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Get Free AccessIntensifying global hydrological cycle has led to significant changes in climate extremes. Recently, compound dry-hot extremes (CDHE) have received considerable attention due to their greater adverse impact than individual extremes. Thus, the present study aims to assess the monsoonal CDHEs over India for past and future. Here, an integrated approach using comprehensive statistical (variance transform method, regression and reliability ensemble averaging-REA) and probabilistic methods (copula) is proposed to study these extremes. The study evaluates the Standardized Compound Event Indicator (SCEI) across two distinct periods: the observed data from 1975 to 2015 and future projections for 2055–2095 under RCP8.5, to analyse the frequency and spatial extent of dry-hot extremes during the monsoon season (JJAS). Furthermore, the estimation of vegetation losses under different dry-hot conditions for 1982–2013 is examined using bivariate copulas. Also, the association of climate indices with SCEI is evaluated using variance transformation method. The results show that SCEI encompasses a negative trend during 1975–2015, which represents a rise in dry-hot extremes. Such extremes exhibit a substantial impact on vegetation, as the bivariate assessment of SCEI and NDVI show that around 65.70% of the country's area is vulnerable to vegetation loss under extreme SCEI condition. Furthermore, the results demonstrate that Nino3.4 (ENSO) is the most influential climate indice for SCEI, for >50% of country's area. Subsequently, for future (2055–2095), it is found that frequency and spatial extent of dry-hot extremes would increase relative to past (1975–2015), across the country. The present study improves the understanding of dry-hot extremes and can contribute towards effective adaptation strategy.
Nikhil Kumar, Manish Kumar Goyal (2024). Projected changes in monsoonal compound dry-hot extremes in India. Atmospheric Research, 310, pp. 107605-107605, DOI: 10.1016/j.atmosres.2024.107605.
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Type
Article
Year
2024
Authors
2
Datasets
0
Total Files
0
Language
English
Journal
Atmospheric Research
DOI
10.1016/j.atmosres.2024.107605
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