Evaluation of CMIP6 model simulations of PM2.5 and its components over China

Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter ( PM 2.5 ) as major climate forcers. Yet the model performance for PM 2.5 components remains little evaluated due in part to a lack of observational data. Here, we evaluate near-surface concentrations of PM 2.5 and its five main components over China as simulated by 14 CMIP6 models, including organic carbon (OC; available in 14 models), black carbon (BC; 14 models), sulfate (14 models), nitrate (4 models), and ammonium (5 models). For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM 2.5 and from 2469 measurement records in the literature for PM 2.5 components. Seven models output total PM 2.5 concentrations, and they all underestimate the observed total PM 2.5 over eastern China, with GFDL-ESM4 ( − 1.5 %) and MPI-ESM-1-2-HAM ( − 1.1 %) exhibiting the smallest biases averaged over the whole country. The other seven models, for which we recalculate total PM 2.5 from the available component output, underestimate the total PM 2.5 concentrations partly because of the missing model representations of nitrate and ammonium. Concentrations of the five individual components are underestimated in almost all models, except that sulfate is overestimated in MPI-ESM-1-2-HAM by 12.6 % and in MRI-ESM2-0 by 24.5 %. The underestimation is the largest for OC (by − 71.2 % to − 37.8 % across the 14 models) and the smallest for BC ( − 47.9 % to − 12.1 %). The multi-model mean (MMM) reproduces the observed spatial pattern for OC ( R   =  0.51), sulfate ( R   =  0.57), nitrate ( R   =  0.70) and ammonium ( R   =  0.74) fairly well, yet the agreement is poorer for BC ( R   =  0.39). The varying performances of ESMs on total PM 2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.