عنوان مقاله [English]
نویسندگان [English]چکیده [English]
The aim of the present study is identifying the relationship between the tele connection patterns with the frequency variability of Iran’s pervasive frosts in three scales namely, monthly, seasonal and annual. To this aim, two data sets were applied for analyzing the correlation between tele connection patterns and monthly, seasonal and annual variations of Iran’s pervasive frost days: first, the minimal daily temperature data of 663 Iranian climatology and synoptic stations during 1962-2004 for October and April months acquired from Iranian Meteorological Organization, and second, tele connection indices extracted from two databases in National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and Climate Prediction Center, a subsidiary of US National Oceanic and Atmospheric Administration. In the next step, pervasive frosts (the days with temperature equal to or less than 0 ̊ C) were counted for each month, season, and year based on a spatial principle which defines such frosts as occurring for 65% or larger percentage of Iran’s surface area. Subsequently, multivariate regression models were used for extracting and determining the most significant tele connection indices and patterns affecting on variability of Iran’s pervasive frosts. Results reveals the fact that frequency of pervasive frost days in winter and also in annual scale showed statistically significant correlation only with east Atlantic pattern; the variables were proved to be inversely related. December showed significant correlation with sum of three indices namely east Atlantic (EA) pattern, north fluctuation index (NOI), and tropical/north hemisphere (THN) patterns. January was only related to east Pacific/west Pacific (EP-NP) and February was correlated with Scandinavian (SCA) and polar or Antarctic oscillation (AO) patterns. Finally, monthly averages of sea level pressure patterns in the positive and negative phases of the indices under study were analyzed for December, January, and February.