Physical mechanisms controlling self‐aggregation of convection in idealized numerical modeling simulations

By A. Wing, Kerry A. Emanuel. 2013

306 citations recorded by the source corpus.

We elucidate the physics of self‐aggregation by applying a new diagnostic technique to the output of a cloud resolving model. Specifically, the System for Atmospheric Modeling is used to perform 3‐ D cloud system resolving simulations of radiative‐convective equilibrium in a nonrotating framework, with interactive radiation and surface fluxes and fixed sea surface temperature (SST). We note that self‐aggregation begins as a dry patch that expands, eventually forcing all the convection into a single clump. Thus, when examining the initiation of self‐aggregation, we focus on processes that can amplify this initial dry patch. We introduce a novel method to quantify the magnitudes of the various feedbacks that control self‐aggregation within the framework of the budget for the spatial variance of column‐integrated frozen moist static energy. The absorption of shortwave radiation by atmospheric water vapor is found to be a key positive feedback in the evolution of aggregation. In addition, we find a positive wind speed‐surface flux feedback whose role is to counteract a negative feedback due to the effect of air‐sea enthalpy disequilibrium on surface fluxes. The longwave radiation feedback can be either positive or negative in the early and intermediate stages of aggregation; however, it is the dominant positive feedback that maintains the aggregated state once it develops. Importantly, the mechanisms that maintain the aggregate state are distinct from those that instigate the evolution of self‐aggregation.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/f56131b182cf68750de7c4107aed2c82f03df761

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Citations in the corpus

What favors convective aggregation and why? (2015, methodology): “…to aggregation may explain its presence in different model runs in various configurations [e.g., Bretherton et al., 2005; Muller and Held, 2012; Jeevanjee and Romps, 2013; Shi and Bretherton, 2014; Wing and Emanuel, 2014] and its hysteresis, since evolving conditions can trigger various feedbacks.”
Could Machine Learning Break the Convection Parameterization Deadlock? (2018, background): “…, 2016] so that Cloud Resolving Models (CRMs) have been the tool of choice to understand convective aggregation [ Jeevanjee and Romps , 2013; Wing and Emanuel , 2014; Arnold and Randall , 2015; Bony et al. , 2015; Bretherton and Khairoutdinov , 2015; Coppin and Bony , 2015; Muller and Bony…”
The MJO as a Dispersive, Convectively Coupled Moisture Wave: Theory and Observations (2016, unknown): “This mechanism may, in turn, be related to the so-called radiative–convective instability discussed by Raymond (2000a), Wing and Emanuel (2014), and Emanuel et al. (2014).”
Global‐scale convective aggregation: Implications for the Madden‐Julian Oscillation (2015, background): “Previous work suggests that gustiness plays only a modest role in MJO dynamics [Araligidad and Maloney, 2008; Riley Dellaripa and Maloney, 2015], although it may contribute to mesoscale aggregation [Wing and Emanuel, 2014].”
Organization of tropical convection in low vertical wind shears: Role of updraft entrainment (2017, background): “The organization leads to a drier mean atmosphere and increased outgoing long wave radiation (OLR) [Bretherton et al., 2005; Tobin et al., 2012; Wing and Emanuel , 2013].”
Convective Self-Aggregation in Numerical Simulations: A Review (2017, methodology): “…e ↵ ect of the longwave feedback in the dry regions at the beginning of the aggregation process, as diagnosed with the MSE variance budget, is large and positive and results from both clear-sky and cloud e ↵ ects (Wing and Emanuel, 2014; Holloway and Woolnough, 2016; Wing and Cronin, 2016).”
Physical mechanisms controlling the initiation of convective self‐aggregation in a General Circulation Model (2015, background): “…in this GCM. Consistently with other studies, self-aggregation exhibits a dependence on surface temperature [e.g., Khairoutdinov and Emanuel, 2010; Wing and Emanuel, 2014], and the mechanisms primarily responsible for its initiation depend on the range of SST considered [e.g., Wing and Cronin,…”
Self‐aggregation of convection in long channel geometry (2014, methodology): “…that convection–moisture–radiation feedbacks, which dry the drier air columns and moisten the moister air columns, are critical for self-aggregation; Wing and Emanuel (2014) clarified that clear-sky longwave radiation and wind-driven surface flux feedbacks dominate the early stages of aggregation.”