A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds

TitleA Stochastic Framework for Modeling the Population Dynamics of Convective Clouds
Publication TypeJournal Article
Year of Publication2018
JournalJournal of Advances in Modeling Earth Systems
Volume10
Number2
Pages448-465
Date Published04/2018
Abstract / Summary

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud‐base mass flux, given a large‐scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud‐base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud‐base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge‐discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud‐base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

URLhttp://dx.doi.org/10.1002/2017ms001214
DOI10.1002/2017ms001214
Funding Program: 
Journal: Journal of Advances in Modeling Earth Systems
Year of Publication: 2018
Volume: 10
Number: 2
Pages: 448-465
Date Published: 04/2018

A stochastic prognostic framework for modeling the population dynamics of convective clouds and representing them in climate models is proposed. The framework follows the nonequilibrium statistical mechanical approach to constructing a master equation for representing the evolution of the number of convective cells of a specific size and their associated cloud‐base mass flux, given a large‐scale forcing. In this framework, referred to as STOchastic framework for Modeling Population dynamics of convective clouds (STOMP), the evolution of convective cell size is predicted from three key characteristics of convective cells: (i) the probability of growth, (ii) the probability of decay, and (iii) the cloud‐base mass flux. STOMP models are constructed and evaluated against CPOL radar observations at Darwin and convection permitting model (CPM) simulations. Multiple models are constructed under various assumptions regarding these three key parameters and the realisms of these models are evaluated. It is shown that in a model where convective plumes prefer to aggregate spatially and the cloud‐base mass flux is a nonlinear function of convective cell area, the mass flux manifests a recharge‐discharge behavior under steady forcing. Such a model also produces observed behavior of convective cell populations and CPM simulated cloud‐base mass flux variability under diurnally varying forcing. In addition to its use in developing understanding of convection processes and the controls on convective cell size distributions, this modeling framework is also designed to serve as a nonequilibrium closure formulations for spectral mass flux parameterizations.

DOI: 10.1002/2017ms001214
Citation:
Hagos, S, Z Feng, R Plant, R Houze, and H Xiao.  2018.  "A Stochastic Framework for Modeling the Population Dynamics of Convective Clouds."  Journal of Advances in Modeling Earth Systems 10(2): 448-465.  https://doi.org/10.1002/2017ms001214.