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Volume 18, issue 20
Atmos. Chem. Phys., 18, 15345-15361, 2018
https://doi.org/10.5194/acp-18-15345-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue: Global and regional assessment of intercontinental transport...

Atmos. Chem. Phys., 18, 15345-15361, 2018
https://doi.org/10.5194/acp-18-15345-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 25 Oct 2018

Research article | 25 Oct 2018

Peroxy acetyl nitrate (PAN) measurements at northern midlatitude mountain sites in April: a constraint on continental source–receptor relationships

Arlene M. Fiore1,2, Emily V. Fischer3, George P. Milly2, Shubha Pandey Deolal4, Oliver Wild5, Daniel A. Jaffe6,7, Johannes Staehelin4, Olivia E. Clifton1,2,a, Dan Bergmann8, William Collins9, Frank Dentener10, Ruth M. Doherty11, Bryan N. Duncan12, Bernd Fischer13, Stefan Gilge14,b, Peter G. Hess15, Larry W. Horowitz16, Alexandru Lupu17,c, Ian A. MacKenzie11, Rokjin Park18, Ludwig Ries19, Michael G. Sanderson20, Martin G. Schultz21, Drew T. Shindell22, Martin Steinbacher23, David S. Stevenson11, Sophie Szopa24, Christoph Zellweger23, and Guang Zeng25 Arlene M. Fiore et al.
  • 1Department of Earth and Environmental Science, Columbia University, Palisades, NY 10964, USA
  • 2Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
  • 3Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80521, USA
  • 4Institute for Atmospheric and Climate Science, ETH Zürich, Switzerland
  • 5Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
  • 6School of STEM, University of Washington, Bothell, WA 98011, USA
  • 7Department of Atmospheric Science, University of Washington, Seattle, WA 98195, USA
  • 8Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
  • 9Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
  • 10European Commission, Joint Research Centre, Ispra, 21027, Italy
  • 11School of GeoSciences, The University of Edinburgh, Edinburgh, EH9 3FF, UK
  • 12Atmospheric Chemistry and Dynamics Laboratory, NASA GSFC, Greenbelt, MD 20720, USA
  • 13Federal Environment Agency (UBA), Schauinsland, 79254, Oberried, Germany
  • 14Meteorological Observatory Hohenpeissenberg, German Meteorological Service (DWD), Hohenpeissenberg, Germany
  • 15Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
  • 16Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration, Princeton, NJ 08540, USA
  • 17Centre for Research in Earth and Space Science, York University, Toronto, M3J 1P3, Canada
  • 18School of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
  • 19II4.5.7, German Environment Agency (UBA), Zugspitze, 82475, Germany
  • 20Met Office, Exeter, EX1 3PB, UK
  • 21Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
  • 22Nicholas School of the Environment, Duke University, Durham, NC 27708, USA
  • 23Laboratory for Air Pollution/Environmental Technology, Empa – Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, 8600, Switzerland
  • 24Laboratoire des Sciences du Climat et de l'Environnement, Institut Pierre Simon Laplace, CEA/CNRS/UVSQ, Gif-sur-Yvette, France
  • 25National Institute of Water and Atmospheric Research, Wellington, 6021, New Zealand
  • anow at: Advanced Study Program, National Center for Atmospheric Research, Boulder, CO, USA
  • bnow at: DWD, Research Center Human Biometeorology, Freiburg, Germany
  • cnow at: Air Quality Research Division, Environment and Climate Change Canada, Toronto, M3H 5T4, Canada

Abstract. Abundance-based model evaluations with observations provide critical tests for the simulated mean state in models of intercontinental pollution transport, and under certain conditions may also offer constraints on model responses to emission changes. We compile multiyear measurements of peroxy acetyl nitrate (PAN) available from five mountaintop sites and apply them in a proof-of-concept approach that exploits an ensemble of global chemical transport models (HTAP1) to identify an observational emergent constraint. In April, when the signal from anthropogenic emissions on PAN is strongest, simulated PAN at northern midlatitude mountaintops correlates strongly with PAN source–receptor relationships (the response to 20% reductions in precursor emissions within northern midlatitude continents; hereafter, SRRs). This finding implies that PAN measurements can provide constraints on PAN SRRs by limiting the SRR range to that spanned by the subset of models simulating PAN within the observed range. In some cases, regional anthropogenic volatile organic compound (AVOC) emissions, tracers of transport from different source regions, and SRRs for ozone also correlate with PAN SRRs. Given the large observed interannual variability in the limited available datasets, establishing strong constraints will require matching meteorology in the models to the PAN measurements. Application of this evaluation approach to the chemistry–climate models used to project changes in atmospheric composition will require routine, long-term mountaintop PAN measurements to discern both the climatological SRR signal and its interannual variability.

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We demonstrate a proof-of-concept approach for applying northern midlatitude mountaintop peroxy acetyl nitrate (PAN) measurements and a multi-model ensemble during April to constrain the influence of continental-scale anthropogenic precursor emissions on PAN. Our findings imply a role for carefully coordinated multi-model ensembles in helping identify observations for discriminating among widely varying (and poorly constrained) model responses of atmospheric constituents to changes in emissions.
We demonstrate a proof-of-concept approach for applying northern midlatitude mountaintop peroxy...
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