Assessment of the MACC reanalysis and its influence as chemical boundary conditions for regional air quality modeling in AQMEII-2

Abstract

The Air Quality Model Evaluation International Initiative (AQMEII) has now reached its second phase which is dedicated to the evaluation of online coupled chemistry-meteorology models. Sixteen modeling groups from Europe and five from North America have run regional air quality models to simulate the year 2010 over one European and one North American domain. The MACC re-analysis has been used as chemical initial (IC) and boundary conditions (BC) by all participating regional models in AQMEII-2. The aim of the present work is to evaluate the MACC re-analysis along with the participating regional models against a set of ground-based measurements (O3, CO, NO, NO2, SO2, SO42−) and vertical profiles (O3 and CO). Results indicate different degrees of agreement between the measurements and the MACC re-analysis, with an overall better performance over the North American domain. The influence of BC on regional air quality simulations is analyzed in a qualitative way by contrasting model performance for the MACC re-analysis with that for the regional models. This approach complements more quantitative approaches documented in the literature that often have involved sensitivity simulations but typically were limited to only one or only a few regional scale models. Results suggest an important influence of the BC on ozone for which the underestimation in winter in the MACC re-analysis is mimicked by the regional models. For CO, it is found that background concentrations near the domain boundaries are rather close to observations while those over the interior of the two continents are underpredicted by both MACC and the regional models over Europe but only by MACC over North America. This indicates that emission differences between the MACC re-analysis and the regional models can have a profound impact on model performance and points to the need for harmonization of inputs in future linked global/regional modeling studies.