Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach

• Main Authors: Kurths, Jürgen, Agarwal, Ankit, Shukla, Roopam, Marwan, Norbert, Rathinasamy, Maheswaran, Caesar, Levke, Krishnan, Raghavan, Merz, Bruno
• Format: Article in Journal/Newspaper
• Language: English
• Published: Katlenburg-Lindau : European Geophysical Society 2019
• Subjects: Atlantic Multidecadal Oscillation; El Nino-Southern Oscillation; Indian Ocean Dipole; monsoon; North Atlantic Oscillation; Pacific Decadal Oscillation; precipitation (climatology); spatial analysis; teleconnection; weather forecasting; India; 550; Indian; Pacific; North Atlantic; North Atlantic oscillation
• Online Access: https://oa.tib.eu/renate/handle/123456789/7539; https://doi.org/10.34657/6586

A better understanding of precipitation dynamics in the Indian subcontinent is required since India's society depends heavily on reliable monsoon forecasts. We introduce a non-linear, multiscale approach, based on wavelets and event synchronization, for unravelling teleconnection influences on precipitation. We consider those climate patterns with the highest relevance for Indian precipitation. Our results suggest significant influences which are not well captured by only the wavelet coherence analysis, the state-of-the-art method in understanding linkages at multiple timescales. We find substantial variation across India and across timescales. In particular, El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) mainly influence precipitation in the south-east at interannual and decadal scales, respectively, whereas the North Atlantic Oscillation (NAO) has a strong connection to precipitation, particularly in the northern regions. The effect of the Pacific Decadal Oscillation (PDO) stretches across the whole country, whereas the Atlantic Multidecadal Oscillation (AMO) influences precipitation particularly in the central arid and semi-arid regions. The proposed method provides a powerful approach for capturing the dynamics of precipitation and, hence, helps improve precipitation forecasting. © 2019 Author(s). publishedVersion