Using Improved Aerosol Optical Thickness (AOT) and Cloud Condensation Nuclei (CCN) Relationship and Aerosol Composition to Study the Impact of Aerosol on Cloud Microphysics

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The aerosol-cloud-interaction (ACI) is one of the largest uncertainties of all known climate forcing mechanisms. In the nutshell, the ACI is ultimately determined by the responses of cloud properties to variations in cloud condensation nuclei (CCN) whose knowledge is thus the key to understanding the ACI. Due to the impracticability of making large-scale continuous observations of CCN, the more readily measured aerosol optical quantities, such as aerosol optical depth (AOD), have been widely used as a proxy for CCN in ACI studies even though both properties represent different aspects of aerosols. On the other hand, the two properties are affected by aerosol size distribution and chemical composition, so are linked to a certain degree. In the first phase of this study funded by the NSF, the relationship between AOD and CCN with a focus on the influences of aerosol physical properties and ambient meteorology has been exploited.The aerosol-cloud-interaction (ACI) is one of the largest uncertainties of all known climate forcing mechanisms. In the nutshell, the ACI is ultimately determined by the responses of cloud properties to variations in cloud condensation nuclei (CCN) whose knowledge is thus the key to understanding the ACI. Due to the impracticability of making large-scale continuous observations of CCN, the more readily measured aerosol optical quantities, such as aerosol optical depth (AOD), have been widely used as a proxy for CCN in ACI studies even though both properties represent different aspects of aerosols. On the other hand, the two properties are affected by aerosol size distribution and chemical composition, so are linked to a certain degree. In the first phase of this study funded by the NSF, the relationship between AOD and CCN with a focus on the influences of aerosol physical properties and ambient meteorology has been exploited. This study will continue the pursuit but focus on investigating the effects of aerosol composition and hygroscopicity on the estimation of the ACI, estimating and reducing uncertainties due to the use of such relationships by means of both observations and modeling. Intellectual Merit:1. A comprehensive analysis on the responses of cloud microphysical properties to aerosol optical, chemical, and hygroscopic properties will help gain deeper insights into the mechanisms behind the aerosol indirect effect.2. Accounting for the effects will help identify and quantify a bias in the estimate of the aerosol indirect effects by the conventional approach using ground or satellite measurements of AOD as a proxy for CCN to obtain better estimate of the ACI for different types of aerosols under diverse meteorological conditions.3. Modeling helps explain the mechanisms behind how cloud microphysical properties respond to aerosol composition/hygroscopicity by accounting for the influence of dynamic and thermodynamic conditions.Broader Impacts:1. With an improved knowledge and understanding of the influence of aerosol chemical composition and/or hygroscopicity on cloud microphysical properties, the mechanism of aerosol indirect effect will be better understood. This will help narrow the large range of uncertainties as identified by the Intergovernmental Panel on Climate Change (IPCC) reports.2. The research will provide interdisciplinary training opportunities for undergraduate and graduate students, and postdoctoral fellows in measuring aerosols, retrieving cloud properties, data analysis, and modeling.3. The study will have an immediate impact on the teaching of cloud physics, atmospheric chemistry, climate, and environmental courses.