[1] We use observations from two aircraft during the ICARTT campaign over the eastern United States and North Atlantic during summer 2004, interpreted with a global 3-D model of tropospheric chemistry (GEOS-Chem) to test current understanding of regional sources, chemical evolution, and export of NO x . The boundary layer NO x data provide top-down verification of a 50% decrease in power plant and industry NO x emissions over the eastern United States between 1999 and 2004. Observed NO x concentrations at 8-12 km altitude were 0.55 ± 0.36 ppbv, much larger than in previous U.S. aircraft campaigns (ELCHEM, SUCCESS, SONEX) though consistent with data from the NOXAR program aboard commercial aircraft. We show that regional lightning is the dominant source of this upper tropospheric NO x and increases upper tropospheric ozone by 10 ppbv. Simulating ICARTT upper tropospheric NO x observations with GEOS-Chem requires a factor of 4 increase in modeled NO x yield per flash (to 500 mol/ flash). Observed OH concentrations were a factor of 2 lower than can be explained from current photochemical models, for reasons that are unclear. A NO y -CO correlation analysis of the fraction f of North American NO x emissions vented to the free troposphere as NO y (sum of NO x and its oxidation products) shows observed f = 16 ± 10% and modeled f = 14 ± 9%, consistent with previous studies. Export to the lower free troposphere is mostly HNO 3 but at higher altitudes is mostly PAN. The model successfully simulates NO y export efficiency and speciation, supporting previous model estimates of a large U.S. anthropogenic contribution to global tropospheric ozone through PAN export.

Abstract. This paper describes the scientific and structural updates to the latest release of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 (v4.7) and points the reader to additional resources for further details. The model updates were evaluated relative to observations and results from previous model versions in a series of simulations conducted to incrementally assess the effect of each change. The focus of this paper is on five major scientific upgrades: (a) updates to the heterogeneous N 2 O 5 parameterization, (b) improvement in the treatment of secondary organic aerosol (SOA), (c) inclusion of dynamic mass transfer for coarse-mode aerosol, (d) revisions to the cloud model, and (e) new options for the calculation of photolysis rates. Incremental test simulations over the eastern United States during January and August 2006 are evaluated to assess the model response to each scientific improvement, providing explanations of differences in results between v4.7 and previously released CMAQ model versions. Particulate sulfate predictions are improved across all monitoring networks during both seasons due to cloud module updates. Numerous updates to the SOA module improve the simulation of seasonal variability and decrease the bias in organic carbon predictions at urban sites in the winter. Bias in the total mass of fine particulate matter (PM 2.5 ) is dominated by overpredictions of unspeciated PM 2.5 (PM other ) in the winter and by underpredictions of carbon in the summer. The CMAQv4.7 model results show slightly worse performance for ozone predictions.Correspondence to: K. M. Foley (foley.kristen@epa.gov) However, changes to the meteorological inputs are found to have a much greater impact on ozone predictions compared to changes to the CMAQ modules described here. Model updates had little effect on existing biases in wet deposition predictions.

With harmful ozone concentrations tied to meteorological conditions, EPA investigates the U.S. air quality implications of a changing climate. Consequently, the 03 NAAQS are most often exceeded during summertime hot spells in places with large natural or anthropogenic precursor emissions (e.g., cities and suburban areas). Table 2 The average maximum or minimum temperature and/or changes in their spatial distribution and duration, leading to a change in reaction rate coefficients and the solubility of gases in cloud water solution;The frequency and pattern of cloud cover, leading to a change in reaction rates and rates of conversion of S02to acid deposition;The frequency and intensity of stagnation episodes or a change in the mixing layer, leading to more or less mixing of polluted air with background air;Background boundary layer concentrations of water vapor, hydrocarbons, NOx, and 03, leading to more or less dilution of polluted air in the boundary layer and altering the chemical transformation rates;

This paper discusses the need for critically evaluating regional-scale (∼200-2,000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.