Collaborative Research: Convective Organization and Climate

This research team will formulate and apply a simplified numerical model to explore conditions favoring the self-aggregation of moist atmospheric convection. Aggregation of cumulus convection into clumps containing many individual convective cells is prominent in nature and in full-physics numerical atmospheric simulations alike. The researchers have performed preliminary analyses using the System for Atmospheric Modeling (SAM) numerical model, and will expand upon these simulations in order to address the hypothesis that self-aggregation of convection will lead to a systematic decline in the surface temperature, and that such decline may in-turn lead to disaggregation of convection and restorative re-warming of the surface. As such, the system would naturally be attracted to the phase transition between aggregated and disaggregated states and would constitute an example of a self-organized critical system. The objectives of the proposed work are to: (1) understand the physics of self-aggregation, (2) determine how much hysteresis there is in the system, (3) determine whether radiative-convective equilibrium systems with surface energy balance are subject to self-organized criticality, and (4) understand the implications of these aforementioned factors upon climates predicated on idealized radiative-convective states as well as more real-world conditions. The intellectual merit of the work derives from improved basic understanding of mechanisms influencing moist atmospheric convection, which is a critical but nonetheless not fully understood component of the climate system. A confirmation of the hypothesis that tropical climate tends toward a state of self-organized criticality could substantially advance the understanding of the interrelationship between convection and climate. The broader impacts of the study include the education and training of a graduate student at each of two collaborative institutions. The work also has potential to lead to improved representations of convection via improved parameterizations suitable for incorporation into coupled climate models, which could in turn contribute to improved climate forecasts.