Evaluation of CMIP6 model simulations of PM_2.5 and its components over China

Abstract

Abstract. Earth system models (ESMs) participating in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate various components of fine particulate matter (PM2.5) as major climate forcers. Yet the model performance for PM2.5 components remains little evaluated due in part to lack of observational data. Here, we evaluate near-surface concentrations of PM2.5 and its five main components over China as simulated by fourteen 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 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 components 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 fairly well the observed spatial pattern for OC (R = 0.51), sulfate (R = 0.57), nitrate (R = 0.70) and ammonium (R = 0.75), 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.

Spatial-temporal patterns of anthropogenic and biomass burning contributions on air pollution and mortality burden changes in India from 1995 to 2014

“…First of all, the coarser resolution in the global model (0.9 × 1.25) is frequently found to be unable to realistically represent the complex physical and chemical processes of regionalscale air pollution, especially for O 3 (Yue et al, 2023). Moreover, missing chemical mechanisms in the model, such as the lack of representations of nitrate and ammonium (Ren et al, 2023) and the secondary organic aerosol (Liu et al, 2021), prevents the model from accurately simulating PM 2.5 concentration, especially during heavily polluted regions, such as China and India (Turnock et al, 2020). Another major uncertainty originates from the inaccurate emission inventory, especially for developing regions in early periods, as reported by the global datasets (Paulot et al, 2018;Wang et al, 2022).…”

“…S5). The underestimation of the surface PM 2.5 was partly caused by the missing model representation of nitrate and ammonium (Ren et al, 2023) and the secondary organic aerosol (Liu et al, 2021). We first assessed the interannual variation of ANTHRO and BB emissions of CO, NOx, NMVOC, sulfur dioxide (SO 2 ), ammonia (NH 3 ), black carbon (BC), and organic carbon (OC) in India between 1995 and 2014 from the CEDS.…”