Influence of large-scale climate signals on the precipitation variability over Iran.

This study investigates relationships between the twenty large-scale climate signals and the precipitation variability during 1960–2018 in Iran. The twenty large-scale climate indicators include atmosphere-ocean teleconnections as well as El Niño-Southern Oscillation (ENSO) signals, and Pacific and Atlantic ocean Sea Surface Temperature (SST). Wavelet Coherence Analysis (WCA) and Pearson correlation analysis were applied to detect the impact of climate signals on Iran's monthly precipitation and also the precipitation of one mountainous region in Iran as a verification example. The results revealed that (a) the significant wavelet coherence and the phase difference between monthly precipitation and climate signals were highly variable in time and periodicity, (b) WCA shows the signals that are leading and most linked to Iran's precipitation at inter-annual scales are Extreme Eastern Tropical Pacific SST(5 ∘ S-5 ∘ N, 170 ∘ -120 ∘ W) (Niño 4), Western Pacific Index (WP), Tropical Southern Atlantic Index (TSA) and Western Hemisphere Warm Pool (WHWP), respectively, and at decadal and inter-decadal scales are Arctic Oscillation (AO), Atlantic Meridional Mode (AMM), and Southern Oscillation Index (SOI), respectively, (c) The WCA results for mountains region precipitation are almost consistent with the results of the country precipitation for all signals except AMM, (d) Iran's precipitation has a statistically significant positive correlation with Niño signals, and a negative one with SOI and TSA based on Pearson correlation analysis, (e) in the most recent decade, the coherence between precipitation and large-scale climate signals has declined in decadal and inter-decadal scales, and an unstable coherence has emerged in the annual scale. [ABSTRACT FROM AUTHOR]