J. Basic. Appl. Sci. Res., 2(2)1152-1158, 2012 ISSN 2090-4304 Journal of Basic and Applied Scientific Research © 2012, TextRoad Publication www.textroad.com
The Investigation of Temperature‘s Changes in Climate Changes in the Latitude of 34° -37° N
Hossain Ebrahimi*1 ; Afshin Tavasoli Farsheh2
1,2Assistant Professor, Department of Agronomy and Plant Breeding, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
ABSTRACT
Several hypotheses were presented with the issue of climate change. Changes in annual mean temperature of each zone would be the most important control on climate change. In this paper we studied the temperature at latitude 34° -37° N. we had used eight synoptic stations to get data. Collected data were analyzed by statistical methods and Mann-Kendall Method in two levels of 99% and 95% were applied. The results showed that there was always a rising trend or sometimes no trend could be seen in temperature. The average maximum monthly temperature was similar in all regions and has a rising trend in eight months of the year. It doesn’t follow a distinctive pattern (at the level of 5%) in the other remaining months. The trend of average minimum monthly temperature data was the same trend as the average monthly maximum temperature data. This trend is more significant for the minimum temperature. The comparison of temperatures in different statistical periods showed that the average triple temperature in the level of 1% and 5% in the past 25 years was significantly different from the previous 25 years. Generally, the results indicated that the temperature change was occurring in recent years and that there will be increasing temperature in the future. The temperatures in statistical 29-years periods, were higher than the average temperature. The results also indicated that rising in temperature in spring and summer was usually higher than autumn and winter. Ten-year average temperature showed that the temperature increase was higher in recent decades than before. KEY WORDS: Climate Change, Statistical, Temperature, Mann-Kendall Method.
INTRODUCTION
The increase trend of the earth’s temperature and decrease of the world water resources are the most critical situation in this century. The increase of the world’s population has resulted in the change of usage of the land, deforestation, increase in farming activities and producing solid or liquid waste [1]. Climate change is one of these results. Most of the climate change is due to human activities, especially the industrial activities and the increase of greenhouse gases [2]. The temperature increase is one of the distinct characteristics of the climate change. Studies showed that the world’s temperature will increase between 4 - 5.2 ° C in the year 2060, while IPCC has been estimated that the increase in temperature will be about 1.5 ° C and 1°-5° C for the years of 2060 and 2100 respectively)[3]. All the predicting models show that based on Global Circulation Modals (GCM), the global temperature will increase 2°C in the year 2100, as the concentration of greenhouse gases rises with the current rate (about 0.7 % a year) [4]. Many researchers emphasize that the areas located in the middle latitudes (15° - 45° N), will experience significant temperature increase and considerable precipitation decrease in future[Hulme]. Therefore, drought Indices is the best measures to assess the regional effects of climate change in these areas. Based on the current evidence, in the tropical and subtropical areas, future climate could be predicted by the spread of extreme droughts [5, 6].The temperature will increase in most parts of the world as the climate changes. Investigating the general circulation models has resulted in different results which depend on the defined models. According to these studies, the average global temperature would be rise between 3.5 and ° C in the year 2050 as the climate change. Also, the rate of change in temperature was significantly different in various regions. Studies showed that the temperature changes in the Mashhad synoptic station has been significantly increased in future. Based on these results, in all weather stations, average spring temperature increase were 3.1 and 3.9 for the years 2025 and 2050, respectively. Average temperature increase for these two years were 3.8 and 4.7 for summer, 3 and 2.3 for autumn and 2 and 4.2 for winter months respectively. In addition, the temperature increase
*Corresponding Author: Hossain Ebrahimi, Assistant Professor, Department of Agronomy and Plant Breeding, Mashhad Branch, Islamic Azad University, Mashhad, Iran. Email: [email protected]
1152 Ebrahimi and Farsheh, 2012
was intensified from the north to the south and from the west to the east. The studies indicated that the average yearly temperature increase would be about 1.8 and 2.35 ° C for the years 2025 and 2050, respectively)[7]. This study was conducted to investigate the trend of temperature and climate changes in Khorasan Razavi Province of Iran. Weather data of statistical period was studied to find if there were significant changes in past periods. Also, future climate changes were investigated.
MATERIALS AND METHODS
The weather stations were selected with the aim that whole parts of studied area were covered. To do this, weather stations of Mashhad, Torbat-e Heydarieh, Quchan, Chenaran, Sabzevar, Kashmar, Gonabad and Neyshabur were selected. A 25 years data were studied. The weather station of Mashhad, The base station of our study, had 50- year statistical data and this period was calculated. Characteristics of the studied stations were shown in table 1.
Table 1 - The characteristics of the weather stations used in the study Station Statistical Year Geographic Specifications Longitude Latitude Altitude Neishabur 1991 58° 48́ 36° 16 ́ 1520 Gonabad 1987 57° 30 ́ 34° 50 ́ 1150 Kashmar 1986 58° 28 ́ 35° 12 ́ 1215 Ghuchan 1984 58° 26 ́ 37° 06 ́ 1217 Sabzevar 1954 57° 43 ́ 36° 12 ́ 1190 Torbate Heidariye 1959 59° 13 ́ 35° 16 ́ 1451 Mashhad 1951 59° 34 ́ 36° 18 ́ 990
The simplest definition of the changes trend in the climatic parameters is the linear regression method in which the dependant variable is Y (the parameter) and the independent variable is X (the number of years). The best fitted line was drawn to analyses the correlation in the studied stations. Another testing methods to analysis the time series data, is the method presented by Man-Kendall. This method usually analysis the series of data and determines the trend of the series. The trend of temperature changes in the stations were studied in the levels of 1 and 5%. In this method, the data were sorted according to the year that data obtained and the value of R was calculated for every series of data by the following formula: R * R S Where R is the numeric value for Man-Kendall and R*, S is the dimensionless quantities that were obtained by the following equations: R* [4M / n(n 1)] 1 S 2(2n 5)/(9n(n 1)) Where n is the total number of data and Xi is the observations and M is the number of the observations where the Xi+i>Xi. The numeric value of R* shows the ascending or descending trend of the data series. If R* > 0 the trend of the series is positive and increasing while if R*<0 the series have a negative and decreasing trend and R*=0 shows that the data don’t have a significant trend. In the levels of 1 and 5%, the series conditions could be determined by calculating the value of R.
Temperature prediction by Man-Kendall method: The values of the weather parameters could be predicted by the Mann-Kendall model [9]. Different methods and formulas were suggested based on this model. One of the weather forecast models which has suggested by Pickering et al in 1988 [8]was as follow: 2 tk t p1t (tk 1 t ) 1 p1,T T K In this equation, the temperature of the k day could be calculated from the average monthly temperature t k, and the coefficient of the correlation with one delay p1k, and the standard deviation of the data would be obtained. This method is suitable when the average monthly data is available and we want to calculate the daily temperature by one day data. This method could also be used for the long-term data if we make some changes on it. So it is possible to predict monthly data for a long-term period based on the available data. The predicted average monthly temperature would be as follow:
1153 J. Basic. Appl. Sci. Res., 2(2)1152-1158, 2012
'T u S (i y ) u p . i .(u u ) 1 p 2 ' T i, j TJ 0 TJ ti ' i, j1 Tj Tj Tj i, j Tj1
Where u is the predicted average monthly temperature in month j, in the year I. ST is the temperature and year T i, j J regression line slope in the month of j. y0 is the number of the year which the prediction starts from it and uTj is the average monthly temperature in the month j in the statistical period. p is the regression coefficient between the T j temperature of month j and month j-1 in the statistical period. ' is the standard deviation of the temperature data in T j the month j and i, j is the normal random variable (the average of random numeric series 0 which the standard deviation is one). This model was used and the results were compared with other methods. In this model, the monthly temperature was predicted based on the average statistical period data. These results would not be correct if the average data of the primary period were used. This model could be changed so that the predicted results in each year were used for the next year. In other words, the data from the statistical period and the predicted years until the year i-1 could be used in order to calculate the temperature in the month J and the year I. This model was changed and temperature prediction was conducted in three ways as follows: ' ' Ti 2 u S TJ (i y ) m p . .(u m ) 1 p ' T i, j 0 j ti ' Ti1, j j Tj Tj i, j Tj1 ' Where S TJ is the temperature and year regression line slope in the month of j that could be calculated from the available data. m j is the average monthly temperature in the month j from the beginning of the statistical period until the year i-1.All the other parameters were defined in the main form of the equation. In the second form, the equation was changed as follows: ' u S (i y ) m R1 . Ti .(u m ) 1 R12 ' Ti, j TJ 0 j J ' Ti, j1 j J Tj i, j Tj 1 Where u is the predicted average temperature for the month j in the year I and S is the temperature and Ti, j TJ year regression line slope in the month j that has been set up on the basis of the available data and could be calculated until the year I-1.
y0 is the number of the year when the predictions start and mj is the average monthly temperature in the month j from the beginning of the statistical period until the year i-1. R1j is the coefficient of the auto regression between the temperature in the month j with one delay (until the year I-1).
Third form of the equation was as follows: ' u S (i y ) m R1 . Ti .(u m ) 1 R12 ' T i, j TJ 0 j J ' Ti1, j j J Tj i, j Tj1 In this form, the temperature in month j in the year i was obtained from the average monthly temperature j in the previous year. All parameters have already been defined.
RESULTS AND DISCUSSION
The trend of temperature changes were shown in tables 2 and 3. It was clearly seen that as the time passed the temperature was increased in all months. The trend of temperature changes was ascending in the level of 99 and 95% in all stations. Sometimes no trend were observed in temperature changes. The results also indicated that the trend of yearly data was ascending in all the stations. The reason of increasing trend of temperature could be the number of years which has data more than average and the difference trend from the average in different statistical periods. In 29 cases (58%), the average yearly temperature of the plain was more than the average. Also, the trend of temperature increase in the recent
1154 Ebrahimi and Farsheh, 2012
twenty five years, was much higher than the whole period. In this period, the temperature in 68% of the years was higher than the average. This was a sign of climate change in the region. Table3. The results of the monthly temperature trend in the region with the Man-Kendall model (the level of 1%)
Table2. The results of the monthly temperature trend in the region with the Man-Kendall model (the level of 5%) STATION JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR Average Temperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * + + + + + + + + + + + Kashmar * * + + + + + + + + + + + MaximumTemperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * + + + + + + + + + + + Kashmar * * + + + + + + + + + + + Minimum Temperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * + + + + + + + + + + + Kashmar * * + + + + + + + + + + +
Table3. The results of the monthly temperature trend in the region with the Man-Kendall model (the level of 1%) STATION JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR Average Temperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * * + * + + + + + + + + Kashmar * * * + * + + + + + + + + MaximumTemperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * * + * + + + + + + + + Kashmar * * * + * + + + + + + + + Minimum Temperature Mashhad * * * + * + + + + + + + + Neishabur * * * + * + + + + + + + + Sabzevar * * + + + + + + + + + + + Ghuchan * * * + * + + + + + + + + Torbate * * * + * + + + + + + + + Heidariye Chenaran * * * + * + + + + + + + + Gonabad * * * + * + + + + + + + + Kashmar * * * + * + + + + + + + +
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The results showed that during the seasons of the year, especially in spring and summer, the trend of temperature increase was significant. The studied temperature trend by statistical methods was investigated and the results were shown in table 4.
Table 4.The trend of monthly changes in the average temperature in the studied stations STATION JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC YEAR Linear model All stations + + + + + + + + + + + + + Quadratic model All stations + + + + + + + + + + + + + Exponential model All stations + + + + + + + + + + + + +
The slope of the linear regression between the time and temperature was calculated. The results showed that the slope of the regression line of time-temperature was positive in all the stations. An example of regression line was shown in figure1.
Mashhad Trend changes in tempratures parameters 25
20 15
10
5
Temperature 0 1940 1950 1960 1970 1980 1990 2000 2010 Year
MIN MAX MEAN
Figure.1. Trend changes in the temperatures Temperature prediction: Because of the lacking weather data in some studied stations, the prediction of temperature in main station (Mashhad) was done by statistical and global models. The results of temperature prediction by statistical methods for 25 years and 50 years were shown in tables 5 and 6 respectively.
Table 5. Prediction of Mashhad yearly temperature Man-Kendall3 Man-Kendall2 Man-Kendall 1 Man-Kendall Exponential Quadratic Linear Year 15.2 15.07 16.33 15.8 17.85 15.27 15.27 2000-2025 16.55 16.27 24.9 18.85 23.91 16.22 16.14 2025-2050 Average yearly temperature of 1951 to2000 = 13.97
Table 6. Prediction of the average 50 years temperature of the Mashhad station (2000-2050) Month JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 1951-2000 Tmt 1.5 3.3 8 14.4 19.3 24.1 26.8 24.5 20 14 8.4 3.9 Prediction by Man-Kendall3 Tmt 3.47 4.7 9.32 15.51 20.44 24.85 27.66 26.9 22.35 16.98 11.74 6.34
Temperature prediction by GCM models In this study, CSIRO (IS92a) model data was studied. In this model, the maximum and minimum temperature data are accessible and the average monthly temperature could be calculated based on these data. The temperature changes for the next years were calculated in three periods. Also the average temperature of years 2025 and 2050 were calculated based on the data in the model. The results were shown in Table 7.
1156 Ebrahimi and Farsheh, 2012
Table7. The average monthly temperature in IS92a model for different periods Average DEC NOV OCT SEP AGU JUL JUN MAY APR MAR FEB JAN Periods 15.56 6.20 12.84 15.30 21.27 25.88 27.58 25.91 19.96 16.27 8.69 4.29 2.58 M1 16.66 7.17 15.19 17.62 22.42 27.09 28.06 26.89 21.43 17.86 9.46 3.94 2.81 M2 16.10 6.67 13.99 14.43 21.83 26.47 27.82 26.39 20.68 17.05 9.07 4.12 2.69 M3 M1=2000-2025 M2=2025-2050 M3=2000-2050
Considering the three GSM models and seven statistical methods, IS92a model has the best fitting line with the real data, so the data of this model were used. The sample of selection a model for the average yearly temperature was shown in table 8. The results indicated that the IS92a data and the Man-Kendall 3 method has the best line fit with the real data, so they used for calculating the water consumption.
Table 8. The results of the comparison between statistical methods with the observed value in 1951-2000. Man-Kendall 1 Man-Kendall Exponential function Quadratic function Linear Statistic test 0.46 0.63 0.52 0.65 0.52 R 3.2 4.76 4.2 4.16 5.97 T 0.01 0 0 0 0 P NCAR IS92a HADLY Man-Kendall 2 Man-Kendall 3 Statistic test 0.13 0.38 0.25 0.78 0.83 R 0.93 2.86 0.436 5.32 5 T 0.356 0.006 0.665 0 0 P
The results of the temperature prediction for the climate change model (GCM) and the statistical model showed that there was a temperature increase for all months of years 2000 to 2050.The results summary of the temperature prediction and the average monthly temperatures in the Mashhad station and in the region were shown in table 9. It was seen that the temperature increase in this station was 2.1°C. So it was possible to predict the temperature in the region by using the relations between the base station and the region. The results of temperature prediction in all stations were obtained by the IS92a model and the correlation formulas. These results were shown as below: Table 9. The observed average monthly temperatures and the IS92a model for the base station Average DEC NOV OCT SEP AGU JUL JUN MAY APR MAR FEB JAN Statistic IS92a 16.10 6.67 13.99 16.43 21.83 26.47 27.82 26.39 20.68 17.05 9.07 4.12 2.69 Prediction 14 3.85 8.41 14 20 24.5 26.3 24.1 19.3 14.4 8.01 3.31 1.5 Observed
Table10. The observed average monthly temperatures in the study region ANNUAL DEC NOV. OCT. SEP. AUG. JULY JUNE MAY APR. MAR. FEB. JAN. Station 14 3.8 8.4 14 20 24.2 26.3 24.1 19.3 14.4 8 3.3 1.5 Mashhad 15.39 5.06 9.98 15.93 20.96 25 26.6 25.1 20.6 15.77 9.27 4.61 2.71 Region
Table11. The predicted average monthly temperatures in the study region ANNUAL DEC NOV. OCT. SEP. AUG. JULY JUNE MAY APR. MAR. FEB. JAN. Station 16.1 6.6 14 16.4 21.8 26.5 27.8 26.4 20.6 17.05 9.07 4.12 2.69 Mashhad 16.9 8.4 15 17.2 22 26.2 27.4 26.1 21 17.7 10.6 6.1 4.8 Region
It was observed that during the period of 2000 to 2050, the temperature was higher than the current temperature. This trend was also observed in all the weather stations. The average difference of the current and the predicted monthly temperatures in the study regions was shown in table 12.
Table 12. Average difference of the current and the predicted monthly temperatures Average Neishabur Sabzevar Gonabad Kashmar Chenaran Ghuchan Torbate Mashhad Station Heidariye 1.5 0.7 1.6 0.8 0.8 3.8 0.8 2.03 2.1 Difference of Temperature
1157 J. Basic. Appl. Sci. Res., 2(2)1152-1158, 2012
The results showed that the status of the temperature will change in the coming future years. It was observed that in the future the temperature will be increase in all the cities. Chenaran and Neyshabur will experience the highest and lowest temperature in future. Average temperature increase for the region was 1.5 °C. The average regime temperature showed that the temperature would increase in the future (Figure 2).
Figure 2. Comparison between current temperature and future changes
Conclusion This study was conducted to investigate the future climate changes and the trend of change in temperature and climate .The following conclusions were drawn from the studies: 1- Temperature change in studied region was observable. 2- In 68 % of statistical years, the temperature of the region was higher than the average temperature. 3- In all studied models, the future predicted temperature was higher than present temperature. 4- Average changes in the region temperature will increase 1.5 °C in 50 years future.
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