mohammad hossein nassrzadeh

In this research, we are trying to determine the “beginning time” as well as the “end” of the climatic seasons; and we will focus on identifying the displacement of these dates, which is influenced by the “climate changes” and “descriptionAbstract The purpose of this study is to investigate possible changes and displacements in Iranchr('39')s climatic seasons due to climate change. To do this, temperature, relative humidity, water vapor, wind and cloud data for 36 stations were received from the Meteorological Agency over 40 years. The data were divided into two 20-year series to allow comparison. Daily temperature data for each clustering time series were determined, then by considering 7-day sequences, the beginning and end of the seasons. The designated times were tested using the Rayman model. The results of comparing the seasons in the two time series indicated that in all stations, changes in climatic seasons occurred from Insignificant to significant. Climatic seasons in Iran do not correspond to calendar seasons, and climate change, especially temperature changes in recent decades, has caused the seasons to shift and shorten and lengthen. Although the beginning and end of the seasons do not generally correspond to their calendar dates, most of the days of these seasons occur in its calendar periods. The changes that have taken place have not only affected the length of the seasons, and these shifts have also changed the quality of the natural seasons. Keywords: Climate change, natural seasons, cluster analysis, Rayman model of the qualitative conditions” created in them, compared to the past climatic periods. “Meteorological Organization” data has been used in this research. Forty years of received data, was divided into two groups of 20. Applying SPSS, each group was divided into four stages representing each seasons. From these stages, the beginning time and the end of seasons were determined and the accuracy of the obtained dates was controlled with the comfort indicators of the Rayman model. The results of the comparison of seasons in two time series indicated that, the changes occurred in natural seasons from an almost non-existent one in all stations. Climatic seasons in Iran are not compatible with the summer season and climate change, especially the change in temperature in recent decades, has caused changes and shortening of seasons. Most of the days in these seasons occur during its monthly periods, although the beginning and end of the seasons generally do not match their calendar dates. Changes have not only affected the duration of the season, and these changes have also led to a change in the natural quality of the season.

Draught is an event which occurs in all types of climatic conditions. This phenomenon can cause severe damages to thesusceptible areas. Since the drought due to its special features will change the agriculture of the Caspian sea, this phenomenon has been studied in the coastal areas of Caspian sea. The purpose of this study was to evaluate the drought during the base period of 1961-2010 and the future period of 2011-2030 byusing the Standardized Precipitation Index (Z-Value) on five stations of southern coastal of Caspian sea. Daily rainfall data from five stations was obtained to calculate the monthly precipitation and the data from HADCM3 model under B1 and A2 scenario. For downscaling the HADCM3 data, two models of LARS-WG and SDSM were used. Downscaled results Showed that LARS-WG model is better than SDSM for downscaling the precipitation data with less error. LARS _WG Model simulation results has estimated that precipitation will increase for February, November and December and it will decrease for the months of August and September, on each of five stations .We Used GIS to mapping the severity of the drought. 3-month SPI- results showed the greatest frequency and severity of droughts during the observation period on Anzali and Rasht stations. During the future, Rasht and Ramsar stations will have the highest intensity of droughts. 6- Months SPI determine the higher frequency on Babolsar, Gorgan, Ramsar Station and severity on Anzali, Rasht and Ramsar have experienced the highest drought. Ramsar, Rasht and Gorgan stations will have higher drought degrees in future. 12 -months SPI results showed the highest intensity was for the Ramsar and anzali, Ramsar, Rasht Anzali, Babolsar will reveale for future. Results indicated periods of normal SPI is more frequently than in any other period of five stations.

Climate is one of the important natural factors that affect all stages of life, particularly human exploitation. Selection of the type of clothing, housing, cultures, architecture, civil engineering, and settlements are influenced by climatic factors. It can be said that the climatic circumstances of the surface of the earth and atmospheric circulation patterns have an important role in shaping and organizing the environment (Alijani, 2009). In some cases, the normal weather conditions become abnormal and cause many damages, which are mostly catastrophes rooted in climatic changes, such as hail, frost, heat and cold waves, floods, storms and so on. Blizzard is one of the atmospheric phenomena, which happens as the result of snow combined with wind (15 meters per second), and low temperatures (below zero°C), and it causes severe losses.
Due to its special geographical location, Iran is placed in the transition region of the large-scale patterns of common tropospheric circulation, and is the intersectional place of the of extra-tropical and tropical circulation system. This feature along with its complex topography caused the land to have a considerable climatic diversity. The climatic diversity makes the various climatic phenomena to be observed with intensity, energy, and different frequencies, therefore, the climatic phenomena with high intensity always causes damage to natural resources and the human civilization. This undesirable phenomenon is called climatic risks. Since the West Azerbaijan Province is located in mountainous areas and high latitudes, the feature is triggered many climatic risks such as flood, hail, snow, snow storm, and so on. Therefore, snowstorm is one of such phenomena that have occurred every year or every few years due to the specific characteristics of the region and have caused damages in the fields of transportation, energy, livestock, closeness of schools and offices.
The purpose of this study is the statistical and synoptic analysis of snowstorm in west Azerbaijan province. Therefore, the data related to the present weather codes were collected during the period 1986 to 2009 from the National Meteorological Agency. The data related to the weather codes entered in Excel, and data related to the snowstorm were selected through Filter tool and isolation of codes related to the strong snowstorms (codes 37and39) and weak snowstorms (codes 36 and 38). Then the data related to the snowstorm was entered in SPSS, and the statistical analysis was performed. In the next step, three cases of the strong and common snowstorm (code 37 and 39) were selected for synoptic analysis. Then, the synoptic maps of the different layers of the atmosphere were selected as the samples for strong snowstorm for the days before the event of the phenomenon, the day of event, and the day after the event of the phenomenon by the using of the accuracy of 2.5 degrees from cdc.noaa.gov website. The study area has been selected in 10 to 80 degrees north latitude, and 15 to 90 degrees east longitude for identifying the patterns that affect West Azerbaijan Province. The data was received on wind speed and direction in digits from the National Center for Environmental Prediction. Then, the maps of the wind direction and speed were provided in Grads. Finally, the daily analysis and interpretation of pressure (500hPa at sea level), instability (700hPa level and the ground level), Earth's surface temperature, wind speed and direction maps for 700hPa level, and identification of patterns that have caused snowstorm in West Azerbaijan province were carried out. Statistical and synoptic analysis of snowstorm phenomenon in West Azerbaijan province during was performed in the period 1986 to 2009. To do this, using codes 36 to 39, which represent a variety of snowstorm (weak and strong), the frequency of snowstorm days on monthly and annual average, distribution of the snowstorm in the extracted stations, the frequency of strong snowstorms (codes 37and39), weak snowstorms (codes 36 and 38), all types of snowstorms (codes 36 to 39), and the frequency of storms in the station level were compared. Out of 322 snowstorms occurred during the period 1986 to 2009 in seven synoptic stations 108 have been determined as strong snowstorm and 214 as weak snowstorm. In order to analyze the synoptic snowstorm in West Azerbaijan province, in the first place, the strong snowstorms were identified, and then five of the strong and comprehensive storms were selected for the synoptic analysis. The snowstorms of choice are as follows: On 18 January 1986, on January 19, 2000, on February 7, 1992, on February 5, 1997, and on December 25, 1990.
For applying the study, pressure maps, Omega (700hp level at ground level), Earth's surface temperature, and wind speed and direction at 700hPa were analyzed, and patterns and conditions that are causing this phenomenon in the West Azerbaijan province were identified.
In this study, to perform statistical and synoptic analysis of snowstorm in Western Azerbaijan province, the statistical data were examined during the period 1986 to 2009 from 7 stations, and the results of the statistical analysis showed that:• Out of a total 322 snowstorm event days of 7 synoptic stations during the period 1986 to 2009, 108 and 214 days were strong and weak snowstorms, respectively.
• Review the annual and monthly snowstorm during the study period showed that the 1992, 1997, and 1989 with a total of 69, 29, and 25 days, as well as the 1999, 2006 and 2007 with 0, 1, and 1 day have the most and the fewest days of snowstorm, respectively. The statistical analysis showed that the snowstorm phenomena happened in January, February, March, April, November, and December. January had the most and April had the fewest snowstorms with 119 and 3 days, respectively. February with 39 days, and April and November, with the number 0 and 1 had the most and the fewest days of strong and constant snowstorms.
• Distribution of the snowstorms in the stations indicated that out of the studied seven synoptic stations, which had a great impact on the synoptic situation of the region, topography, and height, Sardasht-Maku station had the most, and stations of Khoy, Mahabad, and Orumiyeh by having no snowstorms had the fewest days of snowstorm.
• The results of the maps of the different levels of the atmosphere and Earth’s surface in the days before the storm, event day and the day after the snowstorm were selected for the snowstorm pattern, which indicated that the snowstorm in the winter due to low compliance pressure formed in the earth's surface with synoptic patterns of middle levels of the atmosphere have provided the conditions for the event, in a way that among the sample cases of the strong snowstorms occurred in the West Azerbaijan Province two circulation patterns were involved in the formation of natural hazards: The Caspian Sea low pressure pattern- Eastern Europe high pressure pattern and the north of the Black Sea low pressure pattern.

today, the worth of the human basis of tourism planning. A difference in time-where climate in different regions a strong potential for tourism, and the reason is that Iran is a diverse country in the world tourism has five rows. A very beautiful area that has great potential in terms of ecotourism, protected areas Ashtarakoh and consequently Gohar Lake in the province. The level of comfort is influenced by environmental factors and physiological. MEMI model component models the thermal equilibrium thermo - physiological, the three sub-indices PMV, PET, SET formed, the basis of this model based on energy balance equation is based on the human body. In this study, in order to determine the appropriate time for tourism in the Lake Gohar Calendar of air drying temperature climate indices C, amount of cloud in the sky Acta, prevailing wind speed in meters per second, the water vapor pressure Hkto Pascal and relative humidity in percent synoptic stations city Dorud period 1379-1391, and parameters human physiological (age, Genus, weight, height, coverage and activity), as well as situational variables (longitude, latitude and altitude geometric study area), the model MEMI were evaluated. Calculate the comfort index for evaluating the results of this model suggest that PET and PMW output indicators are almost identical, and all year variety of physiological conditions in the lake is ruling Gohar. According to this model, the best time for tourist activity in the months of May, June, September and October are.