Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2017-954
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
28 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).
Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability
Benjamin Brown-Steiner1,2,a, Noelle E. Selin2,3,4, Ronald G. Prinn1,2,4, Erwan Monier1,2, Simone Tilmes5, Louisa Emmons5, and Fernando Garcia-Menendez6 1Center for Global Change Science, Massachusetts Institute of Technology, 7 Massachusetts Ave, Cambridge, MA 02139
2Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 7 Massachusetts Ave, Cambridge, MA 02139
3Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 7 Massachusetts Ave, Cambridge, MA 02139
4Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 7 Massachusetts Ave, Cambridge, MA 02139
5Atmospheric Chemistry Observations and Modeling Lab, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301
6Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695
anow at: Atmospheric and Environmental Research, Lexington, Massachusetts, 02421
Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical and meteorological variabilities that exist both in space and in time. This can present difficulties for both policy-makers and researchers as they attempt to identify the influence or signal of climate trends (e.g. any pauses in warming trends), the impact of enacted emission reductions policies (e.g. United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g. summertime ozone over the Northeastern United States). Here we examine the scale-dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the Continental US. We recommend temporal averaging of at least 10–15 years combined with regional spatial averaging over several hundred kilometer spatial scales. These results are consistent between simulated and observed data, and within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data, and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.

Citation: Brown-Steiner, B., Selin, N. E., Prinn, R. G., Monier, E., Tilmes, S., Emmons, L., and Garcia-Menendez, F.: Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-954, in review, 2017.
Benjamin Brown-Steiner et al.
Benjamin Brown-Steiner et al.
Benjamin Brown-Steiner et al.

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