Improving dust simulations in WRF-Chem model v4.1.3 coupled with GOCART aerosol module

Abstract

<jats:p>Abstract. In this paper, we rectify inconsistencies that emerge in the WRF-Chem code when using the Goddard Chemistry Aerosol Radiation and Transport (GOCART) aerosol module. These inconsistencies have been reported and corrections have been implemented in WRF-Chem v4.1.3. Here, we use a WRF-Chem experimental setup configured over the Middle East (ME) to estimate the effects of these inconsistencies. Firstly, we show that the diagnostic output of PM2.5 surface concentration was underestimated by 7% and PM10 was overestimated by 5 %. Secondly, we demonstrate that the contribution of sub-micron dust particles was underestimated in the calculation of optical properties and thus, Aerosol Optical Depth (AOD) was consequently underestimated by 25–30 %. Thirdly, we show that an inconsistency in the process of gravitational settling led to the overestimation of the dust column loadings by 4–6 %, PM10 surface concentrations by 2–4 %, and the rate of dust gravitational settling by 5–10 %. We present a methodology to calculate diagnostics that can be used to estimate the effects of these applied changes. Our corrections also help to explain the overestimation of PM10 surface concentrations encountered in many WRF-Chem simulations. We also share the developed Merra2BC interpolator, which allows constructing initial and boundary conditions for chemical species and aerosols based on MERRA-2 reanalysis. The results of this work can be useful for those who simulate the dust cycle using the WRF-Chem model coupled with the GOCART aerosol module. </jats:p>

Aerosol vertical distribution and interactions with land/sea breezes over the eastern coast of the Red Sea from lidar data and high-resolution WRF-Chem simulations

“…Therefore, dust is present in the atmosphere over the study site for the entire year. Although our focus in this study is on dust aerosols, which are the dominant aerosol over the study site (Jish Prakash et al, 2015;Parajuli et al, 2019;Ukhov et al, 2020b), some additional aerosol types also contribute to the aerosol loading at KAUST. Our site is located on the coast; thus, sea salt aerosol, which is of natural origin, inevitably contributes considerably to the atmospheric aerosol loading.…”

“…Our site is located on the coast; thus, sea salt aerosol, which is of natural origin, inevitably contributes considerably to the atmospheric aerosol loading. Furthermore, the study site has several industrial areas nearby that produce anthropogenic emissions of sulfur dioxide (SO 2 ) and black and organic carbon (BC and OC) (Ukhov et al, 2020b).…”

“…From AERONET, we also use an aerosol number density and a particle size distribution (PSD) obtained by inversion (Dubovik and King, 2000) to characterize the aerosol particles in the region. We use the AERONET V3, level-2.0 product, which provides volume concentration of aerosols in the atmospheric column in 22 bins between 0.05 and 15 µm in radius (Dubovik and King, 2000;Parajuli et al, 2019;Ukhov et al, 2020b).…”

“…Although our understanding of the dust cycle and the related physical processes has substantially improved in recent decades (Shao et al, 2011), in the present context, two aspects of dust aerosol dynamics remain the least explored: the vertical structure and the diurnal cycle. Understanding the vertical structure is important because the vertical distribution of aerosols affects the radiative budget (Johnson et al, 2008;Osipov et al, 2015) and surface air quality (Chin et al, 2007;Wang et al, 2010;Ukhov et al, 2020b). Moreover, understanding the diurnal cycle of aerosols is important because aerosols scatter and absorb radiation (Sokolik and Toon, 1996;Di Biagio et al, 2017), which ultimately affects the land and sea breezes in coastal areas.…”

Extension of WRF-Chem for birch pollen modelling—a case study for Poland

“…Hence, both meteorological and air quality components use the same physics schemes for the sub-grid scale transport, the same grid on the horizontal and vertical coordinates, and the same atmospheric transport scheme (advection and diffusion) which preserves air and scalar mass (Tsarpalis et al 2018). The GOCART module includes algorithms for dry deposition and gravitational settling (Legrand et al 2018;Ukhov et al 2020). The gravitational settling parameterization is based on calculation of settling velocity, which depends on particle density and size.…”