Abstract. In this study, a full description and comprehensive evaluation of a global-regional nested model, the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM), is presented for the first time. Not only the global budgets and distribution, but also a comparison of nested simulation over China against multi-datasets are investigated, benefiting from the access of air quality monitoring data in China since 2013 and the Model Inter-Comparison Study for Asia project. The model results and analysis can greatly help reduce uncertainties and understand model diversity in assessing global and regional aerosol effects, especially over East Asia and areas affected by East Asia. The 1-year simulation for 2014 shows that the IAP-AACM is within the range of other models, and well reproduces both spatial distribution and seasonal variation of trace gases and aerosols over major continents and oceans (mostly within the factor of two). The model nicely captures spatial variation for carbon monoxide except an underestimation over the ocean that also shown in other models, which suggests the need for more accurate emission rate of ocean source. For aerosols, the simulation of fine-mode particulate matter (PM2.5) matches observation well and it has a better simulating ability on primary aerosols than secondary aerosols. This calls for more investigation on aerosol chemistry. Furthermore, IAP-AACM shows the superiority of global model, compared with regional model, on performing regional transportation for the nested simulation over East Asia. For the city evaluation over China, the model reproduces variation of sulfur dioxide (SO2), nitrogen dioxide (NO2) and PM2.5 accurately in most cities, with correlation coefficients above 0.5. Compared to the global simulation, the nested simulation exhibits an improved ability to capture the high temporal and spatial variability over China. In particular, the correlation coefficients for PM2.5, SO2 and NO2 are raised by ~ 0.25, ~ 0.15 and ~ 0.2 respectively in the nested grid. The summary provides constructive information for the application of chemical transport models. In future, we recommend the model's ability to capture high spatial variation of PM2.5 is yet to be improved.
Agile Innovation to transform healthcare: innovating in complex adaptive systems is an everyday process, not a light bulb event
