پديد آورندگان :
عليجاني ، بهلول نويسنده Alijani, B , روشني، احمد نويسنده , , پرك، فاطمه نويسنده , , حيدري، روح الله نويسنده ,
چكيده لاتين :
Introduction
Global temperature has increased about 0.74°C over the last century (IPCC, 2007, 30). In recent studies, the potential of increase in heat waves, heavy precipitation, cold winters, summer storms and drought events due to climate change has noticed (Zhang,2005,11).
On the other hand, it is believed that climate change will affect most aspects of weather and climate, especially precipitation and temperature extreme events (Radinovi and Curi, 2009, 200; Lehner et al., 2006, 293). The socio-economic effects of extreme events (Ryoo et al., 2004, 145), require to give more attention to such studies. Assessment of the temperature extremes changes is done in many regions of the world in the last century (Mudelsee et al. 2003; New et al. 2001; Moberg and Jones 2005; Klein Tank and K¨onnen 2003; Alexander et al. 2006). The socio - economic effects of extreme events in arid and semi arid regions like Iran due to very vulnerable and fragile echosystems are more and their sudden changes may be followed by the devastating events. Spatial and temporal variability of climate in Iran is one of its inherent characteristics and devastative socio-economic effects of climatic disasters such as floods and droughts have been severe in recent years (Nazemosadat and Cordery 2000, 59; Barlow et al. 2002, 697; Nazemosadat and Ghasemi 2004, 4016).
Studies such as Rasouli (2004), about spatial analysis of cold winds in the South-western of Iran and Kaviani et al. (2004) on the effective temperatures in the country, show the variability and instability of climate. The result of global warming over Iran includes the increased frequency of extreme events, especially in cold and heat waves, long time severe droughts and torrential rain (Rahimzadeh et al. 2009, 342). Numerous studies in recent years, have been investigated the average temperature and precipitation variability over Iran (Alijani 1997; Jahadi Toroghi 2000; Rasooli 2002; Rahimzadeh and Asgari 2003; Rahimzadeh and Asgari 2005; Pedram et al. 2005; Asadi and Heydari 2011). Furthermore, Rahimzadeh et al (2009) considered the temperature and precipitation extreme variability in Iran and Taghavi and Mohammadi (2007), stated that the frequency of warm and cold events, respectively, has been associated of what. Moreover, kari (2010), has confirmed significant changes in heat waves and cold periods in Tehran. The purpose of this study is to provide a more detailed analysis about the spatial and temporal distribution of the temperature extremes over Iran.
Study Area
To draw an accurate picture from the variability of climate, temperature extreme are analyzed for 30 synoptic stations which have shown a good spatial distribution over Iran.
Material and Methods
Long-term (1961–2005) data set of daily maximum and minimum temperature is analyzed and has less missing values. To prevent the effects of heterogeneity in the results, some such as the stationʹs relocation, missing values less than five percent and adequate long time continuity have been used in station selection. Other wise, the stations with irrelevant series have removed from dataset. For each station, 18 indicative climatic indices which has recommended by the joint World Meteorological Organization CCL/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated (Nicholls and Murray, 1999, 24). To detect trends in time series the Mann-Kendall test (tau), with null hypothesis as "the lack of trend in time series" is used. In addition, the Mann-Kendal test requires that data to be independent in terms of series. If data has positive serial correlation, Mann-Kendal will significantly over-estimate the temperature, On the other hand, if the data has negative serial correlation, the temperature is Significantly will under-estimate (Tabari, 2011, 314). The linear regression model is used to calculate the trend per decade and to determine the autocorrelation regression error. This test is based on the first order autoregressive error model . Durbin-Watson test based on the difference between the neighboring residuals and calculated using the formula:
Results and Discussion
The annual minimum temperature has increased in most regions except in Azerbaijan province and annual maximum temperature in most regions especially in central region is showing an uptrend. Warm nights, tropical nights and warm spell duration in the station and regional scale show increasing trends about one day per decade and cold spell duration in central region and Alborz Mountains have a significant negative trend wich it is consistent with the IPCC study. DTR significant negative trends over iran and especially over Zagros coordinate with results of previous studies over the Middle East and Southwestern Asia (Zhang et al., 2005, 10; Klein Tank et al., 2006,9). Growing season length, in the other regions except in the northern Zagros and southern Azerbaijan has an incremental process. Cold nights are declining and this decline in Central region, Alborz Mountains and southern regions is more severe. TNx index also shows increasing trends in Zagros, Central region and the Alborz. Warm days in the Zagros, Central Iran and Alborz have increased, that confirms the results of Zhang et al. (2005). Cold process is only seen in the southern Zagros and especially Shahrekord station. ID0 and cold days have a reduction in central regions and summer days are increasing in Alborz and Central region.
Conclusion
In this study, the variability of temperature extreme was analyzed. The results observed in this study, confirm many previous findings and also provide some new results in terms of indices or regions. The results show that the temperature has increased over the whole country and this increase as well as with the global warming (Trenberth et al., 2007), have intensified in recent years. Generally, the maximum variability is visible in central regions of Iran, which area most indices are significant in this area and then, Alborz and Zagros show the greatest variability and the Azerbaijan region has the lowest variability in regional scale.
