Investigating the Relationship Between the Extreme Values of Temperature Parameters and Teleconnection Indexes (Case Study: Chaharmahal and Bakhtiari Province)

Background and Objectives: Teleconnection patterns can affect climate parameters in different regions of the world. Investigating their effects, especially on the climatic extreme values, can provide appropriate information for optimal management of weather processes as well as terrestrial ecosystems, including vegetation. Temperature is one of the important elements of the atmosphere and is involved in climate changes on a regional and global scale, which quantitatively leads to tremendous events affecting the environment, including increased evaporation and transpiration and plant water needs, increased frequency of droughts, and lack of water resources. The effects of extreme events are often large at the local scale and can severely affect certain sectors and regions. The ever-increasing emission of greenhouse gases will cause global warming and cause damages and losses to the water, agriculture, environment, and economy sectors and will have a significant impact on the lives of humans and animals. One of the most important effects of this phenomenon will be the influence on extreme events of atmospheric and climatic elements such as drought, flood, and storm. On the other hand, atmospheric circulations are very variable. These changes lead to the emergence of air patterns and forms of atmospheric currents that occur in different time scales. The teleconnection patterns indicate large-scale changes that occur in the pattern of atmospheric waves and winds and affect the temperature pattern, precipitation, direction of clouds, and position and intensity of the winds in many regions. The effectiveness of these indicators has attracted the attention of researchers in various parts of the world, indicating the importance of these interactions on the atmosphere and surrounding environment. The purpose of present research was to investigate the effect of teleconnection patterns on temperature extreme values ​​(minimum and maximum) in Chaharmahal and Bakhtiari province.
Materials and Methods: Chaharmahal and Bakhtiari province is one of the mountainous parts of the central plateau of Iran, which has an area of ​​16421 km² and is one of the mountainous and rainy provinces of the country, which are the source of two important rivers of the country, Zayandehroud and Karun. This province has one percent of the total area of ​​Iran, which is located in the Zagros-mountain range and in the path of the humid winds of the Mediterranean systems and causes the rise and discharge of these systems. This province has relatively good rainfall. In this research, 8 synoptic meteorological stations of the province in the statistical period of 30 years (1368-1398) were considered. Ten teleconnection indexes were used during this period to investigate their effect on climatic extreme values ​​using Pearson's correlation coefficient.
Results: The changes in average minimum temperature shows that Kohrang station has the coldest minimum temperature in the studied area and annual average fluctuation of this variable has changed from -4.71 to -15.61 degrees Celsius during the studied period. On the other hand, Lordegan station has a higher average minimum temperature than other stations and the range of changes of this variable is from -0.16 to -5.8 degrees Celsius. Lordegan station has the highest values ​​of extreme maximum temperature. The range of changes of this variable fluctuated from 32 to 38.5 degrees Celsius, while the range of these changes for Kohrang station was recorded from 28.7 to 32.2 degrees Celsius during the same period. Based on the results in EGheis and Avergan stations, the average maximum temperature correlation with TNH, NINO3, NINO12, NINO4 and PNA indices was significant at 1% level and with EA/WA, NP and NAO indices at 5% level and had no special relationship with AAO and SOI indices. Among these indices, the highest correlation of this variable with TNH index was 0.728 for EGheis station and 0.71 for Avergan station. At PoleZamankhan and Shahrekord stations, the average minimum temperature correlation was significant at 1% level with TNH, NINO12, and NINO4 indices and with NP, PNA, NAO, and SOI (only for PoleZamankhan station) at 5% significance level. There was no significant correlation with NINO3, AAO and SOI (only for Shahrekord station) and EA/WA indices (only for Kohrang station). The average minimum temperature was significant with TNH, NINO3, NINO12 and NINO4 indices at 1% level and with the EA/WA, PNA, NP and NAO indices at 5% level, and there was no significant correlation with the AAO and SOI indices. The highest correlation coefficient was related to TNH index (0.724) in this station. In Shahrekord and Brojen stations, the average maximum temperature correlation was significant with TNH, NINO3, NINO12, NINO4 and NP indices at 1% level and with EA/WA, PNA and NAO indices at 5% level and there was no significant correlation with the AAO and SOI indices. In PoleZamankhan station, the average maximum temperature correlation was significant with EA/WA, TNH, NINO3, NINO12, NINO4, NAO and PNA indices at 1% level and with NP index at 5% level, and there was no special relationship with the AAO and SOI indices. Results showed that correlation of TNH index with the average maximum temperature of the stations had the highest positive value compared to other indices. The NINO12 index was in the next rank, but its correlation was negative, and it is understood that the occurrence of this index has caused maximum temperature in these stations to decrease. Also, AAO and SOI indices have no significant correlation with this variable in the stations. Also, results reveal that correlation of TNH index with average minimum temperature of the stations had the highest positive value compared to other indices, and the lowest correlation was with Lordegan station and the highest was with Kohrang station. The NINO12 index was in the next rank; but its correlation was negative, and it is understood that the occurrence of this index caused the temperature to decrease at least in these stations. The NINO4 index is also in the third place, with the difference that, like the TNH index, it has a positive correlation in the minimum temperature. The AAO and SOI indices have mainly no significant correlation with this variable in the stations. In Lordegan and Avergan stations, the average minimum temperature correlation was significant with TNH, NINO3, NINO12, NINO4, NAO and PNA indices at 1% level and with EA/WA and NP, NINO3 and NAO indices at 5% level and did not have a significant correlation with AAO and SOI. The highest correlation coefficient related to TNH index was 0.726 and 0.702 for Avergan and Lordegan stations.
Conclusion: In this research, Pearson's correlation was used in 8 meteorological stations in Chaharmahal and Bakhtiari province to investigate the effect of teleconnection indices on extreme maximum and minimum temperature values. In general, it can be said that the teleconnection indices have an effect on the extreme temperature values ​​of the studied area (some have an increasing effect and some have a decreasing effect on these variables). These findings can be used in predicting these extreme values ​​and preventing possible damages to agriculture sector and vegetation. Also, considering the role of temperature values ​​on evaporation and water needs of plants, the results can be used in drought crisis management.