Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)- Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and/or emissions are also evaluated during the same timeframe using spectral decomposition of observed and modeled ozone time series with the aim of identifying the underlying forcing mechanisms that control ozone exceedances and making informed recommendations for the optimal use of regional-scale air quality models. The evaluation is focused on the warm season's (i.e., May-September) daily maximum 8-hr (DM8HR) ozone concentrations, the 4 highest (4) and average of top 10 DM8HR ozone values (top10), as well as the spectrally-decomposed components of the DM8HR ozone time series using the Kolmogorov-Zurbenko (KZ) filter. Results of the dynamic evaluation are presented for six regions in the U.S., consistent with the National Oceanic and Atmospheric Administration (NOAA) climatic regions. During the earlier 11-yr period (1990-2000), the simulated and observed trends are not statistically significant. During the more recent 2000-2010 period, all trends are statistically significant and WRF-CMAQ captures the observed trend in most regions. Given large number of sites for the 2000-2010 period, the model captures the observed trends in the Southwest (SW) and MW but has significantly different trend from that seen in observations for the other regions. Observational analysis reveals that it is the long-term forcing that dictates how high the ozone exceedances will be; there is a strong linear relationship between the long-term forcing and the 4th highest or the average of the top10 ozone concentrations in both observations and model output. This finding indicates that improving the model's ability to reproduce the long-term component will also enable better simulation of ozone extreme values that are of interest to regulatory agencies.

Regional-scale air quality models and observations at routine air quality monitoring sites are used to determine attainment/non-attainment of the ozone air quality standard in the United States. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Because of the stochastic nature of the atmosphere, the same meteorological conditions would not prevail in the future year. Therefore, we use multi-decadal observations to develop a new method for estimating the confidence bounds for the future ozone design value (based on the 4 th highest value in the daily maximum 8-hr ozone concentration time series, DM8HR) for each emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at all monitoring sites in the contiguous United States. To this end, we spectrally decompose the observed DM8HR ozone time series covering the period from 1981 to 2014 using the Kolmogorov-Zurbenko (KZ) filter and examine the variability in the relative strengths of the short-term variations (induced by synoptic-scale weather fluctuations; referred to as synoptic component, SY) and the long-term component (dictated by changes in emissions, seasonality and other slow-changing processes such as climate change; referred to as baseline component, BL). Results indicate that combining the projected change in the ozone baseline level with the adjusted synoptic forcing in historical ozone observations enables us to provide a probabilistic assessment of the efficacy of a selected emissions control strategy in complying with the ozone standard in future years. In addition, attainment demonstration is illustrated with a realworld application of the proposed methodology by using air quality model simulations, thereby helping build confidence in the use of regional-scale air quality models for supporting regulatory policies.

<p><strong>Abstract.</strong> Regional-scale air pollution models are routinely being used world-wide for research, forecasting air quality, and regulatory purposes. It is well known that there are both reducible and irreducible uncertainties in the meteorology-atmospheric chemistry modeling systems. Inherent or irreducible uncertainties stem from our inability to properly characterize stochastic variations in atmospheric dynamics and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations in atmospheric dynamics and emissions forcing influencing the air pollutant concentrations are difficult to explicitly simulate, one can expect to find a percentile value from the distribution of measured concentrations to have much greater variability than that of the model. This paper presents an observation-based methodology to determine the expected errors from regional air quality models even when the model design, physics, chemistry, and numerical analysis techniques as well as its input data were <q>perfect</q>. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8-hr ozone time series are separated from the longer-term forcings using a simple recursive moving average filter. The inherent variability 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. The results reveal that the expected root mean square error at the median and 95<sup>th</sup> percentile is about 2 ppb and 5 ppb, respectively, even for <q>perfect</q> air quality models driven with <q>perfect</q> 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.</p>

Abstract. Regional-scale air quality models are being used for studying the sources, composition, transport, transformation, and deposition of fine particulate matter (PM2.5). The availability of decadal air quality simulations provides a unique opportunity to explore sophisticated model evaluation techniques rather than relying solely on traditional operational evaluations. In this study, we propose a new approach for process-based model evaluation of speciated PM2.5 using improved complete ensemble empirical mode decomposition with adaptive noise (improved CEEMDAN) to assess how well version 5.0.2 of the coupled Weather Research and Forecasting model–Community Multiscale Air Quality model (WRF-CMAQ) simulates the time-dependent long-term trend and cyclical variations in daily average PM2.5 and its species, including sulfate (SO4), nitrate (NO3), ammonium (NH4), chloride (Cl), organic carbon (OC), and elemental carbon (EC). The utility of the proposed approach for model evaluation is demonstrated using PM2.5 data at three monitoring locations. At these locations, the model is generally more capable of simulating the rate of change in the long-term trend component than its absolute magnitude. Amplitudes of the sub-seasonal and annual cycles of total PM2.5, SO4, and OC are well reproduced. However, the time-dependent phase difference in the annual cycles for total PM2.5, OC, and EC reveals a phase shift of up to half a year, indicating the need for proper temporal allocation of emissions and for updating the treatment of organic aerosols compared to the model version used for this set of simulations. Evaluation of sub-seasonal and interannual variations indicates that CMAQ is more capable of replicating the sub-seasonal cycles than interannual variations in magnitude and phase.

Regional air quality models are widely being used to understand the spatial extent and magnitude of the ozone non-attainment problem and to design emission control strategies needed to comply with the relevant ozone standard through direct emission perturbations. In this study, we examine the manageable portion of ground-level ozone using two simulations of the Community Multiscale Air Quality (CMAQ) model for the year 2010 and a probabilistic analysis approach involving 29 years of historical ozone observations. The modeling results reveal that the reduction in the peak ozone levels from total elimination of anthropogenic emissions within the model domain is around 13-21 ppb for the 90 th −100 th percentile range of the daily maximum 8-hr ozone concentrations across the contiguous United States (CONUS). Large reductions in the 4 th highest 8-hr ozone are seen in the regions of West (interquartile range (IQR) of 17-33%), South (IQR 22-34%), Central (IQR 19-31%), Southeast (IQR 25-34%) and Northeast (IQR 24-37%). However, sites in the western portion of the domain generally show smaller reductions even when all anthropogenic emissions are removed, possibly due to the strong influence of global background ozone, including sources such as intercontinental ozone transport, stratospheric ozone intrusions, wildfires, and biogenic precursor emissions. Probabilistic estimates of the exceedances for several hypothetical thresholds of the 4 th highest 8-hr ozone indicate that, in some areas, exceedances of such hypothetical thresholds may occur even with no anthropogenic emissions due to the everpresent atmospheric stochasticity and the current global tropospheric ozone burden.