Articles | Volume 18, issue 11
https://doi.org/10.5194/acp-18-8373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-18-8373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2018
Research article |
| 15 Jun 2018
| 15 Jun 2018
Maximizing ozone signals among chemical, meteorological, and climatological variability
Benjamin Brown-Steiner
CORRESPONDING AUTHOR
Center for Global Change Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
now at: Atmospheric and Environmental Research, 131 Hartwell Avenue, Lexington, MA 02421, USA
Noelle E. Selin
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Institute for Data, Systems, and Society, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Ronald G. Prinn
Center for Global Change Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Erwan Monier
Center for Global Change Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA 02139, USA
Simone Tilmes
Atmospheric Chemistry Observations and Modeling Lab, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA
Louisa Emmons
Atmospheric Chemistry Observations and Modeling Lab, National Center for Atmospheric Research, 3450 Mitchell Lane, Boulder, CO 80301, USA
Fernando Garcia-Menendez
Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA
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Cited
10 citations as recorded by crossref.
- Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution B. Pienkosz et al. https://doi.org/10.1029/2019EF001195
- 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 B. Brown-Steiner et al. https://doi.org/10.5194/gmd-11-4155-2018
- Importance of dry deposition parameterization choice in global simulations of surface ozone A. Wong et al. https://doi.org/10.5194/acp-19-14365-2019
- Rapid Estimation of Climate–Air Quality Interactions in Integrated Assessment Using a Response Surface Model S. Eastham et al. https://doi.org/10.1021/acsenvironau.2c00054
- Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. https://doi.org/10.1016/j.atmosenv.2024.120793
- Multi-decadal surface ozone trends at globally distributed remote locations O. Cooper et al. https://doi.org/10.1525/elementa.420
- Tropospheric Ozone Assessment Report A. Archibald et al. https://doi.org/10.1525/elementa.2020.034
- Assessing ENSO as a Driver of Climate Variability in Cameroon from 1981 To 2023: A Zonal Disaggregation and Composite Based Analysis across Agroecological Zones B. Benoir et al. https://doi.org/10.1007/s41748-025-00848-z
- A Geographically Weighted Gaussian Process Regression (GW-GPR) emulator of anthropogenic PM2.5 from the GEOS-Chem High Performance (GCHP) 13.0.0 global chemical transport model A. Wong et al. https://doi.org/10.5194/gmd-19-3335-2026
- An analysis of 30 years of surface ozone concentrations in Austria: temporal evolution, changes in precursor emissions and chemical regimes, temperature dependence, and lessons for the future M. Mayer et al. https://doi.org/10.1039/D2EA00004K
10 citations as recorded by crossref.
- Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution B. Pienkosz et al. https://doi.org/10.1029/2019EF001195
- 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 B. Brown-Steiner et al. https://doi.org/10.5194/gmd-11-4155-2018
- Importance of dry deposition parameterization choice in global simulations of surface ozone A. Wong et al. https://doi.org/10.5194/acp-19-14365-2019
- Rapid Estimation of Climate–Air Quality Interactions in Integrated Assessment Using a Response Surface Model S. Eastham et al. https://doi.org/10.1021/acsenvironau.2c00054
- Fine particulate matter and ozone variability with regional and local meteorology in Beijing, China S. Guha et al. https://doi.org/10.1016/j.atmosenv.2024.120793
- Multi-decadal surface ozone trends at globally distributed remote locations O. Cooper et al. https://doi.org/10.1525/elementa.420
- Tropospheric Ozone Assessment Report A. Archibald et al. https://doi.org/10.1525/elementa.2020.034
- Assessing ENSO as a Driver of Climate Variability in Cameroon from 1981 To 2023: A Zonal Disaggregation and Composite Based Analysis across Agroecological Zones B. Benoir et al. https://doi.org/10.1007/s41748-025-00848-z
- A Geographically Weighted Gaussian Process Regression (GW-GPR) emulator of anthropogenic PM2.5 from the GEOS-Chem High Performance (GCHP) 13.0.0 global chemical transport model A. Wong et al. https://doi.org/10.5194/gmd-19-3335-2026
- An analysis of 30 years of surface ozone concentrations in Austria: temporal evolution, changes in precursor emissions and chemical regimes, temperature dependence, and lessons for the future M. Mayer et al. https://doi.org/10.1039/D2EA00004K
Saved (final revised paper)
Latest update: 31 May 2026
Short summary
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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