Operational, diagnostic, and probabilistic evaluation of AQMEII-4 regional-scale ozone dry deposition: Time to harmonize our LULC masks

Abstract

We present the collective evaluation of the regional-scale models that took part in the fourth edition of the Air Quality Model Evaluation International Initiative (AQMEII). The activity consists of the evaluation and intercomparison of regional-scale air quality models run over North American (NA) and European (EU) domains for 2016 (NA) and 2010 (EU). The focus of the paper is ozone dry deposition. Dry deposition is among the most important processes of removal of chemical compounds from the atmosphere and an important contributor to the overall chemical budget of the latter. Furthermore ozone dry deposition is very important as it can be severely detrimental to vegetation physiology. The collective evaluation begins with an operational evaluation, namely a direct comparison of model-simulated predictions with monitoring data aiming at assessing model performance (Dennis et al., 2010). Following the AQMEII protocol and Dennis et al. (2010), we also perform a probabilistic evaluation in the form of ensemble analyses and an introductory diagnostic evaluation. The latter analyzes the role of dry deposition in comparison with dynamic and radiative processes and land use/land cover (LULC) types in determining surface ozone variability. Important differences are found across dry deposition results when the same LULC is considered. Furthermore, we found that models use very different LULC masks, thus introducing an additional level of diversity in the model results. The study stresses that, as for other kinds of prior and problem-defining information (emissions, topography, or land-water masks), the choice of LULC mask should not be at modeler discretion. Furthermore, LULC should be considered as a variable to be evaluated in any future model intercomparison, unless set as common input information. The differences in LULC selection can have a substantial impact on model results, making the task of evaluating dry deposition modules across different regional-scale models very difficult.