We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.

The heterogeneous hydrolysis of dinitrogen pentoxide (N₂O₅) has typically been modeled as only producing nitric acid. However, recent field studies have confirmed that the presence of particulate chloride alters the reaction product to produce nitryl chloride (ClNO₂) which undergoes photolysis to generate chlorine atoms and nitrogen dioxide (NO₂). Both chlorine and NO₂ affect atmospheric chemistry and air quality. We present an updated gas-phase chlorine mechanism that can be combined with the Carbon Bond 05 mechanism and incorporate the combined mechanism into the Community Multiscale Air Quality (CMAQ) modeling system. We then update the current model treatment of heterogeneous hydrolysis of N₂O₅ to include ClNO₂ as a product. The model, in combination with a comprehensive inventory of chlorine compounds, reactive nitrogen, particulate matter, and organic compounds, is used to evaluate the impact of the heterogeneous ClNO₂ production on air quality across the United States for the months of February and September in 2006. The heterogeneous production increases ClNO₂ in coastal as well as many in-land areas in the United States. Particulate chloride derived from sea-salts, anthropogenic sources, and forest fires activates the heterogeneous production of ClNO₂. With current estimates of tropospheric emissions, it modestly enhances monthly mean 8-h ozone (up to 1–2 ppbv or 3–4%) but causes large increases (up to 13 ppbv) in isolated episodes. This chemistry also substantially reduces the mean total nitrate by up to 0.8–2.0 μg m⁻³ or 11–21%. Modeled ClNO₂ accounts for up to 6% of the monthly mean total reactive nitrogen. Sensitivity results of the model suggest that heterogeneous production of ClNO₂ can further increase O₃ and reduce TNO₃ if elevated particulate-chloride levels are present in the atmosphere

No abstract

More than ten state-of-the-art regional air quality models have been applied as part of the Air Quality Model Evaluation International Initiative (AQMEII). These models were run by twenty independent groups in Europe and North America. Standardised modelling outputs over a full year (2006) from each group have been shared on the web-distributed ENSEMBLE system, which allows for statistical and ensemble analyses to be performed by each group. The estimated ground-level ozone mixing ratios from the models are collectively examined in an ensemble fashion and evaluated against a large set of observations from both continents. The scale of the exercise is unprecedented and offers a unique opportunity to investigate methodologies for generating skilful ensembles of regional air quality models outputs. Despite the remarkable progress of ensemble air quality modelling over the past decade, there are still outstanding questions regarding this technique. Among them, what is the best and most beneficial way to build an ensemble of members? And how should the optimum size of the ensemble be determined in order to capture data variability as well as keeping the error low? These questions are addressed here by looking at optimal ensemble size and quality of the members. The analysis carried out is based on systematic minimization of the model error and is important for performing diagnostic/probabilistic model evaluation. It is shown that the most commonly used multi-model approach, namely the average over all available members, can be outperformed by subsets of members optimally selected in terms of bias, error, and correlation. More importantly, this result does not strictly depend on the skill of the individual members, but may require the inclusion of low-ranking skill-score members. A clustering methodology is applied to discern among members and to build a skilful ensemble based on model association and data clustering, which makes no use of priori knowledge of model skill. Results show that, while the methodology needs further refinement, by optimally selecting the cluster distance and association criteria, this approach can be useful for model applications beyond those strictly related to model evaluation, such as air quality forecasting

No abstract