquire specific threshold values, then there is a statistically significant trend. The point where they cross each other indicates the approximate year at which the trend begins (Mosmann et al., 2004).
2.3. Serial correlation effect The Mann–Kendall test requires time series to be serially independent. The presence of serial correlation in the time series makes trend tests too liberal, i.e. the null hypothesis of no trend is rejected too frequently, specifically if there is a positive serial correlation (Kulkarni and von Storch, 1995; von Storch, 1995). For this, von Storch and Navarra (1995) suggest that the time series should be ‘pre-whitened’ to eliminate the effect of serial correlation before applying the Mann–Kendall test. This study incorporates this suggestion, and thus possible statistically significant trends in climatic observations (x1, x2, . . ., xn) are examined using the following procedures: 1 Compute the lag-1 serial correlation coefficient (designated by r1). 2 If the calculated r1 is not significant at the 5% level, then the Mann–Kendall test is applied to original values of the time series. 3 If the calculated r1 is significant, prior to application of the Mann–Kendall test, then the ‘pre-whitened’ time series may be obtained as (x2 − r1x1, x3 − r1x2,..., xn − r1xn − 1) (Partal and Kahya, 2006).
Table 2 Results of the statistical tests for annual Tmean over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney
Z
Median (Pre-1985)
Median (Post-1985)
p-value
26.70 26.25 26.90 24.20 17.35 25.10 25.10 12.30 14.10 10.60 11.25 13.25 17.70
27.25 26.80 26.90 25.05 18.55 25.85 25.95 11.40 15.05 11.35 10.80 13.95 16.40
0.0009 0.0032 0.9138 0.0001 0.0000 0.0006 0.0000 0.0039 0.0001 0.0096 0.0510 0.0165 0.0292
u(t)
b
2.48 ⁎ 2.14 ⁎ 0.33 2.33 ⁎ 2.23 ⁎ 2.65 ⁎⁎ 2.32 ⁎
2.15 ⁎ 1.98 ⁎ −1.62 2.01 ⁎ 2.18 ⁎ 2.09 ⁎ 2.02 ⁎
−1.38 2.90 ⁎⁎ 3.21 ⁎⁎ −0.46 1.97 ⁎ 0.27
−1.41 2.12 ⁎ 1.99 ⁎ −0.76 2.29 ⁎ 1.09
0.292 0.354 −0.011 0.382 0.586 0.372 0.603 0.529 0.558 0.268 −0.233 0.291 −0.375
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
136
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
3. Results and discussion
3.2. Trends in the climatic variables
3.1. Serial correlation of the climatic data
The annual trends of Tmean and their magnitude (in °C decade−1) obtained by the Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests and regression analysis are given in Table 2. Trends are considered statistically significant at the 95% and 99% confidence levels — presented in bold character in the tables — when identified by the three statistical methods (Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests). For the Mann–Whitney test application, the data were divided into two parts: one is from 1966 to 1985 and the other is from 1986 to 2005. The trend tests detected a significant increasing trend in annual Tmean at the majority of the stations (9 out of 13 stations). On the contrary, Shahrekord and Saghez stations showed insignificant decreasing trends. The magnitudes of the significant increasing trends in annual Tmean ranged between
Autocorrelation plots for the climatic variables at the stations are presented in Fig. 2. As shown, positive serial correlations were obtained for all the variables except precipitation. The Tmax series had significant correlation at all of the stations exception for Shiraz and Hamedan. The serial correlations of the Tmin and Tmean series were entirely significant. A mix of positive and negative serial correlations was obtained for annual P data. The positive correlations were significant only at Jask, Saghez and Sanandaj stations. The negative serial correlations of the P series were significant at Abadan, Ahwaz and Hamedan stations. As a whole, the strongest and weakest serial correlations were observed at the Tmin and P series, respectively.
28
28
Jask
25
25
19
19
19
19 26
Bandar-Lengeh
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
26
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
26
66
27
69
27
20
Bushehr
Shiraz
19
25
18 17
24
16
22.5 19
19 66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
14
Kermanshah
13
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
19
69
Hamedan
12
15
11
14
15
84
87 19 90 19 93 19 96 19 99 20 02 20 05
19
81
19
19
78 19
75
72
19
19
19
19
16
66
8
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
9
12
69
10
13
19
16
75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
23.5
23
19
24
72
24.5
19
25
Ahwaz
69
25.5
66
26
17
19
26.5
Abadan
19
66 19
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
19
69
19
27
15 19
66
23
Sanandaj
14 13 12 11
19
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
10
Time series
Linear trend
5-year moving average
Fig. 3. Observed, 5-year moving average and trend line of annual mean air temperature at the stations with significant trends for the period 1966–2005.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
137
Table 3 Results of the statistical tests for annual Tmax over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney Median (Pre-1985)
Median (Post-1985)
p-value
30.05 31.15 32.30 29.10 25.85 32.55 33.10 20.60 22.10 18.90 19.20 21.40 25.05
29.90 31.15 32.10 30.00 26.15 33.65 33.15 20.00 23.50 19.60 19.10 21.90 24.95
0.2977 0.9246 0.4094 0.0016 0.1289 0.0026 0.3648 0.0214 0.0007 0.0761 0.4014 0.1160 0.5154
Z
u(t)
b
−0.86 0.92 −0.11 2.07 ⁎ 1.73 2.69 ⁎⁎
−1.05 0.43 −1.98 ⁎ 1.98 ⁎ 0.88 2.26 ⁎
1.78 −0.58 2.84 ⁎⁎ 0.27 2.01 ⁎ 2.74 ⁎⁎ 0.39
0.18 −1.17 2.42 ⁎ 0.08 1.50 1.59 1.92
−0.062 0.046 −0.040 0.364 0.203 0.396 0.262 −0.2.03 0.597 0.168 −0.104 0.356 −0.176
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
(+)0.268 °C per decade at Hamedan station and (+)0.603 °C per decade at Ahwaz station. The increasing trends in annual Tmean data found in this study are in accordance with the results of Ghahraman (2006) who reported a warming trend in annual Tmean by the Student's t-test at the majority of the synoptic stations in Iran. Tabari et al. (2011) also found a significant increasing trend in annual Tmean in 70% of the stations located in the western half of Iran using the Mann– Kendal test without consideration of serial correlation. The time series, along with the 5-year moving average and the linear trend line, were graphically represented for the climatic variables at each station but only the plots for the significant trends in Tmean are presented here (Fig. 3). Results of the statistical methods for annual Tmax over the period 1966–2005 are presented in Table 3. According to these results, it is clear that an overall warming trend is evident in the annual Tmax series (77% of the stations). However, statistically significant cases were found only at Bushehr, Abadan and Kermanshah stations. Based on the slopes of the regression lines, the rates of the significant warming trends in annual
Tmax were (+)0.364, (+)0.396 and (+)5.970 °C per decade at Bushehr, Abadan and Kermanshah stations, respectively. Analysis of the Tmin series indicated warming trends at 10 stations (Table 4). Significant warming trends in the Tmin series were larger than those in the Tmax series, and Tmin increased at higher rate than Tmax which results in a decrease of the diurnal temperature range. Turkes and Sumer (2004) also reported that trend magnitude of Tmin was greater than that for Tmax series in Turkey. Similar to the Tmean time series, Shahrekord and Saghez stations had insignificant cooling trends in Tmin data. The magnitudes of the significant warming trends in annual Tmin varied from (+)0.344 °C per decade at Hamedan station to (+)0.671 °C per decade at Bandar–Lengeh station. Tabari and Marofi (2011) found that air temperature increased in the west of Iran over the last 22 years. It can be inferred from the results presented in Tables 2, 3 and 4 that 9 out of 13 stations revealed positive trends in the total temperature series (Tmean, Tmax and Tmin) and among them Bushehr, Abadan and Kermanshah stations had significant trends.
Table 4 Results of the statistical tests for annual Tmin over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney
Z
Median (Pre-1985)
Median (Post-1985)
p-value
23.35 21.55 21.30 19.45 9.00 17.70 16.80 3.75 5.75 2.30 3.60 4.70 10.00
24.45 22.60 21.75 20.20 11.15 18.60 18.80 3.25 6.90 3.05 2.60 6.05 8.20
0.0000 0.0000 0.1199 0.0001 0.0000 0.0006 0.0000 0.0273 0.0004 0.0160 0.0101 0.0324 0.0068
u(t)
2.04 ⁎ 2.14 ⁎ 0.96 2.50 ⁎ 1.11 2.17 ⁎ 1.74 −1.06 2.82 ⁎⁎ 2.61 ⁎⁎ −0.92 1.58 0.07
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
3.90 ⁎⁎ 3.81 ⁎⁎ 1.36 2.09 ⁎ 2.47 ⁎ 1.98 ⁎ 2.48 ⁎ −1.32 2.62 ⁎⁎ 2.10 ⁎ −0.49 0.82 −1.09
b
0.662 0.671 0.041 0.405 0.991 0.353 0.947 −0.291 0.525 0.344 −0.357 0.256 −0.577
138
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
The graphical analysis for the temperature series was applied to the u(di) and u′(di) statistics of each station to identify the intersection of the curves and thus to detect the beginning year of trend or change. As an example of the positive trends, plots for Tmean at Abadan station are shown in Fig. 4. Analysis of the full range of figures shows the positive or negative trend per station, as well as the approximate year when an abrupt change occurred. This information is summarized in Table 5 for all the climatic variables. Increasing and decreasing trends are represented by (+ ) and (−), respectively; each station is characterized by a year which reflects the initiation of a positive or a negative trend. In some stations a second year is added, reflecting a year when a new change or trend was observed. In addition, the N in some stations denotes that there is no significant trend for the period. According to Table 5, an increasing trend for annual Tmean, Tmax and Tmin values began in the 1970s at the majority of the stations. The increasing trends in air temperature have been related to several factors such as global warming, increased concentrations of anthropogenic green house gases, aerosols which exert cooling effects on the climate, increased cloud cover and urbanization (Smadi, 2006). Overall, the increase of air temperature in the study area will increase dry conditions in the region by increasing potential evapotranspiration and consequently places it in serious risk of desertification. Results of the statistical methods for annual P data were summarized in Table 6. As shown, the majority of the annual P series were characterized by negative trend which were mostly insignificant. Significant decreasing trend was only observed at Sanandaj at the rate of (−)42.610 mm per decade which began in 1975 (Table 5). The plots for precipitation at Sanandaj station are shown in Fig. 5. Modarres and da Silva (2007) found no significant trends in P data at majority of the stations located in the arid and semi-arid regions of Iran. The results also showed that the increasing P trends were found at the stations located on the northern coasts of the Persian Gulf and the Oman Sea. This is probably due to sea surface temperature influences on P variability over the coastal regions (Nazemosadat et al., 1995). Since detecting increasing or decreasing trends in precipitation requires data from hundreds of years and our data was collected in less than a 100 year span, it might be difficult to detect trends in such a short time interval.
u(d)
u(d) u'(d)
4
u'(d)
2
Table 5 Approximate year of beginning of the positive or negative trend according to the Mann–Kendall rank statistic for the climatic variables (1966–2005). Station
Tmean
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
1972+ N 1976+ 1969+ N 1973+ N N N 1972+ (1996+) 1976+ (2003+) 1977+ 1968+ N N 1973+ 1973+ 1972+ 1976+ (1991+) N N N N N 1970+ 1975+ 1978+ 1970+ N 1971+ N N N 1971+ N N N N N
Tmax
Tmin
P N N N N N N N N N N N 1975− N
N: No significant trend at the confidence levels; +: Positive trend; −: Negative trend; ( ): Approximate year of a new change in a trend which is not statistically significant.
According to Smadi (2006), the main sources for abrupt changes in climate data include station relocations, changes in observation times, methods used to calculate daily means, changes in gauged locations, changes in instruments, increased urbanized area and global warming. Another factor that could lead to climate changes is change in atmospheric circulation. The large-scale structure of the atmospheric circulation varies from year to year and region to region. 4. Conclusions This study investigated long-term trends in the annual maximum, minimum, mean air temperatures and precipitation time series at 13 synoptic stations in the west, south and southwest of Iran for the period 1966–2005. Furthermore, pre-whitening was applied to eliminate the effect of serial correlation on the Mann–Kendall test. The results indicated that the climatic data were serially correlated showing the data were not independent. The trend tests detected significant increasing trend in the annual Tmean, Tmax and Tmin series in about 69%, 23% and 46% of the stations, respectively. The Mann–Kendall rank statistic test indicated that the significant increasing trends in air temperature data began in the 1970s in most of the stations. The analysis revealed that there were no visible trends indicating an increase or decrease in the annual P series. The results also suggest the need for further investigation on local anthropogenic intervention in the environment, which could be one of the major causes of climate change.
0
Acknowledgments -2
1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-4
Year Fig. 4. Graphical representation of the series u(di) and the retrograde series u′(di) of the Mann–Kendall rank statistic test for mean air temperature at Abadan station for the period 1966–2005.
The data used to carry out this research were provided by the Islamic Republic of Iran Meteorological Office (IRIMO). The first author would like to especially thank Miss. H. Tabari for her inestimable help for analyzing the data for this paper. We wish to express our gratitude to the anonymous reviewers whose suggestions and remarks have greatly helped us to improve the quality of the manuscript.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
139
Table 6 Results of the statistical tests for annual P over the period 1966–2005. Station
Mann–Whitney
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Median (Pre-1985)
Median (Post-1985)
p-value
168.00 130.50 152.20 229.70 297.60 156.35 258.70 332.40 477.30 326.50 542.00 491.30 549.20
119.30 130.90 183.60 263.20 348.90 166.10 227.55 331.00 438.80 327.30 478.80 406.40 465.30
0.2733 0.8924 0.4094 0.0531 0.0679 0.5250 0.5428 0.7353 0.2503 0.8392 0.0639 0.0043 0.1368
Z
u(t)
b
−2.20a 0.16 0.66 1.90 1.36 0.56 −1.57 −0.85 −2.13a −0.86 −1.96 −2.15a −1.88
−1.97a 1.22 1.32 0.06 0.87 0.38 −0.30 −0.18 −1.79 −0.27 −1.09 −1.97a −1.26
−33.840 −0.711 5.478 31.800 24.410 3.132 −2.553 1.670 −42.430 −6.930 −28.810 −42.610 −22.460
a : Statistically significant trends at the 95% confidence level, b: Statistically significant trends at the 99% confidence level. b: Slope of linear regression (mm/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic.
u(d) u'(d)
u(d) u'(d)
4 2 0 -2 -4
1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-6
Year Fig. 5. Graphical representation of the series u(di) and the retrograde series u′(di) of the Mann–Kendall rank statistic test for precipitation at Sanandaj station for the period 1966–2005.
References de la Casa, A., Nasello, O., 2010. Breakpoints in annual rainfall trends in Córdoba, Argentina. Atmos. Res. 95, 419–427. del Rio, S., Penas, A., Fraile, R., 2005. Analysis of recent climatic variations in Castile and Leon (Spain). Atmos. Res. 73, 69–85. Ghahraman, B., 2006. Time trend in the mean annual temperature of Iran. Turk. J. Agric. For. 30, 439–448. Gilbert, R.O., 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York. Hamdi, M.R., Abu-Allaban, M., Al-Shayeb, A., Jaber, M., Momani, N.M., 2009. Climate change in Jordan: a comprehensive examination approach. Am. J. Environ. Sci. 5 (1), 58–68. Intergovernmental Panel on Climate Change, 1996a. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Kattenburg, A., Maskell, K. (Eds.), Climate Change 1995: the Science of Climate Change. Cambridge University Press, Cambridge. Intergovernmental Panel on Climate Change, 1996b. In: Watson, R., Zinqowera, M., Moss, R. (Eds.), Climate Change 1995: Impacts, Adaptations and Mitigations: Scientific–Technical Analysis. Cambridge University Press, Cambridge. IRIMO, 2007. Iranian Meteorological Office. Data Processing Center, Tehran, Iran. Jaagus, J., 2006. Climatic changes in Estonia during the second half of the 20th century in relationship with changes in large-scale atmospheric circulation. Theor. Appl. Climatol. 83, 77–88. Kampata, J.M., Parida, B.P., Moalafhi, D.B., 2008. Trend analysis of rainfall in the headstreams of the Zambezi River Basin in Zambi. Phys. Chem. Earth 33, 621–625. Kohler, M.A., 1949. Double-mass analysis for testing the consistency of records and for making adjustments. Bull. Amer. Meteor. Soc. 30, 188–189.
Krishnakumar, K.N., Prasada Rao, G.S.L.H.V., Gopakumar, C.S., 2009. Rainfall trends in twentieth century over Kerala, India. Atmos. Environ. 43, 1940–1944. Kulkarni, A., von Storch, H., 1995. Monte-Carlo experiments on the effect of serial correlation on the Mann–Kendall test of trend. Meteorol. Z. 4 (2), 82–85. Liu, Q., Yang, Z., Cui, B., 2008. Spatial and temporal variability of annual precipitation during 1961–2006 in Yellow River Basin China. J. Hydrol. 361, 330–338. Matti, C., Pauling, A., Kuttel, M., Wanner, H., 2009. Winter precipitation trends for two selected European regions over the last 500 years and their possible dynamical background. Theor. Appl. Climatol. 95, 9–26. Modarres, R., da Silva, V.P.R., 2007. Rainfall trends in arid and semi-arid regions of Iran. J. Arid Environ. 70, 344–355. Mosmann, V., Castro, A., Fraile, R., Dessens, J., Sanchez, J.L., 2004. Detection of statistically significant trends in the summer precipitation of mainland Spain. Atmos. Res. 70, 43–53. Nazemosadat, M.J., Cordery, I., Eslamian, S., 1995. The impact of the Persian Gulf sea surface temperature on Iranian rainfall. The Proceedings of the Regional Conference on Water Resources Management, Isfahan, Iran. . Partal, T., Kahya, E., 2006. Trend analysis in Turkish precipitation data. Hydrol. Process. 20, 2011–2026. Peterson, T.C., Easterling, D.R., Karl, T.R., Groisman, P.Y., Nicholis, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, J., Førland, E., Hanssen-Bauer, I., Alexandersson, H., Jones, P., Parker, D., 1998. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517. Price, C., Michaelides, S., Pashiardis, S., Alpert, P., 1999. Long term changes in diurnal temperature range in Cyprus. Atmos. Res. 51, 85–98. Raziei, T., Daneshkar Arasteh, P., Saghfian, B., 2005. Annual Rainfall Trend in Arid and Semi-arid Regions of Iran. ICID 21st European Regional Conference, Germany and Poland. Rio, S., Fraile, R., Herrero, L., Penas, A., 2007. Analysis of recent trends in mean maximum and minimum temperatures in a region of the NW of Spain (Castilla y Leon). Theor. Appl. Climatol. 90, 1–12. Smadi, M.M., 2006. Observed abrupt changes in minimum and maximum temperatures in Jordan in the 20th century. Am. J. Environ. Sci. 2 (3), 114–120. Sneyers, R., 1990. On the statistical analysis of series of observations. WMO. Technical Note (143). World Meteorological Organization, Geneve. 192 pp. Tabari, H., Marofi, S., 2011. Changes of Pan Evaporation in the West of Iran. Water Resour. Manage. 25, 97–111. Tabari, H., Marofi, S., Ahmadi, M., 2010. Long-term variations of water quality parameters in the Maroon River, Iran. Environ. Monit. Assess.10.1007/ s10661-010-1633-y Tabari, H., Marofi, S., Hosseinzadeh Talaee, P., Mohammadi, K., 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol. 151, 128–136. Turkes, M., Sumer, U.M., 2004. Spatial and temporal patterns of trends and variability in diurnal temperature ranges of Turkey. Theor. Appl. Climatol. 77, 195–227.
140
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
Ventura, F., Rossi Pisa, P., Ardizzoni, E., 2002. Temperature and precipitation trends in Bologna (Italy) from 1952 to 1999. Atmos. Res. 61, 203–214. von Storch, H., 1995. Misuses of statistical analysis in climate research. In: Storch, H.V., Navarra, A. (Eds.), Analysis of climate variability: applications of statistical techniques. Springer, Berlin, pp. 11–26. von Storch, H., Navarra, A., 1995. Analysis of Climate Variability — Applications of Statistical Techniques. Springer-Verlag, New York.
Yue, S., Hashino, M., 2003. Temperature trends in Japan: 1900–1996. Theor. Appl. Climatol. 75, 15–27. Yue, S., Wang, C.Y., 2002. The influence of serial correlation on the Mann– Whitney test for detecting a shift in median. Adv. Water Resour. 25, 325–333. Zhang, Q., Xu, C.Y., Zhang, Z., Chen, Y.D., Liu, C.L., 2009. Spatial and temporal variability of precipitation over China, 1951–2005. Theor. Appl. Climatol. 95, 53–68.
Contents lists available at ScienceDirect
Atmospheric Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / a t m o s
Testing for long-term trends in climatic variables in Iran Hossein Tabari a,⁎, Behzad Shifteh Somee b, Mehdi Rezaeian Zadeh c a b c
Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, 65174, Islamic Republic of Iran Department of Irrigation, Faculty of Agriculture, Zabol University, Zabol, Islamic Republic of Iran Department of Water Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Islamic Republic of Iran
a r t i c l e
i n f o
Article history: Received 8 November 2010 Received in revised form 7 January 2011 Accepted 9 January 2011 Keywords: Climate change Climatic variables Trend analysis Statistical tests Serial correlation
a b s t r a c t Analysis of long-term climatic datasets is currently of unprecedented interest to the scientific community. In this study, the trends of the annual maximum (Tmax), minimum (Tmin) and mean (Tmean) air temperatures and precipitation (P) time series were examined in the west, south and southwest of Iran for the period 1966–2005. The magnitude of the climatic trends was derived from the slopes of the regression lines, and the statistical significance was determined by means of the Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests. Pre-whitening was used to eliminate the influence of serial correlation on the Mann– Kendall test. The results showed a warming trend in annual Tmean, Tmax and Tmin at the majority of the stations which mostly began in the 1970s. On average, the magnitudes of the significant positive trends in annual Tmean, Tmax and Tmin were (+)0.412, (+)0.452 and (+)0.493 °C per decade, respectively. However, the variations of the P series were not uniform over the region and there were various patterns (increasing and decreasing trends). © 2011 Elsevier B.V. All rights reserved.
1. Introduction There is mounting evidence that the global climate is changing in response to human activity and that the increase in temperature recorded during the 20th century cannot be entirely attributed to natural causes. Analysis of air temperature data at global scales with respect to climate change indicates a 0.4 °C to 0.8 °C rise since 1860 (Intergovernmental Panel on Climate Change, 1996a, 1996b). While changes in global mean surface temperature are a useful indicator of climate change and variability, a greater understanding of the causes of some of the changes has been gained over the past few decades by considering related changes in other climatic variables (Rio et al., 2007). The analysis of long-term changes in climatic variables is a fundamental task in studies on climate change detection. Several researchers studied variability and trends in temperature and precipitation across the world (de la Casa and
⁎ Corresponding author. Tel.: +98 911 2528074; fax: +98 811 4227012. E-mail address: [email protected] (H. Tabari). 0169-8095/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2011.01.005
Nasello, 2010; del Rio et al., 2005; Kampata et al., 2008; Krishnakumar et al., 2009; Liu et al., 2008; Matti et al., 2009; Partal and Kahya, 2006; Price et al., 1999; Ventura et al., 2002; Yue and Hashino, 2003; Zhang et al., 2009). Turkes and Sumer (2004) studied the spatial and temporal patterns of trends in maximum and minimum temperatures and diurnal temperature range for the period 1929–1999. Maximum temperature has shown weak warming and cooling in comparison with significant warming of minimum temperature in many regions of Turkey and in most seasons. In addition, diurnal temperature range has significantly decreased at most of urbanized and rapidly urbanizing stations throughout the seasons except partly in winter. Smadi (2006) examined changes in annual and seasonal mean minimum and maximum temperatures in Jordan during the 20th century. He showed a significant warming trend after the years 1957 and 1967 for minimum and maximum temperatures, respectively. Hamdi et al. (2009) analyzed climatic variables at six weather stations in Jordan and found an increasing trend in annual minimum temperature and a decreasing trend in diurnal temperature range. However, no visible trends were found in annual precipitation and maximum temperature.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
In Iran, Raziei et al. (2005) investigated the temporal trends in annual precipitation in the central and east of Iran during 1965–2000. Their results showed that there is no evidence of climate change. Although many stations showed negative trends indicating the decrease in precipitation, this trend was not statistically significant at the 95% confidence level. In addition, they indicated that the southeast corner of Iran has experienced climate change in the form of negative precipitation trend. Ghahraman (2006) analyzed the longterm trend of mean annual temperature at 34 synoptic stations in Iran. The results showed that there was a positive trend in 50% of stations, while 41% of stations had a negative trend. Modarres and da Silva (2007) examined the time series of annual rainfall to assess climate variability in the arid and semi-arid regions of Iran. Their results indicated increasing and decreasing monthly rainfall trends over large continuous areas in the study region. These trends were statistically significant mostly during the winter and spring seasons, suggesting a seasonal movement of rainfall concentration. Tabari and Marofi (2011) investigated temporal variations in
133
pan evaporation (Epan) and the associated changes in climatic variables in western Iran for the period 1982–2003. The results showed a significant increasing trend in Epan in 67% of the selected stations, and the increasing Epan was strongly related to air temperature changes. Tabari et al. (2011) analyzed the trends in annual, seasonal and monthly reference evapotranspiration (ETo) in the western half of Iran during 1966–2005. They showed a positive trend in ETo at the majority of the stations. Additionally, the main cause of the increasing trend in ETo was an increase in air temperature in the study area. Previous studies carried out in Iran provided important findings, although they have focused on only one climatic variable at a time. In this study, more climatic variables were considered and several statistical tests were used for trend detection in order to find out how global warming is impacting the climate of the study area. The primary aim of the present research was to understand the variations in maximum, minimum and mean air temperatures and precipitation by means of the Mann–Kendall, Mann–Whitney,
Fig. 1. Spatial distribution of the synoptic stations in Iran map.
134
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
Table 1 IRIMO synoptic stations considered and their data availability. Station
Latitude (N)
Longitude (E)
Elevation (m a.s.l.)
No. of air temperature data a
No. of precipitation data a
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
25° 26° 27° 28° 29° 31° 31° 32° 34° 35° 36° 35° 33°
57° 54° 56° 50° 52° 52° 48° 50° 47° 48° 46° 47° 48°
5.2 23 10 20 1484 2030 23 2049 1319 1680 1523 1373 1148
35 40 40 39 38 37 37 40 40 40 37 39 38
35 40 40 39 38 39 38 40 40 40 38 39 38
46′ 50′ 22′ 50′ 36′ 40′ 40′ 51′ 09′ 43′ 16′ 00′ 17′
Complete dataset have 40 number of records.
linear regression and Mann–Kendall rank statistic tests in the west, south and southwest of Iran. The secondary objective was to assess the serial correlation effect on statistical testing for the test statistic of the Mann–Kendall test and to determine the starting time of trends with the Mann–Kendall rank statistic test. 2. Materials and methods 2.1. Data
Tmax
0.6 0.4 0.2 0
1 0.8
Tmin
0.6 0.4 0.2 0 nd Ja a s Ba r-Le k nd ng e ar -A h bb Bu as sh eh Sh r ira Ab z ad a A n Sh hw ah az Ke rek rm or an d s H hah am ed Sa an g S he Kh an z a or nd ra m aj -A ba d
lag-1 serial correlation coefficient
1 0.8
Ba
Ba
nd ar Jas Ba -Le k nd ng ar eh -A bb Bu as sh eh r Sh ira Ab z ad a A n Sh hw ah az Ke rek rm ord an s H ha am h ed a Sa n g Sa hez na Kh nd or aj ra m -A ba d
lag-1 serial correlation coefficient
Series of annual maximum (Tmax), minimum (Tmin) and mean (Tmean) air temperatures and precipitation (P) were analyzed in the present study. The data were collected from 13 synoptic stations in the west, south and southwest of Iran for the period 1966–2005 and were provided by the Islamic Republic of Iran Meteorological Office (IRIMO, 2007). These stations were selected taking into account the length and com-
pleteness of records, so that most of the region was covered by the corresponding data. The details of data availability for these 13 stations (Fig. 1) are reported in Table 1. Several strategies have been described in the literature, which have been developed to detect non-homogeneities in the data series (Peterson et al., 1998). In this study, both the double-mass curve (Kohler, 1949) method and the correlation analysis were used to the climatic variables time series of each station. A double-mass curve analysis is a graphical method for identifying or adjusting inconsistencies in a station record by comparing its time trend with those of other relatively stable records of a station, or an average of several nearby surrounding stations. The results of the correlation analysis and the doublemass curve analysis were checked in order to contrast both series and to use them alternatively when a segment of anyone of the series was missing. The first method showed considerable close correlations with average correlation coefficients
Stations
Tmean
0.6 0.4 0.2
nd Ja a s Ba r-Le k nd ng ar eh -A bb Bu as sh eh Sh r ira Ab z ad a A n Sh hw ah az Ke rek rm or an d s H hah am ed Sa an g S he Kh an z or an d ra m aj -A ba d
0
Stations
0.6
P
0.4 0.2 0 -0.2 -0.4 nd Ja a s Ba r-Le k nd ng ar eh -A bb Bu as sh eh r Sh ira Ab z ad a Ah n Sh w ah az Ke rek rm ord an s H hah am ed a Sa n g Sa hez Kh n or and ra m aj -A ba d
lag-1 serial correlation coefficient
1 0.8
Ba
lag-1 serial correlation coefficient
Stations
Ba
a
38′ 32′ 13′ 59′ 32′ 11′ 20′ 17′ 21′ 12′ 15′ 20′ 26′
Stations
Fig. 2. Lag-1 serial correlation coefficient for the climatic variables at the stations.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
between 0.91 and 0.95 for the climatic variables time series. The second method indicated average correlation coefficients between 0.96 and 0.98. Results of the double-mass curves of all stations are almost a straight line, and no any obvious breakpoints are detected in the time series. In the case series with a few missing observations at a station, the recorded values in neighboring stations with high correlation (r greater than 0.8 at the 95% confidence level) were used to complete the air temperature and precipitation records. The estimated amount of missing data was about 3.8% and 3.1% for the air temperature and precipitation series, respectively. 2.2. The used techniques The Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests were applied to detect and describe significant trends in the climatic variables. In addition, the magnitude of the trends was derived from the slope of the regression line. 2.2.1. Mann–Whitney test The Mann–Whitney test is a non-parametric test for assessing the significance of a difference in median or central tendency or mean of two series. The test is often viewed as the non-parametric equivalent of the Student's t-test. The major difference between the Mann–Whitney test and the Student's t-test involves the concept of normal distribution. The Mann– Whitney test does not require sample data to be normal. The null hypothesis for the test is that the two series from which samples have been drawn have equal medians or means. The alternatives are that the series do not have equal medians (Yue and Wang, 2002). 2.2.2. Mann–Kendall test The Mann–Kendall test is one of the widely used nonparametric tests to detect significant trends in hydrological and meteorological time series (e.g., Modarres and da Silva, 2007; Partal and Kahya, 2006; Tabari et al., 2010, 2011). The test compares the relative magnitudes of sample data rather than the data values themselves (Gilbert, 1987). One advantage of this test is that the data need not conform to any
135
particular distribution. The second advantage of the test is its low sensitivity to abrupt breaks due to inhomogeneous time series (Jaagus, 2006).
2.2.3. Mann–Kendall rank statistic test The Mann–Kendall rank statistic test proposed by Sneyers (1990) is used for determining approximate year of beginning of the significant trend (e.g., del Rio et al., 2005; Partal and Kahya, 2006). This test sets up two series, a progressive one and a backward one. If they cross each other, and then diverge and acquire specific threshold values, then there is a statistically significant trend. The point where they cross each other indicates the approximate year at which the trend begins (Mosmann et al., 2004).
2.3. Serial correlation effect The Mann–Kendall test requires time series to be serially independent. The presence of serial correlation in the time series makes trend tests too liberal, i.e. the null hypothesis of no trend is rejected too frequently, specifically if there is a positive serial correlation (Kulkarni and von Storch, 1995; von Storch, 1995). For this, von Storch and Navarra (1995) suggest that the time series should be ‘pre-whitened’ to eliminate the effect of serial correlation before applying the Mann–Kendall test. This study incorporates this suggestion, and thus possible statistically significant trends in climatic observations (x1, x2, . . ., xn) are examined using the following procedures: 1 Compute the lag-1 serial correlation coefficient (designated by r1). 2 If the calculated r1 is not significant at the 5% level, then the Mann–Kendall test is applied to original values of the time series. 3 If the calculated r1 is significant, prior to application of the Mann–Kendall test, then the ‘pre-whitened’ time series may be obtained as (x2 − r1x1, x3 − r1x2,..., xn − r1xn − 1) (Partal and Kahya, 2006).
Table 2 Results of the statistical tests for annual Tmean over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney
Z
Median (Pre-1985)
Median (Post-1985)
p-value
26.70 26.25 26.90 24.20 17.35 25.10 25.10 12.30 14.10 10.60 11.25 13.25 17.70
27.25 26.80 26.90 25.05 18.55 25.85 25.95 11.40 15.05 11.35 10.80 13.95 16.40
0.0009 0.0032 0.9138 0.0001 0.0000 0.0006 0.0000 0.0039 0.0001 0.0096 0.0510 0.0165 0.0292
u(t)
b
2.48 ⁎ 2.14 ⁎ 0.33 2.33 ⁎ 2.23 ⁎ 2.65 ⁎⁎ 2.32 ⁎
2.15 ⁎ 1.98 ⁎ −1.62 2.01 ⁎ 2.18 ⁎ 2.09 ⁎ 2.02 ⁎
−1.38 2.90 ⁎⁎ 3.21 ⁎⁎ −0.46 1.97 ⁎ 0.27
−1.41 2.12 ⁎ 1.99 ⁎ −0.76 2.29 ⁎ 1.09
0.292 0.354 −0.011 0.382 0.586 0.372 0.603 0.529 0.558 0.268 −0.233 0.291 −0.375
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
136
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
3. Results and discussion
3.2. Trends in the climatic variables
3.1. Serial correlation of the climatic data
The annual trends of Tmean and their magnitude (in °C decade−1) obtained by the Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests and regression analysis are given in Table 2. Trends are considered statistically significant at the 95% and 99% confidence levels — presented in bold character in the tables — when identified by the three statistical methods (Mann–Kendall, Mann–Whitney and Mann–Kendall rank statistic tests). For the Mann–Whitney test application, the data were divided into two parts: one is from 1966 to 1985 and the other is from 1986 to 2005. The trend tests detected a significant increasing trend in annual Tmean at the majority of the stations (9 out of 13 stations). On the contrary, Shahrekord and Saghez stations showed insignificant decreasing trends. The magnitudes of the significant increasing trends in annual Tmean ranged between
Autocorrelation plots for the climatic variables at the stations are presented in Fig. 2. As shown, positive serial correlations were obtained for all the variables except precipitation. The Tmax series had significant correlation at all of the stations exception for Shiraz and Hamedan. The serial correlations of the Tmin and Tmean series were entirely significant. A mix of positive and negative serial correlations was obtained for annual P data. The positive correlations were significant only at Jask, Saghez and Sanandaj stations. The negative serial correlations of the P series were significant at Abadan, Ahwaz and Hamedan stations. As a whole, the strongest and weakest serial correlations were observed at the Tmin and P series, respectively.
28
28
Jask
25
25
19
19
19
19 26
Bandar-Lengeh
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
26
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
26
66
27
69
27
20
Bushehr
Shiraz
19
25
18 17
24
16
22.5 19
19 66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
14
Kermanshah
13
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
19
69
Hamedan
12
15
11
14
15
84
87 19 90 19 93 19 96 19 99 20 02 20 05
19
81
19
19
78 19
75
72
19
19
19
19
16
66
8
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
9
12
69
10
13
19
16
75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
23.5
23
19
24
72
24.5
19
25
Ahwaz
69
25.5
66
26
17
19
26.5
Abadan
19
66 19
72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
19
69
19
27
15 19
66
23
Sanandaj
14 13 12 11
19
66 19 69 19 72 19 75 19 78 19 81 19 84 19 87 19 90 19 93 19 96 19 99 20 02 20 05
10
Time series
Linear trend
5-year moving average
Fig. 3. Observed, 5-year moving average and trend line of annual mean air temperature at the stations with significant trends for the period 1966–2005.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
137
Table 3 Results of the statistical tests for annual Tmax over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney Median (Pre-1985)
Median (Post-1985)
p-value
30.05 31.15 32.30 29.10 25.85 32.55 33.10 20.60 22.10 18.90 19.20 21.40 25.05
29.90 31.15 32.10 30.00 26.15 33.65 33.15 20.00 23.50 19.60 19.10 21.90 24.95
0.2977 0.9246 0.4094 0.0016 0.1289 0.0026 0.3648 0.0214 0.0007 0.0761 0.4014 0.1160 0.5154
Z
u(t)
b
−0.86 0.92 −0.11 2.07 ⁎ 1.73 2.69 ⁎⁎
−1.05 0.43 −1.98 ⁎ 1.98 ⁎ 0.88 2.26 ⁎
1.78 −0.58 2.84 ⁎⁎ 0.27 2.01 ⁎ 2.74 ⁎⁎ 0.39
0.18 −1.17 2.42 ⁎ 0.08 1.50 1.59 1.92
−0.062 0.046 −0.040 0.364 0.203 0.396 0.262 −0.2.03 0.597 0.168 −0.104 0.356 −0.176
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
(+)0.268 °C per decade at Hamedan station and (+)0.603 °C per decade at Ahwaz station. The increasing trends in annual Tmean data found in this study are in accordance with the results of Ghahraman (2006) who reported a warming trend in annual Tmean by the Student's t-test at the majority of the synoptic stations in Iran. Tabari et al. (2011) also found a significant increasing trend in annual Tmean in 70% of the stations located in the western half of Iran using the Mann– Kendal test without consideration of serial correlation. The time series, along with the 5-year moving average and the linear trend line, were graphically represented for the climatic variables at each station but only the plots for the significant trends in Tmean are presented here (Fig. 3). Results of the statistical methods for annual Tmax over the period 1966–2005 are presented in Table 3. According to these results, it is clear that an overall warming trend is evident in the annual Tmax series (77% of the stations). However, statistically significant cases were found only at Bushehr, Abadan and Kermanshah stations. Based on the slopes of the regression lines, the rates of the significant warming trends in annual
Tmax were (+)0.364, (+)0.396 and (+)5.970 °C per decade at Bushehr, Abadan and Kermanshah stations, respectively. Analysis of the Tmin series indicated warming trends at 10 stations (Table 4). Significant warming trends in the Tmin series were larger than those in the Tmax series, and Tmin increased at higher rate than Tmax which results in a decrease of the diurnal temperature range. Turkes and Sumer (2004) also reported that trend magnitude of Tmin was greater than that for Tmax series in Turkey. Similar to the Tmean time series, Shahrekord and Saghez stations had insignificant cooling trends in Tmin data. The magnitudes of the significant warming trends in annual Tmin varied from (+)0.344 °C per decade at Hamedan station to (+)0.671 °C per decade at Bandar–Lengeh station. Tabari and Marofi (2011) found that air temperature increased in the west of Iran over the last 22 years. It can be inferred from the results presented in Tables 2, 3 and 4 that 9 out of 13 stations revealed positive trends in the total temperature series (Tmean, Tmax and Tmin) and among them Bushehr, Abadan and Kermanshah stations had significant trends.
Table 4 Results of the statistical tests for annual Tmin over the period 1966–2005. Station
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Mann–Whitney
Z
Median (Pre-1985)
Median (Post-1985)
p-value
23.35 21.55 21.30 19.45 9.00 17.70 16.80 3.75 5.75 2.30 3.60 4.70 10.00
24.45 22.60 21.75 20.20 11.15 18.60 18.80 3.25 6.90 3.05 2.60 6.05 8.20
0.0000 0.0000 0.1199 0.0001 0.0000 0.0006 0.0000 0.0273 0.0004 0.0160 0.0101 0.0324 0.0068
u(t)
2.04 ⁎ 2.14 ⁎ 0.96 2.50 ⁎ 1.11 2.17 ⁎ 1.74 −1.06 2.82 ⁎⁎ 2.61 ⁎⁎ −0.92 1.58 0.07
b: Slope of linear regression (°C/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic. ⁎ Statistically significant trends at the 95% confidence level. ⁎⁎ Statistically significant trends at the 99% confidence level.
3.90 ⁎⁎ 3.81 ⁎⁎ 1.36 2.09 ⁎ 2.47 ⁎ 1.98 ⁎ 2.48 ⁎ −1.32 2.62 ⁎⁎ 2.10 ⁎ −0.49 0.82 −1.09
b
0.662 0.671 0.041 0.405 0.991 0.353 0.947 −0.291 0.525 0.344 −0.357 0.256 −0.577
138
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
The graphical analysis for the temperature series was applied to the u(di) and u′(di) statistics of each station to identify the intersection of the curves and thus to detect the beginning year of trend or change. As an example of the positive trends, plots for Tmean at Abadan station are shown in Fig. 4. Analysis of the full range of figures shows the positive or negative trend per station, as well as the approximate year when an abrupt change occurred. This information is summarized in Table 5 for all the climatic variables. Increasing and decreasing trends are represented by (+ ) and (−), respectively; each station is characterized by a year which reflects the initiation of a positive or a negative trend. In some stations a second year is added, reflecting a year when a new change or trend was observed. In addition, the N in some stations denotes that there is no significant trend for the period. According to Table 5, an increasing trend for annual Tmean, Tmax and Tmin values began in the 1970s at the majority of the stations. The increasing trends in air temperature have been related to several factors such as global warming, increased concentrations of anthropogenic green house gases, aerosols which exert cooling effects on the climate, increased cloud cover and urbanization (Smadi, 2006). Overall, the increase of air temperature in the study area will increase dry conditions in the region by increasing potential evapotranspiration and consequently places it in serious risk of desertification. Results of the statistical methods for annual P data were summarized in Table 6. As shown, the majority of the annual P series were characterized by negative trend which were mostly insignificant. Significant decreasing trend was only observed at Sanandaj at the rate of (−)42.610 mm per decade which began in 1975 (Table 5). The plots for precipitation at Sanandaj station are shown in Fig. 5. Modarres and da Silva (2007) found no significant trends in P data at majority of the stations located in the arid and semi-arid regions of Iran. The results also showed that the increasing P trends were found at the stations located on the northern coasts of the Persian Gulf and the Oman Sea. This is probably due to sea surface temperature influences on P variability over the coastal regions (Nazemosadat et al., 1995). Since detecting increasing or decreasing trends in precipitation requires data from hundreds of years and our data was collected in less than a 100 year span, it might be difficult to detect trends in such a short time interval.
u(d)
u(d) u'(d)
4
u'(d)
2
Table 5 Approximate year of beginning of the positive or negative trend according to the Mann–Kendall rank statistic for the climatic variables (1966–2005). Station
Tmean
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
1972+ N 1976+ 1969+ N 1973+ N N N 1972+ (1996+) 1976+ (2003+) 1977+ 1968+ N N 1973+ 1973+ 1972+ 1976+ (1991+) N N N N N 1970+ 1975+ 1978+ 1970+ N 1971+ N N N 1971+ N N N N N
Tmax
Tmin
P N N N N N N N N N N N 1975− N
N: No significant trend at the confidence levels; +: Positive trend; −: Negative trend; ( ): Approximate year of a new change in a trend which is not statistically significant.
According to Smadi (2006), the main sources for abrupt changes in climate data include station relocations, changes in observation times, methods used to calculate daily means, changes in gauged locations, changes in instruments, increased urbanized area and global warming. Another factor that could lead to climate changes is change in atmospheric circulation. The large-scale structure of the atmospheric circulation varies from year to year and region to region. 4. Conclusions This study investigated long-term trends in the annual maximum, minimum, mean air temperatures and precipitation time series at 13 synoptic stations in the west, south and southwest of Iran for the period 1966–2005. Furthermore, pre-whitening was applied to eliminate the effect of serial correlation on the Mann–Kendall test. The results indicated that the climatic data were serially correlated showing the data were not independent. The trend tests detected significant increasing trend in the annual Tmean, Tmax and Tmin series in about 69%, 23% and 46% of the stations, respectively. The Mann–Kendall rank statistic test indicated that the significant increasing trends in air temperature data began in the 1970s in most of the stations. The analysis revealed that there were no visible trends indicating an increase or decrease in the annual P series. The results also suggest the need for further investigation on local anthropogenic intervention in the environment, which could be one of the major causes of climate change.
0
Acknowledgments -2
1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-4
Year Fig. 4. Graphical representation of the series u(di) and the retrograde series u′(di) of the Mann–Kendall rank statistic test for mean air temperature at Abadan station for the period 1966–2005.
The data used to carry out this research were provided by the Islamic Republic of Iran Meteorological Office (IRIMO). The first author would like to especially thank Miss. H. Tabari for her inestimable help for analyzing the data for this paper. We wish to express our gratitude to the anonymous reviewers whose suggestions and remarks have greatly helped us to improve the quality of the manuscript.
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
139
Table 6 Results of the statistical tests for annual P over the period 1966–2005. Station
Mann–Whitney
Jask Bandar–Lengeh Bandar–Abbas Bushehr Shiraz Abadan Ahwaz Shahrekord Kermanshah Hamedan Saghez Sanandaj Khorram–Abad
Median (Pre-1985)
Median (Post-1985)
p-value
168.00 130.50 152.20 229.70 297.60 156.35 258.70 332.40 477.30 326.50 542.00 491.30 549.20
119.30 130.90 183.60 263.20 348.90 166.10 227.55 331.00 438.80 327.30 478.80 406.40 465.30
0.2733 0.8924 0.4094 0.0531 0.0679 0.5250 0.5428 0.7353 0.2503 0.8392 0.0639 0.0043 0.1368
Z
u(t)
b
−2.20a 0.16 0.66 1.90 1.36 0.56 −1.57 −0.85 −2.13a −0.86 −1.96 −2.15a −1.88
−1.97a 1.22 1.32 0.06 0.87 0.38 −0.30 −0.18 −1.79 −0.27 −1.09 −1.97a −1.26
−33.840 −0.711 5.478 31.800 24.410 3.132 −2.553 1.670 −42.430 −6.930 −28.810 −42.610 −22.460
a : Statistically significant trends at the 95% confidence level, b: Statistically significant trends at the 99% confidence level. b: Slope of linear regression (mm/decade), Z: Statistic of the Mann–Kendall test, u(t): Mann–Kendall rank statistic.
u(d) u'(d)
u(d) u'(d)
4 2 0 -2 -4
1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-6
Year Fig. 5. Graphical representation of the series u(di) and the retrograde series u′(di) of the Mann–Kendall rank statistic test for precipitation at Sanandaj station for the period 1966–2005.
References de la Casa, A., Nasello, O., 2010. Breakpoints in annual rainfall trends in Córdoba, Argentina. Atmos. Res. 95, 419–427. del Rio, S., Penas, A., Fraile, R., 2005. Analysis of recent climatic variations in Castile and Leon (Spain). Atmos. Res. 73, 69–85. Ghahraman, B., 2006. Time trend in the mean annual temperature of Iran. Turk. J. Agric. For. 30, 439–448. Gilbert, R.O., 1987. Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York. Hamdi, M.R., Abu-Allaban, M., Al-Shayeb, A., Jaber, M., Momani, N.M., 2009. Climate change in Jordan: a comprehensive examination approach. Am. J. Environ. Sci. 5 (1), 58–68. Intergovernmental Panel on Climate Change, 1996a. In: Houghton, J.T., Meira Filho, L.G., Callander, B.A., Kattenburg, A., Maskell, K. (Eds.), Climate Change 1995: the Science of Climate Change. Cambridge University Press, Cambridge. Intergovernmental Panel on Climate Change, 1996b. In: Watson, R., Zinqowera, M., Moss, R. (Eds.), Climate Change 1995: Impacts, Adaptations and Mitigations: Scientific–Technical Analysis. Cambridge University Press, Cambridge. IRIMO, 2007. Iranian Meteorological Office. Data Processing Center, Tehran, Iran. Jaagus, J., 2006. Climatic changes in Estonia during the second half of the 20th century in relationship with changes in large-scale atmospheric circulation. Theor. Appl. Climatol. 83, 77–88. Kampata, J.M., Parida, B.P., Moalafhi, D.B., 2008. Trend analysis of rainfall in the headstreams of the Zambezi River Basin in Zambi. Phys. Chem. Earth 33, 621–625. Kohler, M.A., 1949. Double-mass analysis for testing the consistency of records and for making adjustments. Bull. Amer. Meteor. Soc. 30, 188–189.
Krishnakumar, K.N., Prasada Rao, G.S.L.H.V., Gopakumar, C.S., 2009. Rainfall trends in twentieth century over Kerala, India. Atmos. Environ. 43, 1940–1944. Kulkarni, A., von Storch, H., 1995. Monte-Carlo experiments on the effect of serial correlation on the Mann–Kendall test of trend. Meteorol. Z. 4 (2), 82–85. Liu, Q., Yang, Z., Cui, B., 2008. Spatial and temporal variability of annual precipitation during 1961–2006 in Yellow River Basin China. J. Hydrol. 361, 330–338. Matti, C., Pauling, A., Kuttel, M., Wanner, H., 2009. Winter precipitation trends for two selected European regions over the last 500 years and their possible dynamical background. Theor. Appl. Climatol. 95, 9–26. Modarres, R., da Silva, V.P.R., 2007. Rainfall trends in arid and semi-arid regions of Iran. J. Arid Environ. 70, 344–355. Mosmann, V., Castro, A., Fraile, R., Dessens, J., Sanchez, J.L., 2004. Detection of statistically significant trends in the summer precipitation of mainland Spain. Atmos. Res. 70, 43–53. Nazemosadat, M.J., Cordery, I., Eslamian, S., 1995. The impact of the Persian Gulf sea surface temperature on Iranian rainfall. The Proceedings of the Regional Conference on Water Resources Management, Isfahan, Iran. . Partal, T., Kahya, E., 2006. Trend analysis in Turkish precipitation data. Hydrol. Process. 20, 2011–2026. Peterson, T.C., Easterling, D.R., Karl, T.R., Groisman, P.Y., Nicholis, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, J., Førland, E., Hanssen-Bauer, I., Alexandersson, H., Jones, P., Parker, D., 1998. Homogeneity adjustments of in situ atmospheric climate data: a review. Int. J. Climatol. 18, 1493–1517. Price, C., Michaelides, S., Pashiardis, S., Alpert, P., 1999. Long term changes in diurnal temperature range in Cyprus. Atmos. Res. 51, 85–98. Raziei, T., Daneshkar Arasteh, P., Saghfian, B., 2005. Annual Rainfall Trend in Arid and Semi-arid Regions of Iran. ICID 21st European Regional Conference, Germany and Poland. Rio, S., Fraile, R., Herrero, L., Penas, A., 2007. Analysis of recent trends in mean maximum and minimum temperatures in a region of the NW of Spain (Castilla y Leon). Theor. Appl. Climatol. 90, 1–12. Smadi, M.M., 2006. Observed abrupt changes in minimum and maximum temperatures in Jordan in the 20th century. Am. J. Environ. Sci. 2 (3), 114–120. Sneyers, R., 1990. On the statistical analysis of series of observations. WMO. Technical Note (143). World Meteorological Organization, Geneve. 192 pp. Tabari, H., Marofi, S., 2011. Changes of Pan Evaporation in the West of Iran. Water Resour. Manage. 25, 97–111. Tabari, H., Marofi, S., Ahmadi, M., 2010. Long-term variations of water quality parameters in the Maroon River, Iran. Environ. Monit. Assess.10.1007/ s10661-010-1633-y Tabari, H., Marofi, S., Hosseinzadeh Talaee, P., Mohammadi, K., 2011. Trend analysis of reference evapotranspiration in the western half of Iran. Agric. For. Meteorol. 151, 128–136. Turkes, M., Sumer, U.M., 2004. Spatial and temporal patterns of trends and variability in diurnal temperature ranges of Turkey. Theor. Appl. Climatol. 77, 195–227.
140
H. Tabari et al. / Atmospheric Research 100 (2011) 132–140
Ventura, F., Rossi Pisa, P., Ardizzoni, E., 2002. Temperature and precipitation trends in Bologna (Italy) from 1952 to 1999. Atmos. Res. 61, 203–214. von Storch, H., 1995. Misuses of statistical analysis in climate research. In: Storch, H.V., Navarra, A. (Eds.), Analysis of climate variability: applications of statistical techniques. Springer, Berlin, pp. 11–26. von Storch, H., Navarra, A., 1995. Analysis of Climate Variability — Applications of Statistical Techniques. Springer-Verlag, New York.
Yue, S., Hashino, M., 2003. Temperature trends in Japan: 1900–1996. Theor. Appl. Climatol. 75, 15–27. Yue, S., Wang, C.Y., 2002. The influence of serial correlation on the Mann– Whitney test for detecting a shift in median. Adv. Water Resour. 25, 325–333. Zhang, Q., Xu, C.Y., Zhang, Z., Chen, Y.D., Liu, C.L., 2009. Spatial and temporal variability of precipitation over China, 1951–2005. Theor. Appl. Climatol. 95, 53–68.