Quantifying the sensitivity of cloud-aerosol interactions to model representation of the aerosol mixing state
Abstract
A key challenge in the prediction of aerosol effects on clouds is the structural uncertainty induced by simplified representation of particle physical and chemical properties. Particles exhibit tremendous variability in size and composition, which affects their activation into cloud condensation nuclei, but this particle-level detail is not easily represented in large-scale models. Variability in composition among particles in a population or within a size bin is often neglected in large-scale aerosol models, and 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 model simulations. We show that aerosol interactions with clouds are accurately represented by new multivariate quadrature model, which accurately captures key features of aerosol size-composition distributions using a small number of representative particles. Using this new framework, we identify environmental regimes in which aerosol activation and growth is most sensitive to variability in the aerosol mixing state. This study lays the groundwork for a new aerosol-cloud simulation framework that accurately and efficiently represents the multiscale complexity of aerosol effects on clouds.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.A54F..08F
- Keywords:
-
- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0320 Cloud physics and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0365 Troposphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES