Towards better black carbon emission estimates in Europe: assimilating observations with a Bayesian inversion framework

Abstract. There are large discrepancies between different black carbon (BC) bottom-up emission estimates. This affects our understanding of how BC affects population health and climate. Previous studies have presented top-down estimates in various domains but estimates in Europe for more than a few months are lacking. With the Lund University Modular Inversion Algorithm (LUMIA) and the FLEXible PARTicle dispersion model (FLEXPART), we aim to calculate top-down estimates of anthropogenic BC emissions by assimilating surface observations from 24 background sites in Europe (15° W–35° E, 33–73° N) during 2021. The results show that the bottom-up BC inventory generally underestimates emissions in the domain. Annually, the resulting top-down emissions are 411 ± 10 Gg of BC, 18 % higher than the bottom-up estimate of 349 ± 30 Gg used as a prior. The largest increases in emissions occur in Eastern parts of Europe during spring and summer, while emissions in Poland and Italy are reduced during winter and autumn. The overall posterior emissions are most sensitive to changes in the observational network on the periphery, resulting in a reduced standard deviation compared to the prior emissions in the central domain, where the network density is high. A cross-validation scheme, where one site at the time is removed for validation, show that the posterior for a majority of sites fit independent observations better than the prior emissions.