PNNL

Abstract

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 CO2. We consider the application of similar techniques to source estimation for atmospheric aerosols, by 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). Simulated particle concentrations in the tropopause region and at high latitudes, as well as transport of particles to tundra and land ice regions are shown to be highly sensitive to scavenging in mixed-phase clouds, which is poorly characterized in most global climate models. This may be a critical uncertainty in correctly simulating the transport of aerosol particles to the Arctic. Source estimation via Monte Carlo Markov Chain is applied to a suite of sensitivity simulations and the global mean emissions are estimated. We present an analysis of the partitioning of uncertainties in the global mean emissions that are attributable to particle size, CCN activity, the ice nucleation scavenging ratios for mixed-phase and cold clouds, and measurement error. Uncertainty due to CCN activity or to a 1 um error in particle size is typically between 10% and 40% of the uncertainty due to data uncertainty, as measured by the 5%-ile to 95%-ile range of the Monte Carlo ensemble. Uncertainty attributable to the ice nucleation scavenging ratio in mized-phase clouds is as high as 10% to 20% of the data uncertainty. Taken together, the four model 20 parameters examined contribute about half as much to the uncertainty in the estimated emissions as do the measurements. This was a surprisingly large contribution from model uncertainty in light of the substantial data uncertainty, which ranges from 81% to 870% 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.