Examining the Impacts of Microphysical-Dynamical Feedbacks on Convective Clouds in Different Aerosol Environments Using Enhanced Observational and Modeling Strategies

Abstract

Deep convective clouds (DCCs) are critical to life on Earth as they provide life-sustaining fresh water and play a vital role in the Earth’s weather and climate system. In spite of their importance, DCCs are poorly represented in weather through climate numerical models. Past studies have pointed to shortfalls in our representations of microphysical and dynamical processes as large sources of model uncertainty. To improve our prediction of DCCs on weather through climate timescales, we therefore need to enhance their representation in numerical models, as well as increase our understanding of the microphysical and dynamical processes impacting DCCs and the factors driving these processes. While DCC processes are impacted by aerosols, the convective lifecycle, and the environment in which the cloud develops, aerosol impacts are also modulated by the lifecycle and the environment. A Model Intercomparison Project (MIP) was recently conducted by the Aerosol, Cloud, Precipitation and Climate (ACPC) Initiative, an international group of aerosol and microphysical experts. They compared the results from seven different modeling frameworks used to simulate aerosol impacts on DCCs within the region of Houston, TX. While a number of common trends were observed, significant differences were found in precipitation amounts, updraft and downdraft velocities and convective anvil properties. ACPC has subsequently played an important role in the motivation and design of the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) field campaign.

The science questions of the proposed research focus on DCC processes. The first question (Q1) asks what are the primary microphysical and dynamical processes responsible for the variability in the precipitation, updraft and downdraft velocities, and convective anvils of isolated DCCs within current state-of-the art CRMs? The second and third questions enquire as to how the primary microphysical and dynamical processes vary as a function of (Q2) aerosol loading and (Q3) the convective life cycle and environment?

The proposed science questions will be addressed through the use of a fully integrated, synergistic model-observation framework comprised of the ACPC MIP database, TRACER observations, and multimodel TRACER case study simulations, all of which are connected through the use of the sophisticated Cloud resolving model Radar SIMulator (CR-SIM) and the Tracking and Object-Based Analysis of Clouds (tobac) tracking tool. We have two pre-TRACER objectives: (1) to determine the predominant microphysical and dynamical processes impacting the convective characteristics of isolated DCCs around Houston as a function of the model physics, aerosol loading and cloud lifecycle for the seven modeling frameworks in ACPC MIP; and (2) to provide optimal radar and lidar sampling strategies for TRACER through the use of Observing System Simulation Experiments (OSSEs) performed using CR-SIM, tobac, and the ACPC MIP model data. Our two post-TRACER objectives are the following: (1) to determine the predominant microphysical and dynamical processes impacting characteristics of isolated DCCs as a function of the model physics, aerosol loading, lifecycle and environment for several multi-model TRACER case study simulations; and (2) to evaluate the model representations of the microphysical and dynamical processes impacting the simulated TRACER case studies through CR-SIM facilitated “apples-to-apples” comparisons of TRACER observations and the model output. The applications of the CR-SIM as a virtual observatory operator of high-resolution model output helps better interpret the differences between model results and observations, and also improves our understanding of the representative errors owing to the sampling limitations of the ground-based observatories. In this way, we are better able to evaluate the shortfalls in the microphysics, aerosol and dynamics parameterizations in multiple state-of-the-art modeling frameworks, thereby enhancing our ability to better predict DCCs on weather through climate scales.