As the rapid growth in East Asia’s economy with surging energy consumption and emissions, air pollution has become an increasingly important scientific topic and political concern in East Asia due to its significant environmental and health effects (Anenberg et al., 2010; Lelieveld et al., 2015). Chemical transport models (CTMs), serving as a critical tool in both the scientific research and policy making, have been applied into various air quality issues, such as air quality prediction, long-range transport of atmospheric pollutants, development of emission control strategies and understanding of observed chemical phenomena (e.g., Cheng et al., 2016; J. Li et al., 2017; Lu et al., 2017; Ma et al., 2019; Tang et al., 2011; Xu et al., 2019; Zhang et al., 2019). Nevertheless, air quality modeling remains a challenge due to the multi-scale and nonlinear nature of the complex atmospheric processes (Carmichael et al., 2008). It still suffers from large uncertainties related to the missing or poorly parameterized physical and chemical processes, inaccurate and/or incomplete emission inventories, as well as the poorly represented initial and boundary conditions (Carmichael et al., 2008; Dabberdt and Miller, 2000; Fine et al., 2003; Gao et al., 1996; Mallet and Sportisse, 2006). Understanding such uncertainties and their impacts on the air quality modeling is of great importance in assessing the robustness of models for their applications in scientific research and operational use.
There are specific techniques to assess these uncertainties. Monte Carlo simulations, based on different values of model parameters or input fields sampled from a predefined probability density function (PDF), can provide an approximation to the PDF of possible model output and serves as an excellent characterization of the uncertainties in simulations (Hanna et al., 2001). However, this method is more suited to deal with the uncertainty related to the continuous variables, such as input data or parameters in parameterization. The ensemble method, based on a set of different models, is an alternative approach to accounting for the range of uncertainties (Galmarini et al., 2004; Mallet and Sportisse, 2006). For example, the Air Quality Model Evaluation International Initiative (AQMEII) has been implemented in Europe and North America to investigate the model uncertainties of their regional-scale model predictions (Rao et al., 2011). To assess the model performances and uncertainties in East Asian applications, the Model Inter-Comparison Study for Asia (MICS-Asia) has been initiated in the year 1998. The first phase of MICS-Asia (MICS-Asia I) was carried out during the period 1998-2002, mainly focusing on the long-range transport and depositions of sulfur in Asia (Carmichael et al., 2002). In 2003, the second phase (MICS-Asia II) was initiated and took more species related to the regional health and ecosystem protection into account, including nitrogen compounds, O3 and aerosols. Launched in 2010, MICS-Asia III has greatly expanded its study scope by covering three individual and interrelated topics: (1) evaluate the strengths and weaknesses of current multi-scale air quality models and provide techniques to reduce uncertainty in Asia; (2) develop reliable anthropogenic emission inventories in Asia and understand the uncertainty of bottom-up emission inventories in Asia; and (3) provide multi-model estimates of radiative forcing and sensitivity analysis of short-lived climate pollutants.
This study addresses one component of topic 1, focusing on the three gas pollutants of NO2, CO and NH3. Compared with MICS-Asia II, more modeling results (14 different models with 13 regional models and 1 global model) were brought together within topic 1 of MICS-Asia III, run by independent modeling groups from China, Japan, Korea, United States of America and other countries/regions. The different models contain differences in their numerical approximations (time step, chemical solver, etc.) and parameterizations, which represent a sampling of uncertainties residing in the air quality modeling. However, it would be difficult to interpret the results from intercomparison studies wherein the models were driven by different meteorological fields and emission inventories. Thus, in MICS-Asia III the models were constrained so that they operated under the same conditions by using common emission inventories, meteorological fields, modeling domain and horizontal resolution. The simulations were also extended from the 4 months in MICS-Asia II to the entire year of 2010.
NO2, CO and NH3 are three important primary gas pollutants that has wide impacts on the atmospheric chemistry. As a major precursor of O3, NO2 plays an important role in the tropospheric O3 chemistry and also contributes to rainwater acidification and the formation of secondary aerosols (Dentener and Crutzen, 1993; Evans and Jacob, 2005). CO is a colorless and toxic gas ubiquitous throughout the atmosphere, which is of interest as an indirect greenhouse gas (Gillenwater, 2008) and a precursor for tropospheric O3 (Seinfeld and Pandis, 1998). Being the major sink of OH, CO also controls the atmosphere’s oxidizing capacity (Levy, 1971; Novelli et al., 1998). As the only primary alkaline gas in the atmosphere, NH3 is closely associated with the acidity of precipitation and it can react with sulfuric acid and nitric acid, forming ammonium sulfate and ammonium nitrate, which account for a large proportion of fine particulate matter (Sun et al., 2012, 2013). Assessing their model performances is thus important to help us better understand their environmental consequences and also help explain the model performances for their related secondary air pollutants, such as O3 and fine particulate matter.
In a previous phase of MICS-Asia, no specific evaluation and intercomparison work was conducted for these gases, especially for CO and NH3. In MICS-Asia II, model performance of NO2 was evaluated as a relevant species to O3 (Han et al., 2008); however, such evaluations were limited to the observation sites from EANET (Acid Deposition Monitoring Network in East Asia). Model evaluations and intercomparisons in industrialized regions of China have not been performed due to the limited number of monitoring sites in China from EANET, which hindered our understanding of the model performance in industrialized regions. More dense observations over highly industrialized regions of China, namely the North China Plain (NCP) and Pearl River Delta (PRD) regions, were first included in MICS-Asia III, allowing the model evaluations over highly industrialized regions. Meanwhile, the emission inventories of these three gases are still subject to the large uncertainties (Kurokawa et al., 2013; M. Li et al., 2017), which is a major source of uncertainties in air quality modeling and forecasts. Evaluating these gases’ emission inventories from a model perspective is also a useful way to identify the uncertainties in emission inventories (Han et al., 2009; van Noije et al., 2006; Pinder et al., 2006; Stein et al., 2014; Uno et al., 2007).
In all, this paper is aimed at evaluating NO2, CO and NH3 simulations using the multi-model data from MICS-Asia III; we try to address three questions: (1) what the performance of current CTMs is for simulating NO2, CO, and NH3 concentrations over highly industrialized regions of China; (2) what potential factors are responsible for the model deviations from observations and differences among models; and (3) how large the impacts are of model uncertainties on the simulations of these gases.