عنوان مقاله [English]
Over the recent decades, human knowledge on earth's climate and his concern about climate change in the future has increased, which has contributed to precise identification of the factors influencing earth's climate. One of the climate phenomena, the change of which causes great anomalies of climate, particularly on temperature and precipitation patterns in many parts of the world, is teleconnection, and it is very important to reveal the relationship between them and climatic parameters for a better understanding of volatility and climate variability in every area. In this study, the relationship between large-scale and well-known patterns such as the Southern Oscillation Index (SOI), North Atlantic oscillation (NAO), Pacific North America (PNA), Multivariate ENSO Index (MEI) and (PDO) with 9 temperature and precipitation variables in the Karaj synoptic station in a monthly basis were analyzed over the 26-year period (2010-1985). First, the normality of the data series based on the Kolmogorov - Smirnov was confirmed. In order to examine the relationship between large-scale patterns with temperature and precipitation variables, the Pearson correlation coefficient was used. The correlations were assessed on a monthly basis without delay and a delay of one month. The results showed that there is a relationship between the NAO index and the temperature and precipitation variables mostly in autumn and winter months and the impact on the autumn months is higher than the winter months. SOI index is more related with the precipitation variables; this index was clearly shown to play a greater role in autumn and winter months, while the MEI index shows a higher correlation with the temperature variables and not particular relation was shown with precipitation variables for this index. Role and impact of this index, in particular on temperature parameters of April and May in spring and December in late autumn, is stronger. The relationship of PDO index with temperature and precipitation variables in May is observed more in the middle of spring. PNA index is effective only on temperature variables, showing higher relationship in December and February. The obtained results are important for greater understanding of the temperature and precipitation variability.