Strong Sensitivity of Simulated Biomass Burning Aerosol Transport and Radiative Effects over the South Atlantic to Carbonaceous Aerosol Aging and Particle Density | AMiner
Strong Sensitivity of Simulated Biomass Burning Aerosol Transport and Radiative Effects over the South Atlantic to Carbonaceous Aerosol Aging and Particle Density
Biomass burning aerosol (BBA) impacts climate through aerosol-cloud-radiation interactions, but models disagree on the sign and magnitude of BBA radiative effects. We quantify the sensitivity of BBA radiative effects and transport to three BBA-relevant processes and properties: parameterized oxidative aging of organic aerosol (OA), a combined change to black carbon (BC) density and the method for calculating aerosol refractive index, and reduction in OA density. We evaluate Unified Model simulations against two aircraft campaigns from summer 2017 over the Southeast Atlantic. The model generally performs well, such that discrepancies between the observational data sets may sometimes limit the precision of the evaluation. Our newly developed aging parameterization reproduces observed OA:BC mass ratios well and allows modeled OA:BC to decrease with smoke age, but increases bias in aerosol extinction and changes the BBA radiative effect little (+0.12 ). We calculate aerosol refractive index using either a volume-weighted component average or the Maxwell-Garnett (MG) mixing assumption, which represents BC as small inclusions in a host material. Compared to MG mixing, the volume-weighted average refractive index and reduced BC density increase aerosol absorption, substantially increasing the total BBA radiative effect (+2.66 ) and amount of BBA transported across the ocean through BC self-lofting. Reducing OA density to better match literature values changes the total BBA radiative effect by -1.96 . Changes to direct radiative effects exceed changes to cloud radiative effects. Our findings emphasize the sensitivity of aerosol radiative effects and transport to these processes and properties, which we suggest could be improved in climate models.