TY  - JOUR
A1  - Maheswaran, Rathinasamy
A1  - Agarwal, Ankit
A1  - Sivakumar, Bellie
A1  - Marwan, Norbert
A1  - Kurths, Jürgen
T1  - Wavelet analysis of precipitation extremes over India and teleconnections to climate indices
JF  - Stochastic Environmental Research and Risk Assessment
N2  - 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, Nino 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 Nino 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.
KW  - Extreme precipitation
KW  - Teleconnection patterns
KW  - Wavelets
KW  - Partial wavelet coherence
KW  - India
Y1  - 2019
U6  - https://doi.org/10.1007/s00477-019-01738-3
SN  - 1436-3240
SN  - 1436-3259
VL  - 33
IS  - 11-12
SP  - 2053
EP  - 2069
PB  - Springer
CY  - New York
ER  -