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Model-simulated transport of atmospheric trace components can be  combined with observed concentrations to obtain estimates of  ground-based sources using various inversion techniques. These  approaches have been applied in the past primarily to obtain source  estimates for long-lived trace gases such as CO<sub>2</sub>. We  consider the application of similar techniques to source estimation  for atmospheric aerosols, using as a case study the estimation of  bacteria emissions from different ecosystem regions in the global  atmospheric chemistry and climate model ECHAM5/MESSy-Atmospheric  Chemistry (EMAC).Source estimation via Markov Chain Monte Carlo is applied to a suite  of sensitivity simulations, and the global mean emissions are  estimated for the example problem of bacteria-containing aerosol  particles. We present an analysis of the uncertainties in the  global mean emissions, and a partitioning of the uncertainties that  are attributable to particle size, activity as cloud condensation  nuclei (CCN), the ice nucleation scavenging ratios for mixed-phase  and cold clouds, and measurement error.For this example, uncertainty due to CCN activity or to  a 1 μm error in particle size is typically between  10% and 40% of the uncertainty due to observation  uncertainty, as measured by the 5–95th percentile range of  the Monte Carlo ensemble. Uncertainty attributable to the ice  nucleation scavenging ratio in mixed-phase clouds is as high as  10–20% of that attributable to observation  uncertainty. Taken together, the four model parameters examined  contribute about half as much to the uncertainty in the estimated  emissions as do the observations. This was a surprisingly large  contribution from model uncertainty in light of the substantial  observation uncertainty, which ranges from 81–870% of  the mean for each of ten ecosystems for this case study. The  effects of these and other model parameters in contributing to the  uncertainties in the transport of atmospheric aerosol particles  should be treated explicitly and systematically in both forward and  inverse modelling studies

Estimating bacteria emissions from inversion of atmospheric transport: sensitivity to modelled particle characteristics