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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 05 Sep 2018

Methods for assessment of models | 05 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

A new method (M3Fusion-v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

Kai-Lan Chang1,2, Owen R. Cooper2,3, J. Jason West4, Marc L. Serre4, Martin G. Schultz5, Meiyun Lin6,7, Virginie Marécal8, Béatrice Josse8, Makoto Deushi9, Kengo Sudo10,11, Junhua Liu12,13, and Christoph A. Keller12,13,14 Kai-Lan Chang et al.
  • 1National Research Council Fellow
  • 2NOAA Earth System Research Laboratory, Boulder, CO, USA
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
  • 4Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, NC, USA
  • 5Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, Germany
  • 6NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
  • 7Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
  • 8Météo-France, Centre National de Recherches Météorologiques, Toulouse, France
  • 9Meteorological Research Institute (MRI), Tsukuba, Japan
  • 10Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 11Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokosuka, Japan
  • 12NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 13Universities Space Research Association, Columbia, MD, USA
  • 14John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA, USA

Abstract. We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly-spaced observations onto a fine resolution grid by using integrated nested Laplace approximations, and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of 8 world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias-corrected within two degrees of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean.

Kai-Lan Chang et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Kai-Lan Chang et al.
Data sets

Tropospheric Ozone Assessment Report: Database and Metrics Data of Global Surface Ozone Observations M. G. Schultz, S. Schröder, O. Lyapina, O. R. Cooper, I. Galbally, I. Petropavlovskikh, E. von Schneidemesser, H. Tanimoto, Y. Elshorbany, M. Naja, R. J. Seguel, U. Dauert, P. Eckhardt, S. Feigenspan, M. Fiebig, A.-G. Hjellbrekke, Y.-D. Hong, P. C. Kjeld, H. Koide, G. Lear, D. Tarasick, M. Ueno, M. Wallasch, D. Baumgardner, M.-T. Chuang, R. Gillett, M. Lee, S. Molloy, R. Moolla, T. Wang, K. Sharps, J. A. Adame, G. Ancellet, F. Apadula, P. Artaxo, M. E. Barlasina, M. Bogucka, P. Bonasoni, L. Chang, A. Colomb, E. Cuevas-Agulló, M. Cupeiro, A. Degorska, A. Ding, M. Fröhlich, M. Frolova, H. Gadhavi, F. Gheusi, S. Gilge, M. Y. Gonzalez, V. Gros, S. H. Hamad, D. Helmig, D. Henriques, O. Hermansen, R. Holla, J. Hueber, U. Im, D. A. Jaffe, N. Komala, D. Kubistin, K.-S. Lam, T. Laurila, H. Lee, I. Levy, C. Mazzoleni, L. R. Mazzoleni, A. McClure-Begley, M. Mohamad, M. Murovec, M. Navarro-Comas, F. Nicodim, D. Parrish, K. A. Read, N. Reid, L. Ries, P. Saxena, J. J. Schwab, Y. Scorgie, I. Senik, P. Simmonds, V. Sinha, A. I. Skorokhod, G. Spain, W. Spangl, R. Spoor, S. R. Springston, K. Steer, M. Steinbacher, E. Suharguniyawan, P. Torre, T. Trickl, L. Weili, R. Weller, X. Xu, L. Xue, and M. Zhiqiang

Kai-Lan Chang et al.
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Short summary
We developed a new method for combining surface ozone observations from thousands of monitoring sites worldwide with the output from multiple atmospheric chemistry models. The result is a global surface ozone distribution with greater accuracy than any single model can achieve. We focused on an ozone metric relevant to human mortality caused by long-term ozone exposure. Our method can be applied to studies that quantify the impacts of ozone on human health and mortality.
We developed a new method for combining surface ozone observations from thousands of monitoring...