Abstract. KORUS-AQ was an international cooperative air quality
field study in South Korea that measured local and remote sources of air
pollution affecting the Korean Peninsula during May–June 2016. Some of the
largest aerosol mass concentrations were measured during a Chinese haze
transport event (24 May). Air quality forecasts using the WRF-Chem
model with aerosol optical depth (AOD) data assimilation captured AOD during
this pollution episode but overpredicted surface particulate matter
concentrations in South Korea, especially PM2.5, often by a factor of 2
or larger. Analysis revealed multiple sources of model deficiency related to
the calculation of optical properties from aerosol mass that explain these
discrepancies. Using in situ observations of aerosol size and composition as
inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency
with which aerosols scatter and absorb light (mass extinction efficiency).
Besides using finer-resolution size bins (8–16 bins), it was also necessary
to increase the refractive indices and hygroscopicity of select aerosol
species within the range of values reported in the literature to achieve
better consistency with measured values of the mass extinction efficiency
(6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH))
due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an
evaluation of the optical properties obtained using modeled aerosol properties
revealed the inability of sectional and modal aerosol representations in
WRF-Chem to properly reproduce the observed size distribution, with the
models displaying a much wider accumulation mode. Other model deficiencies
included an underestimate of organic aerosol density (1.0 g cm−3 in the
model vs. observed average of 1.5 g cm−3) and an overprediction of
the fractional contribution of submicron inorganic aerosols other than
sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust
(17 %–28 % modeled vs. 12 % estimated from observations). These results
illustrate the complexity of achieving an accurate model representation of
optical properties and provide potential solutions that are relevant to
multiple disciplines and applications such as air quality forecasts, health
impact assessments, climate projections, solar power forecasts, and aerosol
data assimilation.
