IAP-AACM v1.0: a global to regional evaluation of the atmospheric chemistry model in CAS-ESM

Abstract

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 are the global budgets and distribution explored, but comparisons of the nested simulation over China against multiple datasets are investigated, which benefit from access to Chinese air quality monitoring data from 2013 to the present and the “Model Inter-Comparison Study for Asia” project. The model results and analysis can help reduce uncertainties and aid with understanding model diversity with respect to assessing global and regional aerosol effects on climate and human health, especially over East Asia and areas affected by East Asia. For the global simulation, the 1-year simulation for 2014 shows that the IAP-AACM is within the range of other models. Overall, it reasonably reproduced spatial distributions and seasonal variations of trace gases and aerosols in both surface concentrations and column burdens (mostly within a factor of 2). The model captured spatial variation for carbon monoxide well with a slight underestimation over ocean, which implicates the uncertainty of the ocean source. The simulation also matched the seasonal cycle of ozone well except for the continents in the Northern Hemisphere, which was partly due to the lack of stratospheric–tropospheric exchange. For aerosols, the simulation of fine-mode particulate matter (PM2.5) matched observations well. The simulation of primary aerosols (normalized mean biases, NMBs, are within ±0.64) is better than that of secondary aerosols (NMB values are greater than 1.0 in some regions). For the nested regional simulation, the IAP-AACM shows the superiority of higher-resolution simulation using the nested domain over East Asia. The model reproduced variation of sulfur dioxide (SO2), nitrogen dioxide (NO2), and PM2.5 accurately in typical cities, with correlation coefficients (R) above 0.5 and NMBs within ±0.5. Compared with the global simulation, the nested simulation exhibits an improved ability to capture the high temporal and spatial variability over China. In particular, the R values for SO2, NO2 and PM2.5 are increased by ∼0.15, ∼0.2, and ∼0.25 respectively in the nested grid. Based on the evaluation and analysis, future model improvements are suggested.