نوع مقاله : مقاله پژوهشی
موضوعات
عنوان مقاله English
نویسندگان English
Aim: Atmospheric teleconnections play an important role in the study of regional circulations and their effect on temperature or precipitation regimes. Therefore, the current study has investigated the relationship between Iran's 6 main watersheds' precipitation oscillation and the Mediterranean teleconnection indices (MTIs) to find a criterion for monitoring and predicting rainfall.
Materials & Methods: For this purpose, the 8th edition of GsMap rainfall data with a spatial resolution of 0.1* 0.1 has been used in Iran. Also, to calculate the MOI1, MOI2, WeMOI, and CACO values from the NCEP/NCAR reanalysis base sea level pressure data and the SaOI values from the ERA5 reanalysis base of the European Center for Medium-Range Weather Forecasts (ECMWF) in reference points during the years 2000 to 2023 has been used. Statistical relationships were estimated using the Pearson correlation test.
Findings: Examining the internal correlation of the MTIs showed that the MOI1 and MOI2 indices, with a correlation coefficient (CC) of 0.9, had the highest correlation; the CACO index, with a CC of 0.58 and 0.56, respectively, had the most significant direct relationship with MOI2 and MOI1. Among the indices, CACO, MOI1, and MOI2 have played the most important role in the rainfall oscillation of Iran. Also, the western watershed has the most significant inverse correlation with MTI values.
Conclusion: Most of the significant relationships of MTIs with the time series of rainfall in Iran's watersheds have been of the inverse correlation type. It means that the positive phase of MTIs is associated with a decrease in rainfall in most of the main catchment basins, and vice versa, their negative phase has increased Iran's rainfall.
Innovation: Spring rainfall in the western, central, and northeastern basins of Iran shows the highest correlation with the indices CACO, MOI1, and MOI2. But contrary to expectation, winter rainfall has not presented a reliable and significant relationship with the MTIs.
کلیدواژهها English
Extended Abstract
1. Introduction
Climatic indices are recognized as key tools in assessing and predicting weather conditions and climate change. These indices are significant in analyzing patterns of atmospheric circulation and their impacts on regional and global weather conditions. The North Atlantic Oscillation (NAO) and Southern Oscillation (SO) related to El Niño (ENSO) are among the teleconnection indices that have been widely studied. These indices can assist in predicting rainfall and temperature in various regions, especially in the Middle East and Iran. Climate change and weather fluctuations, especially in sensitive areas like the Middle East, pose serious challenges to water resources and agriculture. In this context, Iran, as a country with high climatic diversity, faces numerous climate-related crises. Variations in rainfall and temperature changes in this country can have profound impacts on agriculture, water resources, and ecosystems.
Given the significance of rainfall in water resource management and agriculture, a better understanding of these relationships can contribute to improved water resource management and agricultural planning. Literature reviews have shown that global climatic fluctuations, particularly in the oceans and atmosphere, have significant impacts on rainfall patterns in Iran. For example, fluctuations in the North Atlantic Ocean and Southern Oscillation directly influence rainfall and temperature in Iran. Also, Mediterranean teleconnection indices are recognized as tools for examining and predicting climatic fluctuations in the Middle East.
This study aims to investigate the relationship between Mediterranean atmospheric teleconnection indices and rainfall fluctuations in Iran's major watersheds. This examination could help identify rainfall patterns and provide better predictions for water resource management in the country.
2. Materials and Methods
This research was conducted in the Eastern Mediterranean and the Middle East regions. To investigate the relationship between Iran's watersheds' rainfall fluctuations and teleconnection indices, GsMap rainfall data with a spatial resolution of 0.1° by 0.1° was utilized over the period from 2001 to 2022. The indices used include the Mediterranean Oscillation (MOI1 and MOI2), Western Mediterranean Oscillation (WeMOI), Sahara Oscillation Index (SaOI), and Central African-Caspian Oscillation (CACO). These indices were calculated and standardized based on sea level pressure data.
Pearson correlation tests were employed to analyze the relationships between the indices and the rainfall series. Additionally, multiple linear regression analyses were conducted to examine the impact of teleconnection indices on rainfall fluctuations in six major watersheds of Iran.
Data was collected from reputable global databases (NCEP/NCAR & ECMWF) and analyzed using statistical software. These analyses included examining temporal correlations between rainfall and teleconnection indices, as well as analyzing rainfall trends in different seasons of the year.
3. Results and Discussion
The results indicate that MOI1 and MOI2 indices have the highest correlation (0.9) with each other. Additionally, the CACO index shows the highest correlation with MOI1 and MOI2 indices, with correlation coefficients of 0.58 and 0.56, respectively. The rainfall in Iran's watersheds, particularly, correlates inversely with the CACO index, such that positive phases of these indices are associated with decreased rainfall, while negative phases are linked to increased rainfall.
In spring, rainfall in the western, central, and northeastern watersheds of Iran shows the highest correlation with CACO, MOI1, and MOI2 indices. In summer, the MOI2 index has a positive correlation with rainfall in the Persian Gulf-Oman Sea watershed, while in autumn, only the CACO index has a significant relationship with rainfall in the northeastern, southern, and western Caspian watersheds.
These findings highlight the importance of teleconnection indices in predicting rainfall in Iran and emphasize that MTI indices can explain only about 30% of the country's rainfall variability. Therefore, future studies should seek to explore the relationships between regional indices and other climatic factors and discover new indices for explaining climatic changes in the country.
4. Conclusions
This study demonstrates that Mediterranean atmospheric teleconnection indices can serve as useful tools for predicting rainfall in Iran. The results of this research emphasize that MTI indices can explain only about 30% of the country's rainfall variability. Since rainfall is a crucial factor in water resource management and agriculture, a better understanding of the relationships between teleconnection indices and rainfall fluctuations can aid in improved water resource management and agricultural planning.
Furthermore, it is recommended that future research examine the impact of other climatic factors and new indices on rainfall fluctuations. This research could serve as a foundation for further studies on climate change and its impacts on water resources and agriculture in Iran.
5. Acknowledgment & Funding
The manuscript did not receive a grant from any organization.
6. Conflict of Interest
The authors declare no conflict of interest.
