From Measurements to Models: Toward Accurate Representation of Brown Carbon in Climate Calculations

PURPOSE OF REVIEW: The direct radiative effect of brown carbon (BrC) absorption predicated by climate-modeling studies is highly uncertain, with values ranging between +0.03 W/m² and + 0.57 W/m². This review strives to identify sources of this uncertainty stemming from challenges in translating measurements into model inputs and to draw lessons from recent advances that lead to improved BrC representation in models. RECENT FINDINGS: Previously thought to absorb only short-visible and UV light, BrC was recently shown to comprise components that are strongly absorptive in the mid- and long-visible wavelengths, with light-absorption efficiencies approaching that of black carbon. The classic picture of biomass and biofuel combustion being the major sources of atmospheric BrC still holds, with recent measurements indicating a strong correlation between BrC optical properties and combustion conditions. Other combustion sources of BrC, currently not accounted for in models, include low-efficiency coal combustion and ship engines utilizing heavy fuel oil. Gas-phase, aqueous, and particle-phase reactions in the atmosphere produce secondary BrC and bleach/darken the primary BrC. Climate-modeling studies revealed that predicted BrC radiative effects are sensitive to the assumed optical properties and atmospheric aging mechanisms. BrC can be grouped into four optical classes, each separated by an order of magnitude in mid-visible light absorption. The classes are approximately mapped to BrC sources, with secondary BrC being the least absorbing and BrC from high-temperature combustion the most absorbing. There is evidence that each class exhibits characteristic physicochemical properties (molecular size, volatility, and solubility), which can be leveraged to design measurements that quantify distributions of BrC across classes as well as rates of photobleaching/darkening for each class. Utilizing this framework to develop BrC parameterizations promises to enhance its representation in climate models.