Journal of Spatial Analysis

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

  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

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=

Here   is the outputs of ANN models (downscaled data) and is the observation data.

Fujibe et all (2007) used an index based on Normalized Thermal Deviation (NTD) for extracting long-term changes of temperature extremes and day to day variability using following equations:

Where N is the number of days in the summation except missing values. Then nine-day running average was applied three times in order to filter out day-to-day irregularities.

=(i,j,n)=T(i,j,n)-T(I,j)

The departure from the climatic mean is given by

=

If NTD >2 and at least lasts for 2 days it determine as a heat wave.

Results

Table 3 shows the results of downscaling selected GCM models.

Table 4 compares the frequency of heat waves for GCMs and historical data.

For historical data, heat waves are more frequent in Abadan station than other stations. There is an increasing trend in the occurrence of heat waves in historical data and monthly frequency of heat waves show the highest amounts for summer.

For both historical and future data 2 days listening heat waves are more frequent.

Table 5 shows seasonal changes of heat waves for historical data and GCMs.

In recent years the frequency of heat waves is increasing in all studied stations. Coincide with Russia and Europe, the highest amounts of heat waves is occurred in 2010 in northern coast of Persian Gulf and this is adopted Esmaeilnezhad et all (2013), Gavidel (2015) and Azizi (2011).

Climate risks are the inherent features of Earth's climate. The occurence of heat wave is one of these natural phenomena. Heat waves, one of the basic appearances of climate change, are very important because of frequency and damage of life and property, (Haddow et al, 2008). Frequency of heat wave occurence in recent years, is one of the aspects of climatic changes and extreme weather (Matthies et al, 2008), and resulted in heavy financial loss and increasing p mortality. From statistical point of view, heat waves are the positive changes and upper extremes of maximum average daily temperature, which continuing during consecutive days, weeks or months in certain geographical areas. According to the available definitions, two dimensions of time and space are important in the occurrence or non-occurrence of heat waves  (Smith,2013). Due to the positive slope of temperature and increase in temperature extremes and many changes in values of maximum temperature in Iran, main purpose of this study is the spatial and time distribution of heat waves on the plateau of Iran.

The daily maximum temperatures recorded in 49 synoptic stations of 31 years (1980-2010) climate normal period were used for the spatial distribution of heat waves. In order to determine heat waves, using the 95th percentile index, the temperature threshold for each month and each station was determined separately. The reason of studying heat waves in the monthly scale is temperature differences and different consequences in different parts of Iran, as an example, maximum temperature 30 degrees in May for south of Iran is normal, but for the northern regions of Iran is a heat wave and causes damage. So the basis in this study is determining heat waves and spatial differences of these phenomena in monthly scale. In this study, the heat wave has been defined as temperatures above the 95th percentile threshold per month, continuing for three days and more. So with specifying the threshold temperature for each month at each station in different parts of the country, temperatures above the threshold continuing for three days and more, defined as a heat wave for each month and the spatial distribution of heat waves was plotted in the whole area of Iran plateau for each month. In order to determine changes in heat waves in the whole country, the number of heat waves has been specified for the whole country in three decades (80-90-2000).

The spatial distribution of heat waves: Maximum temperature thresholds are related to the southeastern, southwestern and southern stations; and the lowest thresholds are northern coast and northwest mountains stations. In general, the minimum temperature thresholds are visible in the northern half and towards the heights; however, the maximum thresholds are visible in southern half. In this temperature variable, the role of latitude and altitude is dominant in lines with the same threshold of extreme temperature like other temperatures properties in Iran. Spatial variations of this temperature parameter throughout the year, increased from the Caspian Sea and North West of Iran to the South East and South West of Iran. In the entire study period, the number of heat waves in different parts of Iran indicates that most heat waves were occurred in the mountainous regions of Iranbased on the zoning temperature Alijani. The number of heat waves decreased from this area to the north and south coastal areas and East of and Central of Caspian has the lowest number of heat waves during the entire period of the study in Iran.

Time, temporal and decade distribution of heat waves: Time changes in heat waves shows increasing trend, As we can see the increase in the number of heat waves, from mid-90s and then, in 2010 most of it.Also, the 5-year average and decade-long average of heat waves, show a significant increasing trends and the most of the heat waves occur in Iran during 2000s. Time series of heat waves in Iran; show a significant increase over time.Hence, from the late 90's onwards, the spatial average of heat waves rather than the average before these years has increased. Iranian plateau in 1992 and 2010 has experienced the minimum and maximum of heat waves, respectively.

The results showed the minimum temperature threshold along the heights in northern half of the country and maximum temperature threshold at the southern half. Spatial variations of this thermal parameter throughout the year, is increased from the Caspian Sea coast and the North West of Iran toward the South East and the South West of the country. In general, this parameter that is associated with the extreme temperatures in Iran is under latitude and heights distributions the same as distribution of maximum temperature areas in Iran. But spatial distribution of heat waves as a natural hazard is different from the distribution thresholds and maximum temperatures. So that, the most heat waves are in Zagros Mountains, the East foothills of Zagros, South of Western and central Alborz and also southern Binalud foothills in the North East. The number of heat waves is reduced toward the center of Iran and the Great Plains (Lut and Kavir deserts). The minimum heat waves occur on the coasts of Caspian Sea, southern coasts of Iran, South-West and West Zagros and central Iran. The occurrence of heat waves in Iran have an average between 9 and 14 heat waves during all months of the year except for May with a maximum of 6 heat waves and June, with a maximum of 16 heat waves (months of minimum and maximum occurrence, respectively). This shows minimum increase in cold months and maximum increase in warm months. Therefore, the occurrence of heat waves in Iran is possible in warm and cold periods of whole year and there is a little difference between these two periods. This indicates both internal (local) and external factors (air masses) involved in occurrence of heat waves in Iran. The number of heat waves increase and decrease since January and June, respectively. This temporal sequence is disrupted by a sharp decrease in May (6 heat waves less than previous month).

The application of Extreme value analysis method in heat wave hazard climatology; case study in Mid-Southern Iran
Abstract
Greenhouse warming poses the main cause of atmospheric hazards’ exacerbation and emergence in recent years. Earth planet has been witnessing frequent and severe natural hazards from the distant past; however, global warming has strongly influenced the occurrence of some atmospheric hazards, especially the ones induced by temperature and has increased the frequency and severity of those risks. Such extreme risks arising from temperature element and being affected by global warming could be referred to hot days and their frequency more than one day which undergo heat waves. Of the studies conducted worldwide in conjunction with the phenomenon of heat waves, the following can be pointed out; Schär (2015) has focused his studies on the Persian Gulf and the worst heat waves expected in this area. The recent work revealed an upper limit of stability which enables the adaptability of human body with heat stress and humidity. If people are exposed to a combination of temperature and humidity over long periods higher than this level, they will lead to hyperthermia and death, because heat dissipation from the body is physically impossible. Paul and al-Tahrir (2015) using a high-resolution regional climate model demonstrated that such a situation can occur much earlier. In Iran, in relation to heat waves, Ghavidel (2013) analyzed climatic risk of Khuzestan province in 2000 regarding super heat waves using the clustering approach. The obtained results unveiled the establishment of a low pressure at ground level and high pressure dominance at mid-altitudes up to 500 hp as well as the increase in atmosphere thickness having led to the ground overheating. Added to that, the source of heat entering into Khuzestan is advective and hot and dry air transport through Arabian Peninsula, Iraq and Africa. Ghavidel and Rezai (2014) addressed in a study to determine the temperature-related threshold and analyze the synoptic patterns of super heat temperatures in southeast region of Iran; the results of study approved that the only pattern effective on the occurrence of super heat days in Iran’s southeast is the establishment of the Grange’s heat low-pressure at ground level and subtropical Azores high elevation dominance at 500 hPa level. In this study, absolute statistical indicators, also recognized as above-threshold values approach, were used in order to identify, classify and heat waves synoptic analysis in the warm period of the year in the southern half of Iran. To use above-mentioned indicators, firstly daily maximum temperature statistics of studied stations with the highest periods were averaged every day once in June to September and once for the months of July and September. Using statistical indicators of long-term mean and standard deviation or base period, indicators would be defined for the classification of heat waves and days with peak extreme temperatures. In such classifications, usually long-term average or base period is multiplied by 1 to 3 to 4 times standard deviation and each time is account for the factor of each class.
To select the days for synoptic analysis, averaging was performed of all classified waves into four heat wave categories of low, intermediate, strong and super heat; accordingly based on the maximum blocks in each class of heat waves, days that had the highest temperature values were selected as the class representative for mapping and synoptic analysis.
This study dealt with investigating heat waves synoptic during the year’s warm period in the southern half of Iran. Studies showed that a variety of synoptic systems in the year’s warm period affect the study area. As well as, synoptic analyses concluded that in the southern half of Iran over the year’s warm period when occurring heat waves, low-pressure status dominates the ground level (caused by Gang’s low-pressure and local radiant mode); thus high-pressure status with closed curves is prevailing in atmosphere’s upper levels that gives rise to the divergence, air fall and Earth's surface heating. Studying the status of the atmosphere thickness in the days with the heat wave in the study area indicates its high altitude and thickness that this itself implies the existence of very hot air and susceptibility of the conditions for the occurrence of heat waves. In addition, wind maps at atmosphere’s different levels well illustrate the wind of very warm and hot air masses from the surrounding areas to the southern part of Iran; therefore it can be noted that aforementioned hot air masses mainly wind from places like different regions of the Arabian Peninsula, Iraq, North Africa and the low latitudes to the study area.
 
Keywords: Synoptic analysis, heat waves, maximum blocks, southern half of Iran.