Articles | Volume 18, issue 11
Atmos. Chem. Phys., 18, 8373–8388, 2018
https://doi.org/10.5194/acp-18-8373-2018
Atmos. Chem. Phys., 18, 8373–8388, 2018
https://doi.org/10.5194/acp-18-8373-2018

Research article 15 Jun 2018

Research article | 15 Jun 2018

Maximizing ozone signals among chemical, meteorological, and climatological variability

Benjamin Brown-Steiner et al.

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Cited articles

Angélil, O., Stone, D., Perkins-Kirkpatrick, S., Alexander, L. V., Wehner, M., Shiogama, H., Wolski, P., Ciavarella, A., and Christidis, N.: On the nonlinearity of spatial scales in extreme weather attribution statements, Clim. Dynam., 50, 2739–2752, 2017. 
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, 2016. 
Brown-Steiner, B., Hess, P. G., and Lin, M. Y.: On the capabilities and limitations of GCCM simulations of summertime regional air quality: A diagnostic analysis of ozone and temperature simulations in the US using CESM CAM-chem, Atmos. Environ., 101, 134–148, 2015. 
Brown-Steiner, B., Selin, N. E., Prinn, R., Tilmes, S., Emmons, L., Lamarque, J.-F., and Cameron-Smith, P.: Evaluating Simplified Chemical Mechanisms within Present-Day Simulations of CESM Version 1.2 CAM-chem (CAM4): MOZART-4 vs. Reduced Hydrocarbon vs. Super-Fast Chemistry, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-16, in review, 2018. 
Camalier, L., Cox, W., and Dolwick, P.: The effects of meteorology on ozone in urban areaas and their use in assessing ozone trends, Atmos. Environ., 41, 7127–7137, 2007. 
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Detecting signals in observations and simulations of atmospheric chemistry is difficult due to the underlying variability in the chemistry, meteorology, and climatology. Here we examine the scale dependence of ozone variability and explore strategies for reducing or averaging this variability and thereby enhancing ozone signal detection capabilities. We find that 10–15 years of temporal averaging, and some level of spatial averaging, reduces the risk of overconfidence in ozone signals.
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