PNNL

Abstract

Improving the ability of models to simulate precipitation will require breakthroughs in representing several poorly constrained cloud and aerosol processes, including droplet collision-coalescence rates, ice nucleation, scavenging in mixed-phase clouds, and the in-cloud chemical processing of atmospheric aerosols. These complex processes are fundamentally difficult to measure in the free atmosphere and cannot be fully addressed by existing cloud chambers. DOE is exploring the idea of building a new laboratory facility to study these and other important atmospheric processes under representative yet controlled, reproducible, and sustained cloud conditions to improve large-scale simulations of weather and climate. This LDRD project consists of a series of six experimental, instrument development, and model development activities that will position PNNL for a leadership role in a future DOE cloud chamber facility. First, we will use a combination of instruments unique to PNNL–a single particle mass spectrometer coupled to a counterflow virtual impactor–to demonstrate experimental measurements of how cloud droplets grow via collision-coalescence, a key uncertainty in converting clouds to precipitation. Second, we will perform experiments examining the chemical processing of organic gasses in cloud droplets and perform simulations to guide and interpret the experimental results. This task will provide data on the relative importance of cloud-phase production of organic aerosol, a process that has been extensively modeled, but poorly constrained by experimental data. Third, we will develop a new holographic droplet sensing instrument to measure the cloud droplet size distributions inside cloud chambers with minimal disturbance to the chamber environmental conditions and flow. Fourth, we will develop a digital twin of a cloud chamber using a PNNL-developed model capable of representing turbulence and other small-scale processes in the chamber. The digital twin will enable us to reproduce and interpret the cloud droplet microphysics in chamber experiments. The fifth task consists of a series of modeling exercises demonstrating our ability to utilize cloud chamber observational data to improve the representation of cloud microphysical processes in models, thereby demonstrating a pathway for knowledge gained from chamber process studies to impact precipitation. Finally, we study how secondary organic aerosol coatings impact the ability of dust particles to nucleate ice. Collectively, these six tasks will demonstrate new and unique capabilities needed to maximize the scientific impact of cloud chamber research.