Representing droplet size distribution and
cloud processes in aerosol-cloud-climate
Representing droplet size distribution and cloud processes in aerosol-cloud-climate interaction studies
The indirect effect of aerosols expresses how changes in aerosols would influence clouds and cause impacts on Earth's climate and hydrological cycle. The current assessment of the interactions between aerosols and clouds is uncertain and parameterizations used to represent cloud processes are not well constrained. This thesis first evaluates a cloud activation parameterization by investigating cloud droplet number concentration closure for stratocumulus clouds sampled during the 2005 MArine Stratus Experiment (MASE). Further analysis of the droplet size distribution characteristics using the extended parameterization is performed by comparing the predicted droplet spectra with the observed ones. The effect of dynamical variability on the droplet size distribution evolution is also investigated by considering a probability density function for updraft velocity. The cumulus and stratocumulus cloud datasets from in-situ field measurements of NASA's Cirrus Regional Study of Tropical Anvils and Cirrus Layers - Florida Area Cirrus Experiment (CRYSTAL-FACE) and Coastal STRatocumulus Imposed Perturbation Experiment (CSTRIPE) campaigns are used for this task. Using the same datasets, the autoconversion rate is calculated based on direct integration of kinematic collection equation (KCE). Six autoconversion parameterizations are evaluated and the effect of turbulence on magnifying collection process is also considered. Finally, a general circulation model (GCM) is used for studying the effect of different autoconversion parameterizations on indirect forcing estimates. The autoconversion rate given by direct KCE integration is also included by implementing a look-up table for collection kernels. Although these studies add more variability to the current estimate of aerosol indirect forcing, they also provide direction towards a more accurate assessment for climate prediction.