Published March 9, 2025 | Version 3
Dataset Open

Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction

Creators

  • 1. University of North Carolina at Chapel Hill
  • 2. University of Colorado, NOAA
  • 3. Forschungszentrum Julich
  • 4. Sun Yat-Sen University
  • 5. Peking University
  • 6. Meteorological Research Institute, Tsukuba
  • 7. Universite de Toulouse
  • 8. NASA Goddard Space Flight Center, Universities Space Research Association
  • 9. National Center for Atmospheric Research
  • 10. NOAA Geophysical Fluid Dynamics Laboratory, Princeton University
  • 11. Nagoya University, Japan Agency for Marine-Earth Science and Technology
  • 12. NOAA Geophysical Fluid Dynamics Laboratory, Princeton University, Pennsylvania State University
  • 13. Institute for Health Metrics and Evaluation, University of British Columbia

Description

This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution.  Also reported is the variance.  Estimates correspond to this paper:

Becker, J. S., DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, H. Wang, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and J. J. West (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, Elementa Science of the Anthropocene, 11: 1, doi: 10.1525/elementa.2022.00025.

The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models.  Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time.  The estimates here are the final results using a weighted RAMP bias correction. 

Notes

Please contact Jason West (jasonwest@unc.edu) with questions about the dataset. We acknowledge funding from the NASA Health and Air Quality Applied Sciences Team (NNX16AQ30G) and the National Institute for Occupational Safety and Health (T42-OH008673). ORC and KLC were supported by the NOAA Cooperative Agreement with CIRES, NA17OAR4320101.MD was supported by the Japan Society for the Promotion of Science (JP20K04070). The MERRA-2 GMI Replay simulation was supported by the NASA Modeling, Analysis and Prediction (MAP) program, with computer resources provided by the NASA Center for Climate Information.

Series information

Version 2 differs from version 1 mainly in that results are now provided at 0.1 degree resolution, and are provided for the whole world from latitude -60 to 75.  The previous version was missing ozone estimates in some grid cells where population is present (along coasts).  Correcting this required redoing the M3Fusion step, and minor changes were made that will cause minor differences in some grid cells from the original.  Ocean grid cells are now modeled in M3Fusion using an average of weights given to each model over all regions, in each year.  Because less attention is given to ocean grid cells, and because of the lack of ozone observations over oceans, we caution that we have less confidence in these areas.  In addition, Version 2 applies the downscaling to 0.1 degree resolution by scaling within each 0.5 degree cell to a fine resolution global model simulation, repeating the methods we applied earlier (described by DeLang et al., 2021).  
 
Version 3 corrects Verson 2 for errors in the TOAR database in Ireland in 2014-2016. 
 

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