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

Abstract

[1] 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 17.8 ± 5.6 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.

Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model

“…MAM4 shows a better agreement with the HIPPO measurements compared with MAM3 (Tilmes et al, 2015); however, the magnitude is still too low. This could be attributed to the underestimation of BC emissions in East Asia in the IPCC AR5 emission inventory used in CAM5 (Lamarque et al, 2010), as reported by previous studies (e.g., Cohen and Wang, 2014). We also find that the simulated aerosol layers are at higher altitudes than the observations.…”

“…However, similarly to other global models , CAM5 significantly underestimates the nearsurface BC concentrations in remote regions, e.g., in the Arctic Wang et al, 2013;Ma et al, 2013a). While the underestimation of BC emission in Asia (e.g., Cohen and Wang, 2014) and in the high latitudes of the Northern Hemisphere (NH) (e.g., Stohl et al, 2013) can be an important factor, the excessively efficient scavenging of BC by liquid cloud processes (Wang et al, 2013) and the coarse horizontal resolution (∼ 100-200 km) of the model (Ma et al, 2014) also contribute to this model bias.…”

“…In addition, the aerosol emission inventories used in our simulations also likely contribute to the BC low biases in the Arctic. They neglect gas flaring emissions in and around the Arctic (Stohl et al, 2013), and they may underestimate fossil fuel emissions in East Asia (Cohen and Wang, 2014).…”

Simultaneous reductions in emissions of black carbon and co-emitted species will weaken the aerosol net cooling effect

“…One reason for the uncertainty is from the biases of current emission inventories of BC, mostly obtained from the so-called bottom-up approach (Cohen and Wang, 2014). Cohen and Wang (2014) provided a global-scale top-down estimation of BC emissions, a factor of more than 2 higher than commonly used global BC emissions data sets, by using a Kalman Filter method. If present-day BC emissions have been substantially underestimated, increase in aerosol net cooling effect may be larger due to only reduction in BC emission.…”

Standard climate models radiation codes underestimate black carbon radiative forcing

“…However, the diversity in existing estimates of the climate effect of BC is large (Bond et al, 2013;Boucher et al, 2013;Myhre et al, 2013). The causes for the diversity in estimates are many, from emissions (Amann et al, 2013;Cohen and Wang, 2014;Lam et al, 2012;Stohl et al, 2013;Wang et al, 2014b), lifetime and abundance (Hodnebrog et al, 2014;Samset et al, 2014;Wang et al, 2014a) to radiative efficiency Zarzycki and Bond, 2010).…”