Exploring Teleconnection-Drought Relationship in Iran Through Dynamic Conditional Correlation and Cluster Analysis

Abstract

An increase in drought frequency and intensity in most parts of the world is a threat to lives and property. Understanding the underlying climatic drivers of drought occurrence and variability is therefore vital for developing effective early warning systems. Large-scale climate variability patterns, commonly referred to as teleconnections, exert significant influence on regional precipitation and drought dynamics. This study explores the relationship between major teleconnections: Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), and Multivariate El Ni & ntilde;o-Southern Oscillation Index (MEI), and drought in Iran by applying the dynamical conditional correlation (DCC) approach together with cluster analysis to capture regional differences. Monthly precipitation data from 1993 to 2016, sourced from 106 meteorological stations, are used to calculate the standardised precipitation index (SPI) with 1-, 3-, 6-, 9-, and 12-month moving averages. The DCC between SPIs and three teleconnections is then analysed and clustered using the Ward's method. Results demonstrate that the MEI and SOI exhibit a strong correlation with the SPIs, while NAO shows an insignificant association with drought patterns in the region. The influence of teleconnections on SPIs exhibited correlations reaching up to +/- 0.6, reflecting the coherence and density of SPI patterns and the distinct spatial clustering of meteorological stations, with this range varying notably based on terrain complexity and elevation. The strength of teleconnection-SPI relationships appears to be modulated by topographic features, time lag, and shocks, which likely play a crucial role in shaping precipitation dynamics and the spatial distribution of droughts in Iran. The findings underscore the importance of incorporating terrain and elevation when analysing large-scale climatic patterns and their influence on regional climate variability for improved accuracy of weather forecasts of extreme events.