Wavelet analysis of precipitation extremes over India and teleconnections to climate indices

• Published in: Stochastic environmental research and risk assessment Vol. 33; no. 11-12; pp. 2053 - 2069
• Main Authors: Rathinasamy, Maheswaran, Agarwal, Ankit, Sivakumar, Bellie, Marwan, Norbert, Kurths, Jürgen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2019; Springer Nature B.V
• Subjects: Aquatic Pollution; Atmospheric circulation; Chemistry and Earth Sciences; Climate; Coherence analysis; Computational Intelligence; Computer Science; Dipoles; Earth and Environmental Science; Earth Sciences; Environment; Math. Appl. in Environmental Science; Original Paper; Oscillations; Physics; Precipitation; Probability Theory and Stochastic Processes; Statistical analysis; Statistics for Engineering; Teleconnections; Waste Water Technology; Water Management; Water Pollution Control; Wavelet analysis; India; Extreme precipitation; Wavelets; Partial wavelet coherence; Teleconnection patterns; India
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-019-01738-3

Precipitation patterns and extremes are significantly influenced by various climatic factors and large-scale atmospheric circulation patterns. This study uses wavelet coherence analysis to detect significant interannual and interdecadal oscillations in monthly precipitation extremes across India and their teleconnections to three prominent climate indices, namely, Niño 3.4, Pacific Decadal Oscillation, and Indian Ocean Dipole (IOD). Further, partial wavelet coherence analysis is used to estimate the standalone relationship between the climate indices and precipitation after removing the effect of interdependency. The wavelet analysis of monthly precipitation extremes at 30 different locations across India reveals that (a) interannual (2–8 years) and interdecadal (8–32 years) oscillations are statistically significant, and (b) the oscillations vary in both time and space. The results from the partial wavelet coherence analysis reveal that Niño 3.4 and IOD are the significant drivers of Indian precipitation at interannual and interdecadal scales. Intriguingly, the study also confirms that the strength of influence of large-scale atmospheric circulation patterns on Indian precipitation extremes varies with spatial physiography of the region.