Investigation of the relationship of NINO4 and NAO indices with meteorological drought in synoptic stations in northern Iran

Water shortages in a defined area and in a certain period are called drought. This phenomenon is time dependent rather than aridity, and it is quitely temporary. drought in various regions of Iran in arid, semi-arid and even humid areas shows different forms and causes great damage. Drought is calculated using indices such as precipitation and evapotranspiration. The teleconnection is also a new branch of synoptic climatology, which recognizes the relationships between phenomena and climatic elements such as rainfall, drought, and temperature in remote locations. In other words, teleconnection patterns are the occurrence and continuation of large scale models of circulation abnormalities and air pressure that extend over a wide geographical range. These patterns are also oscillatory behavior of low frequencies. Teleconnection mechanisms are one of the important issues in justifying the behavior of the climate, and its effects do not always appear in the same form everywhere (Khosravi, 2004). In worldwide, many studies have been done on climate signals in variety ways. Iran is also affected by rainfall systems, which affects the pattern of rainfall and drought in it.

The purpose of this study was to investigate the relationship between NINO4 and NAO indices with meteorological droughts in synoptic stations in the southern part of the Caspian Sea. For this purpose, data of rainfall were obtained monthly from the Meteorological Organization of Iran. Then, SPI was calculated for different time scales of months including 1, 3, 6, 9, 12, 15, 18, 24, 48. The next step was to obtain the values of the Ocean Atmospheric indices, including NINO4 and NAO, at the monthly scale from the Australian Meteorological Organization. For analysis, the MINITAB16, SPSS16 was used. In this case, the simultaneous and asynchronous relationship between the time series of SPI and the NAO and NINO4 indices was determined. In this stage, Spearman's correlation test and cross-correlation function were determined. In the next step, regression modeling was done. In this way, regression models for the time series of SPI were calculated taking into the simultaneous and asynchronous state of the NAO and NINO4 indices. The lag times were 1-6, 12, 24, 48 months. Finally, errors for regression models were determined at all stations to select the best model.

The results showed relationships between NAO with SPI different series at Ardabil and Babolsar stations in January, April, August and December; the NINO4 with SPI at Ardebil station in all months except October and December. Also, the results of regression equations showed that in SPI time series of NINO4 with the highest coefficient, the highest and NAO with lowest coefficient, had the lowest effect on drought in studied areas. In this case, the Ardabil has the highest coefficient in SPI24 months. At the end, the error statistics including RMSE, R, R2, CE were calculated for model reliability and calculation of errors.

In this case, the best model was found at the Gorgan station by calculating the error statistics. The result suggests that these stations are neighbor to the Caspian Sea, which has affected the rainfall and drought pattern of the regions. This can also be the result of the universality of the Enso phenomenon that affects the world's climate. In order to obtain better results, it is recommended to study the precipitation and temperature pattern of these areas with other Ocean Atmospheric indices such as SOI and AO. Regarding the fact that the distant teleconnection indices do not occur in all directions in one way, it is suggested to study rainfall and temperature pattern of other parts of Iran in relation to climate signals. This study is also in line with another study by researchers who through statistical and synoptic methods have investigated the relationship between climatic factors such as rainfall and temperature with climatic signals. Study of other researchers determine that there is a significant relationship between the annual rainfall of Iran and the South Oscillation Index. temperature pattern of these areas with other Ocean Atmospheric indices such as SOI and AO. Regarding the fact that the distant teleconnection indices do not occur in all directions in one way, it is suggested to study rainfall and temperature pattern of other parts of Iran in relation to climate signals. This study is also in line with another study by researchers who through statistical and synoptic methods have investigated the relationship between climatic factors such as rainfall and temperature with climatic signals. Study of other researchers determine that there is a significant relationship between the annual rainfall of Iran and the South Oscillation Index.