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New framework for extending cloud chemistry in the Community Multiscale Air Quality (CMAQ) modeling
Citation:
Fahey, K. New framework for extending cloud chemistry in the Community Multiscale Air Quality (CMAQ) modeling. Invited seminar for the UNC Group on Atmospheric Science and Pollution, Chapel Hill, NC, April 22, 2016.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
Clouds and fogs significantly impact the amount, composition, and spatial distribution of gas and particulate atmospheric species, not least of which through the chemistry that occurs in cloud droplets. Atmospheric sulfate is an important component of fine aerosol mass and in an environment where clouds or fogs are present, aqueous phase production of SO4 can dominate over gas phase production (Seigneur and Saxena, 1988). More recent studies have suggested that the aqueous phase of clouds and wet aerosols may be an important medium for the production of secondary organic aerosol (Ervens et al., 2014; Ervens et al., 2011; McNeill, 2015). Due in part to computational constraints, historically, only a simple description of aqueous phase chemistry has been implemented in many chemical transport models. Aqueous phase chemistry in CMAQ, for example, is based on a simple sulfur oxidation scheme from RADM (Walcek and Taylor, 1986) with few updates to the mechanism in recent years (Carlton et al., 2008). The current cloud module in CMAQ has the mechanism embedded in the simple solver (forward euler) and as a result it is not suited to the expansion to more complex chemistry for additional species. Here we describe our usage of the Kinetic PreProcessor (KPP) to facilitate the expansion of CMAQ’s cloud chemistry module and describe three applications of CMAQ with the updated cloud chemistry, solver, and mass transfer treatment.