A Lagrangian Study of the Transition from Shallow to Deep Convection using ASR Observations and LES Simulations

Principal Investigator(s):
Zhiming Kuang, President and Fellows of Harvard College

The goal of the proposed research is to better characterize the transition from shallow to deep convection and to improve its representation in Global Climate Models (GCMs). A number of prominent biases seen in GCM simulations can be attributed to inadequate representations of this transition. Building upon the Lagrangian tracking techniques that we have developed with the support from a previous Department of Energy Atmospheric System Research (ASR) grant, we will analyze in detail the processes involved in this transition using large-eddy simulations (LES) guided and constrained by the long-term measurements at the Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site, as well as data from the Routine ARM Aerial Facility (AAF) Clouds with Low Optical Water Depths Optical Radiative Observations (RACORO) campaign. Our proposed research consists of three steps: the first is to use the ARM observations to drive and constrain the numerical models to establish their utility and limitations and to find configurations adequate for simulating the transition from shallow to deep convection. The second step is to use the LES and Lagrangian and Eulerian analyses to quantify the roles and evolution of various characteristics such as entrainment/detrainment, plume overlap, updraft vertical velocity, and sub-cloud layer characteristics during this transition. The last step is to evaluate, develop, and improve cumulus parameterizations using knowledge gained in the second step. We expect this research to improve our understanding of the transition from shallow to deep convection, cumulus schemes, and the representation of a number of phenomena involving clouds and convection in GCMs. Clouds and convection remain a main contributor to the high degree of uncertainty associated with climate change predictions with these models, this research will therefore support the accomplishment of the Office of Biological and Environmental Research Climate and Environmental Science Division’s long term measure of scientific advancement.