Scholarlink

<p>Teleconnection patterns, as major large-scale climate signals, can influence dust sources, transport pathways, and the intensity of dusty days (DD). Understanding these relationships helps improve the identification of dust origins across different time intervals. The main objective of this study is to examine the influence of teleconnection patterns on the temporal and spatial frequency of dusty days in Iran. Using data from 44 synoptic stations (1968–2023), twelve teleconnection indices- including Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic/Western Russia pattern (EAWR), Pacific Decadal Oscillation (PDO), Mediterranean Oscillation (MO), etc.- were analyzed using correlation analysis, multiple regression, Path Analysis (PA), and Transfer Entropy Analysis (TEA). The results indicate that ENSO (Nino 1 + 2, Multivariate ENSO Index (MEI)), EAWR, and Mediterranean Oscillation Index (MOI) exert the strongest influences on dust activity, particularly in the southern and western regions during spring and summer. Northern Hemisphere indices such as AO and NAO generally show inverse relationships with DD frequency, whereas Southern Hemisphere signals tend to exhibit positive associations. The combined use of PA and TEA highlights both linear and nonlinear pathways of information transfer between climate indices and dust variability. Although teleconnections explain only 10–15% of the total variance, they provide meaningful insights into the large-scale climate regulation of dust events. These findings underscore the potential value of teleconnection monitoring for improving early-warning and seasonal forecasting systems in arid and semi-arid regions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated causal and information-theoretic analysis of teleconnection–dust relationships in Iran: identifying linear pathways and nonlinear dependencies

  • 期刊论文

  • 发表日期：

    2026年6月3日

查看全文
  • Yousef Ghavidel,
  • Parasto Baghbanan,
  • Manuchehr Farajzadeh

摘要

Teleconnection patterns, as major large-scale climate signals, can influence dust sources, transport pathways, and the intensity of dusty days (DD). Understanding these relationships helps improve the identification of dust origins across different time intervals. The main objective of this study is to examine the influence of teleconnection patterns on the temporal and spatial frequency of dusty days in Iran. Using data from 44 synoptic stations (1968–2023), twelve teleconnection indices- including Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic/Western Russia pattern (EAWR), Pacific Decadal Oscillation (PDO), Mediterranean Oscillation (MO), etc.- were analyzed using correlation analysis, multiple regression, Path Analysis (PA), and Transfer Entropy Analysis (TEA). The results indicate that ENSO (Nino 1 + 2, Multivariate ENSO Index (MEI)), EAWR, and Mediterranean Oscillation Index (MOI) exert the strongest influences on dust activity, particularly in the southern and western regions during spring and summer. Northern Hemisphere indices such as AO and NAO generally show inverse relationships with DD frequency, whereas Southern Hemisphere signals tend to exhibit positive associations. The combined use of PA and TEA highlights both linear and nonlinear pathways of information transfer between climate indices and dust variability. Although teleconnections explain only 10–15% of the total variance, they provide meaningful insights into the large-scale climate regulation of dust events. These findings underscore the potential value of teleconnection monitoring for improving early-warning and seasonal forecasting systems in arid and semi-arid regions.