Impact of individual and combined influence of large-scale climatic oscillations on Indian summer monsoon rainfall extremes

Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India

“…OA oscillations significantly influence model dynamics by shaping interactions between the ocean and atmosphere, impacting global and regional atmospheric circulation patterns, and influencing teleconnections (Athira et al, 2023; Gadgil et al, 2004; Johnson et al, 2014; Tamaddun et al, 2019; Tian et al, 2017). The zonal sea surface temperature (SST) fluctuations causes changes in the regional and global atmospheric pressure systems, formation of surface winds and affects precipitation and temperature patterns (Tamaddun et al, 2019).…”

“…The large‐scale OA patterns, including El Niño–Southern Oscillation (ENSO), Madden–Julian Oscillation (MJO) and Equatorial Indian Ocean Oscillation (EQ), significantly influence regional and global precipitation variability by modulating sea surface temperatures, atmospheric circulation and convection patterns (Johnson et al, 2014; Tamaddun et al, 2019; Tian et al, 2017). Studies have shown that ENSO, MJO and EQ effects are responsible for droughts across the world (Athira et al, 2023; Francis & Gadgil, 2013; Pai et al, 2011; Sasikumar et al, 2022; Tamaddun et al, 2019; Vishnu et al, 2019; Wilson et al, 2013). ENSO is arguably the most important interannual climate signal for the Tropics and also serves as the primary tropical influencer of the Indian summer monsoon rainfall (ISMR) (Francis & Gadgil, 2013; Krishnamurthy & Goswami, 2000; Roy et al, 2017; Webster et al, 1998).…”

“…Gadgil et al (2004) was the first to state that EQ oscillations affects ISMR by influencing the atmospheric convection and zonal winds. Studies have also reported on the interaction effect of these OA on global and regional precipitation and temperature patterns (Athira et al, 2023; de Beurs et al, 2018; Demir & Koc, 2021; L'Heureux & Higgins, 2008; Wang et al, 2014). A recent study by Athira et al (2023) showed that the phase interactions of the climatic oscillations (AMO, EQUINOO, ENSO and PDO) better explains the variability of ISMR.…”

“…Studies have also reported on the interaction effect of these OA on global and regional precipitation and temperature patterns (Athira et al, 2023; de Beurs et al, 2018; Demir & Koc, 2021; L'Heureux & Higgins, 2008; Wang et al, 2014). A recent study by Athira et al (2023) showed that the phase interactions of the climatic oscillations (AMO, EQUINOO, ENSO and PDO) better explains the variability of ISMR. Therefore, understanding and monitoring these oscillations are essential for improving meteorological forecasts at sub‐seasonal to interannual timescales, as they provide valuable information about the state of the climate system and its potential impacts on weather conditions worldwide (Johnson et al, 2014; Tian et al, 2017).…”

Multiscale association between large‐scale climate variability modes and drought in India via wavelet analysis

“…The major advantage of wavelet analysis is that it helps in analysing the temporal variations of teleconnection by the decomposition of a signal at various timescales. The study done by Torrence and Compo (1998) and updated by Grinsted et al (2004) increased the popularity of the wavelet tool in geophysical studies (Athira et al, 2022; Das et al, 2020; de Oliveira‐Júnior et al, 2022; Hu & Si, 2016; Keitt & Urban, 2005; Kumar & Foufoula‐Georgiou, 1997; Mitra et al, 2014; Rigozo et al, 2002; Zeri et al, 2019). Özger et al (2009) used cross wavelet transform to examine the teleconnections between ENSO and PDO on droughts in Texas.…”