Abstract 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).

中文翻译：

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使用非线性格兰杰因果关系解开 ENSO 与印度干/湿条件之间的遥相关

摘要 厄尔尼诺南方涛动（ENSO）等大规模气候环流过程影响着全球的气候异常。因此，了解ENSO与洪水和干旱等水文气象现象的遥相关性，一直是近几十年来水文气候学家的一个重点研究方向。据报道，世界大部分地区的干旱都受到 ENSO 的影响。由于印度是最容易发生干旱的国家之一，因此，更好地了解这些远程连接将极大地有助于更好地管理干旱灾害。为了量化气候指数和干旱指数之间的因果遥相关，文献中没有很好地解决非线性对因果关系的影响。因此，在本研究中，我们提出了一种基于非线性神经网络的格兰杰因果关系 (NGCT) 方法，用于量化 ENSO 和干旱之间的因果遥相关。使用四个气候指数和两个干旱指数（标准化降水指数 (SPI) 和标准化降水-蒸散指数 (SPEI)）在四个时间尺度上分析了 ENSO 与印度干/湿条件之间的遥相关关系。NGCT 的结果还与传统的格兰杰因果检验 (LGCT) 进行了比较，以阐明非线性方法在遥相关分析中的潜力。结果表明 NGCT 在检查 ENSO 和印度干/湿条件的遥相关性方面具有巨大潜力。与传统的 LGCT 相比，非线性方法的显着因果关系区域显着更高。