Abstract
Soil moisture plays a significant role in partitioning surface energy fluxes into latent and sensible heat components and is fundamental for process level understanding of land-atmosphere interactions. Realistic simulations of such interactions by models are constrained due to intrinsic model assumptions. This study examines convective triggering based on soil moisture by employing a framework of coupling diagnostics called Convective triggering potential (CTP) and humidity index (HILow) to classify land regions into various feedback regimes for each season between 2005 and 2015. Results show that atmospherically controlled cases prevail in most of the regions for all seasons. Temporal regime probability values (TRP) suggested a dominant wet regime in southern India and a dominant dry regime in central and western India. Precipitation events triggered in wet advantage conditions were found to have larger accumulations than similar events triggered in dry advantage conditions for all the seasons. This study highlights separate seasonal regime classification and emphasizes on how potential regime hotspots vary for each season.
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