Investigating the relationships of Tel-connection on temperature and precipitation parameters in Abarkoh-Sirjan basin

The climatic conditions of a region are determined by the frequency and cumulative effects of weather systems passing through that area. The recurrence, change or continuity of weather systems in any given location is of paramount importance in determining and identifying climate. The continuity and change of systems are determined by the process of classifying or determining atmospheric circulation patterns and meteorological types, and for this reason the classification of weather systems is one of the main objectives of synoptic climatology. Ocean-atmosphere interactions are one of the most important discussions in marine meteorology, which has found a wide place in scientific research in the field of meteorology and oceanography. Teleconnection patterns are defined as the simultaneous relationships between fluctuations in climatic elements of a location and changes in atmospheric pressure and sea surface temperature in distant geographical areas. Recent research has focused on explaining climate behavior through the mechanisms of teleconnection patterns. As a global climate feature, teleconnection patterns represent large-scale shifts in atmospheric wave patterns and jet streams, influencing temperature, precipitation, storm tracks, and jet stream positions and intensities across vast regions. Given the significance of teleconnection patterns and their connection to climatic parameters, particularly temperature and precipitation in Iran, this study investigated the monthly temperature and precipitation data from four stations within the AbarkOuh-Sirjan basin, analyzing their relationships with six indices.

In this study, monthly precipitation and average temperature data were used for four stations in the Abarkouh-Sirjan basin (Sirjan, Shahrbabak, Abarkouh and Marvast) during the statistical period of 2003-2022. Data from six teleconnection patterns (IOD, NINO 3+4, NINO1+2, NINO4, NAO and AO) were also used to investigate the effects of teleconnection patterns on changes in the climate data of the study area. The Pearson correlation test was used to evaluate the relationship between the investigated parameters. After determining the correlation of the variables, a multivariate regression was performed to gain a better insight into the effects of the teleconnection patterns on the temperature and precipitation of the stations.

When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns, in January the NINO4 pattern, in March the NINO4, NAO and AO patterns, in April the NINO3.4 index in April, NINO4 in May, NINO3.4, NINO1.2 and NINO4 in June, NINO1.2, NINO3.4, NINO4 in July, NAO in October, IOD in November and NINO3.4 in December showed significant correlations. There was no significant correlation in February, August and September. When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a time lag of three months, the index NINO1.2 in January, NINO4 in March, NINO3.4 and NINO1.2 in April and May, IOD, NINO3.4 and NINO4 in July, NINO4 in August, IOD and NINO1.2 in October showed significant correlations. In February, June, September and November, there was no significant correlation between the index and the temperature. The correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a 6-month time lag showed significant correlations in different months: NINO1.2 in January, NAO in February, NINO1.2 in March and April, NINO3+4 and NINO4 in April, NINO3+4 and NINO4 in May, NINO1.2, NINO3+4, NINO4 in June, IOD in July, NINO1.2 in August, IOD and NINO4 in October and November, and NINO3+4 in December.

When looking at the correlation coefficient between monthly temperatures at different stations and various teleconnection patterns, the NINO3.4 index in the Abarkouh and Marvast stations and the NINO4 index in the Marvast station had the highest correlation at the 99% level. With a 3-month lag between temperature and teleconnection patterns in the Marvast station, the NINO3.4 index in May was significant at the 99% level. With a 6-month lag between temperature and teleconnection patterns, in Sirjan the IOD index, in Shahrbabak and Abarkuh the IOD and NINO1.2 indices had the highest correlation. In the Marvast station, the NAO, IOD, and NINO1.2 indices had the highest correlation with temperature. In the Shahrbabak station, the AO and NINO3.4 indices were significant at the 99% level. In the Abarkouh station, the IOD index had the highest correlation. In the Marvast station, the NAO and NINO4 indices were significant at the 99% level. In examining multivariate regression of temperature with teleconnection patterns, the Marvast station showed the highest correlation in June, with 80% of temperature changes in this station explained by teleconnection patterns. For the regression relationship of precipitation with teleconnection patterns, the highest correlation was identified in the Abarkouh station in July, with 71% of precipitation changes explained by teleconnection patterns. According to the step-by-step regression results, the NINO3.4 pattern in the three stations of Shahrbabak, Abarkouh, and Marvast was identified as the most effective pattern in explaining precipitation changes. In the Sirjan station, none of the indices were identified as an effective pattern for precipitation. In the Sirjan and Shahrbabak stations, the NINO3.4 pattern, and in the Abarkouh and Marvast stations, the NAO pattern were identified as the effective pattern for temperature.