American Association for Aerosol Research - Abstract Submission

AAAR 35th Annual Conference
October 17 - October 21, 2016
Oregon Convention Center
Portland, Oregon, USA

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Common Model Assumptions Increase Uncertainties in Describing Atmospheric New Particle Formation

TINJA OLENIUS, Jan Julin, Ilona Riipinen, Stockholm University

     Abstract Number: 285
     Working Group: Aerosol Physics

Abstract
In climate and air quality models, new particle formation (NPF) from condensable vapors is incorporated by feeding in the particle formation rate at a size around a few nanometers. Traditionally, the formation rate has been obtained from parameterizations based on the classical nucleation theory or observations, both of which involve semi-empirical corrections or scaling factors in order to produce formation rates of a realistic order of magnitude. To improve the understanding of NPF and its effects on climate, modeling studies have now started to replace the scaled approaches by results from quantum mechanics -based molecular modeling or well-controlled chamber experiments that have recently become available.

However, even when using state-of-the-art particle formation rates, incorporating the rates in a climate model involves making certain assumptions or approximations. These assumptions arise from the interface between the molecular-level scheme that describes the initial formation process, i.e. the formation rate, and the aerosol dynamics model that simulates the particle growth to larger sizes. We investigate the uncertainties related to the model interface with a detailed sub-10 nm particle dynamics model that allows simultaneous dynamical modeling of both the initial formation process and the early growth steps. Preliminary results suggest that incorporating the formation rates as is done in conventional aerosol dynamics models may result in uncertainties of factors of at least ca. 2-10 in the concentrations of nanometer-sized particles. These uncertainties may be significant in the interpretation of modeling data and predictions of secondary aerosol concentrations.