Abstract

Aerosols remain a major source of uncertainty in estimates of global radiative forcing. They affect climate through two pathways: directly by interacting with solar radiation, and indirectly by modifying cloud properties. This indirect pathway is especially difficult to constrain because clouds respond to changes in aerosol concentration alongside multiple factors. A better understanding of how aerosols influence cloud properties is essential for developing more robust climate models. Aerosols affect clouds primarily through two pathways. Under the assumption of constant cloud liquid water content, increasing aerosol concentration produces more numerous, smaller cloud droplets that enhance cloud reflectivity. This modification of cloud reflectivity constitutes the first aerosol indirect effect, known as the Twomey effect. The resulting smaller droplets also reduce the efficiency of collision–coalescence processes, suppressing precipitation and prolonging cloud lifetime, which defines the second aerosol indirect effect. Quantifying this precipitation suppression effect is particularly challenging because precipitation is influenced by many factors beyond aerosols. To reduce the influence of non-aerosol factors, precipitation susceptibility has been introduced as a metric to quantify precipitation responses to aerosol perturbations. Precipitation susceptibility quantifies the relative change in precipitation rate (R) in response to fractional changes in aerosol concentration (N), typically expressed through CCN concentration or cloud droplet number concentration. However, previous studies have reported substantial discrepancies in both the magnitude of precipitation susceptibility and its dependence on environmental conditions. Moreover, aerosol impacts on precipitation depend not only on aerosol concentration but also on particle size. This dissertation addresses these challenges of method and scope by developing a robust framework for estimating precipitation susceptibility that accounts for measurement uncertainties and inconsistent data processing choices across previous studies. Building on this improved framework, the dissertation extends susceptibility estimates by explicitly incorporating aerosol size distributions and atmospheric turbulence into the analysis. The dissertation consists of two interconnected studies. The first study develops an uncertainty-aware regression framework to quantify precipitation responses to aerosol concentration. Using numerical simulations, this study evaluates how measurement uncertainties and data preprocessing choices influence regression slope estimates. The framework is then applied to long-term field observations to examine precipitation responses to aerosol perturbations under real atmospheric conditions. This study also assesses how methodological differences contribute to discrepancies among previous susceptibility estimates. Results demonstrate that accurately quantifying precipitation susceptibility requires accounting for intrinsic scatter and measurement uncertainties and adopting consistent methodologies. The second study extends this framework by incorporating aerosol size effects and turbulence, both of which previous studies have identified as additional drivers of precipitation. Using the same long-term observational dataset, this study compares how giant and fine cloud condensation nuclei (CCN) influence precipitation, then evaluates the combined effects of aerosols in these two size modes. Because elevated concentrations of giant CCN are often associated with enhanced turbulence, this analysis also examines the role of turbulence in modulating precipitation responses. The results provide new insights into the coupled effects of aerosol concentration, size, and dynamical conditions on precipitation. Together, these studies establish a more robust and physically grounded framework for quantifying aerosol impacts on precipitation, with the inclusion of aerosol particle size and turbulence. This dissertation contributes to improved representation of aerosol–cloud–precipitation interactions and provides observational constraints that can strengthen the performance of future climate models.

Committee Chair

Jian Wang

Committee Members

Fangqiong Ling; Lu Xu; Randall Martin; Virendra Ghate

Degree

Doctor of Philosophy (PhD)

Author's Department

Energy, Environmental & Chemical Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

8-17-2026

Language

English (en)

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Available for download on Monday, August 14, 2028

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