One of the effects of climate change is the possible increase in both frequency and intensity of extreme weather events. Extreme weather and climate events have a major impact on ecosystems and human society due to their severity and the fact that they often occur unexpectedly. In warmer climates and during transition seasons, cold extremes have agricultural impacts that are manifested in the damage of crops due to frost. The identification of teleconnections and the analysis of their impact on the atmospheric circulation can be very useful for the understanding of anomalous events at many regions of the planet when one assumes that local forcing may influence the atmosphere circulation at remote locations. Teleconnection patterns are simultaneous correlations in the fluctuations of large scale atmospheric parameters at points on the Earth that are wide apart. The effect of these patterns could be significant throughout the dominant modes of the atmospheric variability. Teleconnection patterns reflect large-scale changes in the atmospheric wave and jet stream patterns, and influence temperature intensity over vast areas. Thus, they are often the culprit responsible for abnormal weather patterns occurring simultaneously over seemingly vast distances. The objective on this study is to clarify whether the frequency of extreme cold temperatures occurrence in Iran during cold period have correlation with North Sea–Caspian pattern (NCP) and East Europe– Northeast Iran (ENEI) .
In order to study the relation between the monthly numbers of extreme cold temperature day number of Iran during cold period with North Sea–Caspian pattern (NCP) and East Europe– Northeast Iran (ENEI), temperature data of 663 synoptic and climatic stations during 1/1/1962 to 31/12/2004 has been used. Then temperature on 15×15 kilometer pixels by using Kriging method interpolated over Iran. A matrix that was 7853×7187 has been created that for this period (7853) located on the rows and pixels on the columns (7187). There is no single definition of what constitutes an extreme event. In defining an extreme event some factors that may be taken into consideration include its magnitude, which involves the notion of the exceeding a threshold. The most general and simple, and so more wide used method for defining an extreme event of temperature is based on the definition of frequency of occurrence of the event. In this study, at first the extreme cold days during cold period recognized with Fumiaki Index. Then for each month during cold period, the number of extreme cold temperature occurrence was calculated. Monthly data during cold period of North Sea–Caspian pattern (NCP) and East Europe– Northeast Iran pattern during study period extracted from NCEP/NCAR data site of United States National Oceanic and aAtmospheric Center. The correlation between the monthly numbers of extreme cold temperature days in Iran during the cold period with North Sea–Caspian pattern (NCP) and East Europe– Northeast Iran (ENEI) was calculated.
After extracting the number of extreme cold day’s occurrence for each month during the cold period of the year during the study period, the correlation was calculated with North Sea–Caspian pattern (NCP) and East Europe– Northeast Iran (ENEI). Also, the magnitude of explanation coefficient has been calculated. The map of correlation and explanation coefficient are showed in figures 2 to 6. There is a significant correlation between monthly numbers of extreme cold days during cold period with NCP and ENEI at the 95% confidence level. 
The results showed that there is a positive correlation between the monthly numbers of extreme cold temperature days in Iran during cold period with an North Sea–Caspian pattern. The positive phase results in increase of cold extreme days in western part of Iran. The positive phase of North Sea–Caspian pattern (NCP) accompany with positive anomaly of the 500 hPa geopotential height level in the North Sea and negative anomaly in Caspian Sea. This indicates in cold air advection towards Iran especially in the western parts. In January, the correlation for 95% of Iran area is significant and positive. The highest explained coefficient is observed for the west and northern part of Iran.