تحلیل وردایی زمانی سیگنال های دورپیوند موثر بر اقلیم ایران

Temporal variations in local and global climate are often influenced by changes in teleconnection patterns. These patterns are key phenomena arising from fluctuations in variables such as sea surface temperature (SST) and sea level pressure (SLP), which exhibit spatio-temporal variability governed by stable and recurring atmospheric and oceanic processes. Consequently, teleconnection signals can induce oscillations in the climate system. Among the most influential indices are the East Atlantic-West Russia (EA-WR) pattern, the Mediterranean Oscillation (MO), the North Atlantic Oscillation (NAO), the Quasi-Biennial Oscillation (QBO), and the Southern Oscillation Index (SOI), all of which can significantly affect atmospheric variability across different regions of Iran.

Identifying the dominant variabilities within each climate index and examining the potential relationships among these signals is a critical first step in assessing their impact on climate variables. This is important because some indices may share common fluctuations within specific time scales (i.e., frequency bands), potentially producing similar effects on climatic variables such as precipitation, temperature, and pressure. By accounting for these shared fluctuations, the net influence of each teleconnection index can be more accurately evaluated.

Given the importance of teleconnection indices and their role in shaping various climatic variables in Iran, a comprehensive understanding of the patterns embedded in these signals — across high-, medium-, and low-frequency oscillations — is essential. Moreover, elucidating the temporal relationships between pairs of global circulation indices is critical for understanding climate evolution at multiple frequencies, particularly in the context of Iran.

In this study, monthly data were obtained from relevant international archives, and the signals were decomposed into distinct frequency bands using optimal digital filters. Correlation analyses, including auto- and cross-correlation functions, were applied to examine the linear relationships between similar variabilities across the signals at different time lags. Additionally, power spectral density (PSD) analysis was used to compare the strength of each teleconnection signal within selected frequency bands.

The results reveal significant correlations among certain teleconnection signals at specific time scales. For example, at the annual time scale (10–14 months), the corresponding components of the EA-WR and NAO signals exhibit a strong direct correlation at zero lag (correlation coefficient: 0.564). The variance distribution across frequency bands is also distinctive for each index. Specifically, over 80% of the variability in the monthly QBO signal occurs quasi-cyclically on time scales of 14 months to 3 years, primarily around 28 months. For the EA-WR and NAO indices, a substantial portion of variability (45%–55%) occurs on the 2–5 month scale (high-frequency component), with limited correlation between these short-term fluctuations. High-frequency variations dominate the MO signal, whereas the SOI exhibits substantial variability across most time scales except for the annual scale. For low-frequency variability (time scales greater than 7 years), a significant negative correlation exists between SOI and the other teleconnection indices.