The global Flow-following finite-volume Icosahedra Model (FIM), which was developed in the Global Systems Division of NOAA/ESRL and the Finite-volume cubed-sphere dynamical core (FV3) developed by GFDL, have been coupled online with aerosol and gas-phase chemistry schemes (FIM-Chem and FV3-Chem). Within the aerosol and chemistry modules, the models handle wet and dry deposition, chemical reactions, aerosol direct and semi-direct effect, anthropogenic emissions, biogenic emissions, biomass burning, dust and sea-salt emissions. They are able to provide chemical weather predictions at various spatial resolutions and with different levels of complexity. FIM-Chem is also able to quantify the impact of aerosol on numerical weather predictions (NWP). Currently, three different chemical schemes have been coupled with the FIM model. The simplest aerosol modules are from the GOCART model with its simplified parameterization of sulfur/sulfate chemistry. The photochemical gas-phase mechanism RACM was included to determine the impact of additional complexity on the aerosol and gas simulations. We have also implemented a more sophisticated aerosol scheme that includes secondary organic aerosols (SOA) based on the VBS approach. The model performance has been evaluated by comparing with the ATom-1 observations. FIM-Chem is able to reproduce many observed aerosol and gas features very well. A five-day NWP on 120 km horizontal resolution using FIM-Chem has been done for the end of July, 2016 to quantify the impact of the three different chemical schemes on weather forecasts. Compared to a meteorological run that excludes the model chemical schemes, and is driven only by background AODs from the GFS model, the 5-day forecast results shows significant impact on weather predictions when including the prognostic aerosol schemes. This includes convective precipitation, surface temperature, and 700 hPa air temperature. We also use FIM-Chem to investigate the 2012 South American Biomass Burning Analysis (SAMBBA) campaign period to determine whether more complex chemistry provides benefits for global numerical weather prediction.
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