Quantifying the sensitivity of aerosol-cloud interactions to the representation of aerosol physical and chemical properties

 

Author

Fierce Laura — Brookhaven National Laboratory

Category

Microphysics (cloud, aerosol and/or precipitation)

Description

Atmospheric aerosol is comprised of distinct multicomponent particles of varied size, shape, and chemical composition, but such particle-scale properties are not easily represented in large-scale atmospheric models. Models necessarily simplify the representation of aerosol size and composition distributions. For example, sectional aerosol schemes track the evolution of aerosol in separate size bins, assuming particles of the same size have the same composition, whereas modal aerosol models track the evolution of separate lognormal size distributions, assuming particles within a mode have the same composition even while assuming the distribution shape and breadth. The error in the resulting cloud properties induced by these approximations has not been well quantified. Here we explore the sensitivity of cloud droplet activation and growth to model approximations of particle size and composition through a series of cloud parcel simulations. We show that aerosol interactions with clouds can be modeled with high accuracy using a new multivariate quadrature-based model, which accurately captures key features of aerosol size-composition distributions using a small number of quadrature abscissas and weights. Using this framework, we identify the environmental regimes in which aerosol activation and growth are most sensitive to the representation of aerosol physical and chemical properties, which is a key first step toward identifying the minimal representation of atmospheric aerosol needed to capture aerosol effects on clouds and climate.