Various studies of observational data have established statistical relationships between cloud microphysical properties and aerosol concentrations consistent with an assumed aerosol indirect effect. However, covariance of aerosol concentrations and meteorological cloud-controlling factors leads to a variety of responses beyond the enhancement of CDNC at increased aerosol concentrations. Time history in 3-D data is needed to overcome some of the issues. To interpret correlations found in observational data, process model studies can establish causality, whereas process models applied to larger scales can be used to comprehend aerosol effects beyond instantaneous influences. A better understanding of models at the process scale may help us improve parameter-izations in large-scale models.

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