01 Aug 2022
01 Aug 2022
Status: this preprint is currently under review for the journal ACP.

Assessing the climate and air quality effects of future aerosol mitigation in India using a global climate model combined with statistical downscaling

Tuuli Miinalainen1, Harri Kokkola2, Antti Lipponen2, Antti-Pekka Hyvärinen3, Vijay Kumar Soni4, Kari E. J. Lehtinen1,2, and Thomas Kühn1,2,5 Tuuli Miinalainen et al.
  • 1Department of Applied Physics, University of Eastern Finland (UEF), Kuopio, Finland
  • 2Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute (FMI), Kuopio, Finland
  • 3Atmospheric Composition Research, Finnish Meteorological Institute (FMI), Helsinki, Finland
  • 4India Meteorological Department (IMD), Ministry of Earth Sciences, New Delhi, India
  • 5Weather and Climate Change Research, Finnish Meteorological Institute (FMI), Helsinki, Finland

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.

Tuuli Miinalainen et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-513', Anonymous Referee #1, 12 Sep 2022
  • RC2: 'Comment on acp-2022-513', Anonymous Referee #2, 12 Sep 2022
  • AC1: 'Author response to referee comments', Tuuli Miinalainen, 07 Oct 2022

Tuuli Miinalainen et al.

Tuuli Miinalainen et al.


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Short summary
We simulated the effects of aerosol emission mitigation on both global and regional radiative forcing and city-level air quality with a global-scale climate model. We used a machine learning downscaling approach to bias-correct the PM2.5 values obtained from the global model for the Indian mega-city New Delhi. Our results indicate that aerosol mitigation could result in both improved air quality and less radiative heating for India.