NCL script: cmaq_ensemble_isam_4panels_subdomain.ncl Netcdf input file for NCL script, containing ensemble means and standard deviation of ISAM SO4 and O3 contributions from IPM: test.nc Plot (ps): maps_isam_mean_std_lasthour_ipm_so4_o3_east.ps Plot (pdf): maps_isam_mean_std_lasthour_ipm_so4_o3_east.pdf Plot (ncgm): maps_isam_mean_std_lasthour_ipm_so4_o3_east.ncgm. This dataset is not publicly accessible because: This contains a dataset that is well over 1Gb, so link provided to US EPA's HPC system where all information can be retrieved. It can be accessed through the following means: /asm/grc/JGR_ENSEMBLE_ScienceHub/Figure12.tar.gz. Format: Tar file with scripts and datasets needed to reproduce Figure 12. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Fortran/NCARgraphics program to compute and plot RRF mean and variability:map_rrf_variability_13runs_epimax.f Ioapi files needed by Fortran/NCARGraphics code: CMAQ.CONC.SREF.June2011.New.13runs.o3_8hrdm CMAQ.CONC.SREF.June2011.N50V25.New.13runs.o3_8hrdm GRIDCRO2D_060607 Plotting routines map_rrf_mean_sigma_ne_13runs_epimax.ps map_rrf_mean_sigma_ne_13runs_epimax.ncgm. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Fortran/NCARgraphics program to compute and plot RRF mean and variability:map_rrf_variability_13runs_epimax.f Ioapi files needed by Fortran/NCARGraphics code: CMAQ.CONC.SREF.June2011.New.13runs.o3_8hrdm CMAQ.CONC.SREF.June2011.N50V25.New.13runs.o3_8hrdm GRIDCRO2D_060607 Plotting routines map_rrf_mean_sigma_ne_13runs_epimax.ps map_rrf_mean_sigma_ne_13runs_epimax.ncgm. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

This is a NetCDF file in ioapi format that contains the probability that ozone is above the 8 hr max O3 standard for the four days of the simulation. This dataset is not publicly accessible because: The file size is a large binary NetCDF file of 56Mb. It can be accessed through the following means: File is located on US EPA's HPC system sol file archive: /asm/grc/JGR_ENSEMBLE_ScienceHub/Figure9.nc. Format: NetCDF file that contains the O3 gridded data to reproduce Figure 9. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

This is a NetCDF file in ioapi format that contains the probability that ozone is above the 8 hr max O3 standard for the four days of the simulation. This dataset is not publicly accessible because: The file size is a large binary NetCDF file of 56Mb. It can be accessed through the following means: File is located on US EPA's HPC system sol file archive: /asm/grc/JGR_ENSEMBLE_ScienceHub/Figure9.nc. Format: NetCDF file that contains the O3 gridded data to reproduce Figure 9. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

Two files provided. The ENS.tar file contains text data files (*.csv) used to create Figure 7 and Figure 8. The Figure7.txt is an R script that reads these files and generates the plots for various cities including the four published in Figure 7 and Figure 8. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

R script for the reproduction of Figure6. This script accesses archived CMAQ and WRF model output on US EPA's HPC sol computer system and plots forward trajectories and ozone concentrations from major cities in the US. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

R script: ensemble_rrf_sigma_vs_mean_play.R Data: ensemble_mean_sigma_rrf_allgrids_epismax_new_13runs.csv Plot: boxplot_ensemble_rrf_sigma_vs_mean_nowater_new_13runs_epimax.pdf. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

R script: ensemble_rrf_sigma_vs_mean_play.R Data: ensemble_mean_sigma_rrf_allgrids_epismax_new_13runs.csv Plot: boxplot_ensemble_rrf_sigma_vs_mean_nowater_new_13runs_epimax.pdf. This dataset is associated with the following publication: Gilliam , R., C. Hogrefe , J. Godowitch, S. Napelenok , R. Mathur , and S.T. Rao. Impact of inherent meteorology uncertainty on air quality model predictions. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 120(23): 12,259–12,280, (2015).

The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. This dataset is not publicly accessible because: The size of the data provided to the external researchers (>1TB) exceeds ScienceHub limits. It can be accessed through the following means: Data can be requested by contacting hogrefe.christian@epa.gov (EPA/ORD/CEMM-contributed dataset) and henderson.barron@epa.gov (EPA/OAR/OAQPS-contributed dataset) and providing an external hard to which the data can then be copied by staff at the National Computing Center. The model simulations are stored on the /asm archival system accessible through the atmos high-performance computing (HPC) system. Due to data management policies, files on /asm are subject to expiry depending on the template of the project. Files not requested for extension after the expiry date are deleted permanently from the system. Location of EPA/ORD/CEMM-provided CMAQ model output data on asm: • /asm/grc/NRT_WRF_CMAQ/model_outputs/nhemi108/cctm.conc • /asm/MOD3EVAL/css/NRT/data/gatech/bc • /asm/MOD3EVAL/css/NRT/data/gatech/scripts • /asm/MOD3EVAL/css/NRT/data/gatech/metbdy3d Location of EPA/OAR/OAQPS-provided CMAQ model output data on asm: • /asm/ROMO/global/CMAQv5.2/2016fe_hemi_cb6_16jh/108km/output • /asm/ROMO/global/CMAQv5.2.1/2016fe_hemi_cb6_16jh/108km/ZUSA/output • /asm/ROMO/2016platform/CMAQv521/2016fe_cb6r3_ae6nvpoa_16j/12US2/output • /asm/ROMO/2016platform/CMAQv521/2016fe_zusa_cb6r3_ae6nvpoa_16j/12US2/output. Format: The CMAQ model output datasets used for the analysis presented in this manuscript and documented here were provided by scientists in EPA/ORD/CEMM and EPA/OAR/OAQPS. The EPA/ORD/CEMM-contributed dataset consisted of hourly CMAQ output for all model species from a 2017 simulation over the northern hemisphere along a boundary curtain of a 36 km modeling domain specified over the CONUS. The horizontal and vertical extent of the 36 km modeling domain was specified by the external collaborator and was defined by 524 boundary grid cells and 34 vertical layers. The number of output species from the 2017 hemispheric CMAQ simulation was 191 through to September 23, 2017 and 213 starting September 24, 2017. The EPA/OAR/OAQPS-contributed dataset consistent of hourly gridded CMAQ output for surface ozone concentrations from four model simulations for the year 2016. Two of these simulations were performed over the northern hemisphere at a horizontal resolution of 108 km and the other two simulations were performed over the CONUS at a horizontal resolution of 12 km. The data files with the CMAQ model output provided to the external researchers use the ioapi/netcdf format. Documentation of this format, including definitions of the geographical projection attributes contained in the file headers, are available at https://www.cmascenter.org/ioapi/documentation/all_versions/html. This dataset is associated with the following publication: Skipper, T.N., Y. Hu, M.T. Odman, B. Henderson, C. Hogrefe, R. Mathur, and A. Russell. EST Publication: Estimating US background ozone levels using data fusion. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA,