Optimizing global CO concentrations and emissions based on DART/CAM-CHEM

Abstract:

Atmospheric Carbon Monoxide (CO) is an important trace gas in tropospheric chemistry through its impact on the oxidizing capacity of the troposphere, as precursor of ozone, and as a good tracer of combustion from both anthropogenic sources and wildfires. We will investigate the potential of the assimilation of TERRA/MOPITT observations to constrain the regional to global CO budget using DART (Data assimilation Research Testbed) together with the global Community Atmospheric Model (CAM-Chem). DART/CAM-Chem is based on an ensemble adjustment Kalman filter (EAKF) framework which facilitates statistical estimation of error correlations between chemical states (CO and related species) and parameters (including sources) in the model using the ensemble statistics derived from dynamical and chemical perturbations in the model. Here, we estimate CO emissions within DART/CAM-Chem using a state augmentation approach where CO emissions are added to the CO state vector being analyzed. We compare these optimized emissions to estimates derived from a traditional Bayesian synthesis inversion using the CO analyses (assimilated CO states) as observational constraints. The spatio-temporal distribution of CO and other chemical species will be compared to profile measurements from aircraft and other satellite instruments (e.g., INTEX-B, ARCTAS).