A framework for convection and boundary layer parameterization derived from conditional filtering
Thuburn, J; Weller, HG; Vallis, GK; et al.Beare, RJ; Whitall, M
Date: 28 March 2018
Journal
Journal of the Atmospheric Sciences
Publisher
American Meteorological Society
Publisher DOI
Abstract
A new theoretical framework is derived for parameterization of subgrid physical processes in atmospheric
models; the application to parameterization of convection and boundary layer fluxes is a particular focus. The
derivation is based on conditional filtering, which uses a set of quasi-Lagrangian labels to pick out different
regions ...
A new theoretical framework is derived for parameterization of subgrid physical processes in atmospheric
models; the application to parameterization of convection and boundary layer fluxes is a particular focus. The
derivation is based on conditional filtering, which uses a set of quasi-Lagrangian labels to pick out different
regions of the fluid, such as convective updrafts and environment, before applying a spatial filter. This results
in a set of coupled prognostic equations for the different fluid components, including subfilter-scale flux
terms and entrainment/detrainment terms. The framework can accommodate different types of approaches to
parameterization, such as local turbulence approaches and mass-flux approaches. It provides a natural way to
distinguish between local and nonlocal transport processes, and makes a clearer conceptual link to schemes
based on coherent structures such as convective plumes or thermals than the straightforward application of
a filter without the quasi-Lagrangian labels. The framework should facilitate the unification of different approaches
to parameterization by highlighting the different approximations made, and by helping to ensure
that budgets of energy, entropy, and momentum are handled consistently and without double counting. The
framework also points to various ways in which traditional parameterizations might be extended, for example
by including additional prognostic variables. One possibility is to allow the large-scale dynamics of all the
fluid components to be handled by the dynamical core. This has the potential to improve several aspects of
convection-dynamics coupling, such as dynamical memory, the location of compensating subsidence, and the
propagation of convection to neighboring grid columns.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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