Long-Term Changes in Carbon Monoxide Abundance as Inferred from an Ensemble of Chemical Reanalyses

In recent years, five relatively long term chemical reanalyses have been reported from leading data assimilation and inverse modeling groups. Here, we present an assessment of the state of CO in the past decade (2005-2015) as inferred from these reanalyses using available ground-, aircraft- and satellite-based observations of CO. These reanalyses represent a range of modeling and assimilation approaches, assimilating different sets of chemical observations for multiple species, and in several cases updating monthly emission estimates from various bottom-up inventories. This provides an unprecedented dataset to understand and characterize the uncertainties on the inferred CO estimates and their trends. We present the ensemble statistics (e.g., mean and spread) on the spatial and temporal patterns of CO abundance and associated emissions at megacity to global scales. We will also elucidate and discuss outstanding biases relative to independent CO measurements. The ensemble of reanalyses allows us to identify representativeness and model errors, that cannot be fully address with a single modeling system and traditional evaluation diagnostics. This work serves as a step towards an inter-comparison project on current chemical data assimilation/inverse modeling systems (CDAs) for reactive gases similar to what has been done for greenhouse gases within the TransCom experiments for CO₂and CH₄. The relevance of this activity will be discussed within the context of the new IGAC project (Analysis of eMIssions usinG Observations or AMIGO) which is directed towards a synthesis activity on research related to observations-based analysis techniques aim to better quantify emissions.