Articles | Volume 11, issue 10
https://doi.org/10.5194/gmd-11-4155-2018
https://doi.org/10.5194/gmd-11-4155-2018
Model evaluation paper
 | 
16 Oct 2018
Model evaluation paper |  | 16 Oct 2018

Evaluating simplified chemical mechanisms within present-day simulations of the Community Earth System Model version 1.2 with CAM4 (CESM1.2 CAM-chem): MOZART-4 vs. Reduced Hydrocarbon vs. Super-Fast chemistry

Benjamin Brown-Steiner, Noelle E. Selin, Ronald Prinn, Simone Tilmes, Louisa Emmons, Jean-François Lamarque, and Philip Cameron-Smith

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Cited articles

Abbatt, J., George, C., Melamed, M., Monks, P., Pandis, S., and Rudich, Y.: New Directions: Fundamentals of atmospheric chemistry: Keeping a three-legged stool balanced, Atmos. Environ., 84, 390–391, https://doi.org/10.1016/j.atmosenv.2013.10.025, 2014. 
Aumont, B., Madronich, S., Bey, I., and Tyndall, G. S.: Contribution of secondary VOC to the composition of aqueous atmospheric particles: A modeling approach, J. Atmos. Chem., 35, 59–75, https://doi.org/10.1023/A:1006243509840, 2000. 
Aumont, B., Szopa, S., and Madronich, S.: Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach, Atmos. Chem. Phys., 5, 2497–2517, https://doi.org/10.5194/acp-5-2497-2005, 2005. 
Baker, L. H., Collins, W. J., Olivié, D. J. L., Cherian, R., Hodnebrog, Ø., Myhre, G., and Quaas, J.: Climate responses to anthropogenic emissions of short-lived climate pollutants, Atmos. Chem. Phys., 15, 8201–8216, https://doi.org/10.5194/acp-15-8201-2015, 2015. 
Barnes, E. A., Fiore, A. M., and Horowitz, L. W.: Detection of trends in surface ozone in the presence of climate variability, J. Geophys. Res.-Atmos., 121, 6112–6129, https://doi.org/10.1002/2015JD024397, 2016. 
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Short summary
We conduct three simulations of atmospheric chemistry using chemical mechanisms of different levels of complexity and compare their results to observations. We explore situations in which the simplified mechanisms match the output of the most complex mechanism, as well as when they diverge. We investigate how concurrent utilization of chemical mechanisms of different complexities can further our atmospheric-chemistry understanding at various scales and give some strategies for future research.