اثر همزمانی شاخص‌های پیوند از دور NAO و AMO بر تغییر‌پذیری دما و بارش شهرستان‌های مجاور سبلان

Abstract
Coordination between the temporal changes of two distant points, which is mainly used for pressure changes, is called remote connection. The purpose of this study is to investigate the variability of temperature and precipitation parameters of Ardabil, Meshginshahr, Sarab and Harris stations from two remote connection models of North Atlantic Oscillation Index and Pacific Decades Index using Pearson torque correlation method and synoptic analysis. For this purpose, data related to the two mentioned indicators were prepared from the Nova site for the period 2009-2018. Also, for this statistical period, data related to temperature and precipitation parameters were obtained from Ardabil Meteorological Department. Then, after standardization of temperature and precipitation, using Pearson correlation method for NAO index, it was found that the temperature of all stations in June, October and February precipitation of Ardabil station had the highest significant direct correlation with 99 and 95% confidence level with this index. The AMO index also showed a direct relationship with the January temperature of Harris station with only 5% probability of error. In order to investigate the effect of these patterns on the studied parameters of NETCDF data, monthly mean sea level pressure, geopotential level of 500 hPa, temperature and rainfall water were obtained from Nova site and synoptic maps were drawn in Gardes software environment. The results showed that when NAO index is in negative phase and AMO index is in hot phase, the dominant system in the studied areas is low thermal pressure and rotational conditions prevail and temperature and rainfall increase and wetting occurs, unlike when NAO index is in phase. The AMO index is positive in the cold phase, with the dominance of high-pressure systems and rotation conditions in the study areas, the temperature and rainfall have decreased and the drought conditions have been determined.

Introduction
Temporal variability is an inherent feature of climate. Experimental evidence has shown that many climatic processes are not stable over time. Variability refers to fluctuations or a pattern of fluctuations that occur at certain specific intermediate values. Cases of temporal variability of climate such as day-to-day, inter-seasonal, annual and spatial changes with horizontal gradients can be expressed. Changes in the functioning of the climate system over time are a description of the temporal changes of interest to climatologists. The causes of climate change are time-dependent, so they can be divided into external and internal forces. External forces are related to the natural variability of the climatic system, which is dependent on changes in solar radiation. But the second category of forces is due to the internal dynamics of the climate system. They cause a series of random fluctuations and form possible disordered behaviors. In the time scale of decades, the precipitation parameter is more sensitive to atmospheric circulation changes than the temperature parameter (Zolfaqari, 1390: 297). The term remote linking was introduced by Angstrom in 1935 in the context of patterns of climatic ups and downs; Berkenes then used it in 1969 to describe patterns of reaction to the surrounding surface force. In the 1990s, renewed attention to teleconnection patterns shifted beyond the pressure fluctuations described by Walker and the global anomalies. Their characteristics and global causes were searched using various spatial analysis methods and atlases of remote connection were published (Omidvar, 2012: 276). Two of the most important long-distance link indicators affecting the global climate, which are based in the Atlantic Ocean, are the NAO North Atlantic Oscillation Index and the AMO Atlantic Decade Index. North Atlantic Fluctuation is the predominant method of climate change in the North Atlantic Basin that is associated with changes in atmospheric pressure and winds. This index is described by atmospheric pressure changes associated with atmospheric mass metamorphic changes. The index also depends on changes in polar air masses near Iceland and subtropical air masses across the Atlantic from the Azores to the Iberian Peninsula. The signal for its emergence is highly regional and is obtained by calculating the difference between the anomalies of normalized pressure at sea level from December to March at the station in Lisbon, the capital of Portugal, and the station at Stikosholmor in Iceland (Dhu al-Faqari, 2014: 222). Another important and influential indicator of the global climate in the northern hemisphere is the multi-decade index of the Atlantic Ocean, which has two phases of hot and cold, and its calculation is based on changes in water temperature in the northern Atlantic (30 to 65 degrees north). This index was first identified and expressed by leaf research in 2001 as a result of small changes in the circulation of the Atlantic thermocline. This pattern has temperature fluctuations between 20 and 40 years in the Atlantic Ocean from the tropics to the Greenland glaciers (Omidvar, 1399: 110).
Methodology
The purpose of this study is to investigate the influence of temperature and precipitation parameters of synoptic stations near Sabalan Mountain (Ardabil, Meshginshahr, Sarab and Harris) from different phases of the North Atlantic Oscillation Index and the Decade of the Atlantic Index. For this purpose, monthly data of temperature and precipitation parameters of the mentioned stations for the statistical period of ten years (2009-2018) were obtained from Ardabil Meteorological Department and data related to NAO and AMO remote connection patterns index for the study period were prepared from Nova site. Due to the fact that the data of the mentioned indicators were recorded in a standardized way, it was necessary to standardize the temperature and precipitation parameter data in the SPSS software environment in order to show their relationships using Pearson torque correlation method. Thus, it was determined which months are more related to the mentioned patterns. In the next stage, the months and years of the statistical period were divided according to the different phases of the mentioned indicators and combined anomaly maps of sea level pressure with geopotential level of 500 millibars, surface temperature anomaly and rainwater anomaly from the surface to the upper atmosphere for them. Gardes was drawn in the software environment. To do this, monthly data on mean sea level pressure, geopotential elevation, temperature and rainwater were collected from the NCEP-NCAR site.

Conclusion
The purpose of this study is to determine the relationship between two important far-linking patterns of the Atlantic, namely NAO and AMO with the variability of temperature and precipitation parameters in Ardabil, Meshginshahr, Sarab and Harris stations. Thus, the results of Pearson correlation for temperature parameter showed that between different months of the year, June and October, all stations had a significant direct correlation with 99 and 95% confidence level with NAO index, but in December, Meshginshahr and Harris station had an inverse correlation with confidence percentage. It had 95% with this index. The AMO index also showed a direct correlation with the 95% confidence level only with the January temperature of Harris station. But the results for the precipitation parameter also showed that only the February precipitation of Ardabil station with the North Atlantic Oscillation index had a significant inverse correlation at the 95% confidence level. The results of synoptic analysis also showed that in periods when NAO index is in negative phase and AMO index is in hot phase, the dominant system in the study areas is low thermal pressure and rotational conditions prevail and temperature and rainfall increase and wetting occurs. The opposite is true when the NAO index is in the positive phase and the AMO index is in the cold phase. There are many contradictory results about the mentioned indices. However, the results of Pearson correlation regarding the precipitation parameter with the NAO index are associated with the results of Razmjoo et al. (1399). As they showed, the NAO index in a linear relationship is not able to explain the large share of variability in drought and new years in Iran. Regarding the temperature parameter, the result of Moradi (2004) research showed that when the index was in the positive phase, in winter, the temperature decreased in most parts of the country. According to this result, in this study, it was proved that with the rule of the positive phase period of NAO, the temperature has decreased.