Comment on acp-2022-513

Abstract

We studied the potential of using a global-scale climate model for analyzing simultaneously both city-level air quality and regional and global scale radiative forcing values for anthropogenic aerosols. As the city-level air pollution values are typically underestimated in global-scale models, we used a machine learning approach to downscale fine particulate (PM2.5) concentrations towards measured values. We first simulated the global climate with the aerosol-climate model ECHAM-HAMMOZ, and corrected the PM2.5 values for the Indian mega-city New Delhi. The downscaling procedure clearly improved the seasonal variation when compared to measured PM2.5 values. However, short-term variations showed less extreme values with the downscaling approach. We applied the downscaling model also to simulations where the aerosol emissions were following different future projections. The corrected PM2.5 concentrations for the year 2030 showed that mitigating anthropogenic aerosols improves local air quality in New Delhi, with organic carbon reductions contributing most to these improvements. In addition, aerosol emission mitigation also resulted in negative radiative forcing over most of India. This was mainly due to reductions in absorbing black carbon emissions. This indicates that aerosol mitigation could bring a double benefit in India: better air quality and decreased warming of the climate. Our results demonstrate that downscaling and bias correction allow more versatile utilization of global-scale climate models. With the help of downscaling, global climate models can be used in applications where one aims to analyze both global and regional effects of policies related to mitigating anthropogenic emissions.