Climate Change and Climate Disaster

Abstract Manjil wind is one of the most atmospheric phenomena the Iranian plateau. Behavior and spatial range of Manjil wind has been very limited. In this study, SCData of 30 synoptic stations in the province of Gilan and Iran plateau and also NCEP / NCAR data (horizontal resolution of 2.5°) ECMWF (horizontal resolution of 0.125°) were used for statistics and synoptic analysis. This study indicates the daily specific behaviors and quasi-permanent phenomenon during the days of the year. Manjil wind is starting to blow from around 9 Am local time and peak in 15 to 16 hours, local time. The most frequency of Manjil wind is during June and July.

Statistics study show the influence of Manjil wind on daily and seasonal temperature regime. In this study for the first time, results showed that Manjil wind is expanding to the altitude of 1400 meters above sea to Jirandeh station in the southern slops of the Alborz mountains. In synoptic scale, pressure gradient between Caspian Sea thermal high pressure and Iran plateau thermal low pressure cause to form wind. Daily and seasonal behaviors of Manjil wind especially speed and frequency of occurrence is related to two regional scale atmospheric systems that are affected by thermal properties of the lower levels of the atmosphere. In this study, an index was introduced to identify the factors leading to blowing Manjil wind. This index is pressure difference between the Caspian Sea and Iranian Plateau (CSIP) that confirms Manjil wind formation mechanism.

Abstract One of the characteristics of precipitation is its long-term changes over time. Considering the complexities of precipitation behavior, using nonlinear methods can lead to a better understanding of this phenomenon. Therefore, in this study, an attempt has been made to investigate the annual precipitation trend in northwestern Iran in the statistical period 1970 to 2013 using polynomial models along with the linear method. Discrete wavelet transform has also been used to reduce the effect of variability on the trend. The time series used to survey the trend is obtained from the grid data of the daily precipitation of northwestern Iran from 1/1/1970 to 12/31/2013 (16071 days). These data are the result of interpolation of daily precipitation using kriging method in lambert conformal conic coordinate system. The result showed that the long-term behavior of precipitation in northwestern Iran in the period under study follows a 3 degree pattern; in a way that the mean frequency of annual precipitation in the first decade (1970) had been increasing rapidly. Since the first years of the 80's, a significant downward trend in the data had been formed and continued for nearly 3 decades. However, in the final years, an upward trend had begun again. The results also showed that data decomposition with wavelet transform while maintaining the mean and long-term behavior in the approximate component has also filtered precipitation variability from this component. Hence the low frequency component obtained from series decomposition is a good representative for trend estimation.

Abstract Conclusion

Investigating the time series of the 70-year temperature of the meteorological station of Tabriz city (1951-2020) while investigating the historical identifier of the station, using graphical and quantitative-analytical statistical methods (such as absolute standard normal tests, Cramer test, autocorrelation test, Von Neumann test) and quantile distance, sign test and landmark test) all types of inhomogeneities (such as outliers, jumps, etc.) were investigated and classified according to the homogeneity classification of the World Meteorological Organization. The findings of the present study showed that based on the methods used in the present study, only one of the tests used indicated the homogeneity of the data. The results of graphical statistics methods are also similar to the results of quantitative-analytical statistics. Therefore, based on the evidence and comparison of various methods, it was determined that the observations of the total annual average temperature of the weather station of Tabriz city airport were heterogeneous, and based on the classification of homogeneity, it was placed in class 4.

Abstract Westerlies are the main driver of rainfall in Iran, and its spatial variations are an important parameter to identify the spatial and temporal differentiation of rainfall, especially its role in the temporal distribution of autumn rainfall at the same time as the beginning of the water year is very important. Early and late fall rain is a good criterion for starting the cultivation of autumn (rainy) crops. In order to explain the role of variations in atmospheric circulation on the time delay of autumn rains, rainfall data, weather patterns, wave indices, intensity and high pressure level in the subtropical region were used. The results show the weakening of the westerly wind waves due to the spatial variations of Subtropical High Pressure(STHP). The decrease in tides in October has been associated with the time delay of rainfall and the shortening of rainy days. The delay in the high-pressure southerly regression is associated with a 20-day delay and a decrease of 7 mm of monthly rainfall and finally a decrease of 15 mm of autumn rainfall. From a synoptic point of view, the time delay of precipitation is associated with the positive anomaly of geopotential height and high pressure strengthening. The time shift of the effective autumn rainfall (from October to November) is accompanied by an increase in rainfall due to the provision of suitable instability conditions. The time delay of autumn rainfall is associated with the shortening of the rainy season, the reduction of autumn and annual rainfall and finally drought.

Abstract Clouds are one of the most important components of the climate system of any region, which have an important effect on the regulation and balance of radiant energy and the water cycle, so it is necessary to study their long-term changes. Based on this, in this study, using the data of the cloudiness coefficient of 26 synoptic meteorological stations in the northwestern region of Iran in the time period of 1982-2022, the analysis of the trend of cloudiness changes was carried out based on the Sen's slope estimation method. Also, by using the average annual cloud cover images of EUMETSAT satellite in the period of 1982-2022 and by using the Getis-Ord Gi* spatial statistics index, the spatial changes of cloudiness in the last four decades were analyzed. The results of the monthly trend analysis showed that except for July, which had an increasing trend, the rest of the months had a significant downward trend. The months of June, August and September also had no significant trends. The results of the spatial analysis also showed that the amount of cloudiness in this region has decreased in all four decades and the pattern of changes has also changed from the first decade to the recent decades.

Abstract Climate change is one of the environmental challenges of the 21st century that is of global concern. The share of industrialized countries in the production of greenhouse gases is higher than other countries. Efforts have been made in some industrialized countries, such as the United States and European countries, to reduce greenhouse gas emissions, but China, the largest producer of greenhouse gases, has not made significant efforts. One of the major changes in global warming is changes in rainfall. In northwestern Iran, the Aras catchment area has experienced a significant shortage of rainfall in recent years. It shows itself to Iran. In order to investigate the precipitation changes of Khoy synoptic station, daily precipitation data and total snowfall for the period 1980-2020 have been received from the Meteorological Organization and after sorting and extracting statistics by Mann-Kendall test have been analyzed. The results of the analyzes show that the monthly precipitation does not have a significant trend according to the Kendall I method. Also, the nature of Khoy precipitation indicates that the first day of precipitation, the number of precipitation days per year, annual precipitation and annual snowfall have a significant decreasing trend. Monthly snow does not have a significant trend. From seasonal precipitation, winter precipitation has a significant decreasing trend. In other seasons, time series do not have a significant trend. In general, it can be concluded that the precipitation characteristics at this station do not follow a specific trend.

Abstract The purpose of this research is to investigate the temporal and spatial changes of the low-level cloud cover in Iran. For this purpose, ECMWF ERA5 data with a spatial resolution of 0.25*0.25 was used for the period of 1963-2022. Matlab software was used for statistical and graphical operations. By fitting the linear regression model to the parametric method, the trend of low-level cloud cover in the annual atmosphere was investigated, then the characteristics of the low-level cloud cover of the Iranian atmosphere were investigated using analogs. In the analysis of spatial factors with cloud cover, the role of latitude is more dominant than the influence of other spatial factors The results obtained from the trend analysis showed that about 99.7% of the country has a negative annual trend with a decrease of -0.003% of cloud cover per year. And only 0.2% of the country has positive rainfall and increasing cloud cover per year. The results of the analysis of the cycles, which were not evident in other studies, indicate different cycles during the statistical period, some of these cycles can be attributed to local factors, and others to the influence of macro-scale atmospheric systems and neighbors, and sometimes a combination of the two. It has caused diversity in creating different cycles of cloud cover (low level) in one place

Abstract Temperature changes are of particular importance as one of the symbols of climate change.The use of statistical methods in describing changes is a useful tool. One of the applications of statistics in climatology is modeling the behavior of climatic elements. One of the most widely used statistical models is ARIMA family models. In this family, the values are modeled based on their past behavior from the statistical models, and are projected into the future. In this research, using the annual temperature data of Sanandaj station during the period of 60 years (1961-2020), the general behavior of temperature in this station was investigated. And also using MATLAB software to fit the appropriate model from the polynomial family and ARIMA modeling. The result of modeling the family of polynomials indicates a quadratic trend of the species in Sanandaj temperature. On the other hand, in the family of the ARIMA model, after checking the AIC value, the ARIMA model (2,2,1) which was relatively better than the other models It was determined as a suitable model for predicting the annual temperature of Sanandaj station.

Abstract The zoning of sub-regions and the recognition of the most important factors and elements affecting each area is one of the ways of recognizing the climate identity of the micro-climatic regions. Climatic zoning was carried out with the help of statistical methods such as factor analysis and clustering to obtain an accurate and comprehensive understanding of the central regions of Iran. To this end, 15 climatic variables were selected from 15 central synoptic stations. The results of the study by factor analysis showed that the climate of the studied area is affected by four factors, which are important, namely rainfall, temperature, humidity and thunderstorm. Among all extracted climatic factors, rainfall factor with explanation of 35.08% of total data variance has the most important role in determining the micro-climatic region conditions in the area. In total, these four factors have explained about of climate behavior in central Iran. The results of cluster analysis revealed four micro-climatic regions in the study area on the four climatic factors.

Abstract Abstract

Heavy rainfall leads to floods, among other natural disasters, which is also considered as a climate hazard. In this research, it has been tried to show the connection and continuity of the atmospheric components in the entire vertical column of the atmosphere with the synoptic-dynamic interpretation of these precipitations.

Therefore, after obtaining the data of geopotential height (in meters), orbital and meridional wind components (meters per second), divergence (s-1), specific humidity (g/kg) and humidity (s-1) from the study center In the mid-term of Europe (in the spatial range of 0 to 80 degrees east longitude and 10 to 70 degrees north latitude), the influence of the synoptic pattern of the atmosphere on the dynamics, continuity and connection of these components is displayed using IDV software.

The results of the drawing and analysis of the atmospheric components affecting the precipitation show that in the heavy rain wave from 25 to 30 February 2015, the strong meridian pattern with a level height of 500 hpa and the activity of the STJ cyclonic circulation in the region were effective factors in intensifying the instability of the atmosphere. This pattern, by strengthening the horizontal convergence of the atmosphere and intensifying the convergence of inward flow of moisture from several sources such as the Mediterranean, the Red Sea, the Persian Gulf, the Sea of Oman, etc., has led to the occurrence of heavy and widespread rainfall in 68 synoptic stations in Iran

Abstract The tourism industry is influenced by many sectors due to its multidimensional nature. The most important factors in effective development and stability of tourism are considered to be climate factors especially temperature. The present study aims at probing the variations in the mean temperature and two factors of minimum and maximum temperatures in the north-western highlands of Iran as well as their impacts on tourism. In this study, an approach based on applications is taken by employing a descriptive-analytical method. The statistical data were provided by the Iranian national meteorological organization (IRIMO). The obtained data were analyzed by linear regression method using MATLAB. The mean temperature and variation maps were obtained using Surfer software which exhibit the meaningful variations by color. The final results indicate mean temperature variations as well as trends in two factors of maximum and minimum temperatures all over the studied region. The highest positive variations were found during June, July and August. Moreover, an increasing trend in some areas with meaningful variations were found in January, February and March. These variations show the extended warm tourist season and shortened cold tourist season in the region. Generally, a uniform pattern of meaningful trends was not found in the area. It seems that the temperature trends in uplands and low-lying lands follow different rates and this region shows a different pattern of temperature trends due to its general temperature and environmental conditions such as topography.

Abstract In order to reveal climate changes from a statistical view, parametric and non-parametric statistical tests are used. Among these methods, homogeneity tests, trend analysis, Arima methods and spectral analysis in time series of climate elements including temperature can be mentioned. Natural and human factors are involved in the creation of heterogeneity, which can be investigated through various statistical tests to find out the heterogeneity of information and discover its causes. The results of these tests are not always the same and each one has special features. n this research, after quality control of the data and doing homogeneity tests and trend detection by using MATLAB, fitting and forecasting of Annual and Monthly average temperature of Jolfa and Sarab stations during the statistical period 1986-2018 done by using ARIMA and SARIMA patterns in MINITAB. For studying stationary of Model, Autocorrelation and Partial Autocorrelation Functions was applied and considering model evaluation criteria, finally, ARIMA (0,1,1) and SARIMA (0,0,1) (1,2,3)12 for Jolfa station and ARIMA (1,1,0) SARIMA (0,0,2) (1,2,3)12 for Sarab station was identified as a suitable model for predicting the average annual and monthly temperature.