Identification of Indian monsoon predictors using climate network and density-based spatial clustering

Abstract

The Indian summer monsoon is a complex climatic phenomenon with a large variability over the years. The climatic predictors affecting the phenomenon evolve with time, and consequently new predictors have gained importance. Several statistical approaches are being explored in the literature to identify the potential predictors influencing the Indian summer monsoon. A complex network paradigm involving climatic variables at the grids over the globe has been proposed for predictor identification and monsoon prediction. The approach initiates with the identification of communities in the climate network considering mutual similarity and the influence of climate variables of grids on the Indian summer monsoon. Spatial clustering is performed over the communities to identify the geographical regions of significance. The climatic predictors extracted from variables of these regions are evaluated in terms of their correlation with the monsoon as well as their forecasting skills in predicting the summer monsoon of the country. The newly identified predictors forecast monsoon with an error of 4.2%, which is significant for the prediction of the complex phenomenon of monsoon.