Physical and chemical processes governing SOA formation are very complex, but global atmospheric models have simplistic SOA treatments resulting in large uncertainties in SOA-radiative forcing. Biomass burning constitutes the largest global source of organic aerosols, yet its contributions to global aerosol radiative forcing is uncertain. We investigate the role of biomass and biofuel burning on both directly emitted primary organic aerosols (POA) and SOA. We parameterize complex SOA chemistry within the US Department of Energy (DOE’s) Energy Exascale Earth System Model (E3SM) model using a volatility basis set approach (VBS), which accounts for gas-phase fragmentation and particle-phase oligomerization. The model is run at high vertical (72 vertical levels) resolution and a horizontal resolution of 1 degree. Aerosol transport and wet removal parameterizations in the model cause long SOA lifetimes. Since the POA-SOA split from biomass burning is highly uncertain, we conduct sensitivity studies varying both POA emissions and SOA precursor gas emissions (in the semi-volatile and intermediate volatility range). Results are evaluated with a suite of ground- and aircraft-based field measurements of organic aerosols in different regions including the Arctic and the Amazon. In addition, we evaluate aerosol optical depth (AOD) in regions dominated by biomass burning emissions with satellite retrievals from MODerate resolution Imaging Spectrometers (MODIS) . Our results indicate that high POA-low SOA precursor emissions from open biomass burning sources could produce similar OA (POA+SOA) model-measurement agreement as model formulations with the default POA-high SOA precursor emissions scenarios. In addition, high MODIS AOD in biomass burning regions (e.g. over Africa and South America) can be explained by both high POA-low SOA and default POA-high SOA model formulations. However, a comparison of model-predicted SOA with the global dataset of oxygenated organic aerosol (OOA) deconvolved using the Aerosol Mass Spectrometer (AMS) reveals that high POA-low SOA emissions scenarios substantially underestimate OOA (by ~80%) at rural/background locations. Our results indicate the need to expand the global dataset of OOA/SOA estimates, especially in regions influenced by biomass burning emissions.