Unravelling the teleconnections between ENSO and dry/wet conditions over India using nonlinear Granger causality

The large scale climatic circulation processes such as El Nino Southern Oscillation (ENSO) affect the climatic anomalies throughout the world. Therefore, understanding the teleconnections of ENSO with the hydrometeorological phenomenon, such as floods and droughts, has been a key research direction for hydro-climatologists in the recent few decades. Droughts over most parts of the world have been previously reported to be influenced by the ENSO. Since India is one of the most drought-prone countries, therefore, a better understanding of these teleconnections would help immensely in better management of drought disasters. For the quantification of causal teleconnection between climatic indices and drought indices, the impact of nonlinearities on causalities has not been addressed well in the literature. Therefore, in this study, we present a nonlinear neural network-based Granger causality (NGCT) approach for the quantification of causal teleconnections between ENSO and droughts. The analysis of teleconnections between ENSO and dry/wet conditions over India has been presented using four climatic indices and two drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI)) at four time-scales. The results of the NGCT were also compared with the traditional Granger causality test (LGCT) to elucidate the potential of nonlinear approaches in teleconnection analysis. Results suggest great potential of NGCT for the examination of the teleconnection of ENSO and Indian dry/wet conditions. The area under significant causality was found significantly higher for a nonlinear approach as compared to the traditional LGCT. Further, the impact of ENSO on evapotranspiration-based (i.e., computed using SPEI) drought was found more than precipitation-based drought (i.e., computed using SPI).