Competing Effect of Radiative and Moisture Feedback in Convective Aggregation States in Two CRMs
2023
P. Bongioannini Cerlini | M. Saraceni | L. Silvestri
Abstract The radiative‐convective equilibrium (RCE) of two models exhibiting convective aggregation has been compared. The goal of the work, following the suggestion from the RCE Model Intercomparison Project (RCEMIP), is to identify key parameters controlling self‐aggregation in RCE for both models, to discuss the processes controlled by these parameters and to underline the models similarities and differences. The two cloud resolving models studied, the SAM (System for Atmospheric Modeling) and the ARPS (Advanced Regional Prediction System), present similar statistics concerning precipitation, but different warming, and drying of the atmosphere, within the spread of the RCEMIP values. On the other hand, the two models show different strengths of the moisture feedback, due to the different saturation of the sub‐cloud layer. A saturated sub‐cloud layer in ARPS (which was not artificially imposed in the numerical setup) allows the localization of convection in moist regions, by weakening the negative influence of cold pools. Such a mechanism leads to a lower degree of aggregation (based on three organization metrics) and a weaker effect of the organized state on the average domain statistics in ARPS. Stronger cold pools in SAM, instead, help the creation of shallow clouds in dry regions, increasing the longwave feedback responsible for their expansion; while delocalizing convection in moist regions and therefore opposing high‐cloud radiative‐feedback. Further experiments are needed to generalize such findings to other RCEMIP models, also investigating the role of microphysics and turbulence schemes in regulating such mechanisms.
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