A Framework to Evaluate Convective Aggregation: Examples With Different Microphysics Schemes. Issue 5 (8th March 2022)

• Record Type: Journal Article
• Authors: Huang, Jin‐De; Wu, Chien‐Ming
• Is Part Of: Journal of geophysical research. Volume 127:Issue 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-08
• Subjects: vector vorticity cloud‐resolving model -- radiative‐convective equilibrium -- convective aggregation -- moisture‐convection feedback -- convection‐SST feedback -- Microphysics; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD035886
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: This study introduces a modeling framework to evaluate convective aggregation (CA) under radiative‐convective equilibrium simulations using the vector vorticity equation cloud‐resolving model (VVM) coupled to a mixed‐layer slab ocean. The framework introduces the competing effects between the convection‐sea surface temperature (SST) feedback (CSF) and the moisture‐convection feedback (MCF) by modifying the initial SST gradient and the mixed layer depth. Examples of applying this framework are demonstrated by comparing simulations with different microphysics schemes. The conventional five‐category scheme (VVM‐Lin) and the predicted particle properties scheme (P3) are examined by the matrix formed by these two factors. A clear bifurcation of the aggregated/non‐aggregated states can be identified in both sets of experiments. The change of the bifurcation between two sets of experiments suggests that the P3 experiments tend to develop CA due to the stronger MCF. The budget analysis of spatial frozen moist static energy variance is applied to quantify the stronger MCF in the P3 simulations. Convective systems in P3 simulations develop more organized structures, which enhances MCF and leads to CA. The proposed modeling framework provides a reconciled view of the process‐based evaluation of CA among cloud‐resolving models that use different dynamics and physical parameterizations. Key Points: A modeling framework is designed for evaluating convective aggregation (CA), focusing on two competing feedbacks in cloud‐resolving models A clear bifurcation of the aggregated/non‐aggregated states can be identified in experiments with different microphysics P3 experiments tend to develop CA due to the stronger moisture‐convection feedback owing to more organized convective systems