The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O 3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-hour averaged surface ozone levels below 50-60 g m-3 are overestimated by all models over both continents while levels over 120-140 g m-3 are underestimated, suggesting that models have a tendency to severely under-predict high O 3 values that are of concern for air quality forecast and control policy applications.

The second phase of the Air Quality Model Evaluation International Initiative (AQMEII) brought together sixteen modeling groups from Europe and North America, running eight operational online-coupled air quality models over Europe and North America on common emissions and boundary conditions. With the advent of online-coupled models providing new capability to quantify the effects of feedback processes, the main aim of this study is to compare the response of coupled air quality models to simulate levels of O 3 over the two continental regions. The simulated annual, seasonal, continental and sub-regional ozone surface concentrations and vertical profiles for the year 2010 have been evaluated against a large observational database from different measurement networks operating in Europe and North America. Results show a general model underestimation of the annual surface ozone levels over both continents reaching up to 18% over Europe and 22% over North America. The observed temporal variations are successfully reproduced with correlation coefficients larger than 0.8. Results clearly show that the simulated levels highly depend on the meteorological and chemical configurations used in the models, even within the same modeling system. The seasonal and sub-regional analyses show the models' tendency to overestimate surface ozone in all regions during autumn and underestimate in winter. Boundary conditions strongly influence ozone predictions especially during winter and autumn, whereas during summer local production dominates over regional transport. Daily maximum 8-hour averaged surface ozone levels below 50-60 g m-3 are overestimated by all models over both continents while levels over 120-140 g m-3 are underestimated, suggesting that models have a tendency to severely under-predict high O 3 values that are of concern for air quality forecast and control policy applications.

h i g h l i g h t sWe intercompared tropospheric gas-phase mechanisms used in AQMEII phase 2. Box model results show O 3 differs by 4 ppbv (5%), NO x 25%, isoprene >100% and HCHO 20%. Key radicals OH and HO 2 differ 40 / 25% between mechanisms, NO 3 by more than 100%. Uncertainty due to gas-phase mechanism choice has to be considered in model simulations. a b s t r a c tThe formulations of tropospheric gas-phase chemistry ("mechanisms") used in the regional-scale chemistry-transport models participating in the Air Quality Modelling Evaluation International Initiative (AQMEII) Phase 2 are intercompared by the means of box model studies. Simulations were conducted under idealized meteorological conditions, and the results are representative of mean boundary layer concentrations. Three sets of meteorological conditions e winter, spring/autumn and summer e were used to capture the annual variability, similar to the 3-D model simulations in AQMEII Phase 2. We also employed the same emissions input data used in the 3-D model intercomparison, and sample from these datasets employing different strategies to evaluate mechanism performance under a realistic range of pollution conditions. Box model simulations using the different mechanisms are conducted with tight constraints on all relevant processes and boundary conditions (photolysis, temperature, entrainment, etc.) to ensure that differences in predicted concentrations of pollutants can be attributed to differences in the formulation of gas-phase chemistry. The results are then compared with each other (but not to measurements), leading to an understanding of mechanism-specific biases compared to the multi-model mean. Our results allow us to quantify the uncertainty in predictions of a given compound in the 3-D simulations introduced by the choice of gas-phase mechanisms, to determine mechanism-specific biases under certain pollution conditions, and to identify (or rule out) the gas-phase mechanism as the cause of an observed discrepancy in 3-D model predictions.Atmospheric Environment 115 (2015) 553e568 We find that the predictions of the median diurnal cycle of O 3 over a set of emission conditions representing a network of station observations is within 4 ppbv (5%) across the different mechanisms. This variability is found to be very similar on both continents. There are considerably larger differences in predicted concentrations of NO x (up to ± 25%), key radicals like OH (40%), HO 2 (25%) and especially NO 3 (>100%). Secondary substances like H 2 O 2 (25%) or HNO 3 (10%), as well as key volatile organic compounds like isoprene (>100%) or CH 2 O (20%) differ substantially as well. Calculation of an indicator of the chemical regime leads to up to 20% of simulations being classified differently by different mechanism, which would lead to different predictions of the most efficient emission reduction strategies.All these differences are despite identical meteorological boundary conditions, photolysis rates, as well as identical biogenic and inorganic anthropogenic emi...

Date of Acceptance: 12/12/2014 Copyright The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/)Air pollution simulations critically depend on the quality of the underlying meteorology. In phase 2 of the Air Quality Model Evaluation International Initiative (AQMEII-2), thirteen modeling groups from Europe and four groups from North America operating eight different regional coupled chemistry and meteorology models participated in a coordinated model evaluation exercise. Each group simulated the year 2010 for a domain covering either Europe or North America or both. Here were present an operational analysis of model performance with respect to key meteorological variables relevant for atmospheric chemistry processes and air quality. These parameters include temperature and wind speed at the surface and in the vertical profile, incoming solar radiation at the ground, precipitation, and planetary boundary layer heights. A similar analysis was performed during AQMEII phase 1 (Vautard etal., 2012) for offline air quality models not directly coupled to the meteorological model core as the model systems investigated here. Similar to phase 1, we found significant overpredictions of 10-m wind speeds by most models, more pronounced during night than during daytime. The seasonal evolution of temperature was well captured with monthly mean biases below 2K over all domains. Solar incoming radiation, precipitation and PBL heights, on the other hand, showed significant spread between models and observations suggesting that major challenges still remain in the simulation of meteorological parameters relevant for air quality and for chemistry-climate interactions at the regional scale

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