The influence of Atlantic-Eurasian teleconnection patterns on temperature regimes in South Caspian Sea coastal areas: a study of Golestan Province, North Iran

Document Type : Original Research Paper


1 Department of Geography, Golestan University, Gorgan, Iran

2 Oman National Weather Service, Public Authority for Civil Aviation, Muscat, Oman



The main objective of this study was to reveal the impact of nine climate indices on temperature changes and climate oscillations in Golestan Province along the southern coast of the Caspian Sea. Climate indices data from across the Atlantic-Eurasian sector were collected from the NCEP/NCAR, the Climate Prediction Centre (CPC) and the Climatic Research Unit (CRU) over a period of 40 years (1971-2010). The climate indices are then compared and correlated with temperature observations from 47 weather stations collected from meteorological and energy organizations. The correlations are based on the 12-month moving average. The study results show a significant increasing temperature trend in most months over different regions of Golestan. For maximum temperature, a significant increasing trend was seen in 55.64, 41.8 and 40% of the land area in the province during August, June and July, respectively. In general, summer had the most significant maximum-temperature trends, with an average of 37.8% of the land area. On the other hand, increasing minimum-temperature trends were seen in 58% of the land area of the province compared to the other seasons. It was concluded that there is high correlation between climate indices and temperature components. The correlation coefficients obtained for various indices including North Atlantic Oscillation (NAO), North Sea Caspian Pattern (NCP), Arctic Oscillation Index (AO), East Atlantic (EA), East Atlantic/West Russia (EATL/WRUS), Atlantic Multi-decadal Oscillation (AMO), North Tropical Atlantic (NTA), Polar/Eurasia (PE), and Scandinavia teleconnection index (SCAND) suggest an inverse relationship between these indices and temperature components. Therefore, the higher the values of these indices, the lower the temperature values, and vice versa.