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Table 2

Summary of global model evaluation approaches for tropospheric ozone. DOI: https://doi.org/10.1525/elementa.265.t2

Evaluation TechniqueMeasurementsMetricsModel skill or processExample References

 
1. Basic model evaluation Monthly mean climatology compiled from ground-based, aircraft or satellite measurements. Field campaign data sometimes used, if suitably averaged or model constrained to the appropriate meteorology. Standard statistical metrics: mean bias (MB), mean normalized gross error (MNGE), mean normalized bias error (MNBE), root mean square error (RMSE), temporal/spatial correlation coefficient (r), Fourier-like (sine and cosine) fits Seasonal cycle, spatial distribution Stevenson et al. (2006); Fiore et al. (2009); Bowman et al. (2013); Young et al. (2013); Tilmes et al. (2015); Hu et al. (2017) 
2. Evaluation of high frequency model output Hourly surface ozone measurements Standard statistical metrics, spectral (frequency domain) analysis, empirical orthogonal functions (EOFs) Extreme ozone episodes; timing and amplitude of daily, sub-seasonal, seasonal and annual cycles; spatio-temporal patterns of ozone variability Eder et al. (1993); Fiore et al. (2003); Hess and Mahowald (2009); Zhang et al. (2011); Lin et al. (2012a); Schnell et al. (2014, 2015); Brown-Steiner et al. (2015); Bowdalo et al. (2016); Solazzo and Galmarini (2016) 
3. Evaluation of long-term changes and variability Long records from satellites, aircraft and remote surface sites; indices of climate variability (e.g., ENSO) Standard statistical metrics Long term changes and trends in ozone; sub-decadal to seasonal variability (e.g., ENSO, Madden-Julien Oscillation, etc.) Lamarque and Hess (2004); Oman et al. (2011); Lin et al. (2014); Sekiya and Sudo (2012); Hess and Zbinden (2013); Neu et al. (2014); Parrish et al. (2014); Strode et al. (2015); Ziemke et al. (2015) 
4. Relationship between ozone and meteorological parameters High frequency surface ozone and meteorological parameter measurements Correlation and regression techniques (e.g., ozone-temperature relationships) Processes driving surface ozone levels, extremes Lin et al. (2001); Bloomer et al. (2009); Steiner et al. (2010); Rasmussen et al. (2012); Tawfik and Steiner (2013); Brown-Steiner et al. (2015); Pusede et al. (2015); Camalier et al. (2007) 
5. Relationship between ozone and other chemical species Co-measurements of ozone and other tracers (e.g., CO, NOx, water vapor) Correlation techniques Emissions, origin of air parcels, chemical processing, and atmospheric transport and mixing processes Mauzerall et al. (1998); Auvray et al. (2007); Pan et al. (2007); Hegglin et al. (2009); Voulgarakis et al. (2011); Borbon et al. (2013); Arnold et al. (2015); Hassler et al. (2016) 
Evaluation TechniqueMeasurementsMetricsModel skill or processExample References

 
1. Basic model evaluation Monthly mean climatology compiled from ground-based, aircraft or satellite measurements. Field campaign data sometimes used, if suitably averaged or model constrained to the appropriate meteorology. Standard statistical metrics: mean bias (MB), mean normalized gross error (MNGE), mean normalized bias error (MNBE), root mean square error (RMSE), temporal/spatial correlation coefficient (r), Fourier-like (sine and cosine) fits Seasonal cycle, spatial distribution Stevenson et al. (2006); Fiore et al. (2009); Bowman et al. (2013); Young et al. (2013); Tilmes et al. (2015); Hu et al. (2017) 
2. Evaluation of high frequency model output Hourly surface ozone measurements Standard statistical metrics, spectral (frequency domain) analysis, empirical orthogonal functions (EOFs) Extreme ozone episodes; timing and amplitude of daily, sub-seasonal, seasonal and annual cycles; spatio-temporal patterns of ozone variability Eder et al. (1993); Fiore et al. (2003); Hess and Mahowald (2009); Zhang et al. (2011); Lin et al. (2012a); Schnell et al. (2014, 2015); Brown-Steiner et al. (2015); Bowdalo et al. (2016); Solazzo and Galmarini (2016) 
3. Evaluation of long-term changes and variability Long records from satellites, aircraft and remote surface sites; indices of climate variability (e.g., ENSO) Standard statistical metrics Long term changes and trends in ozone; sub-decadal to seasonal variability (e.g., ENSO, Madden-Julien Oscillation, etc.) Lamarque and Hess (2004); Oman et al. (2011); Lin et al. (2014); Sekiya and Sudo (2012); Hess and Zbinden (2013); Neu et al. (2014); Parrish et al. (2014); Strode et al. (2015); Ziemke et al. (2015) 
4. Relationship between ozone and meteorological parameters High frequency surface ozone and meteorological parameter measurements Correlation and regression techniques (e.g., ozone-temperature relationships) Processes driving surface ozone levels, extremes Lin et al. (2001); Bloomer et al. (2009); Steiner et al. (2010); Rasmussen et al. (2012); Tawfik and Steiner (2013); Brown-Steiner et al. (2015); Pusede et al. (2015); Camalier et al. (2007) 
5. Relationship between ozone and other chemical species Co-measurements of ozone and other tracers (e.g., CO, NOx, water vapor) Correlation techniques Emissions, origin of air parcels, chemical processing, and atmospheric transport and mixing processes Mauzerall et al. (1998); Auvray et al. (2007); Pan et al. (2007); Hegglin et al. (2009); Voulgarakis et al. (2011); Borbon et al. (2013); Arnold et al. (2015); Hassler et al. (2016) 
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