Estimating global black carbon emissions using a top-down Kalman Filter approach

Black carbon (BC) is an important aerosol constituent in the atmosphere and climate forcer. A good understanding of the radiative forcing of BC and associated climate feedback and response is critical to minimize the uncertainty in predicting current and future climate influenced by anthropogenic aerosols. One reason for this uncertainty is that current emission inventories of BC are mostly obtained from the so-called bottom-up approach, an approach that derives emissions based on categorized emitting sources and emission factors used to convert burning mass to emissions. In this work, we provide a first global-scale top-down estimation of global BC emissions, as well as an estimated error range, by using a Kalman Filter. This method uses data of both column aerosol absorption optical depth and surface concentrations from global and regional networks to constrain our fully coupled climate-aerosol-urban model and thus to derive an optimized estimate of BC emissions as 17.85.6 Tg/yr, a factor of more than 2 higher than commonly used global BC emissions data sets. We further perform 22 additional optimization simulations that incorporate the known uncertain ranges of various important physical, model, and measurement parameters and still yield an optimized value within the above given range, from a low of 14.6 Tg/yr to a high of 22.2 Tg/yr. Furthermore, we show that the emissions difference between our optimized and a priori estimation is not uniform, with East Asia, Southeast Asia, and Eastern Europe underestimated, while North America is overestimated in the a priori inventory.