Evaluation of CMIP6 model simulations of PM 2.5 and its components over China

For this purpose, we collect observational data between 2000 and 2014 from a satellite-based dataset for total PM2.5 and from 2469 measurement records in the literature for PM2.5 components. Seven models output total PM2.5 concentrations, and they all underestimate the observed total PM2.5 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 PM2.5 from the available component output, underestimate the total PM2.5 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 PM2.5 and its components have important implications for the modeled magnitude and spatial pattern of aerosol radiative forcing.