Bivariate trend assessment of dust storm frequency in relation to climate drivers

Abstract. Climate variability and change in arid regions are important factors controlling emission, frequency and movement of dust storms. This study provides robust statistical methods to detect trends in dust storm frequency across arid regions of Iran in relation to climate variability and trend in recent decades. The univariate trend assessment based on block bootstrapping method and three bivariate trend assessment methods, Covariance Inversion Test, Covariance Sum Test and The Covariance Eigenvalue Test are applied in this study to find if change in dust storm frequency can be attributed to changes in climatic variables. In this regard, the annual number of dust storms from 25 stations in central arid and semi-arid regions of Iran were selected. In addition, five major climatic variables including annual rainfall, annual maximum and average wind speed, annual maximum and average temperature were also collected. The univariate trend test indicates both increasing and decreasing trend in dust storm frequency and climate variables. The bivariate trend test shows a strong and statistically significant relationship between trend of climate variables and dust storm frequency for most of the stations across the region. Among climate variables, rainfall change has an inverse impact on dust storm frequency while wind speed and temperature have direct covariance structure with dust storm frequency. The wind speed also seems to be the most effective climate driver on dust storm frequency in arid regions of Iran, followed by temperature. The results also shows that local conditions that are not considered in this study may also play significant role in dust storm emission in some parts of the region.