Scrutinizing Entrainment and Mass Flux Closures in Shallow Cumulus Parameterizations using Cloud-Radar Observations and Large-Eddy Simulation
Warm, shallow clouds are responsible for a substantial portion of global climate model (GCM) uncertainty and differences in projections of future climate. The effects of shallow clouds are represented in GCMs using parameterizations. We propose a joint observational and modeling study to scrutinize the entrainment and mass flux closures used in parameterizations of these shallow clouds. This proposal emphasizes entrainment and mass flux of shallow clouds, directly relevant to the Funding Opportunity Announcement (FOA) research goals of pursuing observational and modeling studies to improve fundamental understanding of processes controlling the behavior of boundary-layer clouds and how these clouds are represented in GCMs. Our overarching science question is, “Do entrainment and detrainment rates vary across different shallow convection environments?” Our proposal has three coordinated research thrusts in order to address this question:
- Observational estimates of entrainment and mass flux. Entrainment rate and mass flux will be derived from cloud radar observations and related to meteorological conditions as identified by our regime clustering technique based on self-organizing maps.
- Regional model simulations to test sensitivity of shallow cumulus parameterizations. Multi-week regional simulations from the Weather Research and Forecasting (WRF) model will be used to evaluate the impact of different entrainment and mass-flux closures (and testing new closures) on coarse-scale grids of ~30 km, a value chosen because of its similarity to state-of-the-art GCM grids. WRF results will be evaluated against observed cloud macrophysical properties and radar-derived entrainment rate and mass flux.
- Diagnosing entrainment rates from large-eddy simulation to test entrainment and mass flux closure assumptions. Large-eddy simulation (LES) runs from the System for Atmospheric Modeling (SAM) will be conducted for specific periods of interest using forcing derived from the WRF simulations. This approach promotes consistency between LES and WRF, allowing us to use LES measures of entrainment and mass flux to directly evaluate the closures in the WRF shallow convection parameterizations. In addition, the LES-derived entrainment rates can serve as a cross-check for the two observational methods used to calculate entrainment.
This research will take advantage of the recent Department of Energy Atmospheric Radiation Measurement Program (DOE ARM) cloud radar advancements, new or emphasized sites (The Azores and the newly enhanced Southern Great Plains facility), and select field campaigns in the Eastern North Atlantic and Amazon. Where possible, we will also take advantage of the huge array of historical ARM cloud radar datasets over the tropics and Southern Great Plains, as well as a wealth of archived cloud property insights that are especially well suited for investigating shallow convection and its role on GCM performance. Evaluating entrainment and mass flux closures in WRF is highly likely to directly lead to short-term improvements in regional models (<5 years) and slightly longer-term improvements in DOE GCM efforts like the Accelerated Climate Modeling for Energy (ACME) project. In addition, results from our proposed WRF/LES framework will be directly comparable to LES output from the ARM LASSO (LES ARM Symbiotic Simulation and Observation) program, particularly since LASSO will employ both WRF and SAM. The extensive use of WRF is consistent with the FOA emphasis on community models. This effort is also in line with one of the five WCRP grand challenges (Clouds, circulation, and climate sensitivity; http://www.wcrp-climate.org/index.php/gc-clouds). Expected outcomes of the research are 1. improved physical understanding of entrainment and environmental controls on mass flux; and 2. thorough evaluation of entrainment and mass flux closures commonly used in regional and global climate models.
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