Abstract. Regional-scale air pollution models are routinely being
used worldwide for research, forecasting air quality, and regulatory
purposes. It is well recognized that there are both reducible (systematic)
and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our
inability to properly characterize stochastic variations in atmospheric
dynamics and chemistry and from the incommensurability associated with
comparisons of the volume-averaged model estimates with point measurements.
Because stochastic variations are not being explicitly simulated in
the current generation of regional-scale meteorology–air quality models, one
should expect to find differences between the model estimates and
corresponding observations. This paper presents an observation-based
methodology to determine the expected errors from current-generation
regional air quality models even when the model design, physics, chemistry,
and numerical analysis, as well as its input data, were “perfect”. To this
end, the short-term synoptic-scale fluctuations embedded in the daily
maximum 8 h ozone time series are separated from the longer-term forcing
using a simple recursive moving average filter. The inherent uncertainty
attributable to the stochastic nature of the atmosphere is determined based
on 30+ years of historical ozone time series data measured at various
monitoring sites in the contiguous United States (CONUS). The results reveal that
the expected root mean square error (RMSE) at the median and 95th percentile
is about 2 and 5 ppb, respectively, even for perfect air quality
models driven with perfect input data. Quantitative estimation of the
limit to the model's accuracy will help in objectively assessing the current
state of the science in regional air pollution models, measuring progress in
their evolution, and providing meaningful and firm targets for improvements
in their accuracy relative to ambient measurements.