Convective cloud development during the Indian monsoon helps moisten the atmospheric environment and drive the monsoon trough northwards each year, bringing a large amount of India’s annual rainfall. Therefore, an increased understanding of how monsoon convection develops from observations will help inform model development. In this study, 139 days of India Meteorological Department Doppler weather radar data is analysed for 7 sites across India during the 2016 monsoon season. Convective cell-top heights (CTH) are objectively identified through the season, and compared with near-surface (at 2 km height) reflectivity. These variables are analysed over three time scales of variability during the monsoon: monsoon progression on a month-by-month basis, active-break periods and the diurnal cycle. We find a modal maximum in CTH around 6–8 km for all sites. Cell-averaged reflectivity increases with CTH, at first sharply, then less sharply above the freezing level. Bhopal and Mumbai exhibit lower CTH for monsoon break periods compared to active periods. A clear diurnal cycle in CTH is seen at all sites except Mumbai. For south-eastern India, the phase of the diurnal cycle depends on whether the surface is land or ocean, with the frequency of oceanic cells typically exhibiting an earlier morning peak compared to land, consistent with the diurnal cycle of precipitation. Our findings confirm that Indian monsoon convective regimes are partly regulated by the large-scale synoptic environment within which they are embedded. This demonstrates the excellent potential for weather radars to improve understanding of convection in tropical regions

This study investigates the direct radiative-convective processes that drive and maintain aggregation within convection permitting elongated channel (and smaller square) simulations of the UK Met Office Unified Model (UM). Our simulations are configured using three fixed sea surface temperatures (SSTs) following the radiative-convective equilibrium model intercomparison project (RCEMIP) protocol. By defining cloud types based on the vertical distribution of condensed water, we study the importance of radiative interactions with each cloud type to aggregation. We eliminate the dependence of the vertically-integrated frozen moist static energy (FMSE) variance budget framework on SST by normalizing FMSE between theoretical upper and lower limits based on SST. The elongated channel simulations reach similar degrees of aggregation across SSTs, despite the contributions of normalized shortwave and longwave interactions decreasing with SST. High-cloud longwave interactions are the main drivers and maintainers of aggregation. Their influence decreases with SST as high clouds become less abundant. This SST-dependence is consistent with changes in grid spacing and RHcrit, however the magnitude of high-cloud longwave interactions is likely reduced as grid spacing and RHcrit are reduced. Both factors tend to decrease condensed water path and cloud top height, decreasing the anomalous longwave heating rates of these clouds. Shortwave interactions with water vapor are key maintainers of aggregation and are dependent on SST and the degree of aggregation itself. The analysis method used provides a new framework to compare the effects of radiative-convective processes on self-aggregation across different SSTs and model configurations in order to improve our understanding of self-aggregation.

The responses of tropical anvil cloud and low-level cloud to a warming climate are among the largest sources of uncertainty in our estimates of climate sensitivity. However, most research on cloud feedbacks relies on either global climate models with parameterized convection, which do not explicitly represent small-scale convective processes, or small-domain models, which cannot directly simulate large-scale circulations. We investigate how self-aggregation, the spontaneous clumping of convection in idealized numerical models, depends on cloud-radiative interactions with different cloud types, sea surface temperatures (SSTs), and stages of aggregation in simulations that form part of RCEMIP (the Radiative-Convective Equilibrium Model Intercomparison Project). Analysis shows that the presence of anvil cloud, which tends to enhance aggregation when collocated with anomalously moist environments, is reduced in nearly all models when SSTs are increased, leading to a corresponding reduction in the aggregating influence of cloud-longwave interactions. We also find that cloud-longwave radiation interactions are stronger in the majority of General Circulation Models (GCMs), typically resulting in faster aggregation compared to Cloud-system Resolving Models (CRMs). GCMs that have stronger cloud-longwave interactions tend to aggregate faster, whereas the influence of circulations is the main factor affecting the aggregation rate in CRMs.