Global changes in extremes of the climatic variables that have been observed in recent decades can only be accounted anthropogenic, as well as natural changes. Factors are considered, and under enhanced greenhouse gas forcing the frequency of some of these extreme events is likely to change (IPCC, 2007 Alexander et al., 2007). Folland et al. (2001) showed that in some regions both temperature and precipitation extremes have already shown amplified responses to changes in mean values. Extreme climatic events, such as heat waves, floods and droughts, can have strong impact on society and ecosystems and are thus important to study (Moberg and Jones, 2005). Climate change is characterized by variations of climatic variables both in mean and extremes values, as well as in the shape of their statistical distribution (Toreti and Desiato, 2008) and knowledge of climate extremes is important for everyday life and plays a critical role in the development and in the management of emergency situations. Studying climate change using climate extremes is rather complex, and can be tackled using a set of suitable indices describing the extremes of the climatic variables. The Expert Team on climate change detection, monitoring and indices, sponsored by WMO (World Meteorological Organization) Commission for Climatology (CCL) and the Climate Variability and Predictability project (CLIVAR), an international research program started in 1995 in the framework of the World Climate Research Programme, has developed a set of indices (Peterson et al., 2001) that represents a common guideline for regional analysis of climate. It is widely conceived that with the increase of temperature, the water cycling process will be accelerated, which will possibly result in the increase of precipitation amount and intensity. Wang et al. (2008), show that many outputs from Global Climate Models (GCMs) indicate the possibility of substantial increases in the frequency and magnitude of extreme daily precipitation. eneral circulation models (GCMs) are three-dimensional mathematical models based on principles of fluid dynamics, thermodynamics and radiative heat transfer. These are easily capable of simulating or forecasting present-future values of various climatic parameters. Output of GCMs can be used to analyze Extreme climate. For this study high quality time series data of key climate variables (daily rainfall totals and Maximum and minimum temperature) of 27 Synoptic stations were used across Iran from a network of meteorological stations in the country. In order to get a downscaled time series using a weather generator (LARS-WG), the daily precipitation output of HadCM3 GCM, SRES A2 and A1B scenario for 2011-2040 are estimated. The Nine selected precipitation indices of ETCCDMI[1] core climate indices are used to assess changes in precipitation extremes and monitor their trends in Iran in the standard-normal period 1961–1990 and future (2011-2030). Due to the purpose of this study, at first changes in extreme precipitation indices in the standard-normal period is evaluated and its results show annual maximum 1-day precipitation increased in many regions in the East of Iran. Simple measure of daily rainfall intensity (SDII), annual maximum consecutive 5-day precipitation, annual count of days with daily precipitation greater than 10mm (R10mm), annual count of days when rainfall is equal to or greater than 20 mm (R20mm) have increased in the central areas, regions in the north , north east and southern parts of Iran. Similar results are obtained for the R25mm index. The consecutive dry days (CDD) index has generally increased across the west areas, southwest, north, northwest and southeast of Iran and indices of consecutive wet days (CWD) decreased in these areas. Trends of extreme precipitation indices simulated by HadCM3 SRES A2 showing increases RX1Day in North West expect west Azerbaijan Province, central, southwest, north east and coasts of Caspian Sea. Similar results are obtained for the R5mm index expects northeast. There are mixed changes in R10mm across Iran, increasing in west, southwest, coasts of Caspian Sea, Hormozgan and Ardebil provinces, East Azerbaijan, Zanjan and Qazvin provinces. Similar results are obtained for the R20, 25 mm index in northeast, south of Caspian Sea, and some parts in western and central areas. Same as HadCM3 SRES A2 pattern there are mixed changes in R10mm across the region. Positive trends are seen in part of the Isfahan, Markazi, Kuhkilue , Lorestan, Ilam, Chaharmahaland Khozestan provinces and some part of Hormozgan and Kerman and some areas in north west. Similar results are obtained for the R20mm and R25mm index and in west of Yazd to north of Khozestan provinces have increased. Consecutive wet days (CWD) have increased over most of the west of Iran, Khorasn Razavi and Southern Khorasn provinces, In contrast consecutive dry days (CDD) index has generally increased in many parts of the region.
[1]. Expert Team on Climate Change Detection and Monitoring Indices
Tehran, in the south of Alborz Mountains, is faced with three types of weather risk, weather risk caused by geography, climatic risks caused by air resistance and weather risk due to global warming. The aim of this study is to examine the three types of risk in Tehran. The method of this study was to evaluate the changes of synoptic factors that affect global warming and urban development. In order to detect the height changes of 500 hPa two 5-year periods including 1948 to 1952 and 2010 to 2014, were studied.
The results showed that changes in heights of 500 geopotential, there was an increased risk in the city of Tehran. The effect of climate change in recent decades, increased the stability of air in Tehran. Human factors in the formation of heat islands, increase LCL height and density of the air balance is transferred to a higher altitude. Changing urban wind field, atmospheric turbulence intensified, exacerbated thermodynamic gradient, fat and refugee cyclones, heat island effect of the city.
Thermal stability in the warm period will appear. The thermal stability of all levels of lower, middle and upper troposphere was intensified. Thermal stability couraged the development of subtropical high pressure in the area. With the arrival of the atmospheric pressure during calm and humid days the stability and pollution were increased. Negative vorticity from early June developed the intensive high pressure over the region. Compare the conditions of the two study periods showed that : the height of the high pressure was 100 meters higher than the second period. The number of days of intensified subtropical high increased during the second period. The high pressure has moved to the northern areas during the second period. This change in the subtropical high pressure increased the dry periods motivating the loss of vegetation. Heat island effect was increased as well. More than 90% of the temperature inversions occurred at an altitude of less than 500 meters in both warm and cold periods of year. Wind direction at both stations has shown that the establishment of any pollutant source in the West of Tehran will increase the pollution.
Climate change is one of the most significant threats facing the world today. One of the most important consequences of climate change is increasing frequency of climate hazards, mainly heat waves. This phenomena has a robust impacts on human and other ecosystems. The aim of this study is investigating changes of heat waves in historical (1980-2014) and projected (2040-2074) data in northern cost of Persian Gulf.
The focus here is on Mean daily maximum temperature and Fujibe index to extract heat waves. For this purpose 6 weather stations locating in north coast of Persian Gulf, Iran, are used (table 1).
Table1: weather stations
Station |
Latitude |
Longitude |
Elevation(m) |
Abadan |
30° 22' N |
48° 20' E |
6.6 |
Boushehr |
28° 55' N |
50° 55' E |
9 |
Bandarabbas |
27° 15' N |
56° 15' E |
9.8 |
Bandarlengeh |
26° 35' N |
54° 58' E |
22.7 |
Kish |
26° 54' N |
53° 54' E |
30 |
In addition, 4 model ensemble outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to project future occurrence and severity of heat waves (2040 to 2070), under Representative Concentration Pathways 8.5 (RCP8.5), adopted by the Intergovernmental Panel on Climate Change for its Fifth Assessment Report (AR5) (table 2).
Table2: List of the AR5 CMIP5 Used Models
Model |
Modeling Cener |
Country |
CanESM2 |
Canadian Earth System Model |
Canada |
MPI-ESM-MR |
Max-Planck-Institut für Meteorologie |
Germany |
CSIRO-Mk3-6-0 |
Commonwealth Scientific and Industrial Research Organization |
Australia |
CMCC-CESM |
CMCC Carbon Earth System Model |
Italy |
The output of models is downscaled using artificial neural network method (ANN). A feed-forward network of multi-layer perceptron with an input layer, a hidden layer and an output layer is used for this purpose. 73 percent (1980 – 2000) of the data is used for training and 27 percent (2000-2005) for testing ANN models. Root Mean Square Error (RMSE) is used as an indicator of the accuracy of Models.
RMSE=