Association between three prominent climatic teleconnections and precipitation in Iran using wavelet coherence. (6th September 2016)

• Record Type: Journal Article
• Authors: Araghi, Alireza; Mousavi‐Baygi, Mohammad; Adamowski, Jan; Martinez, Christopher
• Is Part Of: International journal of climatology. Volume 37:Number 6(2017)
• Page Start: 2809
• Page End: 2830
• Publication Date: 2016-09-06
• Subjects: Arctic Oscillation -- North Atlantic Oscillation -- Southern Oscillation Index -- Precipitation -- Wavelet coherence -- Iran; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.4881
• Languages: English
• ISSNs: 0899-8418

Abstract

ABSTRACT: Large‐scale climatic teleconnections have noticeable effects on meteorological events in different regions of the world. In this study, the linkages between three major climatic indices, Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Southern Oscillation Index (SOI), and precipitation in Iran were assessed from 1960 to 2014, at 30 synoptic stations in a time‐frequency space, using wavelet coherence (WCO). The results showed that the SOI is the most effective climatic teleconnection on precipitation in Iran, although the other studied climatic indices have noticeable effects as well. The predominant and effective period of AO on precipitation was equal to or greater than 32 months at most of the stations, while the major effective period of NAO was equal to or greater than 64 months. For the SOI, most parts of the country were affected by a period of less than 64 months, while the predominant period of SOI for the northwestern part of the country was greater than 64 months. A uniform phase difference was not observed between the three studied climatic indices and precipitation in the country; instead the phase differences were usually random. For long‐term periods of SOI, an anti‐phase situation was detected at most of the stations. The study suggested that the WCO is a very powerful and flexible method for studying the relationship between multiple time series in a time–frequency space, and its application in hydrological and meteorological research is expected to increase in the near future.