Abstract
Abstract. The Weather Research and Forecasting model with Chemistry (WRF/Chem) simulation with the 2005 Carbon Bond (CB05) gas-phase mechanism coupled to the Modal for Aerosol Dynamics for Europe (MADE) and the volatility basis set approach for secondary organic aerosol (SOA) are conducted over a domain in North America for 2006 and 2010 as part of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 project. Following the Part 1 paper that focuses on the evaluation of the 2006 simulations, this Part 2 paper focuses on a comparison of model performance in 2006 and 2010 as well as analysis of the responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010. In general, emissions for gaseous and aerosol species decrease from 2006 to 2010, leading to a reduction in gaseous and aerosol concentrations and associated changes in radiation and cloud variables due to various feedback mechanisms. WRF/Chem is able to reproduce most observations and the observed variation trends from 2006 to 2010, despite its slightly worse performance than WRF that is likely due to inaccurate chemistry feedbacks resulting from less accurate emissions and chemical boundary conditions (BCONs) in 2010. Compared to 2006, the performance for most meteorological variables in 2010 gives lower normalized mean biases but higher normalized mean errors and lower correlation coefficients. The model also shows poorer performance for most chemical variables in 2010. This could be attributed to underestimations in emissions of some species, such as primary organic aerosol in some areas of the US in 2010, and inaccurate chemical BCONs and meteorological predictions. The inclusion of chemical feedbacks in WRF/Chem reduces biases in meteorological predictions in 2010; however, it increases errors and weakens correlations comparing to WRF simulations. Sensitivity simulations show that the net changes in meteorological variables from 2006 to 2010 are mostly influenced by changes in meteorology and those of ozone and fine particulate matter are influenced to a large extent by emissions and/or chemical BCONs and to a lesser extent by changes in meteorology. Using a different set of emissions and/or chemical BCONs helps improve the performance of individual variables, although it does not improve the degree of agreement with observed interannual trends. These results indicate a need to further improve the accuracy and consistency of emissions and chemical BCONs, the representations of SOA and chemistry–meteorology feedbacks in the online-coupled models.
Highlights
Changes in meteorology, climate, and emissions affect air quality (e.g., Hogrefe et al, 2004; Leung and Gustafson, 2005; Zhang et al, 2008; Dawson et al, 2009; Gao et al, 2013; Penrod et al, 2014)
Yahya et al.: Application of WRF/Chem over North America under the Air Quality Model Evaluation International Initiative (AQMEII) Phase 2 may be compensated by adverse changes in climatic or meteorological conditions that are directly conducive to the formation and accumulation of air pollutants and that may result in higher biogenic emissions
The reduced emissions and changed meteorology result in decreased concentrations in general for gaseous and aerosol species except for species influenced by high boundary conditions (BCONs), e.g., for OM concentrations over Canada in MAM and JJA
Summary
Climate, and emissions affect air quality (e.g., Hogrefe et al, 2004; Leung and Gustafson, 2005; Zhang et al, 2008; Dawson et al, 2009; Gao et al, 2013; Penrod et al, 2014). The 2-year simulations further enable an examination of the responses of air quality and meteorology–chemistry interactions to changes in emissions and meteorology from 2006 to 2010 that was not possible with offline-coupled models. Similar to offline AQMs, large uncertainties exist in online-coupled AQMs, which will affect the model predictions and implications Such uncertainties lie in the meteorological and chemical inputs such as emissions, initial and boundary conditions (ICONs and BCONs), model representations of atmospheric processes, and model configurations for applications such as horizontal/vertical grid resolutions and nesting techniques. In mechanistic evaluation ( referred to as dynamic evaluation), sensitivity simulations are performed by changing one or a few model inputs or process treatments, while holding others constant This approach can help diagnose the likely sources of biases in the model predictions. The work by Hogrefe et al (2014) involves nudging of temperature, wind speed, water vapor mixing ratio, soil temperature and soil moisture, while the model used for this study did not include any nudging
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