Changes in the relationship between Indian Ocean dipole and Indian summer monsoon rainfall in early and recent multidecadal epochs during different phases of monsoon

Hrudya, Palakkal Puthan Veedu Haridas; Varikoden, Hamza; Vishnu, R.

doi:10.1002/joc.6685

Cited by 18 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is not surprising in regions like Southern Asia (Figures 5a and 5b) with salient contrast between dry and wet seasons and about 80% annual rainfall falls in the monsoon season, that is, June to September (Bhatla et al., 2016). Specifically, the warm‐dry to wet swings concentrate between mid‐June and late‐June (Figure 5a), consistent with the period from central Indian seeing the abrupt monsoon onset (B. Wang & LinHo, 2002) to rainfall starting to cover the entire Indian region (Hrudya et al., 2021). The occurrence of the opposite swings peak in September and October when Indian monsoon gradually withdraws and begins to transform to winter monsoon (Figure 5b).…”

Section: Resultssupporting

confidence: 73%

“…Specifically, the warm-dry to wet swings concentrate between mid-June and late-June (Figure 5a), consistent with the period from central Indian seeing the abrupt monsoon onset (B. Wang & LinHo, 2002) to rainfall starting to cover the entire Indian region (Hrudya et al, 2021). The occurrence of the opposite swings peak in September and October when Indian monsoon gradually withdraws and begins to transform to winter monsoon (Figure 5b).…”

Section: Significant Shift In Swing's Seasonalitysupporting

confidence: 73%

See 1 more Smart Citation

Asia Faces a Growing Threat From Intraseasonal Compound Weather Whiplash

Fang

2023

Earth's Future

View full text Add to dashboard Cite

show abstract

Section: Resultssupporting

confidence: 73%

Section: Significant Shift In Swing's Seasonalitysupporting

confidence: 73%

Asia Faces a Growing Threat From Intraseasonal Compound Weather Whiplash

Fang

2023

Earth's Future

View full text Add to dashboard Cite

show abstract

“…A recent study by Hrudya et al . (2021) revealed a phase change from positive (1951–1980) to negative correlation (1986–2015) of Indian Ocean dipole (exists between Western and southeastern tropical Indian Ocean) with the monsoon rainfall. The phase changes could have weakened the overall correlation in the current study considering a period from 1948 to 2020.…”

Section: Resultsmentioning

confidence: 99%

“…Further mapping the correlation of these teleconnections (ENSO based on SSTs averaged over the Nino 3.4 region and IOD based on DMI) with the gridded drought indices in India show significant values for ENSO (Figure S7) and weak correlation for the IOD (Figure S8). A recent study by Hrudya et al (2021) revealed a phase change from positive to negative correlation of Indian Ocean dipole (exists between Western and southeastern tropical Indian Ocean) with the monsoon rainfall. The phase changes could have weakened the overall correlation in the current study considering a period from 1948 to 2020.…”

Section: Enso Teleconnection With Concurrent Droughtsmentioning

confidence: 99%

Multivariate analysis of concurrent droughts and their effects on Kharif crops—A copula‐based approach

Muthuvel

Mahesha

2021

Intl Journal of Climatology

View full text Add to dashboard Cite

Apart from creating an ecological imbalance, drought events could affect an agrarian country's economy and food security by reducing crop yields. The antecedent meteorological droughts could prolong into hydrological and (or) agricultural droughts and may co-exist as concurrent droughts. The current study aims to comprehensively study Indian concurrent droughts, their effects on crop yield, and possible teleconnection with ENSO (El Niño-Southern Oscillation), adopting a copula-based multivariate approach. The copula functions can replicate the correlation among the variables and keep the dependence structure intact. The concurrent drought characteristics are computed using a multivariate standardized drought index that incorporates the three primary drought indices using the Gaussian copula. Some of the severe concurrent drought years such as 2002, 1987, 1972, and 1965 caused considerable yield losses in Kharif season crops of groundnut, millet, and rice. This prompts to construct quad-variate models involving the crop yield and the three drought indices using the vine copulas that perform better than the elliptical and symmetric Archimedean copula. Though the isolated forms of droughts could cause mild yield losses, the probability of concurrent droughts causing high to exceptional losses is more. Further, the ENSO teleconnection with the concurrent monsoon droughts is analysed and mapped. The above-normal warming of the Nino 3.4 region over the tropical Pacific during the months leading up to the monsoon could signal concurrent monsoon droughts in the areas under the Ganga-Brahmaputra basin at a probability of around 45%. These results could be helpful in drought mitigation measures and policymaking.copula, drought, multivariate analysis, vine copula | INTRODUCTIONIn India, 14.5% of people are malnourished, and 37.9% of children under age five are stunted as of 2018 (Priyadarshini and Abhilash, 2020). Sustaining food security in an evergrowing population is one of the challenges posing the developing countries. With the increasing population, urban expansion could sprawl into agricultural farmlands (Abu Hatab et al., 2019). Thus improving the crop yield with advanced farming machinery, pesticides, insecticides, irrigation practices, or sowing high yielding variety seeds become the need of the hour (Sannigrahi et al., 2021). Despite these measures, the inevitable climate extreme events such as extreme precipitation (Alidoost et al., 2019)

show abstract

“…It starts developing in May and attains its peaks in fall (September-October-November; SON) and decay in winter (December-January-February; DJF) with strong year-to-year variability. IOD profoundly impacts the strength of seasonal Indian summer monsoon rainfall (ISMR) and its variability (Ashok and Saji, 2007;Hrudya et al, 2021). The number of IOD events is projected to increase in a warming climate, particularly the number of intense positive IOD events (Cai et al, 2021(Cai et al, , 2014.…”

Section: Introductionmentioning

confidence: 99%