25 Jul 2022
25 Jul 2022
Status: this preprint is open for discussion.

An Improved Representation of Aerosol Mixing State for Air-Quality-Weather Interactions

Robin Stevens1,2, Andrei Ryjkov1, Mahtab Majdzadeh3, and Ashu Dastoor1 Robin Stevens et al.
  • 1Air Quality Research Division, Environment and Climate Change Canada, 2121 Trans-Canada Highway, Dorval, Québec, Canada
  • 2Université de Montreal, Montréal, Quebec, Canada
  • 3Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, Canada

Abstract. We implement a detailed representation of aerosol mixing-state into the GEM-MACH air quality and weather forecast model. Our mixing-state representation includes three categories: one for more-hygroscopic aerosol, one for less-hygroscopic aerosol with a high black carbon (BC) mass fraction, and one for less-hygroscopic aerosol with a low BC mass fraction. This is the first model with a mixing-state representation of this type simulating a continent-scale domain. The more-detailed representation allows us to better resolve two different aspects of aerosol mixing state: differences in hygroscopicity due to aerosol composition, and the amount of absorption enhancement of BC due to non-absorbing coatings. Notably, this three-category representation allows us to account for BC thickly coated with primary organic mass, which enhances the absorption of the BC but has a low hygroscopicity.

We compare the results of the three-category representation (1L2B) with a simulation that uses two categories, split by hygroscopicity (HYGRO), and a simulation using the original size-resolved internally mixed assumption (SRIM). We find that the more-detailed representation of the aerosol hygroscopicity in both 1L2B and HYGRO decreases wet deposition, which increases aerosol concentrations, particularly of less-hygroscopic species. The concentration of PM2.5 increases by 23 % on average. We show that these increased aerosol concentrations increase cloud droplet number concentrations and cloud reflectivity in the model, decreasing surface temperatures.

Using two categories based on hygroscopicity yields only a modest benefit in resolving the coating thickness on black carbon, however. The 1L2B representation resolves BC with thinner coatings than the HYGRO simulation, resulting in absorption aerosol optical depths that are 3 % less on average. We did not find strong subsequent effects of this decreased absorption on meteorology.

Robin Stevens et al.

Status: open (until 05 Sep 2022)

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Robin Stevens et al.


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Short summary
Absorbing particles like black carbon can be coated with other matter. How much radiation these particles absorb depends on the coating thickness. The removal of these particles by clouds and rain depends on the coating composition. These effects are important for both climate and air quality. We implement a more detailed representation of these particles into an air quality model which accounts for both coating thickness and composition. We find a significant effect on particle concentrations.