Comment on gmd-2023-21

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> In the eastern China region, two-way coupled meteorology and air quality models have been applied aiming to more realistically simulate meteorology and air quality by accounting for the aerosol‒radiation‒cloud interactions. There have been numerous related studies being conducted, but the performances of multiple two-way coupled models simulating meteorology and air quality under equivalent configurations have not been compared in this region. In this study, we systematically evaluated annual and seasonal meteorological and air quality variables simulated by three open-source and widely used two-way coupled models (i.e., WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) by validating the model results with surface and satellite observations for eastern China during 2017. Our comprehensive model evaluations showed that all three two-way coupled models simulated the annual spatiotemporal distributions of meteorological and air quality variables reasonably well, especially the surface temperature (with R up to 0.97) and fine particular matter (PM_2.5) concentrations (with R up to 0.68). The model results of winter PM_2.5 and summer ozone compared better with observations and WRF-CMAQ exhibited the best overall performance. The aerosol feedbacks affected model results of meteorology and air quality in various ways and turning on aerosol-radiation interactions made the PM_2.5 and surface shortwave radiation simulations better, but worse for T2 and Q2. The impacts of aerosol-cloud interactions (ACI) on model performances&rsquo; improvements were limited and several possible improvements on ACI representations in two-way coupled models are further discussed and proposed. When sufficient computational resources become available, two-way coupled models including the aerosol-radiation-cloud interactions should be applied for more accurate air quality prediction and timely warning of air pollution events in atmospheric environmental management.