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Abstract

Utilising a fleet of commercial airliners, MOZAIC/IAGOS provides atmospheric composition data on a
regular basis that are widely used for modelling applications. Due to the specific operational context of the
platforms, such observations are collected close to international airports and hence in an environment
characterised by high anthropogenic emissions. This provides opportunities for assessing emission inventories
of major metropolitan areas around the world, but also challenges in representing the observations in typical
chemical transport models. We assess here the contribution of different sources of error to overall modeldata
mismatch using the example of MOZAIC/IAGOS carbon monoxide (CO) profiles collected over the European
regional domain in a time window of 5 yr (20062011). The different sources of error addressed in the present
study are: 1) mismatch in modelled and observed mixed layer height; 2) bias in emission fluxes and 3) spatial
representation error (related to unresolved spatial variations in emissions). The modelling framework combines
a regional Lagrangian transport model (STILT) with EDGARv4.3 emission inventory and lateral boundary
conditions from the MACC reanalysis. The representation error was derived by coupling STILT with emission
fluxes aggregated to different spatial resolutions. We also use the MACC reanalysis to assess uncertainty
related to uncertainty sources 2) and 3). We treat the random and the bias components of the uncertainty
separately and found that 1) and 3) have a comparable impact on the random component for both models,
while 2) is far less important. On the other hand, the bias component shows comparable impacts from each
source of uncertainty, despite both models being affected by a low bias of a factor of 22.5 in the emission
fluxes. In addition, we suggested methods to correct for biases in emission fluxes and in mixing heights. Lastly,
the evaluation of the spatial representation error against modeldata mismatch between MOZAIC/IAGOS
observations and the MACC reanalysis revealed that the representation error accounts for roughly 1520% of
the modeldata mismatch uncertainty.