It has been historically assumed that most of the uncertainty associated with the aerosol indirect effect on climate can be attributed to the unpredictability of updrafts. We assess the sensitivity of cloud droplet number density to realistic variations in aerosol chemical properties and to variable updraft velocities using a 1-dimensional cloud parcel model. The results suggest that aerosol chemical variability may be as important to the aerosol indirect effect as the effect of unresolved cloud dynamics, especially in polluted environments. We next used a continuous flow streamwise thermal gradient Cloud Condesnation Nuclei counter (CCNc) to study the water-uptake properties of the ambient aerosol, by exposing an aerosol sample to a controlled water vapor supersaturation and counting the resulting number of droplets. The heat transfer properties and droplet growth within the CCNc were first modeled and experimentally characterized. We describe results from the MIRAGE field campaign at a ground-based site during March, 2006. Size-resolved CCN activation spectra and hygroscopic growth factor distributions of the ambient aerosol in Mexico City were obtained, and an analytical technique was developed to quantify a probability distribution of solute volume fractions for the CCN, as well as the aerosol mixing-state. The CCN were shown to be much less CCN active than ammonium sulfate, with water uptake properties more consistent with low molecular weight organic compounds. We also describe results from the GoMACCS field study, an airborne field campaign in Houston, Texas during August-September, 2006. GoMACCS tested our ability to predict CCN for highly polluted conditions with limited chemical information. Assuming the particles were composed purely of ammonium sulfate, CCN closure was obtained with a 10% overprediction bias on average for CCN concentrations ranging from less than 100 cm-3 to over 10,000 cm-3, but with on average 50% variability. Assuming measured concentrations of organics to be internally mixed and insoluble tended to reduce the overprediction bias for less polluted conditions, but led to underprediction bias in the most polluted conditions. Comparing the two campaigns, it is clear that the chemistry of the particles plays an important role in our ability to predict CCN concentrations.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/26700 |
| Date | 14 November 2007 |
| Creators | Lance, Sara |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0145 seconds