Wavelet analysis of precipitation extremes over India and teleconnections to climate indices
- 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 andPrecipitation 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.…
| Author details: | Rathinasamy MaheswaranORCiD, Ankit AgarwalORCiDGND, Bellie SivakumarORCiD, Norbert MarwanORCiDGND, Jürgen KurthsORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1007/s00477-019-01738-3 |
| ISSN: | 1436-3240 |
| ISSN: | 1436-3259 |
| Title of parent work (English): | Stochastic Environmental Research and Risk Assessment |
| Publisher: | Springer |
| Place of publishing: | New York |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2019 |
| Publication year: | 2019 |
| Release date: | 2020/10/06 |
| Tag: | Extreme precipitation; India; Partial wavelet coherence; Teleconnection patterns; Wavelets |
| Volume: | 33 |
| Issue: | 11-12 |
| Number of pages: | 17 |
| First page: | 2053 |
| Last Page: | 2069 |
| Funding institution: | Inspire Faculty Award, Department of Science and Technology, India [IFA-12-ENG/28]; Science and Engineering Research Board (SERB), India [ECRA/16/1721]; Deutsche Forschungsgemeinschaft (DFG) within the graduate research training group Natural risk in a changing world (NatRiskChange) at the University of PotsdamGerman Research Foundation (DFG) [GRK 2043/1] |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Peer review: | Referiert |
