Pilot Study: Improving the Characterization of Cloud Formation Properties and Hygroscopicity of Aerosol Particles in the Southeastern U.S. Region

The influences of aerosol

aerosols

Small particles or droplets, such as wildfire smoke, volcanic gases, or salty sea spray, that float in the air. They are emitted by both natural events and human activities.

Source: NASA Global Climate Change Source

number concentrations and chemical composition on clouds are one of the major uncertainties in predicting future climate. The southeastern United States (SE US) is a warm and humid region with a notable presence of hydrophobic secondary organic aerosols

secondary organic aerosol (SOA)

Secondary Organic Aerosols (SOAs) are air pollutants emitted from natural and man-made sources. They are produced through a complex interaction of sunlight, volatile organic compounds from trees, plants, cars or industrial emissions, and other airborne chemicals.

Source: EPA Environmental Protection Agency Source

(SOA) mixed with anthropogenic hygroscopic inorganic compounds. Recent studies have revealed that the formation of secondary organic aerosols (SOA) from multiphase reactions in the SE US can convert inorganic sulfates to organosulfates. This conversion significantly modifies the hygroscopicity

hygroscopicity

The ability of a particle to take up moisture from the environment and is important for understanding the ability of particles to act as cloud condensation nuclei (CCN).

Source: ARM Source

of aerosol particles, resulting in changes to cloud condensation nuclei

cloud condensation nuclei

Tiny airborne particles suspended in the atmosphere that serve as the seeds around which cloud droplets form.

Source: Source

(CCN), cloud cover, and precipitation. To address the scientific gap on hygroscopicity and to facilitate future analyses of the Atmospheric Radiation Measurement (ARM) data in the SE US, our team proposes a comprehensive examination of factors influencing the hygroscopicity and CCN activity of the aerosols from the ARM Mobile Facility (AMF3) in conjunction with a regional scale model. 

Our study builds on three primary objectives that utilize measurement data to gain insights into key aerosol processes that are significant in the SE US region: First, we will conduct a hygroscopicity-closure pilot study to quantify hygroscopicity derived from modeling results based on chemical composition, and compare it with CCN and hygroscopicity measurements obtained through ground-based measurement;  Second, we will identify key factors related to aerosols and atmospheric dynamics that contribute to the discrepancies between modeled and measured CCN activity and hygroscopicity; Thirdly, we will propose potential explanations and parameterizations to improve hygroscopicity parameterization. The results generated from the above three objectives will contribute to maximizing the scientific impact of the AMF3 deployment.

This project encompasses a three-phase work plan: Comparison, Identification, and Explanation. In the Comparison phase, we plan to use multiple methods, including data from the DOE ARM AMF3 data and regional scale chemical transport model, to reconstruct the hygroscopicity and CCN activity of aerosols in the SE US; The Identification phase will focus on identifying key factors such as chemical composition and meteorological parameters that contribute to the discrepancy between estimated and measured hygroscopicity values; In the Explanation phase, we aim to develop an improved parameterization for estimating CCN activity and hygroscopicity of ambient aerosols in the SE US using advanced machine learning

machine learning (ML)

Machine Learning (ML) refers to the field and practice of using algorithms that are able to “learn” by extracting patterns from a large body of data.

Source: Center of Excellence Source

techniques applicable to large-scale climate models. Our team will strive to involve traditionally underrepresented groups throughout the work plan of this project, aiming to achieve a more inclusive and equitable learning and research experience. 

The results from our proposed project will not only reduce uncertainties in understanding aerosol-cloud-radiation interactions in the SE US during the AMF3 campaign, but also have the potential to enhance the performance of global climate models in regions characterized by strong biosphere and anthropogenic emissions.