On the connection between large-scale climate indices and rainfall variability in Iraq

Abstract

This study investigates the teleconnection between climate indices and rainfall variability in Iraq to identify the factors influencing rainfall variability. The correlation between seven climate indices and rainfall variability across eight Iraqi cities was analyzed for the period 1951–2020, with a focus on January, the month with the highest amount of rainfall for most cities in the country. Bivariate wavelet coherence (WTC) and improved partial wavelet coherence (IPWC) methods were adopted for the analysis, and the significance of the correlations was quantified by the percentage of significant coherence (PoSC). The study aimed to determine whether specific climate indices have major connection with rainfall variability in Iraq, and whether this connection is identified through integration with other indices (i.e. using WTC), or by removing the mutual dependence of these climate indices (i.e. using IPWC). Results indicated that IPWC generally yielded a higher PoSC than WTC. The highest PoSC for the IPWC was achieved not by eliminating all climate indices but by selectively removing certain indices while retaining others. For instance, each of the three indices (PDO, AMO, DMI) produced the highest PoSC by removing four climate indices and keeping both the SOI and the NAO. In addition, the correlation between the reconstructed rainfall and the seven climate indices on different frequency bands explains and confirms the results of deleting some indices and keeping others to gain the greatest revelation on rainfall variability since no single dominant index can fully explain such rainfall variation. In addition, the combined NAO & SOI indices found to be the main connection with rainfall variability over Iraq, especially when this combination is linked to any SST indices. However, the second driver of rainfall variability over Iraq was revealed by the combined WeMO & SOI indices when they are linked to any climate indices. The above findings were found to be helpful and improved the accuracy of rainfall prediction. This study on searching for the drivers that affect the rainfall variation through multiple Large-Scale Climate Oscillation (LSCO) indices is the first in Iraq, and it has importance for other studies such as rainfall prediction, flooding analysis, and flooding mitigation.