Estimating US Background Ozone Using Data Fusion.

US background (US-B) ozone (O) is the O that would be present in the absence of US anthropogenic (US-A) emissions. US-B O varies by location and season and can make up a large, sometimes dominant, portion of total O. Typically, US-B O is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O. We develop and apply a method to fuse observed O with US-B O simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O. Trends in O bias are explored across different simulation years and varying model scales. We found that the CTM US-B O estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O. Across the four estimates, we found annual mean CTM-simulated US-B O ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.