From Boundary Layer to Deep Convection: The Multi-Plume Eddy-Diffusivity/Mass-Flux (EDMF) Fully Unified Parameterization

The key objective of this project is to reduce critical systematic biases in the GFDL model related to the boundary layer, convection and clouds by implementing, and evaluating, in the GFDL model, a new fully unified boundary layer and deep convection parameterization based on the multi-plume Eddy-Diffusivity/Mass-Flux (EDMF) approach.
Turbulence and convection in the atmosphere are at the core of key climate prediction problems. For example: i) to reduce uncertainties in climate projections, it is essential to improve predictions of cloud feedbacks (how clouds respond to, and influence, climate change), which are controlled by the interactions between a turbulent flow with water phase transitions and radiation; ii) to improve extreme weather prediction for the next few decades as climate changes, it is essential to improve our understanding of how moist convection responds to a warmer world.
It is increasingly clear that to realistically represent the different manifestations of turbulence and convection in the atmosphere, new unified parameterizations that consider all types of sub-grid flow in one single scheme, are needed. In this context, a parameterization such as EDMF that unifies boundary layer with moist convection (both shallow and deep) is a promising approach. EDMF is based on the unification of concepts generally used for the parameterization of turbulence in the boundary layer (ED) and of moist convection (MF). Studies have shown the potential of EDMF to represent dry and moist convective boundary layers. In the last few years the Lead PI’s group has developed a new version of EDMF that is particularly well suited to simulate moist convective boundary layers and is able to represent in a realistic manner the dry boundary layer, stratocumulus, shallow and deep cumulus convection. This new version uses a multi-plume approach and the probability density function (PDF) of updraft properties in the surface layer is sampled in a Monte-Carlo manner to start a variety of updraft plumes, with a stochastic lateral entrainment parameterization. The current EDMF version is a turbulence and convection parameterization that can be considered as fully unified, since it is able to represent convective processes from boundary layer convection (dry and with clouds) to deep moist convection.
In this project, we will implement and evaluate the new EDMF parameterization in the GFDL model. Initially we will evaluate the new EDMF implemented into the GFDL SCM versus a variety of LES case-studies and results from field experiments. For the full 3D implementation, we will focus our evaluation on cloud and convection variables as observed by satellite instruments during present climate. In particular, we will evaluate how the new EDMF version of the GFDL model is able to simulate key boundary layer and convection transitions such as (i) from stratocumulus, to cumulus and to deep convection (over the tropical and sub-tropical oceans) and (ii) the diurnal cycle of tropical convection over land from a stable boundary layer to dry convection, shallow convection and deep precipitating convection. We will also investigate in detail the impact of the new EDMF GFDL simulations in present and future climate.
By developing and implementing a new fully unified boundary layer, cloud and convection parameterization in the GFDL model, and reducing key biases in GFDL’s climate predictions, this project will improve NOAA’s Climate Program Office (CPO) capabilities in Earth system science and modeling, will address CPO’s strategic challenges in the areas of (1) Weather and climate and (2) Climate impacts on water resources, and will ultimately advance the scientific understanding and prediction of climate and its impacts, to enable effective decisions.