Source specific bias correction of US background ozone modeled in CMAQ

United States (US) background ozone (O ₃) is the counterfactual O ₃ that would exist with zero US anthropogenic emissions. Estimates of US background O ₃ typically come from chemical transport models (CTMs), but different models vary in their estimates of both background and total O ₃. Here, a measurement-model data fusion approach is used to estimate CTM biases in US anthropogenic O ₃ and multiple US background O ₃ sources, including natural emissions, long-range international emissions, short-range international emissions from Canada and Mexico, and stratospheric O ₃. Spatially and temporally varying bias correction factors adjust each simulated O ₃ component so that the sum of the adjusted components evaluates better against observations compared to unadjusted estimates. The estimated correction factors suggest a seasonally consistent positive bias in US anthropogenic O ₃ in the eastern US, with the bias becoming higher with coarser model resolution and with higher simulated total O ₃ though the bias does not increase much with higher observed O ₃. Correlation among different US background O ₃ components can increase the uncertainty in the estimation of the source-specific adjustment factors. Despite this, results indicate that there may be a negative bias in modeled estimates of the impact of stratospheric O ₃ at the surface. This type of data fusion approach can be extended to include data from multiple models to leverage the strengths of different data sources while reducing uncertainty in the US background ozone estimates.