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Abstract As the community increases climate model horizontal resolutions and experiments with removing moist convective parameterizations entirely, it is imperative to understand how these advances affect the InterTropical Convergence Zone (ITCZ). We investigate how the ITCZ responds to deactivating parameterized convection at two resolutions, 50 and 6 km, in fixed sea surface temperature, aquaplanet simulations with the NOAA GFDL AM4 atmospheric model. Disabling parameterized convection at 50 km resolution narrows the ITCZ and increases its precipitation minus evaporation (P–E) maximum by ∼78%, whereas at 6 km resolution doing so widens the ITCZ and decreases its P–E maximum by ∼50%. Using the column‐integrated moist static energy budget, we decompose these tropical P–E responses into contributions from changes in atmospheric energy input (AEI), gross moist stability, and gross moisture stratification. At 6 km, the ITCZ weakens due to increased gross moist stability. Disabling the convective parameterization at this finer resolution deepens the circulation, favoring more efficient poleward energy transport out of the deep tropics and reduced precipitation in the core of the ITCZ. Conversely, at 50 km the ITCZ strengthening is primarily driven by AEI, which in turn stems primarily from increased low cloud amount and thus longwave cloud radiative cooling in the Hadley cell subsiding branch. The Hadley circulation overturning intensifies to produce poleward energy fluxes that compensate the longwave cooling, yielding a stronger ITCZ. We further show that the low level diabatic heating profiles over the descending region are instrumental in understanding such diverse responses. 

African Easterly Waves (AEWs) are synoptic-scale atmospheric disturbances that serve as precursors to tropical cyclones (TCs) in the North Atlantic and North Africa. As climate changes, TC activities are increasingly frequent, leading to exponentially growing socio-economic losses. So understanding the physical mechanisms governing the tropical cyclogenesis (TCG) of AEWs remains a crucial problem. Competing theoretical frameworks, including baroclinic instability, barotropic instability, and moisture-vortex instability (MVI) have been proposed, but their relative importance and temporal evolution during storm development remain unclear. In this study, machine learning algorithms are used to empirically analyze the governing mechanisms of AEW development based on 40 years of reanalysis data (1979-2018). We develop a computer vision framework utilizing convolutional neural networks (CNNs) and transformer architectures to identify developing AEWs (DAEWs) from non-developing AEWs (NDAEWs) based on wave-centered composites of key thermodynamic and dynamic variables for storm development. The model results suggest that the MVI framework is a critical factor for high classification accuracy in distinguishing developers from non-developers. 

Extreme rainfall during the Indian summer monsoon can be destructive and deadly to the world’s third-largest economy and most populous country. Although El Niño events in the equatorial Pacific are known to suppress total summer rainfall throughout India, we show using observational data spanning 1901 to 2020 that, counterintuitively, they simultaneously intensify extreme daily rainfall. This is partly driven by increases in extreme daily values of convective buoyancy, provided that both the undilute instability of near-surface air and the dilution by mixing with drier air above are considered. El Niño could plausibly drive similar changes in other tropical regions, and our framework could be further applied to changes in hourly extremes, to other internal variability modes, and to forced trends under climate change.