Constructing a comprehensive numerical experiment to study biospheric-atmospheric feedbacks driving dry season cloud formation over the Amazon Basin

The Amazonian hydrological and carbon cycle are controlled by a complex, interconnected and interdependent myriad of surface and atmospheric processes. Improving our understanding and numerical representation of these cycles under a changing climate requires a deeper exploration of the biospheric-atmospheric coupling and the processes governing the formation and deepening of shallow cumulus clouds. Utilising a comprehensive set of surface and upper-air atmospheric measurements from the CloudRoots-Amazon22 campaign alongside an integrated hierarchy of models, we construct a numerical experiment to systematically study these processes throughout the dry season of 2022. The model hierarchy consists of a large eddy simulation resolving turbulence and shallow cumulus formation, a coupled rainforest-atmosphere mixed-layer model to map the sensitivity to surface and atmospheric observations and a moisture tracking model to identify and quantify moisture sources, sinks, and long-range transport. Individual days of observations were characterised into representative shallow convective and shallow-to-deep convective regimes. We accurately replicated the evolution of radiation and the asymmetrical exchange fluxes of energy, momentum, moisture, and carbon during the shallow convective regime. By analysing the diurnal variability of the state variables, we can determine how turbulent mixing controls the morning transition, from strong gradients to well-mixed conditions above the forest. Ongoing work involves improving the representation of in-canopy processes and simulating the shallow-to-deep convective regime by introducing thermodynamic forcings, such as moist air intrusion or increased wind sheared conditions, on the shallow convective experiment.