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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 11
Atmos. Chem. Phys., 18, 8373-8388, 2018
https://doi.org/10.5194/acp-18-8373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 18, 8373-8388, 2018
https://doi.org/10.5194/acp-18-8373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 15 Jun 2018

Research article | 15 Jun 2018

Maximizing ozone signals among chemical, meteorological, and climatological variability

Benjamin Brown-Steiner1,2,a, Noelle E. Selin2,4,5, Ronald G. Prinn1,2,5, Erwan Monier1,2, Simone Tilmes6, Louisa Emmons6, and Fernando Garcia-Menendez3 Benjamin Brown-Steiner et al.
  • 1Center for Global Change Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
  • 2Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
  • 3Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA
  • 4Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
  • 5Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
  • 6Atmospheric Chemistry Observations and Modeling Lab, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA
  • anow at: Atmospheric and Environmental Research, 131 Hartwell Avenue, Lexington, MA 02421, USA

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, meteorological, and climatological variabilities (and their interactions) that exist both in space and in time, and which include variability in emissions and surface processes. This can present difficulties for both policymakers 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. For signals that are large compared to the meteorological variability (e.g., strong emissions reductions), shorter averaging periods and smaller spatial averaging regions may be sufficient, but for many signals that are smaller than or comparable in magnitude to the underlying meteorological variability, we recommend temporal averaging of 10–15 years combined with some level of spatial averaging (up to several hundred kilometers). If this level of averaging is not practical (e.g., the signal being examined is at a local scale), we recommend some exploration of the spatial and temporal variability to provide context and confidence in the robustness of the result. These results are consistent between simulated and observed data, as well as 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.

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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.
Detecting signals in observations and simulations of atmospheric chemistry is difficult due to...
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