Atmospheric aerosols are known to exert a significant influence on the Earth's climate system; however, the magnitude of this influence is highly uncertain because of the complex interaction between aerosols and water vapor to form clouds. Toward reducing this uncertainty, this dissertation outlines a series of laboratory and in-situ field measurements, instrument technique development, and model simulations designed to characterize the ability of aerosols to act as cloud condensation nuclei (CCN) and form cloud droplets. Specifically, we empirically quantify the mixing state and thermodynamic properties of organic aerosols (e.g., hygroscopicity and droplet condensational uptake coefficient) measured in polluted and non-polluted environments including Alaska, California, and Georgia. It is shown that organic aerosols comprise a substantial portion of the aerosol mass and are often water soluble. CCN measurements are compared to predictions from theory in order to determine the error associated with simplified composition and mixing state assumptions employed by current large-scale models, and these errors are used to constrain the uncertainty of global and regional cloud droplet number and albedo using a recently-developed cloud droplet parameterization adjoint coupled with the GMI chemical transport model. These sensitivities are important because they describe the main determinants of climate forcing. We also present two novel techniques for fast measurements of CCN concentrations with high size, supersaturation, and temporal resolution that substantially improve the state of the art by several orders of magnitude. Ultimately, this work represents a step toward better understanding how atmospheric aerosols influence cloud properties and Earth's climate.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/45945 |
| Date | 14 November 2011 |
| Creators | Moore, Richard Herbert |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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