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https://doi.org/10.5194/acp-2020-760
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2020-760
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Aug 2020

31 Aug 2020

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This preprint is currently under review for the journal ACP.

Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations

Tong Sha1, Xiaoyan Ma1, Jun Wang2, Rong Tian1, Jianqi Zhao1, Fang Cao3, and Yan-Lin Zhang3 Tong Sha et al.
  • 1Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 2Department of Chemical and Biochemical Engineering/Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, IA, USA
  • 3Yale–NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change (ILCEC)/Jiangsu Provincial Key Laboratory of Agricultural Meteorology, College of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract. High concentrations of PM2.5 in China have caused severe visibility degradation and health problem. However, it is still a big challenge to accurately predict PM2.5 and its chemical components in the numerical model. In this study, we compared the inorganic aerosol components of PM2.5 (sulfate, nitrate, and ammonium (SNA)) simulated by WRF-Chem with in-situ data during a heavy haze-fog event (November 2018) in Nanjing. The comparisons show that the model underestimates the sulfate concentrations by 81 % and fails to reproduce the significant increase of sulfate concentrations from early morning to noon, which corresponds to the timing of fog dissipation, suggesting that the model underestimates the aqueous-phase formation of sulfate in clouds. In addition, the model overestimates both nitrate and ammonium concentrations by 184 % and 57 %, respectively. These ultimately result in the simulated SNA 77.2 % higher than the observations. However, as the important aqueous-phase reactors, cloud water are simultaneously underestimated by the model. Therefore, the modeled cloud water was constrained based on the MODIS Liquid Water Path (LWP) observations. Results show that the simulation with MODIS-corrected cloud water amount increases the sulfate by a factor of 3, decreases NMB by 53.5 %, and can reproduce its diurnal cycles, i.e. the peak concentration at noon. Also, the model absolute bias of nitrate decreases from 184 % to 50 %, especially for the nocturnal concentrations, which suggests the MODIS-constrained simulation improved the diurnal pattern. Although the simulated ammonium is still higher than the observation, corrected cloud water lead to the decrease of the modeled bias of SNA from 77.2 % to 14.1 %. The strong sensitivity of simulated SNA concentration to the cloud water provides an explanation for the bias of SNA simulation. Hence, the uncertainties of cloud water can lead to model bias in simulating SNA, and can be reduced by constraining the model with satellite observations.

Tong Sha et al.

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Latest update: 29 Sep 2020
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Short summary
Most numerical models perform poorly on simulating the inorganic chemical components in PM2.5 (sulfate, nitrate, and ammonium (SNA)), generally underestimate sulfate but overestimate nitrate concentrations in haze events. Our work aims at investigating the role of cloud water in simulating SNA. We find that the uncertainties of cloud water can lead to model bias in simulating SNA, and can be reduced by constraining the modeled cloud water with MODIS satellite observations.
Most numerical models perform poorly on simulating the inorganic chemical components in PM2.5...
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